Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck

One of the key challenges in training Spiking Neural Networks (SNNs) is that target outputs typically come in the form of natural signals, such as labels for classification or images for generative models, and need to be encoded into spikes. This is done by handcrafting target spiking signals, which in turn implicitly fixes the mechanisms used to decode spikes into natural signals, e.g., rate decoding. The arbitrary choice of target signals and decoding rule generally impairs the capacity of the SNN to encode and process information in the timing of spikes. To address this problem, this work introduces a hybrid variational autoencoder architecture, consisting of an encoding SNN and a decoding Artificial Neural Network (ANN). The role of the decoding ANN is to learn how to best convert the spiking signals output by the SNN into the target natural signal. A novel end-to-end learning rule is introduced that optimizes a directed information bottleneck training criterion via surrogate gradients. We demonstrate the applicability of the technique in an experimental settings on various tasks, including real-life datasets.
Invariant Policy Learning: A Causal Perspective

Contextual bandit and reinforcement learning algorithms have been successfully used in various interactive learning systems such as online advertising, recommender systems, and dynamic pricing. However, they have yet to be widely adopted in high-stakes application domains, such as healthcare. One reason may be that existing approaches assume that the underlying mechanisms are static in the sense that they do not change over different environments. In many real-world systems, however, the mechanisms are subject to shifts across environments which may invalidate the static environment assumption. In this paper, we take a step toward tackling the problem of environmental shifts considering the framework of offline contextual bandits. We view the environmental shift problem through the lens of causality and propose multi-environment contextual bandits that allow for changes in the underlying mechanisms. We adopt the concept of invariance from the causality literature and introduce the notion of policy invariance. We argue that policy invariance is only relevant if unobserved variables are present and show that, in that case, an optimal invariant policy is guaranteed to generalize across environments under suitable assumptions.
Predicting exoplanet mass from radius and incident flux: a Bayesian mixture model

The relationship between mass and radius (M-R relation) is the key for inferring the planetary compositions and thus valuable for the studies of formation and migration models. However, the M-R relation alone is not enough for planetary characterization due to the dependence of it on other confounding variables. This paper provides a non-trivial extension of the M-R relation by including the incident flux as an additional variable. By using Bayesian hierarchical modeling (BHM) that leverages the flexibility of finite mixture models, a probabilistic mass-radius-flux relationship (M-R-F relation) is obtained based on a sample of 319 exoplanets. We find that the flux has nonnegligible impact on the M-R relation, while such impact is strongest for hot-Jupiters. On the population level, the planets with higher level of flux tend to be denser, and high flux could trigger significant mass loss for plants with radii larger than 13R⊕. As a result, failing to account for the flux in mass prediction would cause systematic over or under-estimation. With the recent advent of computing power, although a lot of complex statistical models can be fitted using Monte Carlo methods, it has largely remain illusive how to validate these complex models when the data are observed with large measurement errors. We present two novel methods to examine model assumptions, which can be used not only for the models we present in this paper but can also be adapted for other statistical models.
Bank Payout Motives at the Start of the Crisis of 2007-2009

We study U.S. banks’ payout policy in 2007-2008. We start by benchmarking bank payouts in 2007 and 2008 against a model of payouts before the crisis. We then investigate stock price reactions to dividend changes and cross-sectional variation in the relation between insider information about banks’ future performance and payout policy to gauge the role of wealth transfer and fear of adverse market reactions underlying payout decisions in both years. While we do not find conclusive evidence of either motive in 2007-2008, our results corroborate concerns that it can be necessary to constrain dividends when a crisis unfolds. <br>
Gender, Race and Diversity Values among Local Government Leaders

Despite the increased emphasis placed on diversity and inclusion, there is relatively little research that focuses on diversity values in small and medium sized cities. This research uses data from a 2016 nationally representative survey to investigate how city department leaders’ perceptions of their organizations valuing diversity are related to the identity of the department head, the mayor, and the community. We find that women and people of color are underrepresented in city department leadership. Reporting that one’s organization values racial and gender diversity is significantly related to respondent gender, respondent race (for women), mayoral race (for women), and diversity in the community (for men), and that the interaction of mayoral and community identity is related to perceived diversity values. We conclude with a discussion of what these findings mean for diversity and inclusion in practice in local government departments, which often lack demographic diversity.
Observational vs Experimental Data When Making Automated Decisions Using Machine Learning

With ubiquitous data and computing power, we see machine learning models making countless decisions automatically. Often these models are deployed with the goal of having a causal effect, that is, of improving an outcome by means of an intervention. Common examples are influencing someone's purchasing behavior with an advertisement or affecting customer retention with a special offer. If these models are built using observational data, as is usually the case, they will likely suffer from confounding bias. Investing in experimental data offers a way to build unconfounded models, but such data is costly and therefore might be in short supply. So, would it be better to use small experimental data or big (confounded) observational data? This paper presents a theoretical comparison between the use of observational and experimental data for building models to make automated intervention decisions. The results reveal different regimes where each approach is preferable. Perhaps surprisingly, confounding may help to make better decisions --- such as when larger causal effects are overestimated more. Even when this is not the case, the benefits of a larger data set may outweigh the detrimental effect of confounding on intervention decisions, an unexpected insight given that confounding bias cannot be corrected with more data when estimating causal effects. The upshot is that large, confounded observational data may be preferable to small experimental data when training models for intervention decisions. This result is important for practitioners using machine learning models to make interventions because experiments entail a variety of costs (design, implementation, opportunity, political).
Toward an Ethical Experiment

Randomized Controlled Trials (RCTs) enroll hundreds of millions of subjects and involve many human lives. To improve subjects’ welfare, I propose a design of RCTs that I call Experiment-as-Market (EXAM). EXAM produces a welfare-maximizing allocation of treatment assignment probabilities, is almost incentive compatible for preference elicitation, and unbiasedly estimates any causal effect estimable with standard RCTs. I quantify these properties by applying EXAM to a water cleaning experiment in Kenya. In this empirical setting, compared to standard RCTs, EXAM improves subjects’ predicted well-being while reaching similar treatment effect estimates with similar precision.
Biases in Tax Decisions

We conduct a series of four laboratory experiments and interviews with senior tax professionals to study biases in tax decisions. In our experiments with 303 students and 62 experienced tax professionals, we find a systematic tax-rate bias in decisions under time constraints. Specifically, decision-makers overestimate the relevance of less complex tax-rate information compared to more complex tax-base information. This behavior leads to suboptimal decision-making. Moreover, we find that decision-making is unaffected by professional experience: students and tax professionals are similarly prone to tax-rate bias. Yet, tax professionals in senior positions are more rationally inattentive. These decision-makers are less likely to exhibit a tax-rate bias when exhibiting such bias is relatively costly. In-depth interviews with senior tax professionals corroborate the external validity of these results. Overall, our findings suggest that resource constraints impede the use of complex information, which results in suboptimal tax decisions.
Just DM Me (Politely): Direct Messaging, Politeness, and Hiring Outcomes in Online Labor Markets

This study examines the role of text-based direct messaging systems in online labor markets, which provide a communication channel between workers and employers, adding a personal touch to the exchange of online labor. We propose the effect of workers’ use of the direct messaging system on employers’ hiring decisions and conceptualize the information role of direct messaging. To empirically evaluate the information role of the direct messaging system, we leverage data on the direct messaging activities between workers and employers across more than 470,000 job applications on a leading online labor market. We report evidence that direct messaging with a prospective employer increases a worker’s probability of being hired by 8.9%. However, the degree to which workers benefit from direct messaging is heterogeneous, and the effect amplifies for workers approaching employers from a position of disadvantage (lacking tenure or fit with the job) and attenuates as more workers attempt to message the same prospective employer. The effects also depend on message content. In particular, we find that the benefits of direct messaging for workers depend a great deal on the politeness of the workers, and this “politeness effect” depends on several contextual factors. The beneficial effects are amplified for lower-status workers (i.e., workers lacking tenure and job fit) and workers who share a common language with the employer. At the same time, the beneficial effects weaken in the presence of typographical errors. These findings provide important insights into when and what to message to achieve favorable hiring outcomes in online employment settings.
The Liquidity Production with Scarce Collateral in Shadow Banking

This paper studies in a collateral equilibrium the determination of haircut and interest rate of secured loans in the shadow banking system. I relate the haircut to an equity-debt ratio which increases with the collateral level banks choose. An improved upside quality of collateral raises haircut but does not affect interest rate. An improved downside quality of collateral or a higher depositors’ risk aversion reduces haircut when the upside (downside) quality is high (low) and decreases interest rate. When depositors’ liquidity demand or patience increases or banks’ collateral supply or patience decreases, the haircut falls and the interest rate rises.
Making Trust Safe for AI?A Paradigm-Based Account

The philosophical aim of the Anatomy of Systemic Financial Risk project is to develop a principled non-consequentialist approach to the ethics of risk imposition for financial technologies. This foundational paper develops a novel philosophical account of trust that can serve as a basis for the assessment of trust in non-standard cases, including non-human agents and novel technologies. Our account is functionalist: we argue that a particular form of trust—Communicated Interpersonal Trust—is paradigmatic and lay out how trust as a social practice in this form helps to satisfy fundamental practical, deliberative and relational needs in mutually reinforcing ways. We then argue that derivative (non-paradigmatic) forms of trust connect to the paradigm by generating a positive dynamic between trustor and trustee that is geared towards the realisation of these functions. We call this trust’s proleptic potential. Our functionalist approach does not only provide important insights into the practice of trust and its place in the broader web of social life, but also illuminates existing philosophical debates on border-line cases. Finally, it opens up the conceptual space for the evaluation of trust in agents and objects that do not satisfy the conditions present in paradigmatic interpersonal cases, e.g. AI agents, algorithms, and, more generally, new technologies
How Does Policy Affect Trading Relationships? Evidence from the U.S. Repo Market

We analyze changes in monetary and regulatory policy on trading relationships in the U.S. repo market. We estimate that when the Federal Reserve (Fed) introduced its reverse repo (RRP) facility, money market mutual funds (MMFs) eligible to lend to the Fed cut their lending in the triparty repo market to broker-dealers (dealers) by 16%, on average. By providing a backstop, the RRP facility shifted bargaining power towards MMFs eligible to lend to the Fed, and away from dealers. Although the RRP facility strengthened trading relationships between MMFs and dealers, it also prevented some foreign dealers, who were subject to less stringent implementation of the Basel III leverage ratio, from engaging in regulatory arbitrage as effectively. We find that these policy changes influenced the way MMFs managed their balance sheets and made MMFs that were eligible to lend to the Fed safer.
Efficient Band Structure Calculation of Two-Dimensional Materials from Semilocal Density Functionals

The experimental and theoretical realization of two-dimensional (2D) materials is of utmost importance in semiconducting applications. Computational modeling of these systems with satisfactory accuracy and computational efficiency is only feasible with semilocal density functional theory methods. In the search for the most useful method in predicting the band gap of 2D materials, we assess the accuracy of recently developed semilocal exchange–correlation (XC) energy functionals and potentials. Though the explicit forms of exchange–correlation (XC) potentials are very effective against XC energy functionals for the band gap of bulk solids, their performance needs to be investigated for 2D materials. In particular, the LMBJ [J. Chem. Theory Comput.2020, 16, 265432097004] and GLLB-SC [Phys. Rev. B82, 2010, 115106] potentials are considered for their dominance in bulk band gap calculation. The performance of recently developed meta generalized gradient approximations, like TASK [Phys. Rev. Res.1, 2019, 033082] and MGGAC [Phys. Rev. B. 100, 2019, 155140], is also assessed. We find that the LMBJ potential constructed for 2D materials is not as successful as its parent functional, i.e., MBJ [Phys. Rev. Lett.102, 2009, 22640119658882] in bulk solids. Due to a contribution from the derivative discontinuity, the band gaps obtained with GLLB-SC are in a certain number of cases, albeit not systematically, larger than those obtained with other methods, which leads to better agreement with the quasi-particle band gap obtained from the GW method. The band gaps obtained with TASK and MGGAC can also be quite accurate.
Application of Response Surface Methodology for Optimizing the Therapeutic Activity of ZnO Nanoparticles Biosynthesized from Aspergillus niger

In this study, the biosynthesis of zinc oxide nanoparticles using Aspergillus niger (A/ZnO-NPs) is described. These particles have been characterized by UV–Vis spectrum analysis, X-ray powder diffraction, field emission scanning electron microscopy, and transmission electron microscopy. To use this biosynthesized nanoparticle as an antiproliferative and antimicrobial agent, the IC50 value against the breast cancer cell line and inhibition zone against Escherichia coli were used to optimize the effect of two processing factors including dose of filtrate fungi cell and temperature. The biosynthesized A/ZnO-NPs had an absorbance band at 320 nm and spherical shapes. The mean particles size was 35 nm. RSM (response surface methodology) was utilized to investigate the outcome responses. The Model F-value of 12.21 and 7.29 implies that the model was significant for both responses. The contour plot against inhibition zone for temperature and dose showed that if the dose increases from 3.8 to 17.2 µg/mL, the inhibition zone increases up to 35 mm. As an alternative to chemical and/or physical methods, biosynthesizing zinc oxide NPs through fungi extracts can serve as a more facile and eco-friendly strategy. Additionally, for optimization of the processes, the outcome responses in the biomedical available test can be used in the synthesis of ZnO-NPs that are utilized for large-scale production in various medical applications.
Steinernema sandneri n. sp. (Rhabditida: Steinernematidae), a new entomopathogenic nematode from Poland

Abstract A new species of entomopathogenic nematodes, Steinernema sandneri n. sp. , was recovered by baiting from Poland. Its morphological traits indicate that the new species is a member of the feltiae-kraussei group. A body length of 843 (708–965) μm, a more anterior position of excretory pore (56 μm), and the lower D% value (40 vs > 46) discriminate this species from most of the other group members. The first-generation males of S. sandneri n. sp. can be distinguished from the other clade members by a 60 μm long spicule, a relatively long gubernaculum (GS% = 79), and the position of the excretory pore (80 μm). Phylogenetic analysis of the ITS rDNA, D2D3 of 28 S rDNA, and cox1 sequences confirmed that S. sandneri n. sp. is a new species of the feltiae-kraussei group, closely related to S. kraussei and S. silvaticum.
A Bibliometric Analysis of Research Trends of Acupuncture Therapy in the Treatment of Migraine from 2000 to 2020

Background Migraine is the second-leading cause of disability worldwide. It is often characterized by attacks of severe, mostly unilateral, pulsating headache associated with symptoms such as photophobia, phonophobia, nausea, vomiting, and cutaneous allodynia. Acupuncture therapy has been used worldwide for the treatment of migraine. However, no visual bibliometric analysis has been conducted on the effects of acupuncture on migraine over the past 20 years. Therefore, this study aimed to explore the current status and trends on the use of acupuncture in the treatment of migraine from 2000 to 2020. Purpose The objective of this study is to identify the current status and emerging trends of the global use of acupuncture on migraine from 2000 to 2020 using CiteSpace and VOSviewer. Methods Web of Science databases were searched for publications related to acupuncture therapy for treating migraine between 2000 and 2020. CiteSpace and VOSviewer were used to analyze the number of publications per year, countries, institutions, authors, journals, references, and keywords. Results A total of 572 publications were included in the final analysis. The total number of publications has continued to increase with some fluctuations over the past 20 years. The most productive country and institution in this field were the USA, and Chengdu University of Traditional Chinese Medicine, respectively. The most active and cited authors were Liang FR and Linde K, respectively. Cephalalgia was the most productive, cited, and co-cited journal. The Linde K (2005) had the highest co-citation, citation number and centrality. The keywords “migraine” ranked first in frequency. The common type of migraine (tension-type headache), research method (randomized controlled trial, multicenter, double-blind), acupuncture’s role (prophylactic, quality of life, pain), and evaluation (meta-analysis, systematic review) were the hotspots and frontier trends of acupuncture therapy on migraine between 2000 and 2020. Conclusion The present study examined the research-related trend in acupuncture therapy on migraine using bibliometric methods and identified the statement and research frontiers over the past two decades. This may help researchers to identify potential hotspots and new directions for future research in this field.
Ferroptosis-Related Gene Signature: A New Method for Personalized Risk Assessment in Patients with Diffuse Large B-Cell Lymphoma

Purpose Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease, which makes prognostic prediction challenging. The rapid development of research on ferroptosis provides the possibility of its use in prognosis in cancer patients. The aim of the current investigation was to perform a systematic study of ferroptosis and DLBCL prognosis to identify prognostic biomarkers in DLBCL. Materials and Methods A total of 884 DLBCL patients from the Gene Expression Omnibus database were included in this study and were divided into a training set and a validation set. Univariate Cox regression analysis was used to investigate relationships between gene expression and prognostic values. Ferroptosis-related genes associated with overall survival in the training set were then extracted, and the least absolute shrinkage and selection operator Cox regression model was used to establish an eight-gene signature, comprising ZEB1, PSAT1, NGB, NFE2L2, LAMP2, HIF1A, FH, and CXCL2. Results The signature exhibited significant independent prognostic value in both the training set and the validation set. It also exhibited strong prognostic value in subgroup analysis. A nomogram integrating the eight-gene signature and components of the International Prognostic Index facilitated reliable prognostic prediction. Conclusion A novel and reliable ferroptosis-related gene signature that can effectively classify DLBCL patients into high-risk and low-risk groups in terms of survival rate was developed. It could be used for prognostic prediction in DLBCL patients. Targeting ferroptosis may be a therapeutic alternative in DLBCL.