cssi/SciSciGPT-SciSciNet huggingface dataset:
Psychosocial predictors of virus and social distancing fears in undergraduate students living in a US COVID-19 "hotspot".

The COVID-19 pandemic is positioned to exact a substantial mental health toll on the global population. Heightened fears of viral contamination and fears of the negative consequences of social distancing (e.g., fears related to home confinement, fears of loneliness and isolation) might contribute to the distress caused by the pandemic. Cross-sectional data were collected from undergraduates (N = 608) residing in a U.S. pandemic "hotspot" at the time of data collection (between 7 April to 9 May, 2020). Outcome variables included viral contamination fears and social distancing fears. Predictor variables included biological sex, underlying medical vulnerability, number of recent viral symptoms, presence of positive COVID-19 test in social network, anxiety, depression, stress, emotion dysregulation, intolerance of uncertainty, body vigilance, and health anxiety. Female sex, anxiety severity, intolerance of uncertainty, and health anxiety uniquely predicted fears of viral contamination. Female sex and depression severity uniquely predicted fears of social distancing. Multiple anxiety-related vulnerabilities are potential intervention targets for reducing viral contamination fears. Depression is a potential intervention target for social distancing fears. Females might be at greater risk for both types of fears.
On a characterization of finite-dimensional vector spaces

We provide a characterization of the finite dimensionality of vector spaces in terms of the right-sided invertibility of linear operators on them.
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks

Abstract Researchers often treat data-driven and theory-driven models as two disparate or even conflicting methods in travel behavior analysis. However, the two methods are highly complementary because data-driven methods are more predictive but less interpretable and robust, while theory-driven methods are more interpretable and robust but less predictive. Using their complementary nature, this study designs a theory-based residual neural network (TB-ResNet) framework, which synergizes discrete choice models (DCMs) and deep neural networks (DNNs) based on their shared utility interpretation. The TB-ResNet framework is simple, as it uses a ( δ , 1- δ ) weighting to take advantage of DCMs’ simplicity and DNNs’ richness, and to prevent underfitting from the DCMs and overfitting from the DNNs. This framework is also flexible: three instances of TB-ResNets are designed based on multinomial logit model (MNL-ResNets), prospect theory (PT-ResNets), and hyperbolic discounting (HD-ResNets), which are tested on three data sets. Compared to pure DCMs, the TB-ResNets provide greater prediction accuracy and reveal a richer set of behavioral mechanisms owing to the utility function augmented by the DNN component in the TB-ResNets. Compared to pure DNNs, the TB-ResNets can modestly improve prediction and significantly improve interpretation and robustness, because the DCM component in the TB-ResNets stabilizes the utility functions and input gradients. Overall, this study demonstrates that it is both feasible and desirable to synergize DCMs and DNNs by combining their utility specifications under a TB-ResNet framework. Although some limitations remain, this TB-ResNet framework is an important first step to create mutual benefits between DCMs and DNNs for travel behavior modeling, with joint improvement in prediction, interpretation, and robustness.
Slingshot spiders build tensed, underdamped webs for ultrafast launches and speedy halts

We develop a mathematical model to capture the web dynamics of slingshot spiders (Araneae: Theridiosomatidae), which utilize a tension line to deform their orb webs into conical springs to hunt flying insects. Slingshot spiders are characterized by their ultrafast launch speeds and accelerations (exceeding 1300 $$\mathrm{m/s}^2$$ ), however a theoretical approach to characterize the underlying spatiotemporal web dynamics remains missing. To address this knowledge gap, we develop a 2D-coupled damped oscillator model of the web. Our model reveals three key insights into the dynamics of slingshot motion. First, the tension line plays a dual role: enabling the spider to load elastic energy into the web for a quick launch (in milliseconds) to displacements of 10–15 body lengths, but also enabling the spider to halt quickly, attenuating inertial oscillations. Second, the dominant energy dissipation mechanism is viscous drag by the silk lines - acting as a low Reynolds number parachute. Third, the web exhibits underdamped oscillatory dynamics through a finely-tuned balance between the radial line forces, the tension line force and viscous drag dissipation. Together, our work suggests that the conical geometry and tension-line enables the slingshot web to act as both an elastic spring and a shock absorber, for the multi-functional roles of risky predation and self-preservation.
(Why) Is There a Public/Private Pay Gap?

Abstract The government is facing a severe shortage of skilled workers. The conventional wisdom in branches of policy and public administration is that the shortage is driven by low salaries that are not competitive for attracting top talent. Using longitudinal data on high skilled workers between 1993 and 2013, this paper shows that, if anything, government employees earn more than their private sector counterparts. Although government workers tend to earn less in the raw data, these differences are driven by the correlation between unobserved productivity and selection into private sector jobs. Instead, this paper provides empirical evidence that low non-pecuniary amenities, such as development opportunities and management, can explain earnings differences between the public and private sectors.
Negotiating eldercare in Akuapem, Ghana: Care-scripts and the role of non-kin

In contemporary Ghana, adult children are considered responsible for the care of aged parents. Within this idealized framework, two aspects of eldercare are overlooked. Firstly, such a narrative obscures the role of non-kin and extended kin in providing eldercare in southern Ghana historically and in the present. Secondly, it hides the negotiations over obligations and commitments between those who manage eldercare and those who help with an ageing person’s daily activities. It is in this latter role that non-kin and extended kin are significant in eldercare, while closer kin maintain their kin roles through the management, financial support and recruitment of others. This article examines recruitment to eldercare and the role of kin and non-kin in eldercare in three historical periods: the 1860s, the 1990s and the 2000s, centred on Akuapem, in southern Ghana. In particular, I show that helping an aged person relies on previous and expected entrustments, in which more vulnerable, dependent and indebted persons are most likely to be recruited to provide care. Resume: Dans le Ghana contemporain, les enfants adultes sont consideres comme responsables de la prise en charge de leurs parents âges. Dans ce cadre idealise, deux aspects des soins aux personnes âgees sont occultes. Premierement, ce cadre eclipse le role historique et actuel des non-parents et de la famille etendue dans la prise en charge des personnes âgees dans le sud du Ghana. Deuxiemement, il masque les negociations sur les obligations et les engagements menees entre ceux qui gerent les soins aux personnes âgees et ceux qui aident une personne âgee dans ses activites quotidiennes. C’est dans ce dernier role que les non-parents et la famille etendue sont importants en matiere de soins aux personnes âgees, tandis que la famille proche maintient son role de parente a travers la gestion, le soutien financier et le recrutement d’autres personnes. Cet article examine le recrutement et le role des parents et non-parents en matiere de soins aux personnes âgees a trois periodes historiques: les annees 1860, 1990 et 2000, dans la region d’Akuapem, dans le sud du Ghana. En particulier, l’auteur montre que l’acte d’aider une personne âgee repose sur des actes de confiance precedents et attendus dans lesquels des personnes plus vulnerables, dependantes et redevables sont plus susceptibles d’etre recrutees pour soigner.
Treatment-resistant bipolar disorder

Despite the remarkable increase in medications validated as effective in bipolar disorder, treatment is still plagued by inadequate response in acute manic or depressive episodes or in long-term preventive maintenance treatment. Established first-line treatments include lithium, valproate and second-generation antipsychotics (SGAs) in acute mania, and lithium and valproate as maintenance treatments. Recently validated treatments include extended release carbamazapine for acute mania and lamotrigine, olanzapine and aripiprazole as maintenance treatments. For treatment-resistant mania and as maintenance treatments, a number of newer anticonvulsants, and one older one, phenytoin, have shown some promise as effective. However, not all anticonvulsants are effective and each agent needs to be evaluated individually. Combining multiple agents is the most commonly used clinical strategy for treatment resistant bipolar patients despite a relative lack of data supporting its use, except for acute mania (for which lithium or valproate plus an SGA is optimal treatment). Other approaches that may be effective for treatment-resistant patients include high-dose thyroid augmentation, clozapine, calcium channel blockers and electroconvulsive therapy (ECT). Adjunctive psychotherapies show convincing efficacy using a variety of different techniques, most of which include substantial attention to education and enhancing coping strategies. Only recently, bipolar depression has become a topic of serious inquiry with the dominant controversy focusing on the place of antidepressants in the treatment of bipolar depression. Other than mood stabilizers alone or the combination of mood stabilizers and antidepressants, most of the approaches for treatment-resistant bipolar depression are relatively similar to those used in unipolar depression, with the possible exception of a more prominent place for SGAs, prescribed either alone or in combination with antidepressants. Future work in the area needs to explore the treatments commonly used by clinicians with inadequate research support, such as combination therapy and the use of antidepressants as both acute and adjunctive maintenance treatments for bipolar disorder.
Geometric considerations of a good dictionary for Koopman analysis of dynamical systems: Cardinality, "primary eigenfunction," and efficient representation

Abstract Representation of a dynamical system in terms of simplifying modes is a central premise of reduced order modelling and a primary concern of the increasingly popular DMD (dynamic mode decomposition) empirical interpretation of Koopman operator analysis of complex systems. In the spirit of optimal approximation and reduced order modelling the goal of DMD methods and variants are to describe the dynamical evolution as a linear evolution in an appropriately transformed lower rank space, as best as possible. That Koopman eigenfunctions follow a linear PDE that is solvable by the method of characteristics yields several interesting relationships between geometric and algebraic properties. Corresponding to freedom to arbitrarily define functions on a data surface, for each eigenvalue, there are infinitely many eigenfunctions emanating along characteristics. We focus on contrasting cardinality and equivalence. In particular, we introduce an equivalence class, “primary eigenfunctions,’ consisting of those eigenfunctions with identical sets of level sets, that helps contrast algebraic multiplicity from other geometric aspects. Popularly, Koopman methods and notably dynamic mode decomposition (DMD) and variants, allow data-driven study of how measurable functions evolve along orbits. As far as we know, there has not been an in depth study regarding the underlying geometry as related to an efficient representation. We present a construction that leads to functions on the data surface whose corresponding eigenfunctions are efficient in a least squares sense. We call this construction optimal Koopman eigenfunction DMD, (oKEEDMD), and we highlight with examples.
Adaptation Bounds for Confidence Bands under Self-Similarity

We derive bounds on the scope for a confidence band to adapt to the unknown regularity of a nonparametric function that is observed with noise, such as a regression function or density, under the self-similarity condition proposed by Gine and Nickl (Ann. Statist. 38 (2010) 1122–1170). We find that adaptation can only be achieved up to a term that depends on the choice of the constant used to define self-similarity, and that this term becomes arbitrarily large for conservative choices of the self-similarity constant. We construct a confidence band that achieves this bound, up to a constant term that does not depend on the self-similarity constant. Our results suggest that care must be taken in choosing and interpreting the constant that defines self-similarity, since the dependence of adaptive confidence bands on this constant cannot be made to disappear asymptotically.
Decomposing the Value of Food Labels on Chicken

This study explores the effects of consumers’ beliefs about labels on chicken. We elicit beliefs associated with seven different labels. By varying the presence/absence of labels in a choice experiment, we are able to determine the effects of labels on consumer choices and decompose the value of labels into beliefs and base utility. Health perceptions have the largest positive effect, and impacts of animal welfare vary by information treatment. We explore the convergent validity of our approach by comparing individual’s beliefs to responses to a best-worst scaling question, which were weakly correlated, suggesting the two approaches are measuring different constructs.
Show me the Way: Intrinsic Motivation from Demonstrations

The study of exploration in the domain of decision making has a long history but remains actively debated. From the vast literature that addressed this topic for decades under various points of view (e.g., developmental psychology, experimental design, artificial intelligence), intrinsic motivation emerged as a concept that can practically be transferred to artificial agents. Especially, in the recent field of Deep Reinforcement Learning (RL), agents implement such a concept (mainly using a novelty argument) in the shape of an exploration bonus, added to the task reward, that encourages visiting the whole environment. This approach is supported by the large amount of theory on RL for which convergence to optimality assumes exhaustive exploration. Yet, Human Beings and mammals do not exhaustively explore the world and their motivation is not only based on novelty but also on various other factors (e.g., curiosity, fun, style, pleasure, safety, competition, etc.). They optimize for life-long learning and train to learn transferable skills in playgrounds without obvious goals. They also apply innate or learned priors to save time and stay safe. For these reasons, we propose to learn an exploration bonus from demonstrations that could transfer these motivations to an artificial agent with little assumptions about their rationale. Using an inverse RL approach, we show that complex exploration behaviors, reflecting different motivations, can be learnt and efficiently used by RL agents to solve tasks for which exhaustive exploration is prohibitive.
Extremal Correlators and Random Matrix Theory

We study the correlation functions of Coulomb branch operators of four-dimensional $$ \mathcal{N} $$ = 2 Superconformal Field Theories (SCFTs). We focus on rank-one theories, such as the SU(2) gauge theory with four fundamental hypermultiplets. “Extremal” correlation functions, involving exactly one anti-chiral operator, are perhaps the simplest nontrivial correlation functions in four-dimensional Quantum Field Theory. We show that the large charge limit of extremal correlators is captured by a “dual” description which is a chiral random matrix model of the Wishart-Laguerre type. This gives an analytic handle on the physics in some particular excited states. In the limit of large random matrices we find the physics of a non-relativistic axion-dilaton effective theory. The random matrix model also admits a ’t Hooft expansion in which the matrix is taken to be large and simultaneously the coupling is taken to zero. This explains why the extremal correlators of SU(2) gauge theory obey a nontrivial double scaling limit in states of large charge. We give an exact solution for the first two orders in the ’t Hooft expansion of the random matrix model and compare with expectations from effective field theory, previous weak coupling results, and we analyze the non-perturbative terms in the strong ’t Hooft coupling limit. Finally, we apply the random matrix theory techniques to study extremal correlators in rank-1 Argyres-Douglas theories. We compare our results with effective field theory and with some available numerical bootstrap bounds.
The Use of Dipeptide Supplementation as a Means of Mitigating the Negative Effects of Dietary Soybean Meal on Zebrafish Danio rerio

Abstract Soybean meal (SBM) inclusion in aquaculture diets has been found to negatively affect growth and induce intestinal inflammation in fish. The objective of this study was to determine the effect of health-promoting dipeptide supplementation into SBM-based feeds on growth performance, intestinal health, and muscle free amino acid composition, an indicator of dietary amino acid availability, in a zebrafish model. There were five treatment groups in this study. The first group ((+) Control) received a fishmeal-based diet. The second group ((−) Control) received SBM-based diet. The last three groups (Ala-Glu, Car, and Ans) were fed SBM-based diets, supplemented with alany l -glutamine, carnosine, and anserine respectively. The Ala-Glu and Car groups experienced a significantly higher weight gain than the (−) Control group, weighing 35.38% and 33.96% more, respectively at the conclusion of the study. There were no significant differences in gene expression among the groups, but Ala-Glu had the highest expression of both nutrient absorption genes measured, PepT1 and fabp2. Ala-Glu had significantly longer intestinal villi, and a significantly higher villus length-to-width ratio than the (−) Control group. The Car group had a significantly higher post-prandial tissue concentration of lysine, compared to the (−) Control group. The increase in villus surface area and expression of nutrient absorption genes represent an improvement in intestinal absorptive capacity in the Ala-Glu group. The results from this study provide support for the use of alany l -glutamine and carnosine supplementation as a means of improving growth performance of zebrafish fed with a high level SBM-based diet.
On the Absence of Spurious Local Minima in Nonlinear Low-Rank Matrix Recovery Problems.

The restricted isometry property (RIP) is a well-known condition that guarantees the absence of spurious local minima in low-rank matrix recovery problems with linear measurements. In this paper, for general low-rank matrix recovery problems with nonlinear measurements, a novel property named bound difference property (BDP) is introduced. Using RIP and BDP jointly, we propose a new criterion to certify the nonexistence of spurious local minima in the rank-1 case, and prove that it leads to a much stronger theoretical guarantee than the existing bounds on RIP.
A method to apply Piola-Kirchhoff stress in molecular statics simulation

Abstract A force-based optimization method is proposed to apply the first and second kind of Piola-Kirchhoff stresses in molecular statics simulation. This method is important for finite deformation problems in which the atomistic behavior can be more accurately described using Piola-Kirchhoff stresses. The performance of the method is tested and validated using Silicon as a model material.
Vanishing Fourier coefficients of Hecke eigenforms

We prove that, for fixed level $$(N,p) = 1$$ and $$p > 2$$ , there are only finitely many Hecke eigenforms f of level $$\Gamma _1(N)$$ and even weight with $$a_p(f) = 0$$ which are not CM.
End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales

Robust Mask R-CNN (Mask Regional Convolutional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earthquakes. We curated a new dataset with 2,021 labeled images for training and validation and aimed to find end-to-end deep neural networks for crack detection in the field. With data augmentation and parameters fine-tuning, Path Aggregation Network (PANet) with spatial attention mechanisms and High- resolution Network (HRNet) are introduced into Mask R-CNNs. The tests on three public datasets with low- or high-resolution images demonstrate that the proposed methods can achieve a big improvement over alternative networks, so the proposed method may be sufficient for crack detection for a variety of scales in real applications.
An Auditing Language for Preventing Correlated Failures in the Cloud

Today's cloud services extensively rely on replication techniques to ensure availability and reliability. In complex datacenter network architectures, however, seemingly independent replica servers may inadvertently share deep dependencies (e.g., aggregation switches). Such unexpected common dependencies may potentially result in correlated failures across the entire replication deployments, invalidating the efforts. Although existing cloud management and diagnosis tools have been able to offer post-failure forensics, they, nevertheless, typically lead to quite prolonged failure recovery time. In this paper, we propose a novel language framework, named RepAudit, that manages to prevent correlated failure risks before service outages occur, by allowing cloud administrators to proactively audit the replication deployments of interest. In particular, RepAudit consists of three new components: 1) a declarative domain-specific language, RAL, for cloud administrators to write auditing programs expressing diverse auditing tasks; 2) a high-performance RAL auditing engine that generates the auditing results by accurately and efficiently analyzing the underlying structures of the target replication deployments; and 3) an RAL-code generator that can automatically produce complex RAL programs based on easily written specifications. Our evaluation result shows that RepAudit can determine the top-20 critical correlated failure root causes in a replication system containing 30,528 devices within 1 minute, which is 400x more efficient in auditing time than state-of-the-art efforts. To the best of our knowledge, RepAudit is the first effort capable of simultaneously offering expressive, accurate and efficient correlated failure auditing to the cloud-scale replication systems.
Common Ownership and Entrepreneurship

We complement the literature on common ownership by presenting two new observations from entrepreneurial start-ups. First, given the increase in common ownership of start-ups by venture capital investors, inclusion of high-value start-ups in standard common ownership measures may actually increase aggregate measures of common ownership. Second, we suggest that even if public-firm common ownership leads to collusive inefficiency and higher prices in the short term, it may also create opportunities for entry of innovative high-growth start-ups. Consistent with this, we document that entrepreneurial activity and common ownership of start-ups tends to be higher in industries with higher common ownership among public firms.
Towards Efficient Auctions in an Auto-bidding World

Auto-bidding has become one of the main options for bidding in online advertisements, in which advertisers only need to specify high-level objectives and leave the complex task of bidding to auto-bidders. In this paper, we propose a family of auctions with boosts to improve welfare in auto-bidding environments with both return on ad spend constraints and budget constraints. Our empirical results validate our theoretical findings and show that both the welfare and revenue can be improved by selecting the weight of the boosts properly.
Public-sector pension plans and the discount rate assumption: The role of political incentives

Abstract We provide evidence that political pressure creates incentives for defined benefit pension plans to use higher discount rates, thus artificially reducing the reported cost of these benefit promises. We generate our inferences using a unique panel dataset for all local pension plans from Pennsylvania for 2003–2013, and by comparing the differential response of plans managed by appointed versus elected officials during a period where there was downward pressure on expected investment returns. The differential response we document is 29 basis points, which equates to a reduction in the reported liability of approximately 4.1% due to political incentives.
Buckling without bending morphogenesis: Nonlinearities, spatial confinement, and branching hierarchies

During morphogenesis, a featureless convex cerebellum develops folds. As it does so, the cortex thickness is thinnest at the crest (gyri) and thickest at the trough (sulci) of the folds. This observation cannot be simply explained by elastic theories of buckling. A recent minimal model explained this phenomenon by modeling the developing cortex as a growing fluid under the constraints of radially spanning elastic fibers, a plia membrane and a nongrowing sub-cortex (Engstrom, et. al., PRX 2019). In this minimal buckling without bending morphogenesis (BWBM) model, the elastic fibers were assumed to act linearly with strain. Here, we explore how nonlinear elasticity influences shape development within BWBM. The nonlinear elasticity generates a quadratic nonlinearity in the differential equation governing the system's shape and leads to sharper troughs and wider crests, which is an identifying characteristic of cerebellar folds at later stages in development. As developing organs are typically not in isolation, we also explore the effects of steric confinement, and observe flattening of the crests. Finally, as a paradigmatic example, we propose a hierarchical version of BWBM from which a novel mechanism of branching morphogenesis naturally emerges to qualitatively predict later stages of the morphology of the developing cerebellum.
FAT4 identified as a potential modifier of orofacial cleft laterality.

Orofacial clefts (OFCs) are common (1 in 700 births) congenital malformations that include a cleft lip (CL) and cleft lip and palate (CLP). These OFC subtypes are also heterogeneous themselves, with the CL occurring on the left, right, or both sides of the upper lip. Unilateral CL and CLP have a 2:1 bias towards left-sided clefts, suggesting a nonrandom process. Here, we performed a study of left- and right-sided clefts within the CL and CLP subtypes to better understand the genetic factors controlling cleft laterality. We conducted genome-wide modifier analyses by comparing cases that had right unilateral CL (RCL; N = 130), left unilateral CL (LCL; N = 216), right unilateral CLP (RCLP; N = 416), or left unilateral CLP (LCLP; N = 638), and identified a candidate region on 4q28, 400 kb downstream from FAT4, that approached genome-wide significance for LCL versus RCL (p = 8.4 × 10-8 ). Consistent with its potential involvement as a genetic modifier of CL, we found that Fat4 exhibits a specific domain of expression in the mesenchyme of the medial nasal processes that form the median upper lip. Overall, these results suggest that the epidemiological similarities in left- to right-sided clefts in CL and CLP are not reflected in the genetic association results.
The configuration of the interplanetary magnetic field.

The idealized basic structure of the interplanetary magnetic field is the familiar spiral wound on a cone whose axis is the solar rotation axis. Variations in the radial velocity of the solar wind produce large scale variations in pitch; slight distortions are also produced by the nonradial component of the solar wind velocity. The high velocity streams in the solar wind seem to be more significant than magnetic polarity alternation in the sector structure of the interplanetary medium. Superposed on the ideal spiral are a variety of smaller structures. Outwardly propagating nonsinusoidal Alfven waves with scale lengths of .1 to 10 million kilometers are common. When sharp crested, they are recognized as rotational discontinuities. Other, less easily identified, waves are present part of the time. Tangential discontinuities and other convected structures have been identified, as have interplanetary shock waves.
ECH capacities, Ehrhart theory, and toric varieties

ECH capacities were developed by Hutchings to study embedding problems for symplectic $4$-manifolds with boundary. They have found especial success in the case of certain toric symplectic manifolds where many of the computations resemble calculations found in cohomology of $\mathbb{Q}$-line bundles on toric varieties, or in lattice point counts for rational polytopes. We formalise this observation in the case of convex toric lattice domains $X_\Omega$ by constructing a natural polarised toric variety $(Y_{\Sigma(\Omega)},D_\Omega)$ containing the all the information of the ECH capacities of $X_\Omega$ in purely algebro-geometric terms. Applying the Ehrhart theory of the polytopes involved in this construction gives some new results in the combinatorialisation and asymptotics of ECH capacities for convex toric domains.
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix

We show that aggregated model updates in federated learning may be insecure. An untrusted central server may disaggregate user updates from sums of updates across participants given repeated observations, enabling the server to recover privileged information about individual users' private training data via traditional gradient inference attacks. Our method revolves around reconstructing participant information (e.g: which rounds of training users participated in) from aggregated model updates by leveraging summary information from device analytics commonly used to monitor, debug, and manage federated learning systems. Our attack is parallelizable and we successfully disaggregate user updates on settings with up to thousands of participants. We quantitatively and qualitatively demonstrate significant improvements in the capability of various inference attacks on the disaggregated updates. Our attack enables the attribution of learned properties to individual users, violating anonymity, and shows that a determined central server may undermine the secure aggregation protocol to break individual users' data privacy in federated learning.
Anatomy of a liquidity crisis: Corporate bonds in the COVID-19 crisis

Abstract We examine the microstructure of liquidity provision in the COVID-19 corporate bond liquidity crisis. During the two weeks leading up to Federal Reserve System interventions, volume shifted to liquid securities, transaction costs soared, trade-size pricing inverted, and dealers, particularly non-primary dealers, shifted from buying to selling, causing dealers’ inventories to plummet. Liquidity provisions in electronic customer-to-customer trading increased, though at prohibitively high costs. By improving dealer funding conditions and providing a liquidity backstop, the Primary Dealer Credit Facility and the Secondary Market Corporate Credit Facility (SMCCF) stabilized trading conditions. Most of the impact of SMCCF on bond liquidity seems to have materialized following its announcement. We argue that the Federal Reserve's actions reflect a new role as market maker of last resort.
Sales Policies for a Virtual Assistant

We study the implications of selling through a voice-based virtual assistant (VA). The seller has a set of products available and the VA decides which product to offer and at what price, seeking to maximize its revenue, consumer- or total-surplus. The consumer is impatient and rational, seeking to maximize her expected utility given the information available to her. The VA selects products based on the consumer’s request and other information available to it and then presents them sequentially. Once a product is presented and priced, the consumer evaluates it and decides whether to make a purchase. The consumer’s valuation of each product comprises a pre-evaluation value, which is common knowledge, and a post-evaluation component which is private to the consumer. We solve for the equilibria and develop efficient algorithms for implementing the solution. We examine the effects of information asymmetry on the outcomes and study how incentive misalignment depends on the distribution of private valuations. We find that monotone rankings are optimal in the cases of a highly patient or impatient consumer and provide a good approximation for other levels of patience. The relationship between products’ expected valuations and prices depends on the consumer’s patience level and is monotone increasing (decreasing) when the consumer is highly impatient (patient). Also, the seller’s share of total surplus decreases in the amount of private information. We compare the VA to a traditional web interface, where multiple products are presented simultaneously on each page. We find that within a page, the higher-value products are priced lower than the lower-value products when the private valuations are exponentially distributed. Finally, the web-interface generally achieves a higher profit share for the seller due to the postponement option available to the consumer with the VA.
Pure tensor program rewriting via access patterns (representation pearl)

Tensor kernels in machine learning (ML) often correspond to pure mathematical expressions, making term rewriting an attractive strategy for optimization and mapping to specialized hardware accelerators. However, existing ML intermediate representations (IRs) tend to either be pure but high-level, making low-level rewrites to hardware targets inexpressible, or low-level but impure, hampering the use of term rewriting altogether. This paper introduces Glenside, a pure IR whose core abstraction—the access pattern—enables low-level, layout-aware, hardware-centric program rewrites. We demonstrate how term rewriting in Glenside can be used to map program fragments to hardware accelerator invocations and automatically discover classic data layout transformations like im2col. Glenside establishes a new foundation for exploring further term rewriting techniques in optimizing low-level tensor programs.
On the convergence of WKB approximations of the damped Mathieu equation

The form of the fundamental set of solutions of the damped Mathieu equation is determined by Floquet theory. In the limit as m → 0, we can apply WKB theory to get first order approximations of the fundamental set. WKB theory states that this approximation gets better as m → 0 and T is fixed. However, convergence of the periodic part and characteristic exponent is not addressed. We show that they converge to those predicted by WKB theory. We also provide a rate of convergence that is not dependent on T.
A Maximum Independent Set Method for Scheduling Earth-Observing Satellite Constellations

Operating Earth-observing satellites requires efficient planning methods that coordinate activities of multiple spacecraft. The satellite task-planning problem entails selecting actions that best s...
A Unified Bi-Directional Model for Natural and Artificial Trust in Human–Robot Collaboration

We introduce a novel capabilities-based bi-directional multi-task trust model that can be used for trust prediction from either a human or a robotic trustor agent. Tasks are represented in terms of their capability requirements, while trustee agents are characterized by their individual capabilities. Trustee agents’ capabilities are not deterministic; they are represented by belief distributions. For each task to be executed, a higher level of trust is assigned to trustee agents who have demonstrated that their capabilities exceed the task's requirements. We report results of an online experiment with 284 participants, revealing that our model outperforms existing models for multi-task trust prediction from a human trustor. We also present simulations of the model for determining trust from a robotic trustor. Our model is useful for control authority allocation applications that involve human–robot teams.
Recognizing and Verifying Mathematical Equations using Multiplicative Differential Neural Units

Automated mathematical reasoning is a challenging problem that requires an agent to learn algebraic patterns that contain long-range dependencies. Two particular tasks that test this type of reasoning are (1)mathematical equation verification,which requires determining whether trigonometric and linear algebraic statements are valid identities or not, and (2)equation completion, which entails filling in a blank within an expression to make it true. Solving these tasks with deep learning requires that the neural model learn how to manipulate and compose various algebraic symbols, carrying this ability over to previously unseen expressions. Artificial neural net-works, including recurrent networks and transformers, struggle to generalize on these kinds of difficult compositional problems, often exhibiting poor extrapolation performance.In contrast, recursive neural networks (recursive-NNs) are,theoretically, capable of achieving better extrapolation due to their tree-like design but are very difficult to optimize as the depth of their underlying tree structure increases. To over-come this, we extend recursive-NNs to utilize multiplicative,higher-order synaptic connections and, furthermore, to learn to dynamically control and manipulate an external memory.We argue that this key modification gives the neural system the ability to capture powerful transition functions for each possible input. We demonstrate the effectiveness of our pro-posed higher-order, memory-augmented recursive-NN models on two challenging mathematical equation tasks, showing improved extrapolation, stable performance, and faster convergence. We show that our models achieve 1.53% average improvement over current state-of-the-art methods in equation verification and achieve 2.22% top-1 average accuracy and 2.96% top-5 average accuracy for equation completion.
An exploratory study of the roll behavior of ablating cones

and d) are zero at roll rate. 3) Small trim angles of attack (fr < 0.25°), which can be produced by principal axis inclination angles of similar magnitude, may seriously degrade the performance of re-entry vehicles that have mass asymmetries. 4) Effects of inertia asymmetries on the angular and rolling motions of ballistic re-entry vehicles may be misinterpreted as the effects of mass and aerodynamic asymmetries.
Orlicz-Sobolev inequalities and the doubling condition

In [12] it has been shown that a \((p,q)\) Sobolev inequality with \(p>q\) implies the doubling condition on the underlying measure. We show that even weaker Orlicz-Sobolev inequalities, where the gain on the left-hand side is smaller than any power bump, imply doubling. Moreover, we derive a condition on the quantity that should replace the radius on the righ-hand side (which we call `superradius'), that is necessary to ensure that the space can support the Orlicz-Sobolev inequality and simultaneously be non-doubling.
Loop estimator for discounted values in Markov reward processes

At the working heart of policy iteration algorithms commonly used and studied in the discounted setting of reinforcement learning, the policy evaluation step estimates the value of states with samples from a Markov reward process induced by following a Markov policy in a Markov decision process. We propose a simple and efficient estimator called loop estimator that exploits the regenerative structure of Markov reward processes without explicitly estimating a full model. Our method enjoys a space complexity of O(1) when estimating the value of a single positive recurrent state s unlike TD with O(S) or model-based methods with O(S^2). Moreover, the regenerative structure enables us to show, without relying on the generative model approach, that the estimator has an instance-dependent convergence rate of O~(\sqrt{\tau_s/T}) over steps T on a single sample path, where \tau_s is the maximal expected hitting time to state s. In preliminary numerical experiments, the loop estimator outperforms model-free methods, such as TD(k), and is competitive with the model-based estimator.
Tiny Video Networks

Automatic video understanding is becoming more important for applications where real-time performance is crucial and compute is limited. Yet, accurate solutions so far have been computationally intensive. We propose efficient models for videos - Tiny Video Networks - which are video architectures, automatically designed to comply with fast runtimes and, at the same time are effective at video recognition tasks. The Tiny Video Networks run at faster-than-real-time speeds and demonstrate strong performance across several video benchmarks. These models not only provide new tools for real-time video applications, but also enable fast research and development in video understanding. Code and models are available.
Bistable State Switch Enables Ultrasensitive Feedback Control in Heterogeneous Microbial Populations

Molecular feedback control circuits can improve robustness of gene expression at single cell-level. This achievement can be offset by requirements of rapid protein expression, that may induce cellular stress, known as burden, that reduces colony growth. To begin to address this challenge we take inspiration by ‘division-of-labor’ in heterogeneous cell populations: we propose to combine bistable switches and quorum sensing systems to coordinate gene expression at population-level. We show that bistable switches in individual cells operating in parallel yield an ultrasensitive response, while cells maintain heterogeneous levels of gene expression. Within a feedback loop, these switches can achieve robust reference tracking and adaptation to disturbances at the population-level. We also demonstrate that molecular sequestration enables tunable hysteresis in individual switches, making it possible to obtain a wide range of stable population-level expressions.
Increased Complexity and Fitness of Artificial Cells that Reproduce Using Spatially Distributed Asynchronous Parallel Processes

Replication time is among the most important components of a bacterial cell's reproductive fitness. Paradoxically, larger cells replicate in less time than smaller cells despite the fact that building a larger cell requires increased quantities of raw materials and energy. This feat is primarily accomplished by the massive over expression of ribosomes, which permits translation of mRNA into protein, the limiting step in reproduction, to occur at a scale that would be impossible were it not for the use of parallel processing. In computer science, spatial parallelism is the distribution of work across the nodes of a distributed-memory multicomputer system. Despite the fact that a non-negligible fraction of artificial life research is grounded in formulations based on spatially parallel substrates, there have been no examples of artificial organisms that use spatial parallelism to replicate in less time than smaller organisms. This paper describes artificial cells defined using a combinator-based artificial chemistry that replicate in less time than smaller cells. This is achieved by employing extra copies of programs implementing the limiting steps in the process used by the cells to synthesize their component parts. Significant speedup is demonstrated, despite the increased complexity of control and export processes necessitated by the use of a parallel replication strategy.
Sensitization of FOLFOX-resistant colorectal cancer cells via the modulation of a novel pathway involving protein phosphatase 2A

The treatment of colorectal cancer (CRC) with FOLFOX shows some efficacy, but these tumors quickly develop resistance to this treatment. We have observed an increased phosphorylation of AKT1/mTOR/4EBP1 and levels of p21 in FOLFOX-resistant CRC cells. We have identified a small molecule, NSC49L, that stimulates protein phosphatase 2A (PP2A) activity, downregulates the AKT1/mTOR/4EBP1-axis, and inhibits p21 translation. We have provided evidence that NSC49L- and TRAIL-mediated sensitization is synergistically induced in p21-knockdown CRC cells, which is reversed in p21-overexpressing cells. p21 binds with procaspase 3 and prevents activation of caspase 3. We have shown that TRAIL induces apoptosis through the activation of caspase 3 by NSC49L-mediated downregulation of p21 translation, and thereby cleavage of procaspase 3 into caspase 3. NSC49L does not affect global protein synthesis. These studies provide a mechanistic understanding of NSC49L as a PP2A agonist, and how its combination with TRAIL sensitizes FOLFOX-resistant CRC cells. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/453259v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@bc3332org.highwire.dtl.DTLVardef@1c533b0org.highwire.dtl.DTLVardef@1c4a852org.highwire.dtl.DTLVardef@6aec2_HPS_FORMAT_FIGEXP M_FIG C_FIG
On the number of alternating paths in random graphs

Abstract In the noisy channel model from coding theory, we wish to detect errors introduced during transmission by optimizing various parameters of the code. Bennett, Dudek, and LaForge framed a variation of this problem in the language of alternating paths in edge-colored complete bipartite graphs in 2016. Here, we extend this problem to the random graph G ( n , p ) . We seek the alternating connectivity, κ r , l ( G ) , which is the maximum t such that there is an r -edge-coloring of G such that any pair of vertices is connected by t internally disjoint and alternating (i.e. no consecutive edges of the same color) paths of length l . We have three main results about how this parameter behaves in G ( n , p ) that basically cover all ranges of p : one for paths of length two, one for the dense case, and one for the sparse case. For paths of length two, we found that κ r , l ( G ) is essentially the codegree of a pair of vertices. For the dense case when p is constant, we were able to achieve the natural upper bounds of minimum degree (minus some intersection) or the total number of disjoint paths between a pair of vertices. For the sparse case, we were able to find colorings that achieved the natural obstructions of minimum degree or (in a slightly less precise result) the total number of paths of a certain length in a graph. We broke up this sparse case into ranges of p corresponding to when G ( n , p ) has diameter k or k + 1 . We close with some remarks about a similar parameter and a generalization to pseudorandom graphs.
Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers:

We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the optimal trajectory is ...