cssi/SciSciGPT-SciSciNet huggingface dataset:
Confrontation in Children's Cases: The Dimensions of Limited Coverage

Application of the rules of evidence in cases involving children is often different than for adults. Given this context, it would hardly be surprising if application of Crawford v. Washington and its new testimonial statement approach to confrontation doctrine was more nuanced, complicated, and uncertain in cases involving children. Although the doctrine’s application is undeniably different, it is generally not less certain. The lines in most cases involving children are just as clear as in adult cases - perhaps even clearer for some commonly encountered situations. The problem is not, however, with children’s cases but with the uncertainty brought into the picture by Michigan v. Bryant and the disintegration generally of the Crawford majority. Where the law of confrontation goes from here, in general and in children’s cases, is hardly settled. There is no longer a commanding center as I describe in Part II of the article. Leaving the broader view to one side and looking at its operation, one finds a doctrine that applies clearly only to a limited number of situations regardless of whether the declarant is a child or an adult. Crawford has importantly provided protection to defendants from government-generated hearsay of the most dangerous accusatorial type. When statements are made to the police or their clear surrogates in investigative settings, the protection is substantial. Relatively little else receives any protection under the Confrontation Clause. In the large remainder of cases, lower courts make decisions regarding a potentially testimonial statement using a heavily fact-dependent and discretionary form of analysis that is likely to result in admission of apparently important and reliable, but un-confronted, hearsay. We are moving toward a Confrontation Clause where Crawford is uniformly applied to a very modest footprint of cases and where, for a sizeable group of other cases, its application is unpredictable. This same pattern is found in cases involving children.
Stock price dynamics: nonlinear trend, volume, volatility, resistance and money supply

We present a methodology to study a data set of 119 260 daily closed-end fund prices using mixed-effects regressions with the objective of understanding price dynamics. There is strong statistical support that relative price change depends significantly on (i) the recent trend in a nonlinear manner, (ii) recent changes in valuation, (iii) recent changes in money supply (M2), (iv) longer-term trend, (v) recent volume changes and (vi) proximity to a recent high price. The dependence on the volatility is more subtle, as short-term volatility has a positive influence, while the longer term is negative. The cubic nonlinearity in the weighted price trend shows that a percentage daily gain of up to 2.78% tends to yield higher prices, but larger gains lead to lower prices. Thus, the nonlinearity of price trend establishes an empirical and quantitative basis for both underreaction and overreaction within one large data set, facilitating an understanding of these competing motivations in markets. Increasing money s...
Managing and Monitoring Conflicts of Interest: Empowering the Outside Directors with Independent Counsel

For most of the last one hundred years, the corporate approach toward related party transactions has been they can be managed, not prohibited. The most common approach toward managing conflicts of interests is review by the board's independent directors. The well-received monitoring role for directors of public companies envisions the outside directors carrying out their responsibilities to manage related party transactions, particularly pursuant to state conflict of interest statutes. This article makes the case that the ability of the outside directors to discharge this monitoring task in the case of related party transactions with senior managers is dependent upon their being assisted by "independent" counsel (counsel with no on-going relationship with the company or its senior management). Independent counsel is not now required for the board's review of conflict of interest transactions. However, we see that in analogous conflict situations that an independent counsel is an essential element for assessing the overall independence of the outside directors' actions. Thus, selectively injecting independent counsel is advanced as a modest extension of the law governing conflict of interest transactions that will significant impact in improving the quality of the outside directors' monitoring and managing related party transactions involving senior management.
Discovery Cost Allocation, Due Process, and the Constitution's Role in Civil Litigation

In recent years, both scholars and rule makers have begun to reconsider the long established practice of the producer-pays model of discovery cost allocation. There are many arguments, on both sides of the issue, as to whether this practice represents wise social policy. In this article, however, Professor Redish challenges the constitutionality of the producer-pays model when applied to defendants, under both the Equal Protection and Due Process Clauses. Professor Redish initially characterizes the cost of a litigant’s discovery as the requesting party’s cost, even though the initial outlay for those costs is made by the producing party. In doing so he reasons by analogy to the doctrine of quantum meruit. On the basis of this premise that the costs of discovery are appropriately seen as the costs of the requesting party, he characterizes the requirement that the producer of the discovery bear the costs incurred in making production as simply a forced subsidy of what are properly deemed the requesting party’s costs. Such forced subsidization, he argues, constitutes a deprivation of a defendant’s property, which can be justified under equal protection only if it is at least rational. He argues that absent a finding that plaintiff’s injury was in fact caused by defendant’s violation of plaintiff’s legal rights, it is irrational to distinguish a defendant from any other member of society as a potential subsidizer of plaintiff’s discovery costs. Under the Due Process Clause, unless the plaintiff has factually established the truth of his allegation of defendant’s fault before a neutral adjudicator, imposition of plaintiff’s discovery costs on defendant is unconstitutional because it is impossible to distinguish the defendant from any other potential subsidizer. Because discovery takes place at a point in the litigation process before any evidentiary showing has been made or any factual determinations have been made by a neutral adjudicator, Professor Redish argues, the deprivation of defendant’s property to subsidize plaintiff’s costs constitutes an unconstitutional deprivation of defendant’s property. According to Professor Redish, then, the issue of discovery cost allocation is not one of social or legal policy, but rather one purely of constitutional law.
Automated analysis of lexical features in frontotemporal degeneration.

Abstract We implemented an automated analysis of lexical aspects of semi-structured speech produced by healthy elderly controls (n = 37) and three patient groups with frontotemporal degeneration (FTD): behavioral variant FTD (n = 74), semantic variant primary progressive aphasia (svPPA, n = 42), and nonfluent/agrammatic PPA (naPPA, n = 22). Based on previous findings, we hypothesized that the three patient groups and controls would differ in the counts of part-of-speech (POS) categories and several lexical measures. With a natural language processing program, we automatically tagged POS categories of all words produced during a picture description task. We further counted the number of wh-words, and we rated nouns for abstractness, ambiguity, frequency, familiarity, and age of acquisition. We also computed the cross-entropy estimation, where low cross-entropy indicates high predictability, and lexical diversity for each description. We validated a subset of the POS data that were automatically tagged with the Google Universal POS scheme using gold-standard POS data tagged by a linguist, and we found that the POS categories from our automated methods were more than 90% accurate. For svPPA patients, we found fewer unique nouns than in naPPA and more pronouns and wh-words than in the other groups. We also found high abstractness, ambiguity, frequency, and familiarity for nouns and the lowest cross-entropy estimation among all groups. These measures were associated with cortical thinning in the left temporal lobe. In naPPA patients, we found increased speech errors and partial words compared to controls, and these impairments were associated with cortical thinning in the left middle frontal gyrus. bvFTD patients' adjective production was decreased compared to controls and was correlated with their apathy scores. Their adjective production was associated with cortical thinning in the dorsolateral frontal and orbitofrontal gyri. Our results demonstrate distinct language profiles in subgroups of FTD patients and validate our automated method of analyzing FTD patients' speech.
GeneWalk identifies relevant gene functions for a biological context using network representation learning

A bottleneck in high-throughput functional genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Gene Ontology (GO) enrichment methods provide insight at the gene set level. Here, we introduce GeneWalk ( github.com/churchmanlab/genewalk ) that identifies individual genes and their relevant functions critical for the experimental setting under examination. After the automatic assembly of an experiment-specific gene regulatory network, GeneWalk uses representation learning to quantify the similarity between vector representations of each gene and its GO annotations, yielding annotation significance scores that reflect the experimental context. By performing gene- and condition-specific functional analysis, GeneWalk converts a list of genes into data-driven hypotheses.
Cyber-Physical Energy Systems Security: Threat Modeling, Risk Assessment, Resources, Metrics, and Case Studies

Cyber-physical systems (CPS) are interconnected architectures that employ analog and digital components as well as communication and computational resources for their operation and interaction with the physical environment. CPS constitute the backbone of enterprise (e.g., smart cities), industrial (e.g., smart manufacturing), and critical infrastructure (e.g., energy systems). Thus, their vital importance, interoperability, and plurality of computing devices make them prominent targets for malicious attacks aiming to disrupt their operations. Attacks targeting cyber-physical energy systems (CPES), given their mission-critical nature within the power grid infrastructure, can lead to disastrous consequences. The security of CPES can be enhanced by leveraging testbed capabilities in order to replicate and understand power systems operating conditions, discover vulnerabilities, develop security countermeasures, and evaluate grid operation under fault-induced or maliciously constructed scenarios. Adequately modeling and reproducing the behavior of CPS could be a challenging task. In this paper, we provide a comprehensive overview of the CPS security landscape with an emphasis on CPES. Specifically, we demonstrate a threat modeling methodology to accurately represent the CPS elements, their interdependencies, as well as the possible attack entry points and system vulnerabilities. Leveraging the threat model formulation, we present a CPS framework designed to delineate the hardware, software, and modeling resources required to simulate the CPS and construct high-fidelity models that can be used to evaluate the system’s performance under adverse scenarios. The system performance is assessed using scenario-specific metrics, while risk assessment enables the system vulnerability prioritization factoring the impact on the system operation. The overarching framework for modeling, simulating, assessing, and mitigating attacks in a CPS is illustrated using four representative attack scenarios targeting CPES. The key objective of this paper is to demonstrate a step-by-step process that can be used to enact in-depth cybersecurity analyses, thus leading to more resilient and secure CPS.
RadVR: A 6DOF Virtual Reality Daylighting Analysis Tool

Abstract This work introduces RadVR, a virtual reality tool for daylighting analysis that simultaneously combines qualitative assessments through immersive real-time renderings with quantitative physically correct daylighting simulations in a 6DOF virtual environment. By taking a 3D building model with material properties as input, RadVR allows users to (1) perform physically-based daylighting simulations via Radiance, (2) study sunlight in different hours-of-the-year, (3) interact with a 9-point-in-time matrix for the most representative times of the year, and (4) visualize, compare, and analyze daylighting simulation results. With an end-to-end workflow, RadVR integrates with 3D modeling software that is commonly used by building designers. Additionally, by conducting user experiments we compare the proposed system with DIVA for Rhino, a Radiance-based tool that uses conventional 2D-displays. The results show that RadVR can provide promising assistance in spatial understanding tasks, navigation, and sun position analysis in virtual reality.
Analysis of intensity correlation enhanced plasmonic structured illumination microscopy

We propose to enhance the performance of localized plasmon structured illumination microscopy (LP-SIM) via intensity correlations. LP-SIM uses sub-wavelength illumination patterns to encode high spatial frequency information. It can enhance the resolution up to three-fold before gaps in the optical transfer function (OTF) support arise. For blinking fluorophores or for quantum antibunching, an intensity correlation analysis induces higher harmonics of the illumination pattern and enlarges the effective OTF. This enables ultrahigh resolutions without gaps in the OTF support, and thus a fully deterministic imaging scheme. We present simulations that include shot and external noise and demonstrate the resolution power under realistic photon budgets. The technique has potential in light microscopy where low-intensity illumination is paramount while aiming for high spatial but moderate temporal resolutions.
Defect Reduction Planning (using TimeLIME)

Software comes in releases. An implausible change to software is something that has never been changed in prior releases. When planning how to reduce defects, it is better to use plausible changes, i.e., changes with some precedence in the prior releases. To demonstrate these points, this paper compares several defect reduction planning tools. LIME is a local sensitivity analysis tool that can report the fewest changes needed to alter the classification of some code module (e.g., from "defective" to "non-defective"). TimeLIME is a new tool, introduced in this paper, that improves LIME by restricting its plans to just those attributes which change the most within a project. In this study, we compared the performance of LIME and TimeLIME and several other defect reduction planning algorithms. The generated plans were assessed via (a) the similarity scores between the proposed code changes and the real code changes made by developers; and (b) the improvement scores seen within projects that followed the plans. For nine project trails, we found that TimeLIME outperformed all other algorithms (in 8 out of 9 trials). Hence, we strongly recommend using past releases as a source of knowledge for computing fixes for new releases (using TimeLIME). Apart from these specific results about planning defect reductions and TimeLIME, the more general point of this paper is that our community should be more careful about using off-the-shelf AI tools, without first applying SE knowledge. In this case study, it was not difficult to augment a standard AI algorithm with SE knowledge (that past releases are a good source of knowledge for planning defect reductions). As shown here, once that SE knowledge is applied, this can result in dramatically better systems.
New forms of structure in ecosystems revealed with the Kuramoto model.

Ecological systems, as is often noted, are complex. Equally notable is the generalization that complex systems tend to be oscillatory, whether Huygens' simple patterns of pendulum entrainment or th...
A Self-Updating K-Contingency List for Smart Grid System

A reliable decision making by the operator in a smart grid is contingent upon correct analysis of intra-and-interdependencies between its entities and also on accurate identification of the most critical entities at a given point of time. A measurement based self-updating contingency list can provide real-time information to the operator about current system condition which can help the operator to take the required action. In this paper, the underlying intra-and-interdependencies between entities for a given power-communication network is captured using a dependency model called Modified Implicative Interdependency Model (MIIM) [1]. Given an integer K, the event-driven self-updating contingency list problem gives the list of K-most critical entities, failure of which maximizes the network damage at the current time. Owing to the problem being NP complete, a fast heuristic method to generate a real-time contingency list using system measurements is provided here. The validation of the work is done by comparing the contingency list obtained for different K values using the MIIM model on a smart grid of IEEE 14-Bus system with that obtained by simulating the smart grid using a co-simulation system formed by MATPOWER and Java Network Simulator (JNS). The results also indicate that the network damage predicted by both the ILP based solution [2] and the proposed heuristic solution using MIIM are more realistic compared to that obtained using another dependency model called Implicative Interdependency Model (IIM) [3].
A dynamical mechanism for the Page curve from quantum chaos

If the evaporation of a black hole formed from a pure state is unitary, the entanglement entropy of the Hawking radiation should follow the Page curve, increasing from zero until near the halfway point of the evaporation, and then decreasing back to zero. The general argument for the Page curve is based on the assumption that the quantum state of the black hole plus radiation during the evaporation process is typical. In this paper, we show that the Page curve can result from a simple dynamical input in the evolution of the black hole, based on a recently proposed signature of quantum chaos, without resorting to typicality. Our argument is based on what we refer to as the “operator gas” approach, which allows one to understand the evolution of the microstate of the black hole from generic features of the Heisenberg evolution of operators. One key feature which leads to the Page curve is the possibility of dynamical processes where operators in the “gas” can “jump” outside the black hole, which we refer to as void formation processes. Such processes are initially exponentially suppressed, but dominate after a certain time scale, which can be used as a dynamical definition of the Page time. In the Hayden-Preskill protocol for young and old black holes, we show that void formation is also responsible for the transfer of information from the black hole to the radiation. We conjecture that void formation may provide a microscopic explanation for the recent semi-classical prescription of including islands in the calculation of the entanglement entropy of the radiation.
Analysis of the human connectome data supports the notion of a "Common Model of Cognition" for human and human-like intelligence across domains.

Abstract The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. These findings suggest that a common set of architectural principles that could be used for artificial intelligence also underpins human brain function across multiple cognitive domains.
Source-specific contributions of particulate matter to asthma-related pediatric emergency department utilization.

Ambient particulate matter smaller than 2.5 μm (PM2.5) is associated with different chronic diseases. It is crucial to identify the sources of ambient particulate matter to reduce the impact on health. Still, only a few studies have been linked with specific ambient particulate matter sources. In this study, we estimated the contributions of sources of PM2.5 and examined their association with daily asthma hospital utilization in Cincinnati, Ohio, USA. We used a model-based clustering method to group days with similar source-specific contributions into six distinct clusters. Specifically, elevated PM2.5 concentrations occurring on days characterized by low coal combustion contributions showed a significantly reduced risk of hospital utilization for asthma (rate ratio: 0.86, 95% CI: [0.77, 0.95]) compared to other clusters. Reducing coal combustion contribution to PM2.5 levels could be an effective intervention for lowering asthma-related hospital utilization.
Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control

Abstract Dynamic mode decomposition (DMD) is a versatile approach that enables the construction of low-order models from data. Controller design tasks based on such models require estimates and guarantees on predictive accuracy. In this work, we provide a theoretical analysis of DMD model errors that reveals impacts of model order and data availability. The analysis also establishes conditions under which DMD models can be made asymptotically exact. We numerically validate our theoretical results using a 2D diffusion system.
Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation

Structure-determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure, but is constrained by the need for large, well ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins. However, the first two methods also suffer from the phase problem, while many proteins fall below the molecular-weight threshold required for single-particle reconstruction. Cryo-electron tomography of protein nanocrystals has the potential to overcome these obstacles of mainstream structure-determination methods. Here, a data-processing scheme is presented that combines routines from X-ray crystallography and new algorithms that have been developed to solve structures from tomograms of nanocrystals. This pipeline handles image-processing challenges specific to tomographic sampling of periodic specimens and is validated using simulated crystals. The tolerance of this workflow to the effects of radiation damage is also assessed. The simulations indicate a trade-off between a wider tilt range to facilitate merging data from multiple tomograms and a smaller tilt increment to improve phase accuracy. Since phase errors, but not merging errors, can be overcome with additional data sets, these results recommend distributing the dose over a wide angular range rather than using a finer sampling interval to solve the protein structure.
Solar Wind Helium Abundance Heralds Solar Cycle Onset

We study the solar wind helium-to-hydrogen abundance’s ( $A_{ {\mathrm {He}}}$ ) relationship to solar cycle onset. Using OMNI/Lo data, we show that $A_{ {\mathrm {He}}}$ increases prior to sunspot number (SSN) minima. We also identify a rapid depletion and recovery in $A_{ {\mathrm {He}}}$ that occurs directly prior to cycle onset. This $A_{ {\mathrm {He}}}$ shutoff happens at approximately the same time across solar wind speeds ( $v_{\mathrm{sw}}$ ) and the time between successive $A_{ {\mathrm {He}}}$ shutoffs is typically on the order of the corresponding solar cycle length. In contrast to $A_{ {\mathrm {He}}}$ ’s $v_{\mathrm{sw}}$ -dependent phase lag with respect to SSN (Alterman and Kasper, 2019), $A_{ {\mathrm {He}}}$ shutofff’s concurrence across $v_{\mathrm{sw}}$ likely implies it is independent of solar wind acceleration and driven by a mechanism near or below the photosphere. Using brightpoint (BP) measurements to provide context, we infer that $A_{ {\mathrm {He}}}$ shutoff is likely related to the overlap of adjacent solar cycles and the equatorial flux cancelation of the older, extended solar cycle during solar minima.
Landscape Fairness: Removing Discrimination from the Built Environment

At its core, this Article argues that the everyday landscape is one of the most overlooked instruments of modern race-making. Drawing on evidence from geography and sociology, the Article begins by demonstrating that the built environment inscribes selective and misleading versions of the past in solid, material forms. These narratives—told through street renamings, parks, monuments, and buildings—ultimately marginalize African American communities and transmit ideas about racial power across generations. After demonstrating that the landscape remains the agar upon which racial hierarchies replicate themselves, this Article then pivots and examines current efforts to rid the built environment of discriminatory spaces. It argues that contemporary attacks on the landscape are doomed to fail. The approaches suggested by academics in law and geography either turn a blind eye to the political economy of local decisionmaking or fail to consider entrenched legal precedent. The final section of the Article lays out a policy proposal that could spark a new focus on issues of “landscape fairness.” It argues in favor of a set of basic procedural requirements that would force jurisdictions to reconsider the discriminatory places within their borders. Procedural mandates would force government to internalize values it might otherwise ignore, allow citizen-critics to challenge dominant historical narratives, and push communities to view the past (and future) in much more diverse terms.
Lossless Convexification of Optimal Control Problems with Semi-continuous Inputs

Abstract This paper presents a novel one-shot convex optimization method for finding globally optimal solutions of a class of mixed-integer non-convex optimal control problems. We consider problems with non-convex constraints that restrict the input norms to be either zero or lower-and upper-bounded. The non-convex problem is relaxed to a convex one whose optimal solution is proved to be optimal almost everywhere for the original problem, a procedure known as lossless convexification. The solution relies on second-order cone programming and demonstrates that a meaningful class of optimal control problems with binary variables can be solved reliably and in polynomial time. A rocket landing example with a coupled thrust-gimbal constraint corroborates the effectiveness of the approach.
Au-Sb and Au-Ag-Sb alloys as low-voltage contact materials

A recent investigation has shown that the increase in contact resistance of low-voltage, noble-metal electrical contacts with the number of on-off switch cycles is primarily due to carbon formation on the contact surfaces. Moreover, when contact electrodes were made of the base metals Ni, Fe, Ti, or Sb, carbon formation was delayed, though not eliminated. Among these, Sb was found to be most effective against carbon formation. Because of its high electrical resistivity and poor oxidation resistance, however, pure Sb cannot be used for electrical contacts. Thus in this study Au-Sb and Au-Ag-Sb alloys of various compositions were prepared and tested to evaluate their carbon formation behavior. An impact tester that simulated the operation of electromechanical relay contacts was used, and experiments were conducted in an atmosphere of air-benzene mixture at room temperature. Of the alloys tested, the ternary Au-7Ag-30Sb alloy was found to be the best. >
Commentary on Janet Landa's Paper

In her paper, Janet Landa argues that the key behind the success of the ethnically homogeneous Chinese middleman group (EHMG) is their highly developed in-group cooperation. She places the EHMG phenomenon within the framework of evolutionary biology by calling it 'a cultural transmission unit' subject to the influence of both genes and culture. Landa suggests that the individual members of the EHMG comprise the units of selection for EHMG groups much like the genes comprise the units of selection for individual organisms. However, evolution occurs through the differential survival and reproduction of individual organisms. In order to meet the criteria of biological group selection, it would have to be demonstrated that groups in competition with EHMGs had gone extinct. The fact that the EHMGs are simply faring better than their competitors is not evidence for group selection. The other reason biological group selection is not really applicable to EHMGs is that there are no physical/biological barriers to exogamous mating, and cultural barriers are intrinsically unstable.
Foreign shareholding, corporate governance and firm performance: Evidence from Chinese companies

Abstract This paper examines how a firm’s corporate governance characteristics and institutional environment affect the presence of large foreign shareholding, and how a firm’s foreign ownership influences its performance. For a sample of listed Chinese companies, we find that both firm-level governance characteristics and country-level institutional environment affect the presence and the extent of large foreign shareholding. We also find an inverted U-shaped relation between a firm’s foreign ownership and its return on assets, return on equity and Tobin’s q. The implied optimal foreign ownership increases when changes in institutional environment reduce the opportunity for controlling shareholders to extract private benefits.
Network Interconnectivity and Entry into Platform Markets

Digital technologies have led to the emergence of many platforms in our economy today. In certain platform networks, buyers in one market purchase services from providers in many other markets, whe...
Action Word Prediction for Neural Source Code Summarization

Source code summarization is the task of creating short, natural language descriptions of source code. Code summarization is the backbone of much software documentation such as JavaDocs, in which very brief comments such as "adds the customer object" help programmers quickly understand a snippet of code. In recent years, automatic code summarization has become a high value target of research, with approaches based on neural networks making rapid progress. However, as we will show in this paper, the production of good summaries relies on the production of the action word in those summaries: the meaning of the example above would be completely changed if "removes" were substituted for "adds." In this paper, we advocate for a special emphasis on action word prediction as an important stepping stone problem towards better code summarization – current techniques try to predict the action word along with the whole summary, and yet action word prediction on its own is quite difficult. We show the value of the problem for code summaries, explore the performance of current baselines, and provide recommendations for future research.
Hardware-assisted detection of firmware attacks in inverter-based cyberphysical microgrids

Abstract The electric grid modernization effort relies on the extensive deployment of microgrid (MG) systems. MGs integrate renewable resources and energy storage systems, allowing to generate economic and zero-carbon footprint electricity, deliver sustainable energy to communities using local energy resources, and enhance grid resilience. MGs as cyberphysical systems include interconnected devices that measure, control, and actuate energy resources and loads. For optimal operation, cyberphysical MGs regulate the onsite energy generation through support functions enabled by smart inverters. Smart inverters, being consumer electronic firmware-based devices, are susceptible to increasing security threats. If inverters are maliciously controlled, they can significantly disrupt MG operation and electricity delivery as well as impact the grid stability. In this paper, we demonstrate the impact of denial-of-service (DoS) as well as controller and setpoint modification attacks on a simulated MG system. Furthermore, we employ custom-built hardware performance counters (HPCs) as design-for-security (DfS) primitives to detect malicious firmware modifications on MG inverters. The proposed HPCs measure periodically the order of various instruction types within the MG inverter’s firmware code. Our experiments illustrate that the firmware modifications are successfully identified by our custom-built HPCs utilizing various machine learning-based classifiers.
What Are Bayesian Neural Network Posteriors Really Like

The posterior over Bayesian neural network (BNN) parameters is extremely high-dimensional and non-convex. For computational reasons, researchers approximate this posterior using inexpensive mini-batch methods such as mean-field variational inference or stochastic-gradient Markov chain Monte Carlo (SGMCMC). To investigate foundational questions in Bayesian deep learning, we instead use full-batch Hamiltonian Monte Carlo (HMC) on modern architectures. We show that (1) BNNs can achieve significant performance gains over standard training and deep ensembles; (2) a single long HMC chain can provide a comparable representation of the posterior to multiple shorter chains; (3) in contrast to recent studies, we find posterior tempering is not needed for near-optimal performance, with little evidence for a "cold posterior" effect, which we show is largely an artifact of data augmentation; (4) BMA performance is robust to the choice of prior scale, and relatively similar for diagonal Gaussian, mixture of Gaussian, and logistic priors; (5) Bayesian neural networks show surprisingly poor generalization under domain shift; (6) while cheaper alternatives such as deep ensembles and SGMCMC methods can provide good generalization, they provide distinct predictive distributions from HMC. Notably, deep ensemble predictive distributions are similarly close to HMC as standard SGLD, and closer than standard variational inference.
Train simultaneously, generalize better: Stability of gradient-based minimax learners

The success of minimax learning problems of generative adversarial networks (GANs) has been observed to depend on the minimax optimization algorithm used for their training. This dependence is commonly attributed to the convergence speed and robustness properties of the underlying optimization algorithm. In this paper, we show that the optimization algorithm also plays a key role in the generalization performance of the trained minimax model. To this end, we analyze the generalization properties of standard gradient descent ascent (GDA) and proximal point method (PPM) algorithms through the lens of algorithmic stability under both convex concave and non-convex non-concave minimax settings. While the GDA algorithm is not guaranteed to have a vanishing excess risk in convex concave problems, we show the PPM algorithm enjoys a bounded excess risk in the same setup. For non-convex non-concave problems, we compare the generalization performance of stochastic GDA and GDmax algorithms where the latter fully solves the maximization subproblem at every iteration. Our generalization analysis suggests the superiority of GDA provided that the minimization and maximization subproblems are solved simultaneously with similar learning rates. We discuss several numerical results indicating the role of optimization algorithms in the generalization of the learned minimax models.
Leveraging legislative power: distributive politics and Committee work in Kenya’s National Assembly

How do legislative Committees in emerging democracies spend their time? Committees are organisational devices for expanding valuable legislative time. Existing works posit that Committee work refle...
Combat, casualties, and compensation: Evidence from Iraq and Afghanistan

Our research examines the effect of combat deployments to Iraq and Afghanistan on casualties. We use restricted data from the Defense Manpower Data Center (DMDC) and Social Security Administration (SSA) to construct a panel of all U.S. Active Duty service members having served at some point during the years 2001-2012. Casualties disproportionately occur at higher rates among (i) young, white, males (ii) enlisted personnel (iii) less educated personnel (iv) and those in combat job types. Our estimates indicate that overall U.S. military personnel who deployed in an individual year to Iraq or Afghanistan had a 48 per 100,000 higher probability of death than non-deployed military personnel who remained stateside. The increased fatal injury risk of deployed U.S. military personnel is 15 times higher than the national average civilian workplace fatality rate, but roughly equal to the fatal injury risk faced in some of the most dangerous civilian occupations. Our estimates suggest a compensating wage differential equal to $808 per month would be appropriate, in comparison to the current status quo of $225 per month in danger pay (and additional tax benefits) provided to U.S. military personnel deployed into combat zones. The additional compensation should also be adjusted by service or job type.
A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network

Incorporating a so-called "momentum" dynamic in gradient descent methods is widely used in neural net training as it has been broadly observed that, at least empirically, it often leads to significantly faster convergence. At the same time, there are very few theoretical guarantees in the literature to explain this apparent acceleration effect. Even for the classical strongly convex quadratic problems, several existing results only show Polyak's momentum has an accelerated linear rate asymptotically. In this paper, we first revisit the quadratic problems and show a non-asymptotic accelerated linear rate of Polyak's momentum. Then, we provably show that Polyak's momentum achieves acceleration for training a one-layer wide ReLU network and a deep linear network, which are perhaps the two most popular canonical models for studying optimization and deep learning in the literature. Prior work Du at al. 2019 and Wu et al. 2019 showed that using vanilla gradient descent, and with an use of over-parameterization, the error decays as $(1- \Theta(\frac{1}{ \kappa'}))^t$ after $t$ iterations, where $\kappa'$ is the condition number of a Gram Matrix. Our result shows that with the appropriate choice of parameters Polyak's momentum has a rate of $(1-\Theta(\frac{1}{\sqrt{\kappa'}}))^t$. For the deep linear network, prior work Hu et al. 2020 showed that vanilla gradient descent has a rate of $(1-\Theta(\frac{1}{\kappa}))^t$, where $\kappa$ is the condition number of a data matrix. Our result shows an acceleration rate $(1- \Theta(\frac{1}{\sqrt{\kappa}}))^t$ is achievable by Polyak's momentum. All the results in this work are obtained from a modular analysis, which can be of independent interest. This work establishes that momentum does indeed speed up neural net training.
Quasi anomalous knowledge: searching for new physics with embedded knowledge

Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the sensitivity of these approaches is significantly reduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alternative signal priors that capture some of the salient features of new physics signatures, allowing for the recovery of sensitivity even when the alternative signal is incorrect. This approach can be applied to a broad range of physics models and neural network architectures. In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider utilizing variational autoencoders with normalizing flow.
Sun Safe Mode Controller Design for LADEE

This paper presents the development of sun safe controllers which are designed to keep the spacecraft power positive and thermally balanced in the event an anomaly is detected. Employed by NASA's Lunar Atmosphere and Dust Environment Explorer (LADEE), the controllers utilize the measured sun vector and the spacecraft body rates for feedback control. To improve the accuracy of sun vector estimation, the least square minimization approach is applied to process the sensor data, which is proven to be effective and accurate. To validate the controllers, the LADEE spacecraft model engaging the sun safe mode was first simulated and then compared with the actual LADEE orbital fight data. The results demonstrated the applicability of the proposed sun safe controllers.
Miniature thermal emission spectrometer for the Mars Exploration Rover

Abstract This paper describes results of the calibration of the miniature thermal emission spectrometer (Mini-TES) being built by Raytheon Santa Barbara Remote Sensing (SBRS) under contract to Arizona State University (ASU). This paper also serves as an update to an earlier paper [R.J. Peralta, S. Silverman, D. Bates, Raytheon/Santa Barbara Remote Sensing, P. Christensen, G. Mehall, T. Tourville, R. Keehn, G. Cannon, Arizona State University, Miniature thermal emission spectrometer for the Mars Exploration Rover, Proceedings of the SPIE, vol. 4485-09, August 2001] for mission description and instrument design. Mini-TES is a single detector Fourier transform spectrometer (FTS), covering the spectral range 5 – 29 μ m at 10 cm - 1 spectral resolution. Launched in June 2003, one Mini-TES instrument will fly to Mars aboard each of the two missions of NASA's Mars Exploration Rover Project (MER), named Spirit and Opportunity. Mini-TES is designed to provide a key minerological remote sensing component of the MER mission, which includes several other science instruments. The first Mini-TES unit was required to meet a two-year development schedule with proven, flight-tested instrumentation. Therefore, SBRS designed Mini-TES based on proven heritage from the successful Mars Global Surveyor (MGS) thermal emission spectrometer (TES), which was launched in 1996 and is still operational with over 500 million spectra collected to date. Mini-TES design, performance, integration onto the rovers, as well as details of the calibration are discussed. Full instrument and calibration details are the subject of an upcoming Journal of Geophysical Research Mini-TES paper by Christensen, et al.
On the degeneration of asymptotically conical Calabi–Yau metrics

We study the degenerations of asymptotically conical Ricci-flat Kahler metrics as the Kahler class degenerates to a semi-positive class. We show that under appropriate assumptions, the Ricci-flat Kahler metrics converge to a incomplete smooth Ricci-flat Kahler metric away from a compact subvariety. As a consequence, we construct singular Calabi–Yau metrics with asymptotically conical behaviour at infinity on certain quasi-projective varieties and we show that the metric geometry of these singular metrics are homeomorphic to the topology of the singular variety. Finally, we will apply our results to study several classes of examples of geometric transitions between Calabi–Yau manifolds.
Parallel evaluation of nucleophilic and electrophilic chemical probes for sulfenic acid: Reactivity, selectivity and biocompatibility

Cysteine sulfenic acids (Cys-SOH) are pivotal modifications in thiol-based redox signaling and central intermediates en route to disulfide and sulfinic acid states. A core mission in our lab is to develop bioorthogonal chemical tools with the potential to answer mechanistic questions involving cysteine oxidation. Our group, among others, has contributed to the development of nucleophilic chemical probes for detecting sulfenic acids in living cells. Recently, another class of Cys-SOH probes based on strained alkene and alkyne electrophiles has emerged. However, the use of different models of sulfenic acid and methodologies, has confounded clear comparison of these probes with respect to chemical reactivity, kinetics, and selectivity. Here, we perform a parallel evaluation of nucleophilic and electrophilic chemical probes for Cys-SOH. Among the key findings, we demonstrate that a probe for Cys-SOH based on the norbornene scaffold does not react with any of the validated sulfenic acid models in this study. Furthermore, we show that purported cross-reactivity of dimedone-like probes with electrophiles, like aldehydes and cyclic sulfenamides, is a not meaningful in a biological setting. In summary, nucleophilic probes remain the most viable tools for bioorthogonal detection of Cys-SOH.
Turbulence Modeling for Unsteady Transonic Flows

Conditionally sampled, ensemble-averaged velocity measurements, made with a laser velocimeter, were taken in the flowfield over the rear half of an 18% thick circular arc airfoil at zero incidence tested at M = 0.76 and at a Reynolds number based on chord of 11 x 10(exp 6). Data for one cycle of periodic unsteady flow having a reduced frequency f of 0.49 are analyzed. A series of compression waves, which develop in the early stages of the cycle, strengthen and coalesce into a strong shock wave that moves toward the airfoil leading edge. A thick shear layer forms downstream of the shock wave. The kinetic energy and shear stresses increase dramatically, reach a maximum when dissipation and diffusion of the turbulence exceed production, and then decrease substantially. The response lime of the turbulence to the changes brought about by the shock-wave passage upstream depends on the shock-wave strength and position in the boundary layer. The cycle completes itself when the shock wave passes the midchord, weakens, and the shear layer collapses. Remarkably good comparisons are found with computations that employ the time-dependent Reynolds averaged form of the Navier-Stokes equations using an algebraic eddy viscosity model, developed for steady flows.