cssi/SciSciGPT-SciSciNet huggingface dataset:
Structured prediction energy networks

We introduce structured prediction energy networks (SPENs), a flexible framework for structured prediction. A deep architecture is used to define an energy function of candidate labels, and then predictions are produced by using back-propagation to iteratively optimize the energy with respect to the labels. This deep architecture captures dependencies between labels that would lead to intractable graphical models, and performs structure learning by automatically learning discriminative features of the structured output. One natural application of our technique is multi-label classification, which traditionally has required strict prior assumptions about the interactions between labels to ensure tractable learning and prediction. We are able to apply SPENs to multi-label problems with substantially larger label sets than previous applications of structured prediction, while modeling high-order interactions using minimal structural assumptions. Overall, deep learning provides remarkable tools for learning features of the inputs to a prediction problem, and this work extends these techniques to learning features of structured outputs. Our experiments provide impressive performance on a variety of benchmark multi-label classification tasks, demonstrate that our technique can be used to provide interpretable structure learning, and illuminate fundamental trade-offs between feed-forward and iterative structured prediction.
Near Equality in the Riesz-Sobolev Inequality

The Riesz-Sobolev inequality provides a sharp upper bound for a trilinear expression involving convolution of indicator functions of sets. Equality is known to hold only for indicator functions of appropriately situated intervals. We characterize ordered triples of subsets of ℝ1 that nearly realize equality, with quantitative bounds of power law form with the optimal exponent.
Spectral edge behavior for eventually monotone Jacobi and Verblunsky coefficients

We consider Jacobi matrices with eventually increasing sequences of diagonal and off-diagonal Jacobi parameters. We describe the asymptotic behavior of the subordinate solution at the top of the essential spectrum, and the asymptotic behavior of the spectral density at the top of the essential spectrum. In particular, allowing on both diagonal and off-diagonal Jacobi parameters perturbations of the free case of the form $- \sum_{j=1}^J c_j n^{-\tau_j} + o(n^{-\tau_1-1})$ with $0 0$, we find the asymptotic behavior of the $\log$ of spectral density to order $O(\log(2-x))$ as $x$ approaches $2$. Apart from its intrinsic interest, the above results also allow us to describe the asymptotics of the spectral density for orthogonal polynomials on the unit circle with real-valued Verblunsky coefficients of the same form.
Deep CORAL: Correlation Alignment for Deep Domain Adaptation

Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input distributions. Domain adaptation algorithms have been proposed to compensate for the degradation in performance due to domain shift. In this paper, we address the case when the target domain is unlabeled, requiring unsupervised adaptation. CORAL [18] is a simple unsupervised domain adaptation method that aligns the second-order statistics of the source and target distributions with a linear transformation. Here, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (Deep CORAL). Experiments on standard benchmark datasets show state-of-the-art performance. Our code is available at: https://github.com/VisionLearningGroup/CORAL.
Superexponential estimates and weighted lower bounds for the square function

Author(s): Ivanisvili, Paata; Treil, Sergei | Abstract: We prove the following superexponential distribution inequality: for any integrable $g$ on $[0,1)^{d}$ with zero average, and any $\lambdag0$ \[ |\{ x \in [0,1)^{d} \; :\; g \geq\lambda \}| \leq e^{- \lambda^{2}/(2^{d}\|S(g)\|_{\infty}^{2})}, \] where $S(g)$ denotes the classical dyadic square function in $[0,1)^{d}$. The estimate is sharp when dimension $d$ tends to infinity in the sense that the constant $2^{d}$ in the denominator cannot be replaced by $C2^{d}$ with $01$ they work with a special square function $S_\infty$, and their result does not imply the estimates for the classical square function. Using good $\lambda$ inequalities technique we then obtain unweighted and weighted $L^p$ lower bounds for $S$; to get the corresponding good $\lambda$ inequalities we need to modify the classical construction. We also show how to obtain our superexponential distribution inequality (although with worse constants) from the weighted $L^2$ lower bounds for $S$, obtained in [5].
Surface plastic flow in polishing of rough surfaces.

We present experimental evidence for a new mechanism for how smooth surfaces emerge during repetitive sliding contacts, as in polishing. Electron microscopy observations of Ti-6Al-4V surface with a spherical asperity structure—realized via additive manufacturing—during successive polishing stages suggest that asperity-abrasive contacts exhibit viscous behavior, where the asperity material flows in the form of thin (1–10 μm) fluid-like layers. Subsequent bridging of these layers among neighboring asperities results in progressive surface smoothening. Using analytical asperity-abrasive contact temperature modeling and microstructural characterization, we show that the sliding contacts encounter flash temperatures of the order of 700–900 K which is in the range of the dynamic recrystallization temperature of the material considered, thus supporting the experimental observations. Besides providing a new perspective on the long-held mechanism of polishing, our observations provide a novel approach based on graph theory to quantitatively characterize the evolution of surface morphology. Results suggest that the graph representation offers a more efficient measure to characterize the surface morphology emerging at various stages of polishing. The research findings and observations are of broad relevance to the understanding of plastic flow behavior of sliding contacts ubiquitous in materials processing, tribology, and natural geological processes as well as present unique opportunities to tailor the microstructures by controlling the thermomechanics of the asperity-abrasive contacts.
The recovery of causal poly-trees from statistical data

Poly-trees are singly connected causal networks in which variables may arise from multiple causes. This paper develops a method of recovering poly-trees from empirically measured probability distributions of pairs of variables. The method guarantees that, if the measured distributions are generated by a causal process structured as a poly-tree then the topological structure of such tree can be recovered precisely and, in addition, the causal directionality of the branches can be determined up to the maximum extent possible. The method also pinpoints the minimum (if any) external semantics required to determine the causal relationships among the variables considered.
Timing observations of diffusions

This article addresses when to observe systems that evolve over time. Many processes in biology and chemistry have been modeled by nonlinear stochastic differential equations (SDEs). Obtaining measurements from these systems can be expensive, so experimenters may wish to choose observation times that are as informative as possible. In this paper, we consider Ito diffusions (a class of SDEs) specified by some $$\theta \in {\mathbb {R}}$$, and we assume that we are permitted to observe their sample paths only n times before a terminal time $$\tau < \infty $$. We propose a policy for timing these observations in order to optimally estimate $$\theta $$. Our policy maximizes the expected Fisher information for $$\theta $$ carried by the observations, and it is adaptive, meaning the policy uses data obtained from earlier observations. In numerical studies, this design reduces the variation of estimated parameters by as much as 75% relative to observations spaced uniformly in time. Our policy depends on the value of the parameter being estimated, so we also discuss strategies for incorporating Bayesian priors over $$\theta $$. These methods are illustrated by example problems from pharmacokinetics and experimental ecology.
On normalized Horn systems

We characterize the (regular) holonomicity of Horn systems of differential equations under a hypothesis that captures the most widely studied classical hypergeometric systems.
Trustee affiliation and servicer oversight: Evidence from CMBS markets

We study the effect of a change in trustees' incentives to monitor servicers in CMBS markets. Our identification strategy rests on variation in servicer-trustee affiliation that arises from mergers. We present evidence that affiliation is associated with excessive servicer advances on delinquent loans that are not in the interest of bondholders. We observe that a servicer-trustee affiliation affects the waterfall cash flows of senior, mezzanine, and junior bond-tranches. Additionally, we find that affiliation increases the loss rate by $0.05 or $0.07 per dollar of outstanding debt and estimate an economic impact of $5.5 billion in market-wide liquidation losses.
There's Much Yet to be Done: Diverse Perspectives on HPV Vaccination.

It has been over 10 y since the first HPV vaccines were licensed and introduced in a number of countries around the world. As only the second vaccine (after hepatitis B) that prevents an infection ...
A Megawatt-Scale Medium-Voltage High-Efficiency High Power Density “SiC+Si” Hybrid Three-Level ANPC Inverter for Aircraft Hybrid-Electric Propulsion Systems

A hybrid-electric propulsion system is an enabling technology to make the aircraft more fuel saving, quieter, and lower carbide emission. In this article, a megawatt (MW) scale power inverter based on a three-level active neutral-point-clamped (3L-ANPC) topology will be developed. To achieve high efficiency, the switching devices operating at carrier frequency in the power converter are configured by the emerging silicon carbide (SiC) metal–oxide–semiconductor field-effect transistors, while the conventional silicon (Si) insulated-gate bipolar transistors are selected for switches operating at the fundamental output frequency. To obtain high power density, dc bus voltage is increased from the conventional 270 V to medium voltage of 2.4 kV to reduce cable weight. Also, unlike the traditional 400 Hz dominated aircraft ac systems, the rated fundamental output frequency here is boosted to 1.4 kHz to drive the high-speed motor, which helps further to reduce the motor weight. Main hardware development and control modulation strategies are presented. Experimental results are presented to verify the performance of this MW-scale medium-voltage “SiC+Si” hybrid 3L-ANPC inverter. It is shown that the 1-MW 3L-ANPC inverter can achieve a high efficiency of 99% and a high power density of 12 kVA/kg.
Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant

In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. Earth’s biodiversity is increasingly under threat by sources of anthropogenic change (e.g. resource extraction, land use change, and climate change) and surveying animal populations is critical for developing conservation strategies. However, manually monitoring tropical forests or deep oceans is intractable. For species that communicate acoustically, researchers have argued for placing audio recorders in the habitats as a costeffective and non-invasive method, a strategy known as passive acoustic monitoring (PAM). In collaboration with conservation efforts, we construct a large labeled dataset of passive acoustic recordings of the African Forest Elephant via crowdsourcing, compromising thousands of hours of recordings in the wild. Using state-of-the-art techniques in artificial intelligence we improve upon previously proposed methods for passive acoustic monitoring for classification and segmentation. In real-time detection of elephant calls, network bandwidth quickly becomes a bottleneck and efficient ways to compress the data are needed. Most audio compression schemes are aimed at human listeners and are unsuitable for low-frequency elephant calls. To remedy this, we provide a novel end-to-end differentiable method for compression of audio signals that can be adapted to acoustic monitoring of any species and dramatically improves over naive coding strategies.
Active Regression via Linear-Sample Sparsification

We present an approach that improves the sample complexity for a variety of curve fitting problems, including active learning for linear regression, polynomial regression, and continuous sparse Fourier transforms. In the active linear regression problem, one would like to estimate the least squares solution $\beta^*$ minimizing $\|X\beta - y\|_2$ given the entire unlabeled dataset $X \in \mathbb{R}^{n \times d}$ but only observing a small number of labels $y_i$. We show that $O(d)$ labels suffice to find a constant factor approximation $\tilde{\beta}$: \[ \mathbb{E}[\|X\tilde{\beta} - y\|_2^2] \leq 2 \mathbb{E}[\|X \beta^* - y\|_2^2]. \] This improves on the best previous result of $O(d \log d)$ from leverage score sampling. We also present results for the \emph{inductive} setting, showing when $\tilde{\beta}$ will generalize to fresh samples; these apply to continuous settings such as polynomial regression. Finally, we show how the techniques yield improved results for the non-linear sparse Fourier transform setting.
Evidence of Counter-Streaming Ions near the Inner Pole of the HERMeS Hall Thruster

NASA is continuing the development of a 12.5-kW Hall thruster system to support a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Test Unit (ETU). A new laser-induced fluorescence diagnostic was developed to obtain data for validating the Hall thruster models and for comparing the behavior of the ETU and TDU. Analysis of TDU LIF data obtained during initial deployment of the diagnostics revealed evidence of two streams of ions moving in opposite directions near the inner front pole. These two streams of ions were found to intersect the downstream surface of the front pole at large oblique angles. This data points to a possible explanation for why the erosion rate of polished pole covers were observed to decrease over the course of several hundred hours of thruster operation.
A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection

Drowsiness can put lives of many drivers and workers in danger. It is important to design practical and easy-to-deploy real-world systems to detect the onset of drowsiness. In this paper, we address early drowsiness detection, which can provide early alerts and offer subjects ample time to react. We present a large and public real-life dataset of 60 subjects, with video segments labeled as alert, low vigilant, or drowsy. This dataset consists of around 30 hours of video, with contents ranging from subtle signs of drowsiness to more obvious ones. We also benchmark a temporal model for our dataset, which has low computational and storage demands. The core of our proposed method is a Hierarchical Multiscale Long Short-Term Memory (HM-LSTM) network, that is fed by detected blink features in sequence. Our experiments demonstrate the relationship between the sequential blink features and drowsiness. In the experimental results, our baseline method produces higher accuracy than human judgment.
Propagation of Error and the Reliability of Global Air Temperature Projections

The reliability of general circulation climate model (GCM) global air temperature projections is evaluated for the first time, by way of propagation of model calibration error. An extensive series of demonstrations show that GCM air temperature projections are just linear extrapolations of fractional greenhouse gas (GHG) forcing. Linear projections are subject to linear propagation of error. A directly relevant GCM calibration metric is the annual average 12.1% error in long-wave cloud forcing (LWCF) produced within CMIP5 climate models. This error is strongly pair-wise correlated across models, implying a source in deficient theory. Climate model LWCF error introduces an annual average ±4 Wm-2 uncertainty into the simulated tropospheric thermal energy flux. This annual ±4 Wm-2 simulation uncertainty is 114 larger than the annual average ~0.035 Wm-2 change in tropospheric thermal energy flux produced by increasing GHG forcing since 1979. Tropospheric thermal energy flux is the determinant of global air temperature. Uncertainty in simulated tropospheric thermal energy flux imposes uncertainty on projected air temperature. Propagation of LWCF thermal energy flux error through the historically relevant 1988 projections of GISS Model II scenarios A, B, and C, the IPCC SRES scenarios CCC, B1, A1B, and A2, and the RCP scenarios of the 2013 IPCC Fifth Assessment Report, uncovers a ±15 C uncertainty in air temperature at the end of a centennial-scale projection. Analogously large but previously unrecognized uncertainties must therefore exist in all the past and present air temperature projections and hindcasts of even advanced climate models. The unavoidable conclusion is that an anthropogenic air temperature signal cannot have been, nor presently can be, evidenced in climate observables.
Antigen-specific immune decoys intercept and exhaust autoimmunity to prevent disease.

Abstract Relapsing-remitting patterns of many autoimmune diseases such as multiple sclerosis (MS) are perpetuated by a recurring circuit of adaptive immune cells that amplify in secondary lymphoid organs (SLOs) and traffic to compartments where antigen is abundant to elicit damage. Some of the most effective immunotherapies impede the migration of immune cells through this circuit, however, broadly suppressing immune cell migration can introduce life-threatening risks for patients. We developed antigen-specific immune decoys (ASIDs) to mimic tissues targeted in autoimmunity and selectively intercept autoimmune cells to preserve host tissue. Using Experimental Autoimmune Encephalomyelitis (EAE) as a model, we conjugated autoantigen PLP139-151 to a microporous collagen scaffold. By subcutaneously implanting ASIDs after induction but prior to the onset of symptoms, mice were protected from paralysis. ASID implants were rich with autoimmune cells, however, reactivity to cognate antigen was substantially diminished and apoptosis was prevalent. ASID-implanted mice consistently exhibited engorged spleens when disease normally peaked. In addition, splenocyte antigen-presenting cells were highly activated in response to PLP rechallenge, but CD3+ and CD19 + effector subsets were significantly decreased, suggesting exhaustion. ASID-implanted mice never developed EAE relapse symptoms even though the ASID material had long since degraded, suggesting exhausted autoimmune cells did not recover functionality. Together, data suggested ASIDs were able to sequester and exhaust immune cells in an antigen-specific fashion, thus offering a compelling approach to inhibit the migration circuit underlying autoimmunity.
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses

We study the problem of estimating a nonparametric probability distribution under a family of losses called Besov IPMs. This family is quite large, including, for example, L^p distances, total variation distance, and generalizations of both Wasserstein (earthmover's) and Kolmogorov-Smirnov distances. For a wide variety of settings, we provide both lower and upper bounds, identifying precisely how the choice of loss function and assumptions on the data distribution interact to determine the mini-max optimal convergence rate. We also show that, in many cases, linear distribution estimates, such as the empirical distribution or kernel density estimator, cannot converge at the optimal rate. These bounds generalize, unify, or improve on several recent and classical results. Moreover, IPMs can be used to formalize a statistical model of generative adversarial networks (GANs). Thus, we show how our results imply bounds on the statistical error of a GAN, showing, for example, that, in many cases, GANs can strictly outperform the best linear estimator.
The Structure of the Grothendieck Rings of Wreath Product Deligne Categories and their Generalisations

Given a tensor category $\mathcal{C}$ over an algebraically closed field of characteristic zero, we may form the wreath product category $\mathcal{W}_n(\mathcal{C})$. It was shown in \cite{Ryba} that the Grothendieck rings of these wreath product categories stabilise in some sense as $n \to \infty$. The resulting "limit" ring, $\mathcal{G}_\infty^{\mathbb{Z}}(\mathcal{C})$, is isomorphic to the Grothendieck ring of the wreath product Deligne category $S_t(\mathcal{C})$ as defined by \cite{Mori}. This ring only depends on the Grothendieck ring $\mathcal{G}(\mathcal{C})$. Given a ring $R$ which is free as a $\mathbb{Z}$-module, we construct a ring $\mathcal{G}_\infty^{\mathbb{Z}}(R)$ which specialises to $\mathcal{G}_\infty^{\mathbb{Z}}(\mathcal{C})$ when $R = \mathcal{G}(\mathcal{C})$. We give a description of $\mathcal{G}_\infty^{\mathbb{Z}}(R)$ using generators very similar to the basic hooks of \cite{Nate}. We also show that $\mathcal{G}_\infty^{\mathbb{Z}}(R)$ is a $\lambda$-ring wherever $R$ is, and that $\mathcal{G}_\infty^{\mathbb{Z}}(R)$ is (unconditionally) a Hopf algebra. Finally we show that $\mathcal{G}_\infty^{\mathbb{Z}}(R)$ is isomorphic to the Hopf algebra of distributions on the formal neighbourhood of the identity in $(W\otimes_{\mathbb{Z}} R)^\times$, where $W$ is the ring of Big Witt Vectors.
Optimal Price of Anarchy in Cost-Sharing Games

The design of distributed algorithms is central to the study of multiagent systems control. In this paper, we consider a class of combinatorial cost-minimization problems and propose a framework for designing distributed algorithms with a priori performance guarantees that are near-optimal. We approach this problem from a game-theoretic perspective, assigning agents cost functions such that the equilibrium efficiency (price of anarchy) is optimized. Once agents' cost functions have been specified, any algorithm capable of computing a Nash equilibrium of the system inherits a performance guarantee matching the price of anarchy. Towards this goal, we formulate the problem of computing the price of anarchy as a tractable linear program. We then present a framework for designing agents' local cost functions in order to optimize for the worst-case equilibrium efficiency. Finally, we investigate the implications of our findings when this framework is applied to systems with convex, nondecreasing costs.
Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics

For half a century population genetics studies have put type II restriction endonucleases to work. Now, coupled with massively-parallel, short-read sequencing, the family of RAD protocols that wields these enzymes has generated vast genetic knowledge from the natural world. Here, we describe the first software natively capable of using paired-end sequencing to derive short contigs from de novo RAD data. Stacks version 2 employs a de Bruijn graph assembler to build and connect contigs from forward and reverse reads for each de novo RAD locus, which it then uses as a reference for read alignments. The new architecture allows all the individuals in a metapopulation to be considered at the same time as each RAD locus is processed. This enables a Bayesian genotype caller to provide precise SNPs, and a robust algorithm to phase those SNPs into long haplotypes, generating RAD loci that are 400-800 bp in length. To prove its recall and precision, we tested the software with simulated data and compared reference-aligned and de novo analyses of three empirical data sets. Our study shows that the latest version of Stacks is highly accurate and outperforms other software in assembling and genotyping paired-end de novo data sets.
In situ Auger electron spectroscopy of complex oxide surfaces grown by pulsed laser deposition

The authors report in situ Auger electron spectroscopy (AES) of the surfaces of complex oxide thin films grown by pulsed laser deposition (PLD). The authors demonstrate the utility of the technique in studying elemental composition by collecting characteristic Auger spectra of elements from samples such as complex oxide thin films and single crystals. In the case of thin films, AES studies can be performed with single unit cell precision by monitoring thickness during deposition with reflection high energy electron diffraction. The authors address some of the challenges in achieving in situ and real-time AES studies on complex oxide thin films grown by PLD. Sustained layer-by-layer PLD growth of a CaTiO3/LaMnO3 superlattice allows depth-resolved elemental composition analysis during the growth process. The evolution of the Auger spectra of elements from individual layers was used to perform compositional analysis with monolayer-depth resolution.The authors report in situ Auger electron spectroscopy (AES) of the surfaces of complex oxide thin films grown by pulsed laser deposition (PLD). The authors demonstrate the utility of the technique in studying elemental composition by collecting characteristic Auger spectra of elements from samples such as complex oxide thin films and single crystals. In the case of thin films, AES studies can be performed with single unit cell precision by monitoring thickness during deposition with reflection high energy electron diffraction. The authors address some of the challenges in achieving in situ and real-time AES studies on complex oxide thin films grown by PLD. Sustained layer-by-layer PLD growth of a CaTiO3/LaMnO3 superlattice allows depth-resolved elemental composition analysis during the growth process. The evolution of the Auger spectra of elements from individual layers was used to perform compositional analysis with monolayer-depth resolution.
Bridge burning and escape routes

Thomas Schelling (Arms and influence, Yale University Press, New Haven, 1966) cites bridge burning as a method of commitment. While such a commitment can increase the chances of success in a conflict, it generally will lower one’s payoff if the conflict is lost. I use a contest framework and establish conditions under which such commitment can raise a player’s expected payoff. The comparative static effects of bridge burning never are favorable at an interior equilibrium, but the strategy may induce the opponent to concede the contest’s outcome. I also analyze the strategy, associated with Sun Tzu, of leaving an escape path open for one’s enemy. This strategy always succeeds at an interior equilibrium and raises the expected payoffs of both players. Under certain parameter restrictions, leaving an open escape path also has the potential of inducing the opponent to concede the contest. A special case of the model is offered to explain why a group vulnerable to a wealth transfer might prefer a less efficient tax system to a more efficient system.
Accounting Research: Past, Present, and Future

This paper begins with a description of the accounting research environment prior to, and shortly following, the appearance of Abacus in 1965. During this period, the approach to accounting was predominantly normative in focus, but also reflected historical approaches, as researchers grappled with the accounting issues faced by practising accountants and bodies that established accounting principles. The 1960s witnessed the beginning of a major change in the interests and approach of accounting researchers. Articles increasingly reflected a decline in reliance on the normative approach, accompanied by an increase in empirical analyses. The new focus introduced the ideas and concepts of several sister disciplines, including the social sciences, notably cognitive psychology and mathematics, particularly statistics, into accounting research. This era, which is still with us today, stressed theory, mathematical modelling, and, importantly, statistical testing. Simultaneously, the new directions gradually abandoned the contributions of normative approaches and diminished the interest in history, both of which had enlightened the problems of practice that previously held centre stage. We examine a broad sample of research articles to inform our discussion and analysis, and then we comment on some of the limitations of the new data-driven approaches embedded in current research efforts. We conclude with ten recommendations for accounting researchers to consider as they tackle the complex issue of increasing the relevance of our efforts in the future. We hope that these recommendations, if adopted, will increase the academic relevance of academic research to the problems facing decision makers beyond the academic community.
Detecting Abnormalities in Resting-State Dynamics: An Unsupervised Learning Approach

Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain. While many rs-fMRI studies have focused on static measures of functional connectivity, there has been a recent surge in examining the temporal patterns in these data. In this paper, we explore two strategies for capturing the normal variability in resting-state activity across a healthy population: (a) an autoencoder approach on the rs-fMRI sequence, and (b) a next frame prediction strategy. We show that both approaches can learn useful representations of rs-fMRI data and demonstrate their novel application for abnormality detection in the context of discriminating autism patients from healthy controls.
DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation. Overall, DeepImpute yields better accuracy than other six publicly available scRNA-seq imputation methods on experimental data, as measured by the mean squared error or Pearson’s correlation coefficient. DeepImpute is an accurate, fast, and scalable imputation tool that is suited to handle the ever-increasing volume of scRNA-seq data, and is freely available at https://github.com/lanagarmire/DeepImpute .
Linear Temporal Logic Satisfaction in Adversarial Environments Using Secure Control Barrier Certificates

This paper studies the satisfaction of a class of temporal properties for cyber-physical systems (CPSs) over a finite-time horizon in the presence of an adversary, in an environment described by discrete-time dynamics. The temporal logic specification is given in \(safe-LTL_F\), a fragment of linear temporal logic over traces of finite length. The interaction of the CPS with the adversary is modeled as a two-player zero-sum discrete-time dynamic stochastic game with the CPS as defender. We formulate a dynamic programming based approach to determine a stationary defender policy that maximizes the probability of satisfaction of a \(safe-LTL_F\) formula over a finite time-horizon under any stationary adversary policy. We introduce secure control barrier certificates (S-CBCs), a generalization of barrier certificates and control barrier certificates that accounts for the presence of an adversary, and use S-CBCs to provide a lower bound on the above satisfaction probability. When the dynamics of the evolution of the system state has a specific underlying structure, we present a way to determine an S-CBC as a polynomial in the state variables using sum-of-squares optimization. An illustrative example demonstrates our approach.
Spinning reserve planning under large wind power penetration considering carbon dioxide emission

With the increasing international concern about carbon dioxide (CO2) emissions, it is no longer adequate to plan spinning reserves taking only the lowest production cost into account. Therefore, the purpose of this paper was to present an optimal spinning reserve planning scheme considering CO2 emissions and the variations in the system reliability index while incorporating renewable energy in the power dispatch selection. First, two different objective scheduling modes of production costs and CO2 emissions were introduced, and then solved the unit commitment problem in different spinning reserves and wind penetrations. In order to obtain a higher probability of the global optimum, the simulation process used appropriate coding techniques to meet the constraints. Secondly, the Monte Carlo simulation to acquire the reliability assessment of these unit commitment solutions was utilized. The salient feature of the simulation strategy was that the decision-maker could make a proper spinning reserve level involving environmental, economic and reliability factors simultaneously. Finally, taking the 27-unit system of the Taiwan power system as a test example, the simulation results showed that the proposed approach could indeed assist the power industry in setting the value of the spinning reserve.
Museums as Weavers of the Invisible Strings that Connect us

This paper reviews the scientific research literature on changes over time in social connection and self-focus in the United States. It discusses the implications of these changes for the need for ...
Using a barcoded AAV capsid library to select for clinically relevant gene therapy vectors

While gene transfer using recombinant adeno-associated viral (rAAV) vectors has shown success in some clinical trials, there remain many tissues that are not well transduced. Because of the recent success in reprogramming islet-derived cells into functional β cells in animal models, we constructed 2 highly complex barcoded replication competent capsid shuffled libraries and selected for high-transducing variants on primary human islets. We describe the generation of a chimeric AAV capsid (AAV-KP1) that facilitates transduction of primary human islet cells and human embryonic stem cell-derived β cells with up to 10-fold higher efficiency compared with previously studied best-in-class AAV vectors. Remarkably, this chimeric capsid also enabled transduction of both mouse and human hepatocytes at very high levels in a humanized chimeric mouse model, thus providing a versatile vector that has the potential to be used in both preclinical testing and human clinical trials for liver-based diseases and diabetes.
Logic Built-In Self-Test for Core-Based Designs on System-on-a-Chip

A system-on-a-chip (SoC) built with embedded intellectual property (IP) cores offers attractive methodology design reuse, reconfigurability, and customizability. However, the integration of the design-for-testability (DfT) structures of the IP cores in these complex SoCs presents daunting challenges to designers and ultimately affects the time-to-market goals. In this paper, we introduce a design methodology to reduce the time to market by taking core test data from the design environment and automatically generating DfT structures that can easily be integrated into the SoC. A novel automated synthesis methodology to generate an SoC built-in self-test (BIST) to test the IP and custom logic cores is proposed. The proposed technique, i.e., NonExclusive Xor Test of 2D linear feedback shift register (NEXT 2D LFSR), is modeled after the principle of configurable 2D LFSR design, which generates a deterministic sequence of test vectors for random-vector-resistant faults and then random test vectors for random-vector-detectable faults. The basis of this method is to explore the design solution space for optimal 2D LFSR while embedding the test patterns by nonexclusively considering xor gates as the conventional LFSR does but including a simple logic solution for minimal hardware optimization. Moreover, the proposed approach is capable of optimizing the 2D LFSRs with consideration of the don't-care bits in the incompletely specified test patterns.
The national science foundation and its quest for tomorrow's academic leaders

This paper presents the author's experience as a recipient of a Faculty Early Career Development (CAREER) Program Award from the National Science Foundation (NSF) and as a reviewer of several NSF proposals, including CAREER proposals. The paper also provides some ideas that, in the author's opinion, might be useful to a new faculty member who is planning to submit a CAREER proposal to the NSF. This paper is primarily intended for such faculty members, though some of the ideas contained here may be of value to anyone preparing an NSF proposal for the first time.
Nobody Knew How Complicated: Constraining The President's Power To (Re)Shape Health Reform.

Beginning on inauguration day, President Trump has attempted an executive repeal of the Affordable Care Act. In doing so, he has tested the limits of presidential power. He has challenged the force...
D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4 (GC4), which focused on predicting the binding poses and affinity ranking for compounds targeting the $$\beta$$-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 A RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and DOCK 6 and found that using a reference ligand to guide the docking process is a better strategy for pose prediction and helped HYBRID to perform better here. We also conducted end-point free energy estimates on molecules dynamics based ensembles of protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R GC4 are: (i) the generation of the macrocyclic conformers is a key step for successful pose prediction, (ii) the protonation states of the BACE-1 binding site should be treated carefully, (iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and (iv) the MM-GBSA method does not perform well at predicting protein–ligand binding affinities here.
Secure Quantum Extraction Protocols

Knowledge extraction, typically studied in the classical setting, is at the heart of several cryptographic protocols. The prospect of quantum computers forces us to revisit the concept of knowledge extraction in the presence of quantum adversaries.
Optimal Search Segmentation Mechanisms for Online Platform Markets

Online platforms, such as Airbnb, hotels.com, Amazon, Uber and Lyft, can control and optimize many aspects of product search to improve the efficiency of marketplaces. Here we focus on a common model, called the discriminatory control model, where the platform chooses to display a subset of sellers who sell products at prices determined by the market and a buyer is interested in buying a single product from one of the sellers. Under the commonly-used model for single product selection by a buyer, called the multinomial logit model, and the Bertrand game model for competition among sellers, we show the following result: to maximize social welfare, the optimal strategy for the platform is to display all products; however, to maximize revenue, the optimal strategy is to only display a subset of the products whose qualities are above a certain threshold. This threshold depends on the quality of all products, and can be computed in linear time in the number of products.
Introduction to flavour physics

We give a brief introduction to ﬂavour physics. The ﬁrst part covers the ﬂavour structure of the Standard Model, how the Kobayashi-Maskawa mechanisem is tested and provides examples of searches for new physics using ﬂavour observables, such as meson mixing and rare decays. In the second part we give a brief overview of the recent ﬂavour anomalies and how the Higgs can act as a new ﬂavour probe.
Unsupervised identification of the internal states that shape natural behavior.

Internal states shape stimulus responses and decision-making, but we lack methods to identify them. To address this gap, we developed an unsupervised method to identify internal states from behavioral data and applied it to a dynamic social interaction. During courtship, Drosophila melanogaster males pattern their songs using feedback cues from their partner. Our model uncovers three latent states underlying this behavior and is able to predict moment-to-moment variation in song-patterning decisions. These states correspond to different sensorimotor strategies, each of which is characterized by different mappings from feedback cues to song modes. We show that a pair of neurons previously thought to be command neurons for song production are sufficient to drive switching between states. Our results reveal how animals compose behavior from previously unidentified internal states, which is a necessary step for quantitative descriptions of animal behavior that link environmental cues, internal needs, neuronal activity and motor outputs. Animals compose behaviors from both sensory cues and internal states. Calhoun et al. develop an unsupervised modeling framework to identify the dynamic internal states that shape social interactions in Drosophila and use the model to identify neurons that modulate the male’s internal state.
Sharp lower bounds for the Widom factors on the real line

Abstract We derive lower bounds for the L p ( μ ) norms of monic extremal polynomials with respect to compactly supported probability measures μ. We obtain a sharp universal lower bound for all 0 p ∞ and all measures in the Szegő class and an improved lower bound on L 2 ( μ ) norm for several classes of orthogonal polynomials including Jacobi polynomials, isospectral torus of a finite gap set, and orthogonal polynomials with respect to the equilibrium measure of an arbitrary non-polar compact subset of R .
Hippocampal state transitions at the boundaries between trial epochs

The hippocampus encodes distinct contexts with unique patterns of activity. Representational shifts with changes in context, referred to as remapping, have been extensively studied. However, less is known about transitions between representations. In this study, we leverage a large dataset of neuronal recordings taken while rats performed an olfactory memory task with a predictable temporal structure involving trials and intertrial intervals (ITIs), separated by salient boundaries at the trial start and trial end. We found that trial epochs were associated with stable hippocampal representations despite moment-to-moment variability in stimuli and behavior. Representations of trial and ITI epochs were far more distinct than spatial factors would predict and the transitions between the two were abrupt. The boundary was associated with a large spike in multiunit activity, with many individual cells specifically active at the start or end of each trial. Both epochs and boundaries were encoded by hippocampal populations, and these representations carried information on orthogonal axes readily identified using principal component analysis. We suggest that the hippocampus orthogonalizes representations of the trial and ITI epochs and the activity spike at trial boundaries might serve to drive hippocampal activity from one stable state to the other.
Unequal interactions between alcohol and nicotine co-consumption: suppression and enhancement of concurrent drug intake.

Alcohol and nicotine addiction are prevalent conditions that co-occur. Despite the prevalence of co-use, factors that influence the suppression and enhancement of concurrent alcohol and nicotine intake are largely unknown. Our goals were to assess how nicotine abstinence and availability influenced concurrent alcohol consumption and to determine the impact of quinine adulteration of alcohol on aversion-resistant alcohol consumption and concurrent nicotine consumption. Male and female C57BL/6J mice voluntarily consumed unsweetened alcohol, nicotine, and water in a chronic 3-bottle choice procedure. In experiment 1, nicotine access was removed for 1 week and re-introduced the following week, while the alcohol and water bottles remained available at all times. In experiment 2, quinine (100–1000 μM) was added to the 20% alcohol bottle, while the nicotine and water bottles remained unaltered. In experiment 1, we found that alcohol consumption and preference were unaffected by the presence or absence of nicotine access in both male and female mice. In experiment 2a, we found that quinine temporarily suppressed alcohol intake and enhanced concurrent nicotine, but not water, preference in both male and female mice. In experiment 2b, chronic quinine suppression of alcohol intake increased nicotine consumption and preference in female mice without affecting water preference, whereas it increased water and nicotine preference in male mice. Quinine suppression of alcohol consumption enhanced the preference for concurrent nicotine preference in male and female mice, suggesting that mice compensate for the quinine adulteration of alcohol by increasing their nicotine preference.