Miracle-dz/UniFAR-FTUs huggingface dataset:
No-regret learning for repeated non-cooperative games with lossy bandits

This paper considers no-regret learning for repeated continuous-kernel games with lossy bandit feedback. Since it is difficult to give the explicit model of the utility functions in dynamic environments, the players' action can only be learned with bandit feedback. Moreover, because of unreliable communication channels or privacy protection, the bandit feedback may be lost or dropped at random. Therefore, we study the asynchronous online learning strategy of the players to adaptively adjust the next actions for minimizing the long-term regret loss. The paper provides a novel no-regret learning algorithm, called Online Gradient Descent with lossy bandits (OGD-lb). We first give the regret analysis for concave games with differentiable and Lipschitz utilities. Then we show that the action profile converges to a Nash equilibrium with probability 1 when the game is also strictly monotone. We further provide the mean square convergence rate $\mathcal{O}\left(k^{-2\min\{\beta, 1/6\}}\right)$ when the game is $\beta-$ strongly monotone. In addition, we extend the algorithm to the case when the loss probability of the bandit feedback is unknown, and prove its almost sure convergence to Nash equilibrium for strictly monotone games. Finally, we take the resource management in fog computing as an application example, and carry out numerical experiments to empirically demonstrate the algorithm performance.
Autonomous Electric Vehicle Battery Disassembly Based on NeuroSymbolic Computing

The booming of electric vehicles demands eﬃcient battery disassembly for recycling to be environment-friendly. Due to the unstructured environment and high uncertainties, battery disassembly is still primarily done by humans, probably assisted by robots. It is highly desirable to design autonomous solutions to improve work eﬃciency and lower human risks in high voltage and toxic environments. This paper proposes a novel framework of the NeuroSymbolic task and motion planning method to disassemble batteries in an unstructured environment using robots automatically. It enables robots to independently locate and disassemble battery bolts, with or without obstacles. This study not only provides a solution for intelligently disassembling electric vehicle batteries but also veriﬁes its feasibility through a set of test results with the robot accomplishing the disassembly tasks in a complex and dynamic environment.
Stochastic Continuum Models for High-Entropy Alloys with Short-range Order

High entropy alloys (HEAs) are a class of novel materials that exhibit superb engineering properties. It has been demonstrated by extensive experiments and first principles/atomistic simulations that short-range order in the atomic level randomness strongly influences the properties of HEAs. In this paper, we derive stochastic continuum models for HEAs with short-range order from atomistic models. A proper continuum limit is obtained such that the mean and variance of the atomic level randomness together with the short-range order described by a characteristic length are kept in the process from the atomistic interaction model to the continuum equation. The obtained continuum model with short-range order is in the form of an Ornstein--Uhlenbeck (OU) process. This validates the continuum model based on the OU process adopted phenomenologically by Zhang et al. [Acta Mater. , 166 (2019), pp. 424--434] for HEAs with short-range order. We derive such stochastic continuum models with short-range order for both elasticity in HEAs without defects and HEAs with dislocations (line defects). The obtained stochastic continuum models are based on the energy formulations, whose variations lead to stochastic partial differential equations.
AI and Citizen Science for Serendipity

It has been argued that introducing AI to creative practices destroys spontaneity, intuition and serendipity. However, the design of systems that leverage complex interactions between citizen scientists (members of the public engaged in research tasks) and computational AI methods have the potential to facilitate creative exploration and chance encounters. Drawing from theories and literature about serendipity and computation, this article points to three interrelated aspects that support the emergence of serendipity in hybrid citizen science the task environment; the characteristics of citizen scientists; and anomalies and errors.
Model Agnostic Local Explanations of Reject

The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions. Reject options are a common way of ensuring a sufficiently high certainty of predictions made by the system. While being able to reject uncertain samples is important, it is also of importance to be able to explain why a particular sample was rejected. However, explaining general reject options is still an open problem. We propose a model agnostic method for locally explaining arbitrary reject options by means of interpretable models and counterfactual explanations.
Engineering Higgs dynamics by spectral singularities

We generalize the dynamical phase diagram of a Bardeen-Cooper-Schrieffer condensate, considering attractive to repulsive, i.e., critical quenches (CQ) and a non-constant density of states (DOS). We show that different synchronized Higgs dynamical phases can be stabilized, associated with singularities in the density of states (DOS) and different quench protocols. In particular, the CQ can stabilize an overlooked high-frequency Higgs dynamical phase related to the upper edge of the fermionic band. For a compensated Dirac system we find a Dirac-Higgs mode associated with the cusp singularity at the Fermi level, and we show that synchronized phases become more pervasive across the phase diagram. The relevance of these remarkable phenomena and their realization in ensembles of fermionic cold atoms confined in optical lattices is also discussed.
Reasoning about Procedures with Natural Language Processing: A Tutorial

This tutorial provides a comprehensive and in-depth view of the research on procedures, primarily in Natural Language Processing. A procedure is a sequence of steps intended to achieve some goal. Understanding procedures in natural language has a long history, with recent breakthroughs made possible by advances in technology. First, we discuss established approaches to collect procedures, by human annotation or extraction from web resources. Then, we examine different angles from which procedures can be reasoned about, as well as ways to represent them. Finally, we enumerate scenarios where procedural knowledge can be applied to the real world.
Past, Present, and Future of SPARC64 Processors

SPARC64 is the name of the series of SPARC-V9 architecture processors that Fujitsu has developed. The development of the first SPARC64 started in the 1990s, and the development of the latest generation is actively ongoing as of 2011. The processor frequency of the first SPARC64 was as little as 118 MHz and it had only 20 million transistors, while the processor frequency of the latest SPARC64 VIIIfx is 2 GHz and it has more than 700 million transistors. This paper describes the history of the SPARC64 processor development, and the enhanced points of each generation.
A Study on the Evacuation Time According to the Width of Corridor, Stair and Density of Evacuee in University Education Facilities

The aims of this study are to verify the relationships of evacuee speed and affections to the evacuation time according to the width of corridor and stairs in university education facilities. To achieve these purposes, the present conditions of university education facilities were analyzed, and the evacuation experiments were executed in relation to the width of corridor and stairs. The results of this study are as follows ; (1) The deviations of numbers of residents per stair is very high, and the number and width of stairs, corridors are only depend on the minimum guidelines of building code, not considering the residents' evacuation. (2) The width of corridor and stairs are affects on the evacuation speed and time. The rate of decreasing speed is large in stairs, and also the possibility of danger is increasing depend on the speed and density.
A random walk on the Rado graph

A BSTRACT . The Rado graph, also known as the random graph G p8 , p q , is a classical limit object for ﬁnite graphs. We study natural ball walks as a way of understanding the geometry of this graph. For the walk started at i , we show that order log ˚ 2 i steps are sufﬁcient, and for inﬁnitely many i , necessary for convergence to stationarity. The proof involves an application of Hardy’s inequality for trees.
Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy

Large pre-trained neural language models have supported the effectiveness of many NLP tasks, yet are still prone to generating toxic language hindering the safety of their use. Using empathetic data, we improve over recent work on controllable text generation that aims to reduce the toxicity of generated text. We find we are able to dramatically reduce the size of fine-tuning data to 7.5-30k samples while at the same time making significant improvements over state-of-the-art toxicity mitigation of up to 3.4% absolute reduction (26% relative) from the original work on 2.3m samples, by strategically sampling data based on empathy scores. We observe that the degree of improvements is subject to specific communication components of empathy. In particular, the more cognitive components of empathy significantly beat the original dataset in almost all experiments, while emotional empathy was tied to less improvement and even underperforming random samples of the original data. This is a particularly implicative insight for NLP work concerning empathy as until recently the research and resources built for it have exclusively considered empathy as an emotional concept.
Graph potentials and topological quantum field theories

We introduce graph potentials, which are Laurent polynomials associated to (colored) trivalent graphs. We show that the birational type of the graph potential only depends on the homotopy type of the colored graph, and use this to define a topological quantum field theory. A similar construction was recently introduced independently by Kontsevich--Odesskii under the name of multiplicative kernels. We end our paper by giving an efficient computational method to compute its partition function. This is the first paper in a series, and we give a survey of the applications of graph potentials in the other parts.
Challenges of green supply chain management in Malaysia

With the deterioration of global warming, Green supply chain management (GSCM) has received growing attention in the last few years. Green supply chain management (GSCM) is integrating environment thinking into supply chain management which able to reduce the environmental issues. However, there are many challenges faced by electronics industries when implementing the GSCM. This research attempts to determine the main challenges faced by electronic industries when implementing GSCM. This study was carried out at several electronic industries which implement GSCM in Peninsular Malaysia. The method that used to conduct this research is qualitative and quantitative method with interview a top management in 5 selected electronic companies and distribute questionnaire to 48 respondents in several electronic industries. Qualitative data collected was analyzed by manual analyzing as following process: data reduction, data display and conclusion drawing. The quantitative collected data was analyzed by using Analytic Hierarchy Process (AHP). It is utilized for ranking of these challenges. All pair comparisons in AHP have been made on the basis on the experts' opinions. The results of survey questionnaire and interview were reliable. From the result, the identified challenges were ranked. The quantitative result showed that the implementation cost, government, awareness of customer, information, resources or expertise, supplier commitment and top management commitment are the main challenges faced by electronic industries when GSCM implementation. The qualitative result is similar to quantitative result but public awareness, worker commitment are also the main challenges faced by the electronic industries. Thus, the research objective has been achieved.
Bound states in the continuum in a heavy ﬂuxonium qutrit

The heavy ﬂuxonium at zero external ﬂux has a long-lived state when coupled capacitively to any other system. We analyze it by projecting all the ﬂuxonium relevant operators into the qutrit subspace, as this long-lived conﬁguration corresponds to the second excited ﬂuxonium level. This state becomes a bound-state in the continuum (BIC) when the coupling occurs to an extended state supporting a continuum of modes. In the case without noise, we ﬁnd BIC decay times that can be much larger than seconds T 1 (cid:29) s when the ﬂuxonium is coupled to superconducting waveguide, while typical device frequencies are in the order of GHz . We have also analyzed the noise in a realistic situation, arguing that the most dangerous noise source is the well-known 1/f ﬂux noise. Even in its presence, we show that decay times could reach the range of T 1 ∼ 10ms .
Convergence and Implicit Bias of Gradient Flow on Overparametrized Linear Networks

Neural networks trained via gradient descent with random initialization and without any regularization enjoy good generalization performance in practice despite being highly overparametrized. A promising direction to explain this phenomenon is to study how initialization and overparametrization aﬀect convergence and implicit bias of training algorithms. In this paper, we present a novel analysis of single-hidden-layer linear networks trained under gradient ﬂow, which connects initialization, optimization, and overparametrization. Firstly, we show that the squared loss converges exponentially to its optimum at a rate that depends on the level of imbalance and the margin of the initialization. Secondly, we show that proper initialization constrains the dynamics of the network parameters to lie within an invariant set. In turn, minimizing the loss over this set leads to the min-norm solution. Finally, we show that large hidden layer width, together with (properly scaled) random initialization, ensures proximity to such an invariant set during training, allowing us to derive a novel non-asymptotic upper-bound on the distance between the trained network and the min-norm solution.
Calcium/Calmodulin- dependent Kinase II mediates the Phosphorylation and Activation of NADPH Oxidase 5 Molecular

Excessive synthesis of reactive oxygen species (ROS) contributes to the pathology of many human diseases and originates from changes in the expression and posttranslational regulation of the transmembrane NADPH oxidases (Nox). Nox5 is a novel Nox isoform whose activity is regulated by intracellular calcium levels. We recently reported that the activity and calcium-sensitivity of Nox5 can also be modulated by direct phosphorylation. However, the kinases that phosphorylate Nox5 have not been identified and thus the goal of this study was to determine if calcium activated kinases such as CAMKII are involved. We found that Nox5 activity in BAEC was suppressed by 2 doses of the CAMKII inhibitor, KN-93. In co-transfected COS-7 cells, WT and constitutively active CAMKII, but not a dominant negative, robustly increased basal Nox5 activity. The ability of CAMKII to increase Nox5 activity was also observed with fixed calcium concentrations in an isolated enzyme activity assay. CAMKII did not elevate intracellular calcium or activate other Nox enzymes. In vitro phosphorylation assays revealed that CAMKII can directly phosphorylate Nox5 onT494 and S498 as detected by phosphorylation state-specific antibodies. MS analysis revealed the phosphorylation of additional, novel sites at S475, S502 and S675. Of these phosphorylation sites, mutation of only S475 to alanine prevented CAMKII-induced increases in Nox5 activity. The ability of CAMKII α to phosphorylate S475 in intact cells was supported by the binding of Nox5 to phosphoprotein-affinity columns and via MS/MS analysis. Collectively these results suggest that CAMKII can positively regulate Nox5 activity via the phosphorylation of S475.
Development of a Single Molecule Counting Assay to Differentiate Chromophobe Renal Cancer and Oncocytoma in Clinics

Malignant chromophobe renal cancer (chRCC) and benign oncocytoma (RO) are two renal tumor types difficult to differentiate using histology and immunohistochemistry-based methods because of their similarity in appearance. We previously developed a transcriptomics-based classification pipeline with Chromophobe-Oncocytoma Gene Signature (COGS) on a single-molecule counting platform. Renal cancer patients (n=32, chRCC=17, RO=15) were recruited from Augusta University Medical Center (AUMC). Formalin-fixed paraffin-embedded (FFPE) blocks from their excised tumors were collected. We created a custom single-molecule counting code set for COGS to assay RNA from FFPE blocks. Utilizing hematoxylin-eosin stain, pathologists were able to correctly classify these tumor types (91.8%). Our unsupervised learning with UMAP (accuracy = 0.97) and hierarchical clustering (accuracy = 1.0) identified two clusters congruent with their histology. We next developed and compared four supervised models (random forest, support vector machine, generalized linear model with L2 regularization, and supervised UMAP). Supervised UMAP has shown to classify all the cases correctly (sensitivity = 1, specificity = 1, accuracy = 1) followed by random forest models (sensitivity = 0.84, specificity = 1, accuracy = 1). This pipeline can be used as a clinical tool by pathologists to differentiate chRCC from RO.
Work-family conflict among hotel housekeepers in the Balearic Islands (Spain)

The massive incorporation of women to the labour market has increased academic and applied interest on work-life issues throughout the years. This article aims to describe the domestic burden and difficulties in work-life balance (WLB) and at understanding the intersection of work and family spheres among hotel housekeepers (HHs). A cross-sectional study was conducted through Primary Health Care in the Balearic Islands (Spain); 1,043 HHs were enrolled. 56.7% report difficulties in WLB. Risk factors for perceiving difficulties in WLB were: living with one or two co-habitants, low social support, having difficulties making ends meet, being the main responsible person for domestic tasks, having a dependant, presenting higher levels of stress at work and being younger. Protective factors from experiencing work-family conflict (WFC) were job and wage satisfaction. WFC is strongly influenced by individual, economic, labour and domestic factors: these relationships show that labour and domestic spheres are non-separate worlds.
Using Facebook language to predict and describe excessive alcohol use.

BACKGROUND Assessing risk for excessive alcohol use is important for applications ranging from recruitment into research studies to targeted public health messaging. Social media language provides an ecologically embedded source of information for assessing individuals who may be at risk for harmful drinking. METHODS Using data collected on 3664 respondents from the general population, we examine how accurately language used on social media classifies individuals as at-risk for alcohol problems based on Alcohol Use Disorder Identification Test-Consumption score benchmarks. RESULTS We find that social media language is moderately accurate (area under the curve = 0.75) at identifying individuals at risk for alcohol problems (i.e., hazardous drinking/alcohol use disorders) when used with models based on contextual word embeddings. High-risk alcohol use was predicted by individuals' usage of words related to alcohol, partying, informal expressions, swearing, and anger. Low-risk alcohol use was predicted by individuals' usage of social, affiliative, and faith-based words. CONCLUSIONS The use of social media data to study drinking behavior in the general public is promising and could eventually support primary and secondary prevention efforts among Americans whose at-risk drinking may have otherwise gone "under the radar. "
Challenges facing fMRI studies of systems consolidation

ABSTRACT Tallman and colleagues (this issue) report fMRI findings in support of the classic view of memory consolidation over its main challenger, the multiple trace theory. The present commentary highlights some of the obstacles facing any fMRI study of memory consolidation and notes which challenges were tackled by Tallman and colleagues and which challenges might be insurmountable.
CAQ Corner: Acute liver failure management and liver transplantation

Acute liver failure (ALF) or fulminant hepatic failure is a rare cause of liver disease due to a multitude of etiologies with an estimated annual incidence of only 2000– 3000 cases in the United States. [1] ALF is defined as the sudden onset of encephalopathy and coagulopathy (i.e., an international normalized ratio [INR] of >1.5) in an individual without preexisting liver disease in the past 8– 24 weeks. The encephalopathy in ALF arises due to rapid increases in ammonia and other neurotoxins that can disrupt the blood– brain barrier with resultant cerebral edema. The likelihood of spontaneous recovery or transplantationfree survival (TFS) is strongly related to the etiology of ALF and maximal degree of encephalopathy. [2,3] Significant improvements in TFS over the past two decades among patients with ALF are largely attributed to improvements in critical care including greater use of Nacetylcysteine (NAC) and continuous renal replacement therapy (CRRT) and less frequent use of blood products and mechanical intubation. [2] Nonetheless, nearly onequarter of patients with ALF listed for liver transplantation (LT) will die awaiting an organ. Although LT is done under emergency circumstances in patients with ALF, 1and 5year survival rates and neurological outcomes are now excellent and approaching those of patients with cirrhosis undergoing LT.[4– 6]
TagSeq: Malicious behavior discovery using dynamic analysis

In recent years, studies on malware analysis have noticeably increased in the cybersecurity community. Most recent studies concentrate on malware classification and detection or malicious patterns identification, but as to malware activity, it still relies heavily on manual analysis for high-level semantic descriptions. We develop a sequence-to-sequence (seq2seq) neural network, called TagSeq, to investigate a sequence of Windows API calls recorded from malware execution, and produce tags to label their malicious behavior. We propose embedding modules to transform Windows API function parameters, registry, filenames, and URLs into low-dimension vectors, while still preserving the closeness property. Moreover, we utilize an attention mechanism to capture the relations between generated tags and certain API invocation calls. Results show that the most possible malicious actions are identified by TagSeq. Examples and a case study demonstrate that the proposed embedding modules preserve semantic-physical relations and that the predicted tags reflect malicious intentions. We believe this work is suitable as a tool to help security analysts recognize malicious behavior and intent with easy-to-understand tags.