Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing and using the Lewin-Lieb bounds in density functional theory

Lewin and Lieb have recently proven several new bounds on the exchange-correlation energy that complement the Lieb-Oxford bound. We test these bounds for atoms, for slowly-varying gases, and for Hooke’s atom, finding them usually less strict than the Lieb-Oxford bound. However, we also show that, if a generalized gradient approximation (GGA) is to guarantee satisfaction of the new bounds for all densities, new restrictions on the the exchange-correlation enhancement factor are implied.
UNDECIDABILITY AND THE STRUCTURE OF THE TURING DEGREES

This paper explores the structure and properties of the Turing degrees, or degrees of undecidability. We introduce the Turing machine, an abstract model of computation, in order to develop the concepts of undecidability and Turing reduction. We demonstrate the technique of proof by reduction through a series of examples of undecidable problems related to context-free grammars. We then employ reducibility to consider a partial ordering on the set of Turing degrees, D. Finally, we prove a variety of theorems related to the structure of D.
On Latency and Volatility

How does latency affect the dynamics of asset prices in modern markets? In this paper, we present a simple model of latency. In our model, latency is a delay between the observed asset price and its true, but latent fundamental price. Because of latency, the observed asset price shadows the true price at some deformed distance away. In other words, latency leads to the deformation in the clock of an asset's evolution. Deformation in the clock links latency to fluctuations in volatility; so, latency should be in some way related to the volatility of volatility. A standard way to characterize the volatility of volatility, however, hinges upon first estimating volatility at an intermediate fixed time scale, and then looking at the fluctuations of volatility. This is going to miss the effect of latency. Instead, we define a volatility of instantaneous volatility (VIV), which pushes the notion of the volatility of volatility down to a microscopic level and enables us to link volatility to latency. We demonstrate the link between the VIV and latency first for a fixed latency and then suggest how to generalize it to stochastic, evolving latency. While the intuition that latency ends up in the volatility of volatility is simple, the math quickly gets out of hand as we need to keep track of many moving parts inside deformed clocks. We go through all this math because we believe that with the continuing automation of the trading process, latency itself has become a market factor which should be explicitly modeled and then - when the models become robust like those for volatility - traded. Traders who wish to hedge their exposure to or speculate on marketwide latency can take positions in something akin to the VIX; we call the LIX - the Latency Index. This could help price the latency risk and allocate it around.
Heat transfer and material flow during laser assisted multi-layer additive manufacturing

A three-dimensional, transient, heat transfer, and fluid flow model is developed for the laser assisted multilayer additive manufacturing process with coaxially fed austenitic stainless steel powder. Heat transfer between the laser beam and the powder particles is considered both during their flight between the nozzle and the growth surface and after they deposit on the surface. The geometry of the build layer obtained from independent experiments is compared with that obtained from the model. The spatial variation of melt geometry, cooling rate, and peak temperatures is examined in various layers. The computed cooling rates and solidification parameters are used to estimate the cell spacings and hardness in various layers of the structure. Good agreement is achieved between the computed geometry, cell spacings, and hardness with the corresponding independent experimental results.
Down-regulation of microRNA-28 in peripheral blood mononuclear cell plays a role in pathogenesis of type 1 diabetes

Objective: Differential network analysis was performed with mRNA expression data and microRNA (miRNA) expression data to identify critical miRNAs in peripheral blood mononuclear cell from type 1 diabetes patients. Methods: Gene expression profiles (dataset GSE55100) were downloaded from Gene Expression Omnibus, including 12 peripheral blood mononuclear cell samples from type 1 diabetes (T1D) and 10 from normal controls. Differentially expressed microRNAs (DEMs) were identified using package limma of R. Target genes of DEMs were predicted using miRanda, MirTarget2, PicTar, PITA and TargetScan. Differential analysis was performed for the target genes using t test. Functional enrichment analysis was done using DAVID. MiRNA functional synergistic network, pathway synergistic network and co-regulation network were constructed, from which core miRNAs and mRNAs were disclosed. Results: A total of 20 DEMs were revealed in T1D compared with normal control and 3752 target genes were predicted. A total of 695 differentially expressed miRNA-mRNA interactions were unveiled from the 7 DEMs and 651 mRNAs. Topological analysis of the three networks revealed two critical miRNAs: hsa-miR-28-5p and hsa-miR-146b5p. Conclusion: miR-146 has been implicated in T1D. We speculated that down-regulated miR-28 played a critical role in pathogenesis of T1D. Further studies are needed to disclose the function of miR-28 in the T1D.
Interim Bayesian Nash Equilibrium on Universal Type Spaces for Supermodular Games

We prove the existence of a greatest and a least interim Bayesian Nash equilibrium for supermodular games of incomplete information. There are two main differences from the earlier proofs in Vives (1990) and Milgrom and Roberts (1990): we use the interim formulation of a Bayesian game, in which each player's beliefs are part of his or her type rather than being derived from a prior; we use the interim formulation of a Bayesian Nash equilibrium, in which each player and every type (rather than almost every type) chooses a best response to the strategy profile of the other players. Given also the mild restrictions on the type spaces, we have a proof of interim Bayesian Nash equilibrium for universal type spaces (for the class of supermodular utilities), as constructed, for example, by Mertens and Zami (1985)? We also weaken restrictions on the set of actions.
Learning Lexical Subspaces in a Distributional Vector Space

Abstract In this paper, we propose LexSub, a novel approach towards unifying lexical and distributional semantics. We inject knowledge about lexical-semantic relations into distributional word embeddings by defining subspaces of the distributional vector space in which a lexical relation should hold. Our framework can handle symmetric attract and repel relations (e.g., synonymy and antonymy, respectively), as well as asymmetric relations (e.g., hypernymy and meronomy). In a suite of intrinsic benchmarks, we show that our model outperforms previous approaches on relatedness tasks and on hypernymy classification and detection, while being competitive on word similarity tasks. It also outperforms previous systems on extrinsic classification tasks that benefit from exploiting lexical relational cues. We perform a series of analyses to understand the behaviors of our model.1 Code available at https://github.com/aishikchakraborty/LexSub.
Employee Stock Option Exercise and Firm Cost

This paper is the ﬁrst to perform a comprehensive estimation of employee stock option ex- ercise behavior and option cost to ﬁrms. We develop a GMM-based methodology, robust to heteroskedasticity and correlation across exercises, for estimating the rate of voluntary option exercise as a function of the stock price path and of various ﬁrm and option holder character- istics. We use it to estimate an exercise function for a sample of 1.3 million employee-option grants to 530,266 employees at 103 publicly-traded ﬁrms between 1981–2009. We use the estimated exercise functions in a simulation based valuation model to analyze the eﬀect of diﬀerent ﬁrm and option characteristics on option value, and show that the Black-Scholes- based methods used in practice can create systematic biases.
Monetary and Exchange Rate Stability in South East Asia

Regaining exchange rate stability has been a major monetary policy goal of East Asian countries in the aftermath of the 1997/98 currency crisis. While most countries have abstained from re-establishing a formal US Dollar peg, they have typically managed the US Dollar exchange rate de facto. We show that most of these countries were able to regain their monetary credibility within a relatively short time period. The Argentine crisis in 2001 caused a minor setback in this process for some countries. We measure the credibility of monetary policy by separating the fundamental and excess volatility of the exchange rate on the basis of a chartist fundamentalist model. The degree of excess volatility is interpreted as the ability of the central bank to manage the exchange rate via the coordination channel.
Coreper Revisited

This article builds on earlier accounts of the work of the Committee of Permanent Representatives (Coreper) in two ways. First, it examines the role played by members of the committee under legislative co-decision. Members of Coreper (especially Coreper I) represent the Council in conciliation with the European Parliament and are increasingly involved in negotiations between Council and Parliament at earlier stages in the co-decision procedure. Second, the article considers in what circumstances Coreper can make a significant contribution to decision-taking within the Council. It suggests that this is most likely to occur in negotiation on issues which are both politically sensitive and technically complex; but that Coreper has no monopoly on such negotiations. Coreper should be thought of partly as a coordinator of Council business, partly as a fixer and troubleshooter.
Wages, Rents, and the Quality of Life

This study focuses on the role of wages and rents in allocating workers to locations with various quantities of amenities. The theory demonstrates that if the amenity is also productive, then the sign of the wage gradient is unclear while the rent gradient is positive. The theory is extended to include the housing market and nontraded goods. These extensions require little modification of the conclusion. The empirical work on wages shows that the regional wage differences can be explained largely by these local attributes. With the use of site price data, implicit prices are estimated and quality of life rankings for the cities are computed.
Raising the Alcohol Purchase Age: Its Effects on Fatal Motor Vehicle Crashes in Twenty-Six States

A study was conducted to investigate and to assess longer-term effects of raising the alcohol purchase age and the effect of legislation on "beginning" drinkers of different ages. The study was based on drivers aged 16-24 who were in fatal crashes during 1975-1984 (n = 159, 262) in 48 states. Based on the 87, 153 nighttime driver fatal crash involvements, raising the minimum drinking age was estimated to produce a 13 percent reduction in nighttime driver fatal crash involvements. The results were positive for the 11 four-state regions in which one or more states changed their laws. There was no evidence of erosion in effects when comparing fatal crash experience after one year and after three years of the age changes. Larger relative reductions were found for females than for males. Some evidence suggested that the major positive effects were achieved where the purchase age was raised to 20 and that raising it from 20 to 21 had a smaller effect; however, the evidence is not conclusive. The data suggested that the age changes are not negated by increases among those just attaining legal age to purchase alcohol in law-change states. These data are generally consistent with those found in previous work. Overall, the study data indicate that raising the minimum drinking age has had, and will continue to have, an important effect on reducing the fatal crash involvement of youthful drivers. (Author/TRRL)
Dentate gyrus development requires a cortical hem-derived astrocytic scaffold

During embryonic development, radial glial cells give rise to neurons, then to astrocytes following the gliogenic switch. Timely regulation of the switch, operated by several transcription factors, is fundamental for allowing coordinated interactions between neurons and glia. We deleted the gene for one such factor, SOX9, early during mouse brain development and observed a significantly compromised dentate gyrus (DG). We dissected the origin of the defect, targeting embryonic Sox9 deletion to either the DG neuronal progenitor domain or the adjacent cortical hem (CH). We identified in the latter previously uncharacterized ALDH1L1+ astrocytic progenitors, which form a fimbrial-specific glial scaffold necessary for neuronal progenitor migration towards the developing DG. Our results highlight an early crucial role of SOX9 for DG development through regulation of astroglial potential acquisition in the CH. Moreover, we illustrate how formation of a local network, amidst astrocytic and neuronal progenitors originating from adjacent domains, underlays brain morphogenesis.
An Individual's Chosen Retirement Age: When is the Economically Feasible Retirement Age Chosen Over the Anchor Provided by Known Others?

Do individuals make rational, well-planned retirement age decisions? Evidence is not conclusive; some decisions seem to be quite reasonable, while others, including the long-term trends generated by these decisions, seem irrational. In order to be able to predict and influence this important decision, the process leading up to it needs to be better understood. The process an individual uses to make a retirement decision may be influenced by a rational allocation of money, time, and effort, as suggested by a utility-maximizing Household Production approach. Alternatively, the decision process may be strongly influenced by an anchor, defined by the retirement ages chosen by friends, neighbors, relatives, and colleagues, as suggested by Anchoring and Prospect Theory. Studies investigating anchoring and risk-seeking/risk-aversion behavior, which results when a target is seen as a loss or a gain from the anchor, have found that individuals make irrational decisions under many different circumstances. A set of retirement decision propositions, which hypothesize that the heuristic of Anchoring and the resulting cognitive biases described by Prospect Theory will influence the chosen retirement age, are developed in this paper. Retirement information provided by the employer is a possible moderator that may reduce the influence of the anchor on the retirement decision; a set of moderator hypotheses are also developed in this paper. Propositions strongly supported by existing research predict that, unless sufficient information regarding retirement issues is used by an individual, s/he is likely to choose an inappropriate retirement age. Finally, recommended methods, including settings, variable measurement, and possible methods of data analysis, for completing the proposed study are provided.
Capturing brain dynamics: latent spatiotemporal patterns predict stimuli and individual differences

Insights from functional Magnetic Resonance Imaging (fMRI), and more recently from recordings of large numbers of neurons through calcium imaging, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of neuronal populations. To capture and characterize spatiotemporal properties of brain events, we propose an architecture based on long short-term memory (LSTM) networks to uncover distributed spatiotemporal signatures during dynamic experimental conditions1. We demonstrate the potential of the approach using naturalistic movie-watching fMRI data. We show that movie clips result in complex but distinct spatiotemporal patterns in brain data that can be classified using LSTMs (≈ 90% for 15-way classification), demonstrating that learned representations generalized to unseen participants. LSTMs were also superior to existing methods in predicting behavior and personality traits of individuals. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational properties of brain dynamics. Finally, we employed saliency maps to characterize spatiotemporally-varying brain-region importance. The spatiotemporal saliency maps revealed dynamic but consistent changes in fMRI activation data. We believe our approach provides a powerful framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.
The Impact of Complaints between Frontline Service Employees on Consumers—Based on Critical Incident Technique

The critical incident technique was adopted to collect 174 critical events which were divided into 3 categories and 9 types about the complaint behaviors among the front-line service employees witnessed by the consumers. In addition, this paper discusses the influence mechanism of employees’ complaint behavior on consumers and enriches the theory research of employees' complaints and brand internalization.
Multi-epitope Based Peptide Vaccine Design Using Three Structural Proteins (S, E, and M) of SARS-CoV-2: An In Silico Approach

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the novel coronavirus responsible for the ongoing pandemic of coronavirus disease (COVID-19). No sustainable treatment option is available so far to tackle such a public health threat. Therefore, designing a suitable vaccine to overcome this hurdle asks for immediate attention. In this study, we targeted for a design of multi-epitope based vaccine using immunoinformatics tools. We considered the structural proteins S, E and, M of SARS-CoV-2, since they facilitate the infection of the virus into host cell and using different bioinformatics tools and servers, we predicted multiple B-cell and T-cell epitopes having potential for the required vaccine design. Phylogenetic analysis provided insight on ancestral molecular changes and molecular evolutionary relationship of S, E, and M proteins. Based on the antigenicity and surface accessibility of these proteins, eight epitopes were selected by various B cell and T cell epitope prediction tools. Molecular docking was executed to interpret the binding interactions of these epitopes and three potential epitopes WTAGAAAYY, YVYSRVKNL, and GTITVEELK were selected for their noticeable higher binding affinity scores −9.1, −7.4, and −7.0 kcal/mol, respectively. Targeted epitopes had 91.09% population coverage worldwide. In summary, we identified three epitopes having the most significant properties of designing the peptide-based vaccine against SARS-CoV-2.
Cortical dynamics underlying social behavior in dominance hierarchy and spatial navigation

Rodents establish dominance hierarchy as a social ranking system in which one subject acts as dominant over all the other subordinate individuals. Dominance hierarchy regulates food access and mating opportunities, but little is known of its significance in collective behavior, for instance during navigation for foraging or migration. Here, we implemented a simplified goal-directed spatial navigation task in mice and found that the social context exerts significant influence on individual decision-making, even when efficient navigation rules leading to reward had been previously learned. Thus, decision-making and consequent task performance were strongly dependent on contingent social interactions arising during collective navigation, yet their influence on individual behavior was outlined by dominance hierarchy. Dominant animals did not behave as leaders during navigation; conversely, they were most sensitive to social context. Social ranking in turn was reflected in the neural activity and connectivity patterns of the prefrontal cortex and hippocampus, both in anesthetized and behaving mice. These results suggest that the interplay between contingent social interactions and dominance hierarchy can regulate behavioral performance, supported by the intrinsic matrix of coordinated activity in the hippocampal-prefrontal circuit. Significance Statement Decision-making is shaped by intrinsic features, such as memory-stored information, and external influences, such as social interactions, yet their interplay is not well understood. We studied decision-making during collective behavior and found that instead of prioritizing memory-based pertinent information, mice shifted their individual decisions according to contingent social interactions arising in the social context. Conversely, constitutive social interactions, such as dominance hierarchy, were relevant to outline the effect of the social environment on individual behavior. Our results suggest that intrinsic hippocampal-cortical activity and connectivity patterns define social interactions. Hence, intrinsic cortical dynamics underlie behavioral performance during social decision-making.
Scheduling an Overloaded Multiclass Many-Server Queue with Impatient Customers

We describe a fluid model with time-varying input that approximates a multiclass many-server queue with time-varying arrivals (specifically, the multiclass G/GI/N +GI queue). We show how to use that fluid model to approximately solve scheduling control problems when the arrival rate remains constant over time. The key is to characterize the invariant states of the fluid model, because they typically provide an approximation to the mean steady-state behavior of the queue under a wide range of scheduling policies. The resulting fluid control problem motivates using a static priority scheduling policy, that is consistent with the simple index rule known as cμ, when the objective is to minimize the long-run average abandonment rate. We end by discussing several open problems.
Multi-modal extended target tracking for autonomous vehicles

Autonomous vehicles rely on target tracking to predict the future positions of people, vehicles and other moving objects. Accurate estimates are essential for trajectory planning, obstacle avoidance and general situational awareness. Target tracking is complicated by proximity to the objects and multiple high resolution sensors, resulting in a large number of spatially distributed measurements from each object. Recent advances in deep neural networks (DNNs) allow detection and classification of objects in images with high accuracy and at frame rates comparable to those of range sensors. This work uses a DNN detector to simplify the data association problem by using object bounding boxes to segment 3D Light Detection And Ranging (LiDAR) point cloud data. The resulting 3D detections are used for tracking with a Global Nearest Neighbor Kalman Filter (GNNKF) algorithm. The performance of the methods is evaluated with an annotated dataset of real-world data collected with an autonomous vehicle test platform. The results show good applicability of fused DNN detections and range sensor measurements for reliable detection and tracking of objects in the autonomous vehicle environment.