Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Music Generation using Long Short-Term Memory Neural Networks

- Recent developments in neural networks and sequential models has produced state of the art results in eld signal processing and sequential data generation. For us, music is a pleasing sound, and everyone listens to music very frequently, but computers represent it as an sequential data and all sequential data generation model can be used to generate music. Our work focuses on generating music using LSTMs. LSTMs have good capability to remember previous data, they have memory unlike general RNNs. A good music must not be abruptly changing tones and themes, it must be consistent and for this purpose our model must remember what was generated previously. So, LSTMs are the perfect choice for this context-based data generation. We used Keras[2], an open-source software library that provides a Python interface for artificial neural networks, to create and train model. Most impressive results were produced by Multi-layered Char-RNN with LSTM Cell. The data is represented with ABC le format for easier access and better understanding. We preprocess the data to make it more robust and understandable for neural network and decode it back for human interpretation, the preprocessing algorithms and data representation is thoroughly discussed. The model used in this paper learn the sequences of polyphonic musical notes over a Stacked-Multilayered Char-RNN with LSTM cell. The model required and do have have the potential to recall past details of a musical sequence and its structure for better learning because of memory cells present in LSTM cells. The whole architecture with data ow and training and testing scores are explained.
Homogenization phenomena of surface components of fluorite and calcite

The flotation separation of fluorite and calcite poses one of the most difficult problems in the mineral processing industry. In particular, the surface homogenization of fluorite and calcite worsens the result of fluorite flotation. In this paper, time of flight secondary ion mass spectrometry and principal component analysis are used to study the surface homogenization of fluorite and calcite during grinding and in solution using X-ray photoelectron spectroscopy, infrared spectroscopy, and solution chemical calculations. The results show that the surface composition of calcite converts to fluorite after mixed grinding and that the surface composition of fluorite also converts to calcite in clarified calcite solution.
Anticancer and Antiviral Activity of the Pyridine-Biphenyl Glycoside System

Ahmed I. Khodair1*, Adel M. Attia1, Eman A. Gendy1, Yaseen A. M. M. Elshaier2, Mohammed A. El-Magd3 1Chemistry Department, Faculty of Science, kafrelshiekh University, El-Geish Street, kafrelshiekh, Post Box 33516, Egypt 2Department of Organic and Medicinal chemistry, Faculty of Pharmacy, University of Sadat City, Menoufiya, 32897, Egypt 3Anatomy Department, Faculty of Veterinary Medicine, Kafrelshiekh University, El-Geish Street, kafrelshiekh, Post Box 33516, Egypt
UTILIZATION OF MANGO WASTE FOR BIOETHANOL PRODUCTION USING ASPERGILLUS NIGER AND SACCHAROMYCETES CEREVISAE: A PILOT-SCALE STUDY

The modern life is highly dependent on energy including for fuel, electricity, and industry. Fossil fuels are the main of energy source is used. However, negative environmental effects are needed to be considered. Biomass energy using waste or plant matter produces a lower level of greenhouse gas emissions than fossil fuels. Through this study, we attempt to use mango waste to produce bioethanol. This source is cellulosic material which is abundant in traditional markets, especially when the peak of harvest season comes. We treated the mango waste in pilot-scale experiment with three different ways using alcohol percentage as an indicator of the alcohol production. Monoculture fermentation of Saccharomycetes cerevisiae produced the highest bio-ethanol percentage (83% v/v). A slightly higher in alcohol percentage (79% v/v) was achieved by coculture fermentation of Aspergillus niger and S. cerevisiae. The lowest result was obtained in fermentation of A. niger was followed by the addition of S. cerevisiae (70% v/v). These results indicate that monoculture fermentation of mango waste gives the best results. This research may be useful in production of bio-ethanol for industrial scale.
The Effect of Investment, Free Cash Flow, Earnings Management, and Interest Coverage Ratio on Financial Distress

The purpose of this research is to test investments, free cash flow, earnings management, and interest coverage ratio are affecting the risk of financial distress in healthy enterprises. Healthy companies can be seen from how large the value of working capital, retained earnings, income before tax, market value and sales implemented in the measurement of the financial difficulties model with the Altman Z-score method. Collection of data by purposive sampling and number of samples as many as 33 companies in the category of healthy companies. The results show that free cash flows and interest coverage ratio significant effect on the financial difficulties of healthy companies whereas investment and earnings management had no significant effect on the financial difficulties of healthy companies
A practical approach for uncertainty quantification of high‐frequency soil respiration using Forced Diffusion chambers

This paper examines the sources of uncertainty for the Forced Diffusion (FD) chamber soil respiration (Rs) measurement technique and demonstrates a protocol for uncertainty quantification that could be appropriate with any soil flux technique. Here we sought to quantify and compare the three primary sources of uncertainty in Rs: (1) instrumentation error; (2) scaling error, which stems from the spatial variability of Rs; and (3) random error, which arises from stochastic or unpredictable variation in environmental drivers and was quantified from repeated observations under a narrow temperature, moisture, and time range. In laboratory studies, we found that FD instrumentation error remained constant as Rs increased. In field studies from five North American ecosystems, we found that as Rs increased from winter to peak growing season, random error increased linearly with average flux by about 40% of average Rs. Random error not only scales with soil flux but scales in a consistent way (same slope) across ecosystems. Scaling error, measured at one site, similarly increased linearly with average Rs, by about 50% of average Rs. Our findings are consistent with previous findings for both soil fluxes and eddy covariance fluxes across other northern temperate ecosystems that showed random error scales linearly with flux magnitude with a slope of ~0.2. Although the mechanistic basis for this scaling of random error is unknown, it is suggestive of a broadly applicable rule for predicting flux random error. Also consistent with previous studies, we found the random error of FD follows a Laplace (double‐exponential) rather than a normal (Gaussian) distribution.
Nitrogen-doped truncated carbon nanotubes inserted into nitrogen-doped graphene nanosheets with a sandwich structure: a highly efficient metal-free catalyst for the HER

A new promising metal-free catalyst (N-TCNT@NGS), with a sandwich structure of 1-dimensional nitrogen-doped truncated carbon nanotubes inserted between 2-dimensional nitrogen-doped graphene nanosheets, was synthesized by a facile and green hydrothermal method. Due to the unique structure, multipath charge and mass transport, N-TCNT@NGS exhibits incredible electrocatalytic ability and robust durability for the hydrogen evolution reaction (HER) (Tafel slope: 50 mV dec−1, onset overpotential: −16 mV, operating overpotential at 10 mA cm−2: −62 mV).
Influence of metal and non-metal ions doping on the structural and photocatalytic properties of titania films

The metal (Zn 2+ or Zr 4+ ) and non-metal (nitrogen) doped titania films are synthesized by sol-gel method. High absorption in the visible region is observed and the band gap values are estimated for indirect electron transition. The decrease in the intensity and broadening of the anatase peaks in XRD spectra for TiO 2 /N films with increasing urea contents are observed. Anatase formation in the films with double doping agents is proven by Raman measurements. XPS clarified the chemical state of the elements, their surface ratios and the incorporation of nitrogen in the matrix of the synthesized films. XPS results show that the substitutional nitrogen is only registered for TiO 2 /Zr 4+ /N. Photocatalytic properties are estimated in the process of tetracycline destruction reaction. The increase of the film activity by factor three is noted for TiO 2 /N. Modification TiO 2 /N by metal ions has no influence on the photocatalytic properties under UV and visible light.
Can Regular Physical Activity Prevent Obesity ? Part 2 : Empirical cross-sectional evidence of efficacy

Objective: The objective of this narrative review is to examine cross-sectional empirical data suggesting the efficacy of adequate daily physical activity in the prevention of obesity. Methods: Information obtained from Ovid/Medline and Google Scholar through to March 2019 was supplemented by a search of the author's extensive personal files. Results: Average skin-fold readings are often as low as 5 mm, in male elite longdistance competitors who spend many hours per week in training, and values for female endurance competitors are also often only a half of those found in the general female population. Physical education (PE) students (particularly men) are somewhat thinner than their peers in other faculties, but this advantage is attenuated in averaged data because a proportion of PE students have a lesser commitment to an active lifestyle, and also some students in other faculties engage in physically demanding inter-collegiate sports. Early occupational studies showed low body fat levels in physically active occupations, and a similar difference can still be observed in elite military groups where high daily energy expenditures are required. Amish and Old-order Mennonite families who eschew modern mechanical devices have very high levels of daily physical activity, and this is reflected in their correspondingly low levels of body fat. Similar findings have been reported for Inuit who still follow a hunter-gatherer lifestyle. Studies validating questionnaires and accelerometers in the general population have show correlations of up to -0.50 between scores on such instruments and estimates of body fat content. Finally, cross-sectional comparisons within circumpolar communities have demonstrated higher levels of aerobic fitness in those populations with the least acculturation to a sedentary "white" lifestyle, and this sub-group of circumpolar residents also has a low body fat content. Conclusions: Conclusions about the impact of vigorous daily physical activity upon body fat content are limited by the possible self-selection of a physically active lifestyle, with the adoption of other favorable health behaviours by active individuals. Nevertheless, all of numerous population comparisons show lower levels of body fat in those groups who maintain an adequate level of habitual physical activity. In some cases, the levels of physical activity adopted have been very high, but there are also examples of populations where a favourable body composition has been associated with the regular performance of volumes of physical activity that are appropriate for average middle-aged adults. Health & Fitness Journal of Canada 2019;12(2):64-96. https://doi.org/10.14288/hfjc.v12i2. 273
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

We present PyTorch Geometric Temporal, a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of the library is to make temporal geometric deep learning available for researchers and machine learning practitioners in a unified easy-to-use framework. PyTorch Geometric Temporal was created with foundations on existing libraries in the PyTorch eco-system, streamlined neural network layer definitions, temporal snapshot generators for batching, and integrated benchmark datasets. These features are illustrated with a tutorial-like case study. Experiments demonstrate the predictive performance of the models implemented in the library on real-world problems such as epidemiological forecasting, ride-hail demand prediction, and web traffic management. Our sensitivity analysis of runtime shows that the framework can potentially operate on web-scale datasets with rich temporal features and spatial structure.
Comparison of Grammatical Error Correction Using Back-Translation Models

Grammatical error correction (GEC) suffers from a lack of sufficient parallel data. Studies on GEC have proposed several methods to generate pseudo data, which comprise pairs of grammatical and artificially produced ungrammatical sentences. Currently, a mainstream approach to generate pseudo data is back-translation (BT). Most previous studies using BT have employed the same architecture for both the GEC and BT models. However, GEC models have different correction tendencies depending on the architecture of their models. Thus, in this study, we compare the correction tendencies of GEC models trained on pseudo data generated by three BT models with different architectures, namely, Transformer, CNN, and LSTM. The results confirm that the correction tendencies for each error type are different for every BT model. In addition, we investigate the correction tendencies when using a combination of pseudo data generated by different BT models. As a result, we find that the combination of different BT models improves or interpolates the performance of each error type compared with using a single BT model with different seeds.
SALT3: An Improved Type Ia Supernova Model for Measuring Cosmic Distances

A spectral-energy distribution (SED) model for Type Ia supernovae (SNe Ia) is a critical tool for measuring precise and accurate distances across a large redshift range and constraining cosmological parameters. We present an improved model framework, SALT3, which has several advantages over current models—including the leading SALT2 model (SALT2.4). While SALT3 has a similar philosophy, it differs from SALT2 by having improved estimation of uncertainties, better separation of color and light-curve stretch, and a publicly available training code. We present the application of our training method on a cross-calibrated compilation of 1083 SNe with 1207 spectra. Our compilation is 2.5× larger than the SALT2 training sample and has greatly reduced calibration uncertainties. The resulting trained SALT3.K21 model has an extended wavelength range 2000–11,000 Å (1800 Å redder) and reduced uncertainties compared to SALT2, enabling accurate use of low-z I and iz photometric bands. Including these previously discarded bands, SALT3.K21 reduces the Hubble scatter of the low-z Foundation and CfA3 samples by 15% and 10%, respectively. To check for potential systematic uncertainties, we compare distances of low (0.01 < z < 0.2) and high (0.4 < z < 0.6) redshift SNe in the training compilation, finding an insignificant 3 ± 14 mmag shift between SALT2.4 and SALT3.K21. While the SALT3.K21 model was trained on optical data, our method can be used to build a model for rest-frame NIR samples from the Roman Space Telescope. Our open-source training code, public training data, model, and documentation are available at https://saltshaker.readthedocs. io/en/latest/, and the model is integrated into the sncosmo and SNANA software packages.
A global DC branch model incorporating power system flexibility

In this paper we propose a generalized branch model to be used in DC optimal power flow (DCOPF) applications. Besides AC lines and transformers, the formulation allows for representing variable susceptance branches, phase shifting transformers, HVDC lines, zero impedance lines and open branches. The possibility to model branches with concurrently variable susceptance and controllable phase shift angles is also provided. The model is suited for use in DCOPF formulations aimed at the optimization of remedial actions so as to exploit power system flexibility; applications to small-, medium- and large-scale systems are presented to this purpose.
Probing Across Time: What Does RoBERTa Know and When?

Models of language trained on very large corpora have been demonstrated useful for NLP. As fixed artifacts, they have become the object of intense study, with many researchers"probing"the extent to which linguistic abstractions, factual and commonsense knowledge, and reasoning abilities they acquire and readily demonstrate. Building on this line of work, we consider a new question: for types of knowledge a language model learns, when during (pre)training are they acquired? We plot probing performance across iterations, using RoBERTa as a case study. Among our findings: linguistic knowledge is acquired fast, stably, and robustly across domains. Facts and commonsense are slower and more domain-sensitive. Reasoning abilities are, in general, not stably acquired. As new datasets, pretraining protocols, and probes emerge, we believe that probing-across-time analyses can help researchers understand the complex, intermingled learning that these models undergo and guide us toward more efficient approaches that accomplish necessary learning faster.
Word2rate: training and evaluating multiple word embeddings as statistical transitions

Using pretrained word embeddings has been shown to be a very effective way in improving the performance of natural language processing tasks. In fact almost any natural language tasks that can be thought of has been improved by these pretrained embeddings. These tasks range from sentiment analysis, translation, sequence prediction amongst many others. One of the most successful word embeddings is the Word2vec CBOW model proposed by Mikolov trained by the negative sampling technique. Mai et al. modifies this objective to train CMOW embeddings that are sensitive to word order. We used a modified version of the negative sampling objective for our context words, modelling the context embeddings as a Taylor series of rate matrices. We show that different modes of the Taylor series produce different types of embeddings. We compare these embeddings to their similar counterparts like CBOW and CMOW and show that they achieve comparable performance. We also introduce a novel left-right context split objective that improves performance for tasks sensitive to word order. Our Word2rate model is grounded in a statistical foundation using rate matrices while being competitive in variety of language tasks.
Learning Implicit 3D Representations of Dressed Humans from Sparse Views

Recently, data-driven single-view reconstruction meth-ods have shown great progress in modeling 3D dressed humans. However, such methods suffer heavily from depth ambiguities and occlusions inherent to single view inputs. In this paper, we address such issues by lifting the single-view input with additional views and investigate the best strategy to suitably exploit information from multiple views. We propose an end-to-end approach that learns an implicit 3D representation of dressed humans from sparse camera views. Speciﬁcally, we introduce two key components: ﬁrst an attention-based fusion layer that learns to aggregate visual information from several viewpoints; second a mechanism that encodes local 3D patterns under the multi-view context. In the experiments, we show the proposed approach outperforms the state of the art on standard data both quantitatively and qualitatively. Additionally, we apply our method on real data acquired with a multi-camera platform and demonstrate our approach can obtain results comparable to multi-view stereo with dramatically less views.
Self-supervised Video Retrieval Transformer Network

Content-based video retrieval aims to find videos from a large video database that are similar to or even near-duplicate of a given query video. Video representation and similarity search algorithms are crucial to any video retrieval system. To derive effective video representation, most video retrieval systems require a large amount of manually annotated data for training, making it costly inefficient. In addition, most retrieval systems are based on frame-level features for video similarity searching, making it expensive both storage wise and search wise. We propose a novel video retrieval system, termed SVRTN, that effectively addresses the above shortcomings. It first applies self-supervised training to effectively learn video representation from unlabeled data to avoid the expensive cost of manual annotation. Then, it exploits transformer structure to aggregate frame-level features into clip-level to reduce both storage space and search complexity. It can learn the complementary and discriminative information from the interactions among clip frames, as well as acquire the frame permutation and missing invariant ability to support more flexible retrieval manners. Comprehensive experiments on two challenging video retrieval datasets, namely FIVR-200K and SVD, verify the effectiveness of our proposed SVRTN method, which achieves the best performance of video retrieval on accuracy and efficiency.
Ionization potential of extended systems: Validity of Koopmans’ theorem in the Hartree-Fock theory and invalidity of the IP-theorem in density functional theory

In the Hartree-Fock (HF) theory, the Koopmans’ theorem stipulates that the first ionization potential (IP) is equal to minus the energy of the highest occupied molecular orbital. A similar result, known as the IP-theorem, holds in the Kohn-Sham (KS) density functional theory (DFT). We argue that proofs of these theorems rely critically on the finiteness of the number of particles comprising the considered systems. We revisit the problem with focus on extended systems and we confirm the Koopmans’ theorem validity. On the other hand, we establish the IP-theorem’s violation within the context of KS DFT of extended systems. These findings have an immediate bearing on the role of the KS valence band at solid surfaces, mesoscopic, and other extended systems with respect to the work-function problem. Based on our results, we introduce a concept of the ionization potential band structure as an observable alternative to its unphysical KS counterpart. A method of practical determination of IP band structure in terms of DFT quantities is provided.
Coverage Probability and Energy Efficiency of Reconfigurable Intelligent Surface-Assisted mmWave Networks

Reconfigurable intelligent surface (RIS) emerges as a promising technology for the next generation networks. In this paper, we utilize the tools from stochastic geometry to study the performance of a RIS-assisted millimeter wave (mmWave) cellular network. Specifically, the locations of the base stations (BSs) and the midpoints of the blockages are modeled as two independent Poisson point processes (PPP) where the blockages are modeled by line boolean model and a fraction of the blockages are coated with RISs. The distinguish characteristics of mmWave communications, i.e., the directional beamforming and different path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS), are incorporated into the analysis. We derive the expressions of the coverage probability and the area spectral efficiency. The coverage probability under the special case where the blockage parameter is sufficiently small is also derived. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by the large-scale deployment of RISs. In addition, the tradeoff between the BS and RIS densities is investigated and the results show that the RISs are excellent supplementary for the traditional networks to improve the coverage probability with limited power consumption.