cssi/SciSciGPT-SciSciNet huggingface dataset:
SPA-GAN: Spatial Attention GAN for Image-to-Image Translation

Image-to-image translation is to learn a mapping between images from a source domain and images from a target domain. In this paper, we introduce the attention mechanism directly to the generative adversarial network (GAN) architecture and propose a novel spatial attention GAN model (SPA-GAN) for image-to-image translation tasks. SPA-GAN computes the attention in its discriminator and use it to help the generator focus more on the most discriminative regions between the source and target domains, leading to more realistic output images. We also find it helpful to introduce an additional feature map loss in SPA-GAN training to preserve domain specific features during translation. Compared with existing attention-guided GAN models, SPA-GAN is a lightweight model that does not need additional attention networks or supervision. Qualitative and quantitative comparison against state-of-the-art methods on benchmark datasets demonstrates the superior performance of SPA-GAN.
A Field Experiment on the Role of Socioemotional Skills and Gender for Hiring in Turkey

A vast literature shows the importance of socioemotional skills in earnings and employment, but whether they matter in getting hired remains unanswered. This study seeks to address this question and further investigates whether socioemotional skill signals in job applicants' resumes have the same value for male and female candidates. In a large-scale randomized audit study, an online job portal in Turkey is used to send fictitious resumes to real job openings, collecting a unique data set that enables investigating different stages of candidate screening. The study finds that socioemotional skills appear to be valued only when an employer specifically asks for such skills in the vacancy ad. When not asked for, however, candidates can face a penalty in the form of lower callback rates. A significant penalty is only observed for women, not for men. The study does not find evidence of other gender differences in the hiring process.
Multiview Supervision By Registration

This paper presents a semi-supervised learning framework to train a keypoint detector using multiview image streams given the limited number of labeled instances (typically <4%). We leverage three self-supervisionary signals in multiview tracking to utilize the unlabeled data: (1) a keypoint in one view can be supervised by other views via epipolar geometry; (2) a keypoint detection must be consistent across time; (3) a visible keypoint in one view is likely to be visible in the adjacent view. We design a new end-to-end network that can propagate these self-supervisionary signals across the unlabeled data from the labeled data in a differentiable manner. We show that our approach outperforms existing detectors including DeepLabCut tailored to the keypoint detection of non-human species such as monkeys, dogs, and mice.
Impact of the North Atlantic Warming Hole on Sensible Weather

AbstractIn future climate projections there is a notable lack of warming in the North Atlantic subpolar gyre, known as the North Atlantic warming hole (NAWH). In a set of large-ensemble atmospheric...
How Economically Significant Are Unused Capacity Costs? A Large-Scale Empirical Analysis

This study uses a confidential Census sample of 151,900 U.S. manufacturing plant-years from 1974 to 2011 to investigate the impact of excluding the cost of unused capacity from key financial indica...
Imaging human blood cells in vivo with oblique back-illumination capillaroscopy

We present a non-invasive, label-free method of imaging blood cells flowing through human capillaries in vivo using oblique back-illumination capillaroscopy (OBC). Green light illumination allows simultaneous phase and absorption contrast, enhancing the ability to distinguish red and white blood cells. Single-sided illumination through the objective lens enables 200 Hz imaging with close illumination-detection separation and a simplified setup. Phase contrast is optimized when the illumination axis is offset from the detection axis by approximately 225 µm when imaging ∼80 µm deep in phantoms and human ventral tongue. We demonstrate high-speed imaging of individual red blood cells, white blood cells with sub-cellular detail, and platelets flowing through capillaries and vessels in human tongue. A custom pneumatic cap placed over the objective lens stabilizes the field of view, enabling longitudinal imaging of a single capillary for up to seven minutes. We present high-quality images of blood cells in individuals with Fitzpatrick skin phototypes II, IV, and VI, showing that the technique is robust to high peripheral melanin concentration. The signal quality, speed, simplicity, and robustness of this approach underscores its potential for non-invasive blood cell counting.
Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images.

Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow OA progression, but at later stages, only an invasive option is available: total knee replacement (TKR). Though a generally successful procedure, only 2/3 of patients who undergo the procedure report their knees feeling “normal” post-operation, and complications can arise that require revision. This necessitates a model to identify a population at higher risk of TKR, particularly at less advanced stages of OA, such that appropriate treatments can be implemented that slow OA progression and delay TKR. Here, we present a deep learning pipeline that leverages MRI images and clinical and demographic information to predict TKR with AUC 0.834 ± 0.036 (p < 0.05). Most notably, the pipeline predicts TKR with AUC 0.943 ± 0.057 (p < 0.05) for patients without OA. Furthermore, we develop occlusion maps for case-control pairs in test data and compare regions used by the model in both, thereby identifying TKR imaging biomarkers. As such, this work takes strides towards a pipeline with clinical utility, and the biomarkers identified further our understanding of OA progression and eventual TKR onset.
Earth Orbiting Satellite Theory

First page of article Keywords: earth orbiting satellite theory; two-body expression
Chapter 16 – Phosphatonins

Phosphorus is essential for the proper function of a multitude of systems including skeletal mineralization, energy homeostasis, enzyme function, and cell membrane integrity. Conditions resulting in chronic hypophosphatemia are associated with abnormal mineralization and manifest as rickets in children or osteomalacia in adults. Classically, the major hormones considered to be involved in phosphorus homeostasis are 1α,25-dihydroxyvitamin D3 and parathyroid hormone. More recently, it has become clear that phosphaturic peptides including fibroblast growth factor-23 (FGF23), secreted frizzled-related protein-4 (sFRP-4), and matrix extracellular phosphoglycoprotein (MEPE) play an important role in phosphate homeostasis and skeletal mineralization. FGF23 and sFRP4 inhibit renal phosphate reabsorption and decrease the formation of active vitamin D metabolites leading to changes in intestinal phosphate absorption and bone mineralization. MEPE has also been shown to induce hyperphosphaturia and may play a significant role in skeletal mineralization. A better understanding of the phosphatonins will provide useful insight into normal and abnormal skeletal biology.
Uncertainty, Flexible Exchange Rates, and Agglomeration

This paper shows that exchange rate volatility promotes agglomeration of economic activity. Under flexible rates, firms prefer to locate in large countries, where they would enjoy lower variability of sales, thus reinforcing concentration of firms in such locations. Empirical evidence on OECD countries demonstrates that for small (large) countries or currency areas, exchange rate volatility has a long-run negative (positive) effect on net inward FDI flows. Two implications arise: creating a currency area fosters agglomeration towards the area and dispersion within the area.
On Topological Properties of the Set of Stabilizing Feedback Gains

This article presents a fairly complete account on various topological and metrical aspects of feedback stabilization for single-input single-output (SISO) continuous and discrete time linear time-invariant (LTI) systems. In particular, we prove that the set of stabilizing output feedback gains for a SISO system with $n$ states have at most $\lceil {\frac{n}{2}}\rceil$ connected components. Furthermore, our analysis yields an algorithm for determining intervals of stabilizing gains for general continuous and discrete LTI systems; the proposed algorithm also computes the number of unstable roots in each unstable interval.
Data Driven Resource Allocation for Distributed Learning.

In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points often belong to the same or similar classes, and more generally, classification rules of high accuracy tend to be "locally simple but globally complex" (Vapnik & Bottou 1993), we propose data dependent dispatching that takes advantage of such structure. We present an in-depth analysis of this model, providing new algorithms with provable worst-case guarantees, analysis proving existing scalable heuristics perform well in natural non worst-case conditions, and techniques for extending a dispatching rule from a small sample to the entire distribution. We overcome novel technical challenges to satisfy important conditions for accurate distributed learning, including fault tolerance and balancedness. We empirically compare our approach with baselines based on random partitioning, balanced partition trees, and locality sensitive hashing, showing that we achieve significantly higher accuracy on both synthetic and real world image and advertising datasets. We also demonstrate that our technique strongly scales with the available computing power.
Functional Modularity Enables the Realization of Smooth and Effective Behavior Integration

In this paper we show how evolving robots can develop behaviors displaying a modular organization characterized by semi-discrete and semi-dissociable sub-behavioral units playing different functions. In our experiments, the development of differentiated behaviors is not realized through the sub-division of the control system into modules and/or through the utilization of differentiated training processes. Instead, it simply originates as a consequence of the adaptive advantage provided by the possibility to display and use functionally specialized behaviors. These are selected by evolution not only with respect to their capability to perform a given sub-function but also with respect to the capability to support smooth and effective transition with other behaviors. This is achieved by having different co- adapted behaviors and by evaluating the variation affecting the behaviors on the basis of the impact they have on the overall performance of the robots. Moreover this process enables the development of t...
A model of supply-chain decisions for resource sharing with an application to ventilator allocation to combat COVID-19

We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top-most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst-case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk-aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp-up consideration can be based on a cost-benefit analysis.
Hermitian manifolds with quasi-negative curvature

In this work, we show that along a particular choice of Hermitian curvature flow, the non-positivity of the first Ricci curvature will be preserved if the initial metric has Griffiths non-positive Chern curvature. If in addition, the first Ricci curvature is negative at a point, then the canonical line bundle is ample.
Historical sources of institutional trajectories in economic development: China, Japan and Korea compared †

Aoki, Masahiko.November, 2012.Historical sources of institutional trajectories in economic development,Article,TokyoAsian Development Bank Institute,ADBI Working Paper Series/no. 397,30
Optimal taxation over the life cycle

Abstract We derive the optimal labor income tax schedule for a life cycle model with deterministic productivity variation and complete asset markets. An individual chooses whether and how much to work at each date. The government must finance a given expenditure and does not have access to lump sum taxation. We develop a solution method that uses the primal approach to solve for the optimal non-linear tax function. The average tax rate determines when an individual will work while the marginal tax rate determines how much she will work. Even in the absence of redistributive concerns, the optimal tax schedule has an increasing average tax rate at low levels of income to encourage labor market participation. The marginal tax rate at the top is strictly positive. Finally, the model is used to assess the effects of changing the current tax schedule to the optimal one. Under the preferred parameters, this delivers a welfare gain equivalent to 0.67 percent of lifetime consumption.
Fractal: Post-quantum and Transparent Recursive Proofs from Holography

We present a new methodology to efficiently realize recursive composition of succinct non-interactive arguments of knowledge (SNARKs). Prior to this work, the only known methodology relied on pairing-based SNARKs instantiated on cycles of pairing-friendly elliptic curves, an expensive algebraic object. Our methodology does not rely on any special algebraic objects and, moreover, achieves new desirable properties: it is post-quantum and it is transparent (the setup is public coin).
Clinical practice guidelines for antimicrobial prophylaxis in surgery.

These guidelines were developed jointly by the American Society of Health-System Pharmacists (ASHP), the Infectious Diseases Society of America (IDSA), the Surgical Infection Society (SIS), and the Society for Healthcare Epidemiology of America (SHEA). This work represents an update to the
Factors Affecting Timing and Intensity of Calving Season of Beef Cow-Calf Producers in the Midwest

Despite demonstrated market incentives to adopt controlled calving seasons, many producers still have herds that calve somewhat broadly throughout the year. Primarydata, collected through a coordinated survey effort withUSDA’s National Agricultural Statistics Service, were usedto quantify factors that affect producers’ decisions regardingtiming and intensity of calving season. Ninety-seven, 50, 33,and 26% of farms calve in the spring (Mar, Apr, May),summer (Jun, Jul, Aug), fall (Sep, Oct, Nov), and winter(Dec, Jan, Feb), respectively. Twenty-two percent observeda calving season exclusively in the spring. Sixty-six percentof respondents indicated calving season was dictated byweather, 34% because of labor availability, and 31%because of tradition. Least often reasons for calving seasonwere market timing (16%), feed availability (8%), and other(4%). Producer stated reasons for calving season explained62% of the variation in timing and intensity of calving on anoperation, whereas a model of producer demographic andoperation characteristics explained 83% of the variation.These results highlight the importance of evaluatingproducer and operation characteristics in addition toproducer input when making recommendations to enhanceproduction efficiency and profitability. Furthermore,understanding the factors which impact calving seasonprovides opportunities for improved extension and research programming.
Sum–product phenomena for planar hypercomplex numbers

Abstract We study the sum–product problem for the planar hypercomplex numbers: the dual numbers and double numbers. These number systems are similar to the complex numbers, but it turns out that they have a very different combinatorial behavior. We identify parameters that control the behavior of these problems, and derive sum–product bounds that depend on these parameters. For the dual numbers we expose a range where the minimum value of max { | A + A | , | A A | } is neither close to | A | nor to | A | 2 . To obtain our main sum–product bound, we extend Elekes’ sum–product technique that relies on point–line incidences. Our extension is significantly more involved than the original proof, and in some sense runs the original technique a few times in a bootstrapping manner. We also study point–line incidences in the dual plane and in the double plane, developing analogs of the Szemeredi–Trotter theorem. As in the case of the sum–product problem, it turns out that the dual and double variants behave differently than the complex and real ones.
DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning

The increasing demand for diverse network services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted progressively due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to the interdependence between virtual network functions and infrastructure elements. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some interdependencies exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on Deep Reinforcement Learning (Deep RL). Our simulation results indicate that DeepPR can achieve near-optimal solutions in certain networks and is more robust to adversarial failures, compared to a baseline heuristic algorithm.
Of Peacocks, Tulips, and Shotguns: Intentions and Side Effects in John Finnis' Natural Law Theory

This article constitutes a detailed response to John Finnis’ present-day critique and deconstruction of two famous tort cases decided in England in the first half of the nineteenth century: Ilott v. Wilkes (King’s Bench 1820) and its progeny Bird v. Holbrook (Court of Common Pleas 1828). Both cases involved trespassers who were seriously injured upon entering landowners’ property without permission. Their injuries were caused by means of ‘man-traps,’ i.e., shotguns set outdoors by a landowner and primed to fire upon contact with a tripwire. Finnis concedes that in laying man-traps, landowners may not have had a desire to harm (in fact they may have had a desire not to harm) but merely to deter. Nevertheless, according to Finnis, any landowner setting such devices, even if he posts clear warnings, ineluctably involves that owner in “conditionally, but really” intending to kill or seriously injure. The present article challenges this view. It argues, by applying Finnis’ own natural law theory of human action, intention, and choice, that his conclusion is undermined. While this topic may appear arcane to some, it focuses attention on important general questions in legal theory and philosophy about the meaning of intentions, choices, and side effects.
Gate-tunable spin waves in antiferromagnetic atomic bilayers.

Remarkable properties of two-dimensional (2D) layer magnetic materials, which include spin filtering in magnetic tunnel junctions and the gate control of magnetic states, were demonstrated recently1–12. Whereas these studies focused on static properties, dynamic magnetic properties, such as excitation and control of spin waves, remain elusive. Here we investigate spin-wave dynamics in antiferromagnetic CrI3 bilayers using an ultrafast optical pump/magneto-optical Kerr probe technique. Monolayer WSe2 with a strong excitonic resonance was introduced on CrI3 to enhance the optical excitation of spin waves. We identified subterahertz magnetic resonances under an in-plane magnetic field, from which the anisotropy and interlayer exchange fields were determined. We further showed tuning of the antiferromagnetic resonances by tens of gigahertz through electrostatic gating. Our results shed light on magnetic excitations and spin dynamics in 2D magnetic materials, and demonstrate their potential for applications in ultrafast data storage and processing. Gating dependent laser induced spin dynamics in an antiferromagnetic bilayer are observed and explained, with implications for future spintronic applications.
Sketching Transformed Matrices with Applications to Natural Language Processing.

Suppose we are given a large matrix $A=(a_{i,j})$ that cannot be stored in memory but is in a disk or is presented in a data stream. However, we need to compute a matrix decomposition of the entry-wisely transformed matrix, $f(A):=(f(a_{i,j}))$ for some function $f$. Is it possible to do it in a space efficient way? Many machine learning applications indeed need to deal with such large transformed matrices, for example word embedding method in NLP needs to work with the pointwise mutual information (PMI) matrix, while the entrywise transformation makes it difficult to apply known linear algebraic tools. Existing approaches for this problem either need to store the whole matrix and perform the entry-wise transformation afterwards, which is space consuming or infeasible, or need to redesign the learning method, which is application specific and requires substantial remodeling. In this paper, we first propose a space-efficient sketching algorithm for computing the product of a given small matrix with the transformed matrix. It works for a general family of transformations with provable small error bounds and thus can be used as a primitive in downstream learning tasks. We then apply this primitive to a concrete application: low-rank approximation. We show that our approach obtains small error and is efficient in both space and time. We complement our theoretical results with experiments on synthetic and real data.
Recombination disequilibrium in ideal and natural populations

Abstract Following Hardy-Weinberg disequilibrium (HWD) occurring at a single locus and linkage disequilibrium (LD) between two loci in generations, we here proposed the third genetic disequilibrium in a population: recombination disequilibrium (RD). RD is a measurement of crossover interference among multiple loci in a random mating population. In natural populations besides recombination interference, RD may also be due to selection, mutation, gene conversion, drift and/or migration. Therefore, similarly to LD, RD will also reflect the history of natural selection and mutation. In breeding populations, RD purely results from recombination interference and hence can be used to build or evaluate and correct a linkage map. Practical examples from F2, testcross and human populations indeed demonstrate that RD is useful for measuring recombination interference between two short intervals and evaluating linkage maps. As with LD, RD will be important for studying genetic mapping, association of haplotypes with disease, plant breading and population history.
Microwave Radiometer Radio-Frequency Interference Detection Algorithms: A Comparative Study

Two algorithms used in microwave radiometry for radio-frequency interference (RFI) detection and mitigation are the pulse detection algorithm and the kurtosis detection algorithm. The relative performance of the algorithms is compared both analytically and empirically. Their probabilities of false alarm under RFI-free conditions and of detection when RFI is present are examined. The downlink data rate required to implement each algorithm in a spaceborne application is also considered. The kurtosis algorithm is compared to a pulse detection algorithm operating under optimal RFI detection conditions. The performance of both algorithms is also analyzed as a function of varying characteristics of the RFI. The RFI detection probabilities of both algorithms under varying subsampling conditions are compared and validated using data obtained from a field campaign. Implementation details, resource usage, and postprocessing requirements are also addressed for both algorithms.
Function-on-function kriging, with applications to 3D printing of aortic tissues

Three-dimensional printed medical prototypes, which use synthetic metamaterials to mimic biological tissue, are becoming increasingly important in urgent surgical applications. However, the mimicki...
Coded Distributed Computing with Heterogeneous Function Assignments.

Coded distributed computing (CDC) introduced by Li et. at. is an effective technique to trade computation load for communication load in a MapReduce framework. CDC achieves an optimal trade-off by duplicating map computations at r computing nodes to yield multicasting opportunities such that r nodes are served simultaneously in the Shuffle phase. However, in general, the state-of-the-art CDC scheme is mainly designed only for homogeneous networks, where the computing nodes are assumed to have the same storage, computation and communication capabilities. In this work, we explore two approaches of heterogeneous CDC design. First, we study CDC schemes which operate on multiple, collaborating homogeneous computing networks. Second, we allow heterogeneous function assignment in the CDC design, where nodes are assigned a varying number of reduce functions. We propose an expandable heterogeneous CDC scheme where r-1 nodes are served simultaneously in the Shuffle phase. In comparison to the state-of-the-art homogeneous CDC scheme with an equivalent computation load, we find our newly proposed heterogeneous CDC scheme has a smaller communication load in some cases.
Log-seed pseudorandom generators via iterated restrictions

There are only a few known general approaches for constructing explicit pseudorandom generators (PRGs). The "iterated restrictions" approach, pioneered by Ajtai and Wigderson [2], has provided PRGs with seed length polylog n or even O(log n) for several restricted models of computation. Can this approach ever achieve the optimal seed length of O(log n)?In this work, we answer this question in the affirmative. Using the iterated restrictions approach, we construct an explicit PRG for read-once depth-2 AC0[⊕] formulas with seed length[MATH HERE].In particular, we achieve optimal seed length O(log n) with near-optimal error e = exp(-ΩT(log n)). Even for constant error, the best prior PRG for this model (which includes read-once CNFs and read-once F2-polynomials) has seed length Θ(log n · (log log n)2) [22].A key step in the analysis of our PRG is a tail bound for subset-wise symmetric polynomials, a generalization of elementary symmetric polynomials. Like elementary symmetric polynomials, subset-wise symmetric polynomials provide a way to organize the expansion of [MATH HERE]. Elementary symmetric polynomials simply organize the terms by degree, i.e., they keep track of the number of variables participating in each monomial. Subset-wise symmetric polynomials keep track of more data: for a fixed partition of [m], they keep track of the number of variables from each subset participating in each monomial. Our tail bound extends prior work by Gopalan and Yehudayoff [17] on elementary symmetric polynomials.
QUANOS: adversarial noise sensitivity driven hybrid quantization of neural networks

Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial attacks, wherein, a model gets fooled by applying slight perturbations on the input. In this paper, we investigate the use of quantization to potentially resist adversarial attacks. Several recent studies have reported remarkable results in reducing the energy requirement of a DNN through quantization. However, no prior work has considered the relationship between adversarial sensitivity of a DNN and its effect on quantization. We propose QUANOS- a framework that performs layer-specific hybrid quantization based on Adversarial Noise Sensitivity (ANS). We identify a novel noise stability metric (ANS) for DNNs, i.e., the sensitivity of each layer's computation to adversarial noise. ANS allows for a principled way of determining optimal bit-width per layer that incurs adversarial robustness as well as energy-efficiency with minimal loss in accuracy. Essentially, QUANOS assigns layer significance based on its contribution to adversarial perturbation and accordingly scales the precision of the layers. We evaluate the benefits of QUANOS on precision scalable Multiply and Accumulate (MAC) hardware architectures with data gating and subword parallelism capabilities. Our experiments on CIFAR10, CIFAR100 datasets show that QUANOS outperforms homogeneously quantized 8-bit precision baseline in terms of adversarial robustness (3 -- 4% higher) while yielding improved compression (> 5×) and energy savings (> 2×) at iso-accuracy. At iso-compression rate, QUANOS yields significantly higher adversarial robustness (> 10%) than similar sized baseline against strong white-box attacks. We also find that combining QUANOS with state-of-the-art defense methods outperforms the state-of-the-art in robustness (~ 5% -- 16% higher) against very strong attacks.
Which social comparisons influence happiness with unequal pay

We examine which social comparisons most affect happiness with pay that is unequally distributed (e.g., salaries and bonuses). We find that ensemble representation-attention to statistical properties of distributions such as their range and mean-makes the proximal extreme (i.e., the maximum or minimum) and distribution mean salient social comparison standards. Happiness with a salary or bonus is more affected by how it compares to the distribution mean and proximal extreme than by exemplar-based properties of the payment, like its comparison to the nearest payment or its distribution rank. This holds for randomly assigned and performance-based payments. Process studies demonstrate that ensemble representations lead people to spontaneously select these statistical properties of pay distributions as comparison standards. Exogenously increasing the salience of less extreme exemplars moderates the influence of the maximum on happiness with pay, but exogenously increasing the salience of the distribution maximum does not. As with other social comparison standards, top-down information moderates their selection. Happiness with a bonus payment is influenced by the largest payment made to others who solve the same math problems, for instance, but not by the largest payment made to others who solve different verbal problems. Our findings yield theoretical and practical insights about which members of groups are selected as social comparison standards, effects of relative income on happiness, and the attentional processes involved in ensemble representation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Development of a fragment kinetic Monte Carlo method for efficient prediction of ionic diffusion in perovskite crystals

Abstract A massively parallel kinetic Monte Carlo (kMC) approach is proposed for simulating ionic migration in a crystal system by introducing the atomic fragmentation scheme (fragment kMC). The fragment kMC method achieved a reasonable parallel efficiency with 1728 central processing unit (CPU) cores, and the method enables the simulation of ionic diffusion in μ m-scale perovskite crystals. To demonstrate the feasibility of the proposed approach, the fragment kMC method was applied to predict the diffusion coefficients of hydride ions and oxygen ions in SrTiO ( 3 - x ) H x and BaTiO ( 3 - x ) H x systems. Finally, the fragment kMC method was customized for μ -scale BaTiO3 simulation under an applied bias voltage, and oxygen diffusion in BaTiO3 model was evaluated. The respective grain sizes are sub-nanometre, and we conclude that the proposed fragment kMC method can be applied to calculate the extent of ionic migration in μ -scale materials with fully atomistic simulation models at a reasonable computational cost.
Quantum Information Processing: An Essential Primer

Quantum information science is an exciting, wide, rapidly progressing, cross-disciplinary field, and that very nature makes it both attractive and hard to enter. In this primer, we first provide answers to the three essential questions that any newcomer needs to know: How is quantum information represented? How is quantum information processed? How is classical information extracted from quantum states? We then introduce the most basic quantum information theoretic notions concerning entropy, sources, and channels, as well as secure communications and error correction. We conclude with examples that illustrate the power of quantum correlations. No prior knowledge of quantum mechanics is assumed.
Oxycodone self-administration activates the mitogen-activated protein kinase/ mitogen- and stress-activated protein kinase (MAPK-MSK) signaling pathway in the rat dorsal striatum

To identify signaling pathways activated by oxycodone self-administration (SA), Sprague-Dawley rats self-administered oxycodone for 20 days using short-(ShA, 3 h) and long-access (LgA, 9 h) paradigms. Animals were euthanized 2 h after SA cessation and dorsal striata were used in post-mortem molecular analyses. LgA rats escalated their oxycodone intake and separated into lower (LgA-L) or higher (LgA-H) oxycodone takers. LgA-H rats showed increased striatal protein phosphorylation of ERK1/2 and MSK1/2. Histone H3, phosphorylated at serine 10 and acetylated at lysine 14 (H3S10pK14Ac), a MSK1/2 target, showed increased abundance only in LgA-H rats. RT-qPCR analyses revealed increased AMPA receptor subunits, GluA2 and GluA3 mRNAs, in the LgA-H rats. GluA3, but not GluA2, mRNA expression correlated positively with changes in pMSK1/2 and H3S10pK14Ac. These findings suggest that escalated oxycodone SA results in MSK1/2-dependent histone phosphorylation and increases in striatal gene expression. These observations offer potential avenues for interventions against oxycodone addiction.
Bayesian Evaluation of User App Choices in the Presence of Risk Communication on Android Devices.

In the age of ubiquitous technologies, security- and privacy-focused choices have turned out to be a significant concern for individuals and organizations. Risks of such pervasive technologies are extensive and often misaligned with user risk perception, thus failing to help users in taking privacy-aware decisions. Researchers usually try to find solutions for coherently extending trust into our often inscrutable electronic networked environment. To enable security- and privacy-focused decision-making, we mainly focused on the realm of the mobile marketplace, examining how risk indicators can help people choose more secure and privacy-preserving apps. We performed a naturalistic experiment with \(N=60\) participants, where we asked them to select applications on Android tablets with accurate real-time marketplace data. We found that, in aggregate, app selections changed to be more risk-averse in the presence of user risk-perception-aligned visual indicators. Our study design and research propose practical and usable interactions that enable more informed, risk-aware comparisons for individuals during app selections. We include an explicit argument for the role of human decision-making during app selection, beyond the current trend of using machine learning to automate privacy preferences after selection during run-time.
Neural anomalies during vigilance in schizophrenia: Diagnostic specificity and genetic associations.

Abstract Impaired vigilance is a core cognitive deficit in schizophrenia and may serve as an endophenotype (i.e., mark genetic liability). We used a continuous performance task with perceptually degraded stimuli in schizophrenia patients (N = 48), bipolar disorder patients (N = 26), first-degree biological relatives of schizophrenia patients (N = 55) and bipolar disorder patients (N = 28), as well as healthy controls (N = 68) to clarify whether previously reported vigilance deficits and abnormal neural functions were indicative of genetic liability for schizophrenia as opposed to a generalized liability for severe psychopathology. We also examined variation in the Catechol-O-methyltransferase gene to evaluate whether brain responses were related to genetic variation associated with higher-order cognition. Relatives of schizophrenia patients had an increased rate of misidentification of nontarget stimuli as targets when they were perceptually similar, suggestive of difficulties with contour perception. Larger early visual responses (i.e., N1) were associated with better task performance in patients with schizophrenia consistent with enhanced N1 responses reflecting beneficial neural compensation. Additionally, reduced N2 augmentation to target stimuli was specific to schizophrenia. Both patients with schizophrenia and first-degree relatives displayed reduced late cognitive responses (P3b) that predicted worse performance. First-degree relatives of bipolar patients exhibited performance deficits, and displayed aberrant neural responses that were milder than individuals with liability for schizophrenia and dependent on sex. Variation in the Catechol-O-methyltransferase gene was differentially associated with P3b in schizophrenia and bipolar groups. Poor vigilance in schizophrenia is specifically predicted by a failure to enhance early visual responses, weak augmentation of mid-latency brain responses to targets, and limited engagement of late cognitive responses that may be tied to genetic variation associated with prefrontal dopaminergic availability. Experimental results illustrate specific neural functions that distinguish schizophrenia from bipolar disorder and provides evidence for a putative endophenotype that differentiates genetic liability for schizophrenia from severe mental illness more broadly.
A Case for Lifetime Reliability-Aware Neuromorphic Computing

Neuromorphic computing with non-volatile memory (NVM) can significantly improve performance and lower energy consumption of machine learning tasks implemented using spikebased computations and bio-inspired learning algorithms. High voltages required to operate certain NVMs such as phase-change memory (PCM) can accelerate aging in a neuron’s CMOS circuit, thereby reducing the lifetime of neuromorphic hardware. In this work, we evaluate the long-term, i.e., lifetime reliability impact of executing state-of-the-art machine learning tasks on a neuromorphic hardware, considering failure models such as negative bias temperature instability (NBTI) and time-dependent dielectric breakdown (TDDB). Based on such formulation, we show the reliability-performance trade-off obtained due to periodic relaxation of neuromorphic circuits, i.e., a stop-and-go style of neuromorphic computing.
Improved queue-size scaling for input-queued switches via graph factorization

This paper studies the scaling of the expected total queue size in an input-queued switch, as a function of both the load and the system scale n. We provide a new class of scheduling policies under which the expected total queue size scales as , over all n and , when the arrival rates are uniform. This improves on the best previously known scalings in two regimes: when and when . A key ingredient in our method is a tight characterization of the largest k-factor of a random bipartite multigraph, which may be of independent interest.
File Classification Based on Spiking Neural Networks

In this paper, we propose a system for file classification in large data sets based on spiking neural networks (SNNs). File information contained in key-value metadata pairs is mapped by a novel correlative temporal encoding scheme to spike patterns that are input to an SNN. The correlation between input spike patterns is determined by a file similarity measure. Unsupervised training of such networks using spike-timing-dependent plasticity (STDP) is addressed first. Then, supervised SNN training is considered by backpropagation of an error signal that is obtained by comparing the spike pattern at the output neurons with a target pattern representing the desired class. The classification accuracy is measured for various publicly available data sets with tens of thousands of elements, and compared with other learning algorithms, including logistic regression and support-vector machines. Simulation results indicate that the proposed SNN-based system using memristive synapses may represent a valid alternative to classical machine learning algorithms for inference tasks, especially in environments with asynchronous ingest of input data and limited resources.
Parabolic–Elliptic Chemotaxis Model with Space–Time Dependent Logistic Sources on $$\mathbb {R}^N$$. III: Transition Fronts

The current work is the third of a series of three papers devoted to the study of asymptotic dynamics in the following parabolic–elliptic chemotaxis system with space and time dependent logistic source, 0.1 $$\begin{aligned} {\left\{ \begin{array}{ll} \partial _tu=\Delta u -\chi \nabla \cdot (u\nabla v)+u(a(x,t)-b(x,t)u),&{}\quad x\in {\mathbb R}^N,\\ 0=\Delta v-\lambda v+\mu u ,&{}\quad x\in {\mathbb R}^N, \end{array}\right. } \end{aligned}$$ where $$N\ge 1$$ is a positive integer, $$\chi , \lambda $$ and $$\mu $$ are positive constants, and the functions a(x, t) and b(x, t) are positive and bounded. In the first of the series (Salako and Shen in Math Models Methods Appl Sci 28(11):2237–2273, 2018), we studied the phenomena of pointwise and uniform persistence for solutions with strictly positive initial data, and the asymptotic spreading for solutions with compactly supported or front like initial data. In the second of the series (Salako and Shen in J Math Anal Appl 464(1):883–910, 2018), we investigate the existence, uniqueness and stability of strictly positive entire solutions of (0.1). In particular, in the case of space homogeneous logistic source (i.e. $$a(x,t)\equiv a(t)$$ and $$b(x,t)\equiv b(t)$$ ), we proved in Salako and Shen (J Math Anal Appl 464(1):883–910, 2018) that the unique spatially homogeneous strictly positive entire solution $$(u^*(t),v^*(t))$$ of (0.1) is uniformly and exponentially stable with respect to strictly positive perturbations when $$0<2\chi \mu <\inf _{t\in {\mathbb R}}b(t)$$ . In the current part of the series, we discuss the existence of transition front solutions of (0.1) connecting (0, 0) and $$(u^*(t),v^*(t))$$ in the case of space homogeneous logistic source. We show that for every $$\chi >0$$ with $$\chi \mu \big (1+\frac{\sup _{t\in {\mathbb R}}a(t)}{\inf _{t\in {\mathbb R}}a(t)}\big )<\inf _{t\in {\mathbb R}}b(t)$$ , there is a positive constant $${c}^{*}_\chi $$ such that for every $$\underline{c}> {c}^*_{\chi }$$ and every unit vector $$\xi $$ , (0.1) has a transition front solution of the form $$(u(x,t),v(x,t))=(U(x\cdot \xi -C(t),t),V(x\cdot \xi -C(t),t))$$ satisfying that $$C'(t)=\frac{a(t)+\kappa ^2}{\kappa }$$ for some positive number $$\kappa $$ , $$\liminf _{t-s\rightarrow \infty }\frac{C(t)-C(s)}{t-s}=\underline{c}$$ , and $$\begin{aligned} \lim _{x\rightarrow -\infty }\sup _{t\in {\mathbb R}}|U(x,t)-u^*(t)|=0 \quad \text {and}\quad \lim _{x\rightarrow \infty }\sup _{t\in {\mathbb R}}|\frac{U(x,t)}{e^{-\kappa x}}-1|=0. \end{aligned}$$ Furthermore, we prove that there is no transition front solution $$(u(x,t),v(x,t))=(U(x\cdot \xi -C(t),t),V(x\cdot \xi -C(t),t))$$ of (0.1) connecting (0, 0) and $$(u^*(t),v^*(t))$$ with least mean speed less than $$2\sqrt{\underline{a}}$$ , where $$\underline{a}=\liminf _{t-s\rightarrow \infty }\frac{1}{t-s}\int _{s}^{t}a(\tau )d\tau $$ .
Thermomechanical conversion in metals: dislocation plasticity model evaluation of the Taylor-Quinney coefficient

Abstract Using a partitioned-energy thermodynamic framework which assigns energy to that of atomic configurational stored energy of cold work and kinetic-vibrational, we derive an important constraint on the Taylor-Quinney coefficient, which quantifies the fraction of plastic work that is converted into heat during plastic deformation. Associated with the two energy contributions are two separate temperatures – the ordinary temperature for the thermal energy and the effective temperature for the configurational energy. We show that the Taylor-Quinney coefficient is a function of the thermodynamically defined effective temperature that measures the atomic configurational disorder in the material. Finite-element analysis of recently published experiments on the aluminum alloy 6016-T4 [1], using the thermodynamic dislocation theory (TDT), shows good agreement between theory and experiment for both stress-strain behavior and temporal evolution of the temperature. The simulations include both conductive and convective thermal energy loss during the experiments, and significant thermal gradients exist within the simulation results. Computed values of the differential Taylor-Quinney coefficient are also presented and suggest a value which differs between materials and increases with increasing strain.
How Large is Large? Estimating the Critical Disorder for the Anderson Model

Complete localization is shown to hold for the d-dimensional Anderson model with uniformly distributed random potentials provided the disorder strength \({\lambda > \lambda_{And}}\) where \({\lambda_{\rm And}}\) satisfies \({\lambda_{\rm And}}=\mu_d e \ln \lambda_{\rm And}\) with \({\mu_d}\) the self-avoiding walk connective constant for the lattice \({\mathbb{Z}^d}\) . Notably, \({\lambda_{\rm And}}\) is precisely the large disorder threshold proposed by Anderson in 1958.
Impact of Baryonic Physics on Intrinsic Alignments

We explore the effects of specific assumptions in the subgrid models of star formation and stellar and active galactic nucleus feedback on intrinsic alignments of galaxies in cosmological simulations of the "MassiveBlack-II" family. Using smaller-volume simulations, we explore the parameter space of the subgrid star formation and feedback model and find remarkable robustness of the observable statistical measures to the details of subgrid physics. The one observational probe most sensitive to modeling details is the distribution of misalignment angles. We hypothesize that the amount of angular momentum carried away by the galactic wind is the primary physical quantity that controls the orientation of the stellar distribution. Our results are also consistent with a similar study by the EAGLE simulation team.