cssi/SciSciGPT-SciSciNet huggingface dataset:
Beyond Massive Univariate Tests: Covariance Regression Reveals Complex Patterns of Functional Connectivity Related to Attention-Deficit/Hyperactivity Disorder, Age, Sex, and Response Control

Abstract Background Studies of brain functional connectivity (FC) typically involve massive univariate tests, performing statistical analysis on each individual connection. In this study, we apply a novel whole-matrix regression approach referred to as covariate assisted principal regression to identify resting-state FC brain networks associated with attention-deficit/hyperactivity disorder (ADHD) and response control. Methods Participants included 8- to 12-year-old children with ADHD (n = 115; 29 girls) and typically developing control children (n = 102; 35 girls) who completed a resting-state functional magnetic resonance imaging scan and a Go/NoGo task. We modeled three sets of covariates to identify resting-state networks associated with an ADHD diagnosis, sex, and response inhibition (commission errors) and variability (ex-Gaussian parameter tau). Results The first network includes FC between striatal-cognitive control (CC) network subregions and thalamic-default mode network (DMN) subregions and is positively related to age. The second consists of FC between CC-visual-somatomotor regions and between CC-DMN subregions and is positively associated with response variability in boys with ADHD. The third consists of FC within the DMN and between DMN-CC-visual regions and differs between boys with and without ADHD. The fourth consists of FC between visual-somatomotor regions and between visual-DMN regions and differs between girls and boys with ADHD and is associated with response inhibition and variability in boys with ADHD. Unique networks were also identified in each of the three models, suggesting some specificity to the covariates of interest. Conclusions These findings demonstrate the utility of our novel covariance regression approach to studying functional brain networks relevant for development, behavior, and psychopathology.
Anthrax toxin translocation complex reveals insight into the lethal factor unfolding and refolding mechanism

Translocation is essential to the anthrax toxin mechanism. Protective antigen (PA), the binding component of this AB toxin, forms an oligomeric pore that translocates lethal factor (LF) or edema factor, the active components of the toxin, into the cell. Structural details of the translocation process have remained elusive despite their biological importance. To overcome the technical challenges of studying translocation intermediates, we developed a method to immobilize, transition, and stabilize anthrax toxin to mimic important physiological steps in the intoxication process. Here, we report a cryoEM snapshot of PApore translocating the N-terminal domain of LF (LFN). The resulting 3.3 A structure of the complex shows density of partially unfolded LFN near the canonical PApore binding site. Interestingly, we also observe density consistent with an α helix emerging from the 100 A β barrel channel suggesting LF secondary structural elements begin to refold in the pore channel. We conclude the anthrax toxin β barrel aids in efficient folding of its enzymatic payload prior to channel exit. Our hypothesized refolding mechanism has broader implications for pore length of other protein translocating toxins.
Random Graph Matching with Improved Noise Robustness

Graph matching, also known as network alignment, refers to finding a bijection between the vertex sets of two given graphs so as to maximally align their edges. This fundamental computational problem arises frequently in multiple fields such as computer vision and biology. Recently, there has been a plethora of work studying efficient algorithms for graph matching under probabilistic models. In this work, we propose a new algorithm for graph matching: Our algorithm associates each vertex with a signature vector using a multistage procedure and then matches a pair of vertices from the two graphs if their signature vectors are close to each other. We show that, for two Erd\H{o}s--R\'enyi graphs with edge correlation $1-\alpha$, our algorithm recovers the underlying matching exactly with high probability when $\alpha \le 1 / (\log \log n)^C$, where $n$ is the number of vertices in each graph and $C$ denotes a positive universal constant. This improves the condition $\alpha \le 1 / (\log n)^C$ achieved in previous work.
Review of Malpractice Lawsuits in the Diagnosis and Management of Aortic Aneurysms and Aortic Dissections.

Objective:Aortic aneurysms and dissections are prevalent causes of morbidity and mortality. The management of aortic pathologies may be called into question in malpractice suits. Malpractice claims...
Reactive pedestrian path following from examples

Architectural and urban planning applications require animations of people to present an accurate and compelling view of a new environment. Ideally, these animations would be easy for a non-programmer to construct, just as buildings and streets can be modeled by an architect or artist using commercial modeling software. In this paper, we explore an approach for generating reactive path following based on the user's examples of the desired behavior. The examples are used to build a model of the desired reactive behavior. The model is combined with reactive control methods to produce natural 2D pedestrian trajectories. The system then automatically generates 3D pedestrian locomotion using a motion-graph approach. We discuss the accuracy of the learned model of pedestrian motion and show that simple direction primitives can be recorded and used to build natural, reactive, path-following behaviors.
Computational Evaluation of the Steady and Pulsed Jet Effects on the Performance of a Circulation Control Wing Section

Circulation Control technology is a very effective way of achieving high lift forces required by aircraft during take-off and landing. This technology can also directly control the flow field over the wing. Compared to a conventional high-lift system, a Circulation Control Wing (CCW) can generate comparable or higher lift forces during take-off/landing with fewer or no moving parts and much less complexity. In this work, an unsteady three-dimensional Navier-Stokes analysis procedure has been developed and applied to Circulation Control Wing configurations. The effects of 2-D steady jets and 2-D pulsed jets on the aerodynamic performance of CCW airfoils have been investigated. It is found that a steady jet can generate very high lift at zero angle of attack without stall, and that a small amount of blowing can eliminate vortex shedding at the trailing edge, a potential noise source. It is also found that a pulsed jet can achieve the same high lift as a steady jet at lower mass flow rates, especially at a high frequency, and that the Strouhal number has a more dominant effect on the pulsed jet performance than just the frequency or the free-stream velocity.
Multi-player Bandits for Distributed Cognitive Radar

With new applications for radar networks such as automotive control and indoor localization, the need for spectrum sharing and general interoperability is expected to rise. This paper describes the application of multi-player bandit algorithms for waveform selection to a distributed cognitive radar network that must coexist with a communications system. Specifically, we make the assumption that radar nodes in the network have no dedicated communication channel. As we will discuss later, nodes can communicate indirectly by taking actions which intentionally interfere with other nodes and observing the resulting collisions. The radar nodes attempt to optimize their own spectrum utilization while avoiding collisions, not only with each other, but with the communications system. The communications system is assumed to statically occupy some subset of the bands available to the radar network. First, we examine models that assume each node experiences equivalent channel conditions, and later examine a model that relaxes this assumption.
Policy Improvement from Multiple Experts

Despite its promise, reinforcement learning's real-world adoption has been hampered by its need for costly exploration to learn a good policy. Imitation learning (IL) mitigates this shortcoming by using an expert policy during training as a bootstrap to accelerate the learning process. However, in many practical situations, the learner has access to multiple suboptimal experts, which may provide conflicting advice in a state. The existing IL literature provides a limited treatment of such scenarios. Whereas in the single-expert case, the return of the expert's policy provides an obvious benchmark for the learner to compete against, neither such a benchmark nor principled ways of outperforming it are known for the multi-expert setting. In this paper, we propose the state-wise maximum of the expert policies' values as a natural baseline to resolve conflicting advice from multiple experts. Using a reduction of policy optimization to online learning, we introduce a novel IL algorithm MAMBA, which can provably learn a policy competitive with this benchmark. In particular, MAMBA optimizes policies by using a gradient estimator in the style of generalized advantage estimation (GAE). Our theoretical analysis shows that this design makes MAMBA robust and enables it to outperform the expert policies by a larger margin than IL state of the art, even in the single-expert case. In an evaluation against standard policy gradient with GAE and AggreVaTeD, we showcase MAMBA's ability to leverage demonstrations both from a single and from multiple weak experts, and significantly speed up policy optimization.
Stellarators as a fast path to fusion

This paper is focused on three points: (1) Overcoming obstacles to tokamak power plants may require a configuration modification as large as that of a stellarator. (2) The demonstrated reliability of the computational design of stellarators should change fusion strategy. (3) Deployment of carbon-free energy sources is mandated by the thirty-year doubling of carbon dioxide emissions. Carbon-free energy options must be developed and fully deployed within a few doubling times. Unit size and cost of electricity are only relevant in comparison to alternative worldwide energy solutions. Intermittency, site specificity, waste management, and nuclear proliferation make fusion attractive as the basis for a carbon-free energy system compared to the alternatives. Nonetheless, fusion is not an option for deployment until a power plant has successfully operated. A critical element in a minimal time and risk program is the use of computational design as opposed to just extrapolation. Only the stellarator has an empirical demonstration of the reliable computational design through large changes in configuration properties and scale. The computational design of stellarators should proceed while the inventions necessary for a more tokamak-like power plant are sought. The cost of computational design is extremely small, but adequate time is required for the development of ideas that maximize attractiveness and minimize risk. Rapid power-plant construction without many intermediate steps may seem risky, but the price is small compared to the cost of trillions of dollars for each year's delay in addressing carbon-dioxide emissions.
Optimal Learning for Structured Bandits

We study structured multi-armed bandits, which is the problem of online decision-making under uncertainty in the presence of structural information. In this problem, the decision-maker needs to discover the best course of action despite observing only uncertain rewards over time. The decision-maker is aware of certain structural information regarding the reward distributions and would like to minimize his regret by exploiting this information, where the regret is its performance difference against a benchmark policy that knows the best action ahead of time. In the absence of structural information, the classical upper confidence bound (UCB) and Thomson sampling algorithms are well known to suffer only minimal regret. As recently pointed out by Russo and Van Roy (2018) and Lattimore and Szepesvari (2017), neither algorithms are, however, capable of exploiting structural information that is commonly available in practice. We propose a novel learning algorithm that we call “DUSA” whose worst-case regret matches the information-theoretic regret lower bound up to a constant factor and can handle a wide range of structural information. Our algorithm DUSA solves a dual counterpart of regret lower bound at the empirical reward distribution and follows the suggestion made by the dual problem. Our proposed algorithm is the first computationally viable learning policy for structured bandit problems that has asymptotic minimal regret.
On the Value of Preview Information For Safety Control

Incorporating predictions of external inputs, which can otherwise be treated as disturbances, has been widely studied in control and computer science communities. These predictions are commonly referred to as preview in optimal control and lookahead in temporal logic synthesis. However, little work has been done for analyzing the value of preview information for safety control for systems with continuous state spaces. In this work, we start from showing general properties for discrete-time nonlinear systems with preview and strategies on how to determine a good preview time, and then we study a special class of linear systems, called systems in Brunovsky canonical form, and show special properties for this class of systems. In the end, we provide two numerical examples to further illustrate the value of preview in safety control.
Hive geometry shapes the recruitment rate of honeybee colonies

Honey bees make decisions regarding foraging and nest-site selection in groups ranging from hundreds to thousands of individuals. To effectively make these decisions, bees need to communicate within a spatially distributed group. However, the spatiotemporal dynamics of honey bee communication have been mostly overlooked in models of collective decisions, focusing primarily on mean field models of opinion dynamics. We analyze how the spatial properties of the nest or hive, and the movement of individuals with different belief states (uncommitted or committed) therein affect the rate of information transmission using spatially-extended models of collective decision-making within a hive. Honeybees waggle-dance to recruit conspecifics with an intensity that is a threshold nonlinear function of the waggler concentration. Our models range from treating the hive as a chain of discrete patches to a continuous line (long narrow hive). The combination of population-thresholded recruitment and compartmentalized populations generates tradeoffs between rapid information propagation with strong population dispersal and recruitment failures resulting from excessive population diffusion and also creates an effective colony-level signal-detection mechanism whereby recruitment to low quality objectives is blocked.
Student-Teacher Learning from Clean Inputs to Noisy Inputs

Feature-based student-teacher learning, a training method that encourages the student’s hidden features to mimic those of the teacher network, is empirically successful in transferring the knowledge from a pre-trained teacher network to the student network. Furthermore, recent empirical results demonstrate that, the teacher’s features can boost the student network’s generalization even when the student’s input sample is corrupted by noise. However, there is a lack of theoretical insights into why and when this method of transferring knowledge can be successful between such heterogeneous tasks. We analyze this method theoretically using deep linear networks, and experimentally using nonlinear networks. We identify three vital factors to the success of the method: (1) whether the student is trained to zero training loss; (2) how knowledgeable the teacher is on the clean-input problem; (3) how the teacher decomposes its knowledge in its hidden features. Lack of proper control in any of the three factors leads to failure of the student-teacher learning method.
Research on Reactive Power Compensation Configuration of Wind Farm Integration

The large-scale wind power integration into power system brings great impact on the stability of the grid voltage. In order to reduce the impact of wind power integration on the power system and improve the safety of wind power integration, it is necessary to carry out the research on reactive power compensation configuration of wind farm integration. Firstly, based on the actual situation of wind power project, study the principle of reactive power compensation configuration. Secondly, propose a theoretical calculation method of reactive power compensation configuration. On this basis, combined with the practical 300 MW wind farm project, analyze the reactive power compensation configuration example. Finally, through the modeling and simulation by PSASP software, the accuracy and effectiveness of the calculation method proposed by this paper are verified. The reactive power compensation configuration method studied in this paper is applicable to all wind farms connected to the power system and provides important support for voltage stability in the wind power integration project. It is of great significance to ensure the safe and stable operation of the power grid.
SceneChecker: Boosting Scenario Verification using Symmetry Abstractions

We present \(\mathsf {SceneChecker}\), a tool for verifying scenarios involving vehicles executing complex plans in large cluttered workspaces. \(\mathsf {SceneChecker}\) converts the scenario verification problem to a standard hybrid system verification problem, and solves it effectively by exploiting structural properties in the plan and the vehicle dynamics. \(\mathsf {SceneChecker}\) uses symmetry abstractions, a novel refinement algorithm, and importantly, is built to boost the performance of any existing reachability analysis tool as a plug-in subroutine. We evaluated \(\mathsf {SceneChecker}\) on several scenarios involving ground and aerial vehicles with nonlinear dynamics and neural network controllers, employing different kinds of symmetries, using different reachability subroutines, and following plans with hundreds of waypoints in complex workspaces. Compared to two leading tools, DryVR and Flow*, \(\mathsf {SceneChecker}\) shows 14\(\times \) average speedup in verification time, even while using those very tools as reachability subroutines.
Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management

Recent advances in Natural Language Processing (NLP), and specifically automated Question Answering (QA) systems, have demonstrated both impressive linguistic fluency and a pernicious tendency to reflect social biases. In this study, we introduce Q-Pain, a dataset for assessing bias in medical QA in the context of pain management, one of the most challenging forms of clinical decision-making. Along with the dataset, we propose a new, rigorous framework, including a sample experimental design, to measure the potential biases present when making treatment decisions. We demonstrate its use by assessing two reference Question-Answering systems, GPT-2 and GPT-3, and find statistically significant differences in treatment between intersectional race-gender subgroups, thus reaffirming the risks posed by AI in medical settings, and the need for datasets like ours to ensure safety before medical AI applications are deployed.
A comparison of cell-based and cell-cultured as appropriate common or usual names to label products made from the cells of fish.

Using an online experiment with a nationally representative sample of 1200 adult American consumers, two "common or usual names," "Cell-Based Seafood" and "Cell-Cultured Seafood," were assessed using five criteria. Displayed on packages of frozen Atlantic Salmon, both "Cell-Based" (60.1%) and "Cell-Cultured" (58.9%) enabled participants to differentiate the novel products from "Farm-Raised" and "Wild-Caught" fish and 74% also recognized that those allergic to fish should not consume the product. Thus, both names met key regulatory criteria. Both names were seen as appropriate terms for describing the process for creating the product, meeting the criteria for transparency. There were no significant differences in the perceived safety, naturalness, taste, or nutritiousness of the products bearing the two names. However, participants' overall impressions associated with "Cell-Based" were rated as more positive than those associated with "Cell-Cultured" (P < 0.001, η2 = 0.010), as were their initial thoughts, images, and feelings (P < 0.001, η2 = 0.008). The participants were also slightly more interested in tasting (P < 0.05, η2 = 0.004) and in purchasing (P < 0.01, η2 = 0.006) "Cell-Based" than "Cell-Cultured" seafood. After learning the meaning of the terms, participants' overall impressions of "Cell-Based" remained higher than "Cell-Cultured" (P < 0.05, η2 = 0.003) and they remained slightly more interested in tasting (P < 0.05, η2 = 0.004) and in purchasing (P < 0.05, η2 = 0.005) "Cell-Based" than "Cell-Cultured" seafood. Therefore, "Cell-Based Seafood" should be adopted as the best common or usual name for seafood made from the cells of fish. PRACTICAL APPLICATION: Widespread adoption and consistent use of a single "common or usual name" for "Cell-Based" seafood, meat, poultry, and other products by the food industry, regulators, journalists, marketers, environmental, consumer, and animal rights advocates, and other key stakeholders would help shape public perceptions and understanding of this rapidly advancing technology and its products. This study confirms that "Cell-Based Seafood" is the best performing term to label seafood products made from the cells of fish. It meets relevant FDA regulatory requirements and slightly outperforms "Cell-Cultured Seafood" with regard to positive consumer perceptions, interest in tasting, and likelihood of purchasing these novel products.
The Semiotics of Consent and the American Law Institute’s Reform of the Model Penal Code’s Sexual Assault Provisions

The concept of consent is ubiquitous in the West. It is the foundation of its construction of meaning for sovereignty (and political legitimacy), and for personal autonomy (and human dignity). Ubiquity, however, has come with a price. The making of a transposable meaning for consent that bridges political community and interpersonal relations has drawn sharply into focus the malleability of the concept, and its utility for masking a power of politics behind an orthodoxy of meaning that is both politically correct, and at the same time its own inversion. This short essay on the semiotics of “consent” considers the manifestation of the concept as object, as symbol, and as a cluster of political interpretation that itself contains within it the Janus faced morality of political correctness. It takes as its starting and end point the idea that free consent is the product of a process of management that reduces consent to the sum of status and authority over the thing assented. The exploration is framed around the recent arguments in the American Law institute’s Model Penal Code Project around the meaning of consent in sexual relations. The essay first situates the problematique of consent—as action and object that incarnates power relations and the boundaries of the taboo. It then illustrates the way that semiotic meaning making produces a political correctness that produces paradox by critically chronicling the meaning of consent respecting sexual intimacy in criminal law. It enhances sexual liberation by placing it within a cage of limitations that ultimately transfers the power over consent form the individual to the state. That meaning making suggests the way that consent as an act, and as a state of being, is transposed to the broader context of political economic relations.
Cash-based bonus plans as a strategic communication, coordination and commitment mechanism

Abstract Over the last two decades, executive compensation research has focused primarily on equity-based pay and incentives emanating from executives' firm-specific equity portfolios, while generally ignoring cash-based bonus plans as a second order effect. Exploiting access to new data sources, there has been a revival of interest by accounting researchers in more deeply understanding the value adding roles played by bonus plans. Earlier research viewed accounting measures in bonus plans through the lens of effort incentives-risk premium trade-offs derived from classical principal-agent theory. In contrast, the recent literature emphasizes the idea that cash-based bonus plans play an important communication role in which a board's performance measure choices reveal to outsiders the firm's commitment to specific strategic objectives and facilitate the coordination of behavior across executives inside the firm. Public observability of bonus plans then provides a basis for investors and competitors to assess a firm's strategic direction, and for investors to hold managers accountable for strategy execution. Building on my discussion of Bloomfield et al. (2021) in the 2020 Journal of Accounting and Economics Conference, my objective in this paper is to synthesize and critique key results from this recent literature and offer ideas for future research.
Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time

Abstract The rainflow counting algorithm for material fatigue is both simple to implement and extraordinarily successful for predicting material failure times. However, it neglects memory effects and time-ordering dependence, and therefore runs into difficulties dealing with highly intermittent or transient stochastic loads with heavy tailed distributions. Such loads appear frequently in a wide range of applications in ocean and mechanical engineering, such as wind turbines and offshore structures. In this work we employ the Serebrinsky–Ortiz cohesive envelope model for material fatigue to characterize the effects of load intermittency on the fatigue-crack nucleation time. We first formulate efficient numerical integration schemes, which allow for the direct characterization of the fatigue life in terms of any given load time-series. Subsequently, we consider the case of stochastic intermittent loads with given statistical characteristics. To overcome the need for expensive Monte-Carlo simulations, we formulate the fatigue life as an up-crossing problem of the coherent envelope. Assuming statistical independence for the large intermittent spikes and using probabilistic arguments we derive closed expressions for the up-crossing properties of the coherent envelope and obtain analytical approximations for the probability mass function of the failure time. The analytical expressions are derived directly in terms of the probability density function of the load, as well as the coherent envelope. We examine the accuracy of the analytical approximations and compare the predicted failure time with the standard rainflow algorithm for various loads. Finally, we use the analytical expressions to examine the robustness of the derived probability distribution for the failure time with respect to the coherent envelope geometrical properties.
Relevance of Accounting Research (ROAR) Scores: Ratings of Titles and Abstracts by Accounting Professionals

The relevance of academic accounting research to practice has been frequently discussed in the accounting academy, yet very little data has been put forth in these discussions. We create relevance of accounting research (ROAR) scores by asking accounting professionals to read and evaluate the titles and abstracts of every article published in 12 leading accounting journals for the years 2016 to 2018, inclusive. These scores allow for an evidence-based evaluation and discussion of how academic accounting research is perceived by accounting professionals. The scores also allow us to identify the articles, authors, journals, and accounting topic areas that are perceived to be more relevant to accounting practice. By continuing to produce these scores in perpetuity, we expect this data to help academics and professionals better identify and utilize research relevant to the accounting profession.
Monogenic trinomials with non-squarefree discriminant

For each integer n ≥ 2, we identify new infinite families of monogenic trinomials f(x) = xn + Axm + B with non-squarefree discriminant, many of which have small Galois group. Moreover, in certain s...
Single-cell analysis of cell fate bifurcation in the chordate Ciona

Background Inductive signaling interactions between different cell types are a major mechanism for the further diversification of embryonic cell fates. Most blastomeres in the model chordate Ciona robusta become restricted to a single predominant fate between the 64-cell and mid-gastrula stages. The deeply stereotyped and well-characterized Ciona embryonic cell lineages allow the transcriptomic analysis of newly established cell types very early in their divergence from sibling cell states without the pseudotime inference needed in the analysis of less synchronized cell populations. This is the first ascidian study to use droplet scRNAseq with large numbers of analyzed cells as early as the 64-cell stage when major lineages such as primary notochord first become fate restricted. Results and conclusions We identify 59 distinct cell states, including new subregions of the b-line neural lineage and the early induction of the tail tip epidermis. We find that 34 of these cell states are directly or indirectly dependent on MAPK-mediated signaling critical to early Ciona patterning. Most of the MAPK-dependent bifurcations are canalized with the signal-induced cell fate lost upon MAPK inhibition, but the posterior endoderm is unique in being transformed into a novel state expressing some but not all markers of both endoderm and muscle. Divergent gene expression between newly bifurcated sibling cell types is dominated by upregulation in the induced cell type. The Ets family transcription factor Elk1/3/4 is uniquely upregulated in nearly all the putatively direct inductions. Elk1/3/4 upregulation together with Ets transcription factor binding site enrichment analysis enables inferences about which bifurcations are directly versus indirectly controlled by MAPK signaling. We examine notochord induction in detail and find that the transition between a Zic/Ets-mediated regulatory state and a Brachyury/FoxA-mediated regulatory state is unexpectedly late. This supports a "broad-hourglass" model of cell fate specification in which many early tissue-specific genes are induced in parallel to key tissue-specific transcriptional regulators via the same set of transcriptional inputs.
The Effects of Sister-Store Presence and Market Competition on Product Assortment: Evidence from Book Retailing

When consumers’ preferred products are not carried by the retail chain store they visit, they may switch to purchasing these products from nearby sister stores of the same chain or from nearby comp...
The histone chaperone Spt6 is required for normal recruitment of the capping enzyme Abd1 to transcribed regions.

The histone chaperone Spt6 is involved in promoting elongation of RNA polymerase II (RNAPII), maintaining chromatin structure, regulating cotranscriptional histone modifications, and controlling mRNA processing. These diverse functions of Spt6 are partly mediated through its interactions with RNAPII and other factors in the transcription elongation complex. In this study, we used mass spectrometry to characterize the differences in RNAPII-interacting factors between wildtype cells and those depleted for Spt6, leading to the identification of proteins that depend on Spt6 for their interaction with RNAPII. The altered association of some of these factors could be attributed to changes in steady-state protein levels. However, Abd1, the mRNA cap methyltransferase, had decreased association with RNAPII after Spt6 depletion despite unchanged Abd1 protein levels, showing a requirement for Spt6 in mediating the Abd1–RNAPII interaction. Genome-wide studies showed that Spt6 is required for maintaining the level of Abd1 over transcribed regions, as well as the level of Spt5, another protein known to recruit Abd1 to chromatin. Abd1 levels were particularly decreased at the 5′ ends of genes after Spt6 depletion, suggesting a greater need for Spt6 in Abd1 recruitment over these regions. Together, our results show that Spt6 is important in regulating the composition of the transcription elongation complex and reveal a previously unknown function for Spt6 in the recruitment of Abd1.
Coherent-scatterer enhancement and Klein-tunneling suppression by potential barriers in gapped graphene with chirality-time-reversal symmetry.

We have utilized the finite-difference approach to explore electron-tunneling properties in gapped graphene through various electrostatic-potential barriers ranging from Gaussian to a triangular envelope function in comparison with a square potential barrier. The transmission coefficient is calculated numerically for each case and applied to the corresponding tunneling conductance. It is well known that Klein tunneling in graphene will be greatly reduced in gapped graphene. Our results further demonstrate that such a decrease of transmission can be significantly enhanced for spatially-modulated potential barriers. Moreover, we investigate the effect from a bias field applied to those barrier profiles, from which we show that it enables the control of electron flow under normal incidence. Meanwhile, the suppression of Klein tunneling is found more severe for a non-square barrier and exhibits a strong dependence on bias-field polarity for all kinds of barriers. Finally, roles of a point impurity on electron transmission and conductance are analyzed with a sharp peak appearing in electron conductance as the impurity atom is placed in the middle of a square barrier. For narrow triangular and Gaussian barriers, however, the conductance peaks become significantly broadened, associated with an enhancement in tunneling conductance.
Projected Mushroom Type Phase-Change Memory

Phase-change memory devices have found applications in in-memory computing where the physical attributes of these devices are exploited to compute in place without the need to shuttle data between memory and processing units. However, non-idealities such as temporal variations in the electrical resistance have a detrimental impact on the achievable computational precision. To address this, a promising approach is projecting the phase configuration of phase change material onto some stable element within the device. Here we investigate the projection mechanism in a prominent phase-change memory device architecture, namely mushroom-type phase-change memory. Using nanoscale projected Ge2Sb2Te5 devices we study the key attributes of state-dependent resistance, drift coefficients, and phase configurations, and using them reveal how these devices fundamentally work.
High-order quadrature on multi-component domains implicitly defined by multivariate polynomials

Abstract A high-order quadrature algorithm is presented for computing integrals over curved surfaces and volumes whose geometry is implicitly defined by the level sets of (one or more) multivariate polynomials. The algorithm recasts the implicitly defined geometry as the graph of an implicitly defined, multi-valued height function, and applies a dimension reduction approach needing only one-dimensional quadrature. In particular, we explore the use of Gauss-Legendre and tanh-sinh methods and demonstrate that the quadrature algorithm inherits their high-order convergence rates. Under the action of h-refinement with q fixed, the quadrature schemes yield an order of accuracy of 2q, where q is the one-dimensional node count; numerical experiments demonstrate up to 22nd order. Under the action of q-refinement with the geometry fixed, the convergence is approximately exponential, i.e., doubling q approximately doubles the number of accurate digits of the computed integral. Complex geometry is automatically handled by the algorithm, including, e.g., multi-component domains, tunnels, and junctions arising from multiple polynomial level sets, as well as self-intersections, cusps, and other kinds of singularities. A variety of numerical experiments demonstrates the quadrature algorithm on two- and three-dimensional problems, including: randomly generated geometry involving multiple high-curvature pieces; challenging examples involving high degree singularities such as cusps; adaptation to simplex constraint cells in addition to hyperrectangular constraint cells; and boolean operations to compute integrals on overlapping domains.
Sibling Regression for Generalized Linear Models

Field observations form the basis of many scientific studies, especially in ecological and social sciences. Despite efforts to conduct such surveys in a standardized way, observations can be prone to systematic measurement errors. The removal of systematic variability introduced by the observation process, if possible, can greatly increase the value of this data. Existing non-parametric techniques for correcting such errors assume linear additive noise models. This leads to biased estimates when applied to generalized linear models (GLM). We present an approach based on residual functions to address this limitation. We then demonstrate its effectiveness on synthetic data and show it reduces systematic detection variability in moth surveys.
Cost Sharing in Two-Sided Markets

Motivated by the emergence of popular service-based two-sided markets where sellers can serve multiple buyers at the same time, we formulate and study the two-sided cost sharing problem. In two-sided cost sharing, sellers incur different costs for serving different subsets of buyers and buyers have different values for being served by different sellers. Both buyers and sellers are self-interested agents whose values and costs are private information. We study the problem from the perspective of an intermediary platform that matches buyers to sellers and assigns prices and wages in an effort to maximize gains from trade (i.e., buyer values minus seller costs) subject to budget-balance in an incentive compatible manner. In our markets of interest, agents trade the (often same) services multiple times. Moreover, the value and cost for the same service differs based on the context (e.g., location, urgency, weather conditions, etc.). In this framework, we design mechanisms that are efficient, ex-ante budget-balanced, ex-ante individually rational, dominant strategy incentive compatible, and ex-ante in the core (a natural generalization of the core that we define here).
A strengths-based, culturally responsive family intervention improves Latino kindergarteners' vocabulary and approaches to learning.

Food routines are an ecocultural asset of Latino families. This cluster-randomized trial with 248 children (Mage = 67 months; 50% girls; 13 schools) investigated the impact of a 4-week family program designed to capitalize on food routines in improving Latino kindergarteners' outcomes in the United States. There were moderate-to-large impacts on child vocabulary (especially food-related) at end-of-treatment and the 5-month follow-up, and suggestive evidence of moderate impacts on approaches to learning (ATL; including ATL math) and executive function at the 5-month follow-up (d = .38-.95). There were no statistically significant impacts on children's math or literacy skills. A strengths-based, culturally responsive family intervention that is integrated into Latino family life can improve the skills needed to succeed in school.
The RNA helicase Ded1 regulates translation and granule formation during multiple phases of cellular stress responses.

Ded1 is a conserved RNA helicase that promotes translation initiation in steady-state conditions. Ded1 has also been shown to regulate translation during cellular stress and affect the dynamics of stress granules (SGs), accumulations of RNA and protein linked to translation repression. To better understand its role in stress responses, we examined Ded1 function in two different models: DED1 overexpression and oxidative stress. DED1 overexpression inhibits growth and promotes the formation of SGs. A ded1 mutant lacking the low-complexity C-terminal region (ded1-ΔCT), which mediates Ded1 oligomerization and interaction with the translation factor eIF4G1, suppressed these phenotypes, consistent with other stresses. During oxidative stress, a ded1-ΔCT mutant was defective in growth and in SG formation compared to wild-type cells, although SGs were increased rather than decreased in these conditions. Unlike stress induced by direct TOR inhibition, the phenotypes in both models were only partially dependent on eIF4G1 interaction, suggesting an additional contribution from Ded1 oligomerization. Furthermore, examination of the growth defects and translational changes during oxidative stress suggested that Ded1 plays a role during recovery from stress. Integrating these disparate results, we propose that Ded1 controls multiple aspects of translation and RNP dynamics in both initial stress responses and during recovery.
A Method to Generalize by Recurrence of Some Known Results

A great number of articles widen known results, and this is due to a simple procedure, of which it is good to say a few words.
Stability of the Magnetotail Current Sheet With Normal Magnetic Field and Field-Aligned Plasma Flows

One of the most important problems of magnetotail dynamics is the substorm onset and the related instability of the magneotail current sheet. Since the simplest 2D current sheet configuration with monotonic $B_z$ was proven to be stable to the tearing mode, the focus of the instability investigation moved to more specific configurations, e.g. kinetic current sheets with strong transient ion currents and current sheets with non-monotonic $B_z$ (local $B_z$ minima or/and peaks). Stability of the latter current sheet configuration has been studied both within kinetic and fluid approaches, whereas the investigation of the transient ion effects were limited to kinetic models only. This paper aims to provide detailed analysis of stability of a multi-fluid current sheet configuration that mimics current sheets with transient ions. Using the system with two field-aligned ion flows that mimic the effect of pressure non-gyrotropy, we construct 1D current sheet with a finite $B_z$. This model describes well recent findings of very thin intense magnetotail current sheets. The stability analysis of this two-ion model confirms the stabilizing effect of finite $B_z$ and shows that the most stable current sheet is the one with exactly counter-streaming ion flows and zero net flow. Such field-aligned flows may substitute the contribution of the pressure tensor nongyrotropy to the stress balance, but cannot overtake the stabilizing effect of $B_z$. Obtained results are discussed in the context of magnetotail dynamical models and spacecraft observations.
Performance Bounds for Sampling and Remote Estimation of Gauss-Markov Processes over a Noisy Channel with Random Delay

In this study, we generalize a problem of sampling a scalar Gauss Markov Process, namely, the Ornstein-Uhlenbeck (OU) process, where the samples are sent to a remote estimator and the estimator makes a causal estimate of the observed real-time signal. In recent years, the problem is solved for stable OU processes. We present solutions for the optimal sampling policy that exhibits a smaller estimation error for both stable and unstable cases of the OU process along with a special case when the OU process turns to a Wiener process. The obtained optimal sampling policy is a threshold policy. However, the thresholds are different for all three cases. Later, we consider additional noise with the sample when the sampling decision is made beforehand. The estimator utilizes noisy samples to make an estimate of the current signal value. The mean-square error (mse) is changed from previous due to noise and the additional term in the mse is solved which provides performance upper bound and room for a pursuing further investigation on this problem to find an optimal sampling strategy that minimizes the estimation error when the observed samples are noisy. Numerical results show performance degradation caused by the additive noise.
Two Algorithms for Computing Exact and Approximate Nash Equilibria in Bimatrix Games.

In this paper, we first devise two algorithms to determine whether or not a bimatrix game has a strategically equivalent zero-sum game. If so, we propose an algorithm that computes the strategically equivalent zero-sum game. If a given bimatrix game is not strategically equivalent to a zero-sum game, we then propose an approach to compute a zero-sum game whose saddle-point equilibrium can be mapped to a well-supported approximate Nash equilibrium of the original game. We conduct extensive numerical simulation to establish the efficacy of the two algorithms.
Method for detecting highly eccentric binaries with a gravitational wave burst search

Detection of gravitational waves (GW) from highly eccentric binary black hole (BBH) systems can provide insight into their dynamics and formation. To date, all of the LIGO-Virgo BBH detections have been made using quasicircular templates in their initial discovery. However, recent studies have found some of these systems to be compatible with high eccentricity in the LIGO band, ${e}_{10\text{ }\text{ }\mathrm{Hz}}g0.1$, possibly pointing to a population of sources that are challenging to detect. Current low-latency search methods used with ground-based GW detector data are not well equipped to detect highly eccentric sources. Template-based, matched-filter searches require accurate eccentric waveform models that are computational expensive. Unmodeled burst searches are designed to detected localized excess power and are unable to identify multiple isolated bursts, as would originate from a single highly eccentric BBH. Therefore, we propose a signal-based prior that can be incorporated into an existing GW burst search to target highly eccentric BBHs. Our eccentric burst prior is based on the Newtonian burst model described by [Classical Quantum Gravity 34, 135011 (2017)]. As a proof of concept, we test our method on simulated data and find that for intermediate $\mathrm{SNR}\ensuremath{\sim}3--6$ signals using the eccentric burst prior more effectively localizes GW bursts when compared to a uniform prior.
Graph neural network for Hamiltonian-based material property prediction

Development of next-generation electronic devices calls for the discovery of quantum materials hosting novel electronic, magnetic, and topological properties. Traditional electronic structure methods require expensive computation time and memory consumption, thus a fast and accurate prediction model is desired with increasing importance. Representing the interactions among atomic orbitals in material, a Hamiltonian matrix provides all the essential elements that control the structure–property correlations in inorganic compounds. Learning of Hamiltonian by machine learning therefore offers an approach to accelerate the discovery and design of quantum materials. With this motivation, we present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The information of each orbital includes the name, relative coordinates with respect to the center of super cell and the atom number. The interaction between orbitals is represented by the Hamiltonian matrix. The results show that our model can get a promising prediction accuracy with cross-validation.