Miracle-dz/UniFAR-FTUs huggingface dataset:
SUBPLEX: Towards a Better Understanding of Black Box Model Explanations at the Subpopulation Level

Understanding the interpretation of machine learning (ML) models has been of paramount importance when making decisions with societal impacts such as transport control, financial activities, and medical diagnosis. While current model interpretation methodologies focus on using locally linear functions to approximate the models or creating self-explanatory models that give explanations to each input instance, they do not focus on model interpretation at the subpopulation level, which is the understanding of model interpretations across different subset aggregations in a dataset. To address the challenges of providing explanations of an ML model across the whole dataset, we propose SUBPLEX, a visual analytics system to help users understand black-box model explanations with subpopulation visual analysis. SUBPLEX is designed through an iterative design process with machine learning researchers to address three usage scenarios of real-life machine learning tasks: model debugging, feature selection, and bias detection. The system applies novel subpopulation analysis on ML model explanations and interactive visualization to explore the explanations on a dataset with different levels of granularity. Based on the system, we conduct user evaluation to assess how understanding the interpretation at a subpopulation level influences the sense-making process of interpreting ML models from a user's perspective. Our results suggest that by providing model explanations for different groups of data, SUBPLEX encourages users to generate more ingenious ideas to enrich the interpretations. It also helps users to acquire a tight integration between programming workflow and visual analytics workflow. Last but not least, we summarize the considerations observed in applying visualization to machine learning interpretations.
IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals

This paper describes our efforts in tackling Task 5 of SemEval-2020. The task involved detecting a class of textual expressions known as counterfactuals and separating them into their constituent elements. Our final submitted approaches were an ensemble of various fine-tuned transformer-based and CNN-based models for the first subtask and a transformer model with dependency tree information for the second subtask. We ranked 4-th and 9-th in the overall leaderboard. We also explored various other approaches that involved classical methods, other neural architectures and incorporation of different linguistic features.
Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles

As a combination of various kinds of technologies, autonomous vehicles could complete a series of driving tasks by itself, such as perception, decision-making, planning, and control. Since there is no human driver to handle the emergency situation, future transportation information is significant for automated vehicles. This paper proposes different methods to forecast the time series for autonomous vehicles, which are the nearest neighborhood (NN), fuzzy coding (FC), and long short term memory (LSTM). First, the formulation and operational process for these three approaches are introduced. Then, the vehicle velocity is regarded as a case study and the real-world dataset is utilized to predict future information via these techniques. Finally, the performance, merits, and drawbacks of the presented methods are analyzed and discussed.
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning

In this extended abstract, we propose a new technique for query scheduling with the explicit goal of reducing disk reads and thus implicitly increasing query performance. We introduce \system, a learned scheduler that leverages overlapping data reads among incoming queries and learns a scheduling strategy that improves cache hits. \system relies on deep reinforcement learning to produce workload-specific scheduling strategies that focus on long-term performance benefits while being adaptive to previously-unseen data access patterns. We present results from a proof-of-concept prototype, demonstrating that learned schedulers can offer significant performance improvements over hand-crafted scheduling heuristics. Ultimately, we make the case that this is a promising research direction in the intersection of machine learning and databases.
Structured Extended Finite Element Methods of Solids Defined by Implicit Surfaces

A paradigm is developed for generating structured finite element models from solid models by means of implicit surface definitions. The implicit surfaces are defined by radial basis functions. Internal features, such as material interfaces, sliding interfaces and cracks are treated by enrichment techniques developed in the extended finite element method (X-FEM). Methods for integrating the weak form for such models are proposed. These methods simplify the generation of finite element models. Results presented for several examples show that the accuracy of this method is comparable to standard unstructured finite element methods.
Quantum potential in dust collapse with a negative cosmological constant

In the functional Schrodinger formalism, we obtain the wave function describing collapsing dust in an anti-de-Sitter background, as seen by a co-moving observer, by mapping the resulting variable mass Schrodinger equation to that of the quantum isotonic oscillator. Using this wave function, we perform a causal de Broglie-Bohm analysis, and obtain the corresponding quantum potential. We construct a quantum corrected metric via a disformal transformation, incorporating the effects of the quantum potential. Due to a pressure term originating from the quantum potential, an initially collapsing solution with a negative cosmological constant bounces back after reaching a minimum radius, and thereby avoids the classical singularity predicted by general relativity.
Movement Assessment from Skeleton Videos: A Review

The raising availability of 3D cameras and dramatic improvement of computer vision algorithms in the recent decade, accelerated the research of automatic movement assessment solutions. Such solutions can be implemented at home, using aﬀordable equipment and dedicated software. In this paper, we divide the movement assessment task into secondary tasks and explain why they are needed and how they can be addressed. We review the recent solutions for automatic movement assessment from skeleton videos, comparing them by their objectives, features, movement domains and algorithmic approaches.
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense

The field of cybersecurity has mostly been a cat-and-mouse game with the discovery of new attacks leading the way. To take away an attacker's advantage of reconnaissance, researchers have proposed proactive defense methods such as Moving Target Defense (MTD). To find good movement strategies, researchers have modeled MTD as leader-follower games between the defender and a cyber-adversary. We argue that existing models are inadequate in sequential settings when there is incomplete information about a rational adversary and yield sub-optimal movement strategies. Further, while there exists an array of work on learning defense policies in sequential settings for cyber-security, they are either unpopular due to scalability issues arising out of incomplete information or tend to ignore the strategic nature of the adversary simplifying the scenario to use single-agent reinforcement learning techniques. To address these concerns, we propose (1) a unifying game-theoretic model, called the Bayesian Stackelberg Markov Games (BSMGs), that can model uncertainty over attacker types and the nuances of an MTD system and (2) a Bayesian Strong Stackelberg Q-learning (BSS-Q) approach that can, via interaction, learn the optimal movement policy for BSMGs within a reasonable time. We situate BSMGs in the landscape of incomplete-information Markov games and characterize the notion of Strong Stackelberg Equilibrium (SSE) in them. We show that our learning approach converges to an SSE of a BSMG and then highlight that the learned movement policy (1) improves the state-of-the-art in MTD for web-application security and (2) converges to an optimal policy in MTD domains with incomplete information about adversaries even when prior information about rewards and transitions is absent.
Sparse Nonnegative Tensor Factorization and Completion With Noisy Observations

In this paper, we study the sparse nonnegative tensor factorization and completion problem from partial and noisy observations for third-order tensors. Because of sparsity and nonnegativity, the underlying tensor is decomposed into the tensor-tensor product of one sparse nonnegative tensor and one nonnegative tensor. We propose to minimize the sum of the maximum likelihood estimation for the observations with nonnegativity constraints and the tensor $\ell _{0}$ norm for the sparse factor. We show that the error bounds of the estimator of the proposed model can be established under general noise observations. The detailed error bounds under specific noise distributions including additive Gaussian noise, additive Laplace noise, and Poisson observations can be derived. Moreover, the minimax lower bounds are shown to be matched with the established upper bounds up to a logarithmic factor of the sizes of the underlying tensor. These theoretical results for tensors are better than those obtained for matrices, and this illustrates the advantage of the use of nonnegative sparse tensor models for completion and denoising. Numerical experiments are provided to validate the superiority of the proposed tensor-based method compared with the matrix-based approach.
Check_square at CheckThat! 2020 Claim Detection in Social Media via Fusion of Transformer and Syntactic Features

In this digital age of news consumption, a news reader has the ability to react, express and share opinions with others in a highly interactive and fast manner. As a consequence, fake news has made its way into our daily life because of very limited capacity to verify news on the Internet by large companies as well as individuals. In this paper, we focus on solving two problems which are part of the fact-checking ecosystem that can help to automate fact-checking of claims in an ever increasing stream of content on social media. For the first problem, claim check-worthiness prediction, we explore the fusion of syntactic features and deep transformer Bidirectional Encoder Representations from Transformers (BERT) embeddings, to classify check-worthiness of a tweet, i.e. whether it includes a claim or not. We conduct a detailed feature analysis and present our best performing models for English and Arabic tweets. For the second problem, claim retrieval, we explore the pre-trained embeddings from a Siamese network transformer model (sentence-transformers) specifically trained for semantic textual similarity, and perform KD-search to retrieve verified claims with respect to a query tweet.
Identification of IngA, the structural gene of longus type IV pilus of enterotoxigenic Escherichia coli

Human enterotoxigenic Escherichia coli (ETEC) produces a type IV pilus termed longus which is encoded on large plasmids in association with colonization factor antigens (CFAs) and enterotoxins. A plasmid-derived 7 kbp BamHI DNA fragment hybridizing with an oligonucleotide probe designed from the amino-terminal amino acid sequence of the denatured 22 kDa structural longus pilin subunit was subcloned and sequenced. DNA sequencing analysis revealed an open reading frame, designated IngA, whose predicted amino acid sequence matched perfectly the N-terminal sequence of LngA obtained by Edman degradation. IngA is the first gene described of the longus gene cluster. Cloned IngA encoded and expressed a prepilin protein of 236 residues with a calculated mass of 25·17 kDa. The prepilin is apparently processed into a mature pilin of 206 residues with a calculated mass of 21·5 kDa. The predicted peptide sequence of IngA showed 78·8 and 37% identity to CFA/III pilin (CofA) of ETEC and the toxin-coregulated pilus (TcpA) of Vibrio cholerae. Peptide sequence homology between IngA and cofA was more prominent towards the amino terminus than within the carboxy region. Like other type IV pilins, LngA contains two cysteine residues towards the carboxy-terminal region. Transmission electron microscopy and immunoblot analysis of ETEC strains expressing either longus or CFA/III detected antigenic differences between native and denatured epitopes of these pili. In addition, differential regulation of pilus expression was identified when ETEC strains were grown in different media. Our data indicate that longus and CFA/III are two distinct but yet highly related type IV pili of ETEC.
Structural and dynamical mechanisms of a naturally occurring variant of the human prion protein in preventing prion conversion

Prion diseases are associated with the misfolding of the normal helical cellular form of prion protein (PrPC) into the beta-sheet-rich scrapie form (PrPSc) and the subsequent aggregation of PrPSc into amyloid fibrils. Recent studies demonstrated that a naturally occurring variant V127 of human PrPC is intrinsically resistant to prion conversion and aggregation, and can completely prevent prion diseases. However, the underlying molecular mechanism remains elusive. Herein we perform multiple microsecond molecular dynamics simulations on both wildtype (WT) and V127 variant of human PrPC to understand at atomic level the protective effect of V127 variant. Our simulations show that G127V mutation not only increases the rigidity of the S2-H2 loop between strand-2 (S2) and helix-2 (H2), but also allosterically enhances the stability of the H2 C-terminal region. Interestingly, previous studies reported that animals with rigid S2-H2 loop usually do not develop prion diseases, and the increase in H2 C-terminal stability can prevent misfolding and oligomerization of prion protein. The allosteric paths from G/V127 to H2 C-terminal region are identified using dynamical network analyses. Moreover, community network analyses illustrate that G127V mutation enhances the global correlations and intra-molecular interactions of PrP, thus stabilizing the overall PrPC structure and inhibiting its conversion into PrPSc. This study provides mechanistic understanding of human V127 variant in preventing prion conversion which may be helpful for the rational design of potent anti-prion compounds.
BPS Explained III: The Leading Order Behavior of the BBGKY Hierarchy in a Plasma

This is the third in a series of lectures on the technique of dimensional continuation, employed by Brown, Preston and Singleton (BPS), for calculating Coulomb energy exchange rates in a plasma. Two important examples of such processes are the charged particle stopping power and the temperature equilibration rate between different plasma species. The first lecture was devoted to understanding the machinery of dimensional continuation, and the second concentrated on calculating the electron-ion temperature equilibration rate in the extreme quantum limit. In this lecture, I will examine one of the main theoretical underpinnings of the BPS theory, namely, the dimensional reduction of the BBGKY hierarchy. I will prove that to leading order in the plasma coupling $g$, the BBGKY hierarchy reduces to the BE for dimensions greater then three and to the LBE for dimensions less than three. We must eventually return to three dimensions, and the BPS formalism shows that the simple poles associated with the BE and the LBE exactly cancel, rendering the limit in three dimensions finite. Furthermore, the leading order behavior of the LBE becomes next-to-leading order when the dimensions is analytically continued from less than three to greater than three. This provides the leading and next-to-leading order terms in $g$ exactly, which is equivalent to an exact calculation of the so-called Coulomb logarithm with no use of an integral cut-off. In this way, BPS takes all Coulomb interactions into account to leading and next-to-leading order in $g$.
Flow Sampling: Accurate and Load-balanced Sampling Policies

Software-defined networking simplifies network monitoring by means of per-flow sampling, wherein the controller keeps track of the active flows in the network and samples the switches on each flow path to collect the flow statistics. A tradeoff in this process is between the controller's sampling preference and the balancing of loads among switches. On the one hand, the controller may prefer to sample some of the switches on the flow path because they yield more accurate flow statistics. On the other hand, it is desirable to sample the switches uniformly so that their resource consumptions and lifespan are balanced. Focusing on the application of traffic matrix estimation, this paper formulates the per-flow sampling problem as a Markov decision process and devises policies that can achieve good tradeoffs between sampling accuracy and load balancing. Three classes of policies are investigated: the optimal policy, the state-independent policies, and the index policies, including the Whittle index and a second-order index policies. The second-order index policy is the most desired policy among all: 1) in terms of performance, it is on an equal footing with the Whittle index policy, and outperforms the state-independent policies by much; 2) in terms of complexity, it is much simpler than the optimal policy, and is comparable to state-independent policies and the Whittle index policy; 3) in terms of realizability, it requires no prior information on the network dynamics, hence is much easier to implement in practice.
Towards Multimodal MIR: Predicting individual differences from music-induced movement

As the field of Music Information Retrieval grows, it is important to take into consideration the multi-modality of music and how aspects of musical engagement such as movement and gesture might be taken into account. Bodily movement is universally associated with music and reflective of important individual features related to music preference such as personality, mood, and empathy. Future multimodal MIR systems may benefit from taking these aspects into account. The current study addresses this by identifying individual differences, specifically Big Five personality traits, and scores on the Empathy and Systemizing Quotients (EQ/SQ) from participants' free dance movements. Our model successfully explored the unseen space for personality as well as EQ, SQ, which has not previously been accomplished for the latter. R2 scores for personality, EQ, and SQ were 76.3%, 77.1%, and 86.7% respectively. As a follow-up, we investigated which bodily joints were most important in defining these traits. We discuss how further research may explore how the mapping of these traits to movement patterns can be used to build a more personalized, multi-modal recommendation system, as well as potential therapeutic applications.
Fully Automated Segmentation of the Left Ventricle in Magnetic Resonance Images

Automatic and robust segmentation of the left ventricle (LV) in magnetic resonance images (MRI) has remained challenging for many decades. With the great success of deep learning in object detection and classification, the research focus of LV segmentation has changed to convolutional neural network (CNN) in recent years. However, LV segmentation is a pixel-level classification problem and its categories are intractable compared to object detection and classification. Although lots of CNN based methods have been proposed for LV segmentation, no robust and reproducible results are achieved yet. In this paper, we try to reproduce the CNN based LV segmentation methods with their disclosed codes and trained CNN models. Not surprisingly, the reproduced results are significantly worse than their claimed accuracies. We also proposed a fully automated LV segmentation method based on slope difference distribution (SDD) threshold selection to compare with the reproduced CNN methods. The proposed method achieved 95.44% DICE score on the test set of automated cardiac diagnosis challenge (ACDC) while the two compared CNN methods achieved 90.28% and 87.13% DICE scores. Our achieved accuracy is also higher than the best accuracy reported in the published literatures. The MATLAB codes of our proposed method are freely available on line.
Minimax Policy for Heavy-tailed Multi-armed Bandits

We study the stochastic Multi-Armed Bandit (MAB) problem under worst case regret and heavy-tailed reward distribution. We modify the minimax policy MOSS~\cite{MOSS} for the sub-Gaussian reward distribution by using saturated empirical mean to design a new algorithm called Robust MOSS. We show that if the moment of order $1+\epsilon$ for the reward distribution exists, then the refined strategy has a worst-case regret matching the lower bound while maintaining a distribution dependent logarithm regret.
Resmoltification in wild, anadromous Arctic char (Salvelinus alpinus): a survey of osmoregulatory, metabolic, and endocrine changes preceding annual seawater migration

Anadromous Arctic char (Salvelinus alpinus) migrate to seawater every summer for feeding and spend the rest of the year overwintering in fresh water. We investigated whether annual seawater migration is preceded by changes in hyposmoregulatory capacity, intermediary metabolism, and the plasma levels of hormones known to play a role in salmonid seawater preadaptation (smoltification). Wild, anadromous Arctic char were sampled in their overwintering lake in April and May and during the period of downstream migration in June. Our results demonstrate a fourfold seasonal increase in gill Na+/K+ ATPase activity, with maximum levels in descending migrants that also displayed prime hyposmoregulatory capacity. Hepatosomatic index and activities of key liver enzymes also increased during spring, indicating a general increase in liver metabolic capacity. These changes were accompanied by increased plasma cortisol and thyroxine levels, decreased plasma growth hormone levels, and unchanged plasma levels of insulin-lik.
Learning in School Based on Multimedia

Teaching and learning in schools today almost find a saturation point, where teachers always carry out teaching the same way every day, regardless of course, style and learning strategy is always the same. Students who are getting more and more tired with a myriad of schoolwork and other loads of learning so as to experience high levels of saturation. If continuously allowed, it can be fatal to the goal of learning. This article was written with the aim to solve the problem, namely how the teacher can implement the teaching and learning process by using multimedia so that learning can take place with more fun and of course also the students can better understand the material described by the teacher. In this article also discusses how to choose a good multimedia and how to implement it so that learning in school using multimedia can give maximum results. Keywords— Learning in School, Multimedia
Floquet topological phases of non-Hermitian systems

The non-Hermiticity caused breakdown of the bulk-boundary correspondence (BBC) in topological phase transition was cured by the skin effect for the systems with chiral symmetry and translation invariance. However, periodic driving, as an active tool in engineering exotic topological phases, breaks the chiral symmetry, and the inevitable disorder destroys the translation invariance. Here, we propose a scheme to retrieve the BBC and establish a complete description of the topological phases of the periodically driven non-Hermitian system both with and without the translation invariance. The demonstration of our method in the non-Hermitian Su-Schrieffer-Heeger model shows that exotic non-Hermitian topological phases of widely tunable numbers of edge states and Floquet topological Anderson insulator are induced by the periodic driving and the disorder. Our result supplies a useful way to artificially synthesize exotic phases by periodic driving in the non-Hermitian system.