Miracle-dz/UniFAR-FTUs huggingface dataset:
Input-Output Admissibility Analysis of Continuous Descriptor System With Time-Varying Delay

This letter deals with the input-output admissibility analysis of the continuous descriptor system with a time-varying delay. A two-term approximation transformation-model has been used to convert the original system into two interconnected subsystems. Based on the scaled small gain theorem and a new Lyapunov-Krasovskii functional, a delay-dependent sufficient condition has been presented to ensure that the time-varying delay descriptor system is input-output stable, regular, and impulse free. The derived conditions are given in a set of linear matrix inequalities. Finally, an example has been used to test the merit and validity of the proposed method.
Base Station Switching and Sleep Mode Optimization With LSTM-Based User Prediction

The base station (BS) switching technique has recently attracted considerable attention for reducing power consumption in wireless networks. In this paper, we propose a novel BS switching and sleep mode optimization method to minimize the power consumption, while ensuring that the arriving user traffic is sufficiently covered. First, the user traffic in multiple time slots was predicted using the long-short term memory (LSTM) prediction model. Subsequently, we solved the Lyapunov optimization problem to obtain the optimal BS switching solution from the trade-off relationship between the reduced power consumption by BS switching and the user traffic handled in time series. Finally, we selected the sleep mode for the switched result by calculating the wake-up time and the power consumption ratio of each sleep mode. Simulation results confirm that the proposed algorithm successfully reduces the total power consumption by approximately 15% while preventing the user data queue from diverging in multiple time slots.
A critical assessment of elemental mercury air/water exchange parameters

Abstract Although evasion of elemental mercury from aquatic systems can significantly deplete net mercury accumulation resulting from atmospheric deposition, the current ability to model elemental mercury air/water exchange is limited by uncertainties in our understanding of all gaseous air/water exchange phenomena in addition to limitations unique to this element. A critical review of the technical literature is performed; a new estimate for the diffusivity of elemental mercury in water is obtained; a Setschenow coefficient for elemental mercury is published; and a normalized version of the Cole and Caraco (1998) low to intermediate windspeed air/water gaseous exchange model is developed. Findings from the study include: (1) some previously published estimates of elemental mercury diffusivity in water are likely too high by as much as a factor of four; (2) some published Schmidt number estimates are likely too low (by a similar factor); (3) some previously published elemental mercury air/water exchange estimates are likely too high; (4) overall water side environmental mass transfer coefficients for elemental mercury likely lie within the range 0.006 to 0.45 m/h; (5) there are both experimental and theoretical justifications for using a non-isothermal Henry’s Law Constant in environmental systems over typical diel time periods; and (6) more complex non-isothermal Henry’s Law Constants may be required to model air/water gaseous exchange in hydrothermal and/or industrial systems.
DDLBench: Towards a Scalable Benchmarking Infrastructure for Distributed Deep Learning

Due to its many applications across various fields of research, engineering, and daily life, deep learning has seen a surge in popularity. Therefore, larger and more expressive models have been proposed, with examples like Turing-NLG using as many as 17 billion parameters. Training these very large models becomes increasingly difficult due to the high computational costs and large memory footprint. Therefore, several approaches for distributed training based on data parallelism (e.g., Horovod) and model/pipeline parallelism (e.g., GPipe, PipeDream) have emerged. In this work, we focus on an in-depth comparison of three different parallelism models that address these needs: data, model and pipeline parallelism. To this end, we provide an analytical comparison of the three, both in terms of computation time and memory usage, and introduce DDLBench, a comprehensive (open-source1, ready-to-use) benchmark suite to quantify these differences in practice. Through in-depth performance analysis and experimentation with various models, datasets, distribution models and hardware systems, we demonstrate that DDLBench can accurately quantify the capability of a given system to perform distributed deep learning (DDL). By comparing our analytical models with the benchmarking results, we show how the performance of real-life implementations diverges from these analytical models, thus requiring benchmarking to capture the in-depth complexity of the frameworks themselves.1https://github.com/sara-nl/DDLBench
Hardware-based Fast Real-time Image Classification with Stochastic Computing

Stochastic computing (SC) with its small area and power footprint is a prime candidate for realizing neural networks (NNs) in heavily resource-restricted devices, such as near-sensor computing systems. Complete SCNNs encompassing all network layers have mostly been simulated in software, or synthesized without consideration of potential area limitations. In this work, we present a full FPGA implementation of a complete SCNN for image classification under tight resource constraints. All computational operations of the NN are performed on FPGA primitives. Furthermore, no DSPs and no onboard CPU is required for the computation. Our system operates at 60MHz and can classify an input image within approximately 6ms. Moreover, our basic SC and memory components can be flexibly combined to cover a large variety of NN structures.
OPTIMUM CONDITIONS OF PHYTOREMEDIATION FOR PETROLEUM- CONTAMINATED SOIL USING AGED REFUSE

In this study, the optimum conditions of phytoremediation for oil contaminated soil using aged refuse have demonstrated. It has used four aged refuse ratios (10%, 25%, 50%, 75% wt.) and one control, four plant species (Alfalfa, Ryegrass, Tall Fescue, Suaeda Heteroptera) one control, three moisture content ratios were set as 50%, 60%, 70% of water holding capacity (WHC), all these were selected to enhance remediation of 5% wt. crude oil contaminated soil in the chamber room and keeping the temperature at 25-28°C. First condition has selected was ages refuse ratio. The best one was 50% according results show, it recorded 92,32% of total oil degradation under conditions (Alfalfa plant, 70%WHC, 5% crude oil) after 133 days. The best plant species results showed Alfalfa plant. It's were 91.07%, under conditions (50% aged refuse, 70%WHC, 5% crude oil) after 133 days. Suaeda Heteroptera treatments failed because Suaeda Heteroptera could not grow under these conditions. The best moisture has selected was 70% among (50%,60%,70%). the total oil degradation rate 89.43%, 91.07% ,92.55% respectively under conditions (Alfalfa plant, 50%aged refuse,5% crude oil) after 133 days. In general, the optimum conditions and plants that most appropriate to achieve a success phytoremediation in this study were using Alfalfa, the best-aged refuse percentage is 50% and best moisture 70%. Keywards: Phytoremediation; Oil-contaminated soil; Plant species; Aged refuse.
Electrically Programmable Bistable Capacitor for High‐Frequency Applications Based on Charge Storage at the (Ba,Sr)TiO3/Al2O3 Interface

Hysteresis is induced in paraelectric (Ba,Sr)TiO3 (BST) thin‐film capacitors by inserting an Al2O3 barrier layer of a few nanometers in thickness between the BST layer and the electrode. The observed hysteresis is explained by ambipolar charge carrier injection through the Al2O3 layer and charge storage at the BST/Al2O3 interface. The magnitude of the hysteresis can be directly adjusted by manipulating the thickness ratio between BST and Al2O3. Taking into account the low loss of (Ba,Sr)TiO3 capacitors, the observed switching and retention characteristics are suitable for application as non‐volatile programmable high‐frequency devices, e.g., in radio‐frequency identification.
Applying Graph Theory to Arterial Vascular Tree of the Kidney

The renal vascular development occurs through vasculogenesis and/or angiogenesis. Particularly, there are two types of vascular angiogenesis: sprouting and splitting. We show the graphs can generate binary tree structures by incorporating the physiological laws of the arterial branching of kidney. The graph prescribes a topology where each edge has the dynamics of the physiological phenomena of vascularization.
Lp(a) (Lipoprotein[a]), an Exemplar for Precision Medicine

Lp(a) (lipoprotein[a]) is a strong and independent risk factor for coronary artery disease, stroke, and aortic stenosis.1,2 In contrast to most biochemical risk factors, Lp(a) concentrations are almost entirely genetically determined, limiting strategies to control Lp(a) levels. Genetic studies have indicated a causal role for Lp(a) in coronary artery disease and other atherosclerotic diseases, intensifying interest in Lp(a) as a potential target.3,4 Now, 10 years on, the effects of newly developed powerful and specific Lp(a)-lowering agents are being studied. For example, Lp(a)HORIZON is an ongoing randomized controlled trial assessing the clinical efficacy and safety of a hepatocyte-directed antisense oligonucleotide that can lower Lp(a) levels by ≈80%. 5,6 In the present issue, 2 studies7,8 report analyses of UK Biobank data9 exploring the epidemiological relationships between Lp(a) and atherosclerotic cardiovascular disease (ASCVD) outcomes and further elucidating our understanding of the genetic determinants of Lp(a).
Sensory, Symptomatic, Inflammatory, and Ocular Responses to and the Metabolism of Methyl Tertiary Butyl Ether in a Controlled Human Exposure Experiment

AbstractThe Clean Air Act of 1990 mandates that those areas of the country that do not attain the health-based National Ambient Air Quality Standard for CO must add oxygenates (2.7% by weight) to auto fuels (oxyfuels). In the fall of 1992, the addition of methyl tertiary butyl ether (MTBE) to automotive fuels coincided with complaints of illness in some parts of the country. In Alaska, the reported symptoms included headache, nasal, throat, or ocular irritation, nausea or vomiting, dizziness, and sensations of “spaciness” or disorientation. We conducted a chamber exposure experiment to determine if exposure to pure MTBE would elicit similar responses to those reported to be related to MTBE exposure. Nineteen male and 18 female subjects were exposed in a repeated-measures design to clean air (CA) and 1.39 ppm (5.0 mg/m3) MTBE for 1 h. This level was selected to approximate a typical exposure experienced during refueling. Exposures were separated by at least I wk. Symptom questionnaires were completed befor.
Productivity of Wheat As Affected by Chelated and Nano Zinc Foliar Application and Nitrogen Fertilizer Levels تأثر إنتاجیة القمح بالرش الورقی بالزنک المخلبی والنانو ومستویات السماد النیتروجینی

The most important feature of nanotechnology in the development process of agriculture is Nano fertilizers at the 1-100 nm nanometer scale. Where, the use of nano fertilizers instead of common fertilizers provides plants with nutrients progressively and in a regulated manner, the quality of application increases, contamination decreases and the risk of chemical fertilization decreases. Therefore, Two field studies have been carried out in a private field in Derwatine Village, Nabarouh Center, Governorate of Dakahlia, Egypt, during 2016/2017 and 2017/2018 Seasons to research the impact of foliar application of chelated and nano zinc (without, spraying with chelated zinc at the rate of 2 g/liter and nano zinc at the rate of 200, 400 and 600 mg/L), nitrogen levels (100, 80 and 60 % of the recommended dose i.e. 80, 64 and 48 kg N/fed, respectively) as well as their interaction on productivity of wheat Gemmiza 12 cultivar. The experiments were conducted with three replicates in a strip-plot configuration. Five zinc foliar application treatments were given to the vertical-plots. The horizontalplots were allocated three nitrogen levels.
Morphological Word Families and Learning to Spell by Josée Taylor A thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfillment of the requirements for the degree of Master of Arts

Research has demonstrated the facilitative effect of morphological word families on the development of orthographic representations and children’s spelling accuracy. Specifically, related words that pronounce silent-letter endings increase the accuracy of children’s attempts at spelling these particularly challenging French words. The current study intended to replicate and extend these findings in large-scale analyses assessing spelling accuracy by word for a large corpus of French words spelled by 40 children in Grades 1 through 5. Results supported the word family hypothesis in demonstrating the facilitative effect of morphological word families for all children. The derivative diversity hypothesis extended research on adult orthographic representations in finding that the diversity of derivatives was the stronger predictor of spelling across grades. Finally, the feminine form hypothesis was generally supported in that the feminine inflection was related to spelling only in Grade 1, however, this was only after controlling for the diversity of derivatives.
MACHINE LEARNING IN CYBER-PHYSICAL SYSTEMS AND MANUFACTURING SINGULARITY – IT DOES NOT MEAN TOTAL AUTOMATION, HUMAN IS STILL IN THE CENTRE:
Part I – MANUFACTURING SINGULARITY AND AN INTELLIGENT MACHINE ARCHITECTURE

architecture towards the manufacturing singularity is presented, arguing that the intelligent machine will always be human dependent, and that, concerning the manufacturing, the human will remain in the centre. The argumentation is based on the phenomenon related to computational machine learning paradigm, as intrinsic feature of the AI/ML, through the inductive inference based machine learning algorithms, whose effectiveness is conditioned by the human participation. In the second part, an architecture of the Cyber-Physical (Production) Systems with multiple learning loops is presented, together with a set of experiments demonstrating the indispensable human role. Also, a discussion of the problem from the manufacturing community point of view on future of human role in Industry 4.0 as the environment for advanced AI/ML applications is included in this part.
Complete Diallel Analysis for Nematodes Resistance in some Tomato Genotypes التحلیل الدائری الکامل للمقاومة للنیماتودا فی بعض التراکیب الوراثیة لنبات الطماطم

In this study a complete diallel analysis was done under the condition of nematode infection, using four different parental genotypes of Tomato. Mean squares of the studied genotypes were significant for all studied parameters, which proves the presence of a considerable amount of genetic variation among studied genotypes. Also, GCA and SCA mean squares were highly significant for all studied traits. This means that both GCA and SCA were important in the inheritance of these traits. Likewise, reciprocal effect mean squares were found significant for all the studied traits indicating that these traits are controlled by extra-nuclear factors in addition to the nuclear factors. Also, data showed that significant SCA effects in demand direction for most crosses in some studied traits. The estimations of (σgi) and (σsi) of the parental genotypes for all studied parameters indicated that the genotypes used in this study are of great importance to improve most of the studied traits. The estimation of genetic parameters and heritability for all studied traits proved the predominance of non-additive gene action in the inheritance of these traits. Results revealed that the cross (Cherry x Castle Rock) is a superior hybrid, and it could be selected in the breeding programs to resist nematode. Therefore, using this combination as started material for selection breeding program to produce resistant root-knot nematodes genotypes.
Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16

Article history: Received: 25 August, 2020 Accepted: 21 October, 2020 Online: 10 November, 2020 Facial emotion recognition is one among many popular and challenging tasks in the field of computer vision. Numerous researches have been conducted on this task and each proposed either standaloneor ensemble-based processing technique. While many researches strive for better accuracy, this research also attempts to increase the processing efficiency of computer correctly classifying human emotions based on human face by utilizing a single standalone-based neural network. This research proposes the use of standalone-based modified Convolutional Neural Network (CNN) based on Visual Geometry Group – 16 (VGG-16) classification model which was pretrained on ImageNet dataset and fine-tuned for emotion classification. The classification is performed on the publicly available FER-2013 dataset of over 35,000 face images with in-the-wild setting for 7 distinct emotions with the provided 80% training, 10% validation, and 10% testing data distributions. The proposed approach outperforms most standalone-based model results with 69.40% accuracy.
RealFormer: Transformer Likes Residual Attention

Transformer is the backbone of modern NLP models. In this paper, we propose RealFormer, a simple and generic technique to create Residual Attention Layer Transformer networks that significantly outperform the canonical Transformer and its variants (BERT, ETC, etc.) on a wide spectrum of tasks including Masked Language Modeling, GLUE, SQuAD, Neural Machine Translation, WikiHop, HotpotQA, Natural Questions, and OpenKP. We also observe empirically that RealFormer stabilizes training and leads to models with sparser attention. Source code and pre-trained checkpoints for RealFormer can be found at https://github.com/google-research/google-research/tree/master/realformer.
Single branch of chiral Majorana modes from doubly degenerate Fermi surfaces

Majorana fermions are often proposed to be realized by singling out one Fermi surface without spin degeneracy via spin-orbit coupling, and then imposing boundaries or defects. In this work, we take a different route starting with two degenerate Fermi surfaces without spin-orbit coupling, and show that by the method of “kink on boundary,” the dispersive chiral Majorana fermions can be realized in superconducting systems with p ± is pairings. The surfaces of these systems develop spontaneous magnetizations whose directions are determined by the boundary orientations and the phase difference between the pand s-component gap functions. Along the magnetic domain walls on the surface, there exist chiral Majorana fermions propagating unidirectionally, which can be conveniently dragged and controlled by external magnetic fields. Furthermore, the surface magnetization is shown to be a magnetoelectric effect based on a Ginzburg-Landau free-energy analysis. We also discuss how to use the proximity effects to realize chiral Majorana fermions by performing the kink on boundary method.
Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games

Off-policy evaluation (OPE) is the problem of evaluating new policies using historical data obtained from a different policy. In the recent OPE context, most studies have focused on single-player cases, and not on multi-player cases. In this study, we propose OPE estimators constructed by the doubly robust and double reinforcement learning estimators in two-player zero-sum Markov games. The proposed estimators project exploitability that is often used as a metric for determining how close a policy profile (i.e., a tuple of policies) is to a Nash equilibrium in two-player zero-sum games. We prove the exploitability estimation error bounds for the proposed estimators. We then propose the methods to find the best candidate policy profile by selecting the policy profile that minimizes the estimated exploitability from a given policy profile class. We prove the regret bounds of the policy profiles selected by our methods. Finally, we demonstrate the effectiveness and performance of the proposed estimators through experiments.
The Empirical Relationship Between Economic Growth, ICT, Financial Globalization Uncertainty and Financial Development: Evidence from Selected Leading African Economies

The paper concentrated on six selected major African countries from 1990 to 2011 and describes the effect of ICT-driven institutional efficiency and knowledge exchange systems on economic growth, financial globalization, and economic growth on financial development. That is because the financial markets of both of those nations are underdeveloped. The article applied Larsson's co-integration, the dynamic common correlated effect, and PMG estimates. The findings indicate a positive long-term association for all parameters, which involves the interaction term. Nevertheless, it is worthy of note that financial globalization is inevitable, as its repercussions can only be ignored at the cost of growth in the country. Perhaps it guarantees that ICT-driven knowledge exchange and institutional quality frameworks contribute to the speeding up of the financial development of the six sampled leading African countries. Increasing knowledge sharing powered by ICT across countries could solve one of the major obstacles of African business activities: which is; lack of financial ease of access. Sampled nations can also optimize ICT-driven knowledge-sharing to decrease asymmetric information. The article provides a full set of recommendations for the sampled countries.
Risk Monotonicity in Statistical Learning

Acquisition of data is a difficult task in many applications of machine learning, and it is only natural that one hopes and expects the population risk to decrease (better performance) monotonically with increasing data points. It turns out, somewhat surprisingly, that this is not the case even for the most standard algorithms that minimize the empirical risk. Non-monotonic behavior of the risk and instability in training have manifested and appeared in the popular deep learning paradigm under the description of double descent. These problems highlight the current lack of understanding of learning algorithms and generalization. It is, therefore, crucial to pursue this concern and provide a characterization of such behavior. In this paper, we derive the first consistent and risk-monotonic (in high probability) algorithms for a general statistical learning setting under weak assumptions, consequently answering some questions posed by Viering et al. 2019 on how to avoid non-monotonic behavior of risk curves. We further show that risk monotonicity need not necessarily come at the price of worse excess risk rates. To achieve this, we derive new empirical Bernstein-like concentration inequalities of independent interest that hold for certain non-i.i. d. ~processes such as Martingale Difference Sequences.