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The author has identified the following significant results. Four change detection techniques were designed and implemented for evaluation: (1) post classification comparison change detection, (2) delta data change detection, (3) spectral/temporal change classification, and (4) layered spectral/temporal change classification. The post classification comparison technique reliably identified areas of change and was used as the standard for qualitatively evaluating the other three techniques. The layered spectral/temporal change classification and the delta data change detection results generally agreed with the post classification comparison technique results; however, many small areas of change were not identified. Major discrepancies existed between the post classification comparison and spectral/temporal change detection results.

There are no author-identified significant results in this report. This investigation is concerned with the development of automated interpretation techniques for the recognition and identification of earth resources. The resources will be identified by using both multispectral and spatial signatures. Ground truth data and aircraft underflight photography will be used to train the recognition algorithms. The data processed will be RBV and MSS images acquired by ERTS-1 satellite, over six test sites located in the vicinities of: Phoenix, Arizona; Weslaco, Texas; Cascade Mountains, Washington; New Orleans, Louisiana; Salt Lake City, Utah; and Salton Sea, California. The processed data will be thematic maps of resources consisting of annotated and outlined images. The seasonal changes of hydrologic and agricultural resources will also be identified. The results are expected to be applicable to a future automatic system of resource inventory and management.

This report contains no significant results.

When driving, flying and even when walking one often can see motion-produced blur patterns that are caused by images of textures slipping over the retina and leaving trails of residual afterimages behind them which contain their motion histories. Other studies in this series on blur pattern parameters have shown that human observers can sensitively detect directional information within a blur pattern, for example when there is curvature change, or relative divergence of the individual blur lines. The purpose of this study was to determine whether the detection of blur pattern curvature change, studied in a previous experiment, would be enhanced if stationary reference lines were superimposed on the blur pattern. Reference lines were oriented in three ways, parallel, perpendicular, and at 45 deg to the direction of element flow. Patterns were viewed foveally or with peripheral vision.

Particle beam power density detection and measurement apparatus

The present experiment investigates whether the architecture underlying change detection decisions varies as a function of the particular type of change that occurs.

We consider the first exit time of a nonnegative Harris-recurrent Markov process from the interval $[0,A]$ as $A\to\infty$. We provide an alternative method of proof of asymptotic exponentiality of the first exit time (suitably standardized) that does not rely on embedding in a regeneration process. We show that under certain conditions the moment generating function of a suitably standardized version of the first exit time converges to that of $\Exp(1)$, and we connect between the standardizing constant and the quasi-stationary distribution (assuming it exists). The results are applied to the evaluation of a distribution of run length to false alarm in change-point detection problems.

Encounters with wind shears are of concern and have caused major accidents, particularly during landing approaches. Changes in the longitudinal component of the wind affect the aircraft by changing its kinetic energy with respect to the air. It is shown that an instrument which will measure and display the rate of change of total energy of the aircraft with respect to the air will give a leading indication of wind shear problems. The concept is outlined and some instrumentation and display considerations are discussed.

We propose a new, generic and flexible methodology for nonparametric function estimation, in which we first estimate the number and locations of any features that may be present in the function, and then estimate the function parametrically between each pair of neighbouring detected features. Examples of features handled by our methodology include change-points in the piecewise-constant signal model, kinks in the piecewise-linear signal model, and other similar irregularities, which we also refer to as generalised change-points. Our methodology works with only minor modifications across a range of generalised change-point scenarios, and we achieve such a high degree of generality by proposing and using a new multiple generalised change-point detection device, termed Narrowest-Over-Threshold (NOT). The key ingredient of NOT is its focus on the smallest local sections of the data on which the existence of a feature is suspected. Crucially, this adaptive localisation technique prevents NOT from considering subsamples containing two or more features, a key factor that ensures the general applicability of NOT. For selected scenarios, we show the consistency and near-optimality of NOT in detecting the number and locations of generalised change-points, and discuss how to extend the proof to other settings. The NOT estimators are easy to implement and rapid to compute: the entire threshold-indexed solution path can be computed in close-to-linear time. Importantly, the NOT approach is easy to extend by the user to tailor to their own needs. There is no single competitor, but we show that the performance of NOT matches or surpasses the state of the art in the scenarios tested. Our methodology is implemented in the R package \textbf{not}.

We propose a new, generic and flexible methodology for nonparametric function estimation, in which we first estimate the number and locations of any features that may be present in the function, and then estimate the function parametrically between each pair of neighbouring detected features. Examples of features handled by our methodology include change-points in the piecewise-constant signal model, kinks in the piecewise-linear signal model, and other similar irregularities, which we also refer to as generalised change-points. Our methodology works with only minor modifications across a range of generalised change-point scenarios, and we achieve such a high degree of generality by proposing and using a new multiple generalised change-point detection device, termed Narrowest-Over-Threshold (NOT). The key ingredient of NOT is its focus on the smallest local sections of the data on which the existence of a feature is suspected. Crucially, this adaptive localisation technique prevents NOT from considering subsamples containing two or more features, a key factor that ensures the general applicability of NOT. For selected scenarios, we show the consistency and near-optimality of NOT in detecting the number and locations of generalised change-points, and discuss how to extend the proof to other settings. The NOT estimators are easy to implement and rapid to compute: the entire threshold-indexed solution path can be computed in close-to-linear time. Importantly, the NOT approach is easy to extend by the user to tailor to their own needs. There is no single competitor, but we show that the performance of NOT matches or surpasses the state of the art in the scenarios tested. Our methodology is implemented in the R package \textbf{not}.

Addis Ababa, the capital city of Ethiopia is, a tropical city, found on a high elevation between 2000 m and 3000 m above sea level. The city is bordered by high rise mountains, Entoto Mountain in the North, Wechecha Mountain in the West, Furi Mountain in the South and Yerer Mountain in the East which have a crucial influence on determining the climate of the city. This study, therefore, focuses on landscape structure and forest cover change on Mount Wechecha, which has a great contribution in the regulation of modifying micro climate of the city and the environ, not only due to its elevation but also due to its dense forest cover. Monitoring land cover changes in a mountain ecosystem help to understand and pinpoint the strategy to be followed for the conservation of ecosystems found on the neighbouring mountains, which also have a great influence on regulating the climate of the city. The study uses five Landsat satellite images to derive land cover of Mount Wechecha from 1973 to 2013. Landscape structures and matrixes have been analyzed and mapped based on the patch-corridor-matrix ecological model. The results have shown that the patch analyst result is proportional to the land cover classes derived from the satellite image. The landscape structure indices of the number of patches are decreasing in all years, except for water and settlement. The forest patches are dissolved into agricultural lands. The mean core area of the forest that increases indicates that the forest coverage is limited to certain places. The Mean Core Area of agricultural land decreased in the last decade due to the agricultural land being interrupted by other economic activities, which leads to landscape modifications. Shannon's evenness and diversity index was zero. The overall result indicates that the forest, which play a great role in climate regulation and open land coverages are decreasing at a faster rate (30% and 24.5% in 1973–14% and 2.4%. in 2013 respectively), while settlement is highly increasing (from 1.3% to 26.25% same period). Hence, the priority has to be given to protecting forest loss of Mount Wechecha and other forests on the neighbouring mountains surrounding Addis Ababa in order to have favorable climate conditions in the city. Such type of studies on mountain chains are very vital in considering appropriate environmental planning mechanisms and to formulate policies at the landscape level.

The goal of this project is to develop a computer-aided diagnosis (CAD) system for interval change analysis of lesions on mammograms. An important component of the CAD system is the multistage regional registration technique for identifying corresponding microcalcification clusters on temporal pairs of mammograms. In the first stage, an initial search region was estimated on the prior mammogram based on the cluster location on the current mammogram. In the second stage the search region was refined. In the third stage the cluster was detected within the search region. In the first stage we used the regional registration method (RRM), which outperformed the warping techniques. 175 temporal pairs of mammograms were used for evaluation. The average distance between% the estimated and the true cluster centroids on the previous mammogram after the initial stage was 7.95+-4.73mm. In the second stage, automated detection of microcalcification cluster within the search region is performed. Using our current cluster detection program with standard thresholds, 76.6% (134/175) TP with 0.45 false positives (FP) were detected within the search region. Using a high-sensitivity threshold, 89.1%(156/175) TP with 0.43 FP were detected. In the third stage the correspondence classifier was used and it reduced the FP rate to an average of 0.19 FP cluster with sensitivity of 81% (141/175).

One of the most significant barriers to establishing an early ovarian cancer (OVCA) detection test is the difficulty of identifying patients with early stage OVCA. In earlier approaches to develop an early detection test for OVCA, using serum levels of CA- 125, traditional transvaginal ultrasound and their combination did not improve the detection of OVCA remarkably. The limitation of CA-125 is that it is non-specific in detecting OVCA at the early stage and no imaging target(s) in the ovary corresponding to the elevated serum CA-125 levels has been defined. Our overall goal is to establish an early detection test for OVCA using vascular endothelial growth factor receptor -2 (VEGFR-2) targeted ultrasound molecular imaging in association with serum anti-nuclear matrix protein (anti-NMP) antibodies (a marker of tumor associated nuclear change) and serum IL-16 levels (a marker of ovarian tumor-associated neoangiogenesis, TAN). This goal is being achieved by two specific aims. The results of Aim 1 suggest that VEGFR-2 targeted ultrasound imaging enhances the detection of ovarian tumors in the laying hen model of spontaneous OVCA. Changes in OVCA related VEGFR-2 targeted imaging indices are associated with the prevalence of serum anti-NMP antibodies and the elevation of serum IL-16 levels.

The decade of the 1990s is the warmest decade of the last century while the year 1998 is the warmest year ever observed by modern techniques with 9 out of 12 months of the year being the warmest month. Since the Arctic is expected to provide early signals of a possible warming scenario, detailed examination of changes in the Arctic environment is important. In this study, we examined available satellite ice cover and surface temperature data, wind and pressure data, and ocean hydrographic data to gain insights into the warming phenomenon. The areas of open water in both western and eastern regions of the Arctic were found to follow a cyclical pattern with approximately decadal period but with a lag of about three years between the two regions. The pattern was interrupted by unusually large anomalies in open water area in the western region in 1993 and 1998 and in the eastern region in 1995. The big 1998 open water anomaly occurred at the same time when a large surface temperature anomaly was also occurring in the area and adjacent regions. The infrared temperature data show for the first time the complete spatial scope of the warming anomalies and it is apparent that despite the magnitude of the 1998 anomaly, it is basically confined to North America and the Western Arctic. The large increases in open water areas in the Western Sector form 1996 to 1998 were observed to be coherent with changing wind directions which was predominantly cyclonic in 1996 and anti-cyclonic in 1997 and 1998. Detailed hydrography measurements up to 500 m depth over the same general area in April 1996 and April 1997 also indicate significant freshening and warming in the upper part of the mixed layer suggesting increases in ice melt. Continuous ocean temperature and salinity data from ocean buoys confirm this result and show significant seasonal changes from 1996 to 1998, at depths of 8 m, 45 m, and 75 m. Long data records of temperature and hydrography were also examined and the potential impact of a warming, freshening, and the presence of abnormally large open areas on the state of the Arctic climate system are discussed.

The objectives are to develop techniques to detect and remove long-term instrument drifts from the operational Solar Backscatter Ultraviolet (SBUV/2) data base using periodic measurements made by the Shuttle Solar Backscatter Ultraviolet Spectral Radiometer and to evaluate the potential effects of scattering by atmospheric aerosols on our ability to detect trends in ozone using SBUV-class instruments.

We generalize the classic change-point problem to a "change-set" framework: a spatial Poisson process changes its intensity on an unobservable random set. Optimal detection of the set is defined by maximizing the expected value of a gain function. In the case that the unknown change-set is defined by a locally finite set of incomparable points, we present a sufficient condition for optimal detection of the set using multiparameter martingale techniques. Two examples are discussed.

We generalize the classic change-point problem to a "change-set" framework: a spatial Poisson process changes its intensity on an unobservable random set. Optimal detection of the set is defined by maximizing the expected value of a gain function. In the case that the unknown change-set is defined by a locally finite set of incomparable points, we present a sufficient condition for optimal detection of the set using multiparameter martingale techniques. Two examples are discussed.

We propose a novel and efficient method, that we shall call TopRank in the following paper, for detecting change-points in high-dimensional data. This issue is of growing concern to the network security community since network anomalies such as Denial of Service (DoS) attacks lead to changes in Internet traffic. Our method consists of a data reduction stage based on record filtering, followed by a nonparametric change-point detection test based on $U$-statistics. Using this approach, we can address massive data streams and perform anomaly detection and localization on the fly. We show how it applies to some real Internet traffic provided by France-T\'el\'ecom (a French Internet service provider) in the framework of the ANR-RNRT OSCAR project. This approach is very attractive since it benefits from a low computational load and is able to detect and localize several types of network anomalies. We also assess the performance of the TopRank algorithm using synthetic data and compare it with alternative approaches based on random aggregation.

The purpose of this note is to show that in a widely cited paper by Yakir [Ann. Statist. 25 (1997) 2117--2126, doi:10.1214/aos/1069362390], the proof that the so-called modified Shiryayev--Roberts procedure is exactly optimal is incorrect. We also clarify the issues involved by both mathematical arguments and a simulation study. The correctness of the theorem remains in doubt.

One of the most contentious issues among contemporary climatologists is the existence of climate change and alteration in the trends of atmospheric parameters. The primary aim of this research was to examine the trends in precipitation and temperature changes under climate change conditions, and to predict future temperature and precipitation in the Kabul Drainage Basin, Afghanistan. To evaluate the impact of climate change on precipitation and temperature parameters, detection, trend analysis, prediction, and model accuracy analyses were conducted. In this process, methods such as anomalies, the non-parametric Mann-Kendall test, Theil-Sen's slope, the LARS-WG model (sixth report), and statistical indices such as the correlation coefficient, coefficient of determination (r, R²), RMSE, and NRMSE were used. The research findings indicate that in the Kabul Drainage Basin, the trends in these parameters are positive, with increasing annual average precipitation and increasing annual average temperature. The rainy season has gradually shifted from winter to summer. Specifically, the annual precipitation between 1979 and 2020 increased by approximately 0.30% or 0.93 mm, while the temperature increased significantly by about 8% (1.2°C). Predictions suggest that the trend of increasing temperature and decreasing precipitation will continue from the baseline period (1991–2021) into the 2021–2050 period. Across all scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 the minimum temperature is projected to increase by 0.4°C, 0.5°C, and 0.6°C, respectively, and the maximum temperature by 0.6°C, 0.7°C, and 0.8°C compared to the baseline period. Annual precipitation is also expected to decrease by approximately 11% compared to the baseline period. Therefore, the impacts of climate change, including increased temperatures and altered precipitation patterns, are likely to exacerbate the risk of floods and related hazards in the future. The study of precipitation and temperature parameters under climate change is crucial. Accordingly, adaptation strategies for climate change and the enhancement of resilience are recommended. Extended Abstract Introduction Climate change, as one of the most critical challenges of the present century, has had widespread effects on the environment, water resources, agriculture, and human livelihoods worldwide. It remains one of the most controversial topics in climatology. Most climatologists believe that changes in temperature or precipitation cannot simply be interpreted as long-term or short-term climatic fluctuations. Studies indicate that climate change is a global threat that disrupts the sustainability of various components of the Earth's system. Therefore, understanding current conditions and making climate projections are essential for identifying vulnerabilities and developing strategies to adapt to climate change. Long-standing internal conflicts, diverse climates, complex topography, and a lack of meteorological stations have hindered extensive research in this field. To date, no comprehensive scientific studies have been conducted on precipitation and temperature trends in the Kabul Basin. While numerous global studies exist, research efforts in Afghanistan have been limited. Among the few studies in Afghanistan are those by Babar et al. (2016), Aliyar, Dhungana, and Shrestha (2022), Broomand (2015), Mestarshed (2013, 2014), Nasrati (2018), Sharafat (2020), and Salehi (2022). These studies indicate a declining trend in precipitation, particularly during spring and winter, and a rising trend in temperature across Afghanistan. The Kabul River Basin, due to its unique geographical location and climatic conditions, has been significantly impacted by climate change, experiencing notable alterations in precipitation and temperature parameters. This region not only holds critical importance for water supply and agriculture for its inhabitants, but also warrants closer examination due to the frequent occurrence of floods and droughts. A detailed assessment of precipitation and temperature trends in the basin is essential. The primary objective of this study is to investigate the impacts of climate change on precipitation and temperature variables and to forecast these parameters in the Kabul River Basin. This research employs various methods, including anomaly detection, the non-parametric Mann-Kendall test, Sen’s slope estimator, the LARS-WG model, and correlation coefficients, to analyze precipitation and temperature trends and address the existing research gap. Material and Methods This study utilized ground-based data from 28 meteorological stations over the period 1979–2020, as well as atmospheric data from NASA's MERRA-2 dataset. In 2021, missing precipitation and temperature data for the years 1979–2020 were completed by Japanese researchers using the gap-filling method. Due to the lack of daily ground-based precipitation and temperature data, daily precipitation and temperature records for 1991–2020 were downloaded from NASA's MERRA-2 platform. Missing and anomalous data were completed using statistical methods such as moving averages, and outliers were identified and removed using statistical tests like the Shapiro-Wilk test. Analytical methods, including anomalies, the non-parametric Mann-Kendall test, Sen's slope estimator, and statistical indices such as the correlation coefficient (r), coefficient of determination (R²), RMSE, and NRMSE, were applied in the analysis. Trend analysis was conducted using the anomaly method (Equation 1) and the Mann-Kendall test (Equation 2). Using outputs from the Sixth Assessment Report of the IPCC and the LARS-WG8 model (HadGEM3-GC31-LL-CMIP6), climatic elements were projected for the 2021–2050 period. The validation of the results was conducted using statistical measures, including r, R², RMSE, and NRMSE (Equations 4, 5, and 6). Data analysis was performed using ArcGIS, Excel, and SPSS software. Additionally, using validation indices such as the correlation coefficient (r), root mean square error (RMSE), and normalized root mean square error (NRMSE), along with observational precipitation and temperature data from five meteorological stations in the Kabul River Basin for the period 1991–2020, the outputs of the LARS-WG8 model were validated. The results confirm the satisfactory performance of the model in generating downscaled precipitation and temperature data. Results and Discussion The Mann-Kendall test, recommended by the World Meteorological Organization, was used in this study to analyze the annual rainfall trend. This statistical method is a standard approach for evaluating temporal trends in hydrological data. The results, presented in Table 2, indicate that during the period 1979–2020, the mean annual rainfall was 310 mm, with minimum and maximum values of 116 mm and 358 mm, respectively. Using a 95% confidence level (α = 0.05), the calculated Z-statistic for the Mann-Kendall test was 1.96, confirming a statistically significant increasing trend. An annual rainfall increase of 0.30% was observed in the Kabul River Basin. Similarly, the trends in annual minimum, maximum, and mean temperatures were analyzed, as detailed in Table 3. The results reveal significant increases in all three temperature parameters. Specifically, the Mann-Kendall test showed that the annual minimum temperature exhibited the most pronounced trend, with a Z-value of 5.25, followed by significant increases in the annual mean and maximum temperatures. Analysis of the data in Table 2 confirms that all temperature types have risen considerably over the period 1979–2020. Figure 5, based on Table 2, illustrates the temporal trend of annual mean temperature in the Kabul River Basin, highlighting a clear and substantial increase over the study period. These findings, combined with the lack of effective adaptation measures to address climate change, suggest that the observed trends are likely to continue. This ongoing temperature rise is expected to accelerate glacier melting, leading to a higher frequency of floods in the basin in the coming years. Conclusion The results of this study indicate that the annual precipitation in the Kabul Basin during the period 1979–2020 experienced an insignificant increase of approximately 0.3%, equivalent to 0.93 mm, while temperature exhibited a significant rise of nearly 8%, corresponding to an increase of 1.2°C. Projections suggest that the upward trend in temperature and the downward trend in precipitation will persist during 2021–2050 compared to the baseline period (1991–2021). Under all Shared Socioeconomic Pathway (SSP) scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—minimum temperatures are projected to increase by 0.4°C, 0.5°C, and 0.6°C, respectively, and maximum temperatures by 0.6°C, 0.7°C, and 0.8°C, respectively, compared to the baseline period. Additionally, annual precipitation is expected to decrease by approximately 11% relative to the baseline. The forecasts further suggest changes in the region’s precipitation regime, specifically a reduction in the proportion of precipitation occurring during colder months compared to other months. Consequently, the ratio of solid precipitation (snow) to liquid precipitation (rain) is projected to decline in the coming years.

There are no author-identified significant results in this report.

Climate change occurs when the Earth's energy budget changes due to natural or possibly anthropogenic forcings. These forcings cause the climate system to adjust resulting in a new climate state that is warmer or cooler than the original. The key question is how to detect and attribute climate change. The inference of infrared spectral signatures of climate change has been discussed in the literature for nearly 30 years. Pioneering work in the 1980s noted that distinct spectral signatures would be evident in changes in the infrared radiance emitted by the Earth and its atmosphere, and that these could be observed from orbiting satellites. Since then, a number of other studies have advanced the concepts of spectral signatures of climate change. Today the concept of using spectral signatures to identify and attribute atmospheric composition change is firmly accepted and is the foundation of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) satellite mission being developed at NASA. In this work, we will present an overview of the current climate change detection concept using climate model calculations as surrogates for climate change. Any future research work improving the methodology to achieve this concept will be valuable to our society.

This paper offers a passive islanding detection method that is effective for distributed generation. When a distributed generator (DG) keeps a location powered even when access to the external electrical grid is lost, this circumstance is referred to as islanding. The power distribution system currently includes distributed generators (DGs), which provide inexpensive electricity and have fewer environmental impacts. Sometimes, these DGs continue to supply the nearby loads because of line outages and islands made by system separations. As a result, there are scenarios with unacceptable power quality. The islanding is identified if the result of the rate of change of frequency over reactive power exceeds the threshold value. The MATLAB test results from this study demonstrate the effectiveness of the suggested approach for different islanding and non-islanding scenarios. 

This is a behavioral counterpart of the ongoing neurofeedback Y-maze study, with the application of a change point detection - strategy preference estimation algorithm. The purpose of this study is to estimate how likely the Y-maze task results would be if no feedback were provided in extended presentations of the Y-maze, and how the Y-Maze and Dual Solution Paradigm strategies relate to one another.

 Access to accurate and up-to-date information on the extent and distribution of individual crop types, associated with land use changes and practices, has significant value in intensively agricultural regions. Explicit information of croplands can be useful for sustainable water resources, land and agriculture planning and management. Remote sensing, has been proven to be a more cost-effective alternative to the traditional statistically-based ground surveys for crop coverage areas that are costly and provide insufficient information. Satellite images along with ground surveys can provide the necessary information of spatial coverage and spectral responses of croplands for sustainable agricultural management. This study strives to differentiate different crop types and agricultural practices to achieve a higher detailed crop map of the Narsinghpur district.

To what extent do individuals use an optimal working memory encoding strategy? Previous work has suggested that individuals with higher working memory capacity are more likely to adopt a more optimal encoding strategy, such as encoding only a small subset of information (e.g., Cusack et al., 2009; Linke et al., 2011). These studies have mainly focused on examining how we prioritize the most relevant or valuable information. However, it is still not well understood how people select optimal strategies to boost memory performance when no explicit cues or strategic constraints are given. In visual search, studies using the adaptive choice visual search (ACVS) paradigm have found large yet stable individual differences in optimal search strategy when participants are free to search for either the optimal or the non-optimal target (Irons & Leber, 2016, 2018, 2020). Here we use a similar approach in a flicker change detection paradigm (Pailian et al., 2020; Pailian & Halberda, 2015) to investigate individual differences in optimal strategy in working memory. In the task, participants need to find one of the two changing items (a red bar and a blue bar) between two alternating displays. We are interested in how frequently participants select the optimal target, whose colour has fewer items in the display. Further, to assess the stability of individual strategies, we will examine the internal consistency and test-retest reliability of optimal target choice.

This is a follow-up experiment on our initial experiment, motivated by the finding of Souza & Oberauer (2020) that change detection procedures did not lead to a credible learning effect for repeatedly presented visual arrays. To investigate the influence of different testing conditions for the long-term learning of visuospatial arrays, the aim of this experiment is to directly compare a discrete-color-reproduction task with a change-detection procedure.

Non-stationarity of the rate or variance of events is a well-known problem in the description and analysis of time series of events, such as neuronal spike trains. A multiple filter test (MFT) for homogeneity of the rate has been proposed earlier that detects change points on multiple time scales simultaneously. It is based on a filtered derivative approach, and the rejection threshold derives from a Gaussian limit process $L$ which is independent of the point process parameters. Here we extend the MFT to the analysis of variance homogeneity of life times. When the rate is constant, the MFT extends directly to the null hypothesis of constant variance and also to higher order moments under the assumption of homogeneity of lower order moments. In the presence of rate change points, we propose an adaptation of the test statistic. In order to ensure that rate changes are not falsely interpreted as variance changes, we propose to estimate the rate change points first, irrespective of potential variance changes, and incorporate these estimates in the statistical test for variance homogeneity. The resulting limit process shows slight deviations from $L$ that depend on unknown process parameters. We use simulations to show that these deviations tend to be small for a wide range of parameter values, suggesting that the use of $L$ does not considerably change the properties of the statistical test. Finally, the procedure is applied to empirical spike trains obtained from dopaminergic neurons in anaesthetized mice, indicating various profiles of rate and variance change points.

Change detection (CD) provides information about the changes on earth’s surface over a period of time. Many algorithms have been proposed over the years for effective CD of satellite imagery. This paper presents the steps to preprocess the captured satellite images, which can be used to perform predictive analysis of earth’s surface by CD techniques. To design a highly effective system for CD, it is recommended that algorithm designers select efficient algorithms from any given application. Thus, this paper presents and investigates the review of most appropriate literature on CD, where CD techniques have been presented into three groups; i) thresholding, ii) clustering, and iii) deep learning. The first two categories mainly rely on the traditional machine learning, whereas the last one exploits the state-of-the art deep learning models. At the end, the standard methods are summarized based on advantages, limitation, and research gap.

This study looks at participant report of how many hours of sleep the night before testing as well as self-reported tiredness level in relation to processing during an auditory oddball task. The component elicited by the oddball task is the MMN and this will be examined in relation to measures of sleep/tiredness.

Human-influenced climate change is an observed phenomenon affecting physical and biological systems across the globe. The majority of observed impacts are related to temperature changes and are located in the northern high- and midlatitudes. However, new evidence is emerging that demonstrates that impacts are related to precipitation changes as well as temperature, and that climate change is impacting systems and sectors beyond the Northern Hemisphere. In this paper, we highlight some of this new evidence-focusing on regions and sectors that the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) noted as under-represented-in the context of observed climate change impacts, direct and indirect drivers of change (including carbon dioxide itself), and methods of detection. We also present methods and studies attributing observed impacts to anthropogenic forcing. We argue that the expansion of methods of detection (in terms of a broader array of climate variables and data sources, inclusion of the major modes of climate variability, and incorporation of other drivers of change) is key to discerning the climate sensitivities of sectors and systems in regions where the impacts of climate change currently remain elusive. Attributing such changes to human forcing of the climate system, where possible, is important for development of effective mitigation and adaptation. Current challenges in documenting adaptation and the role of indigenous knowledge in detection and attribution are described.

Overall goal of this study was to examine the feasibility of vascular endothelial growth factor receptor -2 (VEGFR-2)- targeted ultrasound molecular imaging for the detection of spontaneous ovarian cancer (OVCA) at early stage in association with serum anti-nuclear matrix protein (anti-NMP) antibodies (a marker of ovarian malignant transformation) and serum IL-16 levels (a marker of ovarian tumor-associated neoangiogenesis, TAN). This goal was realized by two specific aims. The results of this study showed that VEGFR-2-targeted ultrasound molecular imaging agent bound with its target expressed by ovarian tumors and enhanced the signal intensity of ultrasound imaging. This study also showed that serum prevalence of anti-NMP antibodies and levels of IL-16 increased in association with ovarian malignant transformation and OVCA development. Furthermore, tumor-associated serum IL-16 levels were elevated even before the formation of a solid tumor mass in the ovary. VEGFR-2-targeted molecular ultrasound imaging together with serum anti-NMP antibodies and IL-16 levels, improved early detection of OVCA and detected ovarian tumors when it is limited to a part of the ovary. Thus VEGFR-2-targeted imaging agents showed potential to be a feasible imaging agent for early OVCA detection. Additionally, laying hens offer a feasible preclinical model to develop OVCA-preventive antiangiogenic therapeutics targeting VEGFR-2 expressed by tumor associated microvessels.

This article is from Frontiers in Psychology , volume 5 . Abstract Visual task performance is generally stronger in familiar environments. One reason for this familiarity benefit is that we learn where to direct our visual attention and effective attentional deployment enhances performance. Visual working memory plays a central role in supporting long-term memory guidance of visuospatial attention. We modified a change detection task to create a new paradigm for investigating long-term memory guidance of attention. During the training phase, subjects viewed images in a flicker paradigm and were asked to detect between one and three changes in the images. The test phase required subjects to detect a single change in a one-shot change detection task in which they held all possible locations of changes in visual working memory and deployed attention to those locations to determine if a change occurred. Subjects detected significantly more changes in images for which they had been trained to detect the changes, demonstrating that memory of the images guided subjects in deploying their attention. Moreover, capacity to detect changes was greater for images that had multiple changes during the training phase. In Experiment 2, we observed that capacity to detect changes for the 3-studied change condition increased significantly with more study exposures and capacity was significantly higher than 1, indicating that subjects were able to attend to more than one location. Together, these findings suggest memory and attentional systems interact via working memory such that long-term memory can be used to direct visual spatial attention to multiple locations based on previous experience.

במחקר זה אנו עוסקים בהשפעת חשיפה לסטטוס חברתי גבוה או נמוך על הערנות לשינויים. נחקור את ההשערה לפיה אדם בעל סטטוס חברתי גבוה ימשוך יותר קשב, ולכן המודעות לשינויים בהופעתו תהיה מוגברת.

במחקר זה אנו עוסקים בהשפעת חשיפה לסטטוס חברתי גבוה או נמוך על הערנות לשינויים. נחקור את ההשערה לפיה אדם בעל סטטוס חברתי גבוה ימשוך יותר קשב, ולכן המודעות לשינויים בהופעתו תהיה מוגברת.

Several relative radiometric normalization (RRN) techniques have been proposed till date most of which involve selection of pseudo invariant features whose reflectance are nearly invariant from image to image and are independent of seasonal cycles. Extraction of such points is quiet tedious and human operator has to provide mutual correspondence by choosing easily recognizable and time invariant points. In this paper, we intend to propose a new automatic radiometric normalization technique to select PIFs in panchromatic images known as Bin-Division Method. For multispectral images, MAD (Multivariate Alteration Detection) has been employed for selecting PIFs based on the assumption that MAD components are invariant to affine transformation. This, followed by robust linear regression constitutes the whole automatic radiometric normalization procedure.

The primary research objective is to initiate long-term monitoring of stratospheric ozone with a ground-based millimeter-wave spectrometer, the first of several such instruments projected to be part of the Network for the Detection of Stratospheric Change. The ultimate goal of this monitoring is twofold. First, to detect any secular trend in stratospheric ozone abundance, whether of natural or anthropogenic origin and, second, to provide ground-truth validation for existing and future satellite measurements of ozone. With this goal in mind, a more immediate objective is to validate the millimeter-wave measurements by tests of the instrument, internal consistency tests on the data, and most importantly, by intercomparison with all other available ozone measurements. The validation process is expected to lead to refinements in the instrument and its operating procedures and in the data analysis. The final objective is to perform short-term scientific studies with the data, including studies of the ozone diurnal and seasonal variations, and comparison of ozone variations with changes in other geophysical parameters, notably temperature and water vapor. Routine observations are now ongoing; these will allow continuing intercomparisons with the Stratospheric Aerosol and Gas Experiment (SAGE II) and one of the lidars, which is permanently on site. The experience gained during the Stratospheric Ozone Intercomparison (STOIC) caused us to refine our calibration procedures and identify the need for internal shielding of the millimeter receiver from radio frequency interference. Installation of this shielding is planned for the near future and should allow improvements in the instrument calibration and a higher signal-to-noise ratio, both of which will result in improved measurement precision.

This viewgraph presentation reviews the use Automatic Fusion of Image Data System (AFIDS) for Automatic Co-Registration of QuickBird Data to ascertain if changes have occurred in images. The process is outlined, and views from Iraq and Los Angelels are shown to illustrate the process.

This viewgraph presentation reviews the use Automatic Fusion of Image Data System (AFIDS) for Automatic Co-Registration of QuickBird Data to ascertain if changes have occurred in images. The process is outlined, and views from Iraq and Los Angelels are shown to illustrate the process.

This report discusses the final phase of a project to develop multispectral change detection methods with emphasis on human activities. Two new algorithms were developed and tested: a Spectral/Spatial Classifier and a Feature Vector Spectral Classifier. The Spectral/Spatial method appears to be a powerful new tool. The Feature Vector results were inconclusive.

We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track it's linguistic displacement over time. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book-ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.

A random sequence having two segments being the homogeneous Markov processes is registered. Each segment has his own transition probability law and the length of the segment is unknown and random. The transition probabilities of each process are known and a priori distribution of the disorder moment is given. The decision maker aim is to detect the moment of the transition probabilities change. The detection of the disorder rarely is precise. The decision maker accepts some deviation in estimation of the disorder moment. In the considered model the aim is to indicate the change point with fixed, bounded error with maximal probability. The case with various precision for over and under estimation of this point is analysed. The case when the disorder does not appears with positive probability is also included. The results insignificantly extends range of application, explain the structure of optimal detector in various circumstances and shows new details of the solution construction. The motivation for this investigation is the modelling of the attacks in the node of networks. The objectives is to detect one of the attack immediately or in very short time before or after it appearance with highest probability. The problem is reformulated to optimal stopping of the observed sequences. The detailed analysis of the problem is presented to show the form of optimal decision function.

An energy efficient distributed Change Detection scheme based on Page's CUSUM algorithm was presented in \cite{icassp}. In this paper we consider a nonparametric version of this algorithm. In the algorithm in \cite{icassp}, each sensor runs CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a Multiple Access Channel (MAC) corrupted with noise. The fusion center performs another CUSUM to detect the change. In this paper, we generalize the algorithm to also include nonparametric CUSUM and provide a unified analysis.

This paper examines the joint problem of detection and identification of a sudden and unobservable change in the probability distribution function (pdf) of a sequence of independent and identically distributed (i.i.d.) random variables to one of finitely many alternative pdf's. The objective is quick detection of the change and accurate inference of the ensuing pdf. Following a Bayesian approach, a new sequential decision strategy for this problem is revealed and is proven optimal. Geometrical properties of this strategy are demonstrated via numerical examples.

This paper examines the joint problem of detection and identification of a sudden and unobservable change in the probability distribution function (pdf) of a sequence of independent and identically distributed (i.i.d.) random variables to one of finitely many alternative pdf's. The objective is quick detection of the change and accurate inference of the ensuing pdf. Following a Bayesian approach, a new sequential decision strategy for this problem is revealed and is proven optimal. Geometrical properties of this strategy are demonstrated via numerical examples.

Recent attention in quickest change detection in the multi-sensor setting has been on the case where the densities of the observations change at the same instant at all the sensors due to the disruption. In this work, a more general scenario is considered where the change propagates across the sensors, and its propagation can be modeled as a Markov process. A centralized, Bayesian version of this problem, with a fusion center that has perfect information about the observations and a priori knowledge of the statistics of the change process, is considered. The problem of minimizing the average detection delay subject to false alarm constraints is formulated as a partially observable Markov decision process (POMDP). Insights into the structure of the optimal stopping rule are presented. In the limiting case of rare disruptions, we show that the structure of the optimal test reduces to thresholding the a posteriori probability of the hypothesis that no change has happened. We establish the asymptotic optimality (in the vanishing false alarm probability regime) of this threshold test under a certain condition on the Kullback-Leibler (K-L) divergence between the post- and the pre-change densities. In the special case of near-instantaneous change propagation across the sensors, this condition reduces to the mild condition that the K-L divergence be positive. Numerical studies show that this low complexity threshold test results in a substantial improvement in performance over naive tests such as a single-sensor test or a test that wrongly assumes that the change propagates instantaneously.

A radiometric normalization technique for compensating illumination and atmospheric differences between multi-temporal images should allow classification of the images with a single classification algorithm. This allows a simpler approach to land cover change detection. Land cover classification of Landsat Thematic Mapper Imagery with and without Pseudo Invariant Feature Normalization was performed to demonstrate the effect on classification and change detection accuracy. A post-classification change detection method using two separate classification algorithms, one for each data, was performed as a baseline comparison. Land cover classification using one classification algorithm was attempted with and without gain and offset correction to serve as another comparison. Accuracy verification was performed on the classification results by comparing random samples against ground truth. Theses.

A radiometric normalization technique for compensating illumination and atmospheric differences between multi-temporal images should allow classification of the images with a single classification algorithm. This allows a simpler approach to land cover change detection. Land cover classification of Landsat Thematic Mapper Imagery with and without Pseudo Invariant Feature Normalization was performed to demonstrate the effect on classification and change detection accuracy. A post-classification change detection method using two separate classification algorithms, one for each data, was performed as a baseline comparison. Land cover classification using one classification algorithm was attempted with and without gain and offset correction to serve as another comparison. Accuracy verification was performed on the classification results by comparing random samples against ground truth. Theses.

The purpose of this research project is to measure the ability of University students and caregivers of a young child to detect and correctly identify changes in facial emotion expressions. For this purpose an interactive digital child is set up to randomly change facial expressions (Anger, Joy, Fear, Surprise, Disgust, Sadness) while talking with the participant. The participant's task is to keep the conversation going while detecting and identifying changes in the digital child's facial emotion expressions with a button press. The participant's response time and emotion identification is recorded. We expect that the participants who are caregivers of a young child will be faster to detect the changes in facial emotion expressions as well as more accurate in identifying the specific emotions than Undergraduate students (who are not caregivers of a young child).

This article is from Frontiers in Human Neuroscience , volume 7 . Abstract Our brain is able to automatically detect changes in sensory stimulation, including in vision. A large variety of changes of features in stimulation elicit a deviance-reflecting event-related potential (ERP) component known as the mismatch negativity (MMN). The present study has three main goals: (1) to register vMMN using a rapidly presented stream of schematic faces (neutral, happy, and angry; adapted from Öhman etal., 2001); (2) to compare elicited vMMNs to angry and happy schematic faces in two different paradigms, in a traditional oddball design with frequent standard and rare target and deviant stimuli (12.5% each) and in an version of an optimal multi-feature paradigm with several deviant stimuli (altogether 37.5%) in the stimulus block; (3) to compare vMMNs to subjective ratings of valence, arousal and attention capture for happy and angry schematic faces, i.e., to estimate the effect of affective value of stimuli on their automatic detection. Eleven observers (19–32 years, six women) took part in both experiments, an oddball and optimum paradigm. Stimuli were rapidly presented schematic faces and an object with face-features that served as the target stimulus to be detected by a button-press. Results show that a vMMN-type response at posterior sites was equally elicited in both experiments. Post-experimental reports confirmed that the angry face attracted more automatic attention than the happy face but the difference did not emerge directly at the ERP level. Thus, when interested in studying change detection in facial expressions we encourage the use of the optimum (multi-feature) design in order to save time and other experimental resources.