Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Magnetostatic and Magnetodynamic Properties in Fe/Co Substituted Ni–Mn–In Alloy Films

The effects of the partial substitution of Mn by Co/Fe on the structural, magnetostatic, and magnetodynamic properties of Ni–Mn–In Heusler alloy films were studied using X-ray diffraction, field-dependent magnetization, and ferromagnetic resonance studies. It was observed that the substitution of up to 3 at. % Co or Fe atoms in Mn sites does not alter the crystal structure much. However, an increase in grain size and surface roughness was noticed upon substitution of the fourth element. The magnetic moment of the films could be increased up to 120 kA/m by introducing Co/Fe in Mn sites. For the first time, low Gilbert damping constant (<inline-formula> <tex-math notation="LaTeX">$\alpha$ </tex-math></inline-formula>) of 0.008 is observed in Co substituted Ni–Mn–In film together with high saturation magnetization, high perpendicular anisotropy, and an easy axis along film plane, which are the promising factors for application in high-density random-access memory devices.
Superior robustness of ExEm‐spFRET to IIem‐spFRET method in live‐cell FRET measurement

We recently developed two quantitative fluorescence resonance energy transfer (FRET) measurement methods based on spectral unmixing of emission spectra (IIem‐spFRET) and excitation–emission spectra (ExEm‐spFRET), respectively. We here evaluated robustness of the two methods by implementing them on a self‐assembled quantitative FRET measurement system using the cells expressing different constructs. For the cells with larger signal‐to‐noise (S/N) ratio (>9), the two methods obtained consistent FRET efficiency (E) values and total concentration ratio (RC) values of acceptor to donor for all constructs; for the cells with 3 < S/N < 9, IIem‐spFRET obtained bigger RC values than the expected value for VCV construct; for the cells with S/N < 3, although IIem‐spFRET method obtained inaccurate E and RC values for VCV construct, the two methods also obtained consistent E and RC values for all other constructs. Collectively, both ExEm‐spFRET and IIem‐spFRET methods are very applicable for live‐cell FRET measurement, and ExEm‐spFRET has superior robustness especially for the cells with low S/N ratio.
A Novel Method for Automatic Detection and Classification of Movement Patterns in Short Duration Playing Activities

Autonomous devices able to evaluate diverse situations without external help have become especially relevant in recent years because they can be used as an important source of relevant information about the activities performed by people (daily habits, sports performance, and health-related activities). Specifically, the use of this kind of device in childhood games might help in the early detection of developmental problems in children. In this paper, we propose a method for the detection and classification of movements performed with an object, based on an acceleration signal. This method can automatically generate patterns associated with a given movement using a set of reference signals, analyze sequences of acceleration trends, and classify the sequences according to the previously established patterns. This method has been implemented, and a series of experiments has been carried out using the data from a sensor-embedded toy. For the validation of the obtained results, we have, in parallel, developed two other classification systems based on popular techniques, i.e., a similarity search based on Euclidean distances and machine-learning techniques, specifically a support vector machine model. When comparing the results of each method, we show that our proposed method achieves a higher number of successes and higher accuracy in the detection and classification of isolated movement signals as well as in sequences of movements.
Long-Distance Vehicle Detection Algorithm at Night for Driving Assistance

In recent years, the Advanced Driver Assistance System (ADAS) system has received increasing attention. In the ADAS field, front vehicle detection is a popular technology. With this technology, we can alert drivers to avoid unnecessary collisions and accidents. Nighttime vehicle detection is indispensable. The spirit and attention of people at night will fall, and it will make the warning system more important. How to accurately detect vehicles in the night has become an important issue. At night, the headlights have a stronger feature than the vehicle contours, and have a better effect for vehicle detection. This paper uses a single camera and uses the lights as a method of night vehicle detection. And use RCCC camera to enhance image quality and improve the success rate of detection.
Understanding and Predicting the Memorability of Outdoor Natural Scenes

Memorability measures how easily an image is to be memorized after glancing, which may contribute to designing magazine covers, tourism publicity materials, and so forth. Recent works have shed light on the visual features that make generic images, object images or face photographs memorable. However, these methods are not able to effectively predict the memorability of outdoor natural scene images. To overcome this shortcoming of previous works, in this paper, we provide an attempt to answer: “what exactly makes outdoor natural scenes memorable”. To this end, we first establish a large-scale outdoor natural scene image memorability (LNSIM) database, containing 2,632 outdoor natural scene images with their ground truth memorability scores and the multi-label scene category annotations. Then, similar to previous works, we mine our database to investigate how low-, middle- and high-level handcrafted features affect the memorability of outdoor natural scenes. In particular, we find that the high-level feature of scene category is rather correlated with outdoor natural scene memorability, and the deep features learnt by deep neural network (DNN) are also effective in predicting the memorability scores. Moreover, combining the deep features with the category feature can further boost the performance of memorability prediction. Therefore, we propose an end-to-end DNN based outdoor natural scene memorability (DeepNSM) predictor, which takes advantage of the learned category-related features. Then, the experimental results validate the effectiveness of our DeepNSM model, exceeding the state-of-the-art methods. Finally, we try to understand the reason of the good performance for our DeepNSM model, and also study the cases that our DeepNSM model succeeds or fails to accurately predict the memorability of outdoor natural scenes.
Oxygen Uptake Slow Component and the Efficiency of Resistance Exercises.

Garnacho-Castaño, MV, Albesa-Albiol, L, Serra-Payá, N, Gomis Bataller, M, Pleguezuelos Cobo, E, Guirao Cano, L, Guodemar-Pérez, J, Carbonell, T, Domínguez, R, and Maté-Muñoz, JL. Oxygen uptake slow component and the efficiency of resistance exercises. J Strength Cond Res XX(X): 000-000, 2018-This study aimed to evaluate oxygen uptake slow component (V[Combining Dot Above]O2sc) and mechanical economy/efficiency in half squat (HS) exercise during constant-load tests conducted at lactate threshold (LT) intensity. Nineteen healthy young men completed 3 HS exercise tests separated by 48-hour rest periods: 1 repetition maximum (1RM), incremental-load HS test to establish the %1RM corresponding to the LT, and constant-load HS test at the LT. During the last test, cardiorespiratory, lactate, and mechanical responses were monitored. Fatigue in the lower limbs was assessed before and after the constant-load test using a countermovement jump test. A slight and sustained increase of the V[Combining Dot Above]O2sc and energy expended (EE) was observed (p < 0.001). In blood lactate, no differences were observed between set 3 to set 21 (p > 0.05). A slight and sustained decrease of half squat efficiency and gross mechanical efficiency (GME) was detected (p < 0.001). Significant inverse correlations were observed between V[Combining Dot Above]O2 and GME (r = -0.93, p < 0.001). Inverse correlations were detected between EE and GME (r = -0.94, p < 0.001). Significant losses were observed in jump height ability and in mean power output (p < 0.001) in response to the constant-load HS test. In conclusion, V[Combining Dot Above]O2sc and EE tended to rise slowly during constant-load HS exercise testing. This slight increase was associated with lowered efficiency throughout constant-load test and a decrease in jump capacity after testing. These findings would allow to elucidate the underlying fatigue mechanisms produced by resistance exercises in a constant-load test at LT intensity.
Proposal for an Easier Obtention of Piezoresistance Coefficients

An alternative procedure to obtain the piezoresistance coefficients is proposed and implemented. It consists on the integration of the piezoresistance equation that relates the resistance change with the pressure change, via the piezoresistance coefficients. This yields a linear dependence between the logarithm of the measured resistance (R) and pressure (P). When implemented experimentally, this facilitates to make one single measurement of R vs P. The linear fit of the logarithm plot yields the piezoresistance coefficients. This technique is compared with the usual procedure, yielding smaller errors, while saving time and experimental and calculation effort, as only one measurement is necessary, as compared with the former procedure.
Experimental Validation of a Torque-Controlled Variable Stiffness Actuator Tuned by Gain Scheduling

A variable stiffness actuator (VSA) is an inherently parameter-dependent system due to the controllable stiffness element. Within the torque-controlled framework, the VSA is distinguished from the classical series elastic actuator (SEA). A frozen torque controller can directly determine the SEA performance with a fixed-stiffness spring selection. However, since the VSA operates at a set of operating points, the aim is to achieve an adaptive control approach. For this, we propose a gain-scheduled torque controller. The control performance is expected to recover robustness when stiffness values are varied from smaller to larger ones. Simultaneously the bandwidth is maximized, taking into account hardware limitations. In this way, a good tradeoff between stability and performance can be achieved. A key step in the gain-scheduled controller design is to implement the linear controllers, where the linear quadratic Gaussian (LQG) technique was applied to deal with the multiloop feedback (a cascade control scheme) in the VSA plant. A lever-arm based VSA [i.e., a mechanical-rotary variable impedance actuator (MeRIA)] was used to verify this new controller design approach by the simulations and experiments. The resulting gain-scheduled controller was also evaluated by taking into account the impedance control testing on a motion-supported platform for the knee joint.
A New Approach for Dynamic Energy Storage System

Battery packs with a large number of cells are becoming more and more prominent technology. Monitoring and controlling these packs is now a subject with increased importance. The proposed implemented system consists of only three switches per cell, allowing several types of connection between the cells. The battery management system relies on a distributed hierarchical architecture with n-1local controllers and a global controller. In the proposed algorithm the cells act as a single swarm functioning autonomously and depending on the parameters (voltage, current and temperature) of each cell of the pack. The hardware for each cell is explained and demonstrated. The results obtained both in simulation and in experimental tests show the excellent performance of the proposed methodology. Also, the charging approach based on the traditional multistage method exhibited an excellent performance on charging supercapacitors, concerning its efficiency, charging time and overvoltage protection.
Concomitant targeting of the mTOR/MAPK pathways: novel therapeutic strategy in subsets of RICTOR/KRAS-altered non-small cell lung cancer

Despite a therapeutic paradigm shift into targeted-driven medicinal approaches, resistance to therapy remains a hallmark of lung cancer, driven by biological and molecular diversity. Using genomic and expression data from advanced non-small cell lung cancer (NSCLC) patients enrolled in the BATTLE-2 clinical trial, we identified RICTOR alterations in a subset of lung adenocarcinomas and found RICTOR expression to carry worse overall survival. RICTOR-altered cohort was significantly enriched in KRAS/MAPK axis mutations, suggesting a co-oncogenic driver role in these molecular settings. Using NSCLC cell lines, we showed that, distinctly in KRAS mutant backgrounds, RICTOR blockade impairs malignant properties and generates a compensatory enhanced activation of the MAPK pathway, exposing a unique therapeutic vulnerability. In vitro and in vivo concomitant pharmacologic inhibition of mTORC1/2 and MEK1/2 resulted in synergistic responses of anti-tumor effects. Our study provides evidence of a distinctive therapeutic opportunity in a subset of NSCLC carrying concomitant RICTOR/KRAS alterations.
An Effective Approach to ANN-Based Short-Term Load Forecasting Model Using Hybrid Algorithm GA-PSO

In recent years, research of optimal algorithms for short-term load forecasting has become popular. Optimal algorithms can improve forecast results in two main directions: precision and speed of convergence. To achieve both of these goals, researchers often use a combination of different algorithms. This paper describes a new combination of two optimal algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The forecasting of the 24-hour daily-load on the normal working day of the SPC-Southern Power Corporation (a big local Vietnam Company) showed a significant improvement in the error as well as the speed of convergence with several selected days. In the most accurate case, the average error of prediction reaches 1.15% while the biggest error is 3.74% and the smallest is 0.02%
Reachability Analysis for Neural Agent-Environment Systems

We develop a novel model for studying agent-environment systems, where the agents are implemented via feed-forward ReLU neural networks. We provide a semantics and develop a method to verify automatically that no unwanted states are reached by the system during its evolution. We study several reachability problems for the system, ranging from one-step reachability, to ﬁxed multi-step and arbitrary-step to study the system evolution. We also study the decision problem of whether an agent, realised via feed-forward ReLU networks will perform an action in a system run. Whenever possible, we give tight complexity bounds to decision problems introduced. We automate the various reachability problems studied by recasting them as mixed-integer linear programming problems. We present an implementation and discuss the experimental results obtained on a range of test cases.
Distributed k-Clustering for Data with Heavy Noise

In this paper, we consider the $k$-center/median/means clustering with outliers problems (or the $(k, z)$-center/median/means problems) in the distributed setting. Most previous distributed algorithms have their communication costs linearly depending on $z$, the number of outliers. Recently Guha et al.[10] overcame this dependence issue by considering bi-criteria approximation algorithms that output solutions with $2z$ outliers. For the case where $z$ is large, the extra $z$ outliers discarded by the algorithms might be too large, considering that the data gathering process might be costly. In this paper, we improve the number of outliers to the best possible $(1+\epsilon)z$, while maintaining the $O(1)$-approximation ratio and independence of communication cost on $z$. The problems we consider include the $(k, z)$-center problem, and $(k, z)$-median/means problems in Euclidean metrics. Implementation of the our algorithm for $(k, z)$-center shows that it outperforms many previous algorithms, both in terms of the communication cost and quality of the output solution.
A Distributed Algorithm via Consensus and Saddle Point Dynamics for Economic Dispatch Problem in Energy Networked Systems

This paper proposes a distributed algorithm in tackling the problem of economic dispatch under generation constraints, which is a constrained optimization problem, in energy networked systems. The designed algorithm utilizes a consensus protocol and a saddle point dynamics idea to solve the problem with strictly convex objective functions in a fully distributed manner. In addition, it is shown that the private information of agents in the interconnected network is guaranteed in our proposed strategy. Furthermore, our algorithm shows a fully distributed property compared to existing works. The theoretical guarantees on the optimality of the solution are provided by rigorous analysis.
Comparative Implementation of Numerical Integration Methods for Transient Stability Constrained Optimal Power Flow

Transient Stability Constrained Optimal Power Flow (TSCOPF) is a non-linear mathematical programming problem that optimizes the operation of power systems considering steady state and dynamic constrains. TSCOPF models extend the OPF solution by considering the dynamic behavior of the system. One of the main approaches in TSCOPF studies discretizes the differential equations representing the dynamics of the system and includes them in the optimization problem. This paper evaluates the impact of numerical integration methods and time steps on TSCOPF. The results show that: 1) the forward Euler method obtains the most conservative representation for all cases evaluated; 2) as the integration time step increases, the backward Euler method and the trapezoidal rule tend to dampen electromechanical oscillations; and 3) the adequate use of a variable integration time step achieves a reduction in the number of equations, variables and computation times, while maintaining the accuracy on the results.
Mathematical Modelling and Simulation of Photovoltaic Module Using MATLAB/SIMULINK

Photovoltaic (PV) array which consists of series and parallel connected modules is the fundamental building block of a photovoltaic energy conversion system. PV array shows nonlinear characteristics and evaluating the operating characteristics under time varying environmental conditions is a too costly and time-consuming task. To avoid these hindrances, an elementary and a basic model of PV array were modeled and incorporated to different software involving MATLAB/SIMULINK. Although, these PV models are not appropriate for operation, including hybrid energy network because these systems require pliant tuning of various parameters and cannot be easy to understand. So, this paper describes a step by step method to simulate PV array using MATLAB/SIMULINK. An MSX 64W PV panel has been taken as a base model. The characteristic's curves of PV module are also evaluated at a broad range of environmental circumstances and physical parameters. The effects of partial shading conditions on PV characteristics are also discussed.
Adversarial TableQA: Attention Supervision for Question Answering on Tables

The task of answering a question given a text passage has shown great developments on model performance thanks to community efforts in building useful datasets. Recently, there have been doubts whether such rapid progress has been based on truly understanding language. The same question has not been asked in the table question answering (TableQA) task, where we are tasked to answer a query given a table. We show that existing efforts, of using "answers" for both evaluation and supervision for TableQA, show deteriorating performances in adversarial settings of perturbations that do not affect the answer. This insight naturally motivates to develop new models that understand question and table more precisely. For this goal, we propose Neural Operator (NeOp), a multi-layer sequential network with attention supervision to answer the query given a table. NeOp uses multiple Selective Recurrent Units (SelRUs) to further help the interpretability of the answers of the model. Experiments show that the use of operand information to train the model significantly improves the performance and interpretability of TableQA models. NeOp outperforms all the previous models by a big margin.
Control of Magnetic Anisotropy by Lattice Distortion in Cobalt Ferrite Thin Film

We have investigated the properties of cobalt ferrite (CFO)(001) epitaxial thin films grown by RF magnetron sputtering method on various buffer layers to induce large perpendicular magnetic anisotropy (PMA) through the magneto-elastic effect. Among five different compounds examined as the buffer layers, we found that only CFO thin films grown on (Mg, Sn)<sub>3</sub>O<sub>4</sub> buffer layers showed a strong PMA, and the value of the saturation magnetization was 460 emu/cm<sup>3</sup>, which is larger than the 425 emu/cm<sup>3</sup> for bulk CFO. Torque measurements revealed the PMA energy <inline-formula> <tex-math notation="LaTeX">$K_{u} = 24.6$ </tex-math></inline-formula> Merg/cm<sup>3</sup> at room temperature, which is extraordinarily large for a ferromagnetic oxide.
Alzheimer Disease: Perspectives from Epidemiology and Genetics

Alzheimer disease (AD) is a huge and growing societal problem with upwards of 35% of the population over the age of 80 developing the disease. AD results in a loss of memory, the ability to make reasoned and sound decisions, and ultimately the inability to take care of oneself. AD has an impact not only on the sufferer, but their caretakers and loved ones, who must take on a costly and time-consuming burden of care. AD is found in virtually all racial and ethnic groups. Genetic influences on AD are substantial, and there has been a 30 year history of both success and failure. Mutations for rare early onset forms of the disease have been identified, but this information has not yet led to an effective treatment. Multiple common genetic variations have also been identified, and have led to new insights into the potential role of microglia cells in addition to neuronal cells in the brain. Despite intensive efforts, a significant portion of the genetic etiology of AD remains unknown and must be identified.
The Significance of Power Electronics Components on Sustainable Energy Sources

Power Electronics is increasingly becoming popular in the applications of sustainable energy sources such as Solar, Wind. Extracting Electrical energy from sources of Sustainable and Non Sustainable energy, for examples solar, wind, coal, gas, requires processing unit. This processing unit must be a high efficient electronic circuit. This unit made of power electronics Components (PEC) plays a significant role for optimum efficiency. This paper briefly describes the importance of Energy Processing Unit (EPU) based on Power Electronics Components (PEC). The goal of this paper is to focus on the essence of PECs in the sustainable energy sources. To better understand the important of power electronics on SE, Solar power and wind power are highlighted in this paper.
Synthesis of Monitors for Networked Systems With Heterogeneous Safety Requirements

Complex embedded systems such as automobiles and IoT-systems feature a wide range of applications with varying degrees of safety relevance. As many applications on these devices interlace more and more, new ways to guarantee sufficient isolation between safety levels become necessary. Previous work only regarded monitoring of timing properties for individual tasks on single resources and neglected functional cause-effect chains. This makes their application costly because nearly every task would require monitoring due to unknown system-level timing dependencies. We present cross-layer dependency analysis of timing properties and system-level synthesis strategies for monitors utilizing a reachability formulation of the problem. Our approach regards distributed cause-effect chains on networked platforms for which the accuracy of timing parameters can vary and heterogeneous safety requirements can be specified.