cssi/SciSciGPT-SciSciNet huggingface dataset:
Using Grammar Patterns to Interpret Test Method Name Evolution

It is good practice to name test methods such that they are comprehensible to developers; they must be written in such a way that their purpose and functionality are clear to those who will maintain them. Unfortunately, there is little automated support for writing or maintaining the names of test methods. This can lead to inconsistent and low-quality test names and increase the maintenance cost of supporting these methods. Due to this risk, it is essential to help developers in maintaining their test method names over time. In this paper, we use grammar patterns, and how they relate to test method behavior, to understand test naming practices. This data will be used to support an automated tool for maintaining test names.
Internal Control Quality and Bank Risk-Taking and Performance

Using a sample of bank-years from 2005 to 2017, we examine the effect of internal control quality on future risk-taking and performance. We find that banks that disclose a material weakness in internal controls have higher risk-taking and worse performance in the future, including having a higher (lower) likelihood of experiencing large losses (gains). These findings suggest that weak controls increase (reduce) downside (upside) risk-taking or conversely that strong controls increase (reduce) upside (downside) risk-taking. Path analyses suggest that 22.3 to 43.7 percent of the effect of internal control quality on future performance is through risk-taking. Additionally, material weaknesses are negatively associated with total asset, loan, interest income, and non-interest income growth, suggesting that internal control quality affects both core and non-core activities of banks. Overall, results suggest that strong internal controls improve bank risk-taking, in part through asymmetrically reducing downside risk-taking while facilitating upside risk-taking, ultimately improving bank performance.
Glycosaminoglycan content of a mineralized collagen scaffold promotes mesenchymal stem cell secretion of factors to modulate angiogenesis and monocyte differentiation

Abstract Effective design of biomaterials to aid regenerative repair of craniomaxillofacial (CMF) bone defects requires approaches that modulate the complex interplay between exogenously added progenitor cells and cells in the wound microenvironment, such as osteoblasts, osteoclasts, endothelial cells, and immune cells. We are exploring the role of the glycosaminoglycan (GAG) content in a class of mineralized collagen scaffolds recently shown to promote osteogenesis and healing of craniofacial bone defects. We previously showed that incorporating chondroitin-6-sulfate or heparin improved mineral deposition by seeded human mesenchymal stem cells (hMSCs). Here, we examine the effect of varying scaffold GAG content on hMSC behavior, and their ability to modulate osteoclastogenesis, vasculogenesis, and the immune response. We report the role of hMSC-conditioned media produced in scaffolds containing chondroitin-6-sulfate (CS6), chondroitin-4-sulfate (CS4), or heparin (Heparin) GAGs on endothelial tube formation and monocyte differentiation. Notably, endogenous production by hMSCs within Heparin scaffolds most significantly inhibits osteoclastogenesis via secreted osteoprotegerin (OPG), while the secretome generated by CS6 scaffolds reduced pro-inflammatory immune response and increased endothelial tube formation. All conditioned media down-regulated many pro- and anti-inflammatory cytokines, such as IL6, IL-1β, and CCL18 and CCL17 respectively. Together, these findings demonstrate that modifying mineralized collagen scaffold GAG content can both directly (hMSC activity) and indirectly (production of secreted factors) influence overall osteogenic potential and mineral biosynthesis as well as angiogenic potential and monocyte differentiation towards osteoclastic and macrophage lineages. Scaffold GAG content is therefore a powerful stimulus to modulate reciprocal signaling between multiple cell populations within the bone healing microenvironment.
Increasing Iterate Averaging for Solving Saddle-Point Problems.

Many problems in machine learning and game theory can be formulated as saddle-point problems, for which various first-order methods have been developed and proven efficient in practice. Under the general convex-concave assumption, most first-order methods only guarantee an ergodic convergence rate, that is, the uniform averages of the iterates converge at a O(1/T) rate in terms of the saddle-point residual. However, numerically, the iterates themselves can often converge much faster than the uniform averages. This observation motivates increasing averaging schemes that put more weight on later iterates, in contrast to the usual uniform averaging. We show that such increasing averaging schemes, applied to various first-order methods, are able to preserve the O(1/T) convergence rate with no additional assumptions or computational overhead. Extensive numerical experiments on zero-sum game solving, market equilibrium computation and image denoising demonstrate the effectiveness of the proposed schemes. In particular, the increasing averages consistently outperform the uniform averages in all test problems by orders of magnitude. When solving matrix and extensive-form games, increasing averages consistently outperform the last iterates as well. For matrix games, a first-order method equipped with increasing averaging outperforms the highly competitive CFR+ algorithm.
Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference

Often when biological entities are measured in multiple ways, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other information is hard-to-obtain information (HI) and can only be gathered on some. We propose building a model to make probabilistic predictions of HI using EI. Our feature mapping GAN (FMGAN), based on the conditional GAN framework, uses an embedding network to process conditions as part of the conditional GAN training to create manifold structure when it is not readily present in the conditions. We experiment on generating RNA sequencing of cell lines perturbed with a drug conditioned on the drug's chemical structure and generating FACS data from clinical monitoring variables on a cohort of COVID-19 patients, effectively describing their immune response in great detail.
End-of-discharge and End-of-life Prediction in Lithium-ion Batteries with Electrochemistry-based Aging Models

As batteries become increasingly prevalent in complex systems such as aircraft and electric cars, monitoring and predicting battery state of charge and state of health becomes critical. In order to accurately predict the remaining battery power to support system operations for informed operational decision-making, age-dependent changes in dynamics must be accounted for. Using an electrochemistry-based model, we investigate how key parameters of the battery change as aging occurs, and develop models to describe aging through these key parameters. Using these models, we demonstrate how we can (i) accurately predict end-of-discharge for aged batteries, and (ii) predict the end-of-life of a battery as a function of anticipated usage. The approach is validated through an experimental set of randomized discharge profiles.
Perspective—On the thermodynamics of perfect unconditional security

A secure key distribution (exchange) scheme is unconditionally secure if it is unbreakable against arbitrary technological improvements of computing power and/or any development of new algorithms. There are only two families of experimentally realized and tested unconditionally secure key distribution technologies: quantum key distribution (QKD), the base of quantum cryptography, which utilizes quantum physical photonic features, and the Kirchhoff-Law–Johnson-Noise (KLJN) system that is based on classical statistical physics (fluctuation–dissipation theorem). The focus topic of this paper is the thermodynamical situation of the KLJN system. In all the original works, the proposed KLJN schemes required thermal equilibrium between the devices of the communicating parties to achieve perfect security. However, Vadai et al., in (Nature) Sci. Rep. 5, 13653 (2015) show a modified scheme, where there is a non-zero thermal noise energy flow between the parties, yet the system seems to resist all the known attack types. We introduce an attack type against their system. The attack utilizes coincidence events between the line current and voltages. We show that there is a non-zero information leak toward the Eavesdropper, even under idealized conditions. As soon as the thermal equilibrium is restored, the system becomes perfectly secure again. In conclusion, perfect unconditional security requires thermal equilibrium.
Signatures of rapid synaptic learning in the hippocampus during novel experiences

Neurons in the hippocampus exhibit striking selectivity for specific combinations of sensory features, forming representations which are thought to subserve episodic memory. Even during a completely novel experience, ensembles of hippocampal ``place cells99 are rapidly configured such that the population sparsely encodes visited locations, stabilizing within minutes of the first exposure to a new environment. What cellular mechanisms enable this fast encoding of experience? Here we leverage virtual reality and large scale neural recordings to dissect the effects of novelty and experience on the dynamics of place field formation. We show that the place fields of many CA1 neurons transiently shift locations and modulate the amplitude of their activity immediately after place field formation, consistent with rapid plasticity mechanisms driven by plateau potentials and somatic burst spiking. These motifs were particularly enriched during initial exploration of a novel context and decayed with experience. Our data suggest that novelty modulates the effective learning rate in CA1, favoring burst-driven field formation to support fast synaptic updating during new experience.
Neuroevolution of a recurrent neural network for spatial and working memory in a simulated robotic environment

We evolved weights in a recurrent neural network (RNN) to replicate the behavior and neural activity observed in rats during a spatial and working memory task. The rat was simulated using a robot simulator to navigate a virtual maze. After evolving weights from sensory inputs to the RNN, within the RNN, and from the RNN to the robot's motors, the robot successfully navigated the space to reach four reward arms with minimal repeats before the timeout. Our current findings suggest that it is the RNN dynamics that are key to performance, and that performance is not dependent on any one sensory type, which suggests that neurons in the RNN are performing mixed selectivity and conjunctive coding. The RNN activity resembles spatial information and trajectory-dependent coding observed in the hippocampus. The evolved RNN exhibits navigation skills, spatial memory, and working memory.
Open quantum systems for quarkonia

I review recent applications of the open quantum system framework in the understanding of quarkonium suppression in heavy-ion collisions, which has been used as a probe of the quark–gluon plasma fo...
Liver-Resident Bystander CD8+ T Cells Contribute to Liver Disease Pathogenesis in Chronic Hepatitis D Virus Infection.

Background & Aims The hepatitis D virus (HDV) causes the most severe form of chronic hepatitis, often progressing to cirrhosis within 5 to 10 years. There is no curative treatment, and the mechanisms underlying the accelerated liver disease progression are unknown. Methods Innate and adaptive immune responses were studied in blood and liver of 24 patients infected with HDV and 30 uninfected controls by multiparameter flow cytometry in correlation with disease severity and stage. Results The 2 main intrahepatic innate immune-cell populations, mucosal-associated invariant T cells and natural killer (NK) cells, were reduced in the livers of patients infected with HDV compared with those of uninfected controls but were more frequently activated in the liver compared with the blood. Most intrahepatic cluster of differentiation (CD) 8-positive (CD8+) T cells were memory cells or terminal effector memory cells, and most of the activated and degranulating (CD107a+) HDV-specific and total CD8+ T cells were liver-resident (CD69+C-X-C motif chemokine receptor 6+). Unsupervised analysis of flow cytometry data identified an activated, memory-like, tissue-resident HDV-specific CD8+ T-cell cluster with expression of innate-like NK protein 30 (NKp30) and NK group 2D (NKG2D) receptors. The size of this population correlated with liver enzyme activity (r = 1.0). NKp30 and NKG2D expression extended beyond the HDV-specific to the total intrahepatic CD8+ T-cell population, suggesting global bystander activation. This was supported by the correlations between (i) NKG2D expression with degranulation of intrahepatic CD8+ T cells, (ii) frequency of degranulating CD8+ T cells with liver enzyme activity and the aspartate aminotransferase-to-platelet ratio index score, and by the in vitro demonstration of cytokine-induced NKG2D-dependent cytotoxicity. Conclusion Antigen-nonspecific activation of liver-resident CD8+ T cells may contribute to inflammation and disease stage in HDV infection.
Optimal Tolling for Multitype Mixed Autonomous Traffic Networks

When selfish users share a road network and minimize their individual travel costs, the equilibrium they reach can be worse than the socially optimal routing. Tolls are often used to mitigate this effect in traditional congestion games, where all vehicles contribute identically to congestion. However, with the proliferation of autonomous vehicles and driver-assistance technology, vehicles become heterogeneous in how they contribute to road latency. This magnifies the potential inefficiencies due to selfish routing and invalidates traditional tolling methods. To address this, we consider a network of parallel roads where the latency on each road is an affine function of the quantity of flow of each vehicle type. We provide tolls (which differentiate between vehicle types) which are guaranteed to minimize social cost at equilibrium. The tolls are a function of a calculated optimal routing; to enable this tolling, we prove that some element in the set of optimal routings has a lack of cycles in a graph representing the way vehicles types share roads. We then show that unless a planner can differentiate between vehicle types in the tolls given, the resulting equilibrium can be unboundedly worse than the optimal routing, and that marginal cost tolling fails in our setting.
Hamiltonian simulation in the low-energy subspace

We study the problem of simulating the dynamics of spin systems when the initial state is supported on a subspace of low energy of a Hamiltonian H. This is a central problem in physics with vast applications in many-body systems and beyond, where the interesting physics takes place in the low-energy sector. We analyze error bounds induced by product formulas that approximate the evolution operator and show that these bounds depend on an effective low-energy norm of H. We find improvements over the best previous complexities of product formulas that apply to the general case, and these improvements are more significant for long evolution times that scale with the system size and/or small approximation errors. To obtain these improvements, we prove exponentially decaying upper bounds on the leakage to high-energy subspaces due to the product formula. Our results provide a path to a systematic study of Hamiltonian simulation at low energies, which will be required to push quantum simulation closer to reality.
Marine Vessel Tracking using a Monocular Camera.

In this paper, a new technique for camera calibration using only GPS data is presented. A new way of tracking objects that move on a plane in a video is achieved by using the location and size of the bounding box to estimate the distance, achieving an average prediction error of 5.55m per 100m distance from the camera. This solution can be run in real-time at the edge, achieving efficient inference in a low-powered IoT environment while also being able to track multiple different vessels.
Socially Aware Navigation: A Non-linear Multi-objective Optimization Approach

Mobile robots are increasingly populating homes, hospitals, shopping malls, factory floors, and other human environments. Human society has social norms that people mutually accept; obeying these norms is an essential signal that someone is participating socially with respect to the rest of the population. For robots to be socially compatible with humans, it is crucial for robots to obey these social norms. In prior work, we demonstrated a Socially-Aware Navigation (SAN) planner, based on Pareto Concavity Elimination Transformation (PaCcET), in a hallway scenario, optimizing two objectives so the robot does not invade the personal space of people. This article extends our PaCcET-based SAN planner to multiple scenarios with more than two objectives. We modified the Robot Operating System’s (ROS) navigation stack to include PaCcET in the local planning task. We show that our approach can accommodate multiple Human-Robot Interaction (HRI) scenarios. Using the proposed approach, we achieved successful HRI in multiple scenarios such as hallway interactions, an art gallery, waiting in a queue, and interacting with a group. We implemented our method on a simulated PR2 robot in a 2D simulator (Stage) and a pioneer-3DX mobile robot in the real-world to validate all the scenarios. A comprehensive set of experiments shows that our approach can handle multiple interaction scenarios on both holonomic and non-holonomic robots; hence, it can be a viable option for a Unified Socially-Aware Navigation (USAN).
Control Synthesis using Signal Temporal Logic Specifications with Integral and Derivative Predicates

In many applications, the integrals and derivatives of signals carry valuable information (e.g., cumulative success over a time window, the rate of change) regarding the behavior of the underlying system. In this paper, we extend the expressiveness of Signal Temporal Logic (STL) by introducing predicates that can define rich properties related to the integral and derivative of a signal. For control synthesis, the new predicates are encoded into mixed-integer linear inequalities and are used in the formulation of a mixed-integer linear program to find a trajectory that satisfies an STL specification. We discuss the benefits of using the new predicates and illustrate them in a case study showing the influence of the new predicates on the trajectories of an autonomous robot.
Extreme Rotation Estimation using Dense Correlation Volumes

We present a technique for estimating the relative 3D rotation of an RGB image pair in an extreme setting, where the images have little or no overlap. We observe that, even when images do not overlap, there may be rich hidden cues as to their geometric relationship, such as light source directions, vanishing points, and symmetries present in the scene. We propose a network design that can automatically learn such implicit cues by comparing all pairs of points between the two input images. Our method therefore constructs dense feature correlation volumes and processes these to predict relative 3D rotations. Our predictions are formed over a fine-grained discretization of rotations, bypassing difficulties associated with regressing 3D rotations. We demonstrate our approach on a large variety of extreme RGB image pairs, including indoor and outdoor images captured under different lighting conditions and geographic locations. Our evaluation shows that our model can successfully estimate relative rotations among non-overlapping images without compromising performance over overlapping image pairs.1
Public Legal Reason

This Essay deveopes an ideal of public legal reason - a normative theory of legal reasons that is appropriate for a society characterized by religious and moral pluralism. One of the implications of this theory is that normative theorizing about public and private law should eschew reliance on the deep premises of deontology or consequentialism and should instead rely on what I shall call public values - values that can be affirmed without relying on the deep and controversial premises of particular comprehensive moral doctrines. The ideal of public legal reason is then applied to a particular question - whether welfarism (a particular form of normative law and economics) provides the sort of reasons that appropriate for legal practice. The answer to that question is no - to the extent that welfarism contends that the normative assessment of legal policies should rely exclusively on information about individual preferences, welfarism relies on deep and controversial premises of consequentialist moral theory that are fail the test of public reason. The Essay also investigates the thesis - advanced by Louis Kaplow and Steven Shavell - that any fairness principle (a nonwelfarist method of policy assessment) can violate weak Pareto (making everyone worse off). Whatever the implications of Kaplow and Shavell's argument, it does not show that welfarism can provide public legal reasons. The essay concludes that law's justifications should rely on normative principles that are accessible to reasonable citizens, whether they are theists or atheists, deontologists or consequentialists, moral philosophers or economists. Law's deliberations should be shallow and not deep. Law's reason should be public.
Client concerns about information spillovers from sharing audit partners

Abstract We hypothesize that companies in the same product market avoid sharing the same audit partner when they are concerned about possible information spillovers. Consistent with our hypothesis, we find that product market rivals are less likely to share the same partner when they perceive that information spillovers are more costly. While concerns about information spillovers significantly reduce the likelihood of product market rivals sharing the same audit partner, we find that such concerns do not deter them from sharing the same audit office. Lastly, when companies are unconcerned with information spillovers, our results suggest that partner sharing can be beneficial because it can result in lower audit fees and fewer accounting misstatements.
Functional Interactomes of the Ebola Virus Polymerase Identified by Proximity Proteomics in the Context of Viral Replication

Ebola virus (EBOV) critically depends on the viral polymerase to replicate and transcribe the viral RNA genome. To examine whether interactions between EBOV polymerase and cellular and viral factors affect distinct viral RNA synthesis events, we applied proximity proteomics to define the cellular interactome of EBOV polymerase, under conditions that recapitulate viral transcription and replication. We engineered EBOV polymerase tagged with the split-biotin ligase split-TurboID, which successfully biotinylated the proximal proteome while retaining polymerase activity. We further analyzed the interactomes in an siRNA-based, functional screen and uncovered 35 host factors, which, when depleted, affect EBOV infection. We validated one host factor, eukaryotic peptide chain release factor subunit 3a (eRF3a/GSPT1), which we show physically and functionally associates with EBOV polymerase to facilitate viral transcription termination. Our work demonstrates the utility of proximity proteomics to capture the functional host-interactome of the EBOV polymerase and to illuminate host-dependent regulations of viral RNA synthesis.
Originalism as Transformative Politics

The debate between originalists and nonoriginalists has begun to suffer a fate similar to these earlier debates in constitutional theory and practice. This essay is one of a growing number of recent attempts to look back at the originalism debate now that both the critics and defenders of originalism have stated their positions and replied to arguments of their opponents. As originalism has been modified and defined in reaction to nonoriginalist critiques, the originalist's theory has become more and more plausible as a theory of constitutional interpretation. When I say plausible as a theory of constitutional interpretation, I mean that the most sophisticated forms of originalism provide an accurate description of the phenomenology of constitutional practice. The Constitution is interpreted in light of the purposes and concerns that animated its framing and ratification. As a matter of the theory of interpretation, originalism captures an important aspect of constitutional practice. But the originalists have won a Pyrrhic victory. As originalism has been clarified in response to its critics, it has gradually become more and more evident that it has no force as a critique of constitutional interpretation as practiced by the Warren Court. Originalism has become indistinguishable from nonoriginalism. Quite to the contrary, I will argue that originalism can serve as the basis for what we might call transformative politics. This essay was originally published in 1989 and is anthologized in Gadamer and the Law (edited by Francis Mootz and published by Ashgate in 2007).
Evaluation of SARS-CoV-2 in human semen and effect on total sperm number: A prospective observational study

PURPOSE: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has created a surge of research to help better understand the breadth of possible sequelae. However, little is known regarding the impact on semen parameters and fertility potential. We sought to investigate for presence of viral RNA in semen of men with SARS-CoV-2 infection and to evaluate its effect on semen parameters in ejaculate. MATERIALS AND METHODS: We prospectively recruited thirty men diagnosed with acute SARS-CoV-2 infection using real-time reverse transcriptase polymerase chain reaction (RT-PCR) of pharyngeal swab specimens. Semen samples were collected from each individual using mailed kits. Follow-up semen samples were done with mailed kits or in-person in office setting. Semen analysis and PCR was performed after samples were received. RESULTS: Thirty semen samples from recovered men were obtained 11-64 days after testing positive for SAR-CoV-2 infection. The median duration between positive SAR-CoV-2 test and semen collection was 37 days (interquartile range [IQR]=23). The median total sperm number (TSN) in ejaculate was 12.5 million (IQR=52.1). When compared with age-matched SARS-CoV-2(-) men, TSN was lower among SARS-CoV-2(+) men (p=0.0024). Five men completed a follow-up sperm analysis (median 3 months) and had a median TSN of 18 million (IQR=21.6). No RNA was detected by means of RT-PCR in the semen in 16 samples tested. CONCLUSIONS: SARS-CoV-2 infection, though not detected in semen of recovered men, can affect TSN in ejaculate in the acute setting. Whether SARS-CoV-2 can affect spermatogenic function long-term remains to be evaluated.
Bankruptcy in the time of COVID-19: Special measures adopted by the People's Republic of China courts during the period of COVID-19 prevention and control

Business bankruptcy in China is governed by the Enterprise Bankruptcy Law (EBL), a national insolvency code enacted in 2007. The EBL contains provisions for business liquidation, reorganization, and compromise of debt. Although adjustment of debt through bankruptcy is far less common in China than in western nations, Chinese courts have established a body of bankruptcy procedures and judicial interpretations that give insolvency in China a measure of predictability and effectiveness. Notwithstanding the EBL provisions, soon after the onset of the pandemic, PRC courts began to adopt ad-hoc rules and guidelines in bankruptcy cases for businesses whose financial woes were caused or exacerbated by coronavirus, or for enterprises that produced medical equipment and supplies to help prevent and control the virus. This paper examines these court measures, explores their political and judicial context, and demonstrates how they produced bankruptcy outcomes that were often significantly different than what would have resulted if the EBL had been applied based on pre-COVID-19 EBL practices. © 2021 INSOL International and John Wiley & Sons Ltd
Meta Dynamic Pricing: Transfer Learning Across Experiments

We study the problem of learning shared structure across a sequence of dynamic pricing experiments for related products. We consider a practical formulation in which the unknown demand parameters f...
Rotorcraft RPM on Mars

The Ingenuity helicopter test flights on Mars in April 2021 marked the first time a powered aircraft has flown on another world. Students who have access to model helicopters and drones may wonder, how well could those hover on the Red Planet? The answer can be found in this journal [The Physics Teacher], using appropriate scaling of the surface gravity and atmospheric density to Martian values.
Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination.

Abstract Background: The current development of vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unprecedented. Little is known, however, about the nuanced public opinions on the vaccines on social media. Methods: We adopt a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine. After feature inference and opinion mining, 10,945 unique Twitter users are included in the study population. Multinomial logistic regression and counterfactual analysis are conducted. Results: Socioeconomically disadvantaged groups are more likely to hold polarized opinions on coronavirus disease 2019 (COVID-19) vaccines - either pro-vaccine ( B = 0.40 , SE = 0.08 , P 0.001 , OR = 1.49 ; 95 % CI = 1.26 -- 1.75 ) or anti-vaccine ( B = 0.52 , SE = 0.06 , P 0.001 , OR = 1.69 ; 95 % CI = 1.49 -- 1.91 ). People who have the worst personal pandemic experience are more likely to hold the anti-vaccine opinion ( B = − 0.18 , SE = 0.04 , P 0.001 , OR = 0.84 ; 95 % CI = 0.77 -- 0.90 ). The U.S. public is most concerned about the safety, effectiveness, and political issues regarding vaccines for COVID-19, and improving personal pandemic experience increases the vaccine acceptance level. Conclusion: Opinion on COVID-19 vaccine uptake varies across people of different characteristics.
Trace Reconstruction with Bounded Edit Distance

The trace reconstruction problem studies the number of noisy samples needed to recover an unknown string $\mathrm{x} \in \{0,1\}^{n}$ with high probability, where the samples are independently obtained by passing x through a random deletion channel with deletion probability $q$ . The problem is receiving significant attention recently due to its applications in DNA sequencing and DNA storage. Yet, there is still an exponential gap between upper and lower bounds for the trace reconstruction problem. In this paper we study the trace reconstruction problem when x is confined to an edit distance ball of radius $k$ , which is essentially equivalent to distinguishing two strings with edit distance at most $k$ . It is shown that $n^{O(k)}$ samples suffice to achieve this task with high probability.
The efficacy of event isotropy as an event shape observable

Event isotropy $$ {\mathcal{I}}^{\mathrm{sph}} $$ , an event shape observable that measures the distance of a final state from a spherically symmetric state, is designed for new physics signals that are far from QCD-like. Using a new technique [1] for producing a wide variety of signals that can range from near-spherical to jetty, we compare event isotropy to other observables. We show that thrust T and the C parameter (and λmax, the largest eigenvalue of the sphericity matrix) are strongly correlated and thus redundant, to a good approximation. By contrast, event isotropy adds considerable information, often serving to break degeneracies between signals that would have almost identical T and C distributions. Signals with broad distributions in T (or λmax) and in $$ {\mathcal{I}}^{\mathrm{sph}} $$ separately often have much narrower distributions, and are more easily distinguished, in the ( $$ {\mathcal{I}}^{\mathrm{sph}} $$ , λmax) plane. An intuitive, semi-analytic estimation technique clarifies why this is the case and assists with the interpretation of the distributions.
Informative fundraising: The signaling value of seed money and matching gifts

Abstract While existing theory predicts that matching leadership gifts raise more voluntary contributions for public goods than seed money, recent field experiments find otherwise. We reconcile the two by studying a model of leadership giving with incomplete information about the quality of the public good provided by a charity. Both the fundraising scheme employed by the charity and the contribution decision by the lead donor may signal the charity’s quality to subsequent donors. The charity solicits optimally for a matching gift if the lead donor is informed about the quality of the public good. Intuitively, an informed lead donor conveys quality information to downstream donors through the size of her contribution. As a result, the charity has no signaling concerns and opts for matching because of its ability to mitigate free-riding. The preference for matching, however, reduces when the lead donor’s information is limited. Then, the lead donor’s contribution is less informative and the high quality charity uses seed money as a costly signal of quality. As a result, seed money is associated with higher expected quality and higher expected contributions compared to matching.
Benchmarking Inference Performance of Deep Learning Models on Analog Devices

Analog hardware implemented deep learning models are promising for computation and energy constrained systems such as edge computing devices. However, the analog nature of the device and the many associated noise sources will cause changes to the value of the weights in the trained deep learning models deployed on such devices. In this study, systematic evaluation of the inference performance of trained popular deep learning models for image classification deployed on analog devices has been carried out, where additive white Gaussian noise has been added to the weights of the trained models during inference. It is observed that deeper models and models with more redundancy in design, such as VGG, are more robust to the noise in general. Also, it is observed that the performance is affected by the design philosophy of the model, the detailed structure of the model, the exact machine learning task, as well as the datasets.
Sourcing from a Self-Reporting Supplier: Strategic Communication of Social Responsibility in a Supply Chain

Problem definition: To manage supplier social responsibility (SR), some firms have adopted a self-assessment strategy whereby they ask suppliers to self-report SR capabilities. Self-reported inform...
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning

For autonomous vehicles, effective behavior planning is crucial to ensure safety of the ego car. In many urban scenarios, it is hard to create sufficiently general heuristic rules, especially for challenging scenarios that some new human drivers find difficult. In this work, we propose a behavior planning structure based on reinforcement learning (RL) which is capable of performing autonomous vehicle behavior planning with a hierarchical structure in simulated urban environments. Application of the hierarchical structure [1] allows the various layers of the behavior planning system to be satisfied. Our algorithms can perform better than heuristic-rule-based methods for elective decisions such as when to turn left between vehicles approaching from the opposite direction or possible lane-change when approaching an intersection due to lane blockage or delay in front of the ego car. Such behavior is hard to evaluate as correct or incorrect, but some aggressive expert human drivers handle such scenarios effectively and quickly. On the other hand, compared to traditional RL methods, our algorithm is more sample-efficient, due to the use of a hybrid reward mechanism and heuristic exploration during the training process. The results also show that the proposed method converges to an optimal policy faster than traditional RL methods.
Many-server asymptotics for join-the-shortest-queue: Large deviations and rare events

The join-the-shortest-queue routing policy is studied in an asymptotic regime where the number of processors n scales with the arrival rate. A large deviation principle (LDP) for the occupancy process is established, as n→∞, in a suitable infinite-dimensional path space. Model features that present technical challenges include, Markovian dynamics with discontinuous statistics, a diminishing rate property of the transition probability rates, and an infinite-dimensional state space. The difficulty is in the proof of the Laplace lower bound which requires establishing the uniqueness of solutions of certain infinite-dimensional systems of controlled ordinary differential equations. The LDP gives information on the rate of decay of probabilities of various types of rare events associated with the system. We illustrate this by establishing explicit exponential decay rates for probabilities of long queues. In particular, denoting by Ejn(T) the event that there is at least one queue with j or more jobs at some time instant over [0,T], we show that, in the critical case, for large n and T, P(Ejn(T))≈exp[−n(j−2)24T].
Unbridled spirit: Illicit markets for bourbon whiskey

Abstract The market for bourbon whiskey is booming. Demand is so great that bourbon aficionados regularly line up overnight for the chance to purchase a single bottle and high-end bourbons are allocated to consumers via lotteries. The prevalence of such non-price allocation mechanisms, however, leads to reselling in secondary markets. To characterize bourbon secondary markets, we first provide a rich description of how they function. We then use 2011 to 2019 whiskey auction data in a repeat sales regression framework to develop a novel price index for rare and vintage bourbons. Our repeat sales approach suggests that bourbon prices are increasing by about 9.1% per year over our sample period. We complement those estimates by developing a second price index using unique data from an illicit peer-to-peer secondary market that operated on a major social network between 2014 and 2017. Our estimates suggest that price increases are similar, at least during common sample periods, in our auction-house and peer-to-peer settings. We also examine how bourbon’s hedonic characteristics are related to secondary market prices and develop hedonic estimates of annual returns that range between 13.1% and 18.8%. While our sample period is relatively short, when examining bourbon’s potential as an alternative investment we find that the addition of bourbon can improve the risk-return ratio of typical stock/bond portfolios.
Using Transformers to Provide Teachers with Personalized Feedback on their Classroom Discourse: The TalkMoves Application

TalkMoves is an innovative application designed to support K-12 mathematics teachers to reflect on, and continuously improve their instructional practices. This application combines state-of-the-art natural language processing capabilities with automated speech recognition to automatically analyze classroom recordings and provide teachers with personalized feedback on their use of specific types of discourse aimed at broadening and deepening classroom conversations about mathematics. These specific discourse strategies are referred to as "talk moves" within the mathematics education community and prior research has documented the ways in which systematic use of these discourse strategies can positively impact student engagement and learning. In this article, we describe the TalkMoves application's cloud-based infrastructure for managing and processing classroom recordings, and its interface for providing teachers with feedback on their use of talk moves during individual teaching episodes. We present the series of model architectures we developed, and the studies we conducted, to develop our best-performing, transformer-based model (F1 = 79.3%). We also discuss several technical challenges that need to be addressed when working with real-world speech and language data from noisy K-12 classrooms.
Regulation of REM sleep by inhibitory neurons in the dorsomedial medulla

Summary The two major stages of mammalian sleep—rapid eye movement sleep (REMs) and non-REM sleep (NREMs)—are characterized by distinct brain rhythms ranging from millisecond to minute-long (infraslow) oscillations. The mechanisms controlling transitions between sleep stages and how they are synchronized with infraslow rhythms remain poorly understood. Using opto- and chemogenetic manipulation in mice, we show that GABAergic neurons in the dorsomedial medulla (dmM) promote the initiation and maintenance of REMs, in part through their projections to the dorsal and median raphe nuclei. Fiber photometry revealed that their activity is strongly increased during REMs and fluctuates during NREMs in close synchrony with infraslow oscillations in the sleep spindle band of the electroencephalogram. The phase of this rhythm influenced the latency and probability with which dmM activation induced REMs. Thus, dmM inhibitory neurons strongly promote REMs, and their slow activity fluctuations may coordinate the timing of REMs episodes with infraslow brain rhythms.
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance

Individuals often make different decisions when faced with the same context, due to personal preferences and background. For instance, judges may vary in their leniency towards certain drug-related offenses, and doctors may vary in their preference for how to start treatment for certain types of patients. With these examples in mind, we present an algorithm for identifying types of contexts (e.g., types of cases or patients) with high inter-decision-maker disagreement. We formalize this as a causal inference problem, seeking a region where the assignment of decision-maker has a large causal effect on the decision. Our algorithm finds such a region by maximizing an empirical objective, and we give a generalization bound for its performance. In a semi-synthetic experiment, we show that our algorithm recovers the correct region of heterogeneity accurately compared to baselines. Finally, we apply our algorithm to real-world healthcare datasets, recovering variation that aligns with existing clinical knowledge.
Towards Emotion-Aware Agents For Negotiation Dialogues

Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the prediction of two important subjective goals in a negotiation – outcome satisfaction and partner perception. Specifically, we analyze the extent to which emotion attributes extracted from the negotiation help in the prediction, above and beyond the individual difference variables. We focus on a recent dataset in chat-based negotiations, grounded in a realistic camping scenario. We study three degrees of emotion dimensions – emoticons, lexical, and contextual by leveraging affective lexicons and a state-of-the-art deep learning architecture. Our insights will be helpful in designing adaptive negotiation agents that interact through realistic communication interfaces.
Beyond the "Ferguson Effect" on crime: Examining its influence on law enforcement personnel

The purpose of the current study is to examine the influence tensions over high-profile officer-involved shootings have had on Atlanta police officers’ ability to do their jobs and whether these im...
Enhancement of electromagnetically induced transparency based Rydberg-atom electrometry through population repumping

We demonstrate improved sensitivity of Rydberg electrometry based on electromagnetically induced transparency (EIT) with a ground state repumping laser. Though there are many factors that limit the sensitivity of radio frequency field measurements, we show that repumping can enhance the interaction strength while avoiding additional Doppler or power broadening. Through this method, we nearly double the EIT amplitude without an increase in the width of the peak. A similar increase in amplitude without the repumping field is not possible through simple optimization. We also establish that one of the key limits to detection is the photon shot noise of the probe laser. We show an improvement on the sensitivity of the device by a factor of nearly 2 in the presence of the repump field.
Deformed W-algebras in Type A for Rectangular Nilpotent

For any $$n,r \in {\mathbb {N}}$$ , we construct an algebra $${\mathcal {P}}_n^r$$ via generators and quadratic relations, and show that it deforms the W–algebra of $${\mathfrak {gl}}_{nr}$$ with respect to a nilpotent with Jordan block decomposition $$r+ \ldots +r$$ . We introduce a surjective morphism from half of the quantum toroidal algebra of $${\mathfrak {gl}}_n$$ to $${\mathcal {P}}_n^r$$ , and show that the action of the quantum toroidal algebra on the K–theory groups of the moduli spaces of parabolic sheaves factors through $${\mathcal {P}}_n^r$$ .
BRAFV600E induces reversible mitotic arrest in human melanocytes via microrna-mediated suppression of AURKB.

Lots of people have small dark patches on their skin known as moles. Most moles form when individual cells known as melanocytes in the skin acquire a specific genetic mutation in a gene called BRAF. This mutation causes the cells to divide rapidly to form the mole. After a while, most moles stop growing and remain harmless for the rest of a person’s life. Melanoma is a type of skin cancer that develops from damaged melanocytes. The same mutation in BRAF that is found in moles is also present in half of all cases of melanoma. Unlike in moles, the melanoma-causing mutation makes the melanocytes divide rapidly to form a tumor that keeps on growing indefinitely. It remains unclear why the same genetic mutation in the BRAF gene has such different consequences in moles and melanomas. To address this question, McNeal et al. used genetic approaches to study melanocytes from moles and melanomas. The experiments identified some molecules known as microRNAs that are present at higher levels in moles than in melanomas. Increasing the levels of two of these microRNAs in melanocytes from human skin stopped the cells from growing and dividing by inhibiting a gene called AURKB. This suggested that these microRNAs are responsible for halting the growth of moles. Introducing the mutated form of BRAF into melanocytes also stopped cells from growing and dividing by inhibiting AURKB. However, changing the environment surrounding the cells reversed this effect and allowed the melanocytes to resume dividing. In this way the mutated form of BRAF acts like a switch that allows melanocytes in skin cancers to start growing again under certain conditions. Further experiments found that a drug called barasertib is able to inhibit the growth of melanoma cells with the mutant form of BRAF. Future work will investigate whether it is possible to use this drug and other tools to stop skin cancer tumors from growing, and possibly even prevent skin tumors from forming in the first place.