cssi/SciSciGPT-SciSciNet huggingface dataset:
Interpreting the 1997 Amendment to the IDEA: Did Congress Intend to Limit the Remedy of Private School Tuition Reimbursement for Disabled Children?

The Individuals with Disabilities Education Act (IDEA) requires school districts to fund private school placements for disabled children who cannot be educated appropriately in public school. This Note explores the conflict over whether private school tuition reimbursement should be available under the IDEA to parents who place their disabled children in private school without previously receiving special education from a public agency. The conflict hinges on whether a 1997 amendment to the IDEA foreclosed the equitable considerations previously utilized by the courts to determine whether tuition reimbursement was appropriate and limited the remedy to cases where the child previously received public special education services. This Note argues that the 1997 amendment to the IDEA did not limit the remedy of tuition reimbursement. It suggests that the U.S. Supreme Court uphold the remedy of tuition reimbursement when appropriate, whether or not the disabled child previously received public special education services, when it decides the issue later this Term.
Privileged Places: Race, Uneven Development and the Geography of Opportunity in Urban America:

David Rusk, former Mayor of Albuquerque, New Mexico, has observed that "bad neighborhoods defeat good programs". This paper identifies the underlying causes of bad neighbourhoods along with their c...
Responsibility for Cost Management Hinders Learning to Avoid the Winner's Curse

Errors in estimated product costs lead firms to win business that is unprofitable, because firms are more likely to win business when underestimated product costs lead them to bid below actual cost (Cooper et al. 1992; Hilton 2005). Feedback from repeated competitive bidding markets can teach people to bid well above estimated costs to avoid this winner's curse (Kagel 1995; Kagel and Levin 2002). We present experimental evidence that such learning is substantially hampered by sellers' sense of responsibility for the costs. This effect is consistent with psychological evidence that people tend to attribute bad outcomes to environmental factors out of their control, such as cost‐estimation errors, and attribute good outcomes to their own skills, such as their ability to choose effective cost‐management initiatives (Miller and Ross 1975; Zuckerman 1979). The results suggest that responsibility structures that combine pricing and production decisions may have unexpected drawbacks.
Foreign Direct Investment and Income Inequality in Mexico, 1990–2000

In this article we explore the relationship between the investments of multinational corporations (foreign direct investment) and income inequality in Mexico. We argue that Mexico's liberalization of foreign direct investment (FDI) inflows in the 1990s provides a natural experiment to test how FDI affects income inequality in a middle-income country. We use an instrumental variables approach as our identification strategy to mitigate problems of endogeneity and omitted variable bias. In an empirical test of the determinants of changes in income inequality from 1990 to 2000, we find that increased FDI inflows are associated with a decrease in income inequality within Mexico's thirty-two states.The authors would like to thank Lawrence Broz, John Freeman, Matt Gabel, Geoff Garrett, Quan Li, Eddy Malesky, Layna Mosley, Katie Ridgeway, Pablo Pinto, John Stringer, and Andy Sobel for comments and suggestions. Jacob Gerber and Mariana Medina provided excellent research assistance. Thanks also to Patricio Aroca Gonzalez for generously providing us with his data. We acknowledge the financial support of the Weidenbaum Center on the Economy, Government, and Public Policy. Nate Jensen's contribution to this article was written as a Global Fellow at UCLA's International Institute.
Making Sense of One Dollar CEO Salaries

We examine the determinants and outcomes of Chief Executive Officers (CEOs) accepting a $1 salary, a compensation practice that occurs relatively frequently in high-profile firms and is debated by regulators, investors, and the media. Using a hand-collected sample of 93 CEOs from 91 firms between 1993 and 2011, we examine the triggers preceding the $1 salary decision, the factors associated with the decision, subsequent stock returns, and the outcomes for the CEOs. Our evidence is consistent with two explanations for the phenomenon: (i) it is a gesture of sacrifice by CEOs of firms in crisis, and (ii) it is a signal of better future performance by CEOs of growing firms. Our analyses highlight the two different circumstances and shed light on an interesting debate that has thus far been supported only by anecdotal evidence
The quality of medical care, behavioral risk factors, and longevity growth.

The rate of increase of longevity has varied considerably across U.S. states since 1991. This paper examines the effect of the quality of medical care, behavioral risk factors (obesity, smoking, and AIDS incidence), and other variables (education, income, and health insurance coverage) on life expectancy and medical expenditure using longitudinal state-level data. We examine the effects of three different measures of the quality of medical care. The first is the average quality of diagnostic imaging procedures, defined as the fraction of procedures that are advanced procedures. The second is the average quality of practicing physicians, defined as the fraction of physicians that were trained at top-ranked medical schools. The third is the mean vintage (FDA approval year) of outpatient and inpatient prescription drugs. Life expectancy increased more rapidly in states where (1) the fraction of Medicare diagnostic imaging procedures that were advanced procedures increased more rapidly; (2) the vintage of self- and provider-administered drugs increased more rapidly; and (3) the quality of medical schools previously attended by physicians increased more rapidly. States with larger increases in the quality of diagnostic procedures, drugs, and physicians did not have larger increases in per capita medical expenditure. We perform several tests of the robustness of the life expectancy model. Controlling for per capita health expenditure (the “quantity” of healthcare), and eliminating the influence of infant mortality, has virtually no effect on the healthcare quality coefficients. Controlling for the adoption of an important nonmedical innovation also has little influence on the estimated effects of medical innovation adoption on life expectancy.
The Demise of the Rights Issue

This article suggests that the lack of use of rights offerings in the United States, a phenomenon referred to as the equity underwriting paradox, can be explained by transaction-cost conditions. A sample of underwritten rights offerings provides supportfor the explanation. Firms making underwritten rights offerings paid lower underwriterfees but incurred significantly larger price drops Just prior to the offering than didfirms making underwritten public offerings. Further analysis reveals that the underwritten-rights-offering price concessions are aform of transaction cost that is notfound in underwritten public offerings.
Effective Interview Practices for Accurately Assessing Counterproductive Traits

A first line of defense for an organization against counterproductive behavior in the workplace is the accurate detection during the interview process of a job candidate’s counterproductive traits. This article discusses aspects of the interview format and other considerations relevant to increasing the accuracy of the assessment of a job candidate’s personality. Recent research suggests that the unstructured interview may be of high value in comparison to the traditional structured interview format, when accurate personality prediction is the criterion (Blackman, in press–a; Ickes, Snyder and Garcia 1997). Interviewers can also become more sophisticated in evaluating the probable accuracy of their judgments by learning about four important moderators of accuracy: properties of the judge, of the target, of the trait being judged, and of the information upon which the judgment is based (Funder 1995).
A map of sentencing and a compass for judges: Sentencing information systems, transparency, and the next generation of reform

No modern structured sentencing system provides easily accessible data describing individual sentences or dynamic sentencing patterns and practices. Limited availability of individual and systemic data (in contrast to annual and other special reports) goes hand-in-hand with limited efforts by state reformers to compare sentencing law and experience across states or to compare states to the federal system. The limited access to information and lack of visible efforts to craft an active sentencing reform dialogue may help to explain the undue scholarly focus on the failed federal reforms over far more positive state sentencing reform experiments. Sentencing reform everywhere can be improved if state actors make sentencing information and sentencing data publicly available and easily accessible and speak to other systems. One promising approach to improve sentencing law and sentencing discourse is the development of sentencing information systems (SIS). An SIS depicts decisions within each system and allows observers without technical data skills, including judges, to ask a variety of questions that relate to individual case decisions, assessments of particular sentencing factors, sentencing variation, sentencing process, and even sentencing purposes.
Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties

As they carry great potential for modeling complex interactions, graph neural network (GNN)-based methods have been widely used to predict quantum mechanical properties of molecules. Most of the existing methods treat molecules as molecular graphs in which atoms are modeled as nodes. They characterize each atom's chemical environment by modeling its pairwise interactions with other atoms in the molecule. Although these methods achieve a great success, limited amount of works explicitly take many-body interactions, i.e., interactions between three and more atoms, into consideration. In this paper, we introduce a novel graph representation of molecules, heterogeneous molecular graph (HMG) in which nodes and edges are of various types, to model many-body interactions. HMGs have the potential to carry complex geometric information. To leverage the rich information stored in HMGs for chemical prediction problems, we build heterogeneous molecular graph neural networks (HMGNN) on the basis of a neural message passing scheme. HMGNN incorporates global molecule representations and an attention mechanism into the prediction process. The predictions of HMGNN are invariant to translation and rotation of atom coordinates, and permutation of atom indices. Our model achieves state-of-the-art performance in 9 out of 12 tasks on the QM9 dataset.
Pandora's Box with Correlations: Learning and Approximation

The Pandora's Box problem and its extensions capture optimization problems with stochastic input where the algorithm can obtain instantiations of input random variables at some cost. To our knowledge, all previous work on this class of problems assumes that different random variables in the input are distributed independently. As such it does not capture many real-world settings. In this paper, we provide the first approximation algorithms for Pandora's Box-type problems with correlations. We assume that the algorithm has access to samples drawn from the joint distribution on input. Algorithms for these problems must determine an order in which to probe random variables, as well as when to stop and return the best solution found so far. In general, an optimal algorithm may make both decisions adaptively based on instantiations observed previously. Such fully adaptive (FA) strategies cannot be efficiently approximated to within any sub-linear factor with sample access. We therefore focus on the simpler objective of approximating partially adaptive (PA) strategies that probe random variables in a fixed predetermined order but decide when to stop based on the instantiations observed. We consider a number of different feasibility constraints and provide simple PA strategies that are approximately optimal with respect to the best PA strategy for each case. All of our algorithms have polynomial sample complexity. We further show that our results are tight within constant factors: better factors cannot be achieved even using the full power of FA strategies.
Towards a Secure and Resilient All-Renewable Energy Grid for Smart Cities

The concept of smart cities is driven by the need to enhance citizens' quality of life. It is estimated that 70% of the world population will live in urban areas by 2050. The electric grid is the energy backbone of smart city deployments. An electric energy system immune to adverse events, both cyber and physical risks, and able to support the integration of renewable sources will drive a transformational development approach for future smart cities. This article describes how the future electric energy system with 100% electricity supply from renewable energy sources requires the “birth of security and resiliency” incorporated with its ecosystem.
Is the behavior of sellers with expected gains and losses relevant to cycles in house prices

Abstract We examine anchoring to the price paid at purchase during an important cycle in Connecticut, 2000–2017 which is similar to cycles in many other states. Our repeat sales model, which mitigates endogeneity and unobserved heterogeneity, provides robust estimates of negotiated price premiums (discounts) of sellers with expected losses (expected gains). Our model bridges from individual to aggregate price responses, and it supports new stylized facts about housing market cycles. Results suggest that anchoring was associated with reductions in observed changes in house prices during the boom (2004–2006) as sellers with gains dominate with their price discounts, and with reduced price declines during the bust (2007–2012) when the behavior of those with losses becomes important. Additional results making minimal model assumptions suggest that loss behavior is statistically significant and important at turning points, i.e., during the transition from a boom to a bust and vice versa. Double mean differences suggest that those with losses used the mild recovery as an opportunity to realize losses at reduced premiums after long delays.
Learning Skills to Patch Plans Based on Inaccurate Models

Planners using accurate models can be effective for accomplishing manipulation tasks in the real world, but are typically highly specialized and require significant fine-tuning to be reliable. Meanwhile, learning is useful for adaptation, but can require a substantial amount of data collection. In this paper, we propose a method that improves the efficiency of sub-optimal planners with approximate but simple and fast models by switching to a model-free policy when unexpected transitions are observed. Unlike previous work, our method specifically addresses when the planner fails due to transition model error by patching with a local policy only where needed. First, we use a sub-optimal model-based planner to perform a task until model failure is detected. Next, we learn a local model-free policy from expert demonstrations to complete the task in regions where the model failed. To show the efficacy of our method, we perform experiments with a shape insertion puzzle and compare our results to both pure planning and imitation learning approaches. We then apply our method to a door opening task. Our experiments demonstrate that our patch-enhanced planner performs more reliably than pure planning and with lower overall sample complexity than pure imitation learning.
The Voter Basis and the Admissibility of Tree Characters

Let ≽ be a total order on the power set of a finite set [n]. A subset S ⊂ [n] is separable when for any X,Y ⊂ S and any Z ⊂ [n] − S, the ordering of X and Y is the same as the ordering of X ∪ Z and Y ∪ Z. The character of a preference order is the collection of all separable subsets. Motivated by questions in the theories of voting, marketing and social choice, the admissibility problem asks which collections $\mathcal {C} \subset \mathcal {P}({[n]})$ can arise as characters of preference orders. We introduce a linear algebraic technique to construct preference orders. Each vector in our 2n-dimensional voter basis induces a simple preference preorder (where ties are allowed) with nice separability properties. Given any collection $\mathcal {C} \subset \mathcal {P}({[n]})$ that contains both ∅ and [n], and such that all pairs of subsets are either nested or disjoint, we use the voter basis to construct a preference order with character $\mathcal {C}$ .
Shilov boundaries determine irreducible bounded symmetric domains

We prove that irreducible symmetric domains are uniquely determined by the homotopy equivalence classes of their Shilov boundaries.
Deep Depth Estimation from Visual-Inertial SLAM

This paper addresses the problem of learning to complete a scene’s depth from sparse depth points and images of indoor scenes. Specifically, we study the case in which the sparse depth is computed from a visual-inertial simultaneous localization and mapping (VI-SLAM) system. The resulting point cloud has low density, it is noisy, and has nonuniform spatial distribution, as compared to the input from active depth sensors, e.g., LiDAR or Kinect. Since the VI-SLAM produces point clouds only over textured areas, we compensate for the missing depth of the low-texture surfaces by leveraging their planar structures and their surface normals which is an important intermediate representation. The pre-trained surface normal network, however, suffers from large performance degradation when there is a significant difference in the viewing direction (especially the roll angle) of the test image as compared to the trained ones. To address this limitation, we use the available gravity estimate from the VI-SLAM to warp the input image to the orientation prevailing in the training dataset. This results in a significant performance gain for the surface normal estimate, and thus the dense depth estimates. Finally, we show that our method outperforms other state-of-the-art approaches both on training (ScanNet [1] and NYUv2 [2]) and testing (collected with Azure Kinect [3]) datasets.
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach

In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate. The open-source community1 collectively has made efforts to collect and annotate the data, but it is not enough to train an accurate deep learning model. Few-shot learning2 is a sub-field of machine learning that aims to learn the objective with less amount of data. In this work, we have experimented with well-known solutions for data scarcity in deep learning to detect COVID-19. These include data augmentation, transfer learning, and few-shot learning, and unsupervised learning. We have also proposed a custom few-shot learning approach to detect COVID-19 using siamese networks.3 Our experimental results showcased that we can implement an efficient and accurate deep learning model for COVID-19 detection by adopting the few-shot learning approaches even with less amount of data. Using our proposed approach we were able to achieve 96.4% accuracy an improvement from 83% using baseline models. Our code is available on github: https://github.com/shruti-jadon/Covid-19-Detection
Channelrhodopsin C1C2: Photocycle kinetics and interactions near the central gate

Abstract Channelrhodopsins (ChR) are light-sensitive cation channels used in optogenetics, a technique that applies light to control cells (e.g., neurons) that have been modified genetically to express those channels. Although mutations are known to affect pore kinetics, little is known about how mutations induce changes at the molecular scale. To address this issue, we first measured channel opening and closing rates of a ChR chimera (C1C2) and selected variants (N297D, N297V, and V125L). Then, we used atomistic simulations to correlate those rates with changes in pore structure, hydration, and chemical interactions among key gating residues of C1C2 in both closed and open states. Overall, the experimental results show that C1C2 and its mutants do not behave like ChR2 or its analogous variants, except V125L, making C1C2 a unique channel. Our atomistic simulations confirmed that opening of the channel and initial hydration of the gating regions between helices I, II, III, and VII of the channel occurs with 1) the presence of 13-cis retinal; 2) deprotonation of a glutamic acid gating residue, E129; and 3) subsequent weakening of the central gate hydrogen bond between the same glutamic acid E129 and asparagine N297 in the central region of the pore. Also, an aspartate (D292) is the unambiguous primary proton acceptor for the retinal Schiff base in the hydrated channel.
Environmental justice, settler colonialism, and more-than-humans in the occupied West Bank: An introduction:

Our special issue provides a first-of-its kind attempt to examine environmental injustices in the occupied West Bank through interdisciplinary perspectives, pointing to the broader settler colonial...
Natural disasters, aggregate trade resilience, and local disruptions: Evidence from Hurricane Katrina

What drives the resilience of international trade against increasingly destructive natural disasters? In this study, I investigate the dynamics and spatial distribution of Hurricane Katrina's trade effects across U.S. infrastructure and break new ground on the mechanisms underlying the static and dynamic trade resilience. Analyzing port-level data, I find that ports subject to this calamity experience significant and lasting trade reductions, while shipments handled by the adjacent ports increase by as much as 13,000%. Interestingly, the duration of this rerouting effect is persistent for eight years uncovering novel path dependencies and providing important policy insights in light of recent events.
Covid-19 in Children: A Narrative Review

Introduction The coronavirus disease 2019 (COVID-19) pandemic is caused by the third known zoonotic coronavirus. It is a disease that does not spare any age group. The scientific community has been inundated with information since January 2020. This review aims to summarise pertinent information relating to COVID-19 in children. Method Literature search was conducted in 2020 on the PubMed, MEDLINE, and Embase databases, with the keywords "COVID 19" and "children". A bibliographic search of articles included was also undertaken. The abstracts were scanned to assess their appropriateness to be included in this narrative review. The data was updated on 11th April 2020. Result The aetiology, transmission, incubation, pathophysiology, clinical features and complications, and management are discussed in this study. Conclusion Our understanding of COVID-19 is evolving as more reports are being published. The growth of SARS-CoV2 is limited in children and they are often asymptomatic. The disease course is also milder. Continued research to understand its effect in children is important to help us manage the disease in these vulnerable populations in a timely fashion.
The role of scale in the estimation of cell-type proportions

Complex tissues are composed of a large number of different types of cells, each involved in a multitude of biological processes. Consequently, an important component to understanding such processes is understanding the cell-type composition of the tissues. Estimating cell-type composition using high-throughput gene expression data is known as cell-type deconvolution. In this paper we first summarize the extensive deconvolution literature by identifying a common regression-like approach to deconvolution. We call this approach the unified deconvolution-as-regression (UDAR) framework. While methods that fall under this framework all use a similar model, they fit using data on different scales. Two popular scales for gene expression data are logarithmic and linear. Unfortunately, each of these scales has problems in the UDAR framework. Using log-scale gene expressions proposes a biologically implausible model and using linear-scale gene expressions will lead to statistically inefficient estimators. To explore ways to address these issues, in this paper we consider how deconvolution methods may use an adjusted model that is a hybrid of the two scales. In analysis on simulations as well as a collection of eleven real benchmark datasets, we find a prototypical hybrid-scale adjustment to the UDAR framework improves statistical efficiency and robustness. More broadly, we believe these hybrid-scale modeling principles may be incorporated into many existing deconvolution methods.
Finite sets containing near-primitive roots

Abstract Fix a ∈ Z , a ∉ { 0 , ± 1 } . A simple argument shows that for each ϵ > 0 , and almost all (asymptotically 100% of) primes p, the multiplicative order of a modulo p exceeds p 1 2 − ϵ . It is an open problem to show the same result with 1 2 replaced by any larger constant. We show that if a , b are multiplicatively independent, then for almost all primes p, one of a , b , a b , a 2 b , a b 2 has order exceeding p 1 2 + 1 30 . The same method allows one to produce, for each ϵ > 0 , explicit finite sets A with the property that for almost all primes p, some element of A has order exceeding p 1 − ϵ . Similar results hold for orders modulo general integers n rather than primes p.
CAN coach: vehicular control through human cyber-physical systems

This work addresses whether a human-in-the-loop cyber-physical system (HCPS) can be effective in improving the longitudinal control of an individual vehicle in a traffic flow. We introduce the CAN Coach, which is a system that gives feedback to the human-in-the-loop using radar data (relative speed and position information to objects ahead) that is available on the controller area network (CAN). Using a cohort of six human subjects driving an instrumented vehicle, we compare the ability of the human-in-the-loop driver to achieve a constant time-gap control policy using only human-based visual perception to the car ahead, and by augmenting human perception with audible feedback from CAN sensor data. The addition of CAN-based feedback reduces the mean time-gap error by an average of 73%, and also improves the consistency of the human by reducing the standard deviation of the time-gap error by 53%. We remove human perception from the loop using a ghost mode in which the human-in-the-loop is coached to track a virtual vehicle on the road, rather than a physical one. The loss of visual perception of the vehicle ahead degrades the performance for most drivers, but by varying amounts. We show that human subjects can match the velocity of the lead vehicle ahead with and without CAN-based feedback, but velocity matching does not offer regulation of vehicle spacing. The viability of dynamic time-gap control is also demonstrated. We conclude that (1) it is possible to coach drivers to improve performance on driving tasks using CAN data, and (2) it is a true HCPS, since removing human perception from the control loop reduces performance at the given control objective.
THE SMALLEST SINGULAR VALUE OF INHOMOGENEOUS SQUARE RANDOM MATRICES

Author(s): Livshyts, Galyna V; Tikhomirov, Konstantin; Vershynin, Roman | Abstract: We show that for an $n\times n$ random matrix $A$ with independent uniformly anti-concentrated entries, such that $\mathbb{E} ||A||^2_{HS}\leq K n^2$, the smallest singular value $\sigma_n(A)$ of $A$ satisfies $$ P\left( \sigma_n(A)\leq \frac{\varepsilon}{\sqrt{n}} \right) \leq C\varepsilon+2e^{-cn},\quad \varepsilon \ge 0. $$ This extends earlier results of Rudelson and Vershynin, and Rebrova and Tikhomirov by removing the assumption of mean zero and identical distribution of the entries across the matrix, as well as the recent result of Livshyts, where the matrix was required to have i.i.d. rows. Our model covers "inhomogeneus" matrices allowing different variances of the entries, as long as the sum of the second moments is of order $O(n^2)$. In the past advances, the assumption of i.i.d. rows was required due to lack of Littlewood--Offord--type inequalities for weighted sums of non-i.i.d. random variables. Here, we overcome this problem by introducing the Randomized Least Common Denominator (RLCD) which allows to study anti-concentration properties of weighted sums of independent but not identically distributed variables. We construct efficient nets on the sphere with lattice structure, and show that the lattice points typically have large RLCD. This allows us to derive strong anti-concentration properties for the distance between a fixed column of $A$ and the linear span of the remaining columns, and prove the main result.
Efficient detection and classification of epigenomic changes under multiple conditions.

Epigenomics, the study of the human genome and its interactions with proteins and other cellular elements, has become of significant interest in recent years. Such interactions have been shown to regulate essential cellular functions and are associated with multiple complex diseases. Therefore, understanding how these interactions may change across conditions is central in biomedical research. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq) is one of several techniques to detect local changes in epigenomic activity (peaks). However, existing methods for differential peak calling are not optimized for the diversity in ChIP-seq signal profiles, are limited to the analysis of two conditions, or cannot classify specific patterns of differential change when multiple patterns exist. To address these limitations, we present a flexible and efficient method for the detection of differential epigenomic activity across multiple conditions. We utilize data from the ENCODE Consortium and show that the presented method, epigraHMM, exhibits superior performance to current tools and it is among the fastest algorithms available, while allowing the classification of combinatorial patterns of differential epigenomic activity and the characterization of chromatin regulatory states.
Movement-related changes in pallidocortical synchrony differentiate action execution and observation in humans.

Abstract Objective Suppression of local and network alpha and beta oscillations in the human basal ganglia-thalamocortical (BGTC) circuit is a prominent feature of movement, including suppression of local alpha/beta power, cross-region beta phase coupling, and cortical and subcortical phase-amplitude coupling (PAC). We hypothesized that network-level coupling is more directly related to movement execution than local power changes, given the role of pathological network hypersynchrony in movement disorders such as Parkinson disease (PD). Understanding the specificity of these movement-related signals is important for designing novel therapeutics. Methods We recorded globus pallidus internus (GPi) and motor cortical local field potentials during movement execution, passive movement observation and rest in 12 patients with PD undergoing deep brain stimulator implantation. Results Local alpha/beta power is suppressed in the globus pallidus and motor cortex during both action execution and action observation, although less so during action observation. In contrast, pallidocortical phase synchrony and GPi and motor cortical alpha/beta-gamma PAC are suppressed only during action execution. Conclusions The functional dissociation across tasks in pallidocortical network activity suggests a particularly important role of network coupling in motor execution. Significance Network level recordings provide important specificity in differentiating motor behavior and may provide significant value for future closed loop therapies.
A Safety and Passivity Filter for Robot Teleoperation Systems

In this paper, we present a way of enforcing safety and passivity properties of robot teleoperation systems, where a human operator interacts with a dynamical system modeling the robot. The approach does so in a holistic fashion, by combining safety and passivity constraints in a single optimization-based controller which effectively filters the desired control input before supplying it to the system. The result is a safety and passivity filter implemented as a convex quadratic program which can be solved efficiently and employed in an online fashion in many robotic teleoperation applications. Simulation results show the benefits of the approach developed in this paper applied to the human teleoperation of a second-order dynamical system.
Genome-wide analysis of retinal transcriptome reveals common genetic network underlying perception of contrast and optical defocus detection

Refractive eye development is regulated by optical defocus in a process of emmetropization. Excessive exposure to negative optical defocus often leads to the development of myopia. However, it is still largely unknown how optical defocus is detected by the retina. Here, we used genome-wide RNA-sequencing to conduct analysis of the retinal gene expression network underlying contrast perception and refractive eye development. We report that the genetic network subserving contrast perception plays an important role in optical defocus detection and emmetropization. Our results demonstrate an interaction between contrast perception, the retinal circadian clock pathway and the signaling pathway underlying optical defocus detection. We also observe that the relative majority of genes causing human myopia are involved in the processing of optical defocus. Together, our results support the hypothesis that optical defocus is perceived by the retina using contrast as a proxy and provide new insights into molecular signaling underlying refractive eye development.
Synthetic Multidimensional Plasma Electron Hole Equilibria

Methods for constructing synthetic multidimensional electron hole equilibria without using particle simulation are investigated. Previous approaches have various limitations and approximations that make them unsuitable within the context of expected velocity diffusion near the trapped-passing boundary. An adjustable model of the distribution function is introduced that avoids unphysical singularities there, and yet it is sufficiently tractable analytically to enable prescription of the potential spatial profiles. It is shown why simple models of the charge density as being a function only of potential cannot give solitary multidimensional electron holes, in contradiction of prior suppositions. Fully self-consistent axisymmetric electron holes in the drift-kinetic limit of electron motion (negligible gyro-radius) are constructed and their properties relevant to observational interpretation and finite-gyro-radius theory are discussed.
A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization

This paper considers decentralized stochastic optimization over a network of $n$ nodes, where each node possesses a smooth non-convex local cost function and the goal of the networked nodes is to find an $\epsilon$-accurate first-order stationary point of the sum of the local costs. We focus on an online setting, where each node accesses its local cost only by means of a stochastic first-order oracle that returns a noisy version of the exact gradient. In this context, we propose a novel single-loop decentralized hybrid variance-reduced stochastic gradient method, called GT-HSGD, that outperforms the existing approaches in terms of both the oracle complexity and practical implementation. The GT-HSGD algorithm implements specialized local hybrid stochastic gradient estimators that are fused over the network to track the global gradient. Remarkably, GT-HSGD achieves a network topology-independent oracle complexity of $O(n^{-1}\epsilon^{-3})$ when the required error tolerance $\epsilon$ is small enough, leading to a linear speedup with respect to the centralized optimal online variance-reduced approaches that operate on a single node. Numerical experiments are provided to illustrate our main technical results.
Shareholder monitoring and discretionary disclosure

Abstract Theories of delegated monitoring predict that when public disclosure is costly, monitoring by a large investor leads management to supply more private information to that investor, and less public disclosure to other similarly aligned investors who free-ride off the monitor. We test this prediction in the setting where large shareholders contractually bind management to share private information. We find that after the execution of such contracts, firms improve their performance and reduce their public disclosures. Overall, our evidence supports the disclosure prediction of delegated monitoring theories, and is inconsistent with poor-performance and expropriation theories of disclosure.
Teaching Tech to Talk: K-12 Conversational Artificial Intelligence Literacy Curriculum and Development Tools.

With children talking to smart-speakers, smart-phones and even smart-microwaves daily, it is increasingly important to educate students on how these agents work-from underlying mechanisms to societal implications. Researchers are developing tools and curriculum to teach K-12 students broadly about artificial intelligence (AI); however, few studies have evaluated these tools with respect to AI-specific learning outcomes, and even fewer have addressed student learning about AI-based conversational agents. We evaluate our Conversational Agent Interface for MIT App Inventor and workshop curriculum with respect to eight AI competencies from the literature. Furthermore, we analyze teacher (n=9) and student (n=47) feedback from workshops with the interface and recommend that future work leverages design considerations from the literature to optimize engagement, collaborates with teachers, and addresses a range of student abilities through pacing and opportunities for extension. We found students struggled most with the concepts of AI ethics and learning, and recommend emphasizing these topics when teaching. The appendix, including a demo video, can be found here: this https URL
Local Spin Ice Order Induced Planar Hall Effect in Nd–Sn Artificial Honeycomb Lattice

Geometrically frustrated materials, such as spin ice or kagome lattice, are known to exhibit exotic Hall effect phenomena due to spin chirality. We explore Hall effect mechanism in an artificial honeycomb spin ice of Nd--Sn element using Hall probe and polarized neutron reflectivity measurements. In an interesting observation, a strong enhancement in Hall signal at relatively higher temperature of $T$ $\sim$ 20 K is detected. The effect is attributed to the planar Hall effect due to magnetic moment configuration in spin ice state in low field application. In the antiferromagnetic state of neodymium at low temperature, applied field induced coupling between atomic Nd moments and conduction electrons in underlying lattice causes distinct increment in Hall resistivity at very modest field of $H$ $\sim$ 0.015 T. The experimental findings suggest the development of a new research vista to study the planar and the field induced Hall effects in artificial spin ice.
Resilient and Distributed Discrete Optimal Transport With Deceptive Adversary: A Game-Theoretic Approach

Optimal transport (OT) is a framework that can be used to guide the optimal allocation of a limited amount of resources. The classical OT paradigm does not consider malicious attacks in its formulation and thus the designed transport plan lacks resiliency to an adversary. To address this concern, we establish an OT framework that explicitly accounts for the adversarial and stealthy manipulation of participating nodes in the network during the transport strategy design. Specifically, we propose a game-theoretic approach to capture the strategic interactions between the transport planner and the deceptive attacker. We analyze the properties of the established two-person zero-sum game thoroughly. We further develop a fully distributed algorithm to compute the optimal resilient transport strategies, and show the convergence of the algorithm to a saddle-point equilibrium. Finally, we demonstrate the effectiveness of the designed algorithm using case studies.
Optimally Summarizing Data by Small Fact Sets for Concise Answers to Voice Queries

Our goal is to find combinations of facts that optimally summarize data sets. We consider this problem in the context of voice query interfaces for simple, exploratory data analysis. Here, the system answers voice queries with a short summary of relevant data. Finding optimal voice data summaries is computationally expensive. Prior work in this domain has exploited sampling and incremental processing. Instead, we rely on a pre-processing stage generating summaries of data subsets in a batch operation. This step reduces run time overheads by orders of magnitude.We present multiple algorithms for the pre-processing stage, realizing different tradeoffs between optimality and data processing overheads. We analyze our algorithms formally and compare them experimentally with prior methods for generating voice data summaries. We report on multiple user studies with a prototype system implementing our approach. Furthermore, we report on insights gained from a public deployment of our system on the Google Assistant Platform.
Entropy, Information, and the Updating of Probabilities.

This paper is a review of a particular approach to the method of maximum entropy as a general framework for inference. The discussion emphasizes pragmatic elements in the derivation. An epistemic notion of information is defined in terms of its relation to the Bayesian beliefs of ideally rational agents. The method of updating from a prior to posterior probability distribution is designed through an eliminative induction process. The logarithmic relative entropy is singled out as a unique tool for updating (a) that is of universal applicability, (b) that recognizes the value of prior information, and (c) that recognizes the privileged role played by the notion of independence in science. The resulting framework—the ME method—can handle arbitrary priors and arbitrary constraints. It includes the MaxEnt and Bayes’ rules as special cases and, therefore, unifies entropic and Bayesian methods into a single general inference scheme. The ME method goes beyond the mere selection of a single posterior, and also addresses the question of how much less probable other distributions might be, which provides a direct bridge to the theories of fluctuations and large deviations.
Evaluating and integrating spatial capture–recapture models with data of variable individual identifiability

Spatial capture-recapture (SCR) models have become the preferred tool for estimating densities of carnivores. Within this family of models are variants requiring identification of all individuals in each encounter (SCR), a subset of individuals only (generalized spatial mark-resight, gSMR), or no individual identification (spatial count or spatial presence-absence). Although each technique has been shown through simulation to yield unbiased results, the consistency and relative precision of estimates across methods in real-world settings are seldom considered. We tested a suite of models ranging from those only requiring detections of unmarked individuals to others that integrate remote camera, physical capture, genetic, and global positioning system (GPS) data into a hybrid model, to estimate population densities of black bears, bobcats, cougars, and coyotes. For each species, we genotyped fecal DNA collected with detection dogs during a 20-d period. A subset of individuals from each species was affixed with GPS collars bearing unique markings and resighted by remote cameras over 140 d contemporaneous with scat collection. Camera-based gSMR models produced density estimates that differed by <10% from genetic SCR for bears, cougars, and coyotes once important sources of variation (sex or behavioral status) were controlled for. For bobcats, SCR estimates were 33% higher than gSMR. The cause of the discrepancies in estimates was likely attributable to challenges designing a study compatible for species with disparate home range sizes and the difficulty of collecting sufficient data in a timeframe in which demographic closure could be assumed. Unmarked models estimated densities that varied greatly from SCR, but estimates became more consistent in models wherein more individuals were identifiable. Hybrid models containing all data sources exhibited the most precise estimates for all species. For studies in which only sparse data can be obtained and the strictest model assumptions are unlikely to be met, we suggest researchers use caution making inference from models lacking individual identity. For best results, we further recommend the use of methods requiring at least a subset of the population is marked and that multiple data sets are incorporated when possible.
Environmental Consumption, Waste Recycling, and Academic Performance among Selected College Students during the COVID-19 Pandemic.

The environment is suffering so much, and yet humanity is still adamant about saving it. This study determines the awareness of environmental consumption, waste recycling, and its relationship to the academic performance of selected college students during the pandemic period of COVID-19. Using a descriptive research design, the researcher used an online survey method to collect the data for the study. With the use of the convenience sampling technique, 192 college students from a local city college responded to the online survey. As for the instrument, the study adopted and modified an existing questionnaire and subjected it to reliability and validity tests, which yielded an acceptable result. Using SPSS 22, results show that college students “often” observe different environmental consumption and waste recycling schemes in school. Their academic performance also is "good" based on their grade point average from their previous semester. Also, the study showed a low positive relationship between environmental consumption, waste recycling, and academic performance. The study concluded that college students still practice appropriate environmental consumption and waste recycling, even during the COVID-19 pandemic. Based on the results, the researcher provided some implications for the learning system.
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs

Disaggregated evaluations of AI systems, in which system performance is assessed and reported separately for different groups of people, are conceptually simple. However, their design involves a variety of choices. Some of these choices influence the results that will be obtained, and thus the conclusions that can be drawn; others influence the impacts---both beneficial and harmful---that a disaggregated evaluation will have on people, including the people whose data is used to conduct the evaluation. We argue that a deeper understanding of these choices will enable researchers and practitioners to design careful and conclusive disaggregated evaluations. We also argue that better documentation of these choices, along with the underlying considerations and tradeoffs that have been made, will help others when interpreting an evaluation's results and conclusions.