cssi/SciSciGPT-SciSciNet huggingface dataset:
Who Has the Time? Community College Students’ Time-Use Response to Financial Incentives

We evaluate the effect of performance-based scholarship programs for post-secondary students on student time use and effort and whether these effects are different for students we hypothesize may be more or less responsive to incentives. To do so, we administered a time-use survey as part of a randomized experiment in which community college students in New York City were randomly assigned to be eligible for a performance-based scholarship or to a control group that was only eligible for the standard financial aid. This paper contributes to the literature by attempting to get inside the “black box” of how students respond to a monetary incentive to improve their educational attainment. We find that students eligible for a scholarship devoted more time to educational activities, increased the quality of effort toward and engagement with their studies, and allocated less time to leisure. Additional analyses suggest that students who were plausibly more myopic (place less weight on future benefits) were more responsive to the incentives, but we find no evidence that students who are arguably more time constrained were less responsive to the incentives.
Going deep: models for continuous-time within-play valuation of game outcomes in American football with tracking data

Continuous-time assessments of game outcomes in sports have become increasingly common in the last decade. In American football, only discrete-time estimates of play value were possible, since the most advanced public football datasets were recorded at the play-by-play level. While measures such as expected points and win probability are useful for evaluating football plays and game situations, there has been no research into how these values change throughout the course of a play. In this work, we make two main contributions: First, we introduce a general framework for continuous-time within-play valuation in the National Football League using player-tracking data. Our modular framework incorporates several modular sub-models, to easily incorporate recent work involving player tracking data in football. Second, we use a long short-term memory recurrent neural network to construct a ball-carrier model to estimate how many yards the ball-carrier is expected to gain from their current position, conditional on the locations and trajectories of the ball-carrier, their teammates and opponents. Additionally, we demonstrate an extension with conditional density estimation so that the expectation of any measure of play value can be calculated in continuous-time, which was never before possible at such a granular level.
Generalized Optimization of High-Capacity Compressive Imaging Systems

One of the greatest challenges in applying compressive sensing (CS) signal processing techniques to electromagnetic imaging applications is designing a sensing matrix that has good reconstruction capabilities. Compressive reflector antennas (CRAs) are a class of antennas that have been shown to provide enhanced image reconstruction performance over traditional reflector antennas (TRAs) when CS techniques are employed. In this article, we present a unified CRA design method, which considers both the sensing capacity and efficiency of the antenna and can be used for both compressive imaging and multiple-input multiple-output (MIMO) communication applications. The unified design method is assessed for a CRA configuration in which dielectric scatterers are added to the surface of a TRA. The design results demonstrate the ability of the unified design method to enhance the CS reconstruction capabilities of the CRA.
Sparse hierarchical regression with polynomials

We present a novel method for sparse polynomial regression. We are interested in that degree r polynomial which depends on at most k inputs, counting at most $$\ell$$ monomial terms, and minimizes the sum of the squares of its prediction errors. Such highly structured sparse regression was denoted by Bach (Advances in neural information processing systems, pp 105–112, 2009) as sparse hierarchical regression in the context of kernel learning. Hierarchical sparse specification aligns well with modern big data settings where many inputs are not relevant for prediction purposes and the functional complexity of the regressor needs to be controlled as to avoid overfitting. We propose an efficient two-step approach to this hierarchical sparse regression problem. First, we discard irrelevant inputs using an extremely fast input ranking heuristic. Secondly, we take advantage of modern cutting plane methods for integer optimization to solve the remaining reduced hierarchical $$(k, \ell )$$-sparse problem exactly. The ability of our method to identify all k relevant inputs and all $$\ell$$ monomial terms is shown empirically to experience a phase transition. Crucially, the same transition also presents itself in our ability to reject all irrelevant features and monomials as well. In the regime where our method is statistically powerful, its computational complexity is interestingly on par with Lasso based heuristics. Hierarchical sparsity can retain the flexibility of general nonparametric methods such as nearest neighbors or regression trees (CART), without sacrificing much statistical power. The presented work hence fills a void in terms of a lack of powerful disciplined nonlinear sparse regression methods in high-dimensional settings. Our method is shown empirically to scale to regression problems with $$n\approx 10{,}000$$ observations for input dimension $$p\approx 1000$$.
The average number of integral points in orbits

Over a number field $K$, a celebrated result of Silverman states that if $\varphi(z)\in K(z)$ is a rational function whose second iterate is not a polynomial, the set of $S$-integral points in the orbit $\text{Orb}_\varphi(P)=\{\varphi^n(P)\}_{n\geq0}$ is finite for all $P\in \mathbb{P}^1(K)$. In this paper, we show that if we vary $\varphi$ and $P$ in a suitable family, the number of $S$-integral points in $\text{Orb}_\varphi(P)$ is absolutely bounded. In particular, if we fix $\varphi$ and vary the basepoint $P\in \mathbb{P}^1(K)$, then we show that $\#(\text{Orb}_\varphi(P)\cap\mathcal{O}_{K,S})$ is zero on average. Finally, we prove a zero-average result in general, assuming a standard height uniformity conjecture in arithmetic geometry.
A Stable H-Dibaryon: Dark Matter Candidate Within QCD?

Particle physics arguments suggest that the H-dibaryon—a state consisting of two u, two d, and two s quarks—may have a mass ≈ 1.5 ± 0.2 MeV, and that r H≲ $${1 \over 4}$$ r N. Remarkably, the observed stability of nuclei and other experimental limits do not exclude this scenario at present, as discussed here. If they are present in sufficient abundance, relic H's would be the cold dark matter. Tests of this scenario are discussed.
Assessing Graph‐based Deep Learning Models for Predicting Flash Point

Flash points of organic molecules play an important role in preventing flammability hazards and large databases of measured values exist, although millions of compounds remain unmeasured. To rapidly extend existing data to new compounds many researchers have used quantitative structure-property relationship (QSPR) analysis to effectively predict flash points. In recent years graph-based deep learning (GBDL) has emerged as a powerful alternative method to traditional QSPR. In this paper, GBDL models were implemented in predicting flash point for the first time. We assessed the performance of two GBDL models, message-passing neural network (MPNN) and graph convolutional neural network (GCNN), by comparing against 12 previous QSPR studies using more traditional methods. Our result shows that MPNN both outperforms GCNN and yields slightly worse but comparable performance with previous QSPR studies. The average R 2 and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3 % lower and 2.0 K higher than previous comparable studies. To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules. The optimized MPNN gives a test data R 2 of 0.803 and MAE of 17.8 K on the complete dataset. We also extracted 5 datasets from our integrated dataset based on molecular types (acids, organometallics, organogermaniums, organosilicons, and organotins) and explore the quality of the model in these classes.
Deep Actor-Critic Reinforcement Learning for Anomaly Detection

Anomaly detection is widely applied in a variety of domains, involving for instance, smart home systems, network traffic monitoring, IoT applications and sensor networks. In this paper, we study deep reinforcement learning based active sequential testing for anomaly detection. We assume that there is an unknown number of abnormal processes at a time and the agent can only check with one sensor in each sampling step. To maximize the confidence level of the decision and minimize the stopping time concurrently, we propose a deep actor-critic reinforcement learning framework that can dynamically select the sensor based on the posterior probabilities. We provide simulation results for both the training phase and testing phase, and compare the proposed framework with the Chernoff test in terms of claim delay and loss.
Coronavirus Fears and Macroeconomic Expectations

The Federal Reserve cut interest rates on March 3, 2020, in response to COVID-19. On March 5 and 6, I surveyed over 500 consumers about their concerns about COVID-19, awareness of the Fed's announc...
In Vitro Characterization of Protein Effector Export in the Bradyzoite Stage of Toxoplasma gondii.

ABSTRACT The ubiquitous parasite Toxoplasma gondii exhibits an impressive ability to maintain chronic infection of its host for prolonged periods. Despite this, little is known regarding whether and how T. gondii bradyzoites, a quasi-dormant life stage residing within intracellular cysts, manipulate the host cell to maintain persistent infection. A previous proteomic study of the cyst wall, an amorphous layer of proteins that forms underneath the cyst membrane, identified MYR1 as a putative cyst wall protein in vitro. Because MYR1 is known to be involved in the translocation of parasite-derived effector proteins into the host cell, we sought to determine whether parasites transitioning toward the bradyzoite life stage retain the capacity to translocate proteins via this pathway. By epitope tagging the endogenous loci of four known effectors that translocate from the parasitophorous vacuole into the host cell nucleus, we show, by immunofluorescence assays, that most effectors accumulate in the host nucleus at early but not late time points after infection, during the tachyzoite-to-bradyzoite transition and when parasites further along the bradyzoite differentiation continuum invade a new host cell. We demonstrate that the suppression of interferon gamma signaling, which was previously shown to be mediated by the effector TgIST, also occurs in the context of prolonged infection with bradyzoites and that TgIST export is a process that occurs beyond the early stages of host cell infection. These findings have important implications regarding how this highly successful parasite maintains persistent infection of its host. IMPORTANCEToxoplasma bradyzoites persist within tissue cysts and are refractory to current treatments, serving as a reservoir for acute complications in settings of compromised immunity. Much remains to be understood regarding how this life stage successfully establishes and maintains persistent infection. In this study, we investigated whether the export of parasite effector proteins into the host cell occurs during the development of in vitro tissue cysts. We quantified the presence of four previously described effectors in host cell nuclei at different time points after bradyzoite differentiation and found that they accumulated largely during the early stages of infection. Despite a decline in nuclear accumulation, we found that one of these effectors still mediated its function after prolonged infection with bradyzoites, and we provide evidence that this effector is exported beyond early infection stages. These findings suggest that effector export from within developing tissue cysts provides one potential mechanism by which this parasite achieves chronic infection.
Compositional Synthesis of Decentralized Robust Set-Invariance Controllers for Large-scale Linear Systems

Ensuring constraint satisfaction in large-scale systems with hard constraints is vital in many safety critical systems. The challenge is to design controllers that are efficiently synthesized offline, easily implementable online, and provide formal correctness guarantees. In this paper, we provide a method to compute correct-by-construction controllers for a network of coupled linear systems with additive bounded disturbances such that i) the design of the controllers is fully compositional - we use an optimization-based approach that iteratively computes subsystem-level assume-guarantee contracts in the form of robust control invariant sets; and ii) the controllers are decentralized hence online implementation requires only the local state information. We present illustrative examples, including a case study on a system with 1000 dimensions.
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification

Collision prediction in a dynamic and unknown environment relies on knowledge of how the environment is changing. Many collision prediction methods rely on deterministic knowledge of how obstacles are moving in the environment. However, complete deterministic knowledge of the obstacles’ motion is often unavailable. This work proposes a Gaussian process based prediction method that replaces the assumption of deterministic knowledge of each obstacle’s future behavior with probabilistic knowledge, to allow a larger class of obstacles to be considered. The method solely relies on position and velocity measurements to predict collisions with dynamic obstacles. We show that the uncertainty region for obstacle positions can be expressed in terms of a combination of polynomials generated with Gaussian process regression. To control the growth of uncertainty over arbitrary time horizons, a probabilistic obstacle intention is assumed as a distribution over obstacle positions and velocities, which can be naturally included in the Gaussian process framework. Our approach is demonstrated in a case study in which an obstacle overtakes the agent. In this simulation we show that the collision can be predicted despite having limited knowledge of the obstacle’s behavior.
On the size of primitive sets in function fields

Abstract A set is primitive if no element of the set divides another. We consider primitive sets of monic polynomials over a finite field and find natural generalizations of many of the results known for primitive sets of integers. In particular, we show that primitive sets in the function field have lower density zero by showing that the sum ∑ a ∈ A 1 q deg ⁡ a deg ⁡ a , an analogue of a sum considered by Erdős, is uniformly bounded over all primitive sets A. We then adapt a method of Besicovitch to construct primitive sets in F q [ x ] with upper density arbitrarily close to q − 1 q and generalize a result of Martin and Pomerance on the asymptotic growth rate of the counting function of a primitive set. Along the way we prove a quantitative analogue of the Hardy-Ramanujan theorem for function fields, as well as bounds on the size of the k-th irreducible polynomial.
AN IMMUNO-EPIDEMIOLOGICAL VECTOR–HOST MODEL WITH WITHIN-VECTOR VIRAL KINETICS

A current challenge for disease modeling and public health is understanding pathogen dynamics across scales since their ecology and evolution ultimately operate on several coupled scales. This is p...
Performance of electrophysiology procedures at an academic medical center amidst the 2020 coronavirus (COVID-19) pandemic.

A global coronavirus (COVID-19) pandemic occurred at the start of 2020 and is already responsible for more than 74 000 deaths worldwide, just over 100 years after the influenza pandemic of 1918. At the center of the crisis is the highly infectious and deadly SARS-CoV-2, which has altered everything from individual daily lives to the global economy and our collective consciousness. Aside from the pulmonary manifestations of disease, there are likely to be several electrophysiologic (EP) sequelae of COVID-19 infection and its treatment, due to consequences of myocarditis and the use of QT-prolonging drugs. Most crucially, the surge in COVID-19 positive patients that have already overwhelmed the New York City hospital system requires conservation of hospital resources including personal protective equipment (PPE), reassignment of personnel, and reorganization of institutions, including the EP laboratory. In this proposal, we detail the specific protocol changes that our EP department has adopted during the COVID-19 pandemic, including performance of only urgent/emergent procedures, after hours/7-day per week laboratory operation, single attending-only cases to preserve PPE, appropriate use of PPE, telemedicine and video chat follow-up appointments, and daily conferences to collectively manage the clinical and ethical dilemmas to come. We discuss also discuss how we perform EP procedures on presumed COVID positive and COVID tested positive patients to highlight issues that others in the EP community may soon face in their own institution as the virus continues to spread nationally and internationally.
Many‐body response of benzene at monolayer MoS2: Van der Waals interactions and spectral broadening

Models of surface enhancement of molecular electronic response properties are challenging for two reasons: (1) molecule-surface interactions require the simultaneous solution of the molecular and the surface dynamic response (a daunting task); (2) when solving for the electronic structure of the combined molecule+surface system, it is not trivial to single out the particular physical effects responsible for enhancement. To attack this problem, in this work we apply a formally exact decomposition of the system’s response function into subsystem contributions by employing subsystem DFT which grants access to dynamic polarizabilities and optical spectra. In order to access information about the interactions between the subsystems, we extend a previously developed subsystem-based adiabatic connection fluctuation-dissipation theorem of DFT to separate the additive from the nonadditive correlation energy and identify the nonadditive correlation as the van der Waals interactions. As an example, we choose benzene adsorbed on monolayer MoS2. We isolate the contributions to the benzene’s dynamic response arising from the interaction with the surface and for the first time, we evaluate the enhancements to the effective C6 coefficients as a function of benzene-MoS2 distance and adsorption site. We also quantify the spectral broadening of the benzene’s electronic excited states due to their interaction with the surface. We find that the broadening has a similar decay law with the molecule-surface distance as the leading van der Waals interactions (i.e., R-6) and that the surface enhancement of dispersion interactions between benzene molecules is less than 5\%, but still large enough (0.5 kcal/mol) to likely play a role in the prediction of interface morphologies.
A Highly Contiguous Genome for the Golden-Fronted Woodpecker ( Melanerpes aurifrons ) via Hybrid Oxford Nanopore and Short Read Assembly.

Woodpeckers are found in nearly every part of the world and have been important for studies of biogeography, phylogeography, and macroecology. Woodpecker hybrid zones are often studied to understand the dynamics of introgression between bird species. Notably, woodpeckers are gaining attention for their enriched levels of transposable elements (TEs) relative to most other birds. This enrichment of TEs may have substantial effects on molecular evolution. However, comparative studies of woodpecker genomes are hindered by the fact that no high-contiguity genome exists for any woodpecker species. Using hybrid assembly methods combining long-read Oxford Nanopore and short-read Illumina sequencing data, we generated a highly contiguous genome assembly for the Golden-fronted Woodpecker (Melanerpes aurifrons). The final assembly is 1.31 Gb and comprises 441 contigs plus a full mitochondrial genome. Half of the assembly is represented by 28 contigs (contig L50), each of these contigs is at least 16 Mb in size (contig N50). High recovery (92.6%) of bird-specific BUSCO genes suggests our assembly is both relatively complete and relatively accurate. Over a quarter (25.8%) of the genome consists of repetitive elements, with 287 Mb (21.9%) of those elements assignable to the CR1 superfamily of transposable elements, the highest proportion of CR1 repeats reported for any bird genome to date. Our assembly should improve comparative studies of molecular evolution and genomics in woodpeckers and allies. Additionally, the sequencing and bioinformatic resources used to generate this assembly were relatively low-cost and should provide a direction for development of high-quality genomes for studies of animal biodiversity.
Characterization of the Fe metalloproteome of a ubiquitous marine heterotroph, Pseudoalteromonas (BB2-AT2): multiple bacterioferritin copies enable significant Fe storage

Fe is a critical nutrient to the marine biological pump, which is the process that exports photosynthetically fixed carbon in the upper ocean to the deep ocean. Fe limitation controls photosynthetic activity in major regions of the oceans, and the subsequent degradation of exported photosynthetic material is facilitated particularly by marine heterotrophic bacteria. Despite their importance in the carbon cycle and the scarcity of Fe in seawater, the Fe requirements, storage and cytosolic utilization of these marine heterotrophs has been less studied. Here, we characterized the Fe metallome of Pseudoalteromonas (BB2-AT2). We found that with two copies of bacterioferritin (Bfr), Pseudoalteromonas possesses substantial capacity for luxury uptake of Fe. Fe : C in the whole cell metallome was estimated (assuming C : P stoichiometry ∼51 : 1) to be between ∼83 μmol : mol Fe : C, ∼11 fold higher than prior marine bacteria surveys. Under these replete conditions, other major cytosolic Fe-associated proteins were observed including superoxide dismutase (SodA; with other metal SOD isoforms absent under Fe replete conditions) and catalase (KatG) involved in reactive oxygen stress mitigation and aconitase (AcnB), succinate dehydrogenase (FrdB) and cytochromes (QcrA and Cyt1) involved in respiration. With the aid of singular value decomposition (SVD), we were able to computationally attribute peaks within the metallome to specific metalloprotein contributors. A putative Fe complex TonB transporter associated with the closely related Alteromonas bacterium was found to be abundant within the Pacific Ocean mesopelagic environment. Despite the extreme scarcity of Fe in seawater, the marine heterotroph Pseudoalteromonas has expansive Fe storage capacity and utilization strategies, implying that within detritus and sinking particles environments, there is significant opportunity for Fe acquisition. Together these results imply an evolved dedication of marine Pseudoalteromonas to maintaining an Fe metalloproteome, likely due to its dependence on Fe-based respiratory metabolism.
Theory of motion of Jupiter's Galilean satellites

Final results are presented for a theory enabling one to calculate the positions of the Galilean satellites and their partial derivatives. Extensive use of algebraic manipulation software on a digital computer is made to generate the final expressions. The theory is, in effect, a revitalization of Sampson's (1921) theory. Algebraic and mathematical errors existing in Sampson's work are removed, some neglected effects due to solar interactions and the 3-7 commensurability are introduced, allowance is made for nonzero amplitude and phase of the free libration, and the final results are expressed as analytic functions of variations in 49 arbitrary constants of integration and physical parameters. The theory is constructed in a manner which readily allows for future revision, and analytic expressions are provided for the partial derivatives with respect to the 49 parameters.
Ion transport across solid-state ion channels perturbed by directed strain

We combine quantum-chemical calculations and molecular dynamics simulations to consider aqueous ion flow across non-axisymmetric nanopores in monolayer graphene and MoS2. When the pore-containing membrane is subject to uniaxial tensile strains applied in various directions, the corresponding permeability exhibits considerable directional dependence. This anisotropy is shown to arise from directed perturbations of the local electrostatics by the corresponding pore deformation, as enabled by the pore edge geometries and atomic compositions. By considering nanopores with ionic permeability that depends on the strain direction, we present model systems that may yield a detailed understanding of the structure–function relationship in solid-state and biological ion channels. Specifically, the observed anisotropic effects potentially enable the use of permeation measurements across strained membranes to obtain directional profiles of ion–pore energetics as contributed by groups of atoms or even individual atoms at the pore edge. The resulting insight may facilitate the development of subnanoscale pores with novel functionalities arising from locally asymmetric pore edge features.
Macroeconomic Factors and Antidumping Filings: Evidence from Four Countries

Abstract This paper examines the relationship between antidumping filings and macroeconomic factors. Real exchange rate fluctuations affect the two criteria for dumping in opposite ways, making the overall effect on filings ambiguous in theory. Examining the filing patterns of the four major users of AD law during the 1980–98 period we find that real exchange rates and domestic real GDP growth both have statistically significant impacts on filings. Bilateral filing data indicate that a one-standard deviation real appreciation of the domestic currency increases filings by 33%. We also find one-standard deviation fall in domestic real GDP increases filings by 23%.
The Full Surplus Extraction Theorem with Hidden Actions

I consider a situation in which a principal commits to a mechanism first and then agents choose unobservable actions before they draw their types. The agents’ actions affect not only their payoffs directly but also a distribution of private types as well. Thus, the distribution of types is determined endogenously rather than exogeneously unlike standard mechanism design literature. Then I extend Cremer and McLean’s full surplus extraction theorem [10] to such a setting, that is, identify a necessary and sufficient condition on the information structure for the full surplus extration. More importantly, it implies that the full surplus extraction may not be obtained for generic set of information structure in this more general setting. This contrasts with the standard full surplus extraction theorem which holds generically. I show, however, that the principal can extract all the surplus for any completely mixed action profile for almost all information structure by using more general mechanisms in which agents announce both their types and the realizations of their mixed actions. Since any pure action profile can be arbitrarily approximated by a completely mixed action profile, the principal can virtually extracts all the maximized social surplus.
The labor market effects of introducing national health insurance : evidence from Canada

Although national health insurance (NHI) plans in the United States are often opposed on the basis of their potential disemployment effects, there is no existing evidence on the effects of NHI on employment. We provide such evidence by examining the employment consequences of NHI in Canada, using the fact that NHI was introduced on a staggered basis across Canadian provinces. We examine monthly data on employment, wages, and hours across 8 industries and 10 provinces over the 1961–1975 period. We find that employment rose after the introduction of NHI; wages increased as well, and average hours were unchanged. In addition, we find lower rates of employment and wage growth after introduction of NHI in provinces that financed NHI with general revenues rather than lump-sum premiums and lower rates of wage growth in provinces with high initial levels of private insurance coverage.
A Model of Social Interactions and Endogenous Poverty Traps

This paper develops a model of social interactions and endogenous poverty traps. The key idea is captured in a framework in which the likelihood of future social interactions with members of one's group is partly determined by group-specific investments made by individuals. I prove three main results. First, some individuals expected to make group-specific capital investments are worse off because their observed decision is used as a litmus test of group loyalty — creating a trade-off between human capital and cooperation among the group. Second, there exist equilibria which exhibit bipolar human capital investment behavior by individuals of similar ability. Third, as social mobility increases this bipolarization increases. The model's predictions are consistent with the bifurcation of distinctively black names in the mid-1960s, the erosion of black neighborhoods in the 1970s, accusations of `acting white', and the efficacy of certain programs designed to encourage human capital acquisition.
13 – Breeder reactors

Publisher Summary This chapter examines the relationship between reproduction factor and breeding. The chapter also describes the physical features of the liquid metal fast breeder reactor, and takes a look at the compatibility of uranium fuel resources and its requirements. If the value of the neutron reproduction factor, η, is larger than 2 and losses of neutrons are minimized, breeding can be achieved with more fuel produced than is consumed. The conversion ratio (CR) measures the ability of a reactor system to transform a fertile isotope. Complete conversion requires a value of CR of nearly 1. Fast breeder reactors using liquid sodium with breeding ratios (BR) greater than 1 have been built and operated, but several development programs have been canceled. There is a great disparity between uranium resources and uranium use among the countries of the world. Application of the breeder could stretch the fission power option from a few decades to centuries.
Adjusting Image Attributes of Localized Regions with Low-level Dialogue.

Natural Language Image Editing (NLIE) aims to use natural language instructions to edit images. Since novices are inexperienced with image editing techniques, their instructions are often ambiguous and contain high-level abstractions which require complex editing steps. Motivated by this inexperience aspect, we aim to smooth the learning curve by teaching the novices to edit images using low-level command terminologies. Towards this end, we develop a task-oriented dialogue system to investigate low-level instructions for NLIE. Our system grounds language on the level of edit operations, and suggests options for users to choose from. Though compelled to express in low-level terms, user evaluation shows that 25% of users found our system easy-to-use, resonating with our motivation. Analysis shows that users generally adapt to utilizing the proposed low-level language interface. We also identified object segmentation as the key factor to user satisfaction. Our work demonstrates advantages of low-level, direct language-action mapping approach that can be applied to other problem domains beyond image editing such as audio editing or industrial design.
Neural Message Passing for Multi-label Classification

Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul challenge. We propose Label Message Passing (LaMP) Neural Networks to efficiently model the joint prediction of multiple labels. LaMP treats labels as nodes on a label-interaction graph and computes the hidden representation of each label node conditioned on the input using attention-based neural message passing. Attention enables LaMP to assign different importances to neighbor nodes per label, learning how labels interact (implicitly). The proposed models are simple, accurate, interpretable, structure-agnostic, and applicable for predicting dense labels since LaMP is incredibly parallelizable. We validate the benefits of LaMP on seven real-world MLC datasets, covering a broad spectrum of input/output types and outperforming the state-of-the-art results. Notably, LaMP enables intuitive interpretation of how classifying each label depends on the elements of a sample and at the same time rely on its interaction with other labels (We provide our code and datasets at https://github.com/QData/LaMP.).
Faith in Trump, Moral Foundations, and Social Distancing Defiance during the Coronavirus Pandemic

Purpose:Over the past several months, the coronavirus has infected more than six million Americans and killed nearly 200,000. Governors have issued stay-at-home orders, and prosecutors have filed c...
BachGAN: High-Resolution Image Synthesis From Salient Object Layout

We propose a new task towards more practical applications for image generation - high-quality image synthesis from salient object layout. This new setting requires users to provide only the layout of salient objects (i.e., foreground bounding boxes and categories) and lets the model complete the drawing with an invented background and a matching foreground. Two main challenges spring from this new task: (i) how to generate fine-grained details and realistic textures without segmentation map input; and (ii) how to create and weave a background into standalone objects in a seamless way. To tackle this, we propose Background Hallucination Generative Adversarial Network (BachGAN), which leverages a background retrieval module to first select a set of segmentation maps from a large candidate pool, then encodes these candidate layouts via a background fusion module to hallucinate a suitable background for the given objects. By generating the hallucinated background representation dynamically, our model can synthesize high-resolution images with both photo-realistic foreground and integral background. Experiments on Cityscapes and ADE20K datasets demonstrate the advantage of BachGAN over existing approaches, measured on both visual fidelity of generated images and visual alignment between output images and input layouts.
Composing Molecules with Multiple Property Constraints

Drug discovery aims to find novel compounds with specified chemical property profiles. In terms of generative modeling, the goal is to learn to sample molecules in the intersection of multiple property constraints. This task becomes increasingly challenging when there are many property constraints. We propose to offset this complexity by composing molecules from a vocabulary of substructures that we call molecular rationales. These rationales are identified from molecules as substructures that are likely responsible for each property of interest. We then learn to expand rationales into a full molecule using graph generative models. Our final generative model composes molecules as mixtures of multiple rationale completions, and this mixture is fine-tuned to preserve the properties of interest. We evaluate our model on various drug design tasks and demonstrate significant improvements over state-of-the-art baselines in terms of accuracy, diversity, and novelty of generated compounds.
Flex: Closing the Gaps between Usage and Allocation

Most of data centers are significantly underutilized. One of the major reasons is the big gaps between the real usage and the provisioned resources. In this paper, we first conduct an in-depth analysis of a Google cluster trace to unveil the root causes for low utilization and highlight the great potential to improve it. We then developed an online resource manager Flex to maximize the cluster utilization while satisfying the Quality of Service (QoS). Large-scale evaluations show that Flex admits up to 1.74× more requests and 1.6× higher utilization compared to tradition schedulers while maintaining the QoS. The full version of this paper is [4].
Heuristic Strategies in Uncertain Approval Voting Environments

In many collective decision making situations, agents use voting to choose an alternative that best represents the preferences of the group. It is often assumed that voters will vote truthfully rather than expending the effort needed to manipulate the outcome in cognitively and computationally complex situations. However, being truthful is just one possible heuristic that agents may employ. We examine how real voters employ heuristics in a variety of approval voting scenarios. In particular, we consider heuristics where a voter ignores information about other voting profiles and makes their decisions based solely on how much they like each candidate. In a behavioral experiment, we show that people vote truthfully in some situations, but prioritize high utility candidates in others. We show how the structure of the voting environment affects how well each heuristic performs as well as how and when humans employ these different heuristics.
Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions

We study the problem of out-of-sample risk estimation in the high dimensional regime where both the sample size $n$ and number of features $p$ are large, and $n/p$ can be less than one. Extensive empirical evidence confirms the accuracy of leave-one-out cross validation (LO) for out-of-sample risk estimation. Yet, a unifying theoretical evaluation of the accuracy of LO in high-dimensional problems has remained an open problem. This paper aims to fill this gap for penalized regression in the generalized linear family. With minor assumptions about the data generating process, and without any sparsity assumptions on the regression coefficients, our theoretical analysis obtains finite sample upper bounds on the expected squared error of LO in estimating the out-of-sample error. Our bounds show that the error goes to zero as $n,p \rightarrow \infty$, even when the dimension $p$ of the feature vectors is comparable with or greater than the sample size $n$. One technical advantage of the theory is that it can be used to clarify and connect some results from the recent literature on scalable approximate LO.
Two theorems on point-flat incidences

Abstract We improve the theorem of Beck giving a lower bound on the number of k-flats spanned by a set of points in real space, and improve the bound of Elekes and Toth on the number of incidences between points and k-flats in real space.
A Mean Field Game Analysis of Distributed MAC in Ultra-Dense Multichannel Wireless Networks

This report analyzes the performance of distributed Medium Access Control (MAC) protocols in ultra-dense multichannel wireless networks, where $N$ frequency bands (or channels) are shared by $M=mN$ devices, and devices make decisions to probe and then transmit over available frequency bands. While such a system can be formulated as an $M$ -player Bayesian game, it is often infeasible to compute the Nash equilibria of a large-scale system due to the curse of dimensionality . In this report, we exploit the Mean Field Game (MFG) approach and analyze the system in the large population regime ( $N$ tends to $\infty $ and $m$ is a constant). We consider a distributed and low complexity MAC protocol where each device probes $d/k$ channels by following an exponential clock which ticks with rate $k$ when it has a message to transmit, and optimizes the probing strategy to balance throughput and probing cost. We present a comprehensive analysis from the MFG perspective, including the existence and uniqueness of and convergence to the Mean Field Nash Equilibrium and the price of anarchy with respect to the global optimal solution. Our analysis shows that the price of anarchy is at most one half, but is close to zero when the traffic load or the probing cost is low. Our numerical results confirm our analysis and show that the MFNE is a good approximation of the $M$ -player system. Further, this report demonstrates the novelty of MFG analysis, which can be used to study other distributed MAC protocols in ultra-dense wireless networks.
Analytical and numerical solution of one- and two-dimensional steady heat transfer in a coldplate

We develop analytical models for steady-state, one- and two-dimensional heat transfer in a single-material, flat-plate coldplate. Discrete heat sources are mounted on one side of the plate and heat transfer to a flowing fluid occurs on the other. The models are validated numerically using finite differences. We propose a simple procedure for estimating maximum coldplate temperature at the location of each heat source which includes thermal interaction among the sources. Results from one model are compared with data obtained for a composite coldplate operated in the laboratory. We demonstrate the utility of the models as diagnostic tools to be used for predicting the existence and extent of void volumes and delaminations in the composite material that can occur with coldplates of this type. Based on our findings, recommendations for effective coldplate design are given.
Architecting noisy intermediate-scale trapped ion quantum computers

Trapped ions (TI) are a leading candidate for building Noisy Intermediate-Scale Quantum (NISQ) hardware. TI qubits have fundamental advantages over other technologies such as superconducting qubits, including high qubit quality, coherence and connectivity. However, current TI systems are small in size, with 5-20 qubits and typically use a single trap architecture which has fundamental scalability limitations. To progress towards the next major milestone of 50-100 qubit TI devices, a modular architecture termed the Quantum Charge Coupled Device (QCCD) has been proposed. In a QCCD-based TI device, small traps are connected through ion shuttling. While the basic hardware components for such devices have been demonstrated, building a 50-100 qubit system is challenging because of a wide range of design possibilities for trap sizing, communication topology and gate implementations and the need to match diverse application resource requirements. Towards realizing QCCD-based TI systems with 50-100 qubits, we perform an extensive application-driven architectural study evaluating the key design choices of trap sizing, communication topology and operation implementation methods. To enable our study, we built a design toolflow which takes a QCCD architecture's parameters as input, along with a set of applications and realistic hardware performance models. Our toolflow maps the applications onto the target device and simulates their execution to compute metrics such as application run time, reliability and device noise rates. Using six applications and several hardware design points, we show that trap sizing and communication topology choices can impact application reliability by up to three orders of magnitude. Microarchitectural gate implementation choices influence reliability by another order of magnitude. From these studies, we provide concrete recommendations to tune these choices to achieve highly reliable and performant application executions. With industry and academic efforts underway to build TI devices with 50-100 qubits, our insights have the potential to influence QC hardware in the near-future and accelerate the progress towards practical QC systems.
Sequentially congruent partitions and partitions into squares

In recent work, M. Schneider and the first author studied a curious class of integer partitions called “sequentiallyc congruent” partitions: the mth part is congruent to the $$(m+1)$$ th part modulo m, with the smallest part congruent to zero modulo the number of parts. Let $$p_{{\mathcal {S}}}(n)$$ be the number of sequentially congruent partitions of n, and let $$p_{\square }(n)$$ be the number of partitions of n wherein all parts are squares. In this note we prove bijectively, for all $$n\ge 1,$$ that $$p_{{\mathcal {S}}}(n) = p_{\square }(n).$$ Our proof naturally extends to show other exotic classes of partitions of n are in bijection with certain partitions of n into kth powers.
Finite symmetric tensor categories with the Chevalley property in characteristic 2

We prove an analog of Deligne’s theorem for finite symmetric tensor categories &#x1d49e; with the Chevalley property over an algebraically closed field k of characteristic 2. Namely, we prove that every su...
Distributed Stabilization of Two Interdependent Markov Jump Linear Systems With Partial Information

In this letter, we study the stabilization of two interdependent Markov jump linear systems (MJLSs) with partial information, where the interdependency arises as the transition of the mode of one system depends on the states of the other system. First, we formulate a framework for the two interdependent MJLSs to capture the interactions between various entities in the system, where the modes of the system cannot be observed directly. Instead, a signal which contains information of the modes can be observed. Then, depending on the scope of the available system state information (global or local), we design centralized and distributed controllers, respectively, that can stochastically stabilize the overall interdependent MJLS. In addition, we derive the sufficient stabilization conditions for the system under both types of information structure. Finally, we use a numerical example to illustrate the effectiveness of the designed controllers.
Is Deadline Oblivious Scheduling Efficient for Controlling Real-Time Traffic in Cellular Downlink Systems?

The emergence of bandwidth-intensive latency-critical traffic in 5G Networks, such as Virtual Reality and Cloud Gaming, has motivated interest in wireless resource allocation problems for flows with hard-deadlines. Attempting to solve this problem brings about the following two key challenges: (i) The flow arrival and the wireless channel state information are not known to the Base Station (BS) apriori, thus, the allocation decisions need to be made in an online manner. (ii) Resource allocation algorithms that attempt to maximize a reward in the wireless setting will likely be unfair, causing unacceptable service for some users. In the first part of this paper, we model the problem of allocating resources to deadline-sensitive traffic as an online convex optimization problem, where the BS acquires a per-request reward that depends on the amount of traffic transmitted within the required deadline. We address the question of whether we can efficiently solve that problem with low complexity. In particular, whether we can design a constant-competitive scheduling algorithm that is oblivious to requests’ deadlines. To this end, we propose a primal-dual Deadline-Oblivious (DO) algorithm, and show it is approximately 3.6-competitive. Furthermore, we show via simulations that our algorithm tracks the prescient offline solution very closely, significantly outperforming several algorithms that were previously proposed. Our results demonstrate that even though a scheduler may not know the deadlines of each flow, it can still achieve good theoretical and empirical performance. In the second part, we impose a stochastic constraint on the allocation, requiring a guarantee that each user achieves a certain timely throughput (amount of traffic delivered within the deadline over a period of time). We propose a modified version of our algorithm, called the Long-term Fair Deadline Oblivious (LFDO) algorithm for that setup. We combine the Lyapunov framework for stochastic optimization with the Primal-Dual analysis of online algorithms, to show that LFDO retains the high-performance of DO, while satisfying the long-term stochastic constraints.