Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Translation Services for Neglected Languages

Taking advantage of computationally lightweight, but high-quality translators prompt consideration of new applications that address neglected languages. Locally run translators for less popular languages may assist data projects with protected or personal data that may require specific compliance checks before posting to a public translation API, but which could render reasonable, cost-effective solutions if done with an army of local, small-scale pair translators. Like handling a specialist's dialect, this research illustrates translating two historically interesting, but obfuscated languages: 1) hacker-speak ("l33t") and 2) reverse (or "mirror") writing as practiced by Leonardo da Vinci. The work generalizes a deep learning architecture to translatable variants of hacker-speak with lite, medium, and hard vocabularies. The original contribution highlights a fluent translator of hacker-speak in under 50 megabytes and demonstrates a generator for augmenting future datasets with greater than a million bilingual sentence pairs. The long short-term memory, recurrent neural network (LSTM-RNN) extends previous work demonstrating an English-to-foreign translation service built from as little as 10,000 bilingual sentence pairs. This work further solves the equivalent translation problem in twenty-six additional (non-obfuscated) languages and rank orders those models and their proficiency quantitatively with Italian as the most successful and Mandarin Chinese as the most challenging. For neglected languages, the method prototypes novel services for smaller niche translations such as Kabyle (Algerian dialect) which covers between 5-7 million speakers but one which for most enterprise translators, has not yet reached development. One anticipates the extension of this approach to other important dialects, such as translating technical (medical or legal) jargon and processing health records.
Statistical exploration of halo anisotropic clustering and intrinsic alignments with the mass-Peak Patch algorithm

The anisotropy or triaxiality of massive dark matter haloes largely defines the structure of the cosmic web, in particular the filaments that join the haloes together. Here, we investigate such oriented correlations in mass-Peak Patch halo catalogues by using the initial strain tensor of spherical proto-halo regions to orient the haloes. To go beyond the spherically averaged two-point correlation function of haloes, we use oriented stacks to compute oriented two-point correlations: we explicitly break isotropy by imposing a local frame set by the strain tensor of the reference halo before stacking neighbouring haloes. Beyond the exclusion zone of the reference halo, clustering is found to be strongly enhanced along the major direction of the strain tensor as expected. This anisotropic clustering of haloes along filaments is further quantified by using a spherical harmonics decomposition. Furthermore, we compute the evolution of cluster-scale halo principal directions relative to those of their neighbours and show that there are strong correlations extending up to very large scales. In order to provide calculations more suitable to observational confrontations, we also utilize 2D projected versions of some equivalent correlation functions. Finally, we show that the multipole structure of the mass-peak patch halo’s anisotropic clustering can be qualitatively captured in an analytic treatment based on peak theory. Though highly informative, giving the same qualitative features as the oriented correlations found from the simulation catalogue, analytic evaluation involves extensive use of Monte Carlo methods, which is also what the simulated catalogue uses, taking into account as they do the adaptive nature of the mass-peak patch mass hierarchy and all non-local complexities associated with the exclusion of smaller haloes overlapping with larger ones: there is no substitute for the mass-Peak Patch simulation-based determination of oriented and anisotropic correlations.
Data-Efficient Domain Randomization With Bayesian Optimization

When learning policies for robot control, the required real-world data is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunately, such polices are often not transferable to the real world due to a mismatch between the simulation and reality, called ‘reality gap’. Domain randomization methods tackle this problem by randomizing the physics simulator (source domain) during training according to a distribution over domain parameters in order to obtain more robust policies that are able to overcome the reality gap. Most domain randomization approaches sample the domain parameters from a fixed distribution. This solution is suboptimal in the context of sim-to-real transferability, since it yields policies that have been trained without explicitly optimizing for the reward on the real system (target domain). Additionally, a fixed distribution assumes there is prior knowledge about the uncertainty over the domain parameters. In this letter, we propose Bayesian Domain Randomization (BayRn), a black-box sim-to-real algorithm that solves tasks efficiently by adapting the domain parameter distribution during learning given sparse data from the real-world target domain. Bayesian Domain Randomization (BayRn) uses Bayesian optimization to search the space of source domain distribution parameters such that this leads to a policy which maximizes the real-word objective, allowing for adaptive distributions during policy optimization. We experimentally validate the proposed approach in sim-to-sim as well as in sim-to-real experiments, comparing against three baseline methods on two robotic tasks. Our results show that BayRn is able to perform sim-to-real transfer, while significantly reducing the required prior knowledge.
Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling

Semantic Role Labeling (SRL) is a core Natural Language Processing task. For English, recent methods based on Transformer models have allowed for major improvements over the previous state of the art. However, for low resource languages, and in particular for Portuguese, currently available SRL models are hindered by scarce training data. In this paper, we explore a model architecture with only a pre-trained BERT-based model, a linear layer, softmax and Viterbi decoding. We substantially improve the state of the art performance in Portuguese by over 15 F 1 . Additionally, we improve SRL results in Portuguese corpora by exploiting cross-lingual transfer learning using multilingual pre-trained models (XLM-R), and transfer learning from dependency parsing in Portuguese. We evaluate the various proposed approaches empirically and as result we present an heuristic that supports the choice of the most appropriate model considering the available resources.
Causal Inference on Non-linear Spaces: Distribution Functions and Beyond

Understanding causal relationships is one of the most important goals of modern science. So far, the causal inference literature has focused almost exclusively on outcomes coming from a linear space, most commonly the Euclidean space. However, it is increasingly common that complex datasets collected through electronic sources, such as wearable devices and medical imaging, cannot be represented as data points from linear spaces. In this paper, we present a formal definition of causal effects for outcomes from nonlinear spaces, with a focus on the Wasserstein space of cumulative distribution functions. We develop doubly robust estimators and associated asymptotic theory for these causal effects. Our framework extends to outcomes from certain Riemannian manifolds. As an illustration, we use our framework to quantify the causal effect of marriage on physical activity patterns using wearable device data collected through the National Health and Nutrition Examination Survey.
Viewing Angle Effects in Quasar Application to Cosmology

The symmetry axes of active galactic nuclei (AGNs) are randomly distributed in space, but highly inclined sources are heavily obscured and are not seen as quasars with broad emission lines. The obscuring torus geometry determines the average viewing angle, and if the torus geometry changes with the redshift, this average viewing angle will also change. Thus, the ratio between the isotropic luminosity and observed luminosity may change systematically with redshift. Therefore, if we use quasars to measure the luminosity distance by evaluating the isotropic absolute luminosity and measuring the observed flux, we can have a redshift-dependent bias that can propagate to cosmological parameters. We propose a toy model for testing the effect of viewing angle uncertainty on the measurement of the luminosity distance. The model is based on analytical description of the obscuring torus applied to one-parameter observational data. It illustrates the possible change of the torus covering factor between the two chosen redshift ranges. We have estimated the possible errors in specific cosmological parameters (H 0, Ω m ) for the flat Lambda cold dark matter cosmology if a method is calibrated at low redshift and applied to the higher redshift. The errors in the cosmological parameters due to potential dependence of the viewing angle on redshift are found to be potentially significant, and the effect will have to be accommodated in the future in all quasar-based cosmological methods. A careful systematic study of AGNs means that a viewing angle across the redshift is necessary, with the use of appropriate samples and models that uniquely determine the inclination of each source.
Impact of Organizational Climate

In search of a pleasant and productive environment, the restlessness of studying about organizational climate was generated "What is it? And for what? " in the current times, and highlight their contributions. The objectives were: (i) To study its representativeness; (ii) To know the factors that indicate where to start the observations regarding the organizational climate; (iii) To recognize that the organizational climate is an indicator of analyses of its own interactivity. The study is justified due to the attention that employees have been receiving from leaders in their work space in accordance with organizational guidelines, in a globalized context that due to its aspects identified post-modern perspectives according to Zygmunt Bauman (2011) who stated: "we are living in liquid times. Nothing has been done to last". These guidelines are the result of observations of a more demanding scenario based on the quality of services and development of labor, which contributes to accelerated production and, above all, dynamics. The research is characterized as bibliographic with an exploratory aspect, for the interaction of the problem with a qualitative proposal. The researches raised portray that the organizational climate is revealed from the impacts of attitudes of a set of behaviors that can come from the predominant culture in the organization, through the actions and reactions of the members (collaborators, members, individuals) of the staff and the way of leading. Therefore, managers and members should keep themselves updated regarding the design of attitudes and behaviors to sustain a satisfactory organizational climate. Within this premise, they should be aware of the behaviors that determine the impacts that influence behaviors, especially. It is also noted that the decision on the style of leadership developed exercises changes in the organizational climate that may arise in a positive or negative way depending on the expectations and interpretation of employees, through the manner of management of the leader.
Antigen/Antibody Immunocomplex from CdTe Nanoparticle Bioconjugates

Complementary bioconjugates based on antibody−antigen interactions were synthesized from luminescent CdTe nanoparticles (NPs). Antigen (bovine serum albumin) was conjugated to red-emitting CdTe NPs, while green-emitting NPs were attached to the corresponding anti-BSA antibody (IgG). The NP bioconjugates were characterized by native and SDS−PAGE electrophoresis, gel-permeation HPLC, and circular dichroism. Antigen−antibody binding affinity was evaluated by enzyme-linked immunosorbent assay (ELISA). The formation of BSA−IgG immunocomplex resulted in the Forster resonance energy transfer (FRET) between the two different NPs: the luminescence of green-emitting NPs was quenched whereas the emission of the red-emitting NPs was enhanced. The luminescence recovered when the immunocomplex was exposed to an unlabeled antigen. The immunocomplexes can be considered as a prototype of NP superstructures based on biospecific ligands, while the competitive FRET inhibition can be used in an immunoassay protocol.
Soutenir les stratégies volitionnelles et améliorer l’expérience des étudiants en formation à distance. Quels potentiels pour le design tangible ?

Le but de cette etude est d’explorer les potentiels du design tangible pour soutenir la volition en formation a distance. Des entretiens semi-directifs ont ete menes aupres de six etudiantes pour rendre compte de leur experience d’apprentissage a distance. L’analyse phenomenologique interpretative (API) des entretiens met en evidence des difficultes qui peuvent etre attenuees par la mise en œuvre de strategies volitionnelles comme la structuration du temps et de l’environnement, la gestion des emotions et de la motivation, et la recherche de soutien social. L’API revele egalement une heterogeneite des usages en termes de strategies volitionnelles. Une boite a outils a ete developpee selon une approche iterative de type « through design ». Cinq objets tangibles sont proposes pour repondre de facon personnalisee aux besoins des etudiants en termes de strategies : « le tube de recompense » (Reward Tube) pour les inciter a se recompenser apres l’effort ; « l’album des victoires » (Victories Album) pour les aider a documenter leurs reussites ; « le thermometre emotionnel » (Emotional Thermometer) pour les amener a prendre conscience de leurs emotions et de leurs besoins ; « la casquette d’apprentissage » (Learning Cap) pour signaler a leurs proches le besoin de ne pas etre deranges ; et « le gardien du temps » (Time Guard) pour fractionner le temps et prendre regulierement des pauses.
New geodetic constraints on southern San Andreas fault-slip rates, San Gorgonio Pass, California

Assessing fault-slip rates in diffuse plate boundary systems such as the San Andreas fault in southern California is critical both to characterize seismic hazards and to understand how different fault strands work together to accommodate plate boundary motion. In places such as San Gorgonio Pass, the geometric complexity of numerous fault strands interacting in a small area adds an extra obstacle to understanding the rupture potential and behavior of each individual fault. To better understand partitioning of fault-slip rates in this region, we build a new set of elastic fault-block models that test 16 different model fault geometries for the area. These models build on previous studies by incorporating updated campaign GPS measurements from the San Bernardino Mountains and Eastern Transverse Ranges into a newly calculated GPS velocity field that has been removed of long- and short-term postseismic displacements from 12 past large-magnitude earthquakes to estimate model fault-slip rates. Using this postseismic-reduced GPS velocity field produces a best-fitting model geometry that resolves the long-standing geologic-geodetic slip-rate discrepancy in the Eastern California shear zone when off-fault deformation is taken into account, yielding a summed slip rate of 7.2 ± 2.8 mm/yr. Our models indicate that two active strands of the San Andreas system in San Gorgonio Pass are needed to produce sufficiently low geodetic dextral slip rates to match geologic observations. Lastly, results suggest that postseismic deformation may have more of a role to play in affecting the loading of faults in southern California than previously thought.
Chemical Composition of Cynara cardunculus L. var. altilis Bracts Cultivated in Central Greece: The Impact of Harvesting Time

The present study evaluated the effect of maturity stage on the chemical composition of cardoon bracts. Plant material was collected in Greece at eight different maturation stages (C1–C8) and the chemical composition was analyzed in regard to lipidic fraction and the content in fatty acids, tocopherols, organic acids, and free sugars. Samples of late maturity (C6–C8) revealed the lowest lipidic content, while a total of 29 fatty acids was identified in all the samples, with palmitic, stearic, oleic, and eicosatrienoic acids present in the highest levels depending on harvesting time. Immature (C1) and mature (C8) bracts were more abundant in saturated fatty acids (SFA) than bracts of medium-to-late maturity (C5, C6), where the monounsaturated fatty acids (MUFA) were the prevalent class. The α- and γ-tocopherols were the only identified isoforms of vitamin E, while the highest content was observed in sample C8 (199 µg/100 g dry weight (dw). The detected organic acids were oxalic, quinic, malic, citric, and fumaric acids, while fructose, glucose, sucrose, trehalose, and raffinose were the main detected sugars. The results of the present study allowed us to reveal the effect of maturity stage on cardoon bracts chemical composition and further valorize this byproduct by improving its bioactive compounds content.
On the quantum cross sections in dilute gases

The quantal cross section Q(N)qm for collision in dilute gases is given for any order N, where previously in the literature only values up to N=4 were to be found. The quantum‐statistical character of Q(N)qm is discussed and calculations are presented for Q(1) through Q(6) for Boltzmann, Fermi–Dirac, and Bose–Einstein cases for 3He–4He, 3He–3He, and 4He–4He using extensive tables of phase shifts.
Smart Scribbles for Image Matting

Image matting is an ill-posed problem that usually requires additional user input, such as trimaps or scribbles. Drawing a fine trimap requires a large amount of user effort, while using scribbles can hardly obtain satisfactory alpha mattes for non-professional users. Some recent deep learning–based matting networks rely on large-scale composite datasets for training to improve performance, resulting in the occasional appearance of obvious artifacts when processing natural images. In this article, we explore the intrinsic relationship between user input and alpha mattes and strike a balance between user effort and the quality of alpha mattes. In particular, we propose an interactive framework, referred to as smart scribbles, to guide users to draw few scribbles on the input images to produce high-quality alpha mattes. It first infers the most informative regions of an image for drawing scribbles to indicate different categories (foreground, background, or unknown) and then spreads these scribbles (i.e., the category labels) to the rest of the image via our well-designed two-phase propagation. Both neighboring low-level affinities and high-level semantic features are considered during the propagation process. Our method can be optimized without large-scale matting datasets and exhibits more universality in real situations. Extensive experiments demonstrate that smart scribbles can produce more accurate alpha mattes with reduced additional input, compared to the state-of-the-art matting methods.
EXTRACTION OF DIVALENT METALS WITH BIS(2,4,4 TRIMETHYLPENTY) PHOSPHINIC ACID

ABSTRACT The extraction of Cd(II), Cu(II) and Zn(II) from 0.1 mol dmminus3 aqueous nitrate solutions with bis(2,4,4-trimethylpentyl)phosphinic acid (HBTMPP) dissolved in Isopar-H has been studied with relation to the total extractant concentration, the equilibrium pH and the total metal concentration in the aqueous phase. The stoichiometry of the extracted species and their equilibrium constants have been determined by graphical and numerical methods and appear to be CdA2 (HA)2 and CdA2 (HA)3 for Cd(II), CuA2(HA)2 for Cu(II) and ZnAzHA and ZnAz(HA)2 for Zn(II). The extraction efficiency follows the order Zn(II)
Intramolecular excited-state proton transfer in jet-cooled 2-substituted 3-hydroxychromones and their water clusters

Intramolecular excited‐state proton transfer (ESPT) of 3‐hydroxychromone (3‐HC), 3‐hydroxyflavone (3‐HF) and 2‐(2‐naphthyl)‐3‐hydroxychromone (2‐NHC), and of their water clusters, was investigated in a supersonic expansion. The visible tautomer fluorescence excitation spectra of these compounds exhibit considerably well‐resolved vibrational structures, while no significant uv fluorescence excitation spectrum due to the normal form was observed. The upper limit of the rate constant of the tautomer formation was estimated to be 1.77×1012 s−1 for 3‐HC, 6.5×1011 s−1 for 3‐HF, and 1.54×1011 s−1 for 2‐NHC in supersonic expansion by simulation of the linewidth of the respective origin bands in the visible fluorescence excitation spectra. The order of rate constants of the tautomer formations in the supersonic free jet are consistent with that of the ESPT of these compounds in nonpolar solution. The fluorescence excitation and dispersed fluorescence spectra demonstrate 1:1 and 1:2 water complex formations of 3‐HF.
The thermal spectrum of low-temperature energy use in the United States

A detailed analysis of U.S. energy consumption was performed to determine the amount of primary energy consumed as a function of its utilization temperature from 0 to 260 °C. The study highlights the changes that have occurred in U.S. energy use since the 1970s and suggests how renewable energy could provide a large fraction of the energy used for direct use at low end-use temperatures that is currently mostly supplied by high grade fossil fuels. For example, most of the low-temperature energy used for water and space heating is provided by combusting natural gas and oil at very high temperatures. This process downgrades the thermodynamic potential of the fossil fuels for generating power resulting in large reduction in the exergy or availability of the combustion products. By focusing attention on the thermodynamic losses inherent to our current energy system, we suggest a paradigm shift in the way we view and use energy by strategically matching the source providing the energy to the end-use temperature of the application. Thermal energy demands below 260 °C could be supplied more sustainably without large exergetic losses by geothermal or solar thermal energy resources, as well as by waste heat from fuel combustion processes. In addition, direct thermal use of available low-temperature thermal energy results in higher overall efficiencies compared to electricity generation by avoiding the substantial 2nd Law losses incurred in converting thermal energy to electricity. Using the U.S. Energy Information Administration database as a primary source of information, we found that the total thermal demand in the temperature range from 0 to 260 °C in 2008 was 33.5 EJ (31.7 quads), which is about one third of the entire U.S. demand. More than half of the thermal energy demand below 260 °C (55%) comes from the residential sector, while the rest comes from the industrial (24%) and commercial (21%) sectors. Additionally, almost 80% of 33.5 EJ is used to provide heat below 150 °C. Space heating and water heating have end-use temperatures of 40 to 60 °C and are responsible for 38% of the thermal energy consumption below 260 °C in the residential and commercial sectors.