Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALYSIS AND DETECTION OF OUTLIERS AND SYSTEMATIC ERRORS IN INDUSTRIAL PLANT DATA

This article describes the analysis of industrial process data to detect outliers and systematic errors. Data reconciliation is an important step in adjusting mathematical models to plant data. The quality of the data directly affects the quality of adjustment of the model for modeling, simulation, and optimization purposes. To detect these errors in a multivariable system is not an easy task. If the origin of the abnormal values is known, these values can be immediately discarded. On the other hand, if an error or an extreme observation is not clearly justified, the decision whether or not to discard these values must be based on statistical analysis. In this work, in addition to process knowledge, the methodology employed involves an approach based on statistical analysis, first-principle equations, neural network models, and a composite of these. The neural network based approach was used to represent the process in order to classify similar inputs and outputs, i.e., to identify clusters. The elimination of gross errors was performed by the similarity principle or by hypothesis testing for means. The system studied is the Isoprene Production Unit of BRASKEM, the largest Brazilian petrochemical plant. The analysis of the process was undertaken by using a one-year database. The frequency of the data collection of the monitoring variables was 15 minutes.
Reactivities of di(1-naphthyl)methane and hydrogenated di(1-naphthyl)methanes toward hydrocracking over Ni-S

Hydrocracking reactions of di(1-naphthyl)methane (DNM) and hydrogenated di(1-naphthyl)methanes (H-DNMs) were investigated at 300 °C to examine the relationship between structure and reactivity of the substrates. The results show that in the presence of Ni−S DNM is readily hydrocracked to naphthalene and 1-methylnaphthalene under pressurized hydrogen, while the more deeply DNM is hydrogenated, the more slowly the resulting H-DNMs are hydrocracked. The differences between DNM and H-DNMs and among the different H-DNMs in reactivity toward hydrocracking can be ascribed to the hydrogen-accepting abilities of ipso-carbons in the substrates, the stabilities of leaving arylmethyl radicals and the adsorption strength of the substrates on catalyst surface.
Influence of low molecular weight organic acids on zinc distribution within micronutrient pools and zinc uptake by wheat 1

Abstract A greenhouse study was conducted to examine the effects of four soil solution organic acids on the distribution of Zn within various soil micro nutrient pools and their influence on Zn uptake by wheat. L‐malic, malonic, oxalic, and succinic acids at four concentration levels, 0,10‐2 mol/L, 10‐3 mol/L, and 10‐4 mol/L were applied to pots growing wheat (Triticum aestivum) over a period of 10 weeks. A sequential Zn extraction technique was performed on the soil for each treatment, with the quantity amount of Zn in each fraction being determined using atomic absorption spectroscopy. Soil Zn fractions significantly affected by the organic acid treatments were the exchangeable, organic and Mn oxide fractions. An inverse relationship existed between Zn extracted from the exchangeable fraction and organic acid concentration for all acid treatments. Succinic and malonic acids at higher concentrations were the only treatments to be significantly higher in organic Zn compared to the control. All treatments .
Selective Trace Analysis of Mercury (II) Ions in Aqueous Media Using SERS-Based Aptamer Sensor

We report a highly sensitive surface-enhanced Raman scattering (SERS) platform for the selective trace analysis of mercury (II) ions in drinkable water using aptamer-conjugated silver nanoparticles. Here, an aptamer designed to specifically bind to Hg 2+ ions in aqueous solution was labelled with a TAMRA moiety at the 5' end and used as a Raman reporter. Polyamine spermine tetrahydrochloride (spermine) was used to promote surface adsorption of the aptamer probes onto the silver nanoparticles. When Hg 2+ ions are added to the system, binding of Hg 2+ with T-T pairs results in a conformational rearrangement of the aptamer to form a hairpin structure. As a result of the reduced of electrostatic repulsion between silver nanoparticles, aggregation of silver nanoparticles occurs, and the SERS signal is significantly increased upon the addition of Hg 2+ ions. Under optimized assay conditions, the concentration limit of detection was estimated to be 5 nM, and this satisfies a limit of detection below the EPA defined limit of 10 nM in drinkable water
Effect of the recombination function on the collection in a p-i-n solar cell

Abstract A closed-form expression for the recombination function associated with a single type of recombination centre that can exist in three charge states is applied to the problem of bulk collection in a p—i—n solar cell of hydrogenated amorphous silicon. It is shown that a linear approximation of the corresponding recombination function can be applied to determine bulk collection, if the p—i—n diode is under reverse bias voltage, a condition that allows the assumption of a constant electric field. The effects of two kinds of recombination function on the bulk collection are compared: first, the function found for a recombination centre that can exist in two charge states (i.e. the classical Shockley—Read—Hall expression) and second, the function found for a recombination centre that can exist in three charge states (as proposed by the present authors for dangling-bond recombination). The comparison demonstrates a significant difference between the two approaches with respect to the parameters that lim.
(S)-(-)-Limonene Derivatives Containing (1H-1,2,3-Triazol-4- yl)methyl 4-Bromobenzoate

The synthesis of (S)-(-)-limonene derivatives containing (1H-1,2,3-triazol-4-yl)methyl 4-bromobenzoate were obtained by epoxidation, azidolysis and Huisgen’s 1,3-dipolar cycloaddition. Two products were isolated and characterized by FT-IR, 1H NMR, 13C NMR, ESI-MS and optical rotation.
Proving non-termination by program reversal

We present a new approach to proving non-termination of non-deterministic integer programs. Our technique is rather simple but efficient. It relies on a purely syntactic reversal of the program's transition system followed by a constraint-based invariant synthesis with constraints coming from both the original and the reversed transition system. The latter task is performed by a simple call to an off-the-shelf SMT-solver, which allows us to leverage the latest advances in SMT-solving. Moreover, our method offers a combination of features not present (as a whole) in previous approaches: it handles programs with non-determinism, provides relative completeness guarantees and supports programs with polynomial arithmetic. The experiments performed with our prototype tool RevTerm show that our approach, despite its simplicity and stronger theoretical guarantees, is at least on par with the state-of-the-art tools, often achieving a non-trivial improvement under a proper configuration of its parameters.
Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning

Exploiting label hierarchies has become a promising approach to tackling the zero-shot multi-label text classification (ZS-MTC) problem. Conventional methods aim to learn a matching model between text and labels, using a graph encoder to incorporate label hierarchies to obtain effective label representations (Rios and Kavuluru, 2018). More recently, pretrained models like BERT (Devlin et al., 2018) have been used to convert classification tasks into a textual entailment task (Yin et al., 2019). This approach is naturally suitable for the ZS-MTC task. However, pretrained models are underexplored in the existing work because they do not generate individual vector representations for text or labels, making it unintuitive to combine them with conventional graph encoding methods. In this paper, we explore to improve pretrained models with label hierarchies on the ZS-MTC task. We propose a Reinforced Label Hierarchy Reasoning (RLHR) approach to encourage interdependence among labels in the hierarchies during training. Meanwhile, to overcome the weakness of flat predictions, we design a rollback algorithm that can remove logical errors from predictions during inference. Experimental results on three real-life datasets show that our approach achieves better performance and outperforms previous non-pretrained methods on the ZS-MTC task.
Multi-unit association measures

This paper formulates and evaluates a series of multi-unit measures of directional association, building on the pairwiseΔPmeasure, that are able to quantify association in sequences of varying length and type of representation. Multi-unit measures face an additional segmentation problem: once the implicit length constraint of pairwise measures is abandoned, association measures must also identify the borders of meaningful sequences. This paper takes a vector-based approach to the segmentation problem by using 18 unique measures to describe different aspects of multi-unit association. An examination of these measures across eight languages shows that they are stable across languages and that each provides a unique rank of associated sequences. Taken together, these measures expand corpus-based approaches to association by generalizing across varying lengths and types of representation.
LOZENGE TILINGS OF HEXAGONS WITH A VERTICAL INTRUSION

Motivated by the conjecture posed by Fulmek and Krattenthaler, we provide product formulas for the number of lozenge tilings of a hexagon with a vertical intrusion. As a direct corollary, we obtain the product formula for the number of plane partitions fitting inside a box with a certain restriction. We also investigate the asymptotic behavior of the ratio between the number of lozenge tilings of a hexagon with a vertical intrusion and that of one without it.
A Big Data Analysis of the Ethereum Network: from Blockchain to Google Trends

First, a big data analysis of the transactions and smart contracts made on the Ethereum blockchain is performed, revealing interesting trends in motion. Next, these trends are compared with the public's interest in Ether and Bitcoin, measured by the volume of online searches. An analysis of the crypto prices and search trends suggests the existence of big players (and not the regular users), manipulating the market after a drop in prices. Lastly, a cross-correlation study of crypto prices and search trends reveals the pairs providing more accurate and timely predictions of Ether prices.
IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection

In this work, we present our approach and findings for SemEval-2021 Task 5 - Toxic Spans Detection. The task’s main aim was to identify spans to which a given text’s toxicity could be attributed. The task is challenging mainly due to two constraints: the small training dataset and imbalanced class distribution. Our paper investigates two techniques, semi-supervised learning and learning with Self-Adjusting Dice Loss, for tackling these challenges. Our submitted system (ranked ninth on the leader board) consisted of an ensemble of various pre-trained Transformer Language Models trained using either of the above-proposed techniques.
The Coronavirus is a Bioweapon: Analysing Coronavirus Fact-Checked Stories

The 2020 coronavirus pandemic has heightened the need to flag coronavirus-related misinformation, and fact-checking groups have taken to verifying misinformation on the Internet. We explore stories reported by fact-checking groups PolitiFact, Poynter and Snopes from January to June 2020, characterising them into six story clusters before then analyse time-series and story validity trends and the level of agreement across sites. We further break down the story clusters into more granular story types by proposing a unique automated method with a BERT classifier, which can be used to classify diverse story sources, in both fact-checked stories and tweets.
Efficiency Lower Bounds for Distribution-Free Hotelling-Type Two-Sample Tests Based on Optimal Transport

The Wilcoxon rank-sum test is one of the most popular distribution-free procedures for testing the equality of two univariate probability distributions. One of the main reasons for its popularity can be attributed to the remarkable result of Hodges and Lehmann (1956), which shows that the asymptotic relative efficiency of Wilcoxon’s test with respect to Student’s t-test, under location alternatives, never falls below 0.864, despite the former being exactly distribution-free for all sample sizes. Even more striking is the result of Chernoff and Savage (1958), which shows that the efficiency of a Gaussian score transformed Wilcoxon’s test, against the t-test, is lower bounded by 1. In this paper we study the two-sample problem in the multivariate setting and propose distribution-free analogues of the Hotelling T 2 test (the natural multidimensional counterpart of Student’s t-test) based on optimal transport and obtain extensions of the above celebrated results over various natural families of multivariate distributions. Our proposed tests are consistent against a general class of alternatives and satisfy Hodges-Lehmann and Chernoff-Savage-type efficiency lower bounds, despite being entirely agnostic to the underlying data generating mechanism. In particular, a collection of our proposed tests suffer from no loss in asymptotic efficiency, when compared to Hotelling T . To the best of our knowledge, these are the first collection of multivariate, nonparametric, exactly distribution-free tests that provably achieve such attractive efficiency lower bounds. We also demonstrate the broader scope of our methods in optimal transport based nonparametric inference by constructing exactly distribution-free multivariate tests for mutual independence, which suffer from no loss in asymptotic efficiency against the classical Wilks’ likelihood ratio test, under Konijn alternatives.
A Dynamics Perspective of Pursuit-Evasion Games of Intelligent Agents with the Ability to Learn

Pursuit-evasion games are ubiquitous in nature and in an artificial world. In nature, pursuer(s) and evader(s) are intelligent agents that can learn from experience, and dynamics (i.e., Newtonian or Lagrangian) are vital for the pursuer and the evader in some scenarios. To this end, this paper addresses the pursuit-evasion game of intelligent agents from the perspective of dynamics. A bio-inspired dynamics formulation of a pursuit-evasion game and baseline pursuit and evasion strategies are introduced at first. Then, reinforcement learning techniques are used to mimic the ability of intelligent agents to learn from experience. Based on the dynamics formulation and reinforcement learning techniques, the effects of improving both pursuit and evasion strategies based on experience in pursuit-evasion games are investigated at two levels: 1) individual runs and 2) ranges of the parameters of pursuit-evasion games. The results of the investigation are consistent with nature observations and the natural law - survival of the fittest. More importantly, with respect to the result of a pursuit-evasion game of agents with baseline strategies, this study achieves a different result. It is shown that, in a pursuit-evasion game with a dynamics formulation, an evader is not able to escape from a slightly faster pursuer with an effective learned pursuit strategy, based on agile maneuvers and an effective learned evasion strategy.
Reliably fast adversarial training via latent adversarial perturbation

While multi-step adversarial training is widely popular as an effective defense method against strong adversarial attacks, its computational cost is notoriously expensive, compared to standard training. Several single-step adversarial training methods have been proposed to mitigate the above-mentioned overhead cost; however, their performance is not sufficiently reliable depending on the optimization setting. To overcome such limitations, we deviate from the existing input-space-based adversarial training regime and propose a single-step latent adversarial training method (SLAT), which leverages the gradients of latent representation as the latent adversarial perturbation. We demonstrate that the ℓ1 norm of feature gradients is implicitly regularized through the adopted latent perturbation, thereby recovering local linearity and ensuring reliable performance, compared to the existing single-step adversarial training methods. Because latent perturbation is based on the gradients of the latent representations which can be obtained for free in the process of input gradients computation, the proposed method costs roughly the same time as the fast gradient sign method. Experiment results demonstrate that the proposed method, despite its structural simplicity, outperforms state-of-the-art accelerated adversarial training methods.
Planar diagrammatics of self-adjoint functors and recognizable tree series

A pair of biadjoint functors between two categories produces a collection of elements in the centers of these categories, one for each isotopy class of nested circles in the plane. If the centers are equipped with a trace map into the ground field, then one assigns an element of that field to a diagram of nested circles. We focus on the self-adjoint functor case of this construction and study the reverse problem of recovering such a functor and a category given values associated to diagrams of nested circles.
Understanding Continual Learning Settings with Data Distribution Drift Analysis

Classical machine learning algorithms often assume that the data are drawn i.i. d. from a stationary probability distribution. Recently, continual learning emerged as a rapidly growing area of machine learning where this assumption is relaxed, i.e. where the data distribution is non-stationary and changes over time. This paper represents the state of data distribution by a context variable $c$. A drift in $c$ leads to a data distribution drift. A context drift may change the target distribution, the input distribution, or both. Moreover, distribution drifts might be abrupt or gradual. In continual learning, context drifts may interfere with the learning process and erase previously learned knowledge; thus, continual learning algorithms must include specialized mechanisms to deal with such drifts. In this paper, we aim to identify and categorize different types of context drifts and potential assumptions about them, to better characterize various continual-learning scenarios. Moreover, we propose to use the distribution drift framework to provide more precise definitions of several terms commonly used in the continual learning field.
Machine Learning Applications in the Routing in Computer Networks

Development of routing algorithms is of clear importance as the volume of Internet traffic continues to increase. In this survey, there is much research into how Machine Learning techniques can be employed to improve the performance and scalability of routing algorithms. We surveyed both centralized and decentralized ML routing architectures and using a variety of ML techniques broadly divided into supervised learning and reinforcement learning. Many of the papers showed promise in their ability to optimize some aspect of network routing. We also implemented two routing protocols within 14 surveyed routing algorithms and verified the efficacy of their results. While the results of most of the papers showed promise, many of them are based on simulations of potentially unrealistic network configurations. To provide further efficacy to the results, more real-world results are necessary.
What does a typical metric space look like?

The collection $\mathcal{M}_n$ of all metric spaces on $n$ points whose diameter is at most $2$ can naturally be viewed as a compact convex subset of $\mathbb{R}^{\binom{n}{2}}$, known as the metric polytope. In this paper, we study the metric polytope for large $n$ and show that it is close to the cube $[1,2]^{\binom{n}{2}} \subseteq \mathcal{M}_n$ in the following two senses. First, the volume of the polytope is not much larger than that of the cube, with the following quantitative estimates: \[ \left(\tfrac{1}{6}+o(1)\right)n^{3/2} \le \log \mathrm{Vol}(\mathcal{M}_n)\le O(n^{3/2}). \] Second, when sampling a metric space from $\mathcal{M}_n$ uniformly at random, the minimum distance is at least $1 - n^{-c}$ with high probability, for some $c>0$. Our proof is based on entropy techniques. We discuss alternative approaches to estimating the volume of $\mathcal{M}_n$ using exchangeability, Szemer\'edi's regularity lemma, the hypergraph container method, and the K\H{o}v\'ari--S\'os--Tur\'an theorem.
speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment

This paper introduces a new open-source speech corpus named"speechocean762"designed for pronunciation assessment use, consisting of 5000 English utterances from 250 non-native speakers, where half of the speakers are children. Five experts annotated each of the utterances at sentence-level, word-level and phoneme-level. A baseline system is released in open source to illustrate the phoneme-level pronunciation assessment workflow on this corpus. This corpus is allowed to be used freely for commercial and non-commercial purposes. It is available for free download from OpenSLR, and the corresponding baseline system is published in the Kaldi speech recognition toolkit.
Graph Contrastive Clustering

Recently, some contrastive learning methods have been proposed to simultaneously learn representations and clustering assignments, achieving significant improvements. However, these methods do not take the category information and clustering objective into consideration, thus the learned representations are not optimal for clustering and the performance might be limited. Towards this issue, we first propose a novel graph contrastive learning framework, and then apply it to the clustering task, resulting in the Graph Constrastive Clustering (GCC) method. Different from basic contrastive clustering that only assumes an image and its augmentation should share similar representation and clustering assignments, we lift the instance-level consistency to the cluster-level consistency with the assumption that samples in one cluster and their augmentations should all be similar. Specifically, on the one hand, we propose the graph Laplacian based contrastive loss to learn more discriminative and clustering-friendly features. On the other hand, we propose a novel graph-based contrastive learning strategy to learn more compact clustering assignments. Both of them incorporate the latent category information to reduce the intra-cluster variance as well as increase the inter-cluster variance. Experiments on six commonly used datasets demonstrate the superiority of our proposed approach over the state-of-the-art methods. 1
GENERALISED BAYESIAN STRUCTURAL EQUATION MODELLING

We propose a generalised framework for Bayesian Structural Equation Modelling (SEM) that can be applied to a variety of data types. The introduced framework focuses on the approximate zero approach, according to which a hypothesised structure is formulated with approximate rather than exact zero. It extends previously suggested models by Muthén and Asparouhov (2012) and can handle continuous, binary, and ordinal data. Moreover, we propose a novel model assessment paradigm aiming to address shortcomings of posterior predictive p−values, which provide the default metric of fit for Bayesian SEM. The introduced model assessment procedure monitors the out-of-sample predictive performance of the model in question, and draws from a list of principles to answer whether the hypothesised theory is supported by the data. We incorporate scoring rules and cross-validation to supplement existing model assessment metrics for Bayesian SEM. The methodology is illustrated in continuous and categorical data examples via simulation experiments as well as real-world applications on the ‘Big-5’ personality scale and the Fagerstrom test for nicotine dependence.