Miracle-dz/UniFAR-FTUs huggingface dataset:
Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment

Bilingual lexicons map words in one language to their translations in another, and are typically induced by learning linear projections to align monolingual word embedding spaces. In this paper, we show it is possible to produce much higher quality lexicons with methods that combine (1) unsupervised bitext mining and (2) unsupervised word alignment. Directly applying a pipeline that uses recent algorithms for both subproblems significantly improves induced lexicon quality and further gains are possible by learning to filter the resulting lexical entries, with both unsupervised and semi-supervised schemes. Our final model outperforms the state of the art on the BUCC 2020 shared task by 14 F1 points averaged over 12 language pairs, while also providing a more interpretable approach that allows for rich reasoning of word meaning in context. Further analysis of our output and the standard reference lexicons suggests they are of comparable quality, and new benchmarks may be needed to measure further progress on this task.
Sedimenting elastic filaments in turbulent flows

We investigate the gravitational settling of a long, model elastic ﬁlament in homogeneous isotropic turbulence. We show that the ﬂow produces a strongly ﬂuctuating settling velocity, whose mean is moderately enhanced over the still-ﬂuid terminal velocity, and whose variance has a power-law dependence on the ﬁlament’s weight but is surprisingly unaﬀected by its elasticity. In contrast, the tumbling of the ﬁlament is shown to be closely coupled to its stretching, and manifests as a Poisson process with a tumbling time that increases with the elastic relaxation time of the ﬁlament.
Pathwise regularization of the stochastic heat equation with multiplicative noise through irregular perturbation

Existence and uniqueness of solutions to the stochastic heat equation with multiplicative spatial noise is studied. In the spirit of pathwise regularization by noise, we show that a perturbation by a sufficiently irregular continuous path establish wellposedness of such equations, even when the drift and diffusion coefficients are given as generalized functions or distributions. In addition we prove regularity of the averaged field associated to a L\'evy fractional stable motion, and use this as an example of a perturbation regularizing the multiplicative stochastic heat equation.
Copula Flows for Synthetic Data Generation

The ability to generate high-fidelity synthetic data is crucial when available (real) data is limited or where privacy and data protection standards allow only for limited use of the given data, e.g., in medical and financial data-sets. Current state-of-the-art methods for synthetic data generation are based on generative models, such as Generative Adversarial Networks (GANs). Even though GANs have achieved remarkable results in synthetic data generation, they are often challenging to interpret.Furthermore, GAN-based methods can suffer when used with mixed real and categorical variables.Moreover, loss function (discriminator loss) design itself is problem specific, i.e., the generative model may not be useful for tasks it was not explicitly trained for. In this paper, we propose to use a probabilistic model as a synthetic data generator. Learning the probabilistic model for the data is equivalent to estimating the density of the data. Based on the copula theory, we divide the density estimation task into two parts, i.e., estimating univariate marginals and estimating the multivariate copula density over the univariate marginals. We use normalising flows to learn both the copula density and univariate marginals. We benchmark our method on both simulated and real data-sets in terms of density estimation as well as the ability to generate high-fidelity synthetic data
Semi-Definite Relaxation-Based ADMM for Cooperative Planning and Control of Connected Autonomous Vehicles

This paper investigates the cooperative planning and control problem for multiple connected autonomous vehicles (CAVs) in different scenarios. In the existing literature, most of the methods suffer from significant problems in computational efficiency. Furthermore, as the optimization problem is nonlinear and nonconvex, it typically poses great difficulty in determining the optimal solution. To address this issue, this work proposes a novel and completely parallel computation framework by leveraging the alternating direction method of multipliers (ADMM). The nonlinear and nonconvex optimization problem in the autonomous driving problem can be divided into two manageable sub-problems; and the resulting sub-problems can be solved by using effective optimization methods in a parallel framework. Here, the differential dynamic programming (DDP) algorithm is capable of addressing the nonlinearity of the system dynamics rather effectively; and the nonconvex coupling constraints with small dimensions can be resolved by invoking the notion of semi-definite relaxation (SDR), which can also be solved in a very short time. Due to the parallel computation and efficient relaxation of nonconvex constraints, our proposed approach effectively realizes real-time implementation; and thus extra assurance of driving safety is provided. In addition, two transportation scenarios for multiple CAVs are used to illustrate the effectiveness and efficiency of the proposed method.
Learning optimal Bayesian prior probabilities from data

Noninformative uniform priors are staples of Bayesian inference, especially in Bayesian machine learning. This study challenges the assumption that they are optimal and their use in Bayesian inference yields optimal outcomes. Instead of using arbitrary noninformative uniform priors, we propose a machine learning based alternative method, learning optimal priors from data by maximizing a target function of interest. Applying naïve Bayes text classification methodology and a search algorithm developed for this study, our system learned priors from data using the positive predictive value metric as the target function. The task was to find Wikipedia articles that had not (but should have) been categorized under certain Wikipedia categories. We conducted five sets of experiments using separate Wikipedia categories. While the baseline models used the popular Bayes-Laplace priors, the study models learned the optimal priors for each set of experiments separately before using them. The results showed that the study models consistently outperformed the baseline models with a wide margin of statistical significance (p < 0.001). The measured performance improvement of the study model over the baseline was as high as 443% with the mean value of 193% over five Wikipedia categories.
Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey

This is a tutorial and survey paper on factor analysis, probabilistic Principal Component Analysis (PCA), variational inference, and Variational Autoencoder (VAE). These methods, which are tightly related, are dimensionality reduction and generative models. They assume that every data point is generated from or caused by a low-dimensional latent factor. By learning the parameters of distribution of latent space, the corresponding low-dimensional factors are found for the sake of dimensionality reduction. For their stochastic and generative behaviour, these models can also be used for generation of new data points in the data space. In this paper, we first start with variational inference where we derive the Evidence Lower Bound (ELBO) and Expectation Maximization (EM) for learning the parameters. Then, we introduce factor analysis, derive its joint and marginal distributions, and work out its EM steps. Probabilistic PCA is then explained, as a special case of factor analysis, and its closed-form solutions are derived. Finally, VAE is explained where the encoder, decoder and sampling from the latent space are introduced. Training VAE using both EM and backpropagation are explained.
WiMesh: Leveraging Mesh Networking For Disaster Communication in Poor Regions of the World

This paper discusses the design, implementation and field trials of WiMesh - a resilient Wireless Mesh Network (WMN) based disaster communication system purpose-built for underdeveloped and rural parts of the world. Mesh networking is a mature area, and the focus of this paper is not on proposing novel models, protocols or other mesh solutions. Instead, the paper focuses on the identification of important design considerations and justifications for several design trade offs in the context of mesh networking for disaster communication in developing countries with very limited resources. These trade-offs are discussed in the context of key desirable traits including security, low cost, low power, size, availability, customization, portability, ease of installation and deployment, and coverage area among others. We discuss at length the design, implementation, and field trial results of the WiMesh system which enables users spread over large geographical regions, to communicate with each other despite the lack of cellular coverage, power, and other communication infrastructure by leveraging multi-hop mesh networking and Wi-Fi equipped handheld devices. Lessons learned along with real-world results are shared for WiMesh deployment in a remote rural mountainous village of Pakistan, and the source code is shared with the research community.
The structure of conservative gradient fields

The classical Clarke subdifferential alone is inadequate for understanding automatic differentiation in nonsmooth contexts. Instead, we can sometimes rely on enlarged generalized gradients called “conservative fields”, defined through the natural path-wise chain rule: one application is the convergence analysis of gradient-based deep learning algorithms. In the semi-algebraic case, we show that all conservative fields are in fact just Clarke subdifferentials plus normals of manifolds in underlying Whitney stratifications.
Mapping class groups of surfaces with noncompact boundary components

We show that the pure mapping class group is uniformly perfect for a certain class of infinite type surfaces with noncompact boundary components. We then combine this result with recent work in the remaining cases to give a complete classification of the perfect and uniformly perfect pure mapping class groups for infinite type surfaces. We also develop a method to cut a general surface into simpler surfaces and extend some mapping class group results to the general case.
On a Faster R-Linear Convergence Rate of the Barzilai-Borwein Method

The Barzilai-Borwein (BB) method has demonstrated great empirical success in nonlinear optimization. However, the convergence speed of BB method is not well understood, as the known convergence rate of BB method for quadratic problems is much worse than the steepest descent (SD) method. Therefore, there is a large discrepancy between theory and practice. To shrink this gap, we prove that the BB method converges $R$-linearly at a rate of $1-1/\kappa$, where $\kappa$ is the condition number, for strongly convex quadratic problems. In addition, an example with the theoretical rate of convergence is constructed, indicating the tightness of our bound.
Anderson localisation and reentrant delocalisation of tensorial elastic waves in two-dimensional fractured media

We study tensorial elastic wave transport in two-dimensional densely fractured media using numerical simulations and document transitions from propagation to diffusion and to localisation/delocalisation. For large fracture stiffness, waves are propagative at the scale of the system. For small stiffness, multiple scattering prevails, such that waves are diffusive in disconnected fracture networks, and localised in connected ones with a strong multifractality of the intensity field. A re-entrant delocalisation is found in well-connected fracture networks due to energy leakage via evanescent waves and cascades of mode conversion.
Controlled syntheses of carbon spheres in a swirled floating catalytic chemical vapour deposition vertical reactor

Onion-like graphite structures, also called carbon spheres (CSs), represent another new allotropic nanophase of carbon materials, which can be potentially used as single electron devices, magnetic refrigerators, nanodiodes, nanotransistors, nanoball bearings, insulator lubricants as well catalyst supports. In this study, carbon nanospheres were synthesised in a vertical swirled floating catalytic chemical vapour deposition reactor for the first time. This process allows for continuous and large scale production of these materials. The CSs were obtained by the pyrolysis of acetylene in an inert atmosphere. The effect of pyrolysis temperatures and the flow rate of argon carrier gas on the size, quality and quantity of the synthesised CSs were investigated. Transmission electron microscope analysis of the carbon material revealed graphitic spheres with a smooth surface and a uniform diameter that could be controlled by varying reaction conditions (size: 50–250 nm). The materials were spongy with very low density. The CS production rate was found to increase with the increase in pyrolysis temperature (900–1000°C) and with flow rates of the carbon source (70–370 mL min−1) and carrier gas (70–480 mL min−1). Thermogravimetric analysis, powder X-ray diffraction and carbon, hydrogen and nitrogen analysis of the samples revealed that the products mainly contained CSs (98% carbon) and Raman spectroscopy revealed that the degree of graphitisation increased with the increase in pyrolysis temperature (900–1000°C).
Single-Molecule AFM Study of Polystyrene Grafted at Gold Surfaces

ABSTRACT Single-molecule studies under poor solvent conditions show that atomic force microscopy (AFM) measurements of contour lengths of polystyrene 12K and 17K relate well to gel permeation chromatography (GPC) data when grafting concentrations are low (not greater than 0.7 mM). Contact angles and ellipsometry have been used to characterize the surfaces and indicate low grafting densities at this grafting concentration. Persistence lengths (p) of different length polystyrene chains, when fitted to a WLC (worm-like chain) model, show values of p = 0.23 nm±0.10 nm and p = 0.25 nm±0.13 nm, for 12 K and 17 K polystyrene, respectively, when the persistence lengths are fitted to log-normal distributions. These values are close to the expected theoretical value of 0.23 nm and further confirm that mostly single molecules were studied on these polystyrene surfaces. Higher grafting concentrations (≥1 mM) resulted in pulling multiple molecules.
The Effect of Cooking on Total Polyphenolic Content and Antioxidant Activity of Selected Vegetables

The aim of this study to investigate the effect of different cooking methods (boiling, steaming, stir-frying, and microwaving) on the total polyphenolic content and total antioxidant activity of selected vegetables (beet, red cabbage, broccoli, white cabbage, black radish, kale, turnip, and red radish) from Turkey. A wide variation on total phenolic content was observed in raw vegetables, ranging from 44.94 ± 0.47 (beet, Beta vulgaris L. subsp. vulgaris) to 350.94 ± 5.18 (red radish, Raphanus sativus L.) μg gallic acid equivalent/mg of sample. Considering uncooked vegetables, the order of total antioxidant activity determined by β-carotene bleaching assay was beet > red cabbage > broccoli > white cabbage > black radish > kale > turnip > red radish. In DPPH• assay, kale had an exceptionally high antioxidant activity followed by red cabbage, broccoli, turnip, black radish, beet, white cabbage, and red radish.
Adsorption, Bond Formation and Graphitization of Carbon Atoms on Ni(111) Surface

Adsorption, bond formation and graphitization of carbon atoms on Ni(111) surface are investigated using full-potential linear muffin-tin orbital method with Ni(111) surface modeled by a bilayer slab. The present calculations show that the adsorption of a single carbon atom onto the Ni surface occurs on fcc and hcp hollow sites and on the on-top site with strong C-Ni bonds. When another carbon atom approaches the adatom, they form stronger C-C bonds to form single-layer graphene. At the same time, C-Ni bonds are weakened and the graphene tends to float on the surface. The stationary properties of the floating graphene are also examined.
Determinação de compostos de massa molecular alta em folhas de plantas da Amazônia

The fractIons of dichloromethane extracts of leaves from andiroba (Carapa guianensis - Meliaceae), caapi (Banisteriopsis caapi - Malpighiaceae), cocoa (Theobroma cacao - Sterculiaceae), Brazil nut (Bertholletia excelsa - Lecytidaceae), cupuacu (Theobroma grandiflorum - Sterculiaceae), marupa (Simaruba amara - Simaroubaceae) and rubber tree (Hevea brasiliensis - Euphorbiaceae), were analyzed by HT-HRGC and HT-HRGC-MS. Esters of homologous series of fatty acids and long chain alcohols, phytol, amyrines and tocopherols were characterized. The characterization of the compounds was based mainly in mass spectra data and in addition by usual spectrometric data (1H and 13C NMR, IR).
A Survey On Secure Architectures Using Hash Function Based On FPGA for Block Chain Enabled IoT Devices

Currently, a large number of cryptographic techniques have been developed to safeguard users’ original data. In a variety of applications, cryptographic hash functions are employed to safeguard the validity and integrity of data. The most crucial primitive for data integrity and authentication is the hash function (HF) security. A cryptographic hash function serves as the foundation of the blockchain technology, which is built on cryptographic principles. Due to its recent integration with Internet of Things (IoT) technology in different fields, blockchain is gaining popularity. Therefore, it remains difficult to deploy existing blockchain technology. Due to their low power consumption and high performance, Application Specific Integrated Circuits (ASICs) have served as the primary substrate for lightweight cryptography up to this point and only used in high volume applications because to their lengthy design cycle and extremely expensive non-recurring engineering costs. The power consumption difference between ASICs and FPGAs has started to close in recent years thanks to the development of high-power, low-cost Field Programmable Gate Arrays (FPGAs). This research provided an analytical assessment of secure designs for block chain enabled Internet of Things devices employing hash functions based on FPGA while taking into account high speed and small-area hardware architectures.
MAPEAMENTO DE ÁREAS CAFEEIRAS (Coffea arabica L.) DA ZONA DA MATA MINEIRA USANDO SENSORIAMENTO REMOTO

The aim of this work was to map coffee lands in the Zona da Mata region, in Minas Gerais state, using non-conventional aerial photographs and satellite images. A pilot area, representative of the regional coffee lands, was chosen. A non-conventional aerophotogrammetric survey of the study area was carried out (scale 1:10000) and an ETM + Landsat7 satellite image was acquired. This image was registered and transformed into surface reflectance data. Photointerpretation of the limits of land use classes was done over a digital mosaic. These limits were overlaid onto the image, providing reflectance sampling of each land use type for statistical analysis and assessment of the vegetation's spectral response. Statistical analysis showed that bands 3, 4, 5 and 7 were the most representative in the discrimination of vegetation canopies. Although statistical analysis showed a significant difference between the bands for the different land use/land cover types, the classifications did not provide good target discrimination due to shading, to the region's very steep landscape and to the spectral signature similarity between coffee and forest. The mapping accuracy between the classified image and photointerpretation was considered regular to weak and the best results were obtained through a combination of bands. The use of ETM/Landsat7 images to map coffee lands presented limitations, despite the few types of land use. This is due to the shading of the images, owing to the steep topography, and to the fragmentation of most of the coffee lands into small fields.
Declarative Dashboard Generation

Systems of systems are highly dynamic software systems that require flexible monitoring solutions to be observed and controlled. Indeed, operators have to frequently adapt the set of collected indicators according to changing circumstances, to visualize the behavior of the monitored systems and timely take actions, if needed. Unfortunately, dashboard systems are still quite cumbersome to conFigure and adapt to a changing set of indicators that must be visualized.This paper reports our initial effort towards the definition of an automatic dashboard generation process that exploits meta-model layouts to create a full dashboard from a set of indicators selected by operators.
Different Response of Molecular Aggregation Structure of Styrenic Triblock Copolymer under Cyclic Uniaxial and Biaxial Stretching Modes

Mechanical stretching behavior of poly(styrene-b-ethylene-co-butylene-b-styrene) (SEBS) triblock copolymer (87 wt% polyethylene-co-butylene (PEB) block, 13 wt% polystyrene (PS) block) was investigated by three different stretching and in situ small-angle X-ray scattering (SAXS) measurements. Strain energy density function was investigated based on the stress-stretching ratio (λ) relationship under uniaxial, planar extension, and equi-biaxial stretching modes. As the result, cross-effect of strain represented by second invariants of the deformation tensor (I2) existed and only Ogden model can be used to fit the data. In the cyclic stretch testing, SEBS exhibited smaller hysteresis during cyclic equi-biaxial stretching mode than for uniaxial stretching one. λ and stretching ratio obtained from crystal planes by SAXS (λSAXS) were compared to investigate relationship between microdomain structure change and macroscopic mechanical property. SAXS measurement revealed that affine deformation occurred in the smaller λ region for both uniaxial and equi-biaxial stretching modes and deviation from affine deformation occurred for uniaxial stretching mode at the larger λ region. This is because entangled PEB loop chains could work as cross-linking points when films are stretched by equi-biaxial stretching mode.
Are Eliminated Spans Useless for Coreference Resolution? Not at all

Various neural-based methods have been proposed so far for joint mention detection and coreference resolution. However, existing works on coreference resolution are mainly dependent on ﬁltered mention representation, while other spans are largely neglected. In this paper, we aim at increasing the utilization rate of data and investigating whether those eliminated spans are totally useless, or to what extent they can improve the performance of coreference resolution. To achieve this, we propose a mention representation re-ﬁning strategy where spans highly related to mentions are well leveraged using a pointer network for representation enhancing. No-tably, we utilize an additional loss term in this work to encourage the diversity between entity clusters. Experimental results on the document-level CoNLL-2012 Shared Task English dataset show that eliminated spans are indeed much effective and our approach can achieve competitive results when compared with previous state-of-the-art in coreference resolution.