Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cloud Native Platform for Stateful Streaming

We present the architecture of a cloud native version of IBM Streams, with Kubernetes as our target platform. Streams is a general purpose streaming system with its own platform for managing applications and the compute clusters that execute those applications. Cloud native Streams replaces that platform with Kubernetes. By using Kubernetes as its platform, Streams is able to offload job management, life cycle tracking, address translation, fault tolerance and scheduling. This offloading is possible because we define custom resources that natively integrate into Kubernetes, allowing Streams to use Kubernetes' eventing system as its own. We use four design patterns to implement our system: controllers, conductors, coordinators and causal chains. Composing controllers, conductors and coordinators allows us to build deterministic state machines out of an asynchronous distributed system. The resulting implementation eliminates 75% of the original platform code. Our experimental results show that the performance of Kubernetes is an adequate replacement in most cases, but it has problems with oversubscription, networking latency, garbage collection and pod recovery.
Making climate model forecasts more useful

There is considerable potential for seasonal to inter-annual climate forecasts derived from dynamic models of the earth's climate to be used widely to help improve management of important real-world issues in a variety of different areas (e.g. disaster management, agriculture, water management, health, natural resource management, food security, and insurance). Unfortunately, several factors currently inhibit this potential, e.g. low skill, low awareness, mismatches in what model forecasts can provide and what users need, and the complexity and probabilistic nature of the information provided. Substantial effort around the world is currently directed towards reducing these impediments. For example, climate model development continues behind the scenes, and techniques such as multi-model ensemble forecasting are progressing rapidly. Communication strategies that enable probabilistic information to be communicated more effectively have been developed and exciting developments such as the emergence of the Argo float program have dramatically improved our ability to initialise forecast systems. We can also look forward to greater computing power in the future, which will allow us to increase the resolution of the models used to perform forecasts. Research on the integration of climate forecasts with risk-management tools more useful to managers is also occurring. The great potential for much wider use of climate model forecasting cannot be denied. However, it will only be realised if models continue to be developed further, if climatic variability continues to be closely monitored from the surface, the atmosphere, the ocean, and from space, and if these data are made readily available to the research community.
Chapter 23: Fog Computing for Smart Grids: Challenges and Solutions

Smart grids (SGs) enable integration of diverse power sources including renewable energy resources. They are a promising solution to cope with environmental problems, reducing harmful gas emission, and support two-way information flow to enhance energy efficiency. However, with the emerging of mission-critical and delay-sensitive applications, traditional Cloud-based data processing mode becomes less satisfying. The use of Fog computing to empower the edge-side processing capability of Smart grid systems is considered as a potential solution to address the problem. In this chapter, we aim to offer a comprehensive analysis of application of Fog computing in Smart grids. We begin with an overview of Smart grids and Fog computing. Then, by surveying the existing research, we summarize the main Fog computing enabled Smart grid applications, key problems and the possible methods. We conclude the chapter by discussing the research challenges and future directions.
Cytomorphological and Cytochemical Identification of Microglia

Microglia is one of the major resident immune cells in the central nervous system and is considered to be the key cellular mediator of neuroinflammatory processes. Identification of different Microglial states of activation by morphologic means has been one of the major challenges in the field of neurobiology of diseases. Therefore, microglial biology demands techniques to identify differing stages of microglia in different neuroanatomic locations as well as understanding the role of Microglia in different Neurological diseases. This present study is aimed towards summarizing the literature and for understanding the progress made in different Cytomorphological and Cytochemical techniques of identifying Microglia. This study also review recently used Immunohistochemistry techniques, along with Ultrastructural studies determining different morphological features of resting to activated phagocytic Microglia in a viral induced experimental animal model of neuroinflammation. Results revealed that chronic Microglial activation is considered to be an important component of neuronal dysfunction, injury, and loss (and hence to disease progression). Thus, Microglial research with special emphasis on identification of different activation states of Microglia has gradually become significant.
Studies on enzyme activities of Pleurotus tuber‐regium (Fries) Singer and Tricholoma lobayensis Heim at various fruitbody stages

Investigations were carried out on the cellulase, amylase, lipase, peroxidase, proteinase, catalase, poly-phenol oxidase and glucose-6-phosphatase activities in very young, young and mature fruitbody stages of Pleurotus tuber-regium (Fries) Singer and Tricholoma lobayensis Heim. With the exception of glucose-6-phosphatase, both mushrooms showed increases in enzyme activities from the very young to mature fruitbody stages. The highest glucose-6-phosphatase activity was detected in the young fruitbody stage of both mushrooms. Furthermore, the activities of the enzymes were higher in the pilei of both mushrooms except cellulase and β-amylase whose activities were higher in the stipes than in the pilei. By comparison, the activities of total amylase, proteinase, lipase and polyphenol oxidase were higher in T. lobayensis than P. tuber-regium which had higher cellulase activities.
Dinucleotide repeats in coronavirus SARS-CoV-2 genome: evolutionary implications

The ongoing global pandemic of infection disease COVID-19 caused by the 2019 novel coronavirus (SARS-COV-2, formerly 2019-nCoV) presents critical threats to public health and the economy since it was identified in China, December 2019. The genome of SARS-CoV-2 had been sequenced and structurally annotated, yet little is known of the intrinsic organization and evolution of the genome. To this end, we present a mathematical method for the genomic spectrum, a kind of barcode, of SARS-CoV-2 and common human coronaviruses. The genomic spectrum is constructed according to the periodic distributions of nucleotides, and therefore reflects the unique characteristics of the genome. The results demonstrate that coronavirus SARS-CoV-2 exhibits dinucleotide TT islands in the non-structural proteins 3, 4, 5, and 6. Further analysis of the dinucleotide regions suggests that the dinucleotide repeats are increased during evolution and may confer the evolutionary fitness of the virus. The special dinucleotide regions in the SARS-CoV-2 genome identified in this study may become diagnostic and pharmaceutical targets in monitoring and curing the COVID-19 disease.
Meta Clustering for Collaborative Learning

Abstract In collaborative learning, learners coordinate to enhance each of their learning performances. From the perspective of any learner, a critical challenge is to filter out unqualified collaborators. We propose a framework named meta clustering to address the challenge. Unlike the classical problem of clustering data points, meta clustering categorizes learners. Assuming each learner performs a supervised regression on a standalone local dataset, we propose a Select-Exchange-Cluster (SEC) method to classify the learners by their underlying supervised functions. We theoretically show that the SEC can cluster learners into accurate collaboration sets. Empirical studies corroborate the theoretical analysis and demonstrate that SEC can be computationally efficient, robust against learner heterogeneity, and effective in enhancing single-learner performance. Also, we show how the proposed approach may be used to enhance data fairness. Supplementary materials for this article are available online.
Escape and Accretion by Cratering Impacts: Formulation of Scaling Relations for High-speed Ejecta

Numerous small bodies inevitably lead to cratering impacts on large planetary bodies during planet formation and evolution. As a consequence of these small impacts, a fraction of the target material escapes from the gravity of the large body, and a fraction of the impactor material accretes onto the target surface, depending on the impact velocities and angles. Here, we study the mass of the high-speed ejecta that escapes from the target gravity by cratering impacts when material strength is neglected. We perform a large number of cratering impact simulations on a planar rocky target using the smoothed particle hydrodynamics method. We show that the escape mass of the target material obtained from our numerical simulations agrees with the prediction of a scaling law under a point-source assumption when vimp ≳ 12vesc, where vimp is the impact velocity and vesc is the escape velocity of the target. However, we find that the point-source scaling law overestimates the escape mass up to a factor of ∼70, depending on the impact angle, when vimp ≲ 12vesc. Using data obtained from numerical simulations, we derive a new scaling law for the escape mass of the target material for vimp ≲ 12vesc. We also derive a scaling law that predicts the accretion mass of the impactor material onto the target surface upon cratering impacts by numerically evaluating the escape mass of the impactor material. Our newly derived scaling laws are useful for predicting the escape mass of the target material and the accretion mass of the impactor material for a variety of cratering impacts that would occur on large planetary bodies during planet formation.
Control Design for Risk-Based Signal Temporal Logic Specifications

We present a general framework for risk semantics on Signal Temporal Logic (STL) specifications for stochastic dynamical systems using axiomatic risk theory. We show that under our recursive risk semantics, risk constraints on STL formulas can be expressed in terms of risk constraints on atomic predicates. We then show how this allows a (stochastic) STL risk constraint to be transformed into a risk-tightened deterministic STL constraint on a related deterministic nominal system, enabling the application of existing STL methods. For affine predicate functions and a (coherent) Distributionally Robust Value at Risk measure, we show how risk constraints on atomic predicates can be reformulated as tightened deterministic affine constraints. We demonstrate the framework using a Model Predictive Control (MPC) design with an STL risk constraint.
A constrained sparse additive model for treatment effect-modifier selection

Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This paper develops a sparse additive model focused on estimation of treatment effect-modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified. The proposed regression model can effectively identify treatment effect-modifiers that exhibit possibly nonlinear interactions with the treatment variable, that are relevant for making optimal treatment decisions. A set of simulation experiments and an application to a dataset from a randomized clinical trial are presented to demonstrate the method.
Universal Distribution of Non-Fickian Particle Kinetics in Three-Dimensional Porous Media

We study particle transport in three-dimensional random packings of monodispersed spheres at different particle Peclet numbers using Lagrangian simulations. We find a universal non-exponential function that describes the observed particle velocity probability distributions when non-Fickian behavior occurs. The function also reproduces results from particle tracking experiments and yields the Maxwell-Boltzmann distribution in the Fickian limit. We discuss the implications of the new function, with emphasis on its ability to explain non-Fickian behavior, which arises because of energy dissipation events during particle deposition even in mildly heterogeneous media.
Transmission of airborne virus through sneezed and coughed droplets

The spread of COVID19 through droplets ejected by infected individuals during sneezing and coughing has been considered a matter of key concern. Therefore, a quantitative understanding of the propagation of droplets containing the virus assumes immense importance. Here, we investigate the evolution of droplets in space and time under varying external conditions of temperature, humidity, and wind flow by using laws of statistical and fluid mechanics. The effects of drag, diffusion, and gravity on droplets of different sizes and ejection velocities have been considered during their motion in air. In still air, we found that bigger droplets traverse a larger distance, but smaller droplets remain suspended in air for a longer time. Therefore, in still air, the horizontal distance that a healthy individual should maintain from an infected one is based on the bigger droplets, but the time interval to be maintained is based on the smaller droplets. We show that in places with wind flow, the lighter droplets travel a larger distance and remain suspended in air for a longer time. Therefore, we conclude that both temporal and geometric distance that a healthy individual should maintain from an infected one is based on the smaller droplets under flowing air, which makes the use of a mask mandatory to prevent the virus. Maintenance of only stationary separation between healthy and infected individuals is not substantiated. The quantitative results obtained here will be useful to devise strategies for preventing the spread of other types of droplets containing microorganisms.
ExplainIt: Explainable Review Summarization with Opinion Causality Graphs

We present ExplainIt, a review summarization system centered around opinion explainability: the simple notion of high-level opinions (e.g. "noisy room") being explainable by lower-level ones (e.g., "loud fridge"). ExplainIt utilizes a combination of supervised and unsupervised components to mine the opinion phrases from reviews and organize them in an Opinion Causality Graph (OCG), a novel semi-structured representation which summarizes causal relations. To construct an OCG, we cluster semantically similar opinions in single nodes, thus canonicalizing opinion paraphrases, and draw directed edges between node pairs that are likely connected by a causal relation. OCGs can be used to generate structured summaries at different levels of granularity and for certain aspects of interest, while simultaneously providing explanations. In this paper, we present the system's individual components and evaluate their effectiveness on their respective sub-tasks, where we report substantial improvements over baselines across two domains. Finally, we validate these results with a user study, showing that ExplainIt produces reasonable opinion explanations according to human judges.
DeepMark++: CenterNet-based Clothing Detection

The single-stage approach for fast clothing detection as a modification of a multi-target network, CenterNet, is proposed in this paper. We introduce several powerful post-processing techniques that may be applied to increase the quality of keypoint localization tasks. The semantic keypoint grouping approach and post-processing techniques make it possible to achieve a state-of-the-art accuracy of 0.737 mAP for the bounding box detection task and 0.591 mAP for the landmark detection task on the DeepFashion2 validation dataset. We have also achieved the second place in the DeepFashion2 Challenge 2020 with 0.582 mAP on the test dataset. The proposed approach can also be used on low-power devices with relatively high accuracy without requiring any post-processing techniques.
Probabilistic Hysteresis in an Isolated Quantum System: The Microscopic Onset of Irreversibility from a Quantum Phase Space Perspective

Probabilistic hysteresis is a manifestation of irreversibility in a small, isolated classical system [Sci. Rep. 9, 14169]: after a slow cyclic sweep of a control parameter, the probability that a microcanonical ensemble returns to the neighborhood of its initial energy is significantly below one. A similar phenomenon has recently been confirmed in a corresponding quantum system for not too small particle number N . Quantum-classical correspondence has been found to be non-trivial in this case, however; the rate at which the control parameter changes must not be extremely slow and the initial distribution of energies must not be too narrow. In this paper we directly compare the quantum and classical forms of probabilistic hysteresis by making use of the Husimi quantum phase space formalism. In particular we demonstrate that the classical ergodization mechanism, which is a key ingredient in classical probabilistic hysteresis, can lead to a breakdown of quantum-classical correspondence rather than to quantum ergodization. As a result strong quantum effects in the long-term evolution are present, even though the quantum corrections in the equations of motion are proportional to 1/N and therefore would naively seem to be small. We also show, however, that quantum ergodization is restored by averaging over energies, so that for sufficient initial energy width and not-too-slow sweep rate the classical results are recovered after all. Finally we show that the formal incommutability of the classical and adiabatic limits in our system, leading to the breakdown of quantum-classical correspondence in the quasi-static limit, is due to macroscopic quantum tunneling through a large energetic barrier. This explains the extremely slow sweep rates needed to reach the quantum adiabatic limit that were reported in our previous work.
Do Influencers Influence? – Analyzing Players’ Activity in an Online Multiplayer Game

In social and online media, influencers have traditionally been understood as highly visible individuals. Recent outcomes suggest that people are likely to mimic influencers’ behavior, which can be exploited, for instance, in marketing strategies. Also in the Games User Research field, the interest in studying player social networks has emerged due to the heavy reliance on online influencers in marketing campaigns for games, as well as in keeping players engaged. Despite the inherent value of those individuals, it is still difficult to identify influencers, as the definition of influencers is a debated topic. Thus, how can we identify influencers, and are they indeed the individuals impacting others’ behavior? In this work, we focus on influence in retention to verify whether central players impacted others’ permanence in the game. We identified the central players in the social network built from the competitive player-vs-player (PvP) multiplayer (Crucible) matches in the online shooter Destiny. Then, we computed influence scores for each player evaluating the increase in similarity over time between two connected individuals. In this paper, we were able to show the first indications that the traditional metrics for influencers do not necessarily apply for games. On the contrary, we found that the group of central players was distinct from the group of influential players, defined as the individuals with the highest influence scores. Then, we provide an analysis of the two groups.
Balancing Expressiveness and Inexpressiveness in View Design

We study the design of data publishing mechanisms that allow a collection of autonomous distributed data sources to collaborate to support queries. A common mechanism for data publishing is via views: functions that expose derived data to users, usually specified as declarative queries. Our autonomy assumption is that the views must be on individual sources, but with the intention of supporting integrated queries. In deciding what data to expose to users, two considerations must be balanced. The views must be sufficiently expressive to support queries that users want to ask—the utility of the publishing mechanism. But there may also be some expressiveness restrictions. Here, we consider two restrictions, a minimal information requirement, saying that the views should reveal as little as possible while supporting the utility query, and a non-disclosure requirement, formalizing the need to prevent external users from computing information that data owners do not want revealed. We investigate the problem of designing views that satisfy both expressiveness and inexpressiveness requirements, for views in a restricted information systems - query languages (conjunctive queries), and for arbitrary views.
Short‐term effects of manganese toxicity on ribulose 1,5 bisphosphate carboxylase in tobacco chloroplasts

Abstract The short‐term effects of manganese (Mn) toxicity on ribulose 1,5 bisphosphate carboxylase EC 4.1. 1.39 (Rubisco) activity and concentration in tobacco (Nicotiana tabacum L. ‘KY 17') chloroplasts were examined. The activity of the enzyme from both Mn‐treated and control plants was determined 6,12,18, 24, and 48 h after introduction of Mn (80 mg/L). Enzyme activity was determined by monitoring rates of carbon dioxide (14CO2) fixation into acid stable products. A decrease in the enzyme's activity in experimental plants was noted after 48 h of exposure. Visible symptoms, such as chlorosis and decreased leaf size, were also observed after 48 h of Mn exposure in experimental plants. Using Rocket Immunoelectrophoresis, no appreciable differences between Rubisco concentration levels of the experimental plants and the control plants were noted indicating that the effect on Rubisco activity is a post‐translational phenomenon and that Rubisco is not being degraded at an accelerated rate. Manganese accumulat.
Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training

Adversarial training has proven to be effective in hardening networks against adversarial examples. However, the gained robustness is limited by network capacity and number of training samples. Consequently, to build more robust models, it is common practice to train on widened networks with more parameters. To boost robustness, we propose a conditional normalization module to adapt networks when conditioned on input samples. Our adaptive networks, once adversarially trained, can outperform their non-adaptive counterparts on both clean validation accuracy and robustness. Our method is objective agnostic and consistently improves both the conventional adversarial training objective and the TRADES objective. Our adaptive networks also outperform larger widened non-adaptive architectures that have 1.5 times more parameters. We further introduce several practical ``tricks'' in adversarial training to improve robustness and empirically verify their efficiency.