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Jeff talks about his journey from the corporate world to Gym Owner to a lead Performance and Mental Toughness Coach. He shares some of his key tips to up leveling your life in a variety of ways. Jeff Wickersham a peak performance coach focused on guiding clients to implement his 4-Step Morning Fire methodology, the Rise, Fight, Love, Repeat, for more energy, focus, abundance and time in their days.  He is the creator of Kings and Queens of Sparta Masterminds and a best-selling author, podcast host and speaker.  Jeff loves to push the limits of what is possible and most recently hit 100,000 pushups for the year and 4 years straight of meditation.   Learn more here: https://www.themorningfire.com/

Siobhan Magnus sings Think by Aretha Franklin on the American Idol 9 Top 20. Performance. Visit http://kittyeatdog.blogspot.com for weekly Idol Updates and participate in our weekly Idol Polls!

Siobhan Magnus sings 'Wicked Game' on American Idol Top 24 Performance Night. Visit http://kittyeatdog.blogspot.com for more American Idol 9 Updates and Join our Idol polls every week! =D

Lacey Brown sings 'Kiss me' on American Idol Top 20 Performance Night. Visit http://kittyeatdog.blogspot.com for weekly Idol Updates and participate in our weekly Idol Polls!

This publication highlights Rhode Island's EdStat, a system of performance management that has enabled the Rhode Island Department of Education to monitor the progress of its participating LEAs and its own offices on Race to the Top implementation and outcomes.

This publication highlights Rhode Island's EdStat, a system of performance management that has enabled the Rhode Island Department of Education to monitor the progress of its participating LEAs and its own offices on Race to the Top implementation and outcomes.

This publication highlights Rhode Island's EdStat, a system of performance management that has enabled the Rhode Island Department of Education to monitor the progress of its participating LEAs and its own offices on Race to the Top implementation and outcomes.

Didi Benami sings 'The Way I Am' on American Idol Top 24 Performance Night. Visit http://kittyeatdog.blogspot.com for more American Idol 9 Updates and Join our Idol polls every week! =D

In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

The effects of team processes (internal team processes and external team processes) and reflexivity (task reflexivity and emotional reflexivity) of the top management team (TMT) on decision performance (decision quality and decision satisfaction) are becoming more and more critical. However, there are few studies on this topic. In this study, I explored the mediating effects of TMT reflexivity between TMT team processes and decision performance by hierarchical regression analysis and a bootstrap method. Participants were 524 team members from 76 TMTs. The results revealed that TMT team processes had significantly positive effects on TMT reflexivity and decision performance. TMT reflexivity had significantly positive effects on decision performance. TMT reflexivity partially mediated the positive effects of TMT team processes on decision performance. The results extend previous findings on TMT and strategic decisions and clarify the relationships between TMT team processes, TMT reflexivity and decision performance.

TITLE Top-Down and Bottom-Up Contributions to Memory Performance in OCD: A Multi-Level Meta-Analysis with Clinical Implications. AUTHORS Harkin, Ben*. Department of Psychology, Manchester Metropolitan University. Persson, Sofia. School of Social Sciences, Leeds Beckett University Yates, Alan. Department of Psychology, Manchester Metropolitan University. Kessler, Klaus. Aston Neuroscience Institute, Aston University. Jauregi, Ainara. Aston Neuroscience Institute, Aston University. * Corresponding author: Dr Ben Harkin, Department of Psychology, Manchester Metropolitan University, All Saints Building, Manchester, M15 6BH. E-mail: [email protected] RATIONALE In a novel multi-level meta-analytic approach, the current author and team (see Persson et al., in press) showed that central executive functioning played a key role in explaining memory performance in those with OCD. Specifically, we tested the predictive validity of Harkin and Kessler’s (2011) Executive Function, Binding Complexity and Memory Load (EBL) Classification System concerning affected versus unaffected memory performance in OCD. We employed a multi-level meta-analytic approach (Viechtbauer, 2010) to accommodate the interdependent nature of the EBL model and interdependency of effect sizes (305 effect sizes from 144 studies, including 4424 OCD patients). Results revealed that the EBL model predicted memory performance; i.e., as EBL demand increases, those with OCD performed progressively worse on memory tasks. Executive function was the driving mechanism behind the EBL’s impact on OCD memory performance, as it negated binding complexity, memory load, and visual or verbal task differences. The present multi-level meta-analysis builds upon our previous research and aims to investigate specific features of central executive functioning; i.e., the top-down and bottom-up contributions to memory performance in those with OCD. The aim will be to identify specific components of top-down and bottom-up processing generally and within specific memory domains and tasks. This data will then be used to inform the content of clinical interventions. OBJECTIVES (1) To quantify the top-down (attentional control/inhibition; planning; maintenance/updating) and bottom-up (perceptual integration; perceptual salience/complexity) in the memory performance of those with OCD. (2) To quantify the top-down and bottom-up contributions to memory performance across a range of memory domains (e.g., complex visual tasks; span spatial; recall of complex verbal information) and sub-tasks (e.g., Rey Complex Visual Tasks; n-back tasks, WLM-story recall, respectively). (3) To identify how specific components of top-down and bottom-up processing potentially contribute to key symptoms of OCD. (4) To identify how specific components of top-down and bottom-up processing can inform the content of clinical interventions. METHODS Included studies had to compare memory performance on at least one task between adults with OCD or OCD-type traits (e.g., checking) and healthy controls. Studies examining visual, verbal, and WM were all included. Participants with subclinical OCD were included; clinical versus sub-clinical samples were coded as a dichotomous variable. Participants with acquired OCD (e.g., following head injury) were excluded, as this group is generally considered clinically distinct from idiopathic OCD groups and illness will in most cases be associated with neurological injury (Coetzer, 2004). The search terms used to access literature was ‘(wash* OR check* OR hoard* OR obsessive-compulsive* OR OCD OR clean*) AND (executive OR bind* OR load* OR visual OR verbal) AND (memory)’. Studies were searched via the following social scientific databases: Web of Science, Psychinfo, Medline, PubMed, OVID, CINAHL, PsychArticles. ProQuest Theses was searched to identify potential grey literature, and prominent authors (most prolific 10% as determined by the initial literature search) in the field were also contacted enquiring about unpublished material. All authors contacted about additional data (see information below for further details), were asked about any unpublished material they might have. Where information to facilitate effect size calculation was missing, authors were contacted requesting additional data. To this end, we contacted 10 authors, with five providing the requested data. Search hits were converted to RIS-files (Clark, 2019, personal communication), and imported into the web application Rayyan (Ouzzani, Hammady, Fedorowicz, & Elmagarmid, 2016), where they were screened for eligibility. DATA CODING The below variables are of main interest for this review. • Effect sizes; i.e., memory performance of those with OCD vs controls • Top-Down Factors: Attentional Control/Inhibition; Planning; Maintenance/Updating • Bottom-Up Factors: Perceptual Salience/Complexity; Perceptual Integration Memory Domains and Sub-Tasks o Reproduction of Complex Geometric Shapes: VR Tasks, RCFT, WMS-VR o Span-Sequence: N-back, symbol, digit o Span-Spatial:TOL, SOST, CBTT o DMTS: Basic Storage, Distractor Inhibition o Recall – Simple Verbal: Threat, Cued, Neutral o Recall – Complex Verbal: Complex Verbal Tasks, WLM-Story Recall, CVLT/RAVLT/AVLT o Recognition: Visuo-spatial, objects, verbal o Declarative memory: Prospective, Source, False Each task was coded in terms of the identified features of the top-down and bottom-up features identified above. Tasks were scored 1 through to 3, depending on how much demand was placed on the individual component: 1 indicated low to little demand, 2 moderate demand, and 3 indicated high demand. All of these features were coded blindly by the author BH and then blindly second coded by AH. Agreement was high, with disagreements resolved via discussion. STATISTICAL ANALYSIS The current analysis will be conducted using the rma.va function in the Metafor package for the statistical software environment R (R Core Team, 2013); R Core Team, 2014), and recommendations of Viechtbauer (2010). A mixed-effects model will likely initially be fitted, and estimation based on the restricted maximum likelihood estimator. The analysis examined the variance distribution over two (collapsed top-down and bottom-up) and five levels, the overall effect (i.e., memory performance of those with OCD compared to controls), and the effects of a number of moderating variables. As recommended by Hox (2010) and Assink and Wibbelink (2016), moderators will be first examined individually, and then combined into one analysis. This allows for initial significance screening, whilst also accounting for the possibility of variables of interest being intercorrelated, producing multicollinearity in analyses. For overall mean effects of the meta-analysis and mean effects of categorical moderators, we report Cohen’s d, and for mean effects of the continuous moderator analyses, we report standardised betas. This is in line with reporting by past research using the same statistical approach (Asskin & Wibbelink, 2016; Fradkin et al., 2018). Code was adapted from Assink and Wibbelink (2016) and Harrer, Cuijpers and Ebert (2019). Visuals were created using ggplot2 (Wickham, 2016). Assessment of methodological quality will occur by entering it as a moderator of effect sizes. Publication bias will be assessed using the funnel plot function (funnel) in R, as recommended by Harrer et al. (2019), and Egger’s regression coefficients (Egger, Smith, Schneider, & Minder, 1997). There will likely also be a qualitative synthesis of the findings, pertaining to the variables of interest. A number of moderators of effect sizes will potentially be explored as part of the secondary outcomes of the review.

TITLE Top-Down and Bottom-Up Contributions to Memory Performance in OCD: A Multi-Level Meta-Analysis with Clinical Implications. AUTHORS Harkin, Ben*. Department of Psychology, Manchester Metropolitan University. Persson, Sofia. School of Social Sciences, Leeds Beckett University Yates, Alan. Department of Psychology, Manchester Metropolitan University. Kessler, Klaus. Aston Neuroscience Institute, Aston University. Jauregi, Ainara. Aston Neuroscience Institute, Aston University. * Corresponding author: Dr Ben Harkin, Department of Psychology, Manchester Metropolitan University, All Saints Building, Manchester, M15 6BH. E-mail: [email protected] RATIONALE In a novel multi-level meta-analytic approach, the current author and team (see Persson et al., in press) showed that central executive functioning played a key role in explaining memory performance in those with OCD. Specifically, we tested the predictive validity of Harkin and Kessler’s (2011) Executive Function, Binding Complexity and Memory Load (EBL) Classification System concerning affected versus unaffected memory performance in OCD. We employed a multi-level meta-analytic approach (Viechtbauer, 2010) to accommodate the interdependent nature of the EBL model and interdependency of effect sizes (305 effect sizes from 144 studies, including 4424 OCD patients). Results revealed that the EBL model predicted memory performance; i.e., as EBL demand increases, those with OCD performed progressively worse on memory tasks. Executive function was the driving mechanism behind the EBL’s impact on OCD memory performance, as it negated binding complexity, memory load, and visual or verbal task differences. The present multi-level meta-analysis builds upon our previous research and aims to investigate specific features of central executive functioning; i.e., the top-down and bottom-up contributions to memory performance in those with OCD. The aim will be to identify specific components of top-down and bottom-up processing generally and within specific memory domains and tasks. This data will then be used to inform the content of clinical interventions. OBJECTIVES (1) To quantify the top-down (attentional control/inhibition; planning; maintenance/updating) and bottom-up (perceptual integration; perceptual salience/complexity) in the memory performance of those with OCD. (2) To quantify the top-down and bottom-up contributions to memory performance across a range of memory domains (e.g., complex visual tasks; span spatial; recall of complex verbal information) and sub-tasks (e.g., Rey Complex Visual Tasks; n-back tasks, WLM-story recall, respectively). (3) To identify how specific components of top-down and bottom-up processing potentially contribute to key symptoms of OCD. (4) To identify how specific components of top-down and bottom-up processing can inform the content of clinical interventions. METHODS Included studies had to compare memory performance on at least one task between adults with OCD or OCD-type traits (e.g., checking) and healthy controls. Studies examining visual, verbal, and WM were all included. Participants with subclinical OCD were included; clinical versus sub-clinical samples were coded as a dichotomous variable. Participants with acquired OCD (e.g., following head injury) were excluded, as this group is generally considered clinically distinct from idiopathic OCD groups and illness will in most cases be associated with neurological injury (Coetzer, 2004). The search terms used to access literature was ‘(wash* OR check* OR hoard* OR obsessive-compulsive* OR OCD OR clean*) AND (executive OR bind* OR load* OR visual OR verbal) AND (memory)’. Studies were searched via the following social scientific databases: Web of Science, Psychinfo, Medline, PubMed, OVID, CINAHL, PsychArticles. ProQuest Theses was searched to identify potential grey literature, and prominent authors (most prolific 10% as determined by the initial literature search) in the field were also contacted enquiring about unpublished material. All authors contacted about additional data (see information below for further details), were asked about any unpublished material they might have. Where information to facilitate effect size calculation was missing, authors were contacted requesting additional data. To this end, we contacted 10 authors, with five providing the requested data. Search hits were converted to RIS-files (Clark, 2019, personal communication), and imported into the web application Rayyan (Ouzzani, Hammady, Fedorowicz, & Elmagarmid, 2016), where they were screened for eligibility. DATA CODING The below variables are of main interest for this review. • Effect sizes; i.e., memory performance of those with OCD vs controls • Top-Down Factors: Attentional Control/Inhibition; Planning; Maintenance/Updating • Bottom-Up Factors: Perceptual Salience/Complexity; Perceptual Integration Memory Domains and Sub-Tasks o Reproduction of Complex Geometric Shapes: VR Tasks, RCFT, WMS-VR o Span-Sequence: N-back, symbol, digit o Span-Spatial:TOL, SOST, CBTT o DMTS: Basic Storage, Distractor Inhibition o Recall – Simple Verbal: Threat, Cued, Neutral o Recall – Complex Verbal: Complex Verbal Tasks, WLM-Story Recall, CVLT/RAVLT/AVLT o Recognition: Visuo-spatial, objects, verbal o Declarative memory: Prospective, Source, False Each task was coded in terms of the identified features of the top-down and bottom-up features identified above. Tasks were scored 1 through to 3, depending on how much demand was placed on the individual component: 1 indicated low to little demand, 2 moderate demand, and 3 indicated high demand. All of these features were coded blindly by the author BH and then blindly second coded by AH. Agreement was high, with disagreements resolved via discussion. STATISTICAL ANALYSIS The current analysis will be conducted using the rma.va function in the Metafor package for the statistical software environment R (R Core Team, 2013); R Core Team, 2014), and recommendations of Viechtbauer (2010). A mixed-effects model will likely initially be fitted, and estimation based on the restricted maximum likelihood estimator. The analysis examined the variance distribution over two (collapsed top-down and bottom-up) and five levels, the overall effect (i.e., memory performance of those with OCD compared to controls), and the effects of a number of moderating variables. As recommended by Hox (2010) and Assink and Wibbelink (2016), moderators will be first examined individually, and then combined into one analysis. This allows for initial significance screening, whilst also accounting for the possibility of variables of interest being intercorrelated, producing multicollinearity in analyses. For overall mean effects of the meta-analysis and mean effects of categorical moderators, we report Cohen’s d, and for mean effects of the continuous moderator analyses, we report standardised betas. This is in line with reporting by past research using the same statistical approach (Asskin & Wibbelink, 2016; Fradkin et al., 2018). Code was adapted from Assink and Wibbelink (2016) and Harrer, Cuijpers and Ebert (2019). Visuals were created using ggplot2 (Wickham, 2016). Assessment of methodological quality will occur by entering it as a moderator of effect sizes. Publication bias will be assessed using the funnel plot function (funnel) in R, as recommended by Harrer et al. (2019), and Egger’s regression coefficients (Egger, Smith, Schneider, & Minder, 1997). There will likely also be a qualitative synthesis of the findings, pertaining to the variables of interest. A number of moderators of effect sizes will potentially be explored as part of the secondary outcomes of the review.

TITLE Top-Down and Bottom-Up Contributions to Memory Performance in OCD: A Multi-Level Meta-Analysis with Clinical Implications. AUTHORS Harkin, Ben*. Department of Psychology, Manchester Metropolitan University. Persson, Sofia. School of Social Sciences, Leeds Beckett University Yates, Alan. Department of Psychology, Manchester Metropolitan University. Kessler, Klaus. Aston Neuroscience Institute, Aston University. Jauregi, Ainara. Aston Neuroscience Institute, Aston University. * Corresponding author: Dr Ben Harkin, Department of Psychology, Manchester Metropolitan University, All Saints Building, Manchester, M15 6BH. E-mail: [email protected] RATIONALE In a novel multi-level meta-analytic approach, the current author and team (see Persson et al., in press) showed that central executive functioning played a key role in explaining memory performance in those with OCD. Specifically, we tested the predictive validity of Harkin and Kessler’s (2011) Executive Function, Binding Complexity and Memory Load (EBL) Classification System concerning affected versus unaffected memory performance in OCD. We employed a multi-level meta-analytic approach (Viechtbauer, 2010) to accommodate the interdependent nature of the EBL model and interdependency of effect sizes (305 effect sizes from 144 studies, including 4424 OCD patients). Results revealed that the EBL model predicted memory performance; i.e., as EBL demand increases, those with OCD performed progressively worse on memory tasks. Executive function was the driving mechanism behind the EBL’s impact on OCD memory performance, as it negated binding complexity, memory load, and visual or verbal task differences. The present multi-level meta-analysis builds upon our previous research and aims to investigate specific features of central executive functioning; i.e., the top-down and bottom-up contributions to memory performance in those with OCD. The aim will be to identify specific components of top-down and bottom-up processing generally and within specific memory domains and tasks. This data will then be used to inform the content of clinical interventions. OBJECTIVES (1) To quantify the top-down (attentional control/inhibition; planning; maintenance/updating) and bottom-up (perceptual integration; perceptual salience/complexity) in the memory performance of those with OCD. (2) To quantify the top-down and bottom-up contributions to memory performance across a range of memory domains (e.g., complex visual tasks; span spatial; recall of complex verbal information) and sub-tasks (e.g., Rey Complex Visual Tasks; n-back tasks, WLM-story recall, respectively). (3) To identify how specific components of top-down and bottom-up processing potentially contribute to key symptoms of OCD. (4) To identify how specific components of top-down and bottom-up processing can inform the content of clinical interventions. METHODS Included studies had to compare memory performance on at least one task between adults with OCD or OCD-type traits (e.g., checking) and healthy controls. Studies examining visual, verbal, and WM were all included. Participants with subclinical OCD were included; clinical versus sub-clinical samples were coded as a dichotomous variable. Participants with acquired OCD (e.g., following head injury) were excluded, as this group is generally considered clinically distinct from idiopathic OCD groups and illness will in most cases be associated with neurological injury (Coetzer, 2004). The search terms used to access literature was ‘(wash* OR check* OR hoard* OR obsessive-compulsive* OR OCD OR clean*) AND (executive OR bind* OR load* OR visual OR verbal) AND (memory)’. Studies were searched via the following social scientific databases: Web of Science, Psychinfo, Medline, PubMed, OVID, CINAHL, PsychArticles. ProQuest Theses was searched to identify potential grey literature, and prominent authors (most prolific 10% as determined by the initial literature search) in the field were also contacted enquiring about unpublished material. All authors contacted about additional data (see information below for further details), were asked about any unpublished material they might have. Where information to facilitate effect size calculation was missing, authors were contacted requesting additional data. To this end, we contacted 10 authors, with five providing the requested data. Search hits were converted to RIS-files (Clark, 2019, personal communication), and imported into the web application Rayyan (Ouzzani, Hammady, Fedorowicz, & Elmagarmid, 2016), where they were screened for eligibility. DATA CODING The below variables are of main interest for this review. • Effect sizes; i.e., memory performance of those with OCD vs controls • Top-Down Factors: Attentional Control/Inhibition; Planning; Maintenance/Updating • Bottom-Up Factors: Perceptual Salience/Complexity; Perceptual Integration Memory Domains and Sub-Tasks o Reproduction of Complex Geometric Shapes: VR Tasks, RCFT, WMS-VR o Span-Sequence: N-back, symbol, digit o Span-Spatial:TOL, SOST, CBTT o DMTS: Basic Storage, Distractor Inhibition o Recall – Simple Verbal: Threat, Cued, Neutral o Recall – Complex Verbal: Complex Verbal Tasks, WLM-Story Recall, CVLT/RAVLT/AVLT o Recognition: Visuo-spatial, objects, verbal o Declarative memory: Prospective, Source, False Each task was coded in terms of the identified features of the top-down and bottom-up features identified above. Tasks were scored 1 through to 3, depending on how much demand was placed on the individual component: 1 indicated low to little demand, 2 moderate demand, and 3 indicated high demand. All of these features were coded blindly by the author BH and then blindly second coded by AH. Agreement was high, with disagreements resolved via discussion. STATISTICAL ANALYSIS The current analysis will be conducted using the rma.va function in the Metafor package for the statistical software environment R (R Core Team, 2013); R Core Team, 2014), and recommendations of Viechtbauer (2010). A mixed-effects model will likely initially be fitted, and estimation based on the restricted maximum likelihood estimator. The analysis examined the variance distribution over two (collapsed top-down and bottom-up) and five levels, the overall effect (i.e., memory performance of those with OCD compared to controls), and the effects of a number of moderating variables. As recommended by Hox (2010) and Assink and Wibbelink (2016), moderators will be first examined individually, and then combined into one analysis. This allows for initial significance screening, whilst also accounting for the possibility of variables of interest being intercorrelated, producing multicollinearity in analyses. For overall mean effects of the meta-analysis and mean effects of categorical moderators, we report Cohen’s d, and for mean effects of the continuous moderator analyses, we report standardised betas. This is in line with reporting by past research using the same statistical approach (Asskin & Wibbelink, 2016; Fradkin et al., 2018). Code was adapted from Assink and Wibbelink (2016) and Harrer, Cuijpers and Ebert (2019). Visuals were created using ggplot2 (Wickham, 2016). Assessment of methodological quality will occur by entering it as a moderator of effect sizes. Publication bias will be assessed using the funnel plot function (funnel) in R, as recommended by Harrer et al. (2019), and Egger’s regression coefficients (Egger, Smith, Schneider, & Minder, 1997). There will likely also be a qualitative synthesis of the findings, pertaining to the variables of interest. A number of moderators of effect sizes will potentially be explored as part of the secondary outcomes of the review.

24 pages : 23 cm

Master the essentials of application performance monitoring with our top five tips tailored for developers. This video covers key strategies to track and improve your app’s performance, from identifying bottlenecks to using the right tools for the job. Whether you're a beginner or an experienced developer, these tips will help you ensure your applications run smoothly and efficiently. Watch now to boost your monitoring skills -  https://youtu.be/WdJcZjYsd3A

The Chromium Projects Home Chromium ChromiumOS Quick links Report bugs Discuss Other sites Chromium Blog Google Chrome Extensions Except as otherwise  noted , the content of this page is licensed under a  Creative Commons Attribution 2.5 license , and examples are licensed under the  BSD License . Privacy Edit this page For Developers  >  How-Tos  > Debugging Chromium on Windows First see  get the code  for checkout and build instructions. Getting started You can use Visual Studio's built-in debugger or  WinDBG  to debug Chromium. You don't need to use the IDE to build in order to use the debugger: autoninja is used to build Chromium and most developers invoke it from a command prompt, and then open the IDE for debugging as necessary. To start debugging an already-built executable with Visual Studio just launch Visual Studio (2019 or higher) and select File-> Open-> Project/Solution (Ctrl+Shift+O) and select the executable of interest. This will create a solution with that executable as the 'project'. You can then launch the debugger with F5 or F11 or from the Debug menu. If you right-click on the executable in Solution Explorer and select properties then you can edit things such as the executable path, command-line arguments, and working directory. You can add additional executables to the solution by using File-> Add-> Existing Project and selecting another already-built executable. You can select which one to debug by right-clicking on one of them in Solution Explorer and selecting Set as Startup Project. When your solution file is customized to your taste you can save it to a directory such as out\solutions. Saving it there helps ensure that relative paths to source files, printed from build commands, will correctly identify the source files. The Tools menu can be used to add commands to do things like invoke autoninja to build Chrome, compile the selected source file, or other things. Visual Studio 2017 is not recommended for debugging of Chromium - use a newer version for best performance and stability. symbol_level=2  is the default on Windows and gives full debugging information with types, locals, globals, function names, and source/line information.  symbol_level=1  creates smaller PDBs with just function names, and source/line information - source-level debugging is still supported (new from June 2019), but local variables and type information are missing.  symbol_level=0  gives extremely limited debugging abilities, mostly just viewing call stacks when Chromium crashes. Browsing source code If you use a solution file generated by gn ( gn gen --ide=vs ) then Intellisense may help you navigate the code. If this doesn't work or if you use a solution created as above then you may want to install  VsChromium  to help navigate the code, as well as using  https://source.chromium.org . Profiles It's a good idea to use a different Chrome profile for your debugging. If you are debugging Google Chrome branded builds, or use a Chromium build as your primary browser, the profiles can collide so you can't run both at once, and your stable browser might see profile versions from the future (Google Chrome and Chromium use different profile directories by default so won't collide). Use the command-line option: --user-data-dir =C:\tmp\my_debug_profile (replace the path as necessary) Using the IDE, go to the  Debugging  tab of the properties of the chrome project, and set the  Command Arguments. Chrome debug log Enable Chrome debug logging to a file by passing  --enable-logging --v=1  command-line flags at startup. Debug builds place the  chrome_debug.log  file in the  out\Debug  directory. Release builds place the file in the top level of the user data Chromium app directory, which is OS-version-dependent. For more information, see  logging  and  user data directory  details. Symbol server If you are debugging official Google Chrome release builds, use the symbol server: https://chromium-browser-symsrv.commondatastorage.googleapis.com In Visual Studio, this goes in  Tools > Options  under  Debugging > Symbols . You should set up a local cache in a empty directory on your computer. In windbg you can add this to your symbol server search path with the command below, where C:\symbols is a local cache directory: .sympath+ SRV\*C:\symbols\*https://chromium-browser-symsrv.commondatastorage.googleapis.com Alternately, You can set the _NT_SYMBOL_PATH environment variable to include both the Microsoft and Google symbol servers - VS, windbg, and other tools should both respect this environment variable: _NT_SYMBOL_PATH =SRV\*C:\symbols\*https://msdl.microsoft.com/download/symbols ;SRV\*C:\symbols\*https://chromium-browser-symsrv.commondatastorage.googleapis.com Note that symbol servers will let the debuggers download both the PE files (DLLs and EXEs) and the PDB files. Chrome often loads third party libraries and partial symbols for some of these are also public. For example: AMD : https://download.amd.com/dir/bin Nvidia : https://driver-symbols.nvidia.com/ Intel : https://software.intel.com/sites/downloads/symbols/ For example, for completeness, the following symbol server environment variable will resolve all of the above sources - but this is more than is normally needed: _NT_SYMBOL_PATH=SRV\*C:\symbols\*https://msdl.microsoft.com/download/symbols;SRV\*C:\symbols\*https://chromium-browser-symsrv.commondatastorage.googleapis.com;SRV\*C:\symbols\*https://download.amd.com/dir/bin;SRV\*C:\symbols\*https://driver-symbols.nvidia.com/;SRV\*C:\symbols\*https://software.intel.com/sites/downloads/symbols/ Source indexing You should set up source indexing in your debugger ( .srcfix  in windbg, Tools-> Options-> Debugging-> General->  Enable source server support  in Visual Studio) so that the correct source files will automatically be downloaded based on information in the downloaded symbols. Additionally, you must have  python  in your  path  in order for the  command that fetches source files  to succeed; launching the debugger from the same environment as where you build Chromium is an easy way to ensure it's present. This is highly recommended when debugging released Google Chrome builds or looking at crash dumps. Having the correct version of the source files automatically show up saves significant time so you should definitely set this. Multi-process issues Chromium can be challenging to debug because of its  multi-process architecture . When you select  Run  in the debugger, only the main browser process will be debugged. The code that actually renders web pages (the Renderer) and the plugins will be in separate processes that's not (yet!) being debugged. The  ProcessExplorer  tool has a process tree view where you can see how these processes are related. You can also get the process IDs associated with each tab from the Chrome Task Manager (right-click on an empty area of the window title bar to open). Automatically attach to child processes There are two Visual Studio extensions that enable the debugger to automatically attach to all Chrome processes, so you can debug all of Chrome at once. Microsoft's  Child Process Debugging Power Tool  is a standalone extension for this, and  VsChromium  is another option that bundles many other additional features. In addition to installing one of these extensions, you  must  run Visual Studio as Administrator, or it will silently fail to attach to some of Chrome's child processes. Single-process mode One way to debug issues is to run Chromium in single-process mode. This will allow you to see the entire state of the program without extra work (although it will still have many threads). To use single-process mode, add the command-line flag --single-process This approach isn't perfect because some problems won't manifest themselves in this mode and some features don't work and worker threads are still spawned into new processes. Manually attaching to a child process You can attach to the running child processes with the debugger. Select  Tools > Attach to Process  and click the  chrome.exe  process you want to attach to. Before attaching, make sure you have selected only Native code when attaching to the process This is done by clicking Select... in the Attach to Process window and only checking Native. If you forget this, it may attempt to attach in "WebKit" mode to debug JavaScript, and you'll get an error message "An operation is not legal in the current state." You can now debug the two processes as if they were one. When you are debugging multiple processes, open the  Debug > Windows > Processes  window to switch between them. Sometimes you are debugging something that only happens on startup, and want to see the child process as soon as it starts. Use: --renderer-startup-dialog --no-sandbox You have to disable the sandbox or the dialog box will be prohibited from showing. When the dialog appears, visit Tools > Attach to Process and attach to the process showing the Renderer startup dialog. Now you're debugging in the renderer and can continue execution by pressing OK in the dialog. Startup dialogs also exist for other child process types:  --gpu-startup-dialog ,  --ppapi-startup-dialog ,  --utility-startup-dialog ,  --plugin-startup-dialog  (for NPAPI). For utilities, you can add a service type  --utility-startup-dialog=data_decoder.mojom.DataDecoderService . You can also try  the vs-chromium plug-in  to attach to the right processes. Semi-automatically attaching the debugger to child processes The following flags cause child processes to wait for 60 seconds in a busy loop for a debugger to attach to the process. Once either condition is true, it continues on; no exception is thrown. --wait-for-debugger-children [=filter] The filter, if provided, will fire only if it matches the  --type  parameter to the process. Values include  renderer ,  plugin  (for NPAPI),  ppapi ,  gpu-process , and  utility . When using this option, it may be helpful to limit the number of renderer processes spawned, using: --renderer-process-limit = 1 Image File Execution Options Using Image File Execution Options (IFEO) will not work because CreateProcess() returns the handle to the debugger process instead of the intended child process. There are also issues with the sandbox. Time travel debugging You can do  time travel debugging using WinDbg Preview  (must be installed from the Microsoft Store). This lets you execute a program forward and backwards. After capturing a trace, you can set breakpoints and step through code as normal, but also provides 'backwards' commands (g-, t-, p-) so that you can go back and forth through the execution. It is especially useful to set data breakpoints ( ba command ) and reverse continuing, so you can see when a certain variable was last changed to its current value. Chromium specifics: The type of injection the time travel tracer needs to perform is incompatible with the Chromium sandbox. In order to record a trace, you'll need to run with  --no-sandbox . Chromium cannot run elevated with Administrator privileges, so the "Launch executable (advance)" option won't work, you'll need to attach after the process has already launched via the checkbox in the bottom right. If you need to record startup-like things, you'll have to use --{browser,gpu,renderer,utility}-startup-dialog, then attach (and hope the relevant code hasn't executed before that point). JsDbg -- data structure visualization You can install  JsDbg as a plugin for WinDbg or Visual Studio . It interactively lets you look at data structures (such as the DOM tree, Accessibility tree, layout object tree, and others) in a web browser as you debug. See the  JsDbg site  for some screen shots and usage examples. This also works when examining memory dumps (though not minidumps), and also works together with time travel debugging. Visual Studio hints Debug visualizers Chrome's custom debug visualizers should be added to the pdb files and automatically picked up by Visual Studio. The definitions are in  //tools/win/DebugVisualizers  if you need to modify them (the BUILD.gn file there has additional instructions). Don't step into trivial functions The debugger can be configured to automatically not step into functions based on regular expression. Edit  default.natstepfilter  in the following directory: For Visual Studio 2015:  C:\Program Files (x86)\Microsoft Visual Studio 14.0\Common7\Packages\Debugger\Visualizers  (for all users) or  %USERPROFILE%\My Documents\Visual Studio 2015\Visualizers  (for the current user only) For Visual Studio 2017 Pro:  C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\Common7\Packages\Debugger\Visualizers  (for all users) or  %USERPROFILE%\My Documents\Visual Studio 2017\Visualizers  (for the current user only) Add regular expressions of functions to not step into. Remember to regex-escape  and  XML-escape them, e.g. < for < and \. for a literal dot. Example: < Function > < Name > operator new </ Name > < Action > NoStepInto </ Action > </ Function > < Function > < Name > operator delete </ Name > < Action > NoStepInto </ Action > </ Function > <!-- Skip everything in std --> < Function > < Name > std::.* </ Name > < Action > NoStepInto </ Action > </ Function > <!-- all methods on WebKit OwnPtr and variants, ... WTF::*Ptr<*>::* --> < Function > < Name > WTF::.*Ptr<.*>::.* </ Name > < Action > NoStepInto </ Action > </ Function > This file is read at start of a debugging session (F5), so you don't need to restart Visual Studio after changing it. More info:  Microsoft email thread V8 and Chromium V8 supports many command-line flags that are useful for debugging. V8 command-line flags can be set via the Chromium command-line flag --js-flags; for instance: chrome.exe --js-flags= "--trace_exception --heap_stats" Note that some V8 command-line flags exist only in the debug build of V8. For a list of all V8 flags try: chrome.exe --js-flags= "--help" Graphics debugging GPU Acceleration of rendering can be more easily debugged with tools. See: Graphics Debugging in Visual Studio 2013 Graphical debugging with NVIDIA NSight Debugging on another machine Sometimes it's useful to debug installation and execution on a machine other than your primary build box. To run the installer on said other machine, first build the mini_installer target on your main build machine (e.g., autoninja -C out\Debug mini_installer). Next, on the debug machine: Make the build machine's build volume available on the debug machine either by mounting it locally (e.g., Z:\) or by crafting a UNC path to it (e.g., \\builder\src) Open up a command prompt and change to a local disk Run src\tools\win\ copy-installer.bat  in the remote checkout by way of the mount (e.g., Z:\PATHTOCHECKOUT\src\...) or UNC path (e.g., \\builder\src\...). This will copy the installer, DLLs, and PDBs into your debug machine's C:\out or C:\build (depending on if you're rocking the component=shared_library build or not) Run  C:\out\Debug\mini_installer.exe  with the flags of your choice to install Chrome. This can take some time, especially on a slow machine. Watch the Task Manager and wait until mini_installer.exe exits before trying to launch Chrome (by way of the shortcut(s) created by the installer) For extra pleasure, add C:\out\Debug to your _NT_SYMBOL_PATH environment variable Consider reading the documentation at the top of copy-installer.bat to see how you can run it. It tries to be smart and copy the right things, but you may need to be explicit (e.g., "copy-installer.bat out Debug"). It is safe to re-run the script to copy only modified files (after a rebuild, for example). You can also use the zip action of the isolate scripts (tools\mb\mb.py) to package all the files for a target into a single zip file, for example: python tools\mb\mb.py zip out/Release base_unittests base_unittests. zip Finding all memory allocations It is possible to use Heap Snapshots to get call stacks on all outstanding allocations that use the OS heap. This works particularly well if heap snapshots are started as soon as the Chrome browser process is created, but before it starts running. Details can be found in  this batch file . However, with  PartitionAlloc Everywhere  most Chromium allocations no longer use the Windows heap so this will only find a subset of allocations, mostly from OS DLLs. Find memory leaks Note: as with Heap Snapshots the utility of UMDH is greatly reduced now because PartitionAlloc Everywhere has mostly replaced the Windows heap. The Windows heap manager has a really useful debug flag, where it can be asked to capture and store a stack trace with every allocation. The tool to scrape these stack traces out of processes is UMDH, which comes with  WinDbg . UMDH is great. It will capture a snapshot of the heap state as many times as you like, and it'll do it fairly quickly. You then run it again against either a single snapshot, or a pair of snapshots, at which time it'll symbolize the stack traces and aggregate usage up to unique stack traces. Turning on the user stack trace database for chrome.exe with gflags.exe makes it run unbearably slowly; however, turning on the user stack trace database on for the browser alone is just fine. While it's possible to turn on the user stack database with the "!gflag" debugging extension, it's too late to do this by the time the initial debugger breakpoint hits. The only reasonable way to do this is to Launch GFlags.exe, Enable the user stack trace database (per image below), Launch Chrome under the debugger. Set a breakpont when chrome.dll loads with "sxe ld chrome.dll". Step up, to allow Chrome.dll to initialize. Disable the stack trace database in GFlags.exe. Continue chrome, optionally detaching the debugger. GFlags.exe settings for user mode stack trace database. If you then ever suffer a browser memory leak, you can snarf a dump of the process with umdh - p :<my browser pid> > chrome-browser-leak-umdh-dump.txt which can then typically be "trivially" analyzed to find the culprit. Miscellaneous Note that by default Application Verifier only works with non-official builds of Chromium. To use Application Verifier on official builds you need to add --disable-features=RendererCodeIntegrity to avoid sandbox crashes in renderer processes. See  crbug.com/1004989  for details. See also  this page . Application Verifier  is a free tool from Microsoft (available as part of the Windows SDK) that can be used to flush out programming errors. Starting with M68 Application Verifier can be enabled for chrome.exe without needing to disable the sandbox. After adding chrome.exe to the list of applications to be stressed you need to expand the list of Basics checks and disable the  Leak  checks. You may also need to disable  Handles  and  Locks  checks depending on your graphics driver and specific Chrome version, but the eventual goal is to have Chrome run with  Handles  and  Locks  checks enabled. When bugs are found Chrome will trigger a breakpoint so running all Chrome processes under a debugger is recommended. Chrome will run much more slowly because Application Verifier puts every heap allocation on a separate page. Note that with PartitionAlloc Everywhere most Chromium allocations don't actually go through the Windows heap and are therefore unaffected by Application Verifier. You can check the undocumented 'Cuzz' checkbox in Application Verifier to get the Windows thread scheduler to add some extra randomness in order to help expose race conditions in your code. To put a breakpoint on CreateFile(), add this break point: {,,kernel32.dll}_CreateFileW@28 {,,kernel32.dll} specifies the DLL (context operator). _ prefix means extern "C". @28 postfix means _stdcall with the stack pop at the end of the function. i.e. the number of arguments in BYTES. You can use  DebugView  from SysInternals or  sawbuck  to view LOG() messages that normally go to stderr on POSIX.

The Chromium Projects Home Chromium ChromiumOS Quick links Report bugs Discuss Other sites Chromium Blog Google Chrome Extensions Except as otherwise  noted , the content of this page is licensed under a  Creative Commons Attribution 2.5 license , and examples are licensed under the  BSD License . Privacy Edit this page For Developers  >  How-Tos  > Debugging Chromium on Windows First see  get the code  for checkout and build instructions. Getting started You can use Visual Studio's built-in debugger or  WinDBG  to debug Chromium. You don't need to use the IDE to build in order to use the debugger: autoninja is used to build Chromium and most developers invoke it from a command prompt, and then open the IDE for debugging as necessary. To start debugging an already-built executable with Visual Studio just launch Visual Studio (2019 or higher) and select File-> Open-> Project/Solution (Ctrl+Shift+O) and select the executable of interest. This will create a solution with that executable as the 'project'. You can then launch the debugger with F5 or F11 or from the Debug menu. If you right-click on the executable in Solution Explorer and select properties then you can edit things such as the executable path, command-line arguments, and working directory. You can add additional executables to the solution by using File-> Add-> Existing Project and selecting another already-built executable. You can select which one to debug by right-clicking on one of them in Solution Explorer and selecting Set as Startup Project. When your solution file is customized to your taste you can save it to a directory such as out\solutions. Saving it there helps ensure that relative paths to source files, printed from build commands, will correctly identify the source files. The Tools menu can be used to add commands to do things like invoke autoninja to build Chrome, compile the selected source file, or other things. Visual Studio 2017 is not recommended for debugging of Chromium - use a newer version for best performance and stability. symbol_level=2  is the default on Windows and gives full debugging information with types, locals, globals, function names, and source/line information.  symbol_level=1  creates smaller PDBs with just function names, and source/line information - source-level debugging is still supported (new from June 2019), but local variables and type information are missing.  symbol_level=0  gives extremely limited debugging abilities, mostly just viewing call stacks when Chromium crashes. Browsing source code If you use a solution file generated by gn ( gn gen --ide=vs ) then Intellisense may help you navigate the code. If this doesn't work or if you use a solution created as above then you may want to install  VsChromium  to help navigate the code, as well as using  https://source.chromium.org . Profiles It's a good idea to use a different Chrome profile for your debugging. If you are debugging Google Chrome branded builds, or use a Chromium build as your primary browser, the profiles can collide so you can't run both at once, and your stable browser might see profile versions from the future (Google Chrome and Chromium use different profile directories by default so won't collide). Use the command-line option: --user-data-dir =C:\tmp\my_debug_profile (replace the path as necessary) Using the IDE, go to the  Debugging  tab of the properties of the chrome project, and set the  Command Arguments. Chrome debug log Enable Chrome debug logging to a file by passing  --enable-logging --v=1  command-line flags at startup. Debug builds place the  chrome_debug.log  file in the  out\Debug  directory. Release builds place the file in the top level of the user data Chromium app directory, which is OS-version-dependent. For more information, see  logging  and  user data directory  details. Symbol server If you are debugging official Google Chrome release builds, use the symbol server: https://chromium-browser-symsrv.commondatastorage.googleapis.com In Visual Studio, this goes in  Tools > Options  under  Debugging > Symbols . You should set up a local cache in a empty directory on your computer. In windbg you can add this to your symbol server search path with the command below, where C:\symbols is a local cache directory: .sympath+ SRV\*C:\symbols\*https://chromium-browser-symsrv.commondatastorage.googleapis.com Alternately, You can set the _NT_SYMBOL_PATH environment variable to include both the Microsoft and Google symbol servers - VS, windbg, and other tools should both respect this environment variable: _NT_SYMBOL_PATH =SRV\*C:\symbols\*https://msdl.microsoft.com/download/symbols ;SRV\*C:\symbols\*https://chromium-browser-symsrv.commondatastorage.googleapis.com Note that symbol servers will let the debuggers download both the PE files (DLLs and EXEs) and the PDB files. Chrome often loads third party libraries and partial symbols for some of these are also public. For example: AMD : https://download.amd.com/dir/bin Nvidia : https://driver-symbols.nvidia.com/ Intel : https://software.intel.com/sites/downloads/symbols/ For example, for completeness, the following symbol server environment variable will resolve all of the above sources - but this is more than is normally needed: _NT_SYMBOL_PATH=SRV\*C:\symbols\*https://msdl.microsoft.com/download/symbols;SRV\*C:\symbols\*https://chromium-browser-symsrv.commondatastorage.googleapis.com;SRV\*C:\symbols\*https://download.amd.com/dir/bin;SRV\*C:\symbols\*https://driver-symbols.nvidia.com/;SRV\*C:\symbols\*https://software.intel.com/sites/downloads/symbols/ Source indexing You should set up source indexing in your debugger ( .srcfix  in windbg, Tools-> Options-> Debugging-> General->  Enable source server support  in Visual Studio) so that the correct source files will automatically be downloaded based on information in the downloaded symbols. Additionally, you must have  python  in your  path  in order for the  command that fetches source files  to succeed; launching the debugger from the same environment as where you build Chromium is an easy way to ensure it's present. This is highly recommended when debugging released Google Chrome builds or looking at crash dumps. Having the correct version of the source files automatically show up saves significant time so you should definitely set this. Multi-process issues Chromium can be challenging to debug because of its  multi-process architecture . When you select  Run  in the debugger, only the main browser process will be debugged. The code that actually renders web pages (the Renderer) and the plugins will be in separate processes that's not (yet!) being debugged. The  ProcessExplorer  tool has a process tree view where you can see how these processes are related. You can also get the process IDs associated with each tab from the Chrome Task Manager (right-click on an empty area of the window title bar to open). Automatically attach to child processes There are two Visual Studio extensions that enable the debugger to automatically attach to all Chrome processes, so you can debug all of Chrome at once. Microsoft's  Child Process Debugging Power Tool  is a standalone extension for this, and  VsChromium  is another option that bundles many other additional features. In addition to installing one of these extensions, you  must  run Visual Studio as Administrator, or it will silently fail to attach to some of Chrome's child processes. Single-process mode One way to debug issues is to run Chromium in single-process mode. This will allow you to see the entire state of the program without extra work (although it will still have many threads). To use single-process mode, add the command-line flag --single-process This approach isn't perfect because some problems won't manifest themselves in this mode and some features don't work and worker threads are still spawned into new processes. Manually attaching to a child process You can attach to the running child processes with the debugger. Select  Tools > Attach to Process  and click the  chrome.exe  process you want to attach to. Before attaching, make sure you have selected only Native code when attaching to the process This is done by clicking Select... in the Attach to Process window and only checking Native. If you forget this, it may attempt to attach in "WebKit" mode to debug JavaScript, and you'll get an error message "An operation is not legal in the current state." You can now debug the two processes as if they were one. When you are debugging multiple processes, open the  Debug > Windows > Processes  window to switch between them. Sometimes you are debugging something that only happens on startup, and want to see the child process as soon as it starts. Use: --renderer-startup-dialog --no-sandbox You have to disable the sandbox or the dialog box will be prohibited from showing. When the dialog appears, visit Tools > Attach to Process and attach to the process showing the Renderer startup dialog. Now you're debugging in the renderer and can continue execution by pressing OK in the dialog. Startup dialogs also exist for other child process types:  --gpu-startup-dialog ,  --ppapi-startup-dialog ,  --utility-startup-dialog ,  --plugin-startup-dialog  (for NPAPI). For utilities, you can add a service type  --utility-startup-dialog=data_decoder.mojom.DataDecoderService . You can also try  the vs-chromium plug-in  to attach to the right processes. Semi-automatically attaching the debugger to child processes The following flags cause child processes to wait for 60 seconds in a busy loop for a debugger to attach to the process. Once either condition is true, it continues on; no exception is thrown. --wait-for-debugger-children [=filter] The filter, if provided, will fire only if it matches the  --type  parameter to the process. Values include  renderer ,  plugin  (for NPAPI),  ppapi ,  gpu-process , and  utility . When using this option, it may be helpful to limit the number of renderer processes spawned, using: --renderer-process-limit = 1 Image File Execution Options Using Image File Execution Options (IFEO) will not work because CreateProcess() returns the handle to the debugger process instead of the intended child process. There are also issues with the sandbox. Time travel debugging You can do  time travel debugging using WinDbg Preview  (must be installed from the Microsoft Store). This lets you execute a program forward and backwards. After capturing a trace, you can set breakpoints and step through code as normal, but also provides 'backwards' commands (g-, t-, p-) so that you can go back and forth through the execution. It is especially useful to set data breakpoints ( ba command ) and reverse continuing, so you can see when a certain variable was last changed to its current value. Chromium specifics: The type of injection the time travel tracer needs to perform is incompatible with the Chromium sandbox. In order to record a trace, you'll need to run with  --no-sandbox . Chromium cannot run elevated with Administrator privileges, so the "Launch executable (advance)" option won't work, you'll need to attach after the process has already launched via the checkbox in the bottom right. If you need to record startup-like things, you'll have to use --{browser,gpu,renderer,utility}-startup-dialog, then attach (and hope the relevant code hasn't executed before that point). JsDbg -- data structure visualization You can install  JsDbg as a plugin for WinDbg or Visual Studio . It interactively lets you look at data structures (such as the DOM tree, Accessibility tree, layout object tree, and others) in a web browser as you debug. See the  JsDbg site  for some screen shots and usage examples. This also works when examining memory dumps (though not minidumps), and also works together with time travel debugging. Visual Studio hints Debug visualizers Chrome's custom debug visualizers should be added to the pdb files and automatically picked up by Visual Studio. The definitions are in  //tools/win/DebugVisualizers  if you need to modify them (the BUILD.gn file there has additional instructions). Don't step into trivial functions The debugger can be configured to automatically not step into functions based on regular expression. Edit  default.natstepfilter  in the following directory: For Visual Studio 2015:  C:\Program Files (x86)\Microsoft Visual Studio 14.0\Common7\Packages\Debugger\Visualizers  (for all users) or  %USERPROFILE%\My Documents\Visual Studio 2015\Visualizers  (for the current user only) For Visual Studio 2017 Pro:  C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\Common7\Packages\Debugger\Visualizers  (for all users) or  %USERPROFILE%\My Documents\Visual Studio 2017\Visualizers  (for the current user only) Add regular expressions of functions to not step into. Remember to regex-escape  and  XML-escape them, e.g. < for < and \. for a literal dot. Example: < Function > < Name > operator new </ Name > < Action > NoStepInto </ Action > </ Function > < Function > < Name > operator delete </ Name > < Action > NoStepInto </ Action > </ Function > <!-- Skip everything in std --> < Function > < Name > std::.* </ Name > < Action > NoStepInto </ Action > </ Function > <!-- all methods on WebKit OwnPtr and variants, ... WTF::*Ptr<*>::* --> < Function > < Name > WTF::.*Ptr<.*>::.* </ Name > < Action > NoStepInto </ Action > </ Function > This file is read at start of a debugging session (F5), so you don't need to restart Visual Studio after changing it. More info:  Microsoft email thread V8 and Chromium V8 supports many command-line flags that are useful for debugging. V8 command-line flags can be set via the Chromium command-line flag --js-flags; for instance: chrome.exe --js-flags= "--trace_exception --heap_stats" Note that some V8 command-line flags exist only in the debug build of V8. For a list of all V8 flags try: chrome.exe --js-flags= "--help" Graphics debugging GPU Acceleration of rendering can be more easily debugged with tools. See: Graphics Debugging in Visual Studio 2013 Graphical debugging with NVIDIA NSight Debugging on another machine Sometimes it's useful to debug installation and execution on a machine other than your primary build box. To run the installer on said other machine, first build the mini_installer target on your main build machine (e.g., autoninja -C out\Debug mini_installer). Next, on the debug machine: Make the build machine's build volume available on the debug machine either by mounting it locally (e.g., Z:\) or by crafting a UNC path to it (e.g., \\builder\src) Open up a command prompt and change to a local disk Run src\tools\win\ copy-installer.bat  in the remote checkout by way of the mount (e.g., Z:\PATHTOCHECKOUT\src\...) or UNC path (e.g., \\builder\src\...). This will copy the installer, DLLs, and PDBs into your debug machine's C:\out or C:\build (depending on if you're rocking the component=shared_library build or not) Run  C:\out\Debug\mini_installer.exe  with the flags of your choice to install Chrome. This can take some time, especially on a slow machine. Watch the Task Manager and wait until mini_installer.exe exits before trying to launch Chrome (by way of the shortcut(s) created by the installer) For extra pleasure, add C:\out\Debug to your _NT_SYMBOL_PATH environment variable Consider reading the documentation at the top of copy-installer.bat to see how you can run it. It tries to be smart and copy the right things, but you may need to be explicit (e.g., "copy-installer.bat out Debug"). It is safe to re-run the script to copy only modified files (after a rebuild, for example). You can also use the zip action of the isolate scripts (tools\mb\mb.py) to package all the files for a target into a single zip file, for example: python tools\mb\mb.py zip out/Release base_unittests base_unittests. zip Finding all memory allocations It is possible to use Heap Snapshots to get call stacks on all outstanding allocations that use the OS heap. This works particularly well if heap snapshots are started as soon as the Chrome browser process is created, but before it starts running. Details can be found in  this batch file . However, with  PartitionAlloc Everywhere  most Chromium allocations no longer use the Windows heap so this will only find a subset of allocations, mostly from OS DLLs. Find memory leaks Note: as with Heap Snapshots the utility of UMDH is greatly reduced now because PartitionAlloc Everywhere has mostly replaced the Windows heap. The Windows heap manager has a really useful debug flag, where it can be asked to capture and store a stack trace with every allocation. The tool to scrape these stack traces out of processes is UMDH, which comes with  WinDbg . UMDH is great. It will capture a snapshot of the heap state as many times as you like, and it'll do it fairly quickly. You then run it again against either a single snapshot, or a pair of snapshots, at which time it'll symbolize the stack traces and aggregate usage up to unique stack traces. Turning on the user stack trace database for chrome.exe with gflags.exe makes it run unbearably slowly; however, turning on the user stack trace database on for the browser alone is just fine. While it's possible to turn on the user stack database with the "!gflag" debugging extension, it's too late to do this by the time the initial debugger breakpoint hits. The only reasonable way to do this is to Launch GFlags.exe, Enable the user stack trace database (per image below), Launch Chrome under the debugger. Set a breakpont when chrome.dll loads with "sxe ld chrome.dll". Step up, to allow Chrome.dll to initialize. Disable the stack trace database in GFlags.exe. Continue chrome, optionally detaching the debugger. GFlags.exe settings for user mode stack trace database. If you then ever suffer a browser memory leak, you can snarf a dump of the process with umdh - p :<my browser pid> > chrome-browser-leak-umdh-dump.txt which can then typically be "trivially" analyzed to find the culprit. Miscellaneous Note that by default Application Verifier only works with non-official builds of Chromium. To use Application Verifier on official builds you need to add --disable-features=RendererCodeIntegrity to avoid sandbox crashes in renderer processes. See  crbug.com/1004989  for details. See also  this page . Application Verifier  is a free tool from Microsoft (available as part of the Windows SDK) that can be used to flush out programming errors. Starting with M68 Application Verifier can be enabled for chrome.exe without needing to disable the sandbox. After adding chrome.exe to the list of applications to be stressed you need to expand the list of Basics checks and disable the  Leak  checks. You may also need to disable  Handles  and  Locks  checks depending on your graphics driver and specific Chrome version, but the eventual goal is to have Chrome run with  Handles  and  Locks  checks enabled. When bugs are found Chrome will trigger a breakpoint so running all Chrome processes under a debugger is recommended. Chrome will run much more slowly because Application Verifier puts every heap allocation on a separate page. Note that with PartitionAlloc Everywhere most Chromium allocations don't actually go through the Windows heap and are therefore unaffected by Application Verifier. You can check the undocumented 'Cuzz' checkbox in Application Verifier to get the Windows thread scheduler to add some extra randomness in order to help expose race conditions in your code. To put a breakpoint on CreateFile(), add this break point: {,,kernel32.dll}_CreateFileW@28 {,,kernel32.dll} specifies the DLL (context operator). _ prefix means extern "C". @28 postfix means _stdcall with the stack pop at the end of the function. i.e. the number of arguments in BYTES. You can use  DebugView  from SysInternals or  sawbuck  to view LOG() messages that normally go to stderr on POSIX.

1 volume ; 23 cm

1 volume ; 23 cm

John Park performs 'God Bless The Child' on American Idol Top 24 Performance Night. Visit http://kittyeatdog.blogspot.com for more American Idol 9 Updates and Join our Idol polls every week! =D

Michael Lynche sings Its a man's man's world on American Idol Top 20 Performance Night. Visit http://kittyeatdog.blogspot.com for more American Idol 9 Updates

Enhance your agricultural productivity with JRS Parts' durable and reliable top link assemblies. Designed to withstand the rigors of farming, these assemblies ensure optimal tractor performance and smooth operation. Visit JRS Parts to find your perfect match today.

📆 07/29/2025 - ⏲ 1hr 33m 04s ►They are working hard to be the BIGGEST little band IN THE WORLD (or Rochester, whichever comes first). That’s a pretty serious goal. However, you should know…we are very serious about not taking ourselves very seriously. With full-time “real jobs”, responsibilities, and the everyday routine, we perform in JUMBO shrimp to have FUN making music & new friends. And while we’re at it, we’re committed to ensuring you enjoy yourself as much as we are. This video was recorded by Penfield Television. Music was provided by Jumbo Shrimp CHAPTERS 0:00:00 Introduction/Song One 0:07:05 Song Two 0:11:24 Song Three 0:15:38 Song Four 0:19:18 Song Five 0:23:09 Song Six 0:26:32 Song Seven 0:30:23 Song Eight 0:33:53 Song Nine 0:37:46 Song Ten 0:42:02 Song Eleven 0:46:01 Song Twelve 0:51:01 Song Thirteen 0:54:26 Song Fourteen 0:58:18 Song Fifteen 1:01:55 Song Sixteen 1:07:43 Song Seventeen 1:11:24 Song Eighteen 1:16:01 Song Nineteen 1:20:04 Song Twenty 1:24:18 Song Twenty One ►Penfield Amphitheater & Kiwanis Stage in Penfield, N.Y. An excellent place to spend a summer evening! 3100 Atlantic Avenue in Veterans Memorial Park. Bring your family and friends for an unforgettable night of togetherness and shared musical moments. ►Amphitheater performances Playlist: https://www.youtube.com/playlist?list=PLnvYNpja7IUmbfzB2Z-Nn75GkOEWDprJu ►Penfield Pops Band performances Playlist: https://www.youtube.com/playlist?list=PLnvYNpja7IUndr6uw5Dab4oV_Wuj6uPjC ►Thank you for watching! SUBSCRIBE to Penfield Television: https://www.youtube.com/channel/UCLctix0o61bACiWd6Oa-S5Q?sub_confirmation=1 ►Follow Penfield Television: https://www.penfieldTV.org http://www.facebook.com/PenfieldTelevision http://www.twitch.tv/penfieldTV http://www.twitter.com/PenfieldTV ►Penfield Recreation: https://www.penfieldrec.org https://www.facebook.com/PenfieldRecreation https://twitter.com/Penfield_Rec ►Follow the Town of Penfield: https://www.penfield.org https://www.facebook.com/townofpenfieldNY https://twitter.com/townofpenfield #townofpenfield #music #amphitheater

/Documents/04. Service Delivery and Budget Implementation Plans/2019-20/Adopted SDBIP/02. Local municipalities/WC026 Langeberg/Report - A3792 - Key Performance Indicators to be amended - KPI s - The Top Layer SDBIP 2018 2019.pdf

This manuscript reports the monitored performance results of roof top solar photovoltaic (PV) power plants in different parts of Tamilnadu, India. In this work, PV plants of capacities 84 kWp and 18 kWp located at Tirunelveli and Ranipet respectively in Tamilnadu are considered. During an eight month period, of September 2014 to April 2015, these plants had generated 43.99 MWh and 15.55 MWh units of electricity respectively. The average electricity production per day for the considered period of these plants is 181.74 kWh and 62.81 kWh respectively. The performance ratio (PR) of these plants PV1 and PV2 is found to be 0.52 and 0.86 respectively. The characteristics of poly crystalline PV modules and the performance of employed photovoltaic inverters are also analyzed.It is observed that external conditions like climate and bad weather significantly reduces the PV system output, whereas it reduces marginally due to inverter failure as observed from the values of energy yield and performance ratio of these plants. Online monitoring of PV plant with DC/AC line and phase voltages and current waveforms observed for the given day are also presented.

Top-down control is a higher-order process which allows us to utilise information processed at an early at an early stage of processing to direct our attention towards task-relevant items in our environment and away from distractions. Research suggests that young adults can use this skill to ignore eye-catching (or salient) distractions. It is theorised that this skill follows an "inverted-U" pattern in that it gradually develops from childhood to young adulthood and then gradually declines in older adulthood. This research questions the development of this skill from early-childhood (five years) to older adulthood (60+ years). We will use two measures of top-down control (the cued visual search task and AX-Continuous Performance Task) to first understand what age this skill develops in childhood; second, whether children and adults can use top-down control to reduce distraction; and third, whether participants behave similarly or differently in both measures of top-down control. This research will provide a greater insight into the development and effectiveness of top-down control across the lifespan. This pre-registration contains amendments to a previous version.

Top-down control is a higher-order process which allows us to utilise information processed at an early at an early stage of processing to direct our attention towards task-relevant items in our environment and away from distractions. Research suggests that young adults can use this skill to ignore eye-catching (or salient) distractions. It is theorised that this skill follows an "inverted-U" pattern in that it gradually develops from childhood to young adulthood and then gradually declines in older adulthood. This research questions the development of this skill from early-childhood (five years) to older adulthood (60+ years). We will use two measures of top-down control (the cued visual search task and AX-Continuous Performance Task) to first understand what age this skill develops in childhood; second, whether children and adults can use top-down control to reduce distraction; and third, whether participants behave similarly or differently in both measures of top-down control. This research will provide a greater insight into the development and effectiveness of top-down control across the lifespan. This pre-registration contains amendments to a previous version.

Top-down control is a higher-order process which allows us to utilise information processed at an early at an early stage of processing to direct our attention towards task-relevant items in our environment and away from distractions. Research suggests that young adults can use this skill to ignore eye-catching (or salient) distractions. It is theorised that this skill follows an "inverted-U" pattern in that it gradually develops from childhood to young adulthood and then gradually declines in older adulthood. This research questions the development of this skill from early-childhood (five years) to older adulthood (60+ years). We will use two measures of top-down control (the cued visual search task and AX-Continuous Performance Task) to first understand what age this skill develops in childhood; second, whether children and adults can use top-down control to reduce distraction; and third, whether participants behave similarly or differently in both measures of top-down control. This research will provide a greater insight into the development and effectiveness of top-down control across the lifespan. This pre-registration contains amendments to a previous version.

Top-down control is a higher-order process which allows us to utilise information processed at an early at an early stage of processing to direct our attention towards task-relevant items in our environment and away from distractions. Research suggests that young adults can use this skill to ignore eye-catching (or salient) distractions. It is theorised that this skill follows an "inverted-U" pattern in that it gradually develops from childhood to young adulthood and then gradually declines in older adulthood. This research questions the development of this skill from early-childhood (five years) to older adulthood (60+ years). We will use two measures of top-down control (the cued visual search task and AX-Continuous Performance Task) to first understand what age this skill develops in childhood; second, whether children and adults can use top-down control to reduce distraction; and third, whether participants behave similarly or differently in both measures of top-down control. This research will provide a greater insight into the development and effectiveness of top-down control across the lifespan. This pre-registration contains amendments to a previous version.

Top-down control is a higher-order process which allows us to utilise information processed at an early at an early stage of processing to direct our attention towards task-relevant items in our environment and away from distractions. Research suggests that young adults can use this skill to ignore eye-catching (or salient) distractions. It is theorised that this skill follows an "inverted-U" pattern in that it gradually develops from childhood to young adulthood and then gradually declines in older adulthood. This research questions the development of this skill from early-childhood (five years) to older adulthood (60+ years). We will use two measures of top-down control (the cued visual search task and AX-Continuous Performance Task) to first understand what age this skill develops in childhood; second, whether children and adults can use top-down control to reduce distraction; and third, whether participants behave similarly or differently in both measures of top-down control. This research will provide a greater insight into the development and effectiveness of top-down control across the lifespan. This pre-registration contains amendments to a previous version.

Includes bibliographical references and index

Includes bibliographical references and index

Bibliography: p. 285-288

This Race to the Top - Early Learning Challenge (RTT-ELC) annual performance report for the year 2015 describes California's accomplishments, lessons learned, challenges, and strategies California will implement to address those challenges. California's RTT-ELC implements a unique approach that builds upon its local and statewide successes to create sustainable capacity at the local level and addresses the geographic and cultural diversity of California. Approximately 77 percent of the grant funding is being spent at the local level, via 17 original consortia and 14 mentee counties, to support the development and expansion of successful local Quality Rating and Improvement System (QRIS) efforts focused on improved outcomes for children with high needs. Expansion of QRIS in California took root in 2015 with the RTT-ELC grant serving as a foundation that resulted in the expansion of QRIS throughout the state. In 2015, the RTT-ELC QRIS efforts transitioned to a state-wide effort as a result of the release of the California State Preschool Program (CSPP) QRIS Block Grant and F5CA Improve and Maximize Programs so All Children Thrive (IMPACT) grant. During 2015, all 58 counties began participating in either one or both funding opportunities indicating program quality improvement is a major priority at the state and local level in preparing young children for lifelong success. Consortia report QRIS is now seen as the effective umbrella to connect all Quality Improvement (QI) efforts within the counties. During the fourth year of the grant, California moved forward with accomplishments in multiple areas while also encountering some lessons learned and challenges. The list of accomplishments in 2015 far outweighed the list of challenges expressed by consortia and reinforced the direction taken by California to build on local successes to create sustainable local capacity in order to address the geographic and cultural diversity of California. The top three accomplishments include: (1) Tiered Quality Rating and Improvement System (TQRIS) implementation and program participation; (2) stakeholders, partnerships, leveraging, and aligning QRIS efforts; and (3) communication strategies and community outreach. The valuable and powerful partnerships between the Team and the Consortia, as well as across local consortia and regions, continue to provide lessons learned. Flexibility and patience continued to be crucial in the fourth year of implementation. Inherent in any implementation of this scale are logistical challenges, including effective communication, gaining and sustaining buy-in and engagement, and working within the bounds of multiple agencies and stakeholders. [For "Race to the Top - Early Learning Challenge Year 2015 Progress Update," see ED583454.]

Minnesota's vision for its Race to the Top -- Early Learning Challenge (RTT-ELC) work was based in years of research showing the impact of high quality early childhood programs on children's school readiness and success in life. Minnesota's State plan was to increase access to high quality early learning development programs for children across the state and in four specific high needs areas of the state. The first strategy was to improve the quality of programs through a tiered quality improvement rating system, revision of early learning standards, and supports for the workforce. The second strategy was to increase children's access to high quality early childhood programs through early learning scholarships to families and grants to incentivize the expansion of high quality programs. The final strategy sought to enhance assessment and data systems in order to improve the ability to measure progress and support children's learning and development throughout all early childhood experiences. The state plan focused on statewide improvement of quality, access to quality and the ability to make evidence-based policy decisions. Strategies included investing in communities where children with high needs live, improving the quality of where they learn, empowering their families to make choices, focusing on evidence-based professional development for the adults who care for them and providing meaningful data to inform decision-making for local programs and state policymakers.

This Race to the Top - Early Learning Challenge (RTT-ELC) final performance report for the year 2016 describes Delaware's accomplishments, lessons learned, challenges, and strategies Delaware will implement to address those challenges. Delaware made significant progress throughout the Race to the Top Early Learning Challenge in four identified areas: 1) continued success in implementing Early Learning Challenge Activities and exceeding outcome targets; 2) building state and community awareness and support of a high-quality early learning system; 3) establishing a permanent governance structure for the state's early learning system and ongoing leadership; and, 4) building a plan for sustaining key Early Learning Challenge initiatives and progress after the Early Learning Challenge funding expires. Success in these and other key grant performance measures clearly indicate that the grant influenced positive system change which directly benefits Delaware's earliest learners and their families.

In the machine learning problems, the performance measure is used to evaluate the machine learning models. Recently, the number positive data points ranked at the top positions (Pos@Top) has been a popular performance measure in the machine learning community. In this paper, we propose to learn a convolutional neural network (CNN) model to maximize the Pos@Top performance measure. The CNN model is used to represent the multi-instance data point, and a classifier function is used to predict the label from the its CNN representation. We propose to minimize the loss function of Pos@Top over a training set to learn the filters of CNN and the classifier parameter. The classifier parameter vector is solved by the Lagrange multiplier method, and the filters are updated by the gradient descent method alternately in an iterative algorithm. Experiments over benchmark data sets show that the proposed method outperforms the state-of-the-art Pos@Top maximization methods.

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The Blaine High School Orchestra was one of just two orchestras across the state selected to perform at the Minnesota Music Educators Association Midwinter Convention. The students have been working on the pieces for more than five months, and the orchestra director calls is their "superbowl." The performance is on Friday, February 17 at 6:45pm at the Minneapolis Convention Center.

This Race to the Top - Early Learning Challenge (RTT-ELC) annual performance report for the year 2015 describes Delaware's accomplishments, lessons learned, challenges, and strategies Delaware will implement to address those challenges. Delaware made significant progress in four primary areas in Year Four: (1) continued success in implementing Early Learning Challenge activities and exceeding outcome targets; (2) building state and community awareness and support of a high-quality early learning system; (3) establishing a permanent governance structure for the state's early learning system and ongoing leadership; and (4) building a plan for sustaining key Early Learning Challenge initiatives and progress after the Early Learning Challenge funding expires. Key performance targets met or exceeded in Year Four include: (1) The number of early learning programs participating in the state's quality rating and improvement system has grown from 134 to 565, with 66% of participating programs rated in the top quality tiers, exceeding the target for performance; (2) More than 79% of children with high needs (16,974) were served in programs participating in Delaware Stars in 2015; (3) Delaware exceeded its performance measure for serving children with high needs in DE Stars top tier programs, with 63% of children with high needs (10,193) served by Star 3, 4, and 5 programs; and (4) The number of early learning professionals receiving T.E.A.C.H.® Early Childhood scholarships for working toward early childhood degrees or credentials has doubled since 2011; up to 209 scholars in 2015. Success in these and other key grant performance measures clearly indicate that the grant has influenced positive system change--directly benefiting Delaware's earliest learners and their families. The Office of Early Learning (OEL) also learned some valuable lessons on how best to educate and engage new stakeholders in the state's early learning work. Outreach efforts were intensified, and all presentations started with brain research and early learning's connection to not only school success, but economic development, crime reduction, and thriving communities. [For "Race to the Top - Early Learning Challenge Year 2015 Progress Update," see ED583454.]

This Race to the Top - Early Learning Challenge (RTT-ELC) final performance report for the year 2016 describes Rhode Island's vision for RTT-ELC, what has changed in Rhode Island and in early learning programs as a result of RTT-ELC, and the lessons learned in implementing a comprehensive reform agenda. The RTT-ELC grant offered Rhode Island the opportunity to formalize a cohesive, coordinated vision among five state agencies and accelerate Rhode Island's progress toward operationalizing this vision by providing significant resources and supports to early learning programs and families. Specifically, during the five years of RTT-ELC, the following key aspects of the state's vision were realized: (1) A governance system among state agencies was formed to support collective, data-driven decision-making and to monitor project implementation; (2) Aligned sets of standards for programs, workforce, and children were created that resulted in a common, cohesive focus on children's learning and development; (3) Increased numbers of early learning programs engaged in the state's program quality continuum; (4) The early learning workforce was increasingly engaged in high-quality professional development and college courses aligned with workforce competencies for teachers; and (5) More Rhode Island children received comprehensive developmental screening and referral for indicated services or supports prior to kindergarten entry. Key lessons learned include: (1) Agency coordination with strong community engagement is critical; (2) The set-up and administration of grant tasks take time; (3) Evaluation and evidence are needed for sustainability; (4) Building data systems requires specialized skills and a nimble structure; (5) Data governance should be prioritized; (6) Alignment is important for education, programs, and students; (7) A Tiered Quality Rating and Improvement System (TQRIS) can support a mixed delivery system; (8) Policy and supports are critical levers to incentivize program improvements; (9) Remediating teaching and learning is challenging; (10) Standards help make the case for public investment; (11) Clear education pathways help develop the workforce; and (12) Low wages undermine workforce development efforts.

This Race to the Top - Early Learning Challenge (RTT-ELC) annual performance report for the reporting period of 01-01-2016 to 12-31-2016 in Pennsylvania provides a detailed summary of accomplishments, lessons learned, challenges, and strategies that the state of Pennsylvania will implement to address those challenges. Some accomplishments highlighted in the area of Community Innovation Zones include the following: (1) Several Community Innovation Zones (CIZ's) have made improvements in their relationships along the P-3 continuum. Grantees accomplished goals such as curriculum alignment between early learning and school district programs, increasing on-time kindergarten registration through coordinated efforts, and the creation of early learning councils with stakeholder representation across the P-3 continuum; (2) In 2016, Pennsylvania focused on building the sustainability of these successful local projects by making promising practices accessible statewide and building the capacity of the CIZ collaborations to obtain resources to continue work after the grant period ends; (3) The Office of Child Development and Early Learning (OCDEL) approached sustainability through the lens of focusing on the process of systems and adult behavior change, building capacity at the local level, and developing statewide collaborations; and (4) Interim results of the family engagement study to document best family engagement strategies of 12 CIZs. In the area of developing and expanding a statewide Tiered Quality Rating Improvement system (TQRIS), some of the following improvements have been accomplished: (1) Pennsylvania made significant progress towards re-visioning its tiered quality rating and improvement system (TQRIS). The new Keystone Standards, Training and Professional Development, Assistance, Resources and Supports (STARS) system will launch for the 2017-18 state fiscal year; (2) Pennsylvania launched its re-visioning process in February 2016 with the Keystone STARS Think Tank, a diverse group of 53 stakeholders chosen by an Office of Child Development and Early Learning (OCDEL) selection team based on rigorous criteria to reflect geographic, racial, gender, and professional diversity; and (3) Using these Core Values, the Think Tank developed a logic model to build supply of quality diverse early learning programs; increase family access to quality programs; build and maintain a qualified workforce; empower program leadership; integrate data and management systems; create a responsive, strengths-based and individualized technical assistance/coaching system; and create a Keystone STARS administrative system that is consistent in quality and responsive to providers and the technical assistance/coaching system. In the area of improving early learning standards: (1) Pennsylvania participated in the Pre-natal-3rd Grade Governor's Institutes, "P-3 Collaboration: Working Together for Student Success," increased by 40 percent. Eighty-six teams of school district, early childhood professionals, higher education and other representatives attended the four Institutes in Pocono Manor, Erie, Philadelphia, and Pittsburgh; and (2) the Pre-natal-3rd Grade Governor's Institutes, "P-3 Collaboration: Working Together for Student Success," increased by 40 percent. Eighty-six teams of school district, early childhood professionals, higher education and other representatives attended the four Institutes in Pocono Manor, Erie, Philadelphia, and Pittsburgh. It was noted that lack of funding at the local level has proven to be a barrier in the implementation of the new P-3 strategies learned at the institutes. In order to better support the implementation the team action plans, Pennsylvania offered $4,000 prototype mini-grants to all teams attending any of the Institutes since 2014. In the area of Comprehensive Assessment Systems, Pennsylvania leveraged its RTT-ELC grant to meet new Child Care and Development and Block Grant (CCDBG) requirements for a consumer website by enhancing the Pennsylvania's Promise for Children (PA Promise) website. Based on a content audit by Thoughtform, Inc., the PA Promise website contained high-quality content. Pennsylvania decided to expand the existing PA Promise website to meet and CCDBG grant requirements and better serve families. In the area of engaging and supporting families, Pennsylvania expanded its supports to CIZs and other organizations in the state to educate and engage families by providing five Community Innovation Zones training on implementing the Be Strong Parent Cafe approach in their communities. Detailed accounts of accomplishments in supporting the early childhood workforce, understanding the status of children's learning and development at kindergarten entry, and early learning data systems are also provided.

In this weeks episode we cover our top picks for performance enhancing supplements based on the most current research. If you're looking to squat heavier, run faster, jump higher, and push through your workouts with increased performance this episode is for you. We also threw in a little bonus, highlighting the supplements with outrageous claims with little to no research to support them.

This Race to the Top - Early Learning Challenge (RTT-ELC) annual performance report for the year 2014 describes Massachusetts' accomplishments, lessons learned, challenges, and strategies Massachusetts will implement to address those challenges. Massachusetts continues to make progress implementing its 2012-2015 Early Learning Plan, developed to carry out projects funded by the Race to the Top - Early Learning Challenge (RTT-ELC) grant. This plan articulates strategies to build strong partnerships among state agencies and communities to ensure every child in Massachusetts has an opportunity to succeed in school and beyond. The Early Learning Plan consists of the following components: (1) Developing and using statewide, high-quality early learning and development standards; (2) Supporting effective uses of comprehensive assessment systems including the assessment of children's learning and development at kindergarten entry; (3) Promoting and supporting program quality; (4) Engaging and supporting families; (5) Supporting early childhood educators to improve their knowledge, skills, and abilities; and and (6) Building an early learning data system to improve instruction, practices, services, and policies. This report summarizes key accomplishments achieved in 2014 to implement the Massachusetts Early Learning Plan, lessons learned, challenges, and strategies to address those challenges. [For "At a Glance: The Race to the Top - Early Learning Challenge Year 2014 Progress Report," see ED583096.]

This Race to the Top - Early Learning Challenge (RTT-ELC) annual performance report for the year 2014 describes Georgia's accomplishments, lessons learned, challenges, and strategies Georgia will implement to address those challenges. In 2014, the first year of implementation, Georgia was able to use the Early Learning Challenge opportunity to strengthen and build on the state's already strong early education foundation. This is most evident in its successes, such as creating empowerment zones, increasing participation in the Georgia's Tiered Quality Rating and Improvement System, and expanding opportunities for early education teachers to incorporate the use of Early Learning and Development standards. However, the successes are also evident in the way Georgia used the Early Learning Challenge opportunity to solidify the critical role of early learning in the state's educational pipeline. The successes were created through purposeful stakeholder engagement, appropriate and meaningful use of data, and a realignment of many of Georgia's early education programs. Beginning with the Georgia's Pre-K program, the state has historically used a system-building approach in developing and expanding early education opportunities for children and families. This includes the development of a distinct education department dedicated to early learning--Bright from the Start: Georgia Department of Early Care and Learning (DECAL) and the alignment of key health and safety and family engagement programs in the state's Tiered Quality Rating and Improvement System--Quality Rated. The Year One implementation of Georgia's Early Learning Challenge initiative demonstrates the long commitment the state has made to its youngest learners and its eventual success in meeting all Early Learning Challenge targets. [For "At a Glance: The Race to the Top - Early Learning Challenge Year 2014 Progress Report," see ED583096.]

This Race to the Top - Early Learning Challenge (RTT-ELC) annual performance report for the year 2015 describes Pennsylvania's accomplishments, lessons learned, challenges, and strategies Pennsylvania will implement to address those challenges. Pennsylvania's remarkable progress in increasing participation in their tiered quality rating and improvement system, STARS, and the number of programs in the top tiers of STARS, is due to the collective impact of many public/private, cross-sector initiatives. Notable efforts include close collaboration among Pennsylvania Birth to Five, the Child Development Division (CDD), and the cross-agency implementation team of Act 166 led by the Agency of Education. The report provides details on accomplishments that: (1) improved quality and access of early learning and development opportunities; (2) improved early childhood wellness; (3) invested in a highly skilled workforce through professional development; (4) empowered communities to support young children and families; and (5) strengthened Pennsylvania's data systems to ensure that Pennsylvania is making a difference. Lessons learned pertained to financial management, flexibility to promote innovation, and creating stronger linkages between regional and state level work. [For "Race to the Top - Early Learning Challenge Year 2015 Progress Update," see ED583454.]