cssi/SciSciGPT-SciSciNet huggingface dataset:
Allocating Opportunities in a Dynamic Model of Intergenerational Mobility

Opportunities such as higher education can promote intergenerational mobility, leading individuals to achieve levels of socioeconomic status above that of their parents. We develop a dynamic model for allocating such opportunities in a society that exhibits bottlenecks in mobility; the problem of optimal allocation reflects a trade-off between the benefits conferred by the opportunities in the current generation and the potential to elevate the socioeconomic status of recipients, shaping the composition of future generations in ways that can benefit further from the opportunities. We show how optimal allocations in our model arise as solutions to continuous optimization problems over multiple generations, and we find in general that these optimal solutions can favor recipients of low socioeconomic status over slightly higher-performing individuals of high socioeconomic status --- a form of socioeconomic affirmative action that the society in our model discovers in the pursuit of purely payoff-maximizing goals. We characterize how the structure of the model can lead to either temporary or persistent affirmative action, and we consider extensions of the model with more complex processes modulating the movement between different levels of socioeconomic status.
Channel-noise tracking for sub-shot-noise-limited receivers with neural networks

This article develops a method based on neural networks for tracking and correcting for complex noise from multiple sources in the communication channel for non-Gaussian receivers.
Using cyclotron radiation emission for ultra-high resolution x-ray spectroscopy

Cyclotron Radiation Emission Spectroscopy (CRES) is an approach to measuring the energy of an electron trapped in an externally applied magnetic field. The bare electron can come from different interactions, including photoelectric absorption, Compton scatters, beta decay, and pair production. CRES relies on measuring the frequency of the electron's cyclotron motion, and because the measurement times extend over $10^6$-$10^7$ cycles, the energy resolution is on the order of a single electronvolt. To date, CRES has only been performed on internal beta-emitting radioisotopes, but the technology can be applied to X-ray spectrometery through appropriate selection of a target gas and sufficient intensity of the distinct X-ray source. The applications of this technology range from high-precision measurements of atomic energy levels, to calibrations of basic science experiments, to trace element identification. In this work we explore the use of CRES for X-ray spectroscopy within the rubric of measuring the energy levels of argon, although the principles are broadly applicable to many other situations. The issues we explore include target material, density, electron trapping depth, noise levels, and overall efficiency. We also discuss spectral deconvolution and how the multiple peaks obtained from a single target / source pair can be used to enhance the robustness of the measurement.
Adopting SAP at Siemens Power Corporation

This case describes how Siemens Power Corporation, a Richland, Washington, USA-based manufacturer of nuclear fuel assemblies, came to adopt and implement SAP's R/3 application suite, the world's leading enterprise resource planning (ERP) package. The case introduces the reader to the type of decision making related to an ERP adoption and implementation and provides some interesting examples of factors which may influence actual decisions and outcomes. Among other things, the case touches on the following issues: the relationship between restructuring (re-engineering) and software adoption and implementation, the choice of package software, the pros and cons of alternative implementation approaches ('big bang' versus 'phased'), the selection of hardware and the value of consultants. By January 1999 Siemens Power Corporation's (SPC's) restructuring project was more than 2 years under way. To SPC's top management's great relief, the company had made major progress ever since SPC's German parent Siemens AG hi...
Geometric cycles and characteristic classes of manifold bundles

We produce new cohomology for non-uniform arithmetic lattices $\Gamma<SO(p,q)$ using a technique of Millson--Raghunathan. From this, we obtain new characteristic classes of manifold bundles with fiber a closed $4k$-dimensional manifold $M$ with indefinite intersection form of signature $(p,q)$. These classes are defined on a finite cover of $BDiff(M)$ and are shown to be nontrivial for $M=\#_g(S^{2k}\times S^{2k})$. In this case, the classes produced live in degree $g$ and are independent from the algebra generated by the stable (i.e. MMM) classes. We also give an application to bundles with fiber a K3 surface.
On Genetic Correlation Estimation With Summary Statistics From Genome-Wide Association Studies

Cross-trait polygenic risk score (PRS) method has gained popularity for assessing genetic correlation of complex traits using summary statistics from biobank-scale genome-wide association studies (...
Sample Out-of-Sample Inference Based on Wasserstein Distance

Financial institutions make decisions according to a model of uncertainty. At the same time, regulators often evaluate the risk exposure of these institutions using a model of uncertainty, which is...
Automated quality control and cell identification of droplet-based single-cell data using dropkick.

A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in datasets with disparate library sizes confounded by high technical noise (i.e. batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining dataset-specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against conventional thresholding approaches and EmptyDrops, a popular computational method, demonstrating greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low and high-background datasets that dropkick's weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to dataset-specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell Python packages.
Dodd-Frank: Toward First Principles?

INTRODUCTION When confronting a major crisis, tinkering at the margins of policy will likely do precious little either to ameliorate the system or avert the next catastrophe. Rather, lawmakers need to return to first principles by examining the underlying causes of the crisis and stemming them. Participating in a symposium over two years ago, well before the advent of the Dodd-Frank Wall Street Reform and Consumer Protection Act (“Dodd-Frank”),1 I argued that to mitigate, or perhaps even avoid, future disasters, policymakers should focus on remedying four pernicious facilitators to scandal: (1) the dissemination of information that is false or misleading; (2) the ability to abuse regulatory gaps; (3) the willingness to exploit credulous consumers; and (4) the use of corporate size to privatize profits and socialize losses.2 This Article, written a few months after the passage of Dodd-Frank, builds on this prior work to assess whether the statute effectively addresses these four root causes of our financial meltdown. Mapping the statute against these four facilitators, I argue that while a positive step forward in some respects, Dodd-Frank exhibits more of an intricate reaction to our last financial crisis than a concise attempt to address fundamental flaws in how Wall Street is regulated. To some degree, this might be unsurprising, given that regulators were reacting ex post to a crisis rather than averting it ex ante.3 Yet Dodd-Frank makes surprisingly few important decisions. Fascinatingly, along each of these four
On Multiplicative Showers

In I we discuss the status of the quantum theoretic formulae for pair production and radiation in the domain of cosmic-ray energies, and the relevance of these processes to an understanding of showers and bursts. In II we give a qualitative estimate of the course implied by the theory for a shower or burst built up by multiplication from a very energetic primary; we then set up the diffusion equations for the equilibrium of electrons and gamma-rays, and show how these can be simplified. In III we carry through the analytic solution of the diffusion equations, and find the distribution of electrons and gamma-rays as a function of their energy, the primary energy, and the thickness and atomic number of the matter traversed. We treat the effect of ionization losses on the shower, calculate the amount of radiation of low energy to be expected, and treat transition effects in passing from one substance to another. In IV we discuss the results of the calculations, and give a summary of the conclusions to which they lead, and the difficulties.
Reverberant Elastography for the Elastic Characterization of Anisotropic Tissues

We derive closed-form solutions for reverberant elastography in anisotropic elastic media by adapting the framework used in electromagnetic theory to treat transverse isotropic materials. Different sample-setup geometries are analyzed, highlighting their relevance for both optical coherence elastography (OCE) and ultrasound elastography (USE). Numerical simulations using finite elements are used to validate the proposed solutions in practical cases. OCE experiments are conducted in ex vivo chicken muscle samples for the characterization of in-plane and out-of-plane shear modulus assuming a transverse isotropic elastic model. Additionally, we obtained a generalized geometry-independent solution for the isotropic media case, thus unifying previous results for reverberant elastography.
Ab Initio Full Cell GW+DMFT for Correlated Materials

Quantitative prediction of electronic properties in correlated materials requires simulations without empirical truncations and parameters. We present a method to achieve this goal through a new ab initio formulation of dynamical mean-field theory (DMFT). Instead of using small impurities defined in a low-energy subspace, which require complicated downfolded interactions which are often approximated, we describe a full cell GW+DMFT approach, where the impurities comprise all atoms in a unit cell or supercell of the crystal. Our formulation results in large impurity problems, which we treat here using an efficient coupled-cluster impurity solver that works on the real-frequency axis, combined with a one-shot G0W0 treatment of long-range interactions. We apply our full cell approach to bulk Si and two antiferromagnetic correlated insulators, NiO and α-Fe₂O₃, with impurities containing up to 10 atoms and 124 orbitals. We find that spectral properties, magnetic moments, and two-particle spin correlation functions are obtained in good agreement with experiments. In addition, in the metal oxides, the balanced treatment of correlations involving all orbitals in the cell leads to new insights into the orbital character around the insulating gap.
General rogue waves in the three-wave resonant interaction systems

General rogue waves in (1+1)-dimensional three-wave resonant interaction systems are derived by the bilinear method. These solutions are divided into three families, which correspond to a simple root, two simple roots and a double root of a certain quartic equation arising from the dimension reduction respectively. It is shown that while the first family of solutions associated with a simple root exist for all signs of the nonlinear coefficients in the three-wave interaction equations, the other two families of solutions associated with two simple roots and a double root can only exist in the so-called soliton-exchange case, where the nonlinear coefficients have certain signs. Many of these rogue wave solutions, such as those associated with two simple roots, and higher-order solutions associated with a simple root, are new solutions which have not been reported before. Technically, our bilinear derivation of rogue waves for the double-root case is achieved by a generalization to the previous dimension reduction procedure in the bilinear method, and this generalized procedure allows us to treat roots of arbitrary multiplicities. Dynamics of the derived rogue waves is also examined, and new rogue-wave patterns are presented. Connection between these bilinear rogue waves and those derived earlier by Darboux transformation is also explained.
Alternative routes to the institutionalisation of social transfers in sub-Saharan Africa: Political survival strategies and transnational policy coalitions

Abstract The new phase of social protection expansion in the global south remains poorly understood. Current interpretations of the spread of social transfers in sub-Saharan Africa tend to emphasize the influence of elections and donor pressure, often by drawing correlations from statistical data, and focusing on the moment of programme adoption. This study adopts a different approach that traces the actual process through which countries have not just adopted but institutionalized social transfers. We test a new theoretical framework through within and cross-case analysis of the degree to which social protection programmes have become institutionalized in eight African countries. Two main pathways emerge: the first confirms the sense that both donors and elections matter, but goes further in showing the particular ways in which these drivers combine. In particular, transnational policy coalitions tend to play a leading role in adoption, whereas governments pursue the further institutionalization of social transfers as a top-down response to competitive elections. However, we also identify an alternative pathway that involves electorally uncompetitive countries; here, the primary motivation is not elections but elite perceptions of vulnerability in the face of distributional crises, augmented by ideas and resources from transnational policy coalitions. Consequently, the latest phase of social transfer development results from the interplay of political survival strategies and transnational policy coalitions.
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs.

Paraphrase generation plays an essential role in natural language process (NLP), and it has many downstream applications. However, training supervised paraphrase models requires many annotated paraphrase pairs, which are usually costly to obtain. On the other hand, the paraphrases generated by existing unsupervised approaches are usually syntactically similar to the source sentences and are limited in diversity. In this paper, we demonstrate that it is possible to generate syntactically various paraphrases without the need for annotated paraphrase pairs. We propose Syntactically controlled Paraphrase Generator (SynPG), an encoder-decoder based model that learns to disentangle the semantics and the syntax of a sentence from a collection of unannotated texts. The disentanglement enables SynPG to control the syntax of output paraphrases by manipulating the embedding in the syntactic space. Extensive experiments using automatic metrics and human evaluation show that SynPG performs better syntactic control than unsupervised baselines, while the quality of the generated paraphrases is competitive. We also demonstrate that the performance of SynPG is competitive or even better than supervised models when the unannotated data is large. Finally, we show that the syntactically controlled paraphrases generated by SynPG can be utilized for data augmentation to improve the robustness of NLP models.
Isochronous space-time wave packets.

The group delay incurred by an optical wave packet depends on its path length. Therefore, when a wave packet is obliquely incident on a planar homogeneous slab, the group delay upon traversing it inevitably increases with the angle of incidence. Here, we confirm the existence of isochronous “space–time” (ST) wave packets: pulsed beams whose spatiotemporal structure enables them to traverse the layer with a fixed group delay over a wide span of incident angles. This unique behavior stems from the dependence of the group velocity of a refracted ST wave packet on its angle of incidence. Isochronous ST wave packets are observed in slabs of optical materials with indices ranging from 1.38 to 2.5 for angles up to 50° away from normal incidence.
Gene Targeting in Disease Networks

Profiling of whole transcriptomes has become a cornerstone of molecular biology and an invaluable tool for the characterization of clinical phenotypes and the identification of disease subtypes. Analyses of these data are becoming ever more sophisticated as we move beyond simple comparisons to consider networks of higher-order interactions and associations. Gene regulatory networks (GRNs) model the regulatory relationships of transcription factors and genes and have allowed the identification of differentially regulated processes in disease systems. In this perspective, we discuss gene targeting scores, which measure changes in inferred regulatory network interactions, and their use in identifying disease-relevant processes. In addition, we present an example analysis for pancreatic ductal adenocarcinoma (PDAC), demonstrating the power of gene targeting scores to identify differential processes between complex phenotypes, processes that would have been missed by only performing differential expression analysis. This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.
Unknotting with a single twist

Given a knot in the three-sphere, is it possible to unknot it by performing a single twist, and if so, what are the possible linking numbers of such a twist? We develop obstructions to unknotting using a twist of a specified linking number. The obstructions we describe are built using classical knot invariants, Casson-Gordon invariants, and Heegaard Floer theory.
A monoclonal antibody against staphylococcal enterotoxin B superantigen inhibits SARS-CoV-2 entry in vitro.

We recently discovered a superantigen-like motif sequentially and structurally similar to a staphylococcal enterotoxin B (SEB) segment, near the S1/S2 cleavage site of the SARS-CoV-2 spike protein, which might explain the multisystem inflammatory syndrome (MIS-C) observed in children and the cytokine storm in severe COVID-19 patients. We show here that an anti-SEB monoclonal antibody (mAb), 6D3, can bind this viral motif at its polybasic (PRRA) insert to inhibit infection in live virus assays. The overlap between the superantigenic site of the spike and its proteolytic cleavage site suggests that the mAb prevents viral entry by interfering with the proteolytic activity of cell proteases (furin and TMPRSS2). The high affinity of 6D3 for this site originates from a polyacidic segment at its heavy chain CDR2. The study points to the potential utility of 6D3 for possibly treating COVID-19, MIS-C, or common colds caused by human coronaviruses that also possess a furin-like cleavage site.
Who is the best coach of all time? A network-based assessment of the career performance of professional sports coaches

We consider two large datasets consisting of all games played among top-tier European soccer clubs in the last 60 years, and among professional American basketball teams in the past 70 years. We leverage game data to build networks of pairwise interactions between the head coaches of the teams, and measure their career performance in terms of network centrality metrics. We identify Arsene Wenger, Sir Alex Ferguson, Jupp Heynckes, Carlo Ancelotti, and Jose Mourinho as the top 5 European soccer coaches of all time. In American basketball, the first 5 positions of the all-time ranking are occupied by Red Auerbach, Gregg Popovich, Phil Jackson, Don Nelson, and Lenny Wilkens. We further establish rankings by decade and season. We develop a simple methodology to monitor performance throughout a coach's career, and to dynamically compare the performance of two or more coaches at a given time. The manuscript is accompanied by the website this http URL where complete results of our analysis are accessible to the interested readers.
Non-Invasive Evaluation of Acute Effects of Tubulin Binding Agents: A Review of Imaging Vascular Disruption in Tumors

Tumor vasculature proliferates rapidly, generally lacks pericyte coverage, and is uniquely fragile making it an attractive therapeutic target. A subset of small-molecule tubulin binding agents cause disaggregation of the endothelial cytoskeleton leading to enhanced vascular permeability generating increased interstitial pressure. The resulting vascular collapse and ischemia cause downstream hypoxia, ultimately leading to cell death and necrosis. Thus, local damage generates massive amplification and tumor destruction. The tumor vasculature is readily accessed and potentially a common target irrespective of disease site in the body. Development of a therapeutic approach and particularly next generation agents benefits from effective non-invasive assays. Imaging technologies offer varying degrees of sophistication and ease of implementation. This review considers technological strengths and weaknesses with examples from our own laboratory. Methods reveal vascular extent and patency, as well as insights into tissue viability, proliferation and necrosis. Spatiotemporal resolution ranges from cellular microscopy to single slice tomography and full three-dimensional views of whole tumors and measurements can be sufficiently rapid to reveal acute changes or long-term outcomes. Since imaging is non-invasive, each tumor may serve as its own control making investigations particularly efficient and rigorous. The concept of tumor vascular disruption was proposed over 30 years ago and it remains an active area of research.
The Deterrent Effect of Insider Trading Enforcement Actions

We analyze whether exposure to an SEC insider trading enforcement action affects how insiders trade. We find that following an insider trading enforcement action at one firm, exposed insiders earn significantly lower abnormal profits from their trades at other firms compared to non-exposed insiders. The deterrent effect is stronger when a fellow insider is convicted and is similarly significant both pre- and post-SOX. Following the enforcement event, exposed insiders do not trade less frequently, but do trade significantly fewer shares per trade. Insiders who have witnessed an enforcement action have a lower probability for future conviction than their unexposed peers.
High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer

Chromatin accessibility profiling can identify putative regulatory regions genome wide; however, pooled single-cell methods for assessing the effects of regulatory perturbations on accessibility are limited. Here, we report a modified droplet-based single-cell ATAC-seq protocol for perturbing and evaluating dynamic single-cell epigenetic states. This method (Spear-ATAC) enables simultaneous read-out of chromatin accessibility profiles and integrated sgRNA spacer sequences from thousands of individual cells at once. Spear-ATAC profiling of 104,592 cells representing 414 sgRNA knock-down populations reveals the temporal dynamics of epigenetic responses to regulatory perturbations in cancer cells and the associations between transcription factor binding profiles. Transcription factor binding dynamics can drive epigenetic states, enabling a diversity of phenotypes. Here the authors present Spear-ATAC to quantify and map perturbations to chromatin accessibility in single cells at high throughput.
Structural Configuration Systems Analysis for Advanced Aircraft Fuselage Concepts

Structural configuration analysis of an advanced aircraft fuselage concept is investigated. This concept is characterized by a double-bubble section fuselage with rear mounted engines. Based on lessons learned from structural systems analysis of unconventional aircraft, high-fidelity finite-element models (FEM) are developed for evaluating structural performance of three double-bubble section configurations. Structural sizing and stress analysis are applied for design improvement and weight reduction. Among the three double-bubble configurations, the double-D cross-section fuselage design was found to have a relatively lower structural weight. The structural FEM weights of these three double-bubble fuselage section concepts are also compared with several cylindrical fuselage models. Since these fuselage concepts are different in size, shape and material, the fuselage structural FEM weights are normalized by the corresponding passenger floor area for a relative comparison. This structural systems analysis indicates that an advanced composite double-D section fuselage may have a relative structural weight ratio advantage over a conventional aluminum fuselage. Ten commercial and conceptual aircraft fuselage structural weight estimates, which are empirically derived from the corresponding maximum takeoff gross weight, are also presented and compared with the FEM- based estimates for possible correlation. A conceptual full vehicle FEM model with a double-D fuselage is also developed for preliminary structural analysis and weight estimation.
Language Model Augmented Relevance Score

Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements. Newer metrics such as BERTScore have addressed many weaknesses in prior metrics such as BLEU and ROUGE, which rely on n-gram matching. These newer methods, however, are still limited in that they do not consider the generation context, so they cannot properly reward generated text that is correct but deviates from the given reference. In this paper, we propose Language Model Augmented Relevance Score (MARS), a new context-aware metric for NLG evaluation. MARS leverages off-the-shelf language models, guided by reinforcement learning, to create augmented references that consider both the generation context and available human references, which are then used as additional references to score generated text. Compared with seven existing metrics in three common NLG tasks, MARS not only achieves higher correlation with human reference judgements, but also differentiates well-formed candidates from adversarial samples to a larger degree.
Prediction of Future Failures for Heterogeneous Reliability Field Data

This article introduces methods for constructing prediction bounds or intervals for the number of future failures from heterogeneous reliability field data. We focus on within-sample prediction whe...
Color Polymorphism is a Driver of Diversification in the Lizard Family Lacertidae.

Color polymorphism - two or more heritable color phenotypes maintained within a single breeding population - is an extreme type of intra-specific diversity widespread across the tree of life. Color polymorphism is hypothesized to be an engine for speciation, where morph loss or divergence between distinct color morphs within a species results in the rapid evolution of new lineages, and thus, color polymorphic lineages are expected to display elevated diversification rates. Multiple species in the lizard family Lacertidae are color polymorphic, making them an ideal group to investigate the evolutionary history of this trait and its influence on macroevolution. Here, we produce a comprehensive species-level phylogeny of the lizard family Lacertidae to reconstruct the evolutionary history of color polymorphism and test if color polymorphism has been a driver of diversification. Accounting for phylogenetic uncertainty with multiple phylogenies and simulation studies, we estimate an ancient origin of color polymorphism (111 Ma) within the Lacertini tribe (subfamily Lacertinae). Color polymorphism most likely evolved few times in the Lacertidae and has been lost at a much faster rate than gained. Evolutionary transitions to color polymorphism are associated with shifts in increased net diversification rate in this family of lizards. Taken together, our empirical results support long-standing theoretical expectations that color polymorphism is a driver of diversification.
Safe Linear Thompson Sampling With Side Information

The design and performance analysis of bandit algorithms in the presence of stage-wise safety or reliability constraints has recently garnered significant interest. In this work, we consider the linear stochastic bandit problem under additional unknown linear safety constraints that need to be satisfied at each round. For this problem, we present and analyze a new safe algorithm based on linear Thompson Sampling (TS). Our analysis shows that, with high probability, the algorithm chooses actions that are safe at each round and achieve cumulative regret of order $\mathcal {O} (d^{3/2}\log ^{1/2}d \cdot T^{1/2}\log ^{3/2}T)$ . Remarkably, this matches the regret bound provided by [1] , [2] for the standard linear TS algorithm in the absence of safety constraints. Also, our analysis highlights how the inherently randomized nature of the TS selection rule suffices to properly expand the set of safe actions that the algorithm has access to at each round. In particular, we compare this behavior to alternative safe algorithms, which typically require distinct rounds of randomization that are dedicated to learning the unknown constraints.
Optimal paths through downbursts

The control of an aircraft's takeoff path through a downburst is presently formulated as a dynamic optimization problem with minimum-altitude constraint and two different performance measures; a landing path through a downburst is also discussed. Paths are determined which, in addition to maximizing an airspeed/altitude combination immediately after downburst penetration, minimize deviation from the intended flight path. For mild-to-moderate downbursts, the performance strategy maintains altitude at the expense of airspeed loss, while the survival strategy involves a descent of the aircraft to the minimum altitude in order to obtain greater airspeed. For a severe downburst, both optimal paths maintain minimum altitude.
Formalized Haar Measure

We describe the formalization of the existence and uniqueness of the Haar measure in the Lean theorem prover. The Haar measure is an invariant regular measure on locally compact groups, and it has not been formalized in a proof assistant before. We will also discuss the measure theory library in Lean’s mathematical library mathlib, and discuss the construction of product measures and the proof of Fubini’s theorem for the Bochner integral.
Reflecting Ablating Heat Shields for Planetary Entry

Heat shielding for planetary entry probes of future Jovian and Venusian missions will encounter heating levels well beyond those previously experienced. These entries are typically dominated by radiative heating from the shock layer. This paper demonstrates the potential of reflecting this incident radiation diffusely from an ablating material. This technique contrasts with the absorption experienced by char-forming or graphitic ablators. Two dielectric materials, Teflon (polytetra-fluoroethylene) and boron nitride, are examined for their ablative performance, including reflection, in a combined convective- and radiative-heating environment. For Teflon, at the conditions obtained, superimposition of radiative heating upon a convective stream causes no additional increase in surface recession over the convective only results. For boron nitride, an excellent room-temperature reflector in the visible spectrum, a decrease in reflectivity from 90 to 55 percent is experienced when the surface undergoes sublimation at high temperatures. The process of reflection in each of these materials is described in terms of backscattering from crystals. The significance of a sizable reflection as a mode of energy accommodation is demonstrated for Venusian entries as a potential reduction in mass loss due to ablation.
Answer Generation for Retrieval-based Question Answering Systems

Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based systems, have achieved impressive results. While generally effective, these models fail to provide a satisfying answer when all retrieved candidates are of poor quality, even if they contain correct information. In AS2, models are trained to select the best answer sentence among a set of candidates retrieved for a given question. In this work, we propose to generate answers from a set of AS2 top candidates. Rather than selecting the best candidate, we train a sequence to sequence transformer model to generate an answer from a candidate set. Our tests on three English AS2 datasets show improvement up to 32 absolute points in accuracy over the state of the art.
Evaluation of Simulation Motion Fidelity Criteria in the Vertical and Directional Axes

An evaluation of existing motion fidelity criteria was conducted on the NASA Ames Vertical Motion Simulator. Experienced test pilots flew single-axis repositioning tasks in both the vertical and the directional axes. Using a first-order approximation of a hovering helicopter, tasks were flown with variations only in the filters that attenuate the commands to the simulator motion system. These filters had second-order high-pass characteristics, and the variations were made in the filter gain and natural frequency. The variations spanned motion response characteristics from nearly full math-model motion to fixed-base. Between configurations, pilots recalibrated their motion response perception by flying the task with full motion. Pilots subjectively rated the motion fidelity of subsequent configurations relative to this full motion case, which was considered the standard for comparison. The results suggested that the existing vertical-axis criterion was accurate for combinations of gain and natural frequency changes. However, if only the gain or the natural frequency was changed, the rated motion fidelity was better than the criterion predicted. In the vertical axis, the objective and subjective results indicated that a larger gain reduction was tolerated than the existing criterion allowed. The limited data collected in the yaw axis revealed that pilots had difficulty in distinguishing among the variations in the pure yaw motion cues.
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting Agent.

The objective of most Reinforcement Learning painting agents is to minimize the loss between a target image and the paint canvas. Human painter artistry emphasizes important features of the target image rather than simply reproducing it. Using adversarial or L2 losses in the RL painting models, although its final output is generally a work of finesse, produces a stroke sequence that is vastly different from that which a human would produce since the model does not have knowledge about the abstract features in the target image. In order to increase the human-like planning of the model without the use of expensive human data, we introduce a new loss function for use with the model's reward function: Content Masked Loss. In the context of robot painting, Content Masked Loss employs an object detection model to extract features which are used to assign higher weight to regions of the canvas that a human would find important for recognizing content. The results, based on 332 human evaluators, show that the digital paintings produced by our Content Masked model show detectable subject matter earlier in the stroke sequence than existing methods without compromising on the quality of the final painting. Our code is available at https://github.com/pschaldenbrand/ContentMaskedLoss.
Random additions in urns of integers

Consider an urn containing balls labeled with integer values. Define a discrete-time random process by drawing two balls, one at a time and with replacement, and noting the labels. Add a new ball labeled with the sum of the two drawn labels. This model was introduced by Siegmund and Yakir (2005) Ann. Prob. 33, 2036 for labels taking values in a finite group, in which case the distribution defined by the urn converges to the uniform distribution on the group. For the urn of integers, the main result of this paper is an exponential limit law. The mean of the exponential is a random variable with distribution depending on the starting configuration. This is a novel urn model which combines multi-drawing and an infinite type of balls. The proof of convergence uses the contraction method for recursive distributional equations.
When the ventral visual stream is not enough: A deep learning account of medial temporal lobe involvement in perception

The medial temporal lobe (MTL) supports a constellation of memory-related behaviors. Its involvement in perceptual processing, however, has been subject to enduring debate. This debate centers on perirhinal cortex (PRC), an MTL structure at the apex of the ventral visual stream (VVS). Here we leverage a deep learning framework that approximates visual behaviors supported by the VVS (i.e., lacking PRC). We first apply this approach retroactively, modeling 30 published visual discrimination experiments: excluding non-diagnostic stimulus sets, there is a striking correspondence between VVS-modeled and PRC-lesioned behavior, while each is outperformed by PRC-intact participants. We corroborate and extend these results with a novel experiment, directly comparing PRC-intact human performance to electrophysiological recordings from the macaque VVS: PRC-intact participants outperform a linear readout of high-level visual cortex. By situating lesion, electrophysiological, and behavioral results within a shared computational framework, this work resolves decades of seemingly inconsistent findings surrounding PRC involvement in perception.
End-to-End NLP Knowledge Graph Construction

This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers. We focus on extracting four types of relations: evaluatedOn between tasks and datasets, evaluatedBy between tasks and evaluation metrics, as well as coreferent and related relations between the same type of entities. For instance, F1-score is coreferent with F-measure. We introduce novel methods for each of these relation types and apply our final framework (SciNLP-KG) to 30,000 NLP papers from ACL Anthology to build a large-scale KG, which can facilitate automatically constructing scientific leaderboards for the NLP community. The results of our experiments indicate that the resulting KG contains high-quality information.
Decomposable Obfuscation: A Framework for Building Applications of Obfuscation from Polynomial Hardness

There is some evidence that indistinguishability obfuscation (iO) requires either exponentially many assumptions or (sub)exponentially hard assumptions, and indeed, all known ways of building obfuscation suffer one of these two limitations. As such, any application built from iO suffers from these limitations as well. However, for most applications, such limitations do not appear to be inherent to the application, just the approach using iO. Indeed, several recent works have shown how to base applications of iO instead on functional encryption (FE), which can in turn be based on the polynomial hardness of just a few assumptions. However, these constructions are quite complicated and recycle a lot of similar techniques.
Partial Identifiability in Discrete Data With Measurement Error

When data contains measurement errors, it is necessary to make modeling assumptions relating the error-prone measurements to the unobserved true values. Work on measurement error has largely focused on models that fully identify the parameter of interest. As a result, many practically useful models that result in bounds on the target parameter -- known as partial identification -- have been neglected. In this work, we present a method for partial identification in a class of measurement error models involving discrete variables. We focus on models that impose linear constraints on the target parameter, allowing us to compute partial identification bounds using off-the-shelf LP solvers. We show how several common measurement error assumptions can be composed with an extended class of instrumental variable-type models to create such linear constraint sets. We further show how this approach can be used to bound causal parameters, such as the average treatment effect, when treatment or outcome variables are measured with error. Using data from the Oregon Health Insurance Experiment, we apply this method to estimate bounds on the effect Medicaid enrollment has on depression when depression is measured with error.
Spatiospectral brain networks reflective of improvisational experience.

Abstract Musical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical chord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.
Specifically Authorized by Binding Precedent Does Not Mean Suggested by Persuasive Precedent: Applying the Good Faith Exception after Davis v. United States

A number of federal circuit courts have recently refused to apply the Fourth Amendment exclusionary rule to evidence obtained from GPS vehicle trackers that were installed and monitored without a warrant before United States v. Jones. Those courts have largely reached that result by invoking Davis v. United States, which held the exclusionary rule to be inapplicable where an officer relied on binding appellate precedent that was later overruled. More specifically, the circuit courts have viewed the Supreme Court’s 1983 decision in United States v. Knotts (addressing “beeper” tracking devices) as binding precedent that specifically authorized the warrantless installation and monitoring of GPS vehicle trackers prior to Jones. The Fourth Circuit’s decision in United States v. Stephens is representative of the “Knotts is binding precedent under Davis approach” that most circuits have used to resolve the exclusionary rule question. This Article, therefore, uses Stephens as a vehicle for analyzing that approach. This Article argues that circuit court decisions like Stephens have stretched the holding of Davis and interpreted Knotts in a way that contradicts Supreme Court precedent. And, the circuit courts have done so for no good reason — they could have reached the same result (i.e., refusing to exclude the GPS vehicle tracker evidence) by working through the general good faith analysis. Instead, most of the circuit courts have chosen to resolve the exclusionary rule issue by defining the terms “specifically authorized” and “binding precedent” to mean “suggested” and “persuasive precedent.” In the process, the courts have converted the intentionally narrow holding of Davis into a broad decision with unknown boundaries.
An Investigation into the Role of Adhesion in the Erosion of Ductile Metals

Existing theories of erosion of ductile metals based on cutting and deformation mechanisms predict no material removal at normal incidence which is contradictory to experience. Thus, other mechanisms may be involved. The possible role of adhesive material transfer during erosion is investigated by both single-particle impingement experiments and erosion by streams of particles. Examination of the rebounding particles as well as the eroded surfaces yields evidence of a significant adhesive mechanism for the ductile metals investigated. Presented at the 35th Annual Meeting in Anaheim, California, May 5–8, 1980
Distribution of Missing Differences in Diffsets

Lazarev, Miller and O’Bryant [11] investigated the distribution of \(|S+S|\) for S chosen uniformly at random from \(\{0, 1, \dots , n-1\}\), and proved the existence of a divot at missing 7 sums (the probability of missing exactly 7 sums is less than missing 6 or missing 8 sums). We study related questions for \(|S-S|\), and show some divots from one end of the probability distribution, \(P(|S-S|=k)\), as well as a peak at \(k=4\) from the other end, \(P(2n-1-|S-S|=k)\). A corollary of our results is an asymptotic bound for the number of complete rulers of length n.
Infinite-Dimensional Fermionic Symmetry in Supersymmetric Gauge Theories

We establish the existence of an infinite-dimensional fermionic symmetry in four-dimensional supersymmetric gauge theories by analyzing semiclassical photino dynamics in abelian $$ \mathcal{N} $$ = 1 theories with charged matter. The symmetry is parametrized by a spinor-valued function on an asymptotic S2 at null infinity. It is not manifest at the level of the Lagrangian, but acts non-trivially on physical states, and its Ward identity is the soft photino theorem. The infinite-dimensional fermionic symmetry resides in the same $$ \mathcal{N} $$ = 1 supermultiplet as the physically non-trivial large gauge symmetries associated with the soft photon theorem.
The coalgebraic enrichment of algebras in higher categories

Abstract We prove that given C a presentably symmetric monoidal ∞-category, and any essentially small ∞-operad O , the ∞-category of O -algebras in C is enriched, tensored and cotensored over the presentably symmetric monoidal ∞-category of O -coalgebras in C . We provide a higher categorical analogue of the universal measuring coalgebra. For categories in the usual sense, the result was proved by Hyland, Lopez Franco, and Vasilakopoulou.
Poverty and Social Protection in Bulgaria

Absolute poverty has dropped markedly in Bulgaria but income inequality has increased substantially in the aftermath of the GFC. This increase is due to a rise in market income inequality that was compounded by a reduction in fiscal redistribution. The redistributive role of direct taxation has declined with the introduction of a flat tax and social spending is relatively low and decreasing (as a share of GDP), is concentrated on a few social risks, and experienced a decline in its redistributive efficiency. The COVID-19 crisis is likely to deepen income inequality, increasing the room for redistributive policies.