Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the Softmax Bellman Operator: Theoretical Properties and Practical Benefits

The softmax function has been primarily employed in reinforcement learning (RL) to improve exploration and provide a differentiable approximation to the max function, as also observed in the mellowmax paper by Asadi and Littman. This paper instead focuses on using the softmax function in the Bellman updates, independent of the exploration strategy. Our main theory provides a performance bound for the softmax Bellman operator, and shows it converges to the standard Bellman operator exponentially fast in the inverse temperature parameter. We also prove that under certain conditions, the softmax operator can reduce the overestimation error and the gradient noise. A detailed comparison among different Bellman operators is then presented to show the trade-off when selecting them. We apply the softmax operator to deep RL by combining it with the deep Q-network (DQN) and double DQN algorithms in an off-policy fashion, and demonstrate that these variants can often achieve better performance in several Atari games, and compare favorably to their mellowmax counterpart.
About the Possibilities of Subject's Changed State Identification on Face Thermograms After Exposure of Different External Stimuli: Analysis of Identification Feature Space and Key Tasks Determination

The results of an analytical study of the current state in the field of remote recognition of subjects' state are presented. A promising tool in this case is thermal imaging, which has already been represented in medical applications. Field and computational experiments were provided to collect and analyze the thermograms of subjects before and after exposure to external stimuli (images with unpleasant contents, noise, a small dose of alcohol). Conclusions about the possibility of changed state identification even with minor effects on the subject are provided. A new space of identification features of person thermograms is proposed to solve this problem. New challenges for future work are determined.
S2L: Single-Streamline For Complex Video Event Detection

In this paper, we focus on event detection in complex videos. Due to the large scale perspective changing and irregular cam- era moving, optical flow fails to capture accurate motion in these videos. To solve this problem, we propose a Single- StreamLine (S2L) model to implicitly represent motion information from two aspects. Specifically, 1) we build relation- ship between frames through temporal encoder for the fact that motion exists in consecutive video frames; 2) we combine low level appearance feature and high level semantic feature in the reason that motion can be observed via the changing in both appearance and semantics. Our experiments use YLI- MED dataset which is an open TRECVID-style video croups based on YFCC100M and includes ten video events. The visualization of feature shows the great power of our model and the experiment result improves at least 2% in accuracy com- pared with the current state-of-the-art.
Modelling of Hysteresis Phenomenon Based on the Elemental Operator and Wind-Rose Method

This paper introduces a vector hysteresis operator based on the physical mechanisms of the magnetic material. With the help of the schematic diagrams of energy barrier and the traditional wind-rose method, the magnetization orientation of each operator under different rotational magnetic field can be determined, as well as the rotation process of the corresponding magnetization. Meanwhile, a two-dimensional distribution function is utilized to describe the density of the interacting operators in the given specimen. To verify this rotational hysteresis model, the magnetic properties of soft magnetic composite SOMALOY™ 500 under different rotational excitations have been simulated and compared with the experimental results. Besides, the error analysis has been given to demonstrate the significance of parameters in the distribution function. The comparison shows the practicability of this vector operator and the wind-rose method.
Reliability-Oriented Optimization of Computation Offloading for Cooperative Vehicle-Infrastructure Systems

Computation offloading is critical for mobile applications that are sensitive to computational power, while dynamic and random nature of vehicular networks makes it challenging to guarantee the reliability of vehicular computation offloading. In this letter, we propose a reliability-oriented stochastic optimization model based on the dynamic programming for computation offloading in the presence of the deadline constraint on application execution. Specifically, a theoretical lower bound of the expected reliability of computation offloading is derived, and then an optimal data transmission scheduling mechanism is proposed to maximize the lower bound with consideration of randomness in vehicle-to-infrastructure communications. Experimental results demonstrate that our mechanism can outperform the conventional scheme and benefits vehicular computation offloading in terms of reliability performance in stochastic situations.
A New Asymmetric Link-Based Binary Regression Model to Detect Parkinson's Disease by Using Replicated Voice Recordings

Addressing dependent data as independent has become usual for Parkinson's Disease (PD) detection by using features extracted from replicated voice recordings. A binary regression model with an Asymmetric Student $\mathrm{t}$ (AST) distribution as link function has been developed in a classification context by taking into account the within-subject dependence. This opens the possibility of handling situations in which the probabilities of the binary response approach 0 and 1 at different rates. The computational issue has been addressed by proposing and using a representation based on a mixture of normal distributions for the AST distribution. This allows to include latent variables to derive a Gibbs sampling algorithm that is used to generate samples from the posterior distribution. The applicability of the proposed approach has been tested with a simulation-based experiment and has been applied to a real dataset for PD detection.
Performance Evaluation of an Isolated DC-DC Converter with Discontinuous Supply

This paper presents the development, analysis, and performance evaluation of a new topology for an isolated dc power electronic converter (PEC). The proposed topology (called the reverse fly-back dc PEC) is developed to process a discontinuous input voltage and produce a continuous output voltage. The reverse fly-back dc PEC is operated based on charging the inductive element in the high-frequency transformer, while discharging the energy to the output side through On-Off actions of the switching element, which is located on the secondary side of the high-frequency transformer. The proposed reverse fly-back dc PEC is tested for its performance under different operating conditions. Performance results show that the reverse fly-back dc PEC is capable of processing discontinuous input voltages with different duty ratios, and maintain continuous output voltages with a minor sensitivity to the load type of ratings.
Multi-View Classification and 3D Bounding Box Regression Networks

We present a method for jointly classifying objects in depth maps and regressing amodal (extending beyond occluded parts) 3D bounding boxes in a way that is highly robust to occlusions. Our method is based on a novel multi-view convolutional neural network architecture with shared layers for both tasks, improving efficiency. The network processes views that encode object geometry and occlusion information and outputs class scores and bounding box coordinates in world coordinates, requiring no post-processing steps. We demonstrate the effectiveness of our method by example of fall detection, presenting a new dataset of 40k samples rendered from 3D models. On this dataset, our method achieves an average classification accuracy above 97% and a regression error below 10 cm at occlusion ratios of up to 90%. The dataset and trained models are publicly available.
Model based Beamspace Channel Estimation for Millimeter Wave Massive MIMO System

Channel estimation concerning channel power leakage is investigated for millimeter wave (mmWave) massive MIMO system where the base station (BS) is equipped with uniform planar array (UPA). To model the channel leakage, the leakage effect of two dimensional beamspace channel is firstly analyzed. A theorem is proved to show that a four-point star (FS)-based model is better than the existing dual-crossing (DC)-based model. Then a FS-based beamspace channel estimation algorithm is proposed, where the initial selection of dominant entries is computed offline and then the selection is iteratively refined until the stop condition is satisfied. Simulation results verify the effectiveness of our work and show that the FS-based channel estimation algorithm outperforms the DC-based channel estimation algorithm.
Capabilities for Digital Platform Survival: Insights from a Business-to-Business Digital Platform

A digital platform is viewed as the central point of gravity within its business ecosystem to facilitate value co-creation processes among its business ecosystem’s constituent actors. Considering the specificities of business ecosystems, we investigate digital platforms’ sustained growth—referred to as digital platform permanence in this study. We posit that digital platform permanence is contingent on its owner’s capabilities to ensure efficient and effective value co-creation processes among the digital platform’s constituent actors on both service system and service ecosystem levels. Building on the inherent control-generativity dualism of digital platforms and through investigating an existing business-to-business digital platform, we identify four key capabilities for digital platform permanence. While two capabilities (system orchestration and system reformation) reflect the owner’s ability to facilitate value co-creation processes on the service system level, the other two capabilities (ecosystem preservation and ecosystem diversification) reflect the owner’s ability to facilitate value co-creation processes on the service ecosystem level.
A randomized hierarchical trees indexing approach for camera-based information spotting

In this paper, we propose an indexing approach for camera-based document image retrieval and spotting systems. The proposed approach is based on randomized hierarchical trees without storing database vector points in the memory. To construct the trees, k-means-based clustering is used for splitting the data points of every non-leaf node into 2 distinct groups. Instead of using the entire dimensions, only a small number of dimensions is chosen randomly and they are combined with the dimension with the highest variance which is computed along all dimensions and the maximum variance is selected. Experimental results demonstrate the usefulness of the proposed approach for limited memory situations, as the proposed random trees could approximately reach the accuracy of state-of-the-art methods on Tobacco dataset without storing the database descriptors in memory.
EMG Feature Extractions for Upper-Limb Functional Movement During Rehabilitation

Rehabilitation is important treatment for post stroke patient to regain their muscle strength and motor coordination as well as to retrain their nervous system. Electromyography (EMG) has been used by researcher to enhance conventional rehabilitation method as a tool to monitor muscle electrical activity however EMG signal is very stochastic in nature and contains some noise. Special technique is yet to be researched in processing EMG signal to make it useful and effective both to researcher and to patient in general. Feature extraction is among the signal processing technique involved and the best method for specific EMG study needs to be applied. In this works, nine feature extractions techniques are applied to EMG signals recorder from subjects performing upper limb rehabilitation activity based on suggested movement sequence pattern. Three healthy subjects perform the experiment with three trials each and EMG data were recorded from their bicep and deltoid muscle. The applied features for every trials of each subject were analyzed statistically using student T-Test their significant of p-value. The results were then totaled up and compared between the nine features applied and Auto Regressive coefficient (AR) present the best result and consistent with each subjects' data. This feature will be used later in our future research work of Upper-limb Virtual Reality Rehabilitation.
Reliability Investigation of TMR and DMR Systems with Global and Partial Reservation

In this article are considered three types system structures: TMR ("2-of-3"), "2-of-2" global reservation by replacing and "2-of-2" with partition reservation by replacing. Here are considered formulas for calculation of reliability the indices "System Availability" and «Mean Time Between Failures. " On this basis here is conducted an analytical research of the reliability of the three systems. In conclusion, here is made a comparative analysis to determine which structure under what conditions is most reliable.
Performance Analysis of LTE Network using QAM and MIMO Configuration

Day by day increasing demand for higher data rate motivates the innovation in technology which fulfills the demand for growing data usage. It is thought-provoking to identify and analyze the innovations which are currently driving the mobile broadband market globally. Recent standard introduced by Third Generation Partnership Project (3GPP) group promises high-speed data, multimedia unicast and multimedia broadcast services is Long Term Evolution (LTE). This paper insight the performance of LTE network under different radio conditions to improve throughput and spectral efficiency of the system. Evaluation is also done for different Multiple Input Multiple Output (MIMO) configurations using MATLAB and Vienna LTE-A system level simulator. Simulation results show that peak throughput can be improved using 256QAM under good radio condition, Co-ordinated multipoint improve the cell edge throughput and MU-MIMO provides higher capacity with better throughput when the load is high.
Multi-source Domain Adaptation for Face Recognition

For transfer learning, many research works have demonstrated that effective use of information from multi-source domains will improve classification performance. In this paper, we propose a method of Targetize Multi-source Domain Bridged by Common Subspace (TMSD) for face recognition, which transfers rich supervision knowledge from more than one labeled source domains to the unlabeled target domain. Specifically, a common subspace is learnt for several domains by keeping the maximum total correlation. In this way, the discrepancy of each domain is reduced, and the structures of both the source and target domains are well preserved for classification. In the common subspace, each sample projected from the source domains is sparsely represented as a linear combination of several samples projected from the target domain, such that the samples projected from different domains can be well interlaced. Then, in the original image space, each source domain image can be represented as a linear combination of neighbors in the target domain. Finally, the discriminant subspace can be obtained by targetized multi-source domain images using supervised learning algorithm. The experimental results illustrate the superiority of TMSD over those competitive ones.
Reporting Guidelines and Issues to Consider for Using Intracranial Brain Stimulation in Studies of Human Declarative Memory

Participants with stimulating and recording electrodes implanted within the brain for clinical evaluation and treatment provide a rare opportunity to unravel the neuronal correlates of human memory, as well as offer potential for modulation of behavior. Recent intracranial stimulation studies of memory have been inconsistent in methodologies employed and reported conclusions, which renders generalizations and construction of a framework impossible. In an effort to unify future study efforts and enable larger meta-analyses we propose in this mini-review a set of guidelines to consider when pursuing intracranial stimulation studies of human declarative memory and summarize details reported by previous relevant studies. We present technical and safety issues to consider when undertaking such studies and a checklist for researchers and clinicians to use for guidance when reporting results, including targeting, placement, and localization of electrodes, behavioral task design, stimulation and electrophysiological recording methods, details of participants, and statistical analyses. We hope that, as research in invasive stimulation of human declarative memory further progresses, these reporting guidelines will aid in setting standards for multicenter studies, in comparison of findings across studies, and in study replications.
A Low-Profile Coplanar Dual-Polarized and Dual-Band Base Station Antenna Array

This paper presents a low-profile coplanar dual-polarized dual-band base station antenna array. A slotted patch antenna operating from 0.82 to 0.99 GHz with four shorting strips is proposed and analyzed. The four shorting strips are employed not only to improve the impedance matching in the lower band but also to remove the center part of the patch, thus making room for higher band dipoles operating from 1.68 to 2.86 GHz. The height of the proposed array is nearly half of the conventional base station array while other performances are guaranteed. The measured results show that the proposed antenna array has wide impedance bandwidths of 18.7% (0.82–0.99 GHz) and 57.9% (1.68–2.86 GHz) (VSWR < 2) with a low profile ( $0.109\lambda _{0}$ at 820 MHz). The measured gains are 13.8±0.5 dB and 16.7 ± 1 dB in lower and upper bands. Large front-to-back ratio (>24.6 dB) and high isolations (>30 and >25 dB in lower and upper bands, respectively) are achieved. The proposed antenna can be easily built with printed circuit board fabrication technique and applied in low profile multiband base stations for 2G/3G/4G and the next-generation systems.
Stable Propagation of Inhibited Spiking Dynamics in Vertical-Cavity Surface-Emitting Lasers for Neuromorphic Photonic Networks

We investigate experimentally and theoretically the communication of inhibited spiking dynamics between two interlinked photonic neurons based upon the vertical-cavity surface-emitting lasers (VCSELs). We show that the sub-nanosecond speed spiking dynamics fired by a Transmitter-VCSEL (T-VCSEL) can be inhibited under the arrival of suitable external stimuli and that the inhibited spiking behaviors are propagated into another Receiver-VCSEL (R-VCSEL). Both VCSELs exhibit analogous inhibited spiking dynamics in response to stimuli with different temporal durations and strength. In addition, a very good agreement is found between theoretical simulations and experiments. These results offer greater prospects for future networks of VCSEL-based photonic neurons for neuromorphic computing platforms.
Iterative De-Embedding and Extracted Maximum Oscillation Frequency $f_{\text{MAX}}$ in mm-Wave InP DHBTs: Impact of Device Dimensions on Extraction Errors

InP/GaAsSb DHBTs with varying emitter lengths $\pmb{L}_{\text{E}}$, emitter widths WE, and base metal widths $\pmb{W}_{\mathbf{B}}$ are characterized to determine their maximum oscillation cutoff frequency $f_{\text{MAX}}$ using iterative de-embedding in comparison to the standard OPEN-SHORT and SHORT-OPEN de-embedding schemes. With increasing measurement frequencies the OPEN-SHORT and SHORT-OPEN methods become increasingly subject to pathologies in the Mason Unilateral power gain which corrupt the extracted $f_{\mathbf{MAX}}$. In contrast, iterative de-embedding is free of such complications. In the present work, we characterize the $f_{\text{MAX}}$ extraction error introduced by the usual de-embedding techniques with respect to iterative de-embedding as a function of DHBT dimensions $\pmb{L}_{\mathbf{E}},\pmb{W}_{\mathbf{E}}$, and $\pmb{W}_{\mathbf{B}}$ to reveal that extracted $\pmb{f}_{\mathbf{MAX}}$ values are especially sensitive to the emitter width $\pmb{W}_{\mathbf{E}}$. De-embedding errors appear to carry over to extrinsic collector area which sensitively affects $f_{\text{MAX}}$.
Prediction of Lung Cancer from Low-Resolution Nodules in CT-Scan Images by using Deep Features

Prediction of lung cancer from CT-scan images is viewed as a challenging task in medical image analysis. It is because pulmonary nodules occupy less than 5% of a CT-scan image and vary highly in terms of their shape, texture, opacity and location, making it difficult to analyze by naked eye. To overcome this challenge, multiple approaches using digital processing of CT-scans are proposed. These existing approaches lack in addressing three main challenges in lung cancer prediction: the problem of low resolution data, lack of generalized nodule features, and cancer prediction in terms of a malignancy score. These challenges are addressed in the paper. In the proposed approach, CT-scan images are preprocessed and super-resolved to serve as input to a convolutional neural network for nodule region detection. Deep features engineered from different layers of this convolutional neural network are classified into two classes: benign and malignant. Additionally, these features are passed through a regressor to predict malignancy score. CT-scan images from a subset of the LIDC-IDRI dataset, called the LUNA16 dataset, are used for experimentation. An accuracy of 85.7% is achieved for the nodule classification task using the approach proposed in this paper, which is a substantial improvement over previous works in literature.
Enhanced Parameter Convergence for Linear Systems Identification: The DREM Approach*

Dynamic regressor extension and mixing is a new technique for parameter estimation that has proven instrumental in the solution of several open problems in system identification and adaptive control. A key property of the estimator is that, for linear regression models, it guarantees monotonicity of each element of the parameter error vector that is a much stronger property than monotonicity of the vector norm, as ensured with classical gradient or least-squares estimators. On the other hand, the overall performance improvement of the estimator is strongly dependent on the suitable choice of certain operators that enter in the design. In this paper we investigate the impact of these operators on the convergence properties of the estimator in the context of identification of linear time-invariant systems. In particular, we give some guidelines for their selection to ensure convergence under the same (persistence of excitation) conditions as standard identification schemes.
Optimal Study of Resonant Wireless Charging Coils for Electric Vehicles

In order to optimize the coil design of electric vehicle (EV) high power resonant wireless power transfer (WPT) system and improve its transfer efficiency, an equivalent circuit model of two-coil series-series topology is analyzed in this paper. Four optimal factors that affect the system transfer efficiency are proposed, i.e. the number of coil turns, the mean radius of coils, the transfer distance and the load. Based on the consideration of the equivalent series resistance of the coils, it can be found that the number of coil turns, the mean coil radius, and the load have the optimal values for the optimal transfer efficiency, while the shorter the transfer distance is, the higher the transfer efficiency is. By using the simulation software COMSOL, similar relationships between four factors and the transfer efficiency can be achieved with the calculation results. Moreover, by repeating the calculation using the simulation coil resistance, the differences between the calculation results and the simulation results are explained. Therefore, the WPT coil optimal method with proposed four optimal factors is verified through calculation and simulation.