Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Spatial Temporal Dynamic of Brain Network in Disorder of Consciousness via Community Analysis

A critical question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity characterized by perception, action, and adaptive behavior. The topology of network in modularity problem has been widely studied and can reflect many features of human brain. However, there are few researches on spatial structure and dynamic structure. In our research, we collect resting-state electroencephalogram (EEG) from 24 healthy controls, 45 patients with unresponsive wakefulness syndrome (UWS) and 25 patients with minimally conscious state (MCS). After dividing the EEG signal analysis into five bands, function network is constructed based on phase synchronization of the network. Then, we perform statistical analysis between groups of different frequency bands. The results show that there are statistically significant differences in the within-and between –module connectivity, community average spatial distance, community spatial diameter and network flexibility. In addition, most of the parameters are positively correlated with the consciousness level of the subjects. Our results suggest that the analysis of the modularized structure of complex networks including topology structure, spatial structure and dynamic structure may help to clarify the process of consciousness recovery and its pathophysiology in patients with disorder of consciousness (DOC), and may contribute to the clinical treatment and evaluation of patients with DOC.
Cluster-Delay Consensus for the Second-Order Nonlinear Multi-Agent Systems with Random Noises

Cluster-delay consensus in a noisy environment has been investigated for the second-order nonlinear multi-agent systems, which consist of a leader and several followers. A novel protocol with white noise is proposed for the followers of different groups tracking their leader gradually with distinct time delays, i.e., followers are divided into several groups according to the time-delays with the leader and each group approaches its own equilibrium state almost surely and exponentially. Based on the Lyapunov’s method for stability, matrix theory and stochastic differential equations, sufficient conditions are derived to achieve the cluster-delay consensus. Finally, simulation experiments are conducted to illustrate and validate our control algorithm.
Multifrequency Phase-Shifted Control for Multiphase Multiload MCR WPT System to Achieve Targeted Power Distribution and High Misalignment Tolerance

With urgent market demand for multiload electronic applications, multiload magnetically coupled resonant (MCR) wireless power transfer (WPT) system has caused widespread concern. In the practical applications, multiload MCR WPT system should have the capability to realize targeted power distribution among loads and stable output power against misalignment of load. To achieve this goal, a multiphase multifrequency system with multiple loads is proposed in this article, where multi-phase structure of transmitting coils can enhance the misalignment tolerance, and multiple operating frequencies are beneficial to achieve multiload identification and targeted power distribution. For simplicity, this article mainly studies a two-phase dual-frequency system with two loads and proposes two phase-shifted control strategies. The equivalent circuit model of the system is established, and the output power in the open-loop system under two phase-shifted control methodologies are analyzed. According to that, the targeted power distribution strategy by adjusting the resonant frequency of the transmitter is implemented. Moreover, the closed-loop control schemes based on phase-shifted control methodologies are adopted to realize stable output power precisely against the misalignment of loads. Finally, a prototype of two-phase dual-frequency MCR WPT system with two loads is built in the laboratory. The experimental results prove the correctness of the theoretical analysis.
Efficient Image Dehazing by Improving Retinex Algorithm

The traditional Retinex hazy image enhancement algorithm using a fixed-scale parameter filter without considering the disadvantages of hazy density, so we propose an improved Retinex dehazing algorithm based on the dark channel prior. According to the correspondence between filter scale parameters and mean values of image sub-block, this algorithm uses Gaussian filters with different scale parameters for different haze concentration regions. Secondly, this algorithm enhanced image performence with the help of Retinex. Finally, optimized details by restoring color information and enhancing image edges with improved fractional differentiation. Experiments prove that this algorithm improves the sharpness and contrast of the image, makes the image richer in detail, has a stronger edge retention ability, and also restores the image with better layering and global effects.
VARIATIONS IN DIGESTIVE TRACT MORPHOLOGY IN ROOT VOLE(MICROTUS OECONOMUS)AND ITS ADAPTIVE SIGNIFICANCE

The animal gut morphology is closed related to food habit,food quality and energyneed.The seasonal changes in length and weight of total and separate organs ofdigestive tract in root vole(Microtus oeconomus),which lived in Qinghai-Tibetanplateau,was determined.It was showed that the length,weight of with content,emptyand dry tissues of total and every organsstomach,small intestine,large intestine andcaecum)of root vole gut had significant seasonal variations. Each index was the lowestin excuberance period,which is moderate to vole,while in green up and/or whitheringperiod,which the fiber content is high in food and temperature is low,all indexes werehigher.The responsive magnitude of each organ was different and the small intestineresponded strongly to the seasonal acclimatization,This suggested that the adjustmentin morphology and capacity of digestive tract is important in animal's digestivestrategies.Different organs showed different responses to different stress,similarly,different pressure have a different effect to the digestive tract. Our results indicated thatroot vole have the ability physiologically to adapt the high-fiber food and lowtemperature environment in their natural habitats.
Controle de qualidade de ervas medicinais

O controle de qualidade da droga vegetal é imprescindível, pois muitas espécies vegetais são vendidas sem nenhuma garantia de qualidade, o que favorece desde a venda de espécies falsificadas até o armazenamento inadequado da droga vegetal durante a sua comercialização. A análise aqui descrita foi realizado com a droga vegetal adquirida e conhecida popularmente como quebra-pedra. O controle de qualidade dessa droga vegetal foi feito a começar da sua descrição microscópica, para verificar a autenticidade da espécie a fim de evitar equívocos. Também foram realizados testes químicos para confirmar as principais classes de seus constituintes químicos descritos na literatura (De Souza et. al., 2002; Duke, 2000). Os interesses terapêuticos do Phyllanthus niruri L. provêm principalmente de suas propriedades diuréticas para o combate de cálculos renais. Devido a isso, estão sendo desenvolvidos vários estudos para isolar a substância responsável por tais propriedades, além de elucidar a melhor maneira de tratamento (Teske et. al., 1995). Estudos experimentais com as folhas e as sementes também têm demonstrado a sua ação hipoglicemiante, antibacteriana e anticancerígena. Em ensaios especiais, mostrou-se que é ativa contra o vírus da hepatite B in vivo e in vitro. Além disso, possui a virtude de dissolver cálculos renais, impedindo a contração do ureter e promovendo a sua desobstrução. Desenvolve atividade diurética pela elevação da filtração glomerular e excreção urinária dos sais de uratos (Tona et. al., 2001; Teske et. al., 1995; Ogata et. al., 1992). Outro estudo realizado mostrou o efeito do Phyllanthus niruri sobre a cristalização do oxalato de cálcio in vitro. Por meio desse estudo, pôde-se concluir que o extrato de quebra-pedra interfere no crescimento e na agregação dos cristais na urina humana, sugerindo que essa planta tem um papel potencial na prevenção de cálculo renal ou do desenvolvimento (Barros, 2002; Freitas et. al., 2002; Campos et. al., 1999; Dias et. al., 1995).
Bilateral Teleoperation of Wheeled Mobile Robots Subject to Lateral Sliding on Soft Terrains

With the widespread applications of wheeled mobile robots (WMR) in various fields, new challenges have arisen in terms of designing its control system. A special challenge is the soft terrain which can induce both of the longitudinal slipping and the lateral sliding. This paper proposes a new method for haptic teleoperation control of a WMR with lateral sliding. In this teleoperation system, the linear velocity and the angular velocity of the WMR separately follow the master haptic interface’s positions. Due to the functional benefits of displaying the lateral sliding information as haptic feedback to the operator, the lateral velocity is segregated and only perceived by the human operator. Then the teleoperator for the master robot and the slave WMR is designed based on the passivity theory. Experiments of the proposed controller demonstrate that this approach results in stable bilateral teleoperation with a satisfactory tracking performance.
Extraction of Coix Seed Polysaccharide and its Antioxidant Activity

To study antioxidant activity of coix seed polysaccharide.Coix seed polysaccharide was extracted by microwave-assisted enzyme method.The antioxidant activities in vitro of coix seed polysaccharide were investigated by phenanthroline-Fe2+,pyrogallol autoxidation and T-AOC kit.The results showed that the content of total polysaccharide was 42.5 %. In the setting concentration range,the antioxidant activities of coix seed polysaccharide increased with the elevation of concentration.The clearance rates of hydroxyl free radical and superoxide anion free radical of 1mg/mL coix seed polysaccharide were 22.1 % and 24.2 %,respectively,and the total antioxidant activity was 3.1 Unit/mL.Our work provided the theory basis for further application of coix seed polysaccharide on food and drug area.
Resonant LLC DC–DC Converter Employing Fixed Switching Frequency Based on Dual-Transformer With Wide Input-Voltage Range

In this article, a dual-transformer-based LLC resonant converter is proposed, which is a hybrid combination of a full-bridge and half-bridge LLC circuits. The output voltage is regulated through fixed-frequency phase-shift pulsewidth modulation control scheme, whereas the switching frequency is equal to the resonant frequency, which helps to design the magnetic components. The turns-ratio design of the two transformers is analyzed by the current stress and power distribution of both transformers with two different cases. The proposed converter is capable of realizing soft switching over wide input-voltage and whole load range. Finally, a prototype converter of 1 kW is built and the experiments are conducted under wide input-voltage and full load range. The experimental results show that the proposed converter can achieve high efficiency throughout the full load range.
Stochastic input-to-state stability of stochastic nonlinear systems with Markovain switching

The stochastic input-to-state stability (SISS) of stochastic nonlinear systems with Markovian switching (SNSwMS) is studied in this paper. Firstly, the notion of SISS for SNSwMS is introduced. And then SISS for SNSwMS is studied. Finally, by the method of changing the supply functions, the stability of cascaded stochastic input-to-state stable stochastic nonlinear subsystems with Markovian switching is investigated.
Polyhedral Model Guided Automatic GPU Cache Exploitation Framework

We propose a compiler driven acceleration of parallel computations on GPUs by exploiting the various special varieties of caches (texture, surface and constant for NVIDIA GPUs). We show that our method obtains better performance for a class of computations when compared with earlier methods that use on-chip shared memory. We provide an end-to-end solution by developing a fully automatic, sound, static framework within a state-of-art source-to-source Polyhedral compiler (PPCG) to exploit these varieties of GPU caches. We use polyhedral model for profitability modeling of the particular variety of GPU caches. We evaluate our implementation on PolyBench/C benchmark kernels and report up to 1. 5x speedups over the current memory mapping strategy used by PPCG compiler. We also consider sample real-world representative kernels: PageRank, DNN layer (LSTM), solvers (Poisson and DWE-FDTD stencil), and show that using the special GPU caches in these programs results in up to 2. 6x speedup over a standard shared memory based implementation. We believe that our contribution is towards automatic exploitation of GPU cache/memory hierarchy as it shows general purpose computing usage of special GPU caches that were originally designed for image processing applications.
Group consensus of continuous-time second-order multi-agent systems via asynchronous sampled-data control

This paper investigates the group consensus problem of continuous-time second-order multi-agent systems (MASs) with asynchronous sampling, where each agent has its own clocks that are not affected by the others’. The asynchronous sampled-data based protocol is designed by using the neighbors’ state information with uncertain time-varying sampling intervals. In continuous-time second-order MASs, the asynchronous group consensus problem can be equivalently transformed into a stability problem of synchronous discrete-time second-order MASs by using appropriate model transformations. With the help of the nonnegative matrix theory and graph theory, the stability is analyzed and a pretty graphic sufficient condition is established. Besides, a simulation example is given to demonstrate the proposed result.
Energy Management of a Residential Consumer with Uncertain Renewable Generation: A Robust Dual Dynamic Programming Approach

This paper studies the dynamic operation of a residential electric energy system with distributed renewable generation. Energy streams among the power grid, local renewable plant, storage unit, and demand are considered; the temporal volatility of renewable generation is described by an uncertainty set, and the optimal operation problem gives rise to a multi-stage robust optimization. To deal with the non-anticipativity issue, the robust model is solved via robust dual dynamic programming (RDDP) approach. The the cost-to-go function is approximated by iteratively updating the upper and lower bounds, which can be obtained from forward-pass and backward pass procedures. Case studies validate the performance and advantage of the proposed method.
Design of an Isolated Bidirectional DC–DC Converter With Built-in Filters for High Power Density

This article aims to realize an isolated dc–dc converter with high power density, high power efficiency, and low noise performance. Although a dual-active-half-bridge (DAHB) converter can achieve high efficiency, it requires input and output low-pass filters to suppress the operational ripple current and switching noise. Owing to the volume of the filters, a DAHB converter cannot be implemented with high power density. This article presents a design theory for a novel isolated bidirectional dc–dc converter with built-in filters. The proposed converter is derived from a DAHB converter and can eliminate the input and output filters via split windings and tank capacitors; the principle of elimination is based on $LC$ low-pass filter function and zero-ripple-current operation. As the number of transistors, capacitors, and magnetic cores of the proposed converter are equal to those of the DAHB converter, the application of the proposed converter allows volume reduction by eliminating the filter components at the input and output ports. Theoretical equations are derived to design a power flow control function and a filter function simultaneously. Finally, prototypes of a DAHB converter and the proposed converter are constructed for comparison. The results demonstrate that the input port noise of the proposed prototype is lower than that of the DAHB prototype by 10 dB (peak) over 1 MHz, and the proposed approach can achieve a significant improvement in efficiency.
A 20-GHz Bandwidth Power Amplifier for Phased Array 5G New Radio Transmitters

A 27–47-GHz differential cascode power amplifier for millimeter-wave 5G new radio applications is presented. The PA is a three-stage design using inductively coupled impedance transformers with 31-dB nominal power gain. A device periphery ratio of 1:2:6 is adopted for predriver, driver, and final stage, respectively. A gain equalization technique was used in the interstage transformers to obtain the required bandwidth with high gain flatness. To enable the use of 2.7-V supply, a cascode topology was employed in all three stages. A small signal gain of 31 dB was achieved with a 3-dB bandwidth of 20 GHz, equivalent to a 54% fractional bandwidth centered at 37 GHz. A saturated output power of 20.6 dBm was measured at 38.5 GHz. With a 100-MHz 64-QAM OFDM modulated signal at 37 GHz and at EVM = −25 dB, an output power of 11.6 dBm and an ACLR1 of −32/−30.8 dBc were obtained. An SiGe HBT BiCMOS process with $f_{\mathrm{ MAX}} = 330$ GHz was used for fabrication. The PA has an active area of 0.28 mm2 and is measured to a best in class FOM of 94.7 dB.
Quasi-time-dependent static output feedback control of 2-D switched systems with mode-dependent persistent dwell-time

In this paper, the static output feedback control for discrete-time two-dimensional switched systems with mode-dependent persistent dwell-time is studied. The discrete-time two-dimensional switched system is formulated by the famous Fornasini–Marchesini local state-space model. Based on a suitable Lyapunov function for mode-dependent persistent dwell-time, a quasi-time-dependent static output feedback controller which guarantee the resulting closed-loop system exponentially stable is presented. The theoretical result is verified by a numerical example.
Comparative Analysis of Pre-process Pipelines For Automatic Retinal Vessel Segmentation

Retinal vessel structure is an unique individual characteristic and important biology marker of many diseases, like Diabetic Retinopathy (DR), cardiovascular ailment, and so on. Automatic retinal vessel segmentation can be used to assist the early diagnosis of above diseases, but suffers from the poor quality and low contrast of fundus images. To eliminate the noise in the fundus images, many pre-process pipelines are designed to normalize and enhance the fundus images. However, the specific operations in pre-process pipelines of the fundus images haven’t been distinguished from operations in normalization of natural images. This paper collects a dozen of pre-process pipelines from published retinal vessel segmentation researches, and proposes five general patterns of these pre-process pipelines. Furthermore, we test the flexibility of five classical pre-process pipelines on public retinal vessel datasets with a Dense-UNet model. Experiments demonstrate that the "Hiera" pre-process pipeline and the "DUNet" pre-process pipeline outperform the rest pipelines in assisting the Dense-UNet to segment the retinal vessels.
Polyhedral Tensor Schedulers

Compiler optimizations based on the polyhedral model are able to automatically parallelize and optimize loop-based code. We acknowledge that while polyhedral techniques can represent a broad set of program transformations, important classes of programs could be parallelized just as well using less general but more tractable techniques. We apply this general idea to the polyhedral scheduling phase, which is one of the typical performance bottlenecks of polyhedral compilation.We focus on a class of programs in which enough parallelism is already exposed in the source program, and which includes Deep Learning layers and combinations thereof, as well as multilinear algebra kernels. We call these programs “tensor codes and consequently call “tensor schedulers” the tractable polyhedral scheduling techniques presented here.The general idea is that we can significantly speed up polyhedral scheduling by restricting the set of transformations considered. As an extra benefit, having a small search space allows us to introduce non-linear cost models, which fills a gap in polyhedral cost models.
Fixed-point self-tuning CPU performance controller for Linux kernel

Power saving techniques based on dynamic voltage and frequency scaling (DVFS) allow to optimize performance of processors capable of operating in more and more energy-efficient operating modes. In this paper we propose a new design of DVFS-based self-tuning CPU performance controller for the Linux kernel. The controller autonomously adapts to currently observed model of CPU load dynamics and adjusts CPU performance level to compensate for load variations while optimizing energy efficiency of performed operations.
Sphere Margins Softmax for Face Recognition

In conjunction with the cross-entropy loss, softmax function has gained great popularity in supervised deep convolutional neural networks (DCNNs). Although original softmax loss reduces training difficulty and makes multi classification problems easier to converge, the module does not explicitly encourage compactness within class and separability between classes, from this point of view it is not particularly suitable for face recognition tasks. In this paper, we reformulate the softmax loss with sphere margins (SM-Softmax) by normalizing both weights and extracted features of the last fully connected layer and have quantitatively adjustable angular margin by hyperparameter m1 and m2. Extensive experiments on CASIA-WebFace and Labeled Face in the Wild (LFW) validate that our SM-Softmax gives better results than the present state-of-the-art methods while adopting the same experimental configuration (benchmark datasets and network structure) and the convergence speed in the early stage is accelerated significantly.
ScaleML: Machine Learning based Heap Memory Object Scaling Prediction

Memory subsystem contributes 28-40% of total energy consumption. Several studies investigated energy prediction and consumption via profiling memory object access patterns. However, such profiling leads to higher energy consumption due to intense memory object-level profiling to achieve high prediction accuracy. Further, memory object access pattern prediction has been considered through analyzing the variation between memory object access patterns, referred to as scaling rate. The existing techniques for scaling rate prediction, such as Linear Scaling Rate (LSR), suffer from a high error rate in prediction with changes in access patterns, which leads to a high error rate of energy consumption prediction. In this paper, we compare and evaluate several memory object access pattern prediction models including LSR and machine learning (ML) models. Further, we propose ScaleML, a heap memory object scaling rate prediction mechanism that employs an ML model to achieve high access pattern prediction accuracy with variations in memory object access patterns. We evaluate ScaleML using various application benchmarks. The experimental results show that ScaleML achieves about 20% higher accuracy than the LSR model for predicting the scaling rate of object access patterns and energy estimation.
The use of antibiotics and bacterial resistance

The discovery of antibiotics makes bacterial infections treated effectively and controlled. However,unreasonable application of broad-spectrum antibiotics in the clinical treatment and abuse in non- clinical field led to bacterial resistance against antibiotics and clinical antibiotic treatment failure. At present,the issue of antibiotic resistance has become a focus of global concern. In order to improve the therapeutic effect of antimicrobial agents,and reduce bacterial resistance and the etiological diagnosis of infectious diseases,bacterial resistance monitoring,rational use of antibiotics and other aspects were reviewed in this article.