Miracle-dz/UniFAR-FTUs huggingface dataset:
Symmetries of Hamiltonian Dynamical Systems, Momentum Maps and Reductions

Hamiltonian systems on symplectic or Poisson manifolds very often appear in Mechanics and in Mathematical Physics. Their symmetry properties can be described by the use of Lie groups actions on these manifolds. I will first recall the basic notions about symplectic and Poisson manifolds, and about Hamiltonian systems on these manifolds. I will discuss the action of a Lie group on a symplectic manifold; that action is called symplectic when it preserves the symplectic form, Hamiltonian when the infinitesimal generators of the action are Hamiltonian vector fields (such an action admits a momentum map, defined up to addition of an arbitrary constant), and strongly Hamiltonian when the momentum map can be chosen equivariant with respect to the coadjoint action of the group on the dual of its Lie algebra. Any momentum map of a Hamiltonian, but not strongly Hamiltonian action is equivariant with respect to an affine action of the group on the dual of its Lie algebra whose linear part is the coadjoint action, the additional term being a symplectic cocycle of the Lie group. Several examples will be presented. Poisson actions of a Lie group on a Poisson manifold will be described. When a Lie group acts on the phase space of a Hamiltonian system by a Hamiltonian action which leaves the Hamiltonian unchanged, the momentum map is a first integral of the system (Noether’s first theorem in Hamiltonian formalism). The reduction procedure of Marsden and Weinstein, which uses that property, will be discussed and several examples will be presented. Momentum maps have remarkable convexity properties, and can be generalized in various ways. I will present the convexity theorems of Atiyah, Guillemin and Sternberg and their generalizatrion by Frances Kirwan. Poisson-Lie groups were introduced Vladimir Drinfeld. A Poisson-Lie group is a Lie group G with a Poisson structure for which the multiplication is a Poisson map. It is a beautiful mathematical object with remarkable properties: the dual of its Lie algebra has a natural structure of Lie algebra. One can define a notion of Poisson action of a PoissonLie group on a Poisson manifold. The previously defined action of a Lie group on a Poisson manifold appears as the special case in which the Poisson-Lie group is equipped with the zero Poisson structure. For such an action Jiang Hua Lu has defined a generalized momentum map which takes its value in the dual group of the Poisson-Lie group. If I have enough time left, I will define symplectic groupoids and explain how the source and target maps appear as natural generalizations of the momentum maps of the right and left actions of a Lie group on its cotangent bundle.
Generating more Realistic Packet Loss Patterns for Wireless links using Neural Networks

Simulations of wireless network connections are essential forthe development of new technologies because they are farmore scalable than real-world experiments and reproducible.Modeling packet loss realistically provides a highly abstractyet powerful tool for the simulation of wirelesses links. Typi-cally, simple statistical models or replaying of recorded tracesare used for the simulation. For a proper parametrization ofsimple statistical models, recorded traces are required, too.Both approaches have drawbacks: replaying traces is limitedto the length of the traces, a repetition may lead to unwantedeffects in the simulation. The statistical models solve this, butthe resulting packet loss patterns significantly differ from realones. In this paper, we propose using a neural network in-stead. It takes the same kind of input, i.e., a real-world trace,but it can generate longer traces with more realistic loss pat-terns. We share pre-trained neural networks for multiple linksin office and industry scenarios with the community for usein future research.
Dynamic Route Guidance in Vehicle Networks by Simulating Future Traffic Patterns

Roadway congestion leads to wasted time and money and environmental damage. Since adding more roadway capacity is often not possible in urban environments, it is becoming more important to use existing road networks more efficiently. Toward this goal, recent research in real-time, schedule-driven intersection control has shown an ability to significantly reduce the delays incurred at signalized intersections. Such approaches operate by building a predictive model of when locally sensed approaching traffic is expected to arrive at a given intersection, and then using the model to generate a signal timing plan (a phase schedule) that minimizes the cumulative delay of this traffic as it moves through the intersection. In this paper, we consider whether further reduction in delay could be gained by applying this predictive model to dynamically reroute some portion of vehicles willing to share their destinations along less congested paths. We developed an algorithm that simulates the current traffic state forward at each vehicle decision point, based on knowledge of other vehicles’ current routes and traffic signals’ control algorithms, and evaluated it using the SUMO microscopic simulator on different road networks (one as a simple synthetic example and the other taken from the real world) using different traffic signal control algorithms (fixed-timing plans and schedule-driven intersection control). Experiments carried out on combinations of networks and traffic signal control algorithms show that our rerouting protocol reduces delay for both vehicles participating in route guidance (adopters) and those that do not (non-adopters) and that the reduction in delay generally increases as the proportion of adopters does.
Nitric Oxide and Strigolactone Alleviate Mercury-Induced Oxidative Stress in Lens culinaris L. by Modulating Glyoxalase and Antioxidant Defense System

Developmental activities have escalated mercury (Hg) content in the environment and caused food security problems. The present investigation describes mercury-incited stress in Lens culinaris (lentil) and its mitigation by supplementation of sodium nitroprusside (SNP) and strigolactone (GR24). Lentil exposure to Hg decreased root and shoot length, relative water content and biochemical variables. Exogenous application of SNP and GR24 alone or in combination enhanced all of the aforementioned growth parameters. Hg treatment increased electrolyte leakage and malondialdehyde content, but this significantly decreased with combined application (Hg + SNP + GR24). SNP and GR24 boosted mineral uptake and reduced Hg accumulation, thus minimizing the adverse impacts of Hg. An increase in mineral accretion was recorded in lentil roots and shoots in the presence of SNP and GR24, which might support the growth of lentil plants under Hg stress. Hg accumulation was decreased in lentil roots and shoots by supplementation of SNP and GR24. The methylglyoxal level was reduced in lentil plants with increase in glyoxalase enzymes. Antioxidant and glyoxylase enzyme activities were increased by the presence of SNP and GR24. Therefore, synergistic application of nitric oxide and strigolactone protected lentil plants against Hg-incited oxidative pressure by boosting antioxidant defense and the glyoxalase system, which assisted in biochemical processes regulation.
Study on Economy and Carbon Emission Optimization of DC micro-Grid

Aiming at the problem of carbon emission in the operation of photovoltaic - wind power - energy storage DC Micro-grid, this paper establishes a bi-level programming model of DC Micro-grid based on comprehensive consideration of carbon emission cost, environmental benefits of renewable energy, system operation and maintenance cost, equipment depreciation cost, interaction cost between Micro-grid and upper grid, and time-of-use price. The upper model aims to maximize the net economic benefits of DC Micro-grid charging and discharging, and optimize the energy storage operation strategy. The lower model takes the minimum carbon emission of DC Micro-grid as the goal to maximize the utilization of new energy and carry out bi-level optimization. Aiming at the conflict problem between economic optimization objectives and carbon flow optimization objectives under the economic constraints and carbon flow constraints of DC Micro-grid, based on the analysis of carbon flow calculation, the fuzzy algorithm is used to transform the multi-objective problem into a single-objective optimization problem, and then the improved genetic algorithm (GA) is used to solve it. Finally, a case study is carried out for a practical photovoltaic - wind power - energy storage DC Micro-grid system in the southern region. The analysis results show that the multi-objective fuzzy optimization model can achieve a larger carbon reduction target with less economic tolerance, which verifies the rationality of the proposed model and algorithm.
An Empirical Study to Measure the Impact of Information Technology Governance Under the Control Objectives for Information and Related Technologies on Financial Performance

Purpose: To determine the effect of information technology governance (ITG) under the control objectives for information and related technologies (COBIT) on financial performance is the objective of this study. Additionally, the article seeks to look into the relationships between the factors under consideration. Theoretical framework: Information technology and operational processes are evaluated and ensure their compliance with the instructions of the Central Bank of Iraq. Therefore, the research dealt with a conceptual framework by reviewing the literature on the importance of the COBIT framework in assessing financial performance. Design/methodology/approach: To investigate the effect of information technology; we the value-added intellectual coefficient approach and a defined corporate governance index were utilized. The performance of the company was assessed using operating efficiency ratio and Economic value Added (EVA). Findings: the results Show there are the high level of application of ITG in the banks listed in the Iraqi stock exchange. Also, we found the effectiveness of ITG under the COBIT framework in banking financial performance. Research, Practical & Social implications: The findings should inform practitioners and legislative institutions of the necessity to follow strong COBIT procedures and enhance the effectiveness of IT to produce a better financial performance for firms. Originality/value: the study is among the first to consider the casual connections and how COBIT policies for ITG affect financial performance in the setting of Iraq.
Substitution effect of protein source with tea waste on productive performances and ruminal fermentation in crossbred Saanen lactating goats

Abstract Tea waste (TW) has a protein content of approximately 30% and the potential to replace other protein sources. However, the effect of dietary TW supplement is still ambiguous. The objective of this study was to investigate the effect of three levels of a dietary TW supplement on the performances and physiological changes in goats. Nine multiparous crossbred Saanen lactating goats were arranged in three replicates of a 3 × 3 Latin square. The three groups of goats were fed with 0, 5, or 10 by weight [wt%] dry matter [DM] basis TW, respectively, for a 21-day period. All diets were a total mixed ration (TMR) containing a 40:60 (DM basis) ratio of concentrate: roughage. The goats were weighed at 15 and 21 days. Faeces and urine were collected during the last 7 days of each period. The TW addition had no effect on the DM intake (DMI), DMI% and body weight. The milk protein content in goats on a 10 wt% TW diet was significantly higher (p < 0.05) than that of goats on the other diets. The milk lactose of both TW diets significantly decreased when compared to the control diet (p < 0.01). Goats with 5 and 10 wt% TW tended to decrease digestibility of crude protein (p = 0.10). There were no changes in the DM digestibility, blood glucose, blood urea nitrogen, triglyceride, total protein, ruminal pH, ammonia-nitrogen, total volatile fatty acids, acetic acid (A), propionic acid (P), A:P ratio, nitrogen balance, allantoin excretion and methane emission among treatments. Diets with 5 or 10 wt% TW (DM basis) could be used to replace other plant protein sources. Highlights The tea waste replaced as protein source up to 10% in goat feed with no effect on dry matter intake, blood profile and methane production. The 10% tea waste diet increased milk protein content in dairy goat. Low dose of condensed tannin in tea waste provided more protein absorption in small intestine by reducing ruminal NH3–N level.
Approximate Verification of Partially Observable Discrete Time Stochastic Hybrid Systems

Assuring safety in discrete time stochastic hybrid systems is particularly difficult when only partial observations are available. We first review a formulation of the probabilistic viability (i.e. safety) problem under noisy hybrid observations as a dynamic program. Two methods for approximately solving the dynamic program are presented. The first method approximates the hybrid system as an equivalent finite state Markov decision process, so that the information state is a probability mass function. The second approach approximates an indicator function over the safe region using radial basis functions, to represent the information state as a Gaussian mixture. In both cases, we discretize the hybrid observation process and generate a sampled set of information states, then use point-based value iteration to under-approximate the viability probability. We obtain error bounds and convergence results in both cases, assuming additive Gaussian noise in the continuous state dynamics and observations. We compare the performance of the finite state and Gaussian mixture approaches on a simple numerical example.
Detection of Jamming Attacks for the Physical-Layer Authentication

This paper concerns the problem of defending against the jamming attack for the Physical-Layer Authentication (PLA). The problem is important due to the fact that jamming attacks can make the legitimate receiver reject a legitimate signal or accept an impersonating signal. In this paper, we propose two jamming detection schemes to defend against jamming attacks for the PLA. The first scheme is the Jamming-Attack Detection (JAD) scheme, which exploits the difference of noise variances between the handshaking and communication stages. The second scheme is the Composite Jamming-Attack Detection (CJAD) scheme, which exploits the difference of noise variances between Alice and Bob. Moreover, we provide the theoretical analysis of the proposed schemes over wireless fading channels and derive their closed-form expressions. We implement our schemes and conduct extensive performance comparisons. Our theoretical analysis and simulation results show that if security is the priority, the CJAD scheme is the best option. If we further consider the overhead, the JAD scheme is the best option under the strategy of jamming attacks and the CJAD scheme may be a better option under the strategy of enhanced jamming attacks, especially for the larger power of a jamming signal.
Optimizing Winograd convolution on GPUs via multithreaded communication

In advanced High-Performance Computing (HPC), convolution operations take a big proportion in convolutional neural networks, and convolutional neural networks very common in image and video based deep learning applications, because of which, this paper takes improving the performance of convolution operation as the research direction. Convolution can be performance in many ways, such as using mathematical definition to calculate, conversing to Fast Fourier Transform (FFT), conversing to batch matrix multiplication (im2col) or using Winograd algorithm. For small filter, Winograd has unique advantages. AMD based ROCm environment, the implementation of Winograd and an optimization method of Winograd based on multi-thread communication algorithm are introduced in this paper. For the Winograd convolution in ROCm 2.9. 0, the speed of the algorithm was increased by more than 150% after optimization in this paper. Under some certain computing power situations, the performance of the optimization algorithm approaches or even exceeds cuDNN and MIOpen.
Type 1 and 2 symmetric divergences for stochastic neighbor embedding

Stochastic neighbor embedding (SNE) is a method of di- mensionality reduction (DR) that involves softmax similarities measured between all pairs of data points. In order to build a low-dimensional embedding, SNE tries to reproduce the similarities observed in the high- dimensional data space. The capability of softmax similarities to fight the phenomenon of norm concentration has been studied in previous work. This paper investigates a complementary aspect, namely, the cost function that quantifies the mismatch between the high- and low-dimensional sim- ilarities. We show experimentally that switching from a simple Kullback- Leibler divergences to mixtures of dual divergences increases the quality of DR. This modification brings SNE to the performance level of its Student t-distributed variant, without the need to resort to non-identical similarity definitions in the high- and low-dimensional spaces. These results allow us to conclude that future improvements in similarity-based DR will likely emerge from better definitions of the cost function.
RNN-Based Method for Classifying Natural Human Emotional States from Speech

An accuracy of classifying human emotional states from speech has been dependent on emotional speech database, speech signal processing algorithms, and classification methods. Numerous static and dynamic classification techniques are used for emotional state recognition. The paper considers a novel method for classifying natural positive, negative, and neutral human emotional states based on a five-layer Recurrent Neural Network (RNN). The method novelty is due to various neuron activation functions employed for each network layer stemming from the peculiarities of natural emotional speech informative parameters used as input data for the neural network. Local and global informative speech parameters relevant to human emotional states are outlined, and the known classification methods are surveyed. The proposed classification method is described, and the developed Russian database of natural emotional speech is presented. The research results compared with the widely used classification methods have evidenced 95 % accuracy of the developed method, which can be effectively tested in systems for detecting and classifying natural human emotional states from speech.
Group Emotion Detection using Convolutional Neural Network

Deep learning mimics how the human brain processes data to generate patterns that can be used to make decisions. It is a subtype of AI and ML specifically utilizes networks to understand unsupervised input that is unstructured or unlabeled data. When the data amount is large, deep learning outperforms machine learning. While typical programs develop analyses using data in a linear manner, deep learning system’s hierarchical function allows machines to process data in a nonlinear manner. When it comes to complicated tasks like picture categorization, video and audio identification, deep learning is advised. An interesting study area is predicting the environment from a group photograph using people’s expressions. Humans use facial expressions to express their emotions. Humans can quickly discern facial emotions, but computers have a harder time doing so since human facial expressions frequently fluctuate in photographs, particularly in group images with multiple faces. In this study, seven basic human emotions are employed to forecast the situation in the group image. Disgust, anger, happiness, sadness, fear, surprise, and neutral are the seven emotions. One of several concerns in computer vision and deep learning that shall be tackled with CNN is facial recognition. Picture-extraction, picture-preprocessing, picture-detection and picture-classification are four steps of the system’s development. Based on the classification of each extracted face from the group image, the probability will be determined to forecast the type of environment existing in the group image.
An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms

Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The use of face masks causes the performance of conventional face recognition technologies to degrade considerably. The effect of mask-wearing in face recognition is yet an understudied issue. In this paper, we address this issue by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best, besides the ones which poorly perform, in the presence of masked face images. Local Binary Pattern (LBP) is utilized as the feature extraction operator. We generated and used synthesized masked face images. We prepared unmasked, masked, and half-masked training datasets and evaluated the face recognition performance against both masked and unmasked images to present a broad view of this crucial problem. We believe that our study is unique in elaborating the mask-aware facial recognition with almost all possible scenarios including half_masked-to-masked and half_masked-to-unmasked besides evaluating a larger number of conventional machine learning algorithms compared the other studies in the literature.
Bionic Multi-legged Robot Based on End-to-end Artificial Neural Network Control

This paper aims at a conceptual design of a lightweight prototype for autonomous planetary surface exploration. Considering the complex bumpy surface on planets, we design a novel 12-legged reshaping robot inspired by sea urchin structure, which holds the potential to fit unstructured terrains using simple motor skills. The prototype realizes the omnidirectional motion and has the features of no overturning and high fault tolerance. The autonomous locomotion policy is proposed based on a model-free end-to-end reinforcement learning algorithm with only proprioception, holding the feature of fast training and no prior knowledge. The robot with learned policy enables steady autonomous mobility and robust adaptation to the generalized terrains and external perturbation through the virtual simulation experiments in various unstructured environments. Finally, we organized prototype experiments in the laboratory, which validate the dynamic feasibility of the gait to directly deploy.