Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Localization Methods for Discrete Convex Simulation Optimization

We propose a set of new algorithms based on stochastic localization methods for large-scale discrete simulation optimization problems with convexity structure. All proposed algorithms, with the general idea of "localizing" potential good solutions to an adaptively shrinking subset, are guaranteed with high probability to identify a solution that is close enough to the optimal given any precision level. Specifically, for one-dimensional large-scale problems, we propose an enhanced adaptive algorithm with an expected simulation cost asymptotically independent of the problem scale, which is proved to attain the best achievable performance. For multi-dimensional large-scale problems, we propose statistically guaranteed stochastic cutting-plane algorithms, the simulation costs of which have no dependence on model parameters such as the Lipschitz parameter, as well as low polynomial order of dependence on the problem scale and dimension. Numerical experiments are implemented to support our theoretical findings. The theory results, joint the numerical experiments, provide insights and recommendations on which algorithm to use in different real application settings.
Using the generalised-optical differentiation wavefront sensor for laser guide star wavefront sensing

Laser guide stars (LGS) are used in many adaptive optics systems to extend sky coverage. The most common wavefront sensor used in combination with a LGS is a Shack-Hartmann wavefront sensor (SHWFS). The ShackHartmann has a major disadvantage for extended source wavefront sensing because it directly samples the image. In this proceeding we propose to use the generalized-Optical Differentation Wavefront Sensor (g-ODWFS) a wavefront sensor for wavefront sensing of LGS. The g-ODWFS uses only 4 pixels per sub-aperture, has little to no aliasing noise and therefore no spurious low-order errors and has no need for centroid gain calibrations. In this proceeding we show the results of simulations that compare the g-ODWFS with the SHWFS.
COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches

One of the most serious global health threat is COVID-19 pandemic. The emphasis on improving diagnosis and increasing the diagnostic capability helps stopping its spread significantly. Therefore, to assist the radiologist or other medical professional to detect and identify the COVID-19 cases in the shortest possible time, we propose a computer-aided detection (CADe) system that uses the computed tomography (CT) scan images. This proposed boosted deep learning network (CLNet) is based on the implementation of Deep Learning (DL) networks as a complementary to the Compressive Learning (CL). We utilize our inception feature extraction technique in the measurement domain using CL to represent the data features into a new space with less dimensionality before accessing the Convolutional Neural Network. All original features have been contributed equally in the new space using a sensing matrix. Experiments performed on different compressed methods show promising results for COVID-19 detection. In addition, our novel weighted method based on different sensing matrices that used to capture boosted features demonstrates an improvement in the performance of the proposed method.
Evolving Character-level Convolutional Neural Networks for Text Classification

Character-level convolutional neural networks (char-CNN) require no knowledge of the semantic or syntactic structure of the language they classify. This property simplifies its implementation but reduces its classification accuracy. Increasing the depth of char-CNN architectures does not result in breakthrough accuracy improvements. Research has not established which char-CNN architectures are optimal for text classification tasks. Manually designing and training char-CNNs is an iterative and time-consuming process that requires expert domain knowledge. Evolutionary deep learning (EDL) techniques, including surrogate-based versions, have demonstrated success in automatically searching for performant CNN architectures for image analysis tasks. Researchers have not applied EDL techniques to search the architecture space of char-CNNs for text classification tasks. This article demonstrates the first work in evolving char-CNN architectures using a novel EDL algorithm based on genetic programming, an indirect encoding and surrogate models, to search for performant char-CNN architectures automatically. The algorithm is evaluated on eight text classification datasets and benchmarked against five manually designed CNN architecture and one long short-term memory (LSTM) architecture. Experiment results indicate that the algorithm can evolve architectures that outperform the LSTM in terms of classification accuracy and five of the manually designed CNN architectures in terms of classification accuracy and parameter count.
Root of unity quantum cluster algebras and discriminants

We describe a connection between the subjects of cluster algebras and discriminants. For this, we define the notion of root of unity quantum cluster algebras and prove that they are polynomial identity algebras. Inside each such algebra we construct a (large) canonical central subalgebra, which can be viewed as a far reaching generalization of the central subalgebras of big quantum groups constructed by De Concini, Kac and Procesi and used in representation theory. Each such central subalgebra is proved to be isomorphic to the underlying classical cluster algebra of geometric type. When the root of unity quantum cluster algebra is free over its central subalgebra, we prove that the discriminant of the pair is a product of powers of the frozen variables times an integer. An extension of this result is also proved for the discriminants of all subalgebras generated by the cluster variables of nerves in the exchange graph. These results can be used for the effective computation of discriminants. As an application we prove an explicit formula for the discriminant of the integral form over ${{\mathbb Z}}[\varepsilon]$ of each quantum uniponet cells of De Concini, Kac and Procesi for arbitrary symmetrizable Kac-Moody algebras.
Pre-trained language models as knowledge bases for Automotive Complaint Analysis

Recently it has been shown that large pre-trained language models like BERT (Devlin et al., 2018) are able to store commonsense factual knowledge captured in its pre-training corpus (Petroni et al., 2019). In our work we further evaluate this ability with respect to an application from industry creating a set of probes specifically designed to reveal technical quality issues captured as described incidents out of unstructured customer feedback in the automotive industry. After probing the out-of-the-box versions of the pre-trained models with fill-in-the-mask tasks we dynamically provide it with more knowledge via continual pre-training on the Office of Defects Investigation (ODI) Complaints data set. In our experiments the models exhibit performance regarding queries on domain-specific topics compared to when queried on factual knowledge itself, as Petroni et al. (2019) have done. For most of the evaluated architectures the correct token is predicted with a $Precision@1$ ($P@1$) of above 60\%, while for $P@5$ and $P@10$ even values of well above 80\% and up to 90\% respectively are reached. These results show the potential of using language models as a knowledge base for structured analysis of customer feedback.
Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes from a single image. These encompass visual appearance and behavior, and also include the forecasting of road crossing, which is a main safety concern. For this, we introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way. Each field spatially locates pedestrian instances and aggregates attribute predictions over them. This formulation naturally leverages spatial context, making it well suited to low resolution scenarios such as autonomous driving. By increasing the number of attributes jointly learned, we highlight an issue related to the scales of gradients, which arises in MTL with numerous tasks. We solve it by normalizing the gradients coming from different objective functions when they join at the fork in the network architecture during the backward pass, referred to as fork-normalization. Experimental validation is performed on JAAD, a dataset providing numerous attributes for pedestrian analysis from autonomous vehicles, and shows competitive detection and attribute recognition results, as well as a more stable MTL training.
Interpretation of the Ultraviolet Spectrum of Trigonal Selenium

Based on an earlier pseudopotential calculation [1] the imaginary part of the dielectric function for Se is recalculated between 6 and 11 eV including lifetime broadening. Our results agree well with UV reflectivity measurements by Leiga [3], a detailed interpretation of which is given. In particular, the strong polarization dependence of several peaks is explained, and also the crossover of the ∥ and ⟂ spectra at 10 eV. The spectrum is shown to be largely determined by additional gaps between the subbands of both the valence band and the conduction band. Aus den Ergebnissen einer fruheren Pseudopotential-Rechnung [1] wurde der Imaginarteil der Dielektrizitatsfunktion fur Se zwischen 6 und 11 eV mit Berucksichtigung einer Lebensdauerverbreiterung neu berechnet. Unsere Ergebnisse stimmen gut mit UV-Reflektionsmessungen von Leiga [3] uberein, die im Detail diskutiert werden. Insbesondere werden die starke Polarisationsabhangigkeit verschiedener Peaks und das Uberkreuzen der Spektren fur ∥ - und ⟂-Polarisation bei 10 eV erklart. Das Spektrum wird wesentlich beeinflust von zusatzlichen Gaps zwischen den Teilbandern im Valenz- und Leitungsband.
Measurements of CH4 and N2O fluxes at the landscape scale using micrometeorological methods

Flux gradient, eddy covariance and relaxed eddy accumulation methods were applied to measure CH4 and N2O emissions from peatlands and arable land respectively. Measurements of N2O emission by eddy covariance using tunable diode laser spectroscopy provided fluxes ranging from 2 to 60 µ mol N2O m-2 h-1 with a mean value of 22 µ mol N2O m-2 h-1 from 320 h of continuous measurements. Fluxes of CH4 measured above peatland in Caithness (U.K.) during May and June 1993 by eddy covariance and relaxed eddy accumulation methods were in the range 70 to 120 µ mol CH4 m-2 h-1 with means of 14.7 µ mol CH4 m -2 h-1 and 22.7 µ mol CH4 m-2 h-1 respectively. Emissions of CH4 from peatland changed with water table depth and soil temperature; increasing from 25 |Amol CH4 m-2 h-1 at 5% pool area to 50 p.mol CH4 m-2 h-1 with 30% within the flux footprint occupied by pools. A temperature response of 4.9 (xmol CH4 m-2 h-1 °C-1 in the range 6-12 °C was also observed. The close similarity in average CH4 emission fluxes reported for wetlands in Caithness, Hudson Bay and Alaska in the range 11 to 40 jamol CH4 m-2 h-1 suggests that earlier estimates of CH4 emission from high latitude wetlands were too large or that the area of high latitudes contributing to CH4 emission has been seriously underestimated.
Synthesis and characterization of 15% efficient CIGSSe solar cells from nanoparticle inks

We report a total‐area power conversion efficiency of 15% for a copper indium gallium disulfoselenide (CIGSSe) solar cell fabricated from a copper indium gallium disulfide (CIGS) nanoparticle ink based process. Careful optimization of the fabrication process has resulted in a significant improvement in efficiency compared to our previously reported efficiency of 12%. This efficiency ranks among the highest reported in the literature for solution processed CIGSSe. Despite having an absorber thickness of approximately 700–800 nm, which is less than half the thickness of high efficiency devices grown by both coevaporation and solution processes in the literature, our devices show good short‐circuit current (32.1 mA/cm2). Surprisingly, the sintered film shows lateral composition fluctuations, which have not been reported for other high efficiency devices and may be responsible for the lower open circuit voltage (636 mV) observed here. This suggests an avenue for further improvement through optimization of the nanoparticle selenization process to better control composition in the sintered film. Copyright © 2015 John Wiley & Sons, Ltd.
FORMAS DE APLICAÇÃO DE SILICATO DE CÁLCIO E MAGNÉSIO NA CULTURA DO SORGO EM NEOSSOLO QUARTZARÊNICO DE CERRADO

Most of savannah soils are highly weathered and poor in nutrients, demanding chemical correction to become productive. For this purpose, limestone is usually applied, but silicate sources are also used because, besides correcting soil reaction, it supplies silicon to plants. This research had as objective to evaluate the effect of silicon levels and forms in soil and sorghum plants. Two forms of silicate application were evaluated: in the planting furrow and in the whole area. For the applications in the planting furrow, the levels of 100 kg ha-1, 200 kg ha-1, and 300 kg ha-1 of silicate were used, while for the applications in the whole area 1000 kg ha-1, 2000 kg ha-1, and 3000 kg ha-1 were used. Both application forms increased sorghum grain yield and Si concentration in soil and plants, differing, statistically, from control, but with no differences among them. The Si levels also promoted yield and Si concentration in soil and plants. The silicate addition promoted pH increase in the soil, confirming its acidity neutralization effect. KEY-WORDS: Silicon; silicate; sorghum.
Insights into the isoprene photochemical cascade

Isoprene emissions are the single largest source of non-methane reduced carbon to the atmosphere. Proper simulation of the effect of its photooxidation on regional air quality and secondary organic aerosol formation requires detailed multi-generation and multi-phase photochemical mechanisms. In this work, I first demonstrate that CF3O- chemical ionization mass spectrometry (CIMS) is a powerful method to probe isoprene photochemical oxidation. Under high NOx, isoprene nitrates and their photochemical products are directly observed and quantified. As isoprene nitrates are critical to the impact of isoprene photochemistry on ozone, these observations provide an important constraint on the effect of isoprene emissions on air quality in polluted and biogenic rich regions (e.g., Southeastern United States). The use of tandem CIMS in association with isotopically labelled hydroxy radicals is used to identify dihydroxyepoxides as major products of isoprene photooxidation in low NOx conditions. This class of compounds may provide an important precursor for secondary organic aerosols under acidic conditions. The chemical mechanism derived from these laboratory observations is then incorporated in a chemical transport model to assess the impact of isoprene photooxidation on atmospheric chemistry. I examine how the photooxidation of isoprene impacts the budget of formic and acetic acids, two ubiquitous trace gases whose sources are poorly constrained. Comparison with observations from satellite, ground and aircraft platforms, reveal a major missing source that may originate from the aging of secondary organic aerosols. Finally, the consequences of the multi-scale interplay between NOx and isoprene photochemistry on the tropical budget of ozone are examined using forward and adjoint sensitivity simulations.
Absolute optical rotation of CsLiB6O10

The optical rotation (OR) of CsLiB6O10 (CLBO, space group I\bar{4}2d) along the a axis has been determined by the HAUP method [Kobayashi & Uesu (1983). J Appl. Cryst. 16, 204–211] at a wavelength of 632.8 nm and by the TILTER method [Kaminsky & Glazer (1996). Ferroelectrics, 183, 133–141] at 532 nm and 650 nm. The respective rotatory powers were found to be 17 (1), 24 (2) and 19 (2)° mm−1. The absolute chirality has been established by comparing Bijvoet differences, {hkl} and {k\bar{h}l}, on the same crystal on which OR was measured. Atomic positions and electron density Fourier peak heights were exploited as input for semi-empirical calculations of refractive indices and OR, using WinOPTACT [Glazer (2002). J. Appl. Cryst. 35, 652] with only one free parameter fitted to match the average refractive index.
Bioremediation of Contaminated Soils Using the White Rot Fungus Phanerochaete Chrysosporium.

Abstract : Bioremediation is an enhancement of the natural biological oxidation processes that degrade organic matter. This is typically accomplished by introducing oxygen and nutrients to the soil that are necessary for the desired growth of naturally occurring bacteria. Fungal treatment is a specialized bioremediation process using fungi instead of bacteria to destroy contaminants. Recent interest in fungal treatment has grown due to the ability of 'white rot' fungi to reduce a wide variety of organic and chlorinated organic compounds for which bacterial remediation methods have been ineffective. White rot fungi are wood-decay fungi that digest lignin in wood by the secretion of enzymes, giving wood a bleached appearance. Lignin is a complex polymer that gives structural support to woody plants. The ability to secrete extracellular enzymes enables the reduction of complex organic compounds outside the body of the fungi. Constituents of the lignin degrading system include lignin peroxidase, manganese peroxidase, veratryl alcohol, and hydrogen peroxide. In addition to lignin these fungi have been shown to degrade insecticides, herbicides, pentachlorophenol, creosote, coal tars, and heavy fuels. Research has shown that the fungi possess the ability to mineralize those contaminants to carbon dioxide, water, and basic elements. Use of white rot fungi in the treatment of contaminated soil augments the natural system by adding sufficient quantities of the fungal species. This technology can be applied in ex situ and in situ cases. Effective application requires adaptation of laboratory technologies to field conditions by carefully controlling temperature, oxygen, nitrogen level, pH, and moisture content. The development of bioreactors may help overcome some of these stumbling blocks.
Optical Constants and Inelastic Electron-Scattering Data for 17 Elemental Metals

Two new sets of optical data, i.e., values for the real (e1) and imaginary (e2) parts of the complex dielectric constant as well as the energy loss function (ELF) (Im{−1∕e}), are presented for 16 elemental metals (Ti, V, Fe, Co, Ni, Cu, Zn, Mo, Pd, Ag, Ta, W, Pt, Au, Pb, and Bi) and 1 semimetal (Te) and are compared to available data in the literature. One data set is obtained from density functional theory (DFT) calculations and gives e from the infrared to the soft x-ray range of wavelengths. The other set of optical constants, derived from experimental reflection electron energy-loss spectroscopy (REELS) spectra, provides reliable optical data from the near-ultraviolet to the soft x-ray regime. The two data sets exhibit very good mutual consistency and also, overall, compare well with optical data found in the literature, most of which were determined several decades ago. However, exceptions to this rule are also found in some instances, some of them systematic, where the DFT and REELS mutually agree s.
Physicochemical assessment of fresh chilled dairy dessert supplemented with wheat germ

Summary Wheat germ is an available and economical source of protein, vitamins and antioxidants with an increasing application in food products. The main aim of this study was to investigate the effects of different levels (0.0, 2.5, 5.0, 7.5 and 10.0%) and particle sizes (120, 210 and 354 μm) of wheat germ on physicochemical properties of fresh chilled dairy dessert. With increasing wheat germ level, dry matter of the dessert increased while the pH decreased. Particle size had no significant effect on these parameters. Increasing the wheat germ content and particle size enhanced darkness, hardness, cohesiveness and gumminess of the desserts while decreasing springiness and water release of the samples. Wheat germ level had greater effects on different quality aspects of the desserts than its particle size. The most acceptable dessert was produced with no more than 5.0% wheat germ with either of the particle sizes used in this study.
Development and validation of high performance liquid chromatographic and derivative spectrophotometric methods for determination of gadodiamide in liposomal formulations

Gadodiamide is a complex of gadolinium (Gd), whose structure presents two carboxylate groups of diethylenetriaminepentaacetic acid (DTPA) substituted by two amide groups (BMA). The use of liposomes as carriers of gadodiamide (Gd–DTPA–BMA) has shown great potential for cancer therapy. In this study, two methods using high performance liquid chromatography (HPLC) without derivatization and derivative spectrophotometry (DSUV) were developed and validated for determination of Gd–DTPA–BMA in liposomes. No interference was observed by using the HPLC method. Also, the third-order derivative has eliminated the interference from the lipid formulations. The methods have shown to be selective, precise, accurate, and linear in the range of 0.1–0.5 μmol mL−1 for HPLC and 25–285 μmol mL−1 for DSUV. The limits of quantification were 3.50 × 10−2 μmol mL−1 for HPLC and 18.33 μmol mL−1 for DSUV, while the limits of detection were 2.02 × 10−2 μmol mL−1 and 12.42 μmol mL−1 for HPLC and DSUV, respectively. Both methods were employed to determine the concentrations of Gd–DTPA–BMA entrapment in three liposomal formulations with different characteristics. No significant difference was found between the results obtained by the developed methods. HPLC and DSUV methods presented advantages over existing methodologies, such as simplicity, speed, low cost, and applicability to the characterization of liposomes containing Gd–DTPA–BMA.
Antecedents of fathering.

Using data collected before the birth of couples 1st children a variety of antecedents of fathering were explored in a short-term longitudinal study of 30 middle and upper middle class men and their expectant wives. Interviews and rating scales tapped 6 areas: the marital relationship adjustment to expectancy sex typing financial and job concerns anticipated effects of parenthood and background information. 6 months postpartum fathers behavior with their infants was observed in 3 situations and their attitudes and home behaviors were assessed from self-reports. 3 fathering factors were defined: caretaking playfulness and satisfaction with fatherhood. Regression analyses were used to predict the 3 measures of fathering first from the scores of expectant men then from the scores of their expectant wives. The parenting scores of men and women were equally effective in predicting mens subsequent fathering. While there were similar predictors for mens caretaking and playfulness their wives scores yielded differential predictors for these aspects of fathering. The quality and nature of the marital relationship was significantly related to all aspects of fathering.
Antigenic characterization of influenza A virus matrix protein with monoclonal antibodies

Monoclonal antibodies were used to study antigenic variation in three distinct epitopes on the matrix protein of influenza A viruses. We found that two of these epitopes underwent antigenic variation, but in a very limited number of virus strains. A third epitope appeared to be an invariant type-specific determinant for influenza A viruses. Competitive antibody binding assays and Western blot analysis of proteolytically digested matrix protein indicated that at least two of the three epitopes are located in nonoverlapping domains on the matrix protein molecule.