cssi/SciSciGPT-SciSciNet huggingface dataset:
Computational framework for behind-the-meter DER techno-economic modeling and optimization: REopt Lite

The energy system is undergoing a major transformation with the global emphasis on decarbonization. Distributed generation is projected to play a significant role in the new energy system, and energy models are informing how distributed generation can be integrated reliably and economically. In this work, we present an end-to-end computational framework for distributed energy resource (DER) modeling, REopt Lite™, which captures the interface of technology, economics, and policy in the energy modeling process. We describe the problem space, the building blocks of the model, the scaling capabilities of the design, the optimization formulation, and the extensibility of the model. We present a framework for accelerating the techno-economic analysis of behind-the-meter distributed energy resources to enable rapid planning and decision-making, thereby enabling greater renewable energy deployment. This computation framework is open-sourced to facilitate transparency, flexibility, and wider collaboration opportunities within the worldwide energy modeling community.
Partitioning signal classes using transport transforms for data analysis and machine learning

A relatively new set of transport-based transforms (CDT, R-CDT, LOT) have shown their strength and great potential in various image and data processing tasks such as parametric signal estimation, classification, cancer detection among many others. It is hence worthwhile to elucidate some of the mathematical properties that explain the successes of these transforms when they are used as tools in data analysis, signal processing or data classification. In particular, we give conditions under which classes of signals that are created by algebraic generative models are transformed into convex sets by the transport transforms. Such convexification of the classes simplify the classification and other data analysis and processing problems when viewed in the transform domain. More specifically, we study the extent and limitation of the convexification ability of these transforms under an algebraic generative modeling framework. We hope that this paper will serve as an introduction to these transforms and will encourage mathematicians and other researchers to further explore the theoretical underpinnings and algorithmic tools that will help understand the successes of these transforms and lay the groundwork for further successful applications.
Privacy Protection of Grid Users Data with Blockchain and Adversarial Machine Learning

Utilities around the world are reported to invest a total of around \$30 billion over the next few years for installation of more than 300 million smart meters, replacing traditional analog meters \citeinfo. By mid-decade, with full country wide deployment, there will be almost 1.3 billion smart meters in place \citeinfo. Collection of fine-grained energy usage data by these smart meters provides numerous advantages such as energy savings for customers with use of demand optimization, a billing system of higher accuracy with dynamic pricing programs, bidirectional information exchange ability between end-users for better consumer-operator interaction, and so on. However, all these perks associated with fine-grained energy usage data collection threaten the privacy of users. With this technology, customers' personal data such as sleeping cycle, number of occupants, and even type and number of appliances stream into the hands of the utility companies and can be subject to misuse. This research paper addresses privacy violation of consumers' energy usage data collected from smart meters and provides a novel solution for the privacy protection while allowing benefits of energy data analytics. First, we demonstrate the successful application of occupancy detection attacks using a deep neural network method that yields high accuracy results. We then introduce Adversarial Machine Learning Occupancy Detection Avoidance with Blockchain (AMLODA-B) framework as a counter-attack by deploying an algorithm based on the Long Short Term Memory (LSTM) model into the standardized smart metering infrastructure to prevent leakage of consumer's personal information. Our privacy-aware approach protects consumers' privacy without compromising the correctness of billing and preserves operational efficiency without use of authoritative intermediaries.
Differentiable Open-Ended Commonsense Reasoning

Current commonsense reasoning research focuses on developing models that use commonsense knowledge to answer multiple-choice questions. However, systems designed to answer multiple-choice questions may not be useful in applications that do not provide a small list of candidate answers to choose from. As a step towards making commonsense reasoning research more realistic, we propose to study open-ended commonsense reasoning (OpenCSR) — the task of answering a commonsense question without any pre-defined choices — using as a resource only a corpus of commonsense facts written in natural language. OpenCSR is challenging due to a large decision space, and because many questions require implicit multi-hop reasoning. As an approach to OpenCSR, we propose DrFact, an efficient Differentiable model for multi-hop Reasoning over knowledge Facts. To evaluate OpenCSR methods, we adapt several popular commonsense reasoning benchmarks, and collect multiple new answers for each test question via crowd-sourcing. Experiments show that DrFact outperforms strong baseline methods by a large margin.
Determining Ionizing Doses in Medium Earth Orbits Using Long-Term GPS Particle Measurements

We use long-term electron and proton in-situ measurements made by the CXD particle instruments, developed by Los Alamos National Laboratory and carried on board GPS satellites, to determine total ionizing dose (TID) values and daily/yearly dose rate (DR) values in medium Earth orbits (MEOs) caused by the natural space radiation environment. Here measurement-based TID and DR values on a simplified sample geometry-a small (with a radius of 0.1 mm) Silicon detector within an Aluminum shielding sphere with a thickness of 100 mil-are compared to those calculated from empirical radiation models. Results over the solar cycle 24 show that electron TID from measurements in GPS orbit is well above the values calculated from the median/mean fluences from AE8 and AE9 models, but close to model fluences at high percentiles. Also, it is confirmed that in MEOs proton contributions to TID are minor and mainly dominated by solar energetic protons. Several factors affecting those dose calculations are discussed and evaluated. Results from this study provide us another out-of-sample test on the reliability of existing empirical space radiation models, and also help estimate the margin factors on calculated dose values in MEOs that pass through the heart of the Earth's outer radiation belt.
Navigation Between Geosynchronous and Lunar L1 Orbiters

Linked Autonomous Interplanetary Satellite Orbit Navigation (LiAISON) is a new technique that takes advantage of the asymmetrical gravity field present in a three-body system in order to perform absolute tracking of satellites using only relative satellite-to-satellite observations. Previous studies have demonstrated LiAISON's practical applications for lunar missions, including a satellite in a halo orbit about either the Earth-Moon L1 or L2 point. This paper studies the viability of applying LiAISON measurements between a lunar halo orbiter and a satellite in a geosynchronous orbit. Simulations demonstrate that the absolute positions and velocities of both satellites are observable using only relative measurements with an achieved uncertainty on the order of observation noise.
Ultrafast amplitude modulation for molecular and hemodynamic ultrasound imaging.

Ultrasound is playing an emerging role in molecular and cellular imaging thanks to new micro- and nanoscale contrast agents and reporter genes. Acoustic methods for the selective in vivo detection of these imaging agents are needed to maximize their impact in biology and medicine. Existing ultrasound pulse sequences use the nonlinearity in contrast agents' response to acoustic pressure to distinguish them from mostly linear tissue scattering. However, such pulse sequences typically scan the sample using focused transmissions, resulting in a limited frame rate and restricted field of view. Meanwhile, existing wide-field scanning techniques based on plane wave transmissions suffer from limited sensitivity or nonlinear artifacts. To overcome these limitations, we introduce an ultrafast nonlinear imaging modality combining amplitude-modulated pulses, multiplane wave transmissions, and selective coherent compounding. This technique achieves contrast imaging sensitivity comparable to much slower gold-standard amplitude modulation sequences and enables the acquisition of larger and deeper fields of view, while providing a much faster imaging framerate of 3.2 kHz. Additionally, it enables simultaneous nonlinear and linear image formation and allows concurrent monitoring of phenomena accessible only at ultrafast framerates, such as blood volume variations. We demonstrate the performance of this ultrafast amplitude modulation technique by imaging gas vesicles, an emerging class of genetically encodable biomolecular contrast agents, in several in vitro and in vivo contexts. These demonstrations include the rapid discrimination of moving contrast agents and the real-time monitoring of phagolysosomal function in the mouse liver.
The Impacts of Convex Piecewise Linear Cost Formulations on AC Optimal Power Flow

Abstract Despite strong connections through shared application areas, research efforts on power market optimization (e.g., unit commitment) and power network optimization (e.g., optimal power flow) remain largely independent. A notable illustration of this is the treatment of power generation cost functions, where nonlinear network optimization has largely used polynomial representations and market optimization has adopted piecewise linear encodings. This work combines state-of-the-art results from both lines of research to understand the best mathematical formulations of the nonlinear AC optimal power flow problem with piecewise linear generation cost functions. An extensive numerical analysis of non-convex models, linear approximations, and convex relaxations across fifty-four realistic test cases illustrates that nonlinear optimization methods are surprisingly sensitive to the mathematical formulation of piecewise linear functions. The results indicate that a poor formulation choice can slow down algorithm performance by a factor of ten, increasing the runtime from seconds to minutes. These results provide valuable insights into the best formulations of nonlinear optimal power flow problems with piecewise linear cost functions, an important step towards building a new generation of energy markets that incorporate the nonlinear AC power flow model.
A Petri Dish for Histopathology Image Analysis

With the rise of deep learning, there has been increased interest in using neural networks for histopathology image analysis, a field that investigates the properties of biopsy or resected specimens traditionally manually examined under a microscope by pathologists. However, challenges such as limited data, costly annotation, and processing high-resolution and variable-size images make it difficult to quickly iterate over model designs.
TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer

Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Current quantitative approaches, including radiomics and deep learning models, do not explicitly capture the complex and subtle parenchymal structures, such as fibroglandular tissue. In this paper, we propose a novel method to direct a neural network’s attention to a dedicated set of voxels surrounding biologically relevant tissue structures. By extracting multi-dimensional topological structures with high saliency, we build a topology-derived biomarker, TopoTxR. We demonstrate the efficacy of TopoTxR in predicting response to neoadjuvant chemotherapy in breast cancer. Our qualitative and quantitative results suggest differential topological behavior of breast tissue on treatment-naive imaging, in patients who respond favorably to therapy versus those who do not.
Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks

Highly constrained manipulation tasks continue to be challenging for autonomous robots as they require high levels of precision, typically less than 1mm, which is often incompatible with what can be achieved by traditional perception systems. This paper demonstrates that the combination of state-of-the-art object tracking with passively adaptive mechanical hardware can be leveraged to complete precision manipulation tasks with tight, industrially-relevant tolerances (0.25mm). The proposed control method closes the loop through vision by tracking the relative 6D pose of objects in the relevant workspace. It adjusts the control reference of both the compliant manipulator and the hand to complete object insertion tasks via within-hand manipulation. Contrary to previous efforts for insertion, our method does not require expensive force sensors, precision manipulators, or time-consuming, online learning, which is data hungry. Instead, this effort leverages mechanical compliance and utilizes an object-agnostic manipulation model of the hand learned offline, off-the-shelf motion planning, and an RGBD-based object tracker trained solely with synthetic data. These features allow the proposed system to easily generalize and transfer to new tasks and environments. This paper describes in detail the system components and showcases its efficacy with extensive experiments involving tight tolerance peg-in-hole insertion tasks of various geometries as well as open-world constrained placement tasks.
Practical selection of representative sets of RNA-seq samples using a hierarchical approach.

Motivation Despite numerous RNA-seq samples available at large databases, most RNA-seq analysis tools are evaluated on a limited number of RNA-seq samples. This drives a need for methods to select a representative subset from all available RNA-seq samples to facilitate comprehensive, unbiased evaluation of bioinformatics tools. In sequence-based approaches for representative set selection (e.g. a k-mer counting approach that selects a subset based on k-mer similarities between RNA-seq samples), because of the large numbers of available RNA-seq samples and of k-mers/sequences in each sample, computing the full similarity matrix using k-mers/sequences for the entire set of RNA-seq samples in a large database (e.g. the SRA) has memory and runtime challenges; this makes direct representative set selection infeasible with limited computing resources. Results We developed a novel computational method called 'hierarchical representative set selection' to handle this challenge. Hierarchical representative set selection is a divide-and-conquer-like algorithm that breaks representative set selection into sub-selections and hierarchically selects representative samples through multiple levels. We demonstrate that hierarchical representative set selection can achieve summarization quality close to that of direct representative set selection, while largely reducing runtime and memory requirements of computing the full similarity matrix (up to 8.4× runtime reduction and 5.35× memory reduction for 10 000 and 12 000 samples respectively that could be practically run with direct subset selection). We show that hierarchical representative set selection substantially outperforms random sampling on the entire SRA set of RNA-seq samples, making it a practical solution to representative set selection on large databases like the SRA. Availability and implementation The code is available at https://github.com/Kingsford-Group/hierrepsetselection and https://github.com/Kingsford-Group/jellyfishsim. Supplementary information Supplementary data are available at Bioinformatics online.
Accelerating Key Bioinformatics Tasks 100-fold by Improving Memory Access

Most experimental sciences now rely on computing, and biological sciences are no exception. As datasets get bigger, so do the computing costs, making proper optimization of the codes used by scientists increasingly important. Many of the codes developed in recent years are based on the Python-based NumPy, due to its ease of use and good performance characteristics. The composable nature of NumPy, however, does not generally play well with the multi-tier nature of modern CPUs, making any non-trivial multi-step algorithm limited by the external memory access speeds, which are hundreds of times slower than the CPU's compute capabilities. In order to fully utilize the CPU compute capabilities, one must keep the working memory footprint small enough to fit in the CPU caches, which requires splitting the problem into smaller portions and fusing together as many steps as possible. In this paper, we present changes based on these principles to two important functions in the scikit-bio library, principal coordinates analysis and the Mantel test, that resulted in over 100x speed improvement in these widely used, general-purpose tools.
A meta-inference framework to integrate multiple external models into a current study.

It is becoming increasingly common for researchers to consider incorporating external information from large studies to improve the accuracy of statistical inference instead of relying on a modestly sized data set collected internally. With some new predictors only available internally, we aim to build improved regression models based on individual-level data from an "internal" study while incorporating summary-level information from "external" models. We propose a meta-analysis framework along with two weighted estimators as the composite of empirical Bayes estimators, which combines the estimates from different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance trade-off while preserving the most efficiency gain. The proposed estimators are more efficient than the naive analysis of the internal data and other naive combinations of external estimators.
A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations

Abstract Lending decisions are usually made with proprietary models that provide minimally acceptable explanations to users. In a future world without such secrecy, what decision support tools would one want to use for justified lending decisions? This question is timely, since the economy has dramatically shifted due to a pandemic, and a massive number of new loans will be necessary in the short term. We propose a framework for such decisions, including a globally interpretable machine learning model, an interactive visualization of it, and several types of summaries and explanations for any given decision. The machine learning model is a two-layer additive risk model, which resembles a two-layer neural network, but is decomposable into subscales. In this model, each node in the first (hidden) layer represents a meaningful subscale model, and all of the nonlinearities are transparent. Our online visualization tool allows exploration of this model, showing precisely how it came to its conclusion. We provide three types of explanations that are simpler than, but consistent with, the global model: case-based reasoning explanations that use neighboring past cases, a set of features that were the most important for the model's prediction, and summary-explanations that provide a customized sparse explanation for any particular lending decision made by the model. Our framework earned the FICO recognition award for the Explainable Machine Learning Challenge, which was the first public challenge in the domain of explainable machine learning. 1
On the speed of propagation of waves in a deformed compressible elastic material

A sinusoidal wave of infinitesimal amplitude is propagated in an isotropic compressible elastic material subjected to a static pure homogeneous deformation. Necessary and sufficient conditions are obtained for the speed of propagation to be real in the case when the direction of propagation is parallel to a principal plane of the static deformation.
The Quest for Strong Inapproximability Results with Perfect Completeness

The Unique Games Conjecture has pinned down the approximability of all constraint satisfaction problems (CSPs), showing that a natural semidefinite programming relaxation offers the optimal worst-case approximation ratio for any CSP. This elegant picture, however, does not apply for CSP instances that are perfectly satisfiable, due to the imperfect completeness inherent in the Unique Games Conjecture. This work is motivated by the pursuit of a better understanding of the approximability of perfectly satisfiable instances of CSPs. We prove that an “almost Unique” version of Label Cover can be approximated within a constant factor on satisfiable instances. Our main conceptual contribution is the formulation of a (hypergraph) version of Label Cover that we call V Label Cover. Assuming a conjecture concerning the inapproximability of V Label Cover on perfectly satisfiable instances, we prove the following implications: • There is an absolute constant c0 such that for k ≥ 3, given a satisfiable instance of Boolean k-CSP, it is hard to find an assignment satisfying more than c0k2/2k fraction of the constraints. • Given a k-uniform hypergraph, k ≥ 2, for all e > 0, it is hard to tell if it is q-strongly colorable or has no independent set with an e fraction of vertices, where q=⌈k+√k-1/2⌉. • Given a k-uniform hypergraph, k ≥ 3, for all e > 0, it is hard to tell if it is (k-1)-rainbow colorable or has no independent set with an e fraction of vertices.
Structure-preserving Model Reduction of Parametric Power Networks

We develop a structure-preserving parametric model reduction approach for linearized swing equations where parametrization corresponds to variations in operating conditions. We employ a global basis approach to develop the parametric reduced model in which we concatenate the local bases obtained via 1-2-based interpolatory model reduction. The residue of the underlying dynamics corresponding to the simple pole at zero varies with the parameters. Therefore, to have bounded H 2 and H ∞ errors, the reduced model residue for the pole at zero should match the original one over the entire parameter domain. Our framework achieves this goal by enriching the global basis based on a residue analysis. The effectiveness of the proposed method is illustrated through two numerical examples.
Distributed Online Linear Quadratic Control for Linear Time-invariant Systems

Classical linear quadratic (LQ) control centers around linear time-invariant (LTI) systems, where the control-state pairs introduce a quadratic cost with time-invariant parameters. Recent advancement in online optimization and control has provided novel tools to study LQ problems that are robust to time-varying cost parameters. Inspired by this line of research, we study the distributed online LQ problem for identical LTI systems. Consider a multi-agent network where each agent is modeled as an LTI system. The LTI systems are associated with decoupled, time-varying quadratic costs that are revealed sequentially. The goal of the network is to make the control sequence of all agents competitive to that of the best centralized policy in hindsight, captured by the notion of regret. We develop a distributed variant of the online LQ algorithm, which runs distributed online gradient descent with a projection to a semi-definite programming (SDP) to generate controllers. We establish a regret bound scaling as the square root of the finite time-horizon, implying that agents reach consensus as time grows. We further provide numerical experiments verifying our theoretical result.
Best Viewpoints for External Robots or Sensors Assisting Other Robots

This work creates a model of the value of different external viewpoints of a robot performing tasks. The current state of the practice is to use a teleoperated assistant robot to provide a view of a task being performed by a primary robot; however, the choice of viewpoints is ad hoc and does not always lead to improved performance. This research applies a psychomotor approach to develop a model of the relative quality of external viewpoints using Gibsonian affordances. In this approach, viewpoints for the affordances are rated based on the psychomotor behavior of human operators and clustered into manifolds of viewpoints with the equivalent value. The value of 30 viewpoints is quantified in a study with 31 expert robot operators for four affordances (reachability, passability, manipulability, and traversability) using a computer-based simulator of two robots. The adjacent viewpoints with similar values are clustered into ranked manifolds using agglomerative hierarchical clustering. The results show the validity of the affordance-based approach by confirming that there are manifolds of statistically significantly different viewpoint values, viewpoint values are statistically significantly dependent on the affordances, and viewpoint values are independent of a robot. Furthermore, the best manifold for each affordance provides a statistically significant improvement with a large Cohen's $d$ effect size (1.1–2.3) in the performance (improving time by 14%–59% and reducing errors by 87%–100%) and improvement in the performance variation over the worst manifold. This model will enable autonomous selection of the best possible viewpoint and path planning for the assistant robot.
Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development: In Silico Smart Molecular Design

Novel diagnostic and therapeutic radiopharmaceuticals are increasingly becoming a central part of personalized medicine. Continued innovation in the development of new radiopharmaceuticals is key to sustained growth and advancement of precision medicine. Artificial intelligence has been used in multiple fields of medicine to develop and validate better tools for patient diagnosis and therapy, including in radiopharmaceutical design. In this review, we first discuss common in silico approaches and focus on their usefulness and challenges in radiopharmaceutical development. Next, we discuss the practical applications of in silico modeling in design of radiopharmaceuticals in various diseases.
A Multiclass, Multiproduct Covid-19 Convalescent Plasma Donor Equilibrium Model

In this paper, we develop a multiclass, multiproduct equilibrium model for convalescent plasma donations in the Covid-19 pandemic. The potential donors are situated at different locations and the donor population at each location can be separated into different classes based on their motivation and the product for which they provide donations at a collection site. The model captures the competition between nonprofit and for-profit organizations seeking convalescent plasma donations, which is a characteristic of this new market. A variational inequality formulation of the equilibrium conditions and qualitative properties of the model are provided. We also present a capacitated version of the model. Numerical examples of increasing complexity are presented and solved using the modified projection method. The results reveal multiclass, multiproduct donor behavior under different scenarios which can inform policy makers during this pandemic and beyond.
Point Decomposition Problem in Binary Elliptic Curves.

We analyze the point decomposition problem (PDP) in binary elliptic curves. It is known that PDP in an elliptic curve group can be reduced to solving a particular system of multivariate non-linear system of equations derived from the so called Semaev summation polynomials. We modify the underlying system of equations by introducing some auxiliary variables. We argue that the trade-off between lowering the degree of Semaev polynomials and increasing the number of variables provides a significant speed-up.
Impact of pre-transplant induction therapy on outcomes of patients who undergo autologous stem cell transplantation for mantle cell lymphoma in first complete remission.

Mantle cell lymphoma is a rare subtype of non-Hodgkin's lymphoma with poor prognosis and continue to be challenging to treat. The choice of first line induction regimen remains a topic of debate due paucity of clinical trials. We retrospectively evaluated 66 patients diagnosed with mantle cell lymphoma who achieved first complete response after induction chemotherapy followed by autologous stem cell transplant. Treatment groups were divided into low-intensity versus high-intensity regimens. Our data showed the intensity of induction regimen does not impact posttransplant outcomes of mantle cell lymphoma who underwent autologous stem cell transplant in first complete response.
Quantifying the subjective cost of self-control in humans

Since Odysseus committed to resisting the Sirens, mechanisms to limit self-control failure have been a central feature of human behavior. Psychologists have long argued that the use of self-control is an effortful process and, more recently, that its failure arises when the cognitive costs of self-control outweigh its perceived benefits. In a similar way, economists have argued that sophisticated choosers can adopt "precommitment strategies" that tie the hands of their future selves in order to reduce these costs. Yet, we still lack an empirical tool to quantify and demonstrate the cost of self-control. Here, we develop and validate an economic decision-making task to quantify the subjective cost of self-control by determining the monetary cost a person is willing to incur in order to eliminate the need for self-control. We find that humans will pay to avoid having to exert self-control in a way that scales with increasing levels of temptation and that these costs appear to be modulated both by motivational incentives and stress exposure. Our psychophysical approach allows us to index moment-to-moment self-control costs at the within-subject level, validating important theoretical work across multiple disciplines and opening avenues of self-control research in healthy and clinical populations.
Secure Quantum Computation with Classical Communication.

The study of secure multi-party computation (MPC) has thus far been limited to the following two settings: every party is fully classical, or every party has quantum capabilities. This paper studies a notion of MPC that allows some classical and some quantum parties to securely compute a quantum functionality over their joint private inputs.
Adoptability and accuracy of point-of-care ultrasound in screening for valvular heart disease in the primary care setting

PURPOSE Despite continued efforts, a majority of patients with valvular heart disease (VHD) remain undiagnosed and untreated. This study aimed to assess the adoptability and accuracy of point-of-care handheld echocardiographic assessments (POCE) in the primary care setting. METHODS Eleven previously untrained primary care providers were trained to use the Vscan Extend (GE, WI) POCE to assess VHD, left ventricular function (LVEF), and major extra-cardiac findings. Their assessments were compared to those of three blinded expert readers. A total of 175 patients underwent POCE assessments which were evaluated using Kappa statistics (κ) together with their estimated standard error, p value, and 95% CI bounds. RESULTS Each patient had a mean of 3.3 ± 1.1 (±SD) assessments performed. Identical or nearly identical agreement between previously untrained primary providers and expert readers was evident for the diagnosis of tricuspid regurgitation, mitral regurgitation, pericardial effusion, and volume status. These agreements were strongest in apical long axis (κ = 1, p =0.82 p =0.66, p < 0.001). The assessments of aortic stenosis (parasternal/long, κ = 0.42, and parasternal/short, κ = 0.47, both p < 0.001) were weak in their agreement. CONCLUSION Compared to expert echocardiography readers, the untrained providers' use of POCE for VHD shows high user adoptability and diagnostic accuracies in the primary care setting.
Gender Differences in Politician Persistence

Why are women underrepresented in politics? This paper documents gender differences in the career paths of novice politicians by studying the persistence of candidates after they win or lose elections. I track the political trajectories of over 11,000 candidates in local California elections and use a regression discontinuity approach. Losing an election causes 50 percent more attrition among female than male candidates: an electoral loss causes men to be 16 percentage points less likely to run again within the next four years, whereas the drop for women is 25 percentage points. Yet the gender gap in persistence depends on the setting: I find no evidence of a gap among candidates for high female representation offices or among more experienced candidates. These results are inconsistent with behavioral explanations of women’s differential attrition. Instead, the results suggest that in low information environments, voters may penalize novice female politicians, which deters women from running again. I discuss the implications of the results for the gender gap in officeholding.
Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks

Spiking Neural Networks (SNNs) are a promising alternative to traditional deep learning methods since they perform event-driven information processing. However, a major drawback of SNNs is high inference latency. The efficiency of SNNs could be enhanced using compression methods such as pruning and quantization. Notably, SNNs, unlike their non-spiking counterparts, consist of a temporal dimension, the compression of which can lead to latency reduction. In this paper, we propose spatial and temporal pruning of SNNs. First, structured spatial pruning is performed by determining the layer-wise significant dimensions using principal component analysis of the average accumulated membrane potential of the neurons. This step leads to 10-14X model compression. Additionally, it enables inference with lower latency and decreases the spike count per inference. To further reduce latency, temporal pruning is performed by gradually reducing the timesteps while training. The networks are trained using surrogate gradient descent based backpropagation and we validate the results on CIFAR10 and CIFAR100, using VGG architectures. The spatiotemporally pruned SNNs achieve 89.04% and 66.4% accuracy on CIFAR10 and CIFAR100, respectively, while performing inference with 3-30X reduced latency compared to state-of-the-art SNNs. Moreover, they require 8-14X lesser compute energy compared to their unpruned standard deep learning counterparts. The energy numbers are obtained by multiplying the number of operations with energy per operation. These SNNs also provide 1–4% higher robustness against Gaussian noise corrupted inputs. Furthermore, we perform weight quantization and find that performance remains reasonably stable up to 5-bit quantization.
Inflation and growth: the role of institutions

This paper investigates the effects of inflation on per capita income growth for 36 developed and developing countries by using structural vector autoregression models that are robust to the consideration of endogeneity by construction. The results show evidence for heterogeneity of such effects across countries that are shown to be further connected to the strength of their institutions. While the effects of inflation on growth are negative and significant in countries with stronger institutions, they are positive and significant in countries with weaker institutions.
Dark matter absorption via electronic excitations

We revisit the calculation of bosonic dark matter absorption via electronic excitations. Working in an effective field theory framework and consistently taking into account in-medium effects, we clarify the relation between dark matter and photon absorption. As is well-known, for vector (dark photon) and pseudoscalar (axion-like particle) dark matter, the absorption rates can be simply related to the target material’s optical properties. However, this is not the case for scalar dark matter, where the dominant contribution comes from a different operator than the one contributing to photon absorption, which is formally next-to-leading-order and does not suffer from in-medium screening. It is therefore imperative to have reliable first-principles numerical calculations and/or semi-analytic modeling in order to predict the detection rate. We present updated sensitivity projections for semiconductor crystal and superconductor targets for ongoing and proposed direct detection experiments.
Simplified derivation of the Kompaneets equation

An isotropic electromagnetic field in a plasma of thermalized electrons undergoes changes in energy as a result of Compton scattering and an Einstein–Hopf drag force on the electrons, eventually approaching a Bose–Einstein photon distribution at the electron temperature. The rate of change of field energy due to the combined effects of Compton scattering and the drag force is shown to be described by the Kompaneets equation for photon diffusion in frequency space. A similarity is noted between this approach and Einstein's derivation of the Planck spectrum based on the recoil of atoms as they absorb and emit radiation.
Dislocation dynamics and multiplication via atomistic simulations

Molecular Dynamics simulations of edge dislocation mobility under stress in ordered Ll 2 Ni 3 Al have been performed between 10 K and 1000 K, and at applied shear stresses ranging from 0.01 to 0.08 C 44 . In this way it has been possible to determine the Peierls stress and mobility parameters as a function of stress and temperature. {100} edge dislocations were studied, which split into closely spaced partials under stress. Under all levels of applied stress (and at lower temperatures) the initial partial dislocations would intermittently stop moving and recombine, then dissociate and move again. In all cases the dislocations exhibited a soliton-like behavior: infinite acceleration at the onset of movement, and further movement at a steady velocity (which was only weakly dependent on stress) on the order of 25% of the acoustic shear velocity. Nonclassical, highly non-linear behavior was observed indicating the probability that a soliton picture of dislocation motion is more appropriate than the classical, «massive string» model that is traditionally used. Furthermore, as both the temperature and the stress were increased, dislocation multiplication became increasingly frequent, ultimately resulting in a spontaneous amorphisation transition which has signs of being a percolation process
Learning Fast Sample Re-Weighting Without Reward Data

Training sample re-weighting is an effective approach for tackling data biases such as imbalanced and corrupted labels. Recent methods develop learning-based algorithms to learn sample re-weighting strategies jointly with model training based on the frameworks of reinforcement learning and meta learning. However, depending on additional unbiased reward data is limiting their general applicability. Furthermore, existing learning-based sample re-weighting methods require nested optimizations of models and weighting parameters, which requires expensive second-order computation. This paper addresses these two problems and presents a novel learning-based fast sample re-weighting (FSR) method that does not require additional reward data. The method is based on two key ideas: learning from history to build proxy reward data and feature sharing to reduce the optimization cost. Our experiments show the proposed method achieves competitive results compared to state of the arts on label noise robustness and long-tailed recognition, and does so while achieving significantly improved training efficiency. The source code is publicly available at this https URL.
Open-Loop Equilibrium Strategies for Dynamic Influence Maximization Game Over Social Networks

We consider the problem of budget allocation for competitive influence maximization over social networks. In this problem, multiple competing parties (players) want to distribute their limited advertising resources over a set of social individuals to maximize their long-run cumulative payoffs. It is assumed that the individuals are connected via a social network and update their opinions based on the classical DeGroot model. The players must decide on the budget distribution among the individuals at a finite number of campaign times to maximize their overall payoff as a function of individuals’ opinions. Under some assumptions, we show that i) the optimal investment strategy for a single player can be found in polynomial time by solving a concave program, and ii) the open-loop equilibrium strategies for the multiplayer dynamic game can be computed efficiently by following natural regret-minimization dynamics. Our results extend earlier work on the static version of the problem to a dynamic multistage game.
Adversarial Representation Learning with Closed-Form Solvers

Adversarial representation learning aims to learn data representations for a target task while removing unwanted sensitive information at the same time. Existing methods learn model parameters iteratively through stochastic gradient descent-ascent, which is often unstable and unreliable in practice. To overcome this challenge, we adopt closed-form solvers for the adversary and target task. We model them as kernel ridge regressors and analytically determine an upper-bound on the optimal dimensionality of representation. Our solution, dubbed OptNet-ARL, reduces to a stable one one-shot optimization problem that can be solved reliably and efficiently. OptNet-ARL can be easily generalized to the case of multiple target tasks and sensitive attributes. Numerical experiments, on both small and large scale datasets, show that, from an optimization perspective, OptNet-ARL is stable and exhibits three to five times faster convergence. Performance wise, when the target and sensitive attributes are dependent, OptNet-ARL learns representations that offer a better trade-off front between (a) utility and bias for fair classification and (b) utility and privacy by mitigating leakage of private information than existing solutions.
Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors

A growing body of literature has proposed formal approaches to audit algorithmic systems for biased and harmful behaviors. While formal auditing approaches have been greatly impactful, they often suffer major blindspots, with critical issues surfacing only in the context of everyday use once systems are deployed. Recent years have seen many cases in which everyday users of algorithmic systems detect and raise awareness about harmful behaviors that they encounter in the course of their everyday interactions with these systems. However, to date little academic attention has been granted to these bottom-up, user-driven auditing processes. In this paper, we propose and explore the concept of everyday algorithm auditing, a process in which users detect, understand, and interrogate problematic machine behaviors via their day-to-day interactions with algorithmic systems. We argue that everyday users are powerful in surfacing problematic machine behaviors that may elude detection via more centrally-organized forms of auditing, regardless of users' knowledge about the underlying algorithms. We analyze several real-world cases of everyday algorithm auditing, drawing lessons from these cases for the design of future platforms and tools that facilitate such auditing behaviors. Finally, we discuss work that lies ahead, toward bridging the gaps between formal auditing approaches and the organic auditing behaviors that emerge in everyday use of algorithmic systems.
Bulk nanocrystalline Al alloys with hierarchical reinforcement structures via grain boundary segregation and complexion formation

Abstract Grain size engineering, particularly reducing grain size into the nanocrystalline regime, offers a promising pathway to further improve the strength-to-weight ratio of Al alloys. Unfortunately, the fabrication of nanocrystalline metals often requires non-equilibrium processing routes, which typically limit the specimen size and require large energy budgets. In this study, multiple dopant elements in ternary Al alloys are deliberately selected to enable segregation to the grain boundary region and promote the formation of amorphous complexions. Three different fully dense bulk nanocrystalline Al alloys (Al-Mg-Y, Al-Fe-Y, and Al-Ni-Y) with small grain sizes were successfully fabricated using a simple powder metallurgy approach, with full densification connected directly to the onset of amorphous complexion formation. All the compositions demonstrate densities above 99% with grain sizes
Building the mega single-cell transcriptome ocular meta-atlas.

BACKGROUND The development of highly scalable single-cell transcriptome technology has resulted in the creation of thousands of datasets, >30 in the retina alone. Analyzing the transcriptomes between different projects is highly desirable because this would allow for better assessment of which biological effects are consistent across independent studies. However it is difficult to compare and contrast data across different projects because there are substantial batch effects from computational processing, single-cell technology utilized, and the natural biological variation. While many single-cell transcriptome-specific batch correction methods purport to remove the technical noise, it is difficult to ascertain which method functions best. RESULTS We developed a lightweight R package (scPOP, single-cell Pick Optimal Parameters) that brings in batch integration methods and uses a simple heuristic to balance batch merging and cell type/cluster purity. We use this package along with a Snakefile-based workflow system to demonstrate how to optimally merge 766,615 cells from 33 retina datsets and 3 species to create a massive ocular single-cell transcriptome meta-atlas. CONCLUSIONS This provides a model for how to efficiently create meta-atlases for tissues and cells of interest.