cssi/SciSciGPT-SciSciNet huggingface dataset:
The apple and your eye: visual and taste rank-ordered probit analysis with correlated errors.

Abstract I look at data from an experiment in which people rank apples according to how they think they will taste. They are then blindfolded and rank how they actually taste. I estimate a multinomial rank-ordered probit model with correlated errors between the taste and visual rankings. I find that the errors for visual characteristics are correlated based on coloring, while the errors for taste are correlated based on sweetness and tartness. Allowing for correlation between the errors in the two regressions shows that, although people often misperceive apple taste based upon visual cues, they do so in systematic ways. People who prefer the looks of an apple they think to be tart (Granny Smith), will like the taste of other apples which are also tart but less well-known (Jonagold).
How to Create—or Destroy—Wealth in Real Property

Current legal conceptions of property differ in the private and the public law. The former develops a comprehensive conception that gives full protection for each element in the bundle of rights - possession, use and disposition - which allows parties to enter into complex transactions that increase wealth for the parties without prejudicing outsiders. Constitutional doctrine gives strong protection to exclusion, but weak protection to use and disposition, and thus invites a complex range of government strategies that reduce property value, without creating any offsetting gain in third parties.
Do accounting measurement regimes matter? A discussion of mark-to-market accounting and liquidity pricing

Abstract Using a model with banking and insurance sectors, Allen and Carletti show that marking-to-market interacts with liquidity pricing to exacerbate the likelihood of financial contagion between the two sectors. In this discussion, I lay out the main ingredients of their model and explain how they interact with liquidity pricing to generate financial contagion. I then discuss some limitations of their model and propose an interesting extension.
Tending Unmarked Graves: Classification of Post-mortem Content on Social Media

User-generated content is central to social computing scholarship. However, researchers and practitioners often presume that these users are alive. Failing to account for mortality is problematic in social media where an increasing number of profiles represent those who have died. Identifying mortality can empower designers to better manage content and support the bereaved, as well as promote high-quality data science. Based on a computational linguistic analysis of post-mortem social media profiles and content, we report on classifiers developed to detect mortality and show that mortality can be determined after the first few occurrences of post-mortem content. Applying our classifiers to content from two other platforms also provided good results. Finally, we discuss trade-offs between models that emphasize pre- vs. post-mortem precision in this sensitive context. These results mark a first step toward identifying mortality at scale, and show how designers and scientists can attend to mortality in their work.
Doing Well by Doing Good? Community Development Venture Capital

This paper examines the investments and performance of community development venture capital (CDVC). We find substantial differences between CDVCs and traditional VCs: CDVC investments are far more likely to be in nonmetropolitan regions and in regions with little prior venture activity. CDVC investments are likely to be in earlier stage investments and in industries outside the venture capital mainstream that have lower probabilities of successful exit. Even after controlling for this unattractive transaction mixture, the probability of a CDVC investment being successfully exited is lower. One benefit of CDVCs may be their effect in bringing traditional VCs to underserved regions—controlling for the presence of traditional VC investments, each additional CDVC investment results in an additional 0.06 new traditional VC firms in a region
Universal Banking, Asset Management, and Stock Underwriting

This paper examines institutions that underwrite IPOs and have asset management divisions from 1993 through 1998. We provide evidence that these firms use asset management funds as vehicles to help them earn more equity underwriting business. We also show that asset managers affiliated with IPO underwriters use their superior information about their own institution's IPOs to earn annualised market adjusted returns 7.6% above asset managers of firms who did not underwrite the IPO. Superior future returns by asset managers who trade affiliated IPOs are dependent on the information environment for the IPO and the underwriter reputation rank.
A Real Effects Perspective to Accounting Measurement and Disclosure: Implications and Insights for Future Research

Accounting measurement and disclosure rules have a significant impact on the real decisions that firms make. In this essay, we provide an analytical framework to illustrate how such real effects arise. Using this framework, we examine three specific measurement issues that remain controversial: (1) How does the measurement of investments affect a firm's investment efficiency? (2) How does the measurement and disclosure of a firm's derivative transactions affect a firm's choice of intrinsic risk exposures, risk management strategy, and the incentive to speculate? (3) How could marking-to-market the asset portfolios of financial institutions generate procyclical real effects? We draw upon these real effects studies to generate sharper and novel insights that we believe are useful not only for the development of accounting standards, but also for guiding future empirical research.
Can Culture Constrain the Economic Model of Corporate Law

With a few simple “moves,” we can see where the economic model of corporate law could bump up against cultural limits. Or, better put, the economic model works well in the United States because little until now impedes Coasian re-bargaining among shareholders and managers. Begin with the economic model without limit: Takeovers persisted in the face of anti-takeover law in the 1990s, one can argue, because many managers were paid to stop from strongly opposing most takeovers. But managers’ pay cannot be varied everywhere in the world as easily as it was raised in the Untied States. (The recent scandals show how wide that latitude has been here.). Where it cannot be so easily var ied, re-splitting the corporate pie in managers’ favor is harder. More generally, culture could affect the economic model if it affects the relative cost of institutional substitutes, by, say, degrading one form of organization but not others. Basic structures of corporate law—indeed, one could imagine even the public firm with diffuse ownership—could be affected by the degree to which local culture allows parties to vary their deals smoothly. When local norms make key variations costly, boundaries to the economic model of a type seldom thus far confining the American corporation appear. I sketch out, with the help of a Symposium’s papers, where those boundaries can be glimpsed. * Mark Roe is the Berg Professor of Law at Harvard Law School and wishes to thank the John M. Olin Center for Law, Economics, and Business for their support.
The anatomy of a likely donor: econometric evidence on philanthropy to higher education

In 2011, philanthropic giving to higher education institutions totaled $30.3 billion, an 8.2% increase over the previous year. Roughly, 26% of those funds came from alumni donations. This article builds upon existing economic models to create an econometric model to explain and predict the pattern of alumni giving. We test the model using data from over 27,000 alumni at a private liberal arts college, and report on the probable profiles for annual fund donors.
Patching as translation: the data and the metaphor

Machine Learning models from other fields, like Computational Linguistics, have been transplanted to Software Engineering tasks, often quite successfully. Yet a transplanted model's initial success at a given task does not necessarily mean it is well-suited for the task. In this work, we examine a common example of this phenomenon: the conceit that "software patching is like language translation". We demonstrate empirically that there are subtle, but critical distinctions between sequence-to-sequence models and translation model: while program repair benefits greatly from the former, general modeling architecture, it actually suffers from design decisions built into the latter, both in terms of translation accuracy and diversity. Given these findings, we demonstrate how a more principled approach to model design, based on our empirical findings and general knowledge of software development, can lead to better solutions. Our findings also lend strong support to the recent trend towards synthesizing edits of code conditional on the buggy context, to repair bugs. We implement such models ourselves as "proof-of-concept" tools and empirically confirm that they behave in a fundamentally different, more effective way than the studied translation-based architectures. Overall, our results demonstrate the merit of studying the intricacies of machine learned models in software engineering: not only can this help elucidate potential issues that may be overshadowed by increases in accuracy; it can also help innovate on these models to raise the state-of-the-art further. We will publicly release our replication data and materials at https://github.com/ARiSE-Lab/Patch-as-translation.
The Catholic Moment in Legal Education

The article argues that the tradition of social justice that is inherent in Catholic legal thinking can give renewed meaning to those who are educated in the legal tradition.
Distributed Estimation for Principal Component Analysis: An Enlarged Eigenspace Analysis

The growing size of modern datasets brings many challenges to the existing statistical estimation approaches, which calls for new distributed methodologies. This article studies distributed estimat...
Targets, Predictability, and Performance

We study market-timing strategies on a given portfolio to achieve a particular risk or return target. Targeting a constant risk level leads to increasing investment at better investment opportuniti...
Measurement of proton quenching in a plastic scintillator detector

The non-linear energy response of the plastic scintillator EJ-260 is measured with the MicroCHANDLER detector, using neutron beams of energy 5 to 27 MeV at the Triangle Universities Nuclear Laboratory. The first and second order Birks' constants are extracted from the data, and found to be $k_B = (8.70 \pm 0.93)\times 10^{-3}\ {\rm g/cm^2/MeV}$ and $k_C = (1.42 \pm 1.00) \times 10^{-5}\ {\rm (g/cm^2/MeV)^2}$. This result covers a unique energy range that is of direct relevance for fast neutron backgrounds in reactor inverse beta decay detectors. These measurements will improve the energy non-linearity modeling of plastic scintillator detectors. In particular, the updated energy response model will lead to an improvement of fast neutron modeling for detectors based on the CHANDLER reactor neutrino detector technology.
Eruptive mass loss in very massive stars and Population III stars

I discuss the role played by short-duration eruptive mass loss in the evolution of very massive stars. Giant eruptions of Luminous Blue Variables (LBVs) like the 19th century event of eta Carinae can remove large quantities of mass almost instantaneously, making them significant in stellar evolution. They can potentially remove much more mass from the star than line-driven winds, especially if stellar winds are highly clumped such that previous estimates of O star mass-loss rates need to be revised downward. When seen in other galaxies as ``supernova impostors'', these LBV eruptions typically last for less than a decade, and they can remove of order 10 Msun as indicated by massive nebulae around LBVs. Such extreme mass-loss rates cannot be driven by radiation pressure on spectral lines, because the lines will completely saturate during the events. Instead, these outbursts must either be continuum-driven super-Eddington winds or outright hydrodynamic explosions, both of which are insensitive to metallicity. As such, this eruptive mode of mass loss could also play a pivotal role in the evolution and ultimate fate of massive metal-poor stars in the early universe. If they occur in these Population III stars, such eruptions would also profoundly affect the chemical yield and types of remnants from early supernovae and hypernovae thought to be the origin of long gamma ray bursts.
Smooth points on semi-algebraic sets

Many algorithms for determining properties of semi-algebraic sets rely upon the ability to compute smooth points [1]. We present a simple procedure based on computing the critical points of some well-chosen function that guarantees the computation of smooth points in each connected bounded component of a real atomic semi-algebraic set. Our technique is intuitive in principal, performs well on previously difficult examples, and is straightforward to implement using existing numerical algebraic geometry software. The practical efficiency of our approach is demonstrated by solving a conjecture on the number of equilibria of the Kuramoto model for the n = 4 case. We also apply our method to design an efficient algorithm to compute the real dimension of algebraic sets, the original motivation for this research.
The Sample Complexity of Up-to-ε Multi-dimensional Revenue Maximization

We consider the sample complexity of revenue maximization for multiple bidders in unrestricted multi-dimensional settings. Specifically, we study the standard model of additive bidders whose values...
Rethink Training of BERT Rerankers in Multi-stage Retrieval Pipeline

Pre-trained deep language models (LM) have advanced the state-of-the-art of text retrieval. Rerankers fine-tuned from deep LM estimates candidate relevance based on rich contextualized matching signals. Meanwhile, deep LMs can also be leveraged to improve search index, building retrievers with better recall. One would expect a straightforward combination of both in a pipeline to have additive performance gain. In this paper, we discover otherwise and that popular reranker cannot fully exploit the improved retrieval result. We, therefore, propose a Localized Contrastive Estimation (LCE) for training rerankers and demonstrate it significantly improves deep two-stage models (Our codes are open sourced at https://github.com/luyug/Reranker.).
Incentivizing routing choices for safe and efficient transportation in the face of the COVID-19 pandemic

The COVID-19 pandemic has severely affected many aspects of people's daily lives. While many countries are in a reopening stage, some effects of the pandemic on people's behaviors are expected to last much longer, including how they choose between different transport options. Experts predict considerably delayed recovery of the public transport options, as people try to avoid crowded places. In turn, significant increases in traffic congestion are expected, since people are likely to prefer using their own vehicles or taxis as opposed to riskier and more crowded options such as the railway. In this paper, we propose to use financial incentives to set the tradeoff between risk of infection and congestion to achieve safe and efficient transportation networks. To this end, we formulate a network optimization problem to optimize taxi fares. For our framework to be useful in various cities and times of the day without much designer effort, we also propose a data-driven approach to learn human preferences about transport options, which is then used in our taxi fare optimization. Our user studies and simulation experiments show our framework is able to minimize congestion and risk of infection.
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation.

We propose a new active learning (AL) framework, Active Learning++, which can utilize an annotator's labels as well as its rationale. Annotators can provide their rationale for choosing a label by ranking input features based on their importance for a given query. To incorporate this additional input, we modified the disagreement measure for a bagging-based Query by Committee (QBC) sampling strategy. Instead of weighing all committee models equally to select the next instance, we assign higher weight to the committee model with higher agreement with the annotator's ranking. Specifically, we generated a feature importance-based local explanation for each committee model. The similarity score between feature rankings provided by the annotator and the local model explanation is used to assign a weight to each corresponding committee model. This approach is applicable to any kind of ML model using model-agnostic techniques to generate local explanation such as LIME. With a simulation study, we show that our framework significantly outperforms a QBC based vanilla AL framework.
Constraints on Blazar Jet Conditions During Gamma-Ray Flaring from Radiative Transfer Modeling

As part of a program to investigate jet flow conditions during GeV gamma-ray flares detected by Fermi, we are using UMRAO multi-frequency, centimeter-band total flux density and linear polarization monitoring observations to constrain radiative transfer models incorporating propagating shocks orientated at an arbitrary angle to the flow direction. We describe the characteristics of the model, illustrate how the data are used to constrain the models, and present results for three program sources with diverse characteristics: PKS 0420-01, OJ 287, and 1156+295. The modeling of the observed spectral behavior yields information on the sense, strength and orientation of the shocks producing the radio-band flaring; on the energy distribution of the radiating particles; and on the observer’s viewing angle with respect to the jet independent of VLBI data. We present evidence that, while a random component dominates the jet magnetic field, a distinguishing feature of those radio events with an associated γ -ray flare is the presence of a weak but non-negligible ordered magnetic field component along the jet axis.
On the residue method for period integrals

By applying the residue method for period integrals, we prove identities between period integrals on cuspidal automorphic representations and period integrals on residual representations for seven spherical varieties. Combining this with the Langlands–Shahidi theory for residues of Eisenstein series, for each case, we prove some relations between the period integrals and certain automorphic L-functions. In some cases, we also study the local multiplicity of the spherical varieties.
Disagreement and Polarization in Two-Party Social Networks

Abstract We investigate disagreement and polarization in a social network with two polarizing sources of information. First, we define disagreement and polarization indices in two-party leader-follower models of opinion dynamics. We then give expressions for the indices in terms of a graph Laplacian. The expressions show a relationship between these quantities and the concepts of resistance distance and biharmonic distance. We next study the problem of designing the network so as to minimize disagreement and polarization. We give conditions for optimal disagreement and polarization, and further, we show that a linear combination of disagreement and polarization of the follower nodes is a convex function of the edge weights between followers. We propose algorithms to address some related continuous and discrete optimization problems and also present analytic results for some interesting examples.
Implementation of higher-order velocity mapping between marker particles and grid in the particle-in-cell code XGC

The global total- gyrokinetic particle-in-cell code XGC, used to study transport in magnetic fusion plasmas or to couple with a core gyrokinetic code while functioning as an edge gyrokinetic code, implements a five-dimensional continuum grid to perform the dissipative operations, such as plasma collisions, or to exchange the particle distribution function information with a core code. To transfer the distribution function between marker particles and a rectangular two-dimensional velocity-space grid, XGC employs a bilinear mapping. The conservation of particle density and momentum is accurate enough in this bilinear operation, but the error in the particle energy conservation can become undesirably large and cause non-negligible numerical heating in a steep edge pedestal. In the present work we update XGC to use a novel mapping technique, based on the calculation of a pseudo-inverse, to exactly preserve moments up to the order of the discretization space. We describe the details of the implementation and we demonstrate the reduced interpolation error for a tokamak test plasma using first- and second-order elements with the pseudo-inverse method and comparing with the bilinear mapping.
Obstacle problems for nonlocal operators: A brief overview

In this note, we give a brief overview of obstacle problems for nonlocal operators, focusing on the applications to financial mathematics. The class of nonlocal operators that we consider can be viewed as infinitesimal generators of non-Gaussian asset price models, such as Variance Gamma Processes and Regular Levy Processes of Exponential type. In this context, we analyze the existence, uniqueness, and regularity of viscosity solutions to obstacle problems which correspond to prices of perpetual and finite expiry American options.
Sensitivity as a Complexity Measure for Sequence Classification Tasks

We introduce a theoretical framework for understanding and predicting the complexity of sequence classification tasks, using a novel extension of the theory of Boolean function sensitivity. The sensitivity of a function, given a distribution over input sequences, quantifies the number of disjoint subsets of the input sequence that can each be individually changed to change the output. We argue that standard sequence classification methods are biased towards learning low-sensitivity functions, so that tasks requiring high sensitivity are more difficult. To that end, we show analytically that simple lexical classifiers can only express functions of bounded sensitivity, and we show empirically that low-sensitivity functions are easier to learn for LSTMs. We then estimate sensitivity on 15 NLP tasks, finding that sensitivity is higher on challenging tasks collected in GLUE than on simple text classification tasks, and that sensitivity predicts the performance both of simple lexical classifiers and of vanilla BiLSTMs without pretrained contextualized embeddings. Within a task, sensitivity predicts which inputs are hard for such simple models. Our results suggest that the success of massively pretrained contextual representations stems in part because they provide representations from which information can be extracted by low-sensitivity decoders.
Pressure waves in a uv-preionized electric-discharge laser

Results of an experimental study of acoustic disturbances in a uv-preionized electric-discharge laser cavity are presented. It is shown that the electrical energy is deposited very nonuniformly into the gas. This nonuniform energy deposition has a major effect on the shape and magnitude of the acoustic waves. The experiments show that the acoustical field is not one-dimensional. They also show that the resulting deflections of a light ray are much smaller than would be expected from simple refraction calculations in which the density gradients are based upon one-dimensional solutions of the acoustic equations.
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning

Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round. While most prior works assume uniform and unbiased client selection, recent work on biased client selection [1] has shown that selecting clients with higher local losses can improve error convergence speed. However, previously proposed biased selection strategies either require additional communication cost for evaluating the exact local loss or utilize stale local loss, which can even make the model diverge. In this paper, we present a bandit-based communication-efficient client selection strategy UCB-CS that achieves faster convergence with lower communication overhead. We also demonstrate how client selection can be used to improve fairness.
Practical and Private (Deep) Learning Without Sampling or Shuffling

We consider training models with differential privacy (DP) using mini-batch gradients. The existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD), requires privacy amplification by sampling or shuffling to obtain the best privacy/accuracy/computation trade-offs. Unfortunately, the precise requirements on exact sampling and shuffling can be hard to obtain in important practical scenarios, particularly federated learning (FL). We design and analyze a DP variant of Follow-The-Regularized-Leader (DP-FTRL) that compares favorably (both theoretically and empirically) to amplified DP-SGD, while allowing for much more flexible data access patterns. DP-FTRL does not use any form of privacy amplification. The code is available at this https URL and this https URL .
Contributions of the NASA Langley Transonic Dynamics Tunnel to launch vehicle and spacecraft development

The NASA Langley Transonic Dynamics Tunnel (TDT) has provided wind-tunnel experimental validation and research data for numerous launch vehicles and spacecraft throughout its forty year history. Most of these tests have dealt with some aspect of aeroelastic or unsteady-response testing, which is the primary purpose of the TDT facility. However, some space-related test programs that have not involved aeroelasticity have used the TDT to take advantage of specific characteristics of the wind-tunnel facility. In general. the heavy gas test medium, variable pressure, relatively high Reynolds number and large size of the TDT test section have made it the preferred facility for these tests. The space-related tests conducted in the TDT have been divided into five categories. These categories are ground wind loads, launch vehicle dynamics, atmospheric flight of space vehicles, atmospheric reentry. and planetary-probe testing. All known TDT tests of launch vehicles and spacecraft are discussed in this report. An attempt has been made to succinctly summarize each wind-tunnel test, or in the case of multiple. related tests, each wind-tunnel program. Most summaries include model program discussion, description of the physical wind-tunnel model, and some typical or significant test results. When available, references are presented to assist the reader in further pursuing information on the tests.
A Quadratic Programming Approach to Manipulation in Real-Time Using Modular Robots.

Motion planning in high-dimensional space is a challenging task. In order to perform dexterous manipulation in an unstructured environment, a robot with many degrees of freedom is usually necessary, which also complicates its motion planning problem. Real-time control brings about more difficulties in which robots have to maintain the stability while moving towards the target. Redundant systems are common in modular robots that consist of multiple modules and are able to transformed into different configurations with respect to different needs. Different from robots with fixed geometry or configurations, the kinematics model of a modular robotic system can alter as the robot reconfigures itself, and developing a generic control and motion planning approach for such systems is difficult, especially when multiple motion goals are coupled. A new manipulation planning framework is developed in this paper. The problem is formulated as a sequential linearly constrained quadratic program (QP) that can be solved efficiently. Some constraints can be incorporated into this QP, including a novel way to approximate environment obstacles. This solution can be used directly for real-time applications or as an off-line planning tool, and it is validated and demonstrated on the CKBot and SMORES-EP modular robot platforms.
Multiple Plans are Better than One: Diverse Stochastic Planning.

In planning problems, it is often challenging to fully model the desired specifications. In particular, in human-robot interaction, such difficulty may arise due to human's preferences that are either private or complex to model. Consequently, the resulting objective function can only partially capture the specifications and optimizing that may lead to poor performance with respect to the true specifications. Motivated by this challenge, we formulate a problem, called diverse stochastic planning, that aims to generate a set of representative --- small and diverse --- behaviors that are near-optimal with respect to the known objective. In particular, the problem aims to compute a set of diverse and near-optimal policies for systems modeled by a Markov decision process. We cast the problem as a constrained nonlinear optimization for which we propose a solution relying on the Frank-Wolfe method. We then prove that the proposed solution converges to a stationary point and demonstrate its efficacy in several planning problems.
Users’ Perception of Search-Engine Biases and Satisfaction

Search engines could consistently favor certain values over the others, which is considered as biased due to the built-in infrastructures. Many studies have been dedicated to detect, control, and mitigate the impacts of those biases from the perspectives of search engines themselves. In our study, we pitched the perspective from end-users to analyze their perceptions of search engine biases and their satisfaction when the biases are regulated. In the study, we paired a real page from search engine Bing and a synthesized page with more diversities in the results (i.e. less biased). Both pages show the top-10 search items given search queries and we asked participants which one they prefer and why do they prefer the one selected. Statistical analyses revealed that overall, participants prefer the original Bing pages. Additionally, the location where the diversities are introduced is significantly associated with users’ preferences as well. We found out that users prefer results that are more consistent and relevant to the search queries. Introducing diversities undermines the relevance of the search results and impairs users’ satisfaction to some degree. Additionally, users tend to pay more attention to the top portion of the results than the bottom ones.
Regional Flood Risk Projections under Climate Change

Flood-related risks to people and property are expected to increase in the future due to environmental and demographic changes. It is important to quantify and effectively communicate flood hazards and exposure to inform the design and implementation of flood risk management strategies. Here we develop an integrated modeling framework to assess projected changes in regional riverine flood inundation risks. The framework samples climate model outputs to force a hydrologic model and generate streamflow projections. Together with a statistical and hydraulic model, we use the projected streamflow to map the uncertainty of flood inundation projections for extreme flood events. We implement the framework for rivers across the state of Pennsylvania, United States. Our projections suggest that flood hazards and exposure across Pennsylvania are overall increasing with future climate change. Specific regions, including the main stem Susquehanna River, lower portion of the Allegheny basin and central portion of Delaware River basin, demonstrate higher flood inundation risks. In our analysis, the climate uncertainty dominates the overall uncertainty surrounding the flood inundation projection chain. The combined hydrologic and hydraulic uncertainties can account for as much as 37% of the total uncertainty. We discuss how this framework can provide regional and dynamic flood-risk assessments and help to inform the design of risk-management strategies.
Phase-amplitude coupling in neuronal oscillator networks

This paper studies the emergence of phase-amplitude coupling in nonlinearly coupled Stuart-Landau oscillators and characterize how structural parameters modulate the intensity of phase-amplitude coupling.
A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays

We introduce the IBM ANALOG HARDWARE ACCELERATION KIT, a new and first of a kind open source toolkit to simulate analog crossbar arrays in a convenient fashion from within PYTORCH (freely available at https://github.com/IBM/aihwkit). The toolkit is under active development and is centered around the concept of an “analog tile” which captures the computations performed on a crossbar array. Analog tiles are building blocks that can be used to extend existing network modules with analog components and compose arbitrary artificial neural networks (ANNs) using the flexibility of the PYTORCH framework. Analog tiles can be conveniently configured to emulate a plethora of different analog hardware characteristics and their non-idealities, such as device-to-device and cycle-to-cycle variations, resistive device response curves, and weight and output noise. Additionally, the toolkit makes it possible to design custom unit cell configurations and to use advanced analog optimization algorithms such as Tiki-Taka. Moreover, the backward and update behavior can be set to “ideal" to enable hardware-aware training features for chips that target inference acceleration only. To evaluate the inference accuracy of such chips over time, we provide statistical programming noise and drift models calibrated on phase-change memory hardware. Our new toolkit is fully GPU accelerated and can be used to conveniently estimate the impact of material properties and non-idealities of future analog technology on the accuracy for arbitrary ANNs.
Towards reusable network components by learning compatible representations

This paper proposes to make a first step towards compatible and hence reusable network components. Rather than training networks for different tasks independently, we adapt the training process to produce network components that are compatible across tasks. In particular, we split a network into two components, a features extractor and a target task head, and propose various approaches to accomplish compatibility between them. We systematically analyse these approaches on the task of image classification on standard datasets. We demonstrate that we can produce components which are directly compatible without any fine-tuning or compromising accuracy on the original tasks. Afterwards, we demonstrate the use of compatible components on three applications: Unsupervised domain adaptation, transferring classifiers across feature extractors with different architectures, and increasing the computational efficiency of transfer learning.
Comparative host interactomes of the SARS-CoV-2 nonstructural protein 3 and human coronavirus homologs.

Abstract Human coronaviruses have become an increasing threat to global health; three highly pathogenic strains have emerged since the early 2000s, including most recently SARS-CoV-2, the cause of COVID-19. A better understanding of the molecular mechanisms of coronavirus pathogenesis is needed, including how these highly virulent strains differ from those that cause milder, common-cold-like disease. While significant progress has been made in understanding how SARS-CoV-2 proteins interact with the host cell, nonstructural protein 3 (nsp3) has largely been omitted from the analyses. Nsp3 is a viral protease with important roles in viral protein biogenesis, replication complex formation, and modulation of host ubiquitinylation and ISGylation. Herein, we use affinity purification–mass spectrometry to study the host–viral protein–protein interactome of nsp3 from five coronavirus strains: pathogenic strains SARS-CoV-2, SARS-CoV, and MERS-CoV; and endemic common-cold strains hCoV-229E and hCoV-OC43. We divide each nsp3 into three fragments and use tandem mass tag technology to directly compare the interactors across the five strains for each fragment. We find that few interactors are common across all variants for a particular fragment, but we identify shared patterns between select variants, such as ribosomal proteins enriched in the N-terminal fragment (nsp3.1) data set for SARS-CoV-2 and SARS-CoV. We also identify unique biological processes enriched for individual homologs, for instance, nuclear protein import for the middle fragment of hCoV-229E, as well as ribosome biogenesis of the MERS nsp3.2 homolog. Lastly, we further investigate the interaction of the SARS-CoV-2 nsp3 N-terminal fragment with ATF6, a regulator of the unfolded protein response. We show that SARS-CoV-2 nsp3.1 directly binds to ATF6 and can suppress the ATF6 stress response. Characterizing the host interactions of nsp3 widens our understanding of how coronaviruses co-opt cellular pathways and presents new avenues for host-targeted antiviral therapeutics.
Hypersonic Boundary-Layer Receptivity over a Blunt Cone to Freestream Pulse Disturbances

Although receptivity plays a key role in the transition of hypersonic flows, most prior computational receptivity studies have neglected to study broadband frequency disturbance spectra. This work ...
Generalized inferential models for censored data

Abstract Inferential challenges that arise when data are censored have been extensively studied under the classical frameworks. In this paper, we provide an alternative generalized inferential model approach whose output is a data-dependent plausibility function. This construction is driven by an association between the distribution of the relative likelihood function at the interest parameter and an unobserved auxiliary variable. The plausibility function emerges from the distribution of a suitably calibrated random set designed to predict that unobserved auxiliary variable. The evaluation of this plausibility function requires a novel use of the classical Kaplan–Meier estimator to estimate the censoring rather than the event distribution. We prove that the proposed method provides valid inference, at least approximately, and our real- and simulated-data examples demonstrate its superior performance compared to existing methods.
MALOnt: An Ontology for Malware Threat Intelligence

Malware threat intelligence uncovers deep information about malware, threat actors, and their tactics, Indicators of Compromise, and vulnerabilities in different platforms from scattered threat sources. This collective information can guide decision making in cyber defense applications utilized by security operation centers. In this paper, we introduce an open-source malware ontology, MALOnt that allows the structured extraction of information and knowledge graph generation, especially for threat intelligence. The knowledge graph that uses MALOnt is instantiated from a corpus comprising hundreds of annotated malware threat reports. The knowledge graph enables the analysis, detection, classification, and attribution of cyber threats caused by malware. We also demonstrate the annotation process using MALOnt on exemplar threat intelligence reports. A work in progress, this research is part of a larger effort towards auto-generation of knowledge graphs for gathering malware threat intelligence from heterogeneous online resources.
A GA-based Approach to Eco-driving of Electric Vehicles Considering Regenerative Braking

As the deployment of zero emission transportation technologies, specifically electric vehicles (EVs), is increasing, the concept of their eco-driving is gaining significant attention. Contrary to the eco-driving techniques used in conventional internal combustion engine vehicles that do not have the capability of regenerative braking, this paper proposes a genetic algorithm (GA)-based eco-driving technique for EVs considering regenerative braking. In the proposed approach, the optimal or near-optimal combination of variables in the driving cycle of EVs is searched using GA. The proposed approach starts by generating an initial population of chromosomes, where all variables under consideration are encoded in each chromosome. This population of chromosomes is passed through crossover, mutation, and elitist-based selection over a certain number of generations, which results in a driving cycle with the least energy consumption. The proposed method is verified using case studies consisting of two types of driving cycles. The results show the capability of the proposed method in computing the minimum energy driving cycle.
Intra-African trade

The intra-continental trade share in Africa is only 12%, compared to 47% in North America, 53% in Asia, and 69% in Europe. This paper shows that the lack of intra-African exports cannot simply be explained by standard gravity equation terms. The analysis investigates possible explanations and identifies factors that promote trade between African countries. Intra-African exports are found to disproportionately increase with road infrastructure, trade agreements, and declining trade costs. Diversifying the domestic economy away from natural resources and towards services is also associated with more intra-African trade. These results can guide efforts to promote African economic integration.
What Determines ETRs? The Relative Influence of Tax and Other Factors†

Many studies use GAAP effective tax rates (ETRs) as a proxy for tax avoidance and assume that very low (high) ETRs represent the greatest (least) tax avoidance, yet ETRs can be affected by items unrelated to tax avoidance. Despite awareness of the potential limitations of ETRs as a measure of tax avoidance, the literature lacks consistent evidence on the extent to which ETRs capture tax avoidance versus other factors. We take a step toward filling this void using income tax footnote disclosures from 2008 through 2016 to investigate how well ETRs capture cross-sectional differences in tax avoidance versus other factors. We document that ETRs below 5% and above 40% are significantly influenced by items largely unrelated to tax avoidance, such as valuation allowances and goodwill impairments. Truncating ETRs at zero and one, controlling for standard determinants of tax avoidance, and using industry-size-adjusted ETRs or multiyear GAAP ETRs do not eliminate the clustering of factors largely unrelated to tax avoidance in the tails of the ETR distribution. Cash ETRs attenuate but do not eliminate this clustering. Researchers can use ETR rate reconciliation data to construct an adjusted ETR that removes the influence of factors largely unrelated to tax avoidance. Our findings inform researchers about factors largely unrelated to tax avoidance that drive significant deviations in ETRs from the statutory tax rate. This is of increasing importance as the number of studies examining the consequences of very high and very low ETRs grows.
Validation of a Compact Isokinetic Total Water Content Probe for Wind Tunnel Characterization at NASA Glenn Icing Research Tunnel and at NRC Ice Crystal Tunnel

A new compact isokinetic probe to measure total water content in a wind tunnel environment has been developed. The probe has been previously tested under altitude conditions. This paper presents a comprehensive validation of the probe under a range of liquid water conditions at sea level in the NASA Glenn Icing Research Tunnel and with ice crystals at sea level at the NRC wind tunnel. The compact isokinetic probe is compared to tunnel calibrations and other probes.
Partially averaged Navier-Stokes closure modeling for variable-density turbulent flow

We extend the bridging Partially-Averaged Navier-Stokes (PANS) equations model to variable-density flow and develop a scale-dependent closure for complex flows. This includes $a$ $p\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}i$ testing to develop guidelines for the efficient selection of parameters controlling the model resolution (range of resolved scales). Accuracy of the model is evaluated with simulations of two benchmark transitional problems: the Taylor-Green Vortex and Rayleigh-Taylor flow. The results show that the model can accurately and efficiently predict the selected flows while resolving only a fraction of the turbulence field.
A diffuse core in Saturn revealed by ring seismology

The best constraints on the internal structures of giant planets have historically originated from measurements of their gravity fields1–3. These data are inherently mostly sensitive to a planet’s outer regions, stymieing efforts to measure the mass and compactness of the cores of Jupiter2,4,5 and Saturn6,7. However, studies of Saturn’s rings have detected waves driven by pulsation modes within the planet8–11, offering independent seismic probes of Saturn’s interior12–14. The observations reveal gravity-mode pulsations, which indicate that part of Saturn’s deep interior is stable against convection13. Here, we compare structural models with gravity and seismic measurements from Cassini to show that the data can only be explained by a diffuse, stably stratified core–envelope transition region in Saturn extending to approximately 60% of the planet’s radius and containing approximately 17 Earth masses of ice and rock. This gradual distribution of heavy elements constrains mixing processes at work in Saturn, and it may reflect the planet’s primordial structure and accretion history. Like a seismograph, Saturn’s rings are sensitive to oscillations coming from the planet’s interior. State-of-the-art modelling shows that Cassini’s measurements of ring waves point to a convectively stable diffuse core within Saturn, which extends for 60% of its radius and contains 17 Earth masses of ice and rock.
Regulation and income inequality in the United States

Abstract Income inequality in the United States has risen over the past several decades. Over the same period, federal regulatory restrictions have increased. An emerging literature shows that regulations can have regressive effects on the distribution of income, exacerbating inequality. The Federal Regulation and State Enterprise (FRASE) index quantifies the regulatory restrictions that apply to each US state by industrial composition. We construct a panel of 50 US states from 1997 to 2015 to test whether states exposed to more federal regulatory restrictions have higher levels of income inequality. The results indicate that a 10 percent increase in federal regulation is associated with an approximate 0.5 percent increase in income inequality as measured by the Gini coefficient. When states are rank-ordered by average Gini coefficient, a 0.5 percent increase in income inequality will typically result in a two-position decline in state ranking.
On Rearrangement of Items Stored in Stacks

There are \(n \ge 2\) stacks, each filled with d items, and one empty stack. Every stack has capacity \(d > 0\). A robot arm, in one stack operation (step), may pop one item from the top of a non-empty stack and subsequently push it onto a stack not at capacity. In a labeled problem, all nd items are distinguishable and are initially randomly scattered in the n stacks. The items must be rearranged using pop-and-pushs so that in the end, the \(k^\mathrm{th}\) stack holds items \((k-1)d +1, \ldots , kd\), in that order, from the top to the bottom for all \(1 \le k \le n\). In an unlabeled problem, the nd items are of n types of d each. The goal is to rearrange items so that items of type k are located in the \(k^\mathrm{th}\) stack for all \(1 \le k \le n\). In carrying out the rearrangement, a natural question is to find the least number of required pop-and-pushes.