Miracle-dz/UniFAR-FTUs huggingface dataset:
GJTD-LR: A Trainable Grouped Joint Tensor Dictionary With Low-Rank Prior for Single Hyperspectral Image Super-Resolution

Reconstructing a high-resolution hyperspectral image (HR-HSI) using a single low-resolution hyperspectral image (LR-HSI) is a significant technique for increasing the spatial resolution of HSIs and overcoming the physical limitation of the HSI sensor. Most single HSI super-resolution methods have achieved great success recently. However, owning to the difficulty of acquiring an HSI, the available training samples are relatively few, which will inevitably lead to relatively low performance. To address this issue, in this article, we propose a novel single HSI super-resolution method by combining a trainable grouped joint tensor dictionary and a low-rank prior (GJTD-LR). First, we design a trainable grouped joint tensor dictionary, which can build an accurate mapping relationship between training HR-HSIs and their corresponding LR-HSIs with relatively few training samples. To be specific, the training HR-HSI and LR-HSI pairs are decomposed into a joint tensor dictionary and a set of sparse coefficients using tensor–tensor product to fully preserve the spectral correlation. In addition, we apply a grouped strategy to divide the training images into several groups and learn a compact joint dictionary for each group. Second, a tensor low-rank model is forced into the reconstruction model to further capture the spatial correlation. Finally, GJTD-LR is optimized using alternating direction method of multipliers (ADMM), soft threshold algorithm, singular value decomposition, and Fourier domain transform. The experimental results on both remote sensed HSIs and indoor HSIs show the superiority of GJTD-LR to some other traditional and advanced single HSI super-resolution methods.
Leakage and ohmic losses investigation in substrate‐integrated waveguide

Rectangular waveguide (RW) with sidewalls of vertical conducting cylinders (i.e., substrate‐integrated waveguide, SIW) becomes popular with the advent of low‐temperature cofired ceramic (LTCC) structure. The formula of leakage loss through the SIW with small cylinders has been found, based on the surface impedance of cylinder walls derived from an analytical MoM. To complete the study of loss, the ohmic loss in the SIW is investigated also in this paper. The theoretical formulas show good agreements with numerical HFSS simulations and the measured data from hardware experiments, demonstrating potential applications to the research and design of some interesting devices such as leaky antennas, etc.
Time-domain numerical modeling of poroelastic waves : the Biot-JKD model with fractional derivatives

A time-domain numerical modeling of Biot poroelastic waves is proposed. The viscous dissipation occurring in the pores is described using the dynamic permeability model developed by Johnson-Koplik-Dashen (JKD). Some of the coefficients in the Biot-JKD model are proportional to the square root of the frequency: in the time-domain, these coefficients introduce shifted fractional derivatives of order 1/2, involving a convolution product. Based on a diffusive representation, the convolution kernel is replaced by a finite number of memory variables that satisfy local-in-time ordinary differential equations, resulting in the Biot-DA (diffusive approximation) model. The properties of both the Biot-JKD model and the Biot-DA model are analyzed: hyperbolicity, decrease of energy, dispersion. To determine the coefficients of the diffusive approximation, different methods of quadrature are analyzed: Gaussian quadratures, linear or nonlinear optimization procedures in the frequency range of interest. The nonlinear optimization is shown to be the better way of determination. A splitting strategy is then applied numerically: the propagative part of Biot-JKD equations is discretized using a fourth-order ADER scheme on a Cartesian grid, whereas the diffusive part is solved exactly. An immersed interface method is implemented to discretize the geometry on a Cartesian grid and also to discretize the jump conditions at interfaces. Numerical experiments are presented, for isotropic and transversely isotropic media. Comparisons with analytical solutions show the efficiency and the accuracy of this approach. Some numerical experiments are performed to investigate wave phenomena in complex media: influence of the porosity of a cancellous bone, multiple scattering across a set of random scatterers.
Data-Driven Safe Predictive Control Using Spatial Temporal Filter-based Function Approximators

Model predictive control (MPC) is a state-of-the-art control method that can explicitly tackle system constraints. However, its high computational cost still remains an open challenge for embedded systems. To achieve satisfactory performance with manageable computational complexity, a spatial temporal filter (STF)-based data-driven predictive control framework is developed to systematically identify system dynamics and subsequently learn the MPC policy using STF-based function approximations. Specifically, an online nonlinear system identification method that satisfies persistence of excitation (PE) is developed by using a discrete-time concurrent learning technique. An STF-based function approximation is then employed to learn the nonlinear MPC (NMPC) policy based on the identified model. Furthermore, a discrete-time robust control barrier function (RCBF) is introduced to guarantee system safety in the presence of additive disturbances and system identification errors. Finally, simulations on the cart inverted pendulum are performed to demonstrate the efficacy of the proposed control synthesis.
Work difficulties in people with multiple sclerosis: The role of anxiety, depression and coping

Background Symptoms of anxiety and depression affect the daily life of people with multiple sclerosis (MS). This study examined work difficulties and their relationship with anxiety, depression and coping style in people with MS. Methods 219 employed people with MS (median age = 43 years, 79% female) completed questionnaires on anxiety, depression, coping style, demographics and work difficulties, and underwent a neurological examination. Two regression analyses were performed with work difficulties as the dependent variable and either anxiety or depression as continuous independent variables. Coping style, age, gender, educational level, MS-related disability and disease duration were added as additional predictors, as well as interaction terms between coping style and either symptoms of depression or anxiety. Results A significant model was found (F(10,205) = 13.14, p < 0.001, R2 = 0.39) in which anxiety, emotion- and avoidance-oriented coping and MS-related disability were positively related to work difficulties. The analysis of depression resulted in a significant model (F(10,205) = 14.98, p < 0.001, R2 = 0.42) in which depression, emotion- and avoidance-oriented coping and MS-related disability were positively related to work difficulties. None of the interaction effects were significant. Conclusions Work difficulties were positively related to anxiety, depression, emotion- and avoidance-oriented coping and MS-related disability in workers with MS.
Math Balance Aids based on Internet of Things for Arithmetic Operational Learning

—Industry 4.0 has changed various aspects of human life towards an era heavily influenced by information technology. The impact of industry 4.0 on the education sector has led to the emergence of the term education 4.0. The Internet of Things (IoT) is one of the pillars of industry 4.0. With its capabilities, IoT can provide opportunities to develop innovations in the field of education. Several studies show that teaching aids can improve the quality of learning and learning outcomes. In Indonesia, mathematics is a compulsory subject taught from elementary school to higher education. Previous studies that used mathematical (math) balance aids to help students learn mathematical operations showed positive correlations between the learning process and student learning outcomes in the materials related to arithmetic operations. This study aims to develop an IoT-based mathematical balance tool to support three education 4.0 trends: remote access, personalization, and practice and feedback. This study used modifications of Fahmideh and Zogwhi's IoT development method. There are five phases of IoT development: initialization phase, analysis phase, design phase, implementation phase, and evaluation phase. From each phase of IoT development, IoT-based mathematical balance assistance systems have been successfully built and it complies with the functionality described in the analysis phase. The system performance also shows optimal results with 100% accuracy for reading the student's learning activities. Moreover, it uses less than 10 seconds for processing 1000 data requests.
Secret Protections in Discrete-Event Systems with Minimum Costs

In this paper, we study a secret protection problem in discrete-event systems. The system is modeled by an automaton in which several states are assigned with different secret levels. Our aim is to protect some of the events in the system, which incurs some given cost, such that any sequence yielding a secret state contains a number of protected events no less than the required security level. We prove that determining a global optimal solution to this secret protection problem with minimum cost is NP-complete. We then develop a heuristic method to obtain a local optimal solution to protect the secrets using the notion of betweenness centrality.
THE ROLE OF PERSONAL INNOVATIVENESS ON BEHAVIORAL INTENTION OF INFORMATION TECHNOLOGY

The purpose of this study is to investigate PIIT as an appropriate factor in predicting behavioral intention in the information technology acceptance framework. This research is a longitudinal study conducted during one semester in the spring semester of 2017. Data collection using a survey questionnaire in the pilot study, period 1 and period 2. Participants in this study are accounting students who are enrolled in Introductory to Computer Application course at the University of HKBP Nommensen, Medan. Statistics techniques used were Pearson Correlation, means, linear regression and paired sample t-test. The results show that the hypothesis and the research model were supported significant statistically. The finding also suggested that the use of IT overtime increased the innovativeness and intention of the students. This research contributed to conducting training using certain software in practical settings.
Coarse-grained strain dynamics and backwards/forwards dispersion

A Particle Tracking Velocimetry experiment has been performed in a turbulent flow at intermediate Reynolds number. We present experimentally obtained stretching rates for particle pairs in the inertial range. When compensated by a characteristic time scale for coarse-grained strain we observe constant stretching. This indicates that the process of material line stretching taking place in the viscous subrange has its counterpart in the inertial subrange. We investigate both forwards and backwards dispersion. We find a faster backwards stretching and relate it to the problem of relative dispersion and its time asymmetry.
Learning-based State-dependent Coefficient Form Task Space Tracking Control of Soft Robot

In this paper, a data-driven modeling and control framework is developed for task space control of a soft robot gripper which consists of four individual soft fingers. Each of the four fingers is modeled as a manipulator with high degrees of freedom. The corresponding task space dynamics of the manipulator are derived using a rigid-link approximation of the continuum manipulator. A neural network approach is used to learn the derived dynamics in State Dependent Coefficient (SDC) form. Using the learned SDC matrices, an asymptotically stable optimal closed-loop tracking controller which is based on solving the State Dependent Riccati Equation (SDRE) is derived. The model learning and trajectory tracking controller is implemented on an open source Soft Motion (SoMo) platform simulating the soft gripper motion and corresponding tracking results are presented.
The history of approximation theory : from Euler to Bernstein

Dedication Foreword Preface Introduction Forerunners 1.1 Euler's Analysis of Delisle's Map 1.2 Laplace's Approximation of Earth's Surface Pafnuti Lvovich Chebyshev 2.1 Chebyshev's Curriculum Vitae 2.2 Stimuli for the Development of a Theory 2.3 First Theoretical Approaches 2.4 First Theoretical Compositions 2.5 Theory of Orthogonal Polynomials 2.6 Other Contributions of P. L. Chebyshev 2.7 Chebyshev - Euler of the 18th Century? The Saint Petersburg Mathematical School 3.1 Aleksandr Nikolaevich Korkin 3.2 Egor Ivanovich Zolotarev 3.3 Andrey and Vladimir Andreevich Markov 3.4 Julian Karol Sochocki 3.5 Konstantin Aleksandrovich Posse 3.6 A. A. Markov's Lectures 3.7 Resume Development Outside Russia 4.1 The Mediator: Felix Klein 4.2 Blichfeldt's Note 4.3 Kirchberger's Thesis 4.4 Other Non-Quantitative Contributions 4.5 On Convergence and Series Expansions 4.6 Fejer and Runge 4.7 Quantitative Approximation Theory 4.8 Jackson's Thesis 4.9 A Note About Gottingen's Role Constructive Function Theory: Kharkiv 5.1 Antony-Bonifatsi Pavlovich Psheborski 5.2 A Short Biography of Sergey Natanovich Bernstein 5.3 First Contributions to Approximation Theory 5.4 Constructive Function Theory as the Development of Chebyshev's Ideas Biographies. A.1 Matvey Aleksandrovich Tikhomandritski A.2 Nikolaj Yakovlevich Sonin A.3 Aleksandr Vasilevich Vasilev A.4 Ivan Lvovich Ptashitski A.5 Dmitry Fedorovich Selivanov A.6 Aleksandr Mikhaylovich Lyapunov A.7 Ivan Ivanovich Ivanov A.8 Dmitry Alksandrovich Grave A.9 Georgi Feodosievich Voronoy Explanations B.1 Russian Academic Degrees References Index List of Figures List of Tables
Grover’s Algorithm for Data Lake Optimization Queries

—Now-a-days, the use of No-SQL databases is one of the potential options for storing and processing big data lakes. However, searching for large data in No-SQL databases is a complex and time-consuming task. Further, information retrieval from big data management suffers in terms of execution time. To reduce the execution time during the search process, we propose a fast and suitable approach based on the quantum Grover algorithm, which represents one of the best-known approaches for searching in an unstructured database and resolves the unsorted search query in O ( √ ) time complexity. To assess our proposal, a comparative study with linear and binary search algorithms was conducted to prove the effectiveness of Grover's algorithms. Then, we perform extensive experiment evaluations based on ibm_qasm_simulator for searching one item out of eight using Grover’s search algorithm based on three qubits. The experiments outcomes revealed encouraging results, with an accuracy of 0.948, well in accordance with the theoretical result. Moreover, a discussion of the sensitivity of Grover's algorithm through different iterations was carried out. Then, exceeding the optimal number of iterations round ( √ ) , induces low accuracy of the marked state. Furthermore, the incorrect selection of this parameter can outline the solution.
Bioinformatics-Based Analysis: Noncoding RNA-Mediated COL10A1 Is Associated with Poor Prognosis and Immune Cell Infiltration in Pancreatic Cancer

Background Collagen type X alpha 1 (COL10A1) is a structural component of the extracellular matrix that is aberrantly expressed in a variety of cancer tissues. However, its role in pancreatic cancer progression is not well understood. Methods The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Gene Expression Profiling Interaction Analysis (GEPIA) data were employed to explore the expression of COL10A1 in normal and tumor tissues and its prognostic value in pancreatic adenocarcinoma. The clinical data of pancreatic cancer in TCGA were used to explore the relationship between COL10A1 and clinical features. Genes coexpressed with COL10A1 were explored using multiple databases and analyzed for functional enrichment. In addition, the lncRNA/miRNA/COL10A1 axis that may be involved in COL10A1 regulation in pancreatic cancer was explored by constructing a competitive endogenous RNA (ceRNA) regulatory axis. Finally, COL10A1 was analyzed for correlation with immune cell infiltration and various immune checkpoint molecules in pancreatic cancer. Results It was found that the expression of COL10A1 was significantly increased in pancreatic cancer tissues. High expression of COL10A1 was related to the clinicopathological characteristics and the worse prognosis of pancreatic cancer patients. The TUG1/miR-144-3p/COL10A1 axis was identified as the most likely upstream noncoding RNA pathway for COL10A1 in pancreatic cancer. Besides, in pancreatic adenocarcinoma, the expression level of COL10A1 showed a significant positive correlation with tumor immune cell infiltration, biomarkers of immune cells, and expression of immune checkpoint molecules. Conclusion COL10A1 is an early diagnostic marker, and its high expression correlates with immune infiltration in pancreatic cancer. The TUG1/miR-144-3p/COL10A1 axis was identified as the most likely upstream noncoding RNA pathway for COL10A1 in pancreatic cancer.
Shielded Deep Reinforcement Learning for Multi-Sensor Spacecraft Imaging

This paper considers the problem of autonomous spacecraft control for imaging missions, subject to safety constraints. The controller chooses between discrete flight modes to image a target with different sensor types. The safety constraints include maintaining safe battery levels, reaction wheel speeds, and body rates. The proposed approach applies shielded deep reinforcement learning (SDRL) to autonomously command spacecraft flight modes, where the imaging requirements are communicated to the agent through a finite-state machine (FSM). The training is done in a target- and orbit-agnostic manner to create a single artificial neural network that can operate in a range of conditions. The FSM specifies which sensor type the agent should use for the next image. Simulation results based on a spacecraft tasked on Boulder, CO, USA demonstrate that this approach is effective for commanding a spacecraft safely while meeting predefined imaging requirements. This work also demonstrates how an agent trained on Boulder is capable of being applied to other Earth-targets as well as targets on the Moon with similar performance.
A Comparative Analysis of Generative Neural Attention-based Service Chatbot

—Companies constantly rely on customer support to deliver pre-and post-sale services to their clients through websites, mobile devices or social media platforms such as Twitter. In assisting customers, companies employ virtual service agents (chatbots) to provide support via communication devices. The primary focus is to automate the generation of conversational chat between a computer and a human by constructing vir- tual service agents that can predict appropriate and automatic responses to customers’ queries. This paper aims to present and implement a seq2seq-based learning task model based on encoder-decoder architectural solutions by training generative chatbots on customer support Twitter datasets. The model is based on deep Recurrent Neural Networks (RNNs) structures which are uni-directional and bi-directional encoder types of Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). The RNNs are augmented with an attention layer to focus on important information between input and output sequences. Word level embedding such as Word2Vec, GloVe, and FastText are employed as input to the model. Incorporating the base architecture, a comparative analysis is applied where baseline models are compared with and without the use of attention as well as different types of input embedding for each experiment. Bilingual Evaluation Understudy (BLEU) was employed to evaluate the model’s performance. Results revealed that while biLSTM performs better with Glove, biGRU operates better with FastText. Thus, the ﬁnding signiﬁcantly indicated that the attention-based, bi-directional RNNs (LSTM or GRU) model signiﬁcantly outperformed baseline approaches in their BLEU score as a promising use in future works.
3 - Utilisation de l'entropie dans les problèmes de restauration et de reconstruction d'images

In a great number of image reconstruction and restoration problems we have to solve an integral equation of the first kind which is an ill posed inverse problem . Therefore one cannot obtain a unique and stable solution without introducing an a priori information on the solution, The Bayesian approach is a coherent one for solving inverse problems because it lets us to take into account and to process in the same way the a priori information on the solution and the data . This approach can be resumed as the following : (i) Assign an a priori probability distribution to the parameters to translate our knowledge on these parameters . (ii) Assign a probability distribution to the measured data to translate the errors and the noise on the data . (iii) Use the Bayes' rule to transmit the information contained in the data to the parameters, i . e. calculate the a posteriori probability distribution of these parameters . (iv) Define a decision rule to determine the parameters values . One must note that : (i) This approach can be used only in problems which can be described by a finite number of parameters (for example when the integral equation is discretized) . (ii) The notion of probability in this approach is not always connected to the frequency of the realization of a random variable . (iii) While it is easy to assign a probability distribution to the measured data to translate the existence of noise on these data, it is more difficult to assign an a priori probability distribution on the unknown parameters of the problem . The maximum entropy principle permits us to choose a probability distribution which is coherent with our a priori knowledge on these parameters and which is less compromising in the sense that it does not introduce any supplementary information . In this paper we use this approach to establish a method for solving the integral equations of the f rst kind in which the entropy of the solution is used as a regularization functional . This method is then used for solving many inverse problems : image restoration by deconvolution in the situation of missing data, image reconstruction in X ray tomography and diffraction tomography, and the multivariable Fourier syrtthesis problem . A great number of simulation results are showed and a comparison is made between these results and those obtained by other usual linear methods .
Sample efficient transfer in reinforcement learning for high variable cost environments with an inaccurate source reward model

Here we propose an algorithm that combines two classic ideas, transfer learning and temporal abstraction, to accelerate learning in high variable cost environments (HVC-envs). In an HVC-env, each sampling of the environment incurs a high cost, thus methods to accelerate learning are sought to reduce the incurred cost. Transfer learning can be useful for such environments by using prior knowledge from a source environment. As only a small number of samples can be collected from an HVC-env due to high sampling cost, learning becomes challenging when the source environment provides inaccurate rewards. To overcome this challenge we propose a simple but effective way of creating useful temporally extended actions from an inaccurate physics guided model (PGM) that acts as the source task. At first we address this issue theoretically by providing performance bounds between two semi-Markov Decision Processes (SMDPs) with different reward functions. Later we develop two benchmark HVC-envs where learning must happen using a small number of real samples (often on the order of ~ 102 or 103). Finally we show that it is possible to obtain sequential high rewards in both of these environments using ~ 103 real samples by leveraging knowledge from PGMs with inaccurate reward models.