cssi/SciSciGPT-SciSciNet huggingface dataset:
A FLUID LIMIT FOR PROCESSOR-SHARING QUEUES WEIGHTED BY FUNCTIONS OF REMAINING AMOUNTS OF SERVICE

We study a single server queue under a processor-sharing type of scheduling policy, where the weights for determining the sharing are given by functions of each job's remaining service (processing) amount, and obtain a fluid limit for the scaled measure-valued system descriptors.
Exceptional points in dissipatively coupled spin dynamics

Nanoscale or continuum spin systems, including simple ferromagnets and antiferromagnets, can have their spectral properties strongly affected by damping and/or pumping. In particular, dissipation can tune the magnon band structure through a series of exceptional points, which constitute special topological degeneracies with potentially dramatic consequences for response properties of magnetic materials.
Coherence distillation machines are impossible in quantum thermodynamics.

The role of coherence in quantum thermodynamics has been extensively studied in the recent years and it is now well-understood that coherence between different energy eigenstates is a resource independent of other thermodynamics resources, such as work. A fundamental remaining open question is whether the laws of quantum mechanics and thermodynamics allow the existence of a coherence distillation machine, i.e., a machine that, by possibly consuming work, obtains pure coherent states from mixed states, at a nonzero rate. This is related to another fundamental question: Starting from many copies of noisy quantum clocks which are (approximately) synchronized with a reference clock, can one distill synchronized clocks in pure states, at a non-zero rate? Surprisingly, we find that the answer to both questions is negative for generic (full-rank) mixed states. However, at the same time, it is possible to distill a sub-linear number of pure coherent states with a vanishing error. Can an idealised machine obtain pure coherent quantum states from mixed ones at a non-zero rate, or equivalently, can a machine distill synchronized quantum clocks starting from noisy ones? Iman Marvian demonstrates that this is impossible, even if the machine is allowed to spend an arbitrary amount of work.
Time-rate-transformation framework for targeted assembly of short-range attractive colloidal suspensions

Abstract The aggregation of attractive colloids has been extensively studied from both theoretical and experimental perspectives as the fraction of solid particles is changed, and the range, type, and strength of attractive or repulsive forces between particles varies. The resulting gels, consisting of disordered assemblies of attractive colloidal particles, have also been investigated with regards to percolation, phase separation, and the mechanical characteristics of the resulting fractal networks. Despite tremendous progress in our understanding of the gelation process, and the exploration of different routes for arresting the dynamics of attractive colloids, the complex interplay between convective transport processes and many-body effects in such systems has limited our ability to drive the system toward a specific configuration. Here, we study a model attractive colloidal system over a wide range of particle characteristics and flow conditions undergoing aggregation far from equilibrium. The complex multiscale dynamics of the system can be understood using a time-rate-transformation diagram adapted from understanding of materials processing in block copolymers, supercooled liquids, and much stiffer glassy metals to direct targeted assembly of attractive colloidal particles.
Desmosomes pattern cell mechanics to govern epidermal tissue form and function

The epidermis is a stratified epithelium in which structural and functional features are polarized across multiple cell layers. This type of polarity is essential for establishing the epidermal barrier, but how it is created and sustained is poorly understood. Previous work identified a role for the classical cadherin/filamentous-actin network in establishment of epidermal polarity. However, little is known about potential roles of the most prominent epidermal intercellular junction, the desmosome, in establishing epidermal polarity, in spite of the fact that desmosome constituents are patterned across the apical to basal cell layers. Here, we show that desmosomes and their associated intermediate filaments (IF) are key regulators of mechanical polarization in epidermis, whereby basal and suprabasal cells experience different forces that drive layer-specific functions. Uncoupling desmosomes and IF or specific targeting of apical desmosomes through depletion of the superficial desmosomal cadherin, desmoglein 1, impedes basal (stratification) and suprabasal (tight junction barrier) functions. Surprisingly, disengaging desmosomes from IF also uncouples stratification from differentiation, accelerating the expression of differentiation markers. Our data support a model in which the desmosome-IF network supports a reciprocally organized distribution of ErbB1/EGFR activity in the basal layer and mechanosensitive kinase ErbB2 activity in the suprabasal layer to ensure the proper spatiotemporal coordination of cell mechanics and the biochemical program of differentiation.
Effects of proton irradiation on microstructure and mechanical properties of nanocrystalline Cu–10at%Ta alloy

Abstract Duplex nanocrystalline Cu–10at%Ta is attractive for extreme environments because of its grain size stability at high homologous temperatures, owing to dispersed Ta nanoclusters. This study is an initial investigation into the microstructural and mechanical stability of Cu–10at%Ta under irradiation. Proton irradiation is carried out to 1 displacement per atom (dpa) at 500 °C. The Cu–10at%Ta exhibits grain size stability throughout irradiation and resistance to the formation of irradiation-induced defects. However, Ta nanoclusters undergo ballistic dissolution. Nanoindentation reveals there is no significant irradiation hardening, consistent with the relatively irradiation-stable microstructure. Ballistic dissolution of Ta nanoclusters is discussed in the context of Orowan strengthening.
Efficient distribution-free learning of probabilistic concepts

A model of machine learning in which the concept to be learned may exhibit uncertain or probabilistic behavior is investigated. Such probabilistic concepts (or p-concepts) may arise in situations such as weather prediction, where the measured variables and their accuracy are insufficient to determine the outcome with certainty. It is required that learning algorithms be both efficient and general in the sense that they perform well for a wide class of p-concepts and for any distribution over the domain. Many efficient algorithms for learning natural classes of p-concepts are given, and an underlying theory of learning p-concepts is developed in detail. >
Bringing Critical Race Praxis Into the Study of Electrophysiological Substrate of Sudden Cardiac Death: The ARIC Study.

Background Race is an established risk factor for sudden cardiac death (SCD). We sought to determine whether the association of electrophysiological substrate with SCD varies between black and whit...
Low Cost Signal Reconstruction Based Testing of RF Components using Incoherent Undersampling

To meet the testing requirements of high speed components used in modern communication systems, in an efficient and cost effective manner, it is necessary to develop new device performance measurement techniques that are easily scalable to high frequencies. Traditional up/down conversion based transmitter testing architectures are sensitive to the linearity of the mixers and carrier phase noise in the receiver. Direct undersampling based test instrumentation can overcome the limitations imposed by mixers in up/down conversion. A major challenge in direct undersampling based test architecture is to achieve precise phase alignment between different components of the test setup. Such phase alignment of high frequency signals requires the use of expensive test instruments adding to the cost and complexity of the overall test system. To resolve this problem, an incoherent undersampling based test method is developed in this research that eliminates the need for precise phase synchronization between the RF test signal carrier, its amplitude-modulated envelope (generally necessary for measuring amplifier nonlinearity) and the reference sampling clock. A side-benefit, due to the use of signal undersampling, is that signal acquisition is achieved without the use of a Nyquist rate data converter. Multiple RF performance metrics are extracted without the use of a reference receiver. The accuracy of the proposed setup is compared against existing coherent sampling based test setups.
Lawvere theories and C-systems

In this paper we consider the class of l-bijective C-systems, i.e., C-systems for which the length function is a bijection. The main result of the paper is a construction of an isomorphism between two categories - the category of l-bijective C-systems and the category of Lawvere theories.
Quantifying E2F1 protein dynamics in single cells

E2F1 protein, a major effector of the Rb/E2F pathway plays a central role in regulating cell-fate decisions involved in proliferation, apoptosis, and differentiation. Its expression is highly dynamic and tightly modulated through a combination of transcriptional, translational and posttranslational controls. However, the mechanisms by which its expression and activity can promote different cellular outcomes remain to be fully elucidated. To better document E2F1 expression in live cells, we have engineered a series of fluorescent E2F1 protein reporters that quantitatively capture E2F1 protein dynamics. Reporter constructs, under the control of the mouse or human E2F1 proximal promoter, were designed to express an E2F1-Venus fusion protein incapable of binding DNA. In addition, constructs either included or excluded the 3′ untranslated region (3′UTR) of the E2F1 gene. These constructs were introduced into fibroblasts and epithelial cells, and expression of the fusion reporter protein was validated and quantified in single cells using live imaging. In all cases, expression of the reporter protein effectively recapitulated the behavior of E2F1 under various conditions, including cell cycle progression and genotoxic stress. No or little fluorescent signal of the reporter was detected in G0, but as the cycle progressed, expression of the reporter protein steadily increased in the nucleus, peaking a few hours before cell division, but declining to baseline 2–3 h prior to the onset of mitosis. The absence of the E2F1 3′UTR in the constructs led to considerably higher steady-state levels of the fusion protein, which although normally regulated, exhibited a slightly less complex dynamic profile during the cell cycle or genotoxic stress. Lastly, the presence or absence of Rb failed to impact the overall detection and levels of the reporter proteins. Our validated E2F1 protein reporters complement nicely other reporters of the Rb/E2F pathway and provide a unique tool to follow the complex dynamics of E2F1 expression in real time in single cells.
A hardware approach to value function iteration

Abstract This paper proposes a novel approach for the computation of dynamic stochastic equilibrium models. We design an FPGA specialized in the computation of a Bellman equation via value function iteration (VFI). Our hardware approach exhibits significant speed gains vis-a-vis GPU-based data-parallelization techniques. The speed gains arise from two layers of parallelism, accessible to hardware developers: instruction-level and pipeline parallelism at the logical resources level. By and large, the paper documents significant computational speed gains from hardware specialization, so far unexplored by the macroeconomic literature.
Two Synthetic 18-Way Outcrossed Populations of Diploid Budding Yeast with Utility for Complex Trait Dissection.

Advanced-generation multiparent populations (MPPs) are a valuable tool for dissecting complex traits, having more power than genome-wide association studies to detect rare variants and higher resolution than F2 linkage mapping. To extend the advantages of MPPs in budding yeast, we describe the creation and characterization of two outbred MPPs derived from 18 genetically diverse founding strains. We carried out de novo assemblies of the genomes of the 18 founder strains, such that virtually all variation segregating between these strains is known, and represented those assemblies as Santa Cruz Genome Browser tracks. We discovered complex patterns of structural variation segregating among the founders, including a large deletion within the vacuolar ATPase VMA1, several different deletions within the osmosensor MSB2, a series of deletions and insertions at PRM7 and the adjacent BSC1, as well as copy number variation at the dehydrogenase ALD2. Resequenced haploid recombinant clones from the two MPPs have a median unrecombined block size of 66 kb, demonstrating that the population is highly recombined. We pool-sequenced the two MPPs to 3270× and 2226× coverage and demonstrated that we can accurately estimate local haplotype frequencies using pooled data. We further downsampled the pool-sequenced data to ∼20–40× and showed that local haplotype frequency estimates remained accurate, with median error rates 0.8 and 0.6% at 20× and 40×, respectively. Haplotypes frequencies are estimated much more accurately than SNP frequencies obtained directly from the same data. Deep sequencing of the two populations revealed that 10 or more founders are present at a detectable frequency for > 98% of the genome, validating the utility of this resource for the exploration of the role of standing variation in the architecture of complex traits.
Music Gesture for Visual Sound Separation

Recent deep learning approaches have achieved impressive performance on visual sound separation tasks. However, these approaches are mostly built on appearance and optical flow like motion feature representations, which exhibit limited abilities to find the correlations between audio signals and visual points, especially when separating multiple instruments of the same types, such as multiple violins in a scene. To address this, we propose ``Music Gesture," a keypoint-based structured representation to explicitly model the body and finger movements of musicians when they perform music. We first adopt a context-aware graph network to integrate visual semantic context with body dynamics and then apply an audio-visual fusion model to associate body movements with the corresponding audio signals. Experimental results on three music performance datasets show: 1) strong improvements upon benchmark metrics for hetero-musical separation tasks (i.e. different instruments); 2) new ability for effective homo-musical separation for piano, flute, and trumpet duets, which to our best knowledge has never been achieved with alternative methods.
Adaptive target recognition: a case study involving airport baggage screening

This work addresses the question whether it is possible to design a computer-vision based automatic threat recognition (ATR) system so that it can adapt to changing specifications of a threat without having to create a new ATR each time. The changes in threat specifications, which may be warranted by intelligence reports and world events, are typically regarding the physical characteristics of what constitutes a threat: its material composition, its shape, its method of concealment, etc. Here we present our design of an AATR system (Adaptive ATR) that can adapt to changing specifications in materials characterization (meaning density, as measured by its x-ray attenuation coefficient), its mass, and its thickness. Our design uses a two-stage cascaded approach, in which the first stage is characterized by a high recall rate over the entire range of possibilities for the threat parameters that are allowed to change. The purpose of the second stage is to then fine-tune the performance of the overall system for the current threat specifications. The computational effort for this fine-tuning for achieving a desired PD/PFA rate is far less than what it would take to create a new classifier with the same overall performance for the new set of threat specifications.
Nonlinear functional analysis and optimal economic growth

Abstract A problem of existence and characterization of solutions of optimal growth models in many sector economies is studied. The social utility to be optimized is a generalized form of a preference depending additively on consumption at the different dates of the planning period. The optimization is restricted to a set of admissible growth paths defined by production-investment-consumption relations described by a system of differential equations. Sufficient conditions are given for existence of a solution in a Hilbert space of paths, without convexity assumptions on either the utilities or the technology, using techniques of nonlinear functional analysis. A characterization is given of the utilities which are continuous with respect to the Hilbert space norm. Under convexity assumptions a characterization is also given of optimal and efficient solutions by competitive prices.
DELTOID MOMENT ARMS DURING ABDUCTION: A SUBJECT-SPECIFIC MUSCULOSKELETAL MODELING STUDY IN HEALTHY SHOULDERS AND SHOULDERS WITH RTSA

Reverse total shoulder arthroplasty (RTSA) is increasingly used in the United States since approval by the FDA in 2003. RTSA relieves pain and restores mobility in arthritic rotator cuff deficient shoulders. Though many advantages of RTSA have been demonstrated, there still are a variety of complications (implant loosening, shoulder impingement, infection, frozen shoulder) making apparent much still is to be learned how RTSA modifies normal shoulder function. The goal of this study was to assess how RTSA affects deltoid muscle moment generating capacity post-surgery using a subject-specific computational model driven by in vivo kinematic data. A subject-specific 12 degree-of-freedom (DOF) musculoskeletal model was used to analyze the shoulders of 27 subjects (14-RTSA, 12-Normal). The model was modified from the work of Holzbaur et al. to directly input 6 DOF humerus and scapula kinematics obtained using fluoroscopy. Model geometry was scaled according to each subject9s skeletal dimensions. In vivo abduction kinematics for each subject were input to their subject-specific model and muscle moment arms for the anterior, lateral and posterior aspects of the deltoid were measured over the arc of motion. Similar patterns of muscle moment arm changes were observed for normal and RTSA shoulders. The moment arm of the anterior deltoid was positive with the arm at the side and decreased monotonically, crossing zero (the point at which the muscle fibers pass across the joint center) between 50°–60° glenohumeral abduction (Figure 1a). The average moment arm of the lateral deltoid was constant and positive in normal shoulders, but showed a decreasing trend with abduction in RTSA shoulders (Figure 1b). The posterior deltoid moment arm was negative with the arm at the side, and increased monotonically to a positive value with increasing glenohumeral abduction (Figure 1c). Subject-specific moment arm values for RTSA shoulders were highly variable compared to normal shoulders. 2-way repeated measures ANOVA showed significant differences between RTSA and normal shoulders for all three aspects of the deltoid moment arm, where the moment arms in RTSA shoulders were smaller in magnitude. Shoulder functional capacity is a product of the moment generating ability of the shoulder muscles which, in turn, are a function of the muscle moment arms and muscle forces. Placement of implant components during RTSA can directly affect the geometric relationship between the humerus and scapula and, therefore, the muscle moment arms in the RTSA shoulder. Our results show RTSA shoulders maintain the same muscle moment arm patterns as healthy shoulders, but they show much greater inter-subject variation and smaller moment arm magnitudes. These observations show directly how RTSA configuration and implant placement affect deltoid moment arms, and provide an objective basis for determining optimal implant configuration and surgical placement to maximize RTSA function in a patient-specific manner.
Disease-Marker Association†

Disease–marker association can be due to both genetic and nongenetic causes. The former are either that the marker plays a functional role in the disease process or that it is in close proximity to a disease locus (in linkage disequilibrium with it). Association can also be due to population stratification or admixture. Usual contingency table chi-square methods assume independence of alleles and/or individuals. In the absence of this independence, it is possible to allow for population heterogeneity by using genomic control (i.e. markers that are unlinked to the disease) or family-member controls, which provide various degrees of matching on ethnicity. Association studies can also use extended pedigree data provided that allowance is made for the statistical dependence of the members in the same pedigree. Keywords: attributable risks; bias; case–control studies; confounding; Hardy–Weinberg equilibrium; Mc Nemar statistic; odds ratio
Advanced head and neck surgery training during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has significantly impacted medical training. Here we assess its effect on head and neck surgical education. METHODS: Surveys were sent to current accredited program directors and trainees to assess the impact of COVID-19 on the fellow's experience and employment search. Current fellows' operative logs were compared with those of the 2018 to 2019 graduates. RESULTS: Despite reduction in operative volume, 82% of current American Head and Neck Society fellows have reached the number of major surgical operations to support certification. When surveyed, 86% of program directors deemed their fellow ready to enter practice. The majority of fellows felt prepared to practice ablative (96%), and microvascular surgery (73%), and 57% have secured employment to follow graduation. Five (10%) had a pending job position put on hold due to the pandemic. CONCLUSIONS: Despite the impact of the COVID-19 pandemic, current accredited trainees remain well-positioned to obtain proficiency and enter the work-force.
Growth from Globalization? A View from the Very Long Run

What is the connection between different forms of globalization, economic growth, and welfare? International trade, cross-border capital flows, and labor movements are three areas in which economic historians have focused their research. I critically summarize various measures of international integration in each of these spheres. I then move on to discuss and evaluate the ongoing and active debate about whether globalization is significantly associated with growth in the past. I pay particular attention to the role of globalization in the Great Divergence, the tariff-trade-growth debate, and the globalization of capital markets in the 19th century.
Recrystallization and Melting of Partially Melted Stark Rubber

Two specimens from a piece of stark rubber which had a melting temperature originally at 41 °C were partially melted at 38 °C and allowed to stand for 11 years, one at about 25 °C, the other at 38 °C. The first showed a continuous slow decrease in specific volume, while the second increased slowly in volume for 5 months and remained constant for about 5 months more before showing the continuous slow decrease. The melting temperature of the first increased to 45 °C and the melting range was broadened. The melting temperature of the second became 52 °C and the range was narrowed. Perfecting or enlarging of crystals and the effect of higher recrystallization temperatures have influenced the melting temperatures. The effect of heating rate is discussed in connection with slow increases in volume, attributed to relaxation of oriented regions, during melting.
Tensor algebras in finite tensor categories

This paper introduces methods for classifying actions of finite-dimensional Hopf algebras on path algebras of quivers, and more generally on tensor algebras $T_B(V)$ where $B$ is semisimple. We work within the broader framework of finite (multi-)tensor categories $\mathcal{C}$, classifying tensor algebras in $\mathcal{C}$ in terms of $\mathcal{C}$-module categories. We obtain two classification results for actions of semisimple Hopf algebras: the first for actions which preserve the ascending filtration on tensor algebras, and the second for actions which preserve the descending filtration on completed tensor algebras. Extending to more general fusion categories, we illustrate our classification result for tensor algebras in the pointed fusion categories ${\sf Vec}_{G}^{\omega}$ and in group-theoretical fusion categories, especially for the representation category of the Kac-Paljutkin Hopf algebra.
The evolution and limits of spectral bandwidth in free electron lasers

Abstract We study the bandwidth in free electron lasers (FELs) under different circumstances; for weakly saturated FELs in storage rings, the bandwidth is given by the formula derived in the supermode theory, while it is given by the Fourier transform of the electron pulse length in the strongly saturated FELs in linacs. The limiting bandwidth for the case of a dc beam is given by a Schawlow-Townes formula, but the approach to the limit is very slow.
Monetary policy rules and macroeconomic stability: Evidence and some theory

We estimate a forward-looking monetary policy reaction function for the postwar United States economy, before and after Volcker’s appointment as Fed Chairman in 1979. Our results point to substantial differences in the estimated rule across periods. In particular, interest rate policy in the Volcker-Greenspan period appears to have been much more sensitive to changes in expected ineation than in the pre-Volcker period. We then compare some of the implications of the estimated rules for the equilibrium properties of ineation and output, using a simple macroeconomic model, and show that the Volcker-Greenspan rule is stabilizing.
Retinopathy of Prematurity

Retinopathy of prematurity is a common disorder among extremely low-birthweight preterm infants and may cause total vision loss in as many as 2% to 4% of those weighing less than 2 pounds at birth. Regular examinations begun in the neonatal intensive care unit permit early detection and treatment of progressive ROP and allow a reduction in visual impairment. Infants whose ROP has regressed should continue to receive regular ophthalmologic follow-up for the detection and treatment of myopia and strabismus and, if they have cicatricial sequelae, late retinal detachments as teens or adults. Early intervention and special education programs are important for the children whose vision loss is significant.
Factor Analysis: Exploratory†

Exploratory factor analysis (EFA) provides a way to account for intercorrelations or covariances among all pairs of variables in a battery using a relatively small number of latent variables called common factors. The method can be especially helpful for analyzing structural relationships in situations where observed variables are approximately linearly related to one another, and when individual-level observations are fallible, that is, subject to errors of measurement. Exploratory factor analysis has gained prominence in many fields over many years, and it has been the subject of much praise as well as criticism. Psychometric and statistical practice has not fully caught up with contemporary appreciations of factor analytic and related methods, but past and recent developments suggest notable promise for continued applications in applied sciences. Keywords: common factor analysis; exploratory factor analysis; principal component analysis; multivariate methods
Identification of Models of the Labor Market

This chapter discusses identification of common selection models of the labor market. We start with the classic Roy model and show how it can be identified with exclusion restrictions. We then extend the argument to the generalized Roy model, treatment effect models, duration models, search models, and dynamic discrete choice models. In all cases, key ingredients for identification are exclusion restrictions and support conditions.
Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI

We propose a model-based deep learning architecture for the reconstruction of highly accelerated diffusion magnetic resonance imaging (MRI) that enables high resolution imaging. The proposed reconstruction jointly recovers all the diffusion weighted images in a single step from a joint k-q under-sampled acquisition in a parallel MRI setting. We propose the novel use of a pre-trained denoiser as a regularizer in a model-based reconstruction for the recovery of highly under-sampled data. Specifically, we designed the denoiser based on a general diffusion MRI tissue microstructure model for multi-compartmental modeling. By using a wide range of biologically plausible parameter values for the multi-compartmental microstructure model, we simulated diffusion signal that spans the entire microstructure parameter space. A neural network was trained in an unsupervised manner using an autoencoder to learn the diffusion MRI signal subspace. We employed the autoencoder in a model-based reconstruction and show that the autoencoder provides a strong denoising prior to recover the q-space signal. We show reconstruction results on a simulated brain dataset that shows high acceleration capabilities of the proposed method.
An objective Bayesian analysis of life's early start and our late arrival.

Life emerged on Earth within the first quintile of its habitable window, but a technological civilization did not blossom until its last. Efforts to infer the rate of abiogenesis, based on its early emergence, are frustrated by the selection effect that if the evolution of intelligence is a slow process, then life’s early start may simply be a prerequisite to our existence, rather than useful evidence for optimism. In this work, we interpret the chronology of these two events in a Bayesian framework, extending upon previous work by considering that the evolutionary timescale is itself an unknown that needs to be jointly inferred, rather than fiducially set. We further adopt an objective Bayesian approach, such that our results would be agreed upon even by those using wildly different priors for the rates of abiogenesis and evolution—common points of contention for this problem. It is then shown that the earliest microfossil evidence for life indicates that the rate of abiogenesis is at least 2.8 times more likely to be a typically rapid process, rather than a slow one. This modest limiting Bayes factor rises to 8.7 if we accept the more disputed evidence of 13C-depleted zircon deposits [E. A. Bell, P. Boehnke, T. M. Harrison, W. L. Mao, Proc. Natl. Acad. Sci. U.S.A. 112, 14518–14521 (2015)]. For intelligence evolution, it is found that a rare-intelligence scenario is slightly favored at 3:2 betting odds. Thus, if we reran Earth’s clock, one should statistically favor life to frequently reemerge, but intelligence may not be as inevitable.
Identification of Multiple Invalid Pairing-based Signatures in Constrained Batches.

This paper describes a new method in pairing-based signature schemes for identifying the invalid digital signatures in a batch after batch verification has failed. The method more efficiently identifies non-trivial numbers, w, of invalid signatures in constrained sized, N , batches than previously published methods, and does not require that the verifier possess detailed knowledge of w. Our method uses “divide-and-conquer” search to identify the invalid signatures within a batch, pruning the search tree to reduce the number of pairing computations required. The method prunes the search tree more rapidly than previously published techniques and thereby provides performance gains for batch sizes of interest. We are motivated by wireless systems where the verifier seeks to conserve computations or a related resource, such as energy, by using large batches. However, the batch size is constrained by how long the verifier can delay batch verification while accumulating signatures to verify. We compare the expected performance of our method (for a number of different signature schemes at varying security levels) for varying batch sizes and numbers of invalid signatures against earlier methods. We find that our new method provides the best performance for constrained batches, whenever the number of invalid signatures is less than half the batch size. We include recently published methods based on techniques from the group-testing literature in our analysis. Our new method consistently outperforms these group-testing based methods, and substantially reduces the cost (> 50%) when w ≤ N/4.
Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off

Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models, and there is an increasing interest in providing accurate uncertainty estimation on many tasks, such as security cameras and autonomous driving vehicles. In this paper, we focus on the two main use cases of uncertainty estimation, i.e., selective prediction and confidence calibration. We first reveal potential issues of commonly used quality metrics for uncertainty estimation in both use cases, and propose our new metrics to mitigate them. We then apply these new metrics to explore the trade-off between model complexity and uncertainty estimation quality, a critically missing work in the literature. Our empirical experiment results validate the superiority of the proposed metrics, and some interesting trends about the complexity-uncertainty trade-off are observed.
Deep Parametric Shape Predictions Using Distance Fields

Many tasks in graphics and vision demand machinery for converting shapes into consistent representations with sparse sets of parameters; these representations facilitate rendering, editing, and storage. When the source data is noisy or ambiguous, however, artists and engineers often manually construct such representations, a tedious and potentially time-consuming process. While advances in deep learning have been successfully applied to noisy geometric data, the task of generating parametric shapes has so far been difficult for these methods. Hence, we propose a new framework for predicting parametric shape primitives using deep learning. We use distance fields to transition between shape parameters like control points and input data on a pixel grid. We demonstrate efficacy on 2D and 3D tasks, including font vectorization and surface abstraction.
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning

This paper studies the problem of post-hoc calibration of machine learning classifiers. We introduce the following desiderata for uncertainty calibration: (a) accuracy-preserving, (b) data-efficient, and (c) high expressive power. We show that none of the existing methods satisfy all three requirements, and demonstrate how Mix-n-Match calibration strategies (i.e., ensemble and composition) can help achieve remarkably better data-efficiency and expressive power while provably maintaining the classification accuracy of the original classifier. Mix-n-Match strategies are generic in the sense that they can be used to improve the performance of any off-the-shelf calibrator. We also reveal potential issues in standard evaluation practices. Popular approaches (e.g., histogram-based expected calibration error (ECE)) may provide misleading results especially in small-data regime. Therefore, we propose an alternative data-efficient kernel density-based estimator for a reliable evaluation of the calibration performance and prove its asymptotically unbiasedness and consistency. Our approaches outperform state-of-the-art solutions on both the calibration as well as the evaluation tasks in most of the experimental settings. Our codes are available at this https URL.
Understanding Self-Training for Gradual Domain Adaptation

Machine learning systems must adapt to data distributions that evolve over time, in applications ranging from sensor networks and self-driving car perception modules to brain-machine interfaces. We consider gradual domain adaptation, where the goal is to adapt an initial classifier trained on a source domain given only unlabeled data that shifts gradually in distribution towards a target domain. We prove the first non-vacuous upper bound on the error of self-training with gradual shifts, under settings where directly adapting to the target domain can result in unbounded error. The theoretical analysis leads to algorithmic insights, highlighting that regularization and label sharpening are essential even when we have infinite data, and suggesting that self-training works particularly well for shifts with small Wasserstein-infinity distance. Leveraging the gradual shift structure leads to higher accuracies on a rotating MNIST dataset and a realistic Portraits dataset.
Extremal triangle-free and odd-cycle-free colourings of uncountable graphs

The optimality of the Erdős–Rado theorem for pairs is witnessed by the colouring $$\Delta_\kappa : [2^\kappa]^2 \rightarrow \kappa$$ recording the least point of disagreement between two functions. This colouring has no monochromatic triangles or, more generally, odd cycles. We investigate a number of questions investigating the extent to which $$\Delta_\kappa$$ is an extremal such triangle-free or odd-cycle-free colouring. We begin by introducing the notion of $$\Delta$$ -regressive and almost $$\Delta$$ -regressive colourings and studying the structures that must appear as monochromatic subgraphs for such colourings. We also consider the question as to whether $$\Delta_\kappa$$ has the minimal cardinality of any maximal triangle-free or odd-cycle-free colouring into $$\kappa$$ . We resolve the question positively for odd-cycle-free colourings.
Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques

Among all animals, mosquitoes are responsible for the most deaths worldwide. Interestingly, not all types of mosquitoes spread diseases, but rather, a select few alone are competent enough to do so. In the case of any disease outbreak, an important first step is surveillance of vectors (i.e., those mosquitoes capable of spreading diseases). To do this today, public health workers lay several mosquito traps in the area of interest. Hundreds of mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have to identify only the vectors to gauge their density. This process today is manual, requires complex expertise/ training, and is based on visual inspection of each trapped specimen under a microscope. It is long, stressful and self-limiting. This paper presents an innovative solution to this problem. Our technique assumes the presence of an embedded camera (similar to those in smart-phones) that can take pictures of trapped mosquitoes. Our techniques proposed here will then process these images to automatically classify the genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes when trained on 25, 867 images of 250 trapped mosquito vector specimens captured via many smart-phone cameras. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are especially deadly vectors) is amongst the highest. We also present important lessons learned and practical impact of our techniques in this paper.
Generalized Frequency-Domain SAR Processing

The range-Doppler algorithm and the chirp-scaling c algorithm (CSA) process synthetic aperture radar (SAR) data with approximations to ideal SAR processing. These approximations are invalid for data from systems with wide beamwidths, large bandwidths, and/or low center frequencies. While simple and efficient, these frequency-domain methods are thus limited by the SAR parameters. This paper explores these limits and proposes a generalized chirp-scaling approach for extending the utility of frequency-domain processing. We demonstrate how different order approximations of the SAR signal in the 2-D frequency domain affect image focusing for varying SAR parameters. From these results, a guideline is set forth, which suggests the required order of approximation terms for proper focusing. A proposed generalized frequency-domain processing approach is derived. This method is an efficient arbitrary-order CSA that processes the data using the appropriate number of approximation terms. The new method is demonstrated using simulated data.
A Closed-Form Model-Free Implied Volatility Formula through Delta Families

In this article, we derive a closed-form explicit model-free formula for the (Black-Scholes) implied volatility. The method is based on the novel use of the Dirac Delta function, corresponding delta families, and the change of variable technique. The formula is expressed through either a limit or as an infinite series of elementary functions, and we establish that the proposed formula converges to the true implied volatility value. In numerical experiments, we verify the convergence of the formula, and consider several benchmark cases, for which the data generating processes are respectively the stochastic volatility inspired (SVI) model, and the stochastic alpha beta rho (SABR) model. We also establish an explicit formula for the implied volatility expressed directly in terms of respective model parameters, and use the Heston model to illustrate this idea. The delta family and change of variable technique that we develop are of independent interest and can be used to solve inverse problems arising in other applications. TOPIC:Derivatives Key Findings ▪ A novel closed-form representation of the Black-Scholes implied volatility is developed by utilizing a delta-family technique. ▪ Convergence and error analyses of approximate forms of this representations are presented. ▪ This technique is applied to the parametric SVI and SABR models as well as the stochastic volatility Heston model.
Structures of self-assembled n-alkanethiols on gold by reflection high-energy electron diffraction.

The structures of long-chain alkanethiols (C18H37SH) chemisorbed on an Au(111) single crystal were investigated using reflection high-energy electron diffraction (RHEED). The primary structure observed as a major species in the as-deposited films contains gold adatoms below the sulfur headgroups. Between the small ordered domains with the alkyl chains tilting toward six directions are azimuthally disorderly packed regions, with a similar average tilt of 30.2°. In contrast, a significant reduction in the coverage of gold adatoms is found in the thermally-induced phase. This superlattice is shown to contain a mixture of two sulfur arrangements, both of which exhibit a small S–S distance, and the pairing of the aliphatic chains. A microscopic picture is then given for the structural transition. These findings demonstrate how the RHEED technique may be used to resolve structures of nanometer-thick thin films with multiple orders at the interfaces.
Workflow Automation for Cyber Physical System Development Processes

Development of Cyber Physical Systems (CPSs) requires close interaction between developers with expertise in many domains to achieve ever-increasing demands for improved performance, reduced cost, and more system autonomy. Each engineering discipline commonly relies on domain-specific modeling languages, and analysis and execution of these models is often automated with appropriate tooling. However, integration between these heterogeneous models and tools is often lacking, and most of the burden for inter-operation of these tools is placed on system developers. To address this problem, we introduce a workflow modeling language for the automation of complex CPS development processes and implement a platform for execution of these models in the Assurance-based Learning-enabled CPS (ALC) Toolchain. Several illustrative examples are provided which show how these workflow models are able to automate many time-consuming integration tasks previously performed manually by system developers.
A1 reactive astrocytes and a loss of TREM2 are associated with an early stage of pathology in a mouse model of cerebral amyloid angiopathy

Cerebral amyloid angiopathy (CAA) is typified by the cerebrovascular deposition of amyloid. The mechanisms underlying the contribution of CAA to neurodegeneration are not currently understood. Although CAA is highly associated with the accumulation of amyloid beta (Aβ), other amyloids are known to associate with the vasculature. Alzheimer’s disease (AD) is characterized by parenchymal Aβ deposition, intracellular accumulation of tau, and significant neuroinflammation. CAA increases with age and is present in 85–95% of individuals with AD. A substantial amount of research has focused on understanding the connection between parenchymal amyloid and glial activation and neuroinflammation, while associations between vascular amyloid pathology and glial reactivity remain understudied. Here, we dissect the glial and immune responses associated with early-stage CAA with histological, biochemical, and gene expression analyses in a mouse model of familial Danish dementia (FDD), a neurodegenerative disease characterized by the vascular accumulation of Danish amyloid (ADan). Findings observed in this CAA mouse model were complemented with primary culture assays. We demonstrate that early-stage CAA is associated with dysregulation in immune response networks and lipid processing, severe astrogliosis with an A1 astrocytic phenotype, and decreased levels of TREM2 with no reactive microgliosis. Our results also indicate how cholesterol accumulation and ApoE are associated with vascular amyloid deposits at the early stages of pathology. We also demonstrate A1 astrocytic mediation of TREM2 and microglia homeostasis. The initial glial response associated with early-stage CAA is characterized by the upregulation of A1 astrocytes without significant microglial reactivity. Gene expression analysis revealed that several AD risk factors involved in immune response and lipid processing may also play a preponderant role in CAA. This study contributes to the increasing evidence that brain cholesterol metabolism, ApoE, and TREM2 signaling are major players in the pathogenesis of AD-related dementias, including CAA. Understanding the basis for possible differential effects of glial response, ApoE, and TREM2 signaling on parenchymal plaques versus vascular amyloid deposits provides important insight for developing future therapeutic interventions.
In silico identification of conserved cis-acting RNA elements in the SARS-CoV-2 genome

Aim The aim of this study was to computationally predict conserved RNA sequences and structures known as cis-acting RNA elements (CREs) in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome. Materials & methods Bioinformatics tools were used to analyze and predict CREs by obtaining viral sequences from available databases. Results Computational analysis revealed the presence of RNA stem-loop structures within the 3' end of the ORF1ab region analogous to previously identified SARS-CoV genomic packaging signals. Alignment-based RNA secondary structure predictions of the 5' end of the SARS-CoV-2 genome also identified conserved CREs. Conclusion These CREs may be potential vaccine and/or antiviral therapeutic targets; however, further studies are warranted to confirm their roles in the SARS-CoV-2 life cycle.