Miracle-dz/UniFAR-FTUs huggingface dataset:
RECONSIDERING LEAD CORROSION IN DRINKING WATER: PRODUCT TESTING, DIRECT CHLORAMINE ATTACK AND GALVANIC CORROSION

The ban on lead plumbing materials in the Safe Drinking Water Act (1986) and the EPA Lead and Copper Rule (1991) have successfully reduced lead contamination of potable water supplies. The success of these regulations gave rise to a belief that serious lead contamination was an important past problem that had been solved, and that additional fundamental research was therefore unnecessary. This work carefully re-examined the lead contamination issue from the perspective of 1) new regulations causing a shift from chlorine to chloramine disinfectant, 2) assumptions guiding sampling strategies, 3) existing performance standards for brass, and 4) galvanically driven corrosion of lead bearing plumbing materals. The results were instrumental in uncovering and understanding a serious problem with lead contamination in Washington, D.C. A critical reading of the literature indicates that chloramines can accelerate corrosion of lead bearing materials and increase lead contamination of water. When a new sampling protocol was conceived and used in Washington homes to assess the nature of the problem, hazardous levels of lead were found to be present in some drinking water samples. Contrary to the conventional wisdom, lead was not always highest in first draw samples, but often increased with flushing. This has several important implications for monitoring and public health. For instance, well-intentioned public education materials were causing consumers to drink water containing very high levels of lead in some circumstances. Laboratory and field-testing proved that chloramines were causing serious lead corrosion problems. That testing also discovered that, unbeknownst to scientists and utilities, free chlorine itself can act as a corrosion inhibitor, reducing lead solubility and contamination of water. The net result is that changing disinfectant from free chlorine to chloramine can sometimes trigger serious lead contamination of water.
Synthesis and crystal structure of a novel multimodal ligand, 6,6'-bis(2-(di(pyridin-2-yl)methylene)-1-methylhydrazinyl)-2,2'-bipyridine, and its copper(I) complex, {[Cu_3L_2](PF_6)_3° 3H_2O} (1)

The single crystal X-ray structure of the novel multimodal ligand 6,6'-bis(2-(di(pyridin-2-yl)methylene)-1-methylhydrazinyl)-2,2'-bipyridine, C34H28N10 (L), was determined at 120 (2) K. The ligand possesses a centre of symmetry that generates a planar bipyridyl core with the hydrazone substituents in a trans-geometry. The di-2-pyridyl moieties at either terminus of the molecule twist out of the plane (52.90 and 56.32 A). The crystal structure comprises 2-dimensional ribbons that stack about each other so as to optimise van der Waals interactions and packing efficiency. Studies of the coordination properties of the novel multimodal ligand with copper(I) show that 2 ligand strands wrapped around the copper(I) having a preference for tetrahedral geometry to furnish a supramolecular self-assembled of a double-stranded tri-nuclear helical complex, {[Cu3L2](PF6)3\cdot 3H2O} (1), as revealed by the ESI-MS, and further characterised by 1H-NMR, UV-Vis, and FT-IR spectra.
Trace element contents and essential oil yields from wild thyme plant (Thymus serpyllum L.) grown at different natural variable environments, Jordan

The yields of essential oils and concentrations of some heavy metals (Fe, Cu, Ni, Cd, Co, Pb and Cr) were determined in the wild thyme plant (Thymus serpyllum L.) grown at different environments in Jordan. The samples were collected from various natural climatic regions located northern (Jeresh) and southern (Al-Karak, Al-Shouback and Aqaba) regions of Jordan. Our results showed a wide variation of essential oil contents among wild thyme plants grown at different natural variable environments, Jordan. They were 5.6 and 5.4% in Aqaba and Al-Karak regions, respectively, and 3.3 and 2.5% in Jerash and Al-Shouback, respectively. The results showed different heavy metal concentrations in all investigated samples. The highest mean levels of copper (10.40 mg/kg) were recorded at the southern regions of Al-Karak and Al-Shouback but they were within the range of permissible limit for medicinal plants. The average concentrations of lead in T. serpyllum were 1.45, 0.05, and 0.79, 1.26 mg/kg, in samples collected from Jerash, Al-Karak, Al-Shouback and Aqaba, respectively. The lead concentrations were below recommended levels by WHO. The iron content showed a great variation between the different plant samples that can be attributed to the place of growth, concentrations varying from 15.31 to 205.80 mg/kg. Cadmium concentration was below the guidelines toxic levels in samples collected from Jerash and Al-Karak, however, it was not detected in Aqaba and Al-Shouback. The essential oil and heavy metal contents in T. serpyllum are mainly affected by variable natural climatic conditions. Moreover, the current study showed that T. serpyllum species grown in Jordan are characterized by low heavy metal contents and can safely be used for pharmaceutical and edible purposes without any hazardous effect on human health.
Effect of Direct and Residual Effects of Different Sulfur Fertilizers on Groundnut and Wheat Cropping System on Typic Haplaquent Soils

Abstract A field experiment was conducted on sulfur deficient soil during kharif dry seasons of 1993, 1994, and 1995 to study the comparative efficacy of selected sources of sulfur to groundnut (Arachis hypogaea L.). The results of the experiment were tested on farm trial in cultivator's field during the kharif season of 1999. The yield of groundnut crop in experimental field increased significantly with increasing levels of sulfur up to 40 kg S ha−1. Among the various sources tested, gypsum was the best with respect to yield, followed by single super phosphate and ammonium sulfate. In on‐farm trial the application of gypsum at 20 kg S ha−1 increased the yield parameters and yield over un‐treated control. The residual effect of different sulfur sources on wheat (Triticum aestivum) grain yield, the effect of treatments was non significant but the levels of sulfur and the interaction between source and levels were significant.
Observations of the Quiet Sun during the Deepest Solar Minimum of the Past Century with Chandrayaan-2 XSM: Sub-A-class Microflares outside Active Regions

Solar flares, with energies ranging over several orders of magnitude, result from impulsive release of energy due to magnetic reconnection in the corona. Barring a handful, almost all microflares observed in X-rays are associated with the solar active regions. Here we present, for the first time, a comprehensive analysis of a large sample of quiet-Sun microflares observed in soft X-rays by the Solar X-ray Monitor (XSM) on board the Chandrayaan-2 mission during the 2019–2020 solar minimum. A total of 98 microflares having peak flux below GOES A-level were observed by the XSM during observations spanning 76 days. By using the derived plasma temperature and emission measure of these events obtained by fitting the XSM spectra along with volume estimates from concurrent imaging observations in EUV with the Solar Dynamics Observatory/Atmospheric Imaging Assembly, we estimated their thermal energies to be ranging from 3 × 1026 to 6 × 1027 erg. We present the frequency distribution of the quiet-Sun microflares with energy and discuss the implications of these observations of small-scale magnetic reconnection events outside active regions on coronal heating.
Buckling and post-buckling of a compressed elastic half-space coated by double layers

We investigate the buckling and post-buckling properties of a hyperelastic half-space coated by two hyperelastic layers when the composite structure is subjected to a uniaxial compression. In the case of a half-space coated with a single layer, it is known that when the shear modulus μf of the layer is larger than the shear modulus μs of the half-space, a linear analysis predicts the existence of a critical stretch and wave number, whereas a weakly nonlinear analysis predicts the existence of a threshold value of the modulus ratio μs/μf ≈ 0.57 below which the buckling is super-critical and above which the buckling is sub-critical. It is shown in this paper that when another layer is added, a larger variety of behaviour can be observed. For instance, buckling can occur at a preferred wavenumber super-critically even if both layers are softer than the half-space although the top layer would need to be harder than the bottom layer. When the shear modulus of the bottom layer lies in a certain interval, the super-critical to sub-critical transition can happen a number of times as the shear modulus of the top layer is increased gradually. Thus, an extra layer imparts more flexibility in producing wrinkling patterns with desired properties, and our weakly nonlinear analysis provides a road map on the parameter regimes where this can be achieved.
UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language

We present a corpus professionally annotated for grammatical error correction (GEC) and fluency edits in the Ukrainian language. We have built two versions of the corpus – GEC+Fluency and GEC-only – to differentiate the corpus application. To the best of our knowledge, this is the first GEC corpus for the Ukrainian language. We collected texts with errors (33,735 sentences) from a diverse pool of contributors, including both native and non-native speakers. The data cover a wide variety of writing domains, from text chats and essays to formal writing. Professional proofreaders corrected and annotated the corpus for errors relating to fluency, grammar, punctuation, and spelling. This corpus can be used for developing and evaluating GEC systems in Ukrainian. More generally, it can be used for researching multilingual and low-resource NLP, morphologically rich languages, document-level GEC, and fluency correction. The corpus is publicly available at https://github.com/grammarly/ua-gec
Convergence Properties of the Distributed Projected Subgradient Algorithm over General Graphs

In this paper, we revisit a well-known distributed projected subgradient algorithm which aims to minimize a sum of cost functions with a common set constraint. In contrast to most of existing results, weight matrices of the time-varying multi-agent network are assumed to be more general, i.e., they are only required to be row stochastic instead of doubly stochastic. We focus on analyzing convergence properties of this algorithm under general graphs. We first show that there generally exists a graph sequence such that the algorithm is not convergent when the network switches freely within finitely many general graphs. Then to guarantee the convergence of this algorithm under any uniformly jointly strongly connected general graph sequence, we provide a necessary and sufficient condition, i.e., the intersection of optimal solution sets to all local optimization problems is not empty. Furthermore, we surprisingly find that the algorithm is convergent for any periodically switching general graph sequence, and the converged solution minimizes a weighted sum of local cost functions, where the weights depend on the Perron vectors of some product matrices of the underlying periodically switching graphs. Finally, we consider a slightly broader class of quasi-periodically switching graph sequences, and show that the algorithm is convergent for any quasi-periodic graph sequence if and only if the network switches between only two graphs.
Adversarial Attacks and Defenses for Speech Recognition Systems

The ubiquitous presence of machine learning systems in our lives necessitates research into their vulnerabilities and appropriate countermeasures. In particular, we investigate the effectiveness of adversarial attacks and defenses against automatic speech recognition (ASR) systems. We select two ASR models - a thoroughly studied DeepSpeech model and a more recent Espresso framework Transformer encoder-decoder model. We investigate two threat models: a denial-of-service scenario where fast gradient-sign method (FGSM) or weak projected gradient descent (PGD) attacks are used to degrade the model's word error rate (WER); and a targeted scenario where a more potent imperceptible attack forces the system to recognize a specific phrase. We find that the attack transferability across the investigated ASR systems is limited. To defend the model, we use two preprocessing defenses: randomized smoothing and WaveGAN-based vocoder, and find that they significantly improve the model's adversarial robustness. We show that a WaveGAN vocoder can be a useful countermeasure to adversarial attacks on ASR systems - even when it is jointly attacked with the ASR, the target phrases' word error rate is high.
Robust Facial Expression Recognition with Convolutional Visual Transformers

—Facial Expression Recognition (FER) in the wild is extremely challenging due to occlusions, variant head poses, face deformation and motion blur under unconstrained conditions. Although substantial progresses have been made in automatic FER in the past few decades, previous studies were mainly designed for lab-controlled FER. Real-world occlusions, variant head poses and other issues deﬁnitely increase the difﬁculty of FER on account of these information-deﬁcient regions and complex backgrounds. Different from previous pure CNNs based methods, we argue that it is feasible and practical to translate facial images into sequences of visual words and perform expression recognition from a global perspective. Therefore, we propose Convolutional Visual Transformers to tackle FER in the wild by two main steps. First, we propose the attentional selective fusion (ASF) for leveraging two kinds of feature maps generated by two-branch CNNs. The ASF captures discriminative information by fusing multiple features with the global-local attention. The fused feature maps are then ﬂattened and projected into sequences of visual words. Second, inspired by the success of Transformers in natural language processing, we propose to model relationships between these visual words with the global self-attention. The proposed method is evaluated on three public in-the-wild facial expression datasets (RAF-DB, FERPlus and AffectNet). Under the same settings, extensive experiments demonstrate that our method shows superior performance over other methods, setting new state of the art on RAF-DB with 88.14%, FERPlus with 88.81% and AffectNet with 61.85%. The cross-dataset evaluation on CK+ shows the promising generalization capability of the proposed method.
Using activation histograms to bound the number of affine regions in ReLU feed-forward neural networks

Several current bounds on the maximal number of affine regions of a ReLU feed-forward neural network are special cases of the framework [1] which relies on layer-wise activation histogram bounds. We analyze and partially solve a problem in algebraic topology the solution of which would fully exploit this framework. Our partial solution already induces slightly tighter bounds and suggests insight in how parameter initialization methods can affect the number of regions. Furthermore, we extend the framework to allow the composition of subnetwork instead of layer-wise activation histogram bounds to reduce the number of required compositions which negatively affect the tightness of the resulting bound.
No Keyword is an Island: In search of covert associations

This paper describes how corpus-assisted discourse analysis based on keyword identification and interpretation can benefit from employing Market Basket Analysis (mba) after keyword extraction. mba is a data mining technique used originally in marketing that can reveal consistent associations between items in a shopping cart, but also between keywords in a corpus of many texts. By identifying recurring associations between keywords, we can compensate for the lack of wider context which is a major issue impeding the interpretation of isolated keywords (especially when analysing large data). To showcase the advantages of mba in ‘re-contextualising’ keywords within the discourse, we conducted a pilot study on the topic of migration, contrasting anti-system and centre-right Czech Internet media. The results show that mba is useful in identifying the dominant strategy of anti-system news portals: to weave in a confounding ideological undercurrent and connect the concept of migrants to a multitude of other topics (i.e., flooding the discourse).
DER: Dynamically Expandable Representation for Class Incremental Learning

We address the problem of class incremental learning, which is a core step towards achieving adaptive vision intelligence. In particular, we consider the task setting of incremental learning with limited memory and aim to achieve better stability-plasticity trade-off. To this end, we propose a novel two-stage learning approach that utilizes a dynamically expandable representation for more effective incremental concept modeling. Specifically, at each incremental step, we freeze the previously learned representation and augment it with additional feature dimensions from a new learnable feature extractor. This enables us to integrate new visual concepts with retaining learned knowledge. We dynamically expand the representation according to the complexity of novel concepts by introducing a channel-level mask-based pruning strategy. Moreover, we introduce an auxiliary loss to encourage the model to learn diverse and discriminate features for novel concepts. We conduct extensive experiments on the three class incremental learning benchmarks and our method consistently outperforms other methods with a large margin.1
Reaction-Diffusion-Advection Systems with Discontinuous Diffusion and Mass Control

Abstract. In this paper, we study unique, globally defined uniformly bounded weak solutions for a class of semilinear reaction-diffusion-advection systems. The coefficients of the differential operators and the initial data are only required to be measurable and uniformly bounded. The nonlinearities are quasi-positive and satisfy a commonly called mass control or dissipation of mass property. Moreover, we assume the intermediate sum condition of a certain order. The key feature of this work is the surprising discovery that quasi-positive systems that satisfy an intermediate sum condition automatically give rise to a new class of L-energy type functionals that allow us to obtain requisite uniform a priori bounds. Our methods are sufficiently robust to extend to different boundary conditions, or to certain quasi-linear systems. We also show that in case of mass dissipation, the solution is bounded in sup-norm uniformly in time. We illustrate the applicability of results by showing global existence and large time behavior of models arising from a spatio-temporal spread of infectious disease.
Two Real Scalar WIMP Model in the Assisted Freeze-Out Scenario

We study a simple dark matter model given by two interacting real singlet scalars, with only one of them coupled to the Higgs. The model therefore presents a minimal assisted-freeze-out framework: both scalars contribute to the dark matter relic density, but only one of them takes part in the elastic scattering with nuclei. This reduces the expected interaction rate in direct detection experiments in such a way that the model in some regions of the parameter space may evade XENON1T constraints. We explore the model under theoretical (perturbativity, stability potential and unitarity) and experimental constraints (Higgs to invisible, relic density, direct detection), with the use of the narrow-width-approximation near the Higgs resonance to calculate the average annihilation cross section. We show that the model is viable in the Higgs resonance region and for scalar singlets masses of hundreds of GeV, although the model becomes highly constrained at high energies.
Statistical inference for individual fairness

As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating or even exacerbating undesirable historical biases (e.g., gender and racial biases) has come to the fore of the public's attention. In this paper, we focus on the problem of detecting violations of individual fairness in ML models. We formalize the problem as measuring the susceptibility of ML models against a form of adversarial attack and develop a suite of inference tools for the adversarial cost function. The tools allow auditors to assess the individual fairness of ML models in a statistically-principled way: form confidence intervals for the worst-case performance differential between similar individuals and test hypotheses of model fairness with (asymptotic) non-coverage/Type I error rate control. We demonstrate the utility of our tools in a real-world case study.
Geometric Unsupervised Domain Adaptation for Semantic Segmentation

Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation. However, they introduce a domain gap that severely hurts real-world performance. We propose to use self-supervised monocular depth estimation as a proxy task to bridge this gap and improve sim-to-real unsupervised domain adaptation (UDA). Our Geometric Unsupervised Domain Adaptation method (GUDA)1 learns a domain-invariant representation via a multi-task objective combining synthetic semantic supervision with real-world geometric constraints on videos. GUDA establishes a new state of the art in UDA for semantic segmentation on three benchmarks, outperforming methods that use domain adversarial learning, self-training, or other self-supervised proxy tasks. Furthermore, we show that our method scales well with the quality and quantity of synthetic data while also improving depth prediction.
Complete invariants and parametrization of expansive Lorenz maps

We obtain the complete conjugacy invariants of expansive Lorenz maps and for any given two expansive Lorenz maps, there are two unique sequences of $(\beta_{i},\alpha_{i})$ pairs. In this way, we can define the classification of expansive Lorenz maps. Moreover, we investigate the uniform linearization of expansive Lorenz maps through periodic renormalization.
Distribuição espacial de fósforo em Latossolo tratado com lodo de esgoto e adubação mineral

In agricultural soils receiving sewage sludge and subjected to water erosion, may occur intense transport of phosphorus (P) to the lower parts of the landscape and eventually to water bodies. The objective of this study was to evaluate the spatial distribution of P in an Oxisol cultivated with maize. The treatments were evaluated with application of mineral fertilization (MF) and sewage sludge (SS). Soil samples were collected following a sampling grid of 69 points in each treatment at the beginning and at the end of the crop cycle, in an experimental area with 10% slope. There was a moderate spatial dependence of P in the soil in the treatment of MF, resulting in higher concentrations of P in the deeper layers of the soil caused by erosion. The same standard was observed for the soil attributes sum of bases, cation exchange capacity and soil organic matter. Conversely, when SS was applied there was a randomized distribution of P in the plots because of the non-uniform manner of its application. Such random distribution of P showed correlation with soil organic matter.