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(55 mins) Jeremy Pesner, Kate Forscey, Bob Frankston, Sam Gustin, Alfredo Lopez, Jesse Sowell -- This spring, the FCC's net neutrality rules were upheld in court, giving the commission license to regulate the Internet as a public utility and ensure that all users are treated fairly. However, the question remains as to exactly how net neutrality should be implemented and how well the concept applies to not only the Internet of today, but tomorrow. Panelists will discuss the tensions between applying the idea of net neutrality to the pragmatics of the Internet's operations and the very real social and policy consequences of such decisions. By combining and contrasting legal, activist, technical, journalistic, and academic perspectives, they will dig deep into the thoughts and aims behind net neutrality and derive a more nuanced and effective assessment of what is needed to create an Internet that works for everyone. The panelists have discussed, taught, and deliberated these issues in university, government, and social settings, and boast employment/affiliations with MIT, Harvard University's Berkman Center for Internet and Society, ACM, IEEE, Columbia University's School of Journalism, VICE Media, May First/People (direct link), and Public Knowledge. Source: http://livestream.com/internetsociety2/hopeconf/videos/130680779 Uploader: tubeup.py Upload date: 2016-07-23

We analyze the dispersions of distributed lossless source coding (the Slepian-Wolf problem), the multiple-access channel and the asymmetric broadcast channel. For the two-encoder Slepian-Wolf problem, we introduce a quantity known as the entropy dispersion matrix, which is analogous to the scalar dispersions that have gained interest recently. We prove a global dispersion result that can be expressed in terms of this entropy dispersion matrix and provides intuition on the approximate rate losses at a given blocklength and error probability. To gain better intuition about the rate at which the non-asymptotic rate region converges to the Slepian-Wolf boundary, we define and characterize two operational dispersions: the local dispersion and the weighted sum-rate dispersion. The former represents the rate of convergence to a point on the Slepian-Wolf boundary while the latter represents the fastest rate for which a weighted sum of the two rates converges to its asymptotic fundamental limit. Interestingly, when we approach either of the two corner points, the local dispersion is characterized not by a univariate Gaussian but a bivariate one as well as a subset of off-diagonal elements of the aforementioned entropy dispersion matrix. Finally, we demonstrate the versatility of our achievability proof technique by providing inner bounds for the multiple-access channel and the asymmetric broadcast channel in terms of dispersion matrices. All our proofs are unified a so-called vector rate redundancy theorem which is proved using the multidimensional Berry-Esseen theorem.

Given a real-analytic manifold M, a compact connected Lie group G and a principal G-bundle P -> M, there is a canonical `generalized measure' on the space A/G of smooth connections on P modulo gauge transformations. This allows one to define a Hilbert space L^2(A/G). Here we construct a set of vectors spanning L^2(A/G). These vectors are described in terms of `spin networks': graphs phi embedded in M, with oriented edges labelled by irreducible unitary representations of G, and with vertices labelled by intertwining operators from the tensor product of representations labelling the incoming edges to the tensor product of representations labelling the outgoing edges. We also describe an orthonormal basis of spin networks associated to any fixed graph phi. We conclude with a discussion of spin networks in the loop representation of quantum gravity, and give a category-theoretic interpretation of the spin network states.

Across the sciences, a fundamental setting for representing and interpreting information about entities, the structure and organization of communities, and changes in these over time, is a stochastic network that is topologically rewiring and semantically evolving over time, or over a genealogy. While there is a rich literature in modeling invariant networks, until recently, little has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable. In this talk, I will present some recent developments in analyzing what we refer to as the dynamic tomography of evolving networks. I will first present a new class of statistical models known as dynamic exponential random graph models for evolving social networks, which offers both good statistical property and rich expressivity; then, I will present new sparse-coding algorithms for estimating the topological structures of latent evolving networks underlying nonstationary time-series or tree-series of nodal attributes, along with theoretical results on the asymptotic sparsistency of the proposed methods; finally, I will present a new Bayesian model for estimating and visualizing the trajectories of latent multi-functionality of nodal states in the evolving networks. I will show some promising empirical results on recovering and analyzing the latent evolving social networks in the US Senate and the Enron Corporation at a time resolution only limited by sample frequency. In all cases, our methods reveal interesting dynamic patterns in the networks. ©2011 Microsoft Corporation. All rights reserved.

Across the sciences, a fundamental setting for representing and interpreting information about entities, the structure and organization of communities, and changes in these over time, is a stochastic network that is topologically rewiring and semantically evolving over time, or over a genealogy. While there is a rich literature in modeling invariant networks, until recently, little has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable. In this talk, I will present some recent developments in analyzing what we refer to as the dynamic tomography of evolving networks. I will first present a new class of statistical models known as dynamic exponential random graph models for evolving social networks, which offers both good statistical property and rich expressivity; then, I will present new sparse-coding algorithms for estimating the topological structures of latent evolving networks underlying nonstationary time-series or tree-series of nodal attributes, along with theoretical results on the asymptotic sparsistency of the proposed methods; finally, I will present a new Bayesian model for estimating and visualizing the trajectories of latent multi-functionality of nodal states in the evolving networks. I will show some promising empirical results on recovering and analyzing the latent evolving social networks in the US Senate and the Enron Corporation at a time resolution only limited by sample frequency. In all cases, our methods reveal interesting dynamic patterns in the networks. ©2011 Microsoft Corporation. All rights reserved.

The present study explores how the visualization of an actor network enables researchers to understand mediating processes better by investigating relationships and effects among elements and entities other than human-actor accounts. In particular, the study presents how visualization may facilitate an understanding of the dynamics and structures of an ecological classroom and thus provide insightful reflections for analyzing data and for drawing conclusions. In doing so, the study explores the possibility that object-oriented thinking guided by actor-network theory (ANT) may allow researchers to understand what data means when the initial design/theoretical conjecture does not work. [For the complete proceedings, see ED608557.]

The present study explores how the visualization of an actor network enables researchers to understand mediating processes better by investigating relationships and effects among elements and entities other than human-actor accounts. In particular, the study presents how visualization may facilitate an understanding of the dynamics and structures of an ecological classroom and thus provide insightful reflections for analyzing data and for drawing conclusions. In doing so, the study explores the possibility that object-oriented thinking guided by actor-network theory (ANT) may allow researchers to understand what data means when the initial design/theoretical conjecture does not work. [For the complete proceedings, see ED608557.]

Production Networks (PN) fundamentally differ from hierarchical organisations, as they emphasise on speed, re-linking, and reconfiguration. For the decisions for realignment of units and the reallocations of resources, configurations on changing levels of detail and control actions, generic models in line with concurrency modes give an adequate base for the description, the control and the evolution of PNs. Simultaneous application of selected interrelated models may generate very efficient procedures for PN management. Moreover, collaboration between dispersed locations is well ICT supported. However, for lack of overall conjectures, management solutions are fragmented. PNs may generally be modelled as Hausdorff spaces and the respective tangent spaces. Specific mappings as well as applications of concurrency modes may be introduced for improving coherence and speeding up decisions. Methods and models appear as embedded structures, carrying the fold/unfold properties of graphs and systems. Interoperability requirements induce standardizations for the models. The specific synthesis of the concurrency modes with criticality thinking results in procedures for gradual and evolving adaptations of production networksâ structures, most adequate to PNâ complexity. Ground laying theory always strengthens the convergence of terminology, methods and models that are developed and applied on a research area. In this sense, this paper intends to contribute to a coherent body of knowledge for PN design and management by theory building.

We motivate the study of a certain class of random Fibonacci sequences - which we call continuous random Fibonacci sequences - by demonstrating that their exponential growth rate can be used to establish capacity and power scaling laws for multihop cooperative amplify-and-forward (AF) relay networks. With these laws, we show that it is possible to construct multihop cooperative AF networks that simultaneously avoid 1) exponential capacity decay and 2) exponential transmit power growth across the network. This is achieved by ensuring the network's Lyapunov exponent is zero.

We motivate the study of a certain class of random Fibonacci sequences - which we call continuous random Fibonacci sequences - by demonstrating that their exponential growth rate can be used to establish capacity and power scaling laws for multihop cooperative amplify-and-forward (AF) relay networks. With these laws, we show that it is possible to construct multihop cooperative AF networks that simultaneously avoid 1) exponential capacity decay and 2) exponential transmit power growth across the network. This is achieved by ensuring the network's Lyapunov exponent is zero.

Complex dynamical systems are often modeled as networks, with nodes representing dynamical units which interact through the network's links. Gene regulatory networks, responsible for the production of proteins inside a cell, are an example of system that can be described as a network of interacting genes. The behavior of a complex dynamical system is characterized by cooperativity of its units at various scales, leading to emergent dynamics which is related to the system's function. Among the key problems concerning complex systems is the issue of stability of their functioning, in relation to different internal and external interaction parameters. In this Thesis we study two-dimensional chaotic maps coupled through non-directed networks with different topologies. We use a non-symplectic coupling which involves a time delay in the interaction among the maps. We test the stability of network topologies through investigation of their collective motion, done by analyzing the departures from chaotic nature of the isolated units. The study is done on two network scales: (a) full-size networks (a computer generated scalefree tree and a tree with addition of cliques); (b) tree's characteristic sub-graph 4-star, as a tree's typical dynamical motif which captures its topology in smallest possible number of nodes and is suitable for time-delayed interaction. We study the dynamical relationship between these two network structures, examining the emergence of cooperativity on a large scale (trees) as a consequence of mesoscale dynamical patterns exhibited by the 4-star. (FULL ABSTRACT INSIDE THE TEXT)

Shannon's information theory was a towering achievement of 20th century science and laid a theoretical foundation for understanding how to design communication systems, and to judge their optimality. However, the bedrock assumptions in the Shannon framework of unbounded delay and reliability are not appropriate for mobile ad hoc networks, which has rapidly changing topologies and end-to-end performance constraints. The lack of a descriptive theory for ad hoc networks has greatly impeded their design and commercialization. In response to this roadblock, DARPA has recently initiated a $13.5m Grand Challenge to develop a new information theory capable of describing the performance limits of mobile wireless networks. UT Austin is leading this effort, with one of the two winning proposals, entitled 'Non-equilibrium information theory'. In this talk, I will describe our vision for progressing towards this daunting goal, will present some preliminary results, and attempt to explain how this might affect the future design of wireless networks. (Project page: http://wncg.ece.utexas.edu/itmanet/index.php) ©2007 Microsoft Corporation. All rights reserved.

In this paper, we take a unified approach for network information theory and prove a coding theorem, which can recover most of the achievability results in network information theory that are based on random coding. The final single-letter expression has a very simple form, which was made possible by many novel elements such as a unified framework that represents various network problems in a simple and unified way, a unified coding strategy that consists of a few basic ingredients but can emulate many known coding techniques if needed, and new proof techniques beyond the use of standard covering and packing lemmas. For example, in our framework, sources, channels, states and side information are treated in a unified way and various constraints such as cost and distortion constraints are unified as a single joint-typicality constraint. Our theorem can be useful in proving many new achievability results easily and in some cases gives simpler rate expressions than those obtained using conventional approaches. Furthermore, our unified coding can strictly outperform existing schemes. For example, we obtain a generalized decode-compress-amplify-and-forward bound as a simple corollary of our main theorem and show it strictly outperforms previously known coding schemes. Using our unified framework, we formally define and characterize three types of network duality based on channel input-output reversal and network flow reversal combined with packing-covering duality.

Adaptive terrorist organizational structure and the lack of intelligence sharing were to blame for terrorist attacks on September 11, 2001. Because terrorist groups are moving toward a less predictable, but more diverse, dynamic, and fluid structure, effective combativeness of terrorism requires fighting terrorists with a network. This network must be capable of collecting and sharing credible, reliable and corroborative information on an unprecedented scale, transcending geographic, agency, and political boundaries. This thesis demonstrates utilization of a network-theory approach for sharing information, which will be argued, can provide insight into the system dynamics of the U.S. IC because it allows a systematic, comparative analysis of the system representation and fundamental problems associated with information sharing. The problems associated with past intelligence failures can be overcome with such a system because the use of a dedicated, nationally networked system will allow completion of three primary tasks: (1) examination of the strength of criminal/terrorist connections, (2) identification of suspects and mapping of networks, and (3) prediction of future behavior and better likelihood of prevention, response, and prosecution. A dedicated national networked intelligence-sharing system called DNIN (Dedicated National Intelligence Network), including geographic areas, regional centers, personnel, computer IT networks, and policy options is discussed.

In this paper, subgraphs and complementary graphs are used to analyze the network synchronizability. Some sharp and attainable bounds are provided for the eigenratio of the network structural matrix, which characterizes the network synchronizability, especially when the network's corresponding graph has cycles, chains, bipartite graphs or product graphs as its subgraphs.

A theory is developed to describe the superfluid turbulence on the base of kinetics of the merging and splitting vortex loops. Because of very frequent reconnections the vortex loops (as a whole) do not live long enough to perform any essential evolution due to the deterministic motion. On the contrary, they rapidly merge and split, and these random recombination processes prevail over other slower dynamic processes. To develop quantitative description we take the vortex loops to have a Brownian structure with the only degree of freedom, which is the length $l$ of the loop. We perform investigation on the base of the Boltzmann type kinetic equation for the distribution function $n(l)$ of number of loops with length $l$. By use of the special ansatz in the collision integral we have found the exact power-like solution to kinetic equation in the stationary case. This solution is not (thermodynamically) equilibrium, but on the contrary, it describes the state with two mutual fluxes of the length (or energy) in space of sizes of the vortex loops. The term flux means just redistribution of length (or energy) among the loops of different sizes due to reconnections. Analyzing this solution we drew several results on the structure and dynamics of the vortex tangle in the turbulent superfluid helium. In particular, we evaluated the mean radius of the curvature and the full rate of the reconnection events. We also studied the evolution of the full length of vortex loops per unit volume-the so-called vortex line density. It is shown this evolution to obey the famous Vinen equation. The properties of the Vinen equation from the point of view of the developed approach had been discussed.

In these few pages, reader you will find Complete a theory, from McMillan does it stem, To satisfy the criticizing mind. The topic network's pure foundation treats. With definitions it begins, such is system; Then n-port properties one here soon meets. For general networks are such defined; Then comes the distributional extension For special type n-ports this holds refined. The end has exponential variables to greet, With generalized descriptions linear, the mention, And paradoxes as an unsolved feat.

In these few pages, reader you will find Complete a theory, from McMillan does it stem, To satisfy the criticizing mind. The topic network's pure foundation treats. With definitions it begins, such is system; Then n-port properties one here soon meets. For general networks are such defined; Then comes the distributional extension For special type n-ports this holds refined. The end has exponential variables to greet, With generalized descriptions linear, the mention, And paradoxes as an unsolved feat.

This study investigated the use of the PLATO III computer assisted instruction (CAI) system to assist in the teaching of an electrical network theory course. It sought to: 1) identify the topics and teaching strategies most amenable to CAI system; 2) develop computer programs needed to teach those topics; 3) use, evaluate and service the programs; and 4) evaluate the overall effectiveness of this research. Using the TUTOR language, the researcher developed a network drawing utility routine, wrote a lesson on Kirchhoff's laws, and developed a DC analysis version of the on-line network analyzer. Two one-semester sections of the course were taught with students having 20% of their instructional time on the CAI system. Results which showed that student attendance and achievement were high and their attitudes positive led to the conclusions that network theory can be taught by CAI systems and that TUTOR is appropriate language. Further implications were that future research should study additional instruction instructional modes and investigate the utility of interstation communication, pre-recorded feedback, and the information structure oriented approach. (PB)

Application of modern network theory to analysis of manned systems

A recurring problem in network traffic analysis is to automatically distinguish legitimate traffic from malicious or spurious traffic. This problem arises in several guises in network security (e.g., spam mitigation, worm detection), and is, at core, a machine learning or data mining problem. However, traffic analysis for network security has many fundamental challenges that are not present in typical machine learning or data mining problems, and a blackbox application of classical algorithms may not address these challenges adequately. For example, many standard machine learning algorithms may not scale to the volume and diversity of network traffic, or perform well in the presence of a malicious adversary who aims to evade detection. It is, therefore, necessary to design algorithms that meet these challenges, and provide formal guarantees on how well they have been met by the algorithms and the extent to which they can be met by any algorithm. In this thesis, we consider four problems in network security with these challenges, and we use tools from computational learning theory and streaming algorithm design to address them. In each of these four problems, the difference between the malicious traffic and normal traffic is characterized by a specific structure of the traffic distributions: temporal structure, structure in content, structure in communication patterns of hosts and network structure given by host IP addresses.

This report lists the author's publications, lectures, symposia and consulting activities.

In recent years, the increasing sophistication of surveillance systems and tracking mechanisms has generated a great deal of interest in the development of new computational structures and architectures for detecting and tracking multiple targets, using data from many sensors. The design of spatially distributed target-detection-and-tracking systems involves the integration of solutions obtained by solving subproblems in data-association, hypothesis testing, data fusion, etc. This must include the cooperative solution of problems by a decentralized and loosely coupled collection of processors, each of which integrates information received from a cluster of spatially distributed sensors into a manageable and reliable output for further integration at a higher level. This process requires the design of efficient architectures and computational techniques to abstractly represent and integrate sensor information.

As regular attendees at the Academy of Human Resource Development (AHRD) Conference, the authors enjoyed interacting with the highly relevant study by Diane Chapman and her colleagues (Chapman, Wiessner, Morton, Fire, Jones, & Majekodunmi, 2009). In the study, they use several theoretical tools to understand the learning experiences of doctoral students at a professional conference. In the authors' view, the concept of the "learning organization" serves as a foundation for the creation of new knowledge at conferences. "Communities of practice" and the idea of "legitimate peripheral participation" represent the process that occurs at conferences. "Knowledge creation" is the outcome of these events. The authors concluded that an additional tool, social network analysis, could help to expand these ideas further. In this reaction article, the authors explain how the tools of social network analysis might be used to enhance their understanding of doctoral student participation in scholarly conferences. As formation of developmental relationships is central to the purpose of scholarly conferences such as AHRD, the authors argue that it is worth exploring the ties that doctoral students might develop in these conferences through the lens of Social Network Theory.

As regular attendees at the Academy of Human Resource Development (AHRD) Conference, the authors enjoyed interacting with the highly relevant study by Diane Chapman and her colleagues (Chapman, Wiessner, Morton, Fire, Jones, & Majekodunmi, 2009). In the study, they use several theoretical tools to understand the learning experiences of doctoral students at a professional conference. In the authors' view, the concept of the "learning organization" serves as a foundation for the creation of new knowledge at conferences. "Communities of practice" and the idea of "legitimate peripheral participation" represent the process that occurs at conferences. "Knowledge creation" is the outcome of these events. The authors concluded that an additional tool, social network analysis, could help to expand these ideas further. In this reaction article, the authors explain how the tools of social network analysis might be used to enhance their understanding of doctoral student participation in scholarly conferences. As formation of developmental relationships is central to the purpose of scholarly conferences such as AHRD, the authors argue that it is worth exploring the ties that doctoral students might develop in these conferences through the lens of Social Network Theory.

The establishment of an 'open door' policy in 1978 marked China's return to the capitalist world-economy. The self-reliance policies of the Maoist era were renounced in favor of modernization. As such, China has adopted a vigorous program of attracting foreign investments. Foreign investors responded favorably to China's invitation and over the course of the past ten years thousands of direct investment projects, particularly joint ventures, were set up. Recently, however the investment atmosphere has soured. Many foreign investors, like the American Motor Company, have found it increasingly difficult and less beneficial to establish ventures in China. The dependency and world system perspectives have been the most prevalent of these in recent times. These political economy perspectives claim to provide a framework through which capitalist penetration can be analyzed; each one examining the core-periphery relations in the capitalist work economy from a different vantage point.

The network theory of mental disorder describes psychological problems as a network of interacting symptoms. This theory introduces several claims regarding the nature, development and treatment of mental disorders, and several possibilities were suggested to apply the network approach in clinical practice. Very little is known about the attitudes of mental health professionals regarding the network theory of mental disorders. Filling this gap seems especially important considering that the network theory was developed based on methodological reasoning. Therefore, we aim to perform a systematic assessment of to what extent the claims of the network theory of mental disorders fit with the clinical perceptions of mental health professionals. Further, assessing if and to which degree mental health professionals find possible applications of the network approach acceptable and appropriate can give direction on how this approach could be implemented in clinical care.

This project is part of a larger study investigating neural correlates of negative emotions and emotion regulation in opponents and endorsers of bathroom bills, an exclusionary social policy restricting access for transgender individuals to use public bathrooms of their gender identity. In this study, the authors collected fMRI data during a task where subjects imagined social interactions with cis-gender men in neutral, fearful, or disgusting scenarios; as well as with transgender women in neutral scenarios. Participants also completed a theory of mind fMRI localizer (Dodell-Feder et al., 2010). Data were collected from subjects who either endorse or oppose transgender bathroom laws, based on preliminary questionnaire data preceding participation. In the current project, we conduct new analyses of the data collected from this larger study , to evaluate neural activity in the canonical theory of mind network during neutral imagined interactions with transgender women as opposed to cisgender males, in endorsers vs. opponents of bathroom bills. Theory of mind network activations to transgender women over cis-males In a set of prior exploratory analyses using data from the imagined interactions task, we used neural data collected from the theory of mind localizer to define the top 100 voxels in each canonical theory of mind ROI (Dufour et al., 2013), and extracted beta estimates in these voxels to the tgn-gt-cgn (trans neutral > cis male neutral) condition from the imagined interactions (“read”) task. We found that: The theory of mind network as a whole was not activated by the transgender neutral condition more than cisgender neutral condition, on average; Activations to the transgender neutral condition were higher, only in dorsomedial prefrontal cortex (DMPFC) and mediomedial prefrontal cortex (MMPFC), and only in opponents of bathroom bills, not in endorsers (see Figure 1 in PDF of this pre-registration). Social cognitive processes in mPFC Theory of mind (i.e., representing the mental states of others), activates a network of regions in the brain, including dmPFC, mmPFC, but also particularly right temporal-parietal junction (rTPJ), along with other regions (Dodell-Feder et al, 2010, Saxe and Kanwisher, 2003). If we expect higher theory of mind activation to condition A over condition B, we would expect not only higher activations in prefrontal regions, but also higher activations in other theory of mind regions, especially rTPJ. However, mPFC is not domain specific to mentalizing about others. DMPFC is also involved in a variety of self-relevant and social cognitive processes, e.g., self-reflection, moral judgements, or identifying social behavior or personality traits of others (without representing mental states) (Jenkins and Mitchell, 2010, Koenigs et al., 2007, Saxe et al., 2006). Thus, mPFC activation in social cognition is dissociable from rTPJ activation under some circumstances (Saxe et al., 2006). Since we saw higher mPFC activations for imagined interactions with trans women > cis men, but not higher activations in other key theory of mind regions (rTPJ), we hypothesize that this activation does not reflect greater theory of mind processing, but another social cognitive process. We see this effect in opponents but not endorsers of bathroom bills, and from anecdotal experience with participants, one possibility is that this activity derives from a group difference in moral convictions about transgender rights. If so, activity in dMPFC may be related to more self-relevant processes, namely a group difference in moral convictions regarding the imagined interaction partners. We will explore this speculation relating moral convictions about transgender rights using independent evidence (e.g. surveys). Here we are pre-registering confirmatory analyses of fMRI data, to test the reliability of our serendipitous observation. The observation of differential activity only in dMPFC and mMPFC, only in endorsers, was not predicted a priori -- this was observed in an exploratory analysis. We therefore seek to replicate these results in independent data from the test runs of the study. For the current project, we are pre-registering a confirmatory analysis of theory of mind network responses to trans female over cis male neutral faces in imagined interactions, using an independent set of fMRI data collected during the same sessions. During each session, in interleaved runs, participants were (i) (‘READ’) shown a novel image and a verbal scenario and instructed explicitly to imagine interacting in the scenario with the person; and then (ii) (‘TEST’) shown the same scenario with two faces, and asked to identify the face previously paired with that scenario. Note, in trials where the scenario was paired with a transgender woman’s face, both faces in the memory task are transgender women; likewise, if the face was cis male, both faces during the memory task are cis male. In our previous analyses, we used fMRI data only from runs of the ‘Read’ task; here we will test whether the same patterns of activity are reinstated during the interleaved ‘Test’ runs. Correspondence of read and test data in our study It is plausible that some cognitive and emotional states from the ‘read’ runs are reinstated during the ‘test’ runs. In another set of pre-registered analyses (https://osf.io/fbpnw/), we found that neural responses to disgusting over neutral imagined interactions (cgd>cgn) with cis men was significantly reinstated in ‘test’ runs (though this was not true for the cgf>cgn contrast), where cgn = cis male neutral, cgd = cis male disgust, and cgf = cis male fear. In this test, we estimated betas to each main condition of cis male faces. In regions defined via a meta-analysis to respond more to disgusting or fearful stimuli over neutral stimuli (see list below), we defined subject-specific fROIs using cgd>cgn (for disgust ROIs) and cgf>cgn (for fear ROIs) in an iterative leave-one-out process, extracting from the independent data the mean beta estimate in the top 100 voxel fROIs to the cgd>cgn (disgust ROIs) and cgf>cgn (fear ROIs) contrasts. Disgust > neutral parcels: -left amygdala -left insula/inferior frontal gyrus (IFG) -left inferior parietal lobe -right amygdala -right claustrum Fear > neutral parcels: -left amygdala -left IFG -right amygdala -right IFG We conducted this beta extraction process for both read runs and test runs, separately. We then used the following models to predict the mean contrast betas (across voxels), averaged across the leave-one-run-out extraction process: Mean_beta_test_cgd-gt-cgn ~ mean_beta_read_cgd-gt-cgn + (1 | subjID) Mean_beta_test_cgf-gt-cgn ~ mean_beta_read_cgf-gt-cgn + (1 | subjID) We found that for the cgd>cgn contrast, mean contrast betas at read were significantly predictive of contrast betas at test (coefficient = 0.51, CI = 0.31-0.71, p<.001), though we did not find a significant prediction for cgf>cgn contrasts (coefficient = 0.24, CI = -0.04-0.53, p = 0.097). Note, as per our pre-registration (https://osf.io/fbpnw/), we did not include data from the tgn condition in any analyses. Because we find that for some other conditions and relevant ROIs, neural activations during the read task are predictive of those at test, we expect to see similar results replicating the above theory of mind analyses in the test data, including the group differences. Given also that we do not believe the patterns observed in the read data reflect theory of mind processes but rather a social-cognitive process relating to the self, the repetition of the trans vs. cis male face stimuli at test may be sufficient to re-evoke the observed cognitive patterns in mPFC. However, given the nature of task differences between read and test runs (imagined interaction vs. memory task), this correspondence may be weak or absent in the current analyses.

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Presented herein is the free vibration analysis of functionally graded beams (FGMs) via higher-order shear deformation theory and an artificial neural network method (ANN). The transverse displacement (w) is expressed as bending (wb) and shear (ws) components to define the deformation of the beam. The higher-order variation of the transverse shear strains is accounted for through the thickness direction of the FGM beam, and satisfies boundary conditions. The governing equations are derived with the help of Hamilton’s principle. Non-dimensional frequencies are obtained using Navier’s solution. To validate and enrich the proposed research, an artificial neural network method (ANN) was developed in order to predict the dimensionless frequencies. Material properties and previous studies were used to generate the ANN dataset. The obtained frequency values from the analytical solution and ANN method were compared and discussed with respect to the mean error. In conclusion, the solutions were demonstrated for various deformation theories, and all of the results were thereupon tabularized and visualized using 2D and 3D plots.

Network theory investigates the operation of diverse sectors such as commerce, electrical and electronic circuits, and computer networks, among other things. It has operation that is the graphical display of data in numerous areas of activity. Network theory can graph symmetrical and asymmetrical connections between discrete items. In the computer science sector, for example, network theory is defined as a graphical illustration of systems having nodes and edges. The notion of network theory was discussed in this article, as well as its use in numerous fields, as well as its limits and benefits. In this post, we'll look at how network theory is used in electrical engineering to address problems. The numbers of current and voltage component in a circuit is first determined using network theory, and then the circuit is simplified using network theory. By applying complex network analysis, this article presents a concept on complex network analysis (CNA) as well as brief information on micro grids in electric distributions system. Network theory is learning of electrical network that can aid in the future, such as improving the network stability of a major power station, increasing grid resilience, and protecting power stations from blackouts. The study of network theory is demonstrated in this article using MATLAB software

Terrorist networks have evolved from locally-oriented political organizations into complex, adaptive, loosely structured groups that span international borders to promote larger regional and global goals through violent asymmetric attacks dependant on compartmentalization and deception. This adaptive terrorist organizational structure and the lack of U.S. IC and LE intelligence sharing were to blame for the terrorist attacks on September 11, 2001. Because terrorist groups are moving toward a less predictable, but more diverse, dynamic, and fluid structure, effective combativeness of terrorism will require fighting terrorist networks with a network capable of collecting and sharing credible, reliable and corroborative information on an unprecedented scale, transcending geographic, agency, and political boundaries.

Polar coding is a method for communication over noisy classical channels which is provably capacity-achieving and has an efficient encoding and decoding. Recently, this method has been generalized to the realm of quantum information processing, for tasks such as classical communication, private classical communication, and quantum communication. In the present work, we apply the polar coding method to network quantum information theory, by making use of recent advances for related classical tasks. In particular, we consider problems such as the compound multiple access channel and the quantum interference channel. The main result of our work is that it is possible to achieve the best known inner bounds on the achievable rate regions for these tasks, without requiring a so-called quantum simultaneous decoder. Thus, our work paves the way for developing network quantum information theory further without requiring a quantum simultaneous decoder.

A discussion of a recent significant advance in network synthesis theory is presented. This 'breakthrough' was accomplished by D. Hazony of the Case Institute of Technology and by D. C. Youla of the Polytechnic Institute of Brooklyn, who independently of each other developed methods for unifying the theory of two-port cascade synthesis. Both methods are based on Richards' theorem, and both introduce the gyrator artificually. Different methods of proof are used, however. A valuable 'cookbook recipe' was developed by Youla. Hazony managed to extend the method to n-ports. In all, this epoch-making achievement has resulted in an important, simple, and beautiful method of network synthesis.

The report contains a set of lectures presented as a final year one semester course at L'Universite Catholique de Louvain in 1967-1968. The material presents network theory from a state space approach. The topics covered include introduction to the state, the state-intuitively, formulation of canonical equations, integrated and analog circuit configurations, minimal realization creation, equivalence, controllability and observability, sensitivity and transition matrices, positive-real admittance synthesis, lumped-distributed lossless synthesis, and time variable synthesis. Appropriate exercises are presented at the end of each chapter.

The report contains a set of lectures presented as a final year one semester course at L'Universite Catholique de Louvain in 1967-1968. The material presents network theory from a state space approach. The topics covered include introduction to the state, the state-intuitively, formulation of canonical equations, integrated and analog circuit configurations, minimal realization creation, equivalence, controllability and observability, sensitivity and transition matrices, positive-real admittance synthesis, lumped-distributed lossless synthesis, and time variable synthesis. Appropriate exercises are presented at the end of each chapter.

Recent advances in neuroscience found that brain connectivity exhibits characteristics of complex networks – such as small-world topology, highly connected hubs and modularity (Bullmore & Sporns, 2009). These network properties are different in Alzheimer’s disease (AD) patients (Supekar et al., 2008) which can be used to classify AD patients and healthy controls (Lama & Kwon, 2021). The change in brain networks occur before the onset of symptoms (Brier et al., 2014) and was found to follow a cascading network failure model which begins in the posterior default mode network and then shifts to other systems containing prominent connectivity hubs (Jones et al., 2016), which makes it a potential biomarker for diagnosis and treatment assessment of preclinical AD. However, it is unclear how individual regions and global network resilience change related to the estimated time of symptom onset for AD in individuals with autosomal dominant Alzheimer's disease (ADAD), and whether the change is gender-dependent. Here we draw the power of estimating time to expected symptom onset in individuals with ADAD and quantify the change in the network properties in several highly-connected brain regions (hubs) as a function of time to expected symptom onset.

Anorexia Nervosa (AN) maintains the highest mortality rate of the mental disorders, and less than 50% of individuals will recover by the tenth year of illness. Despite this, there has been little innovation in clinical treatment and theory of illness is seldom explored. This study aims to return to phenomenological and theoretical research by interrogating presumed constructs of illness, with those who live it, those who treat it, and those who have studied it extensively.

A new proposal for group key exchange is introduced which proves to be both efficient and secure and compares favorably with state of the art protocols.

This article is from Cerebral Cortex (New York, NY) , volume 24 . Abstract Eye contact plays a key role in social interaction and is frequently reported to be atypical in individuals with autism spectrum conditions (ASCs). Despite the importance of direct gaze, previous functional magnetic resonance imaging in ASC has generally focused on paradigms using averted gaze. The current study sought to determine the neural processing of faces displaying direct and averted gaze in 18 males with ASC and 23 matched controls. Controls showed an increased response to direct gaze in brain areas implicated in theory-of-mind and gaze perception, including medial prefrontal cortex, temporoparietal junction, posterior superior temporal sulcus region, and amygdala. In contrast, the same regions showed an increased response to averted gaze in individuals with an ASC. This difference was confirmed by a significant gaze direction × group interaction. Relative to controls, participants with ASC also showed reduced functional connectivity between these regions. We suggest that, in the typical brain, perceiving another person gazing directly at you triggers spontaneous attributions of mental states (e.g. he is “interested” in me), and that such mental state attributions to direct gaze may be reduced or absent in the autistic brain.

This study proposes a method based on Dempster-Shafer theory (DST) and fuzzy neural network (FNN) to improve the reliability of recognizing fatigue driving. Thismethodmeasures driving states usingmultifeature fusion. First, FNNis introduced to obtain the basic probability assignment (BPA) of each piece of evidence given the lack of a general solution to the definition of BPA function. Second, a modified algorithm that revises conflict evidence is proposed to reduce unreasonable fusion results when unreliable information exists. Finally, the recognition result is given according to the combination of revised evidence based on Dempster’s rule. Experiment results demonstrate that the recognition method proposed in this paper can obtain reasonable results with the combination of information given by multiple features. The proposed method can also effectively and accurately describe driving states.

On this episode of The Police Accountability Report, we tell a harrowing tale of a firsthand experience with drug dealing cops reveals the long and troubling history of police selling narcotics, and calls into question the idea that police corruption is limited to a few bad actors.Subscribe to our page and support our work at https://therealnews.com/donate.

How is the link between learner and technology made in mobile learning? What is the value of the concept of "affordances"? And how does research articulating this concept act to position mobile devices as "technologies for learning"? This literature review used both unstructured and structured search samples of published research on mobile learning to critically evaluate the prevalence and influence of the concept of the affordances of mobile technologies. Actor-network theory is drawn on as a theoretical lens through which to critically consider how this concept is articulated, and in particular to explore the way it positions and controls mobile devices as technologies for learning. Parallels in contemporary accounts of mobile learning are drawn with classifications of previous discourses around the introduction of computers into schools. An alternative agenda for mobile learning research is suggested with a focus on authentic and informal contexts rather than controlled experiments.

STICAP (Stiff Circuit Analysis Program) is a FORTRAN 4 computer program written for the CDC-6400-6600 computer series and SCOPE 3.0 operating system. It provides the circuit analyst a tool for automatically computing the transient responses and frequency responses of large linear time invariant networks, both stiff and nonstiff (algorithms and numerical integration techniques are described). The circuit description and user's program input language is engineer-oriented, making simple the task of using the program. Engineering theories underlying STICAP are examined. A user's manual is included which explains user interaction with the program and gives results of typical circuit design applications. Also, the program structure from a systems programmer's viewpoint is depicted and flow charts and other software documentation are given.

We present a set of five axioms for fairness measures in resource allocation. A family of fairness measures satisfying the axioms is constructed. Well-known notions such as alpha-fairness, Jain's index, and entropy are shown to be special cases. Properties of fairness measures satisfying the axioms are proven, including Schur-concavity. Among the engineering implications is a generalized Jain's index that tunes the resolution of the fairness measure, a new understanding of alpha-fair utility functions, and an interpretation of "larger alpha is more fair". We also construct an alternative set of four axioms to capture efficiency objectives and feasibility constraints.

Alcohol use disorders in adolescents has been a major health and social concern across the globe demanding timely intervention in order to prevent the great deal of morbidity and mortality that are often associated with maladaptive pattern of substance use. Understanding the causation, maintenance and progression of alcohol use disorder is of utmost importance in formulating strategies for its treatment and prevention. There has been a major paradigm shift in understanding of the complex phenomenon of alcohol use in the form of application of concept of network theory. In this review a sincere attempt has been made to give insight into the various issues related to alcohol use disorders in adolescents. Key Words: Social network, Alcohol dependence, Drinking habit 

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Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.