Miracle-dz/UniFAR-FTUs huggingface dataset:
A PSO-Optimized Fuzzy Reinforcement Learning Method for Making the Minimally Invasive Surgical Arm Cleverer

For robotic-assisted surgery, the preoperative preparation procedure within which the surgical arms need to be adjusted manually to their expected configuration can pose interlocking problems in terms of accuracy, system robustness, and human-robot interaction experience. Previous work about variable impedance/admittance control methods did improve the smoothness of this procedure while individual characteristics of different operating personnel haven’t yet been adequately considered. To further improve this process on the basis of existing methods, a novel strategy based on fuzzy Sarsa( $\lambda $ )-learning algorithm is both proposed and incorporated into the virtual parameter adjustment strategy to achieve physical human–robot interaction (pHRI) so that the robot can acclimatize various handling characteristics of different operators through enough online learning. To also shorten the online training cycle, reduce the undesirable subjective factors and improve the overall training performance, a particle-swarm-optimization-based (PSO-based) algorithm is as well employed for optimizing the partition of state variable space and the distribution of discrete actions. Several groups of experiments have demonstrated the validity of this scheme and the effectiveness of the PSO optimization element.
Vitamin D supplementation for nonalcoholic fatty liver disease in type 2 diabetes mellitus

Abstract Background: Nonalcoholic fatty liver disease (NAFLD) is strongly associated with type 2 diabetes mellitus (T2DM), and low vitamin D levels are positively associated with NAFLD and T2DM. But there is absence of convincing evidence-based medicine to confirm the efficacy of vitamin D supplementation for T2DM with NAFLD. Thus, we aimed to conduct this meta-analysis to summarize the efficacy of vitamin D supplementation for T2DM combined with NAFLD, and help to further clarify its beneficial action on diabetic patients with NAFLD. Methods: The study only selects clinical randomized controlled trials of vitamin D supplementation for T2DM combined with NAFLD. We will search each database from the built-in until July 2020. The English literature mainly searches Cochrane Library, Pubmed, EMBASE, and Web of Science. While the Chinese literature comes from CNKI, CBM, VIP, and Wangfang database. Meanwhile, we will retrieve clinical trial registries and grey literature. Two researchers worked independently on literature selection, data extraction, and quality assessment. The dichotomous data is represented by relative risk (RR), and the continuous is expressed by mean difference (MD) or standard mean difference (SMD), eventually the data is synthesized using a fixed effect model (FEM) or a random effect model (REM) depending on the heterogeneity. The imaging markers of liver, biomarkers of hepatic steatosis, serological indexes of hepatic fibrosis, serum NAFLD liver fat score were evaluated as the main outcomes. While several secondary outcomes were also evaluated in this study. The statistical analysis of this meta-analysis was conducted by RevMan software version 5.3. Results: This meta-analysis will further determine the beneficial efficacy of vitamin D supplementation for T2DM combined with NAFLD. Conclusion: This study determines the positive efficacy of vitamin D supplementation for diabetic patients with NAFLD.
Racial-ethnic differences in social networks and perceived support: Measurement considerations and implications for disparities research.

OBJECTIVES Racial-ethnic differences in physical/mental health are well documented as being associated with disparities; however, emerging conceptual models increasingly suggest that group differences in social functioning and organization contribute to these relationships. There is little work examining whether racial-ethnic groups respond similarly to classic measures of social networks and perceived support and whether there are significant between-groups differences on these measures. METHOD A multisite, cross-sectional study of 2,793 non-Hispanic White (NHW), non-Hispanic Black (NHB), and Hispanic participants was conducted using common measures of social networks and perceived support. A confirmatory factor analytic model was used to test for the invariance of factor covariance and mean structures in a three latent constructs model including social network, social provisions, and interpersonal support. Between-group differences in structural and functional support were assessed. RESULTS We established measurement invariance of the latent representations of these measures suggesting that racial-ethnic groups responded comparably. In direct comparisons, Hispanics and NHWs demonstrated similar levels of network structure and support. In contrast, NHWs reported support advantages on a majority of measures compared with NHBs. CONCLUSIONS Findings support the use of these measures across groups and provide initial support for potential differences in this hypothesized mediator of racial-ethnic health disparities. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
TS Cache: A Fast Cache With Timing-Speculation Mechanism Under Low Supply Voltages

To mitigate the ever-worsening “power wall” problem, more and more applications need to expand their working voltage to the wide-voltage range including the near-threshold region. However, the read delay distribution of the static random access memory (SRAM) cells under the near-threshold voltage shows a more serious long-tail characteristic than that under the nominal voltage due to the process fluctuation. Such degradation of SRAM delay makes the SRAM-based cache a performance bottleneck of systems as well. To avoid unreliable data reading, circuit-level studies use larger/more transistors in a bitcell by sacrificing chip area and the static power of cache arrays. Architectural studies propose the auxiliary error correction or block disabling/remapping methods in fault-tolerant caches, which worsen both the hit latency and energy efficiency due to the complex accessing logic. This article proposes a timing-speculation (TS) cache to boost the cache frequency and improve energy efficiency under low supply voltages. In the TS cache, the voltage differences of bitlines (BLs) are continuously evaluated twice by a sense amplifier (SA), and the access timing error can be detected much earlier than that in prior methods. According to the measurement results from the fabricated chips, the TS L1 cache aggressively increases its frequency to $1.62\times $ and $1.92\times $ compared with the conventional scheme at 0.5- and 0.6-V supply voltages, respectively.
MHC class I polymorphic Alu insertion (POALIN) allele and haplotype frequencies in the Arabs of the United Arab Emirates and other world populations

Polymorphic Alu insertions (POALINs) are found throughout the human genome and have been used in various studies to infer geographic origin of human populations. The main aim of this study was to determine the allele and haplotype frequencies of five POALINs, AluHF, AluHG, AluHJ, AluTF and AluMICB, within the major histocompatibility complex (MHC) class I region of 95 UAE Arabs, and correlate their frequencies to those of the HLA‐A, HLA‐C and HLA‐B class I allele lineages. Evolutionary relationships between the POALINs of the Arabs and those previously studied in populations of African, Asian and European descent were compared. At each of the five Alu loci (AluHF, AluHG, AluHJ, AluTF and AluMICB), Alu insertion was designated as Alu(locus)*02 and absence was Alu(locus)*01. The AluHG insertion (AluHG*02) had the highest frequency (0.332), followed by AluHF*02 (0.300), AluHJ*02 (0.263), AluMICB*02 (0.111) and AluTF*02 (0.058). Of the 270 Alu‐HLA haplotypes pairs in the UAE Arabs, 110 had no Alu insertion, and 54 had an Alu insertion at >50% per haplotype. An Alu insertion >75% per haplotype was found between AluMICB*02 and HLA‐B*14, HLA‐B*22, HLA‐B*44, HLA‐B*55, HLA‐B*57 and HLA‐B*73, and with HLA‐C*01 and HLA‐C*18; AluHJ*02 with HLA‐A*01, HLA‐A*19, HLA‐A*24 and HLA‐A*32; AluHG*02 with HLA‐A*02 and HLA‐B*18; and AluHF*02 with HLA‐A*10. The genotyped allele and haplotype frequencies of the MHC POALINs in UAE Arabs were compared with the results of 30 previously published Asian, European, American and African populations. Phylogenetic and multidimensional scaling (MDS) analysis of the relative MHC POALINs allele and haplotype frequencies revealed that the UAE Arabs have a similar lineage to Caucasians and the most distant genetic relationship to the Waorani native American population of Ecuador. The structure of both the phylogenetic tree and the MDS analysis supports the Out of Africa theory of human evolution. The nature of the clusters suggests the Arabian Middle East represents a crossroads from which human populations migrated towards Asia in the east and Europe to the north‐west.
A Simple Local Variational Iteration Method and Related Algorithm for Nonlinear Science and Engineering

A very simple and efficient local variational iteration method for solving problems of nonlinear science is proposed in this paper. The analytical iteration formula of this method is derived first using a general form of first order nonlinear differential equations, followed by straightforward discretization using Chebyshev polynomials and collocation method. The resulting numerical algorithm is very concise and easy to use, only involving highly sparse matrix operations of addition and multiplication, and no inversion of the Jacobian in nonlinear problems. Apart from the simple yet efficient iteration formula, another extraordinary feature of LVIM is that in each local domain, all the collocation nodes participate in the calculation simultaneously, thus each local domain can be regarded as one node in calculation through GPU acceleration and parallel processing. For illustration, the proposed algorithm of LVIM is applied to various nonlinear problems including Blasius equations in fluid mechanics, buckled bar equations in solid mechanics, the Chandrasekhar equation in astrophysics, the low-Earth-orbit equation in orbital mechanics, etc. Using the built-in highly optimized ode45 function of MATLAB as a comparison, it is found that the LVIM is not only very accurate, but also much faster by an order of magnitude than ode45 in all the numerical examples, especially when the nonlinear terms are very complicated and difficult to evaluate.
Holistic Large Scale Video Understanding

Action recognition has been advanced in recent years by benchmarks with rich annotations. However, research is still mainly limited to human action or sports recognition - focusing on a highly specific video understanding task and thus leaving a significant gap towards describing the overall content of a video. We fill in this gap by presenting a large-scale "Holistic Video Understanding Dataset"~(HVU). HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene. HVU contains approx.~577k videos in total with 13M annotations for training and validation set spanning over {4378} classes. HVU encompasses semantic aspects defined on categories of scenes, objects, actions, events, attributes and concepts, which naturally captures the real-world scenarios. Further, we introduce a new spatio-temporal deep neural network architecture called "Holistic Appearance and Temporal Network"~(HATNet) that builds on fusing 2D and 3D architectures into one by combining intermediate representations of appearance and temporal cues. HATNet focuses on the multi-label and multi-task learning problem and is trained in an end-to-end manner. The experiments show that HATNet trained on HVU outperforms current state-of-the-art methods on challenging human action datasets: HMDB51, UCF101, and Kinetics. The dataset and codes will be made publicly available.
Differential tissue expression of extracellular vesicle‐derived proteins in prostate cancer

Proteomic profiling of extracellular vesicles (EVs) from prostate cancer (PCa) and normal prostate cell lines, led to the identification of new candidate PCa markers. These proteins included the nuclear exportin proteins XPO1 (also known as CRM1), the EV‐associated PDCD6IP (also known as ALIX), and the previously published fatty acid synthase FASN. In this study, we investigated differences in expression of XPO1 and PDCD6IP on well‐characterized prostate cancer cohorts using mass spectrometry and tissue microarray (TMA) immunohistochemistry to determine their diagnostic and prognostic value.
OpenCL Implementation of FPGA-Based Signal Generation and Measurement

Signal generation and measurement have been widely used in many engineering applications, such as for creating test signals in radar, communication, and software-defined radio. In field programmable gate array (FPGA) design, to generate and measure an analog signal, compatible software and hardware interfaces are required. Traditionally, hardware description language (HDL) is required to program an FPGA. HDL programming provides an efficient logic resource with low latency. However, it is time-consuming for designs that are more complex. Currently, OpenCL is implemented for FPGA programming. OpenCL reduces the FPGA development time because it increases the abstraction level of the code. OpenCL is an open and royalty-free framework for accelerating the algorithm executed on a heterogeneous system, such as a GPU, CPU, DSP, or FPGA. OpenCL implementation on FPGAs yields high-performance results for the computation process. However, compared to HDL design, OpenCL does not provide a particular function to access the FPGA hardware directly. In this paper, we have demonstrated the implementation of OpenCL programming on an FPGA for signal generation and measurement. We have developed OpenCL components that can interact with the FPGA hardware directly. An OpenCL I/O channel extension is employed in the kernel to read data from and write data to the OpenCL components. The experimental results indicate that OpenCL can be used for signal measurement and generation using FPGAs.
BP $k$ NN: $k$ -Nearest Neighbor Classifier With Pairwise Distance Metrics and Belief Function Theory

The <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN) rule is one of the most popular classification algorithms in pattern recognition field because it is very simple to understand but works quite well in practice. However, the performance of the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN rule depends critically on its being given a good distance metric over the input space, especially in small data set situations. In this paper, a new <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN-based classifier, called BP<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN, is developed based on pairwise distance metrics and belief function theory. The idea of the proposal is that instead of learning a global distance metric, we first decompose it into learning a group of pairwise distance metrics. Then, based on each learned pairwise distance metric, a pairwise <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN (<inline-formula> <tex-math notation="LaTeX">$\text{P}k$ </tex-math></inline-formula>NN) sub-classifier can be adaptively designed to separate two classes. Finally, a polychotomous classification problem is solved by combining the outputs of these <inline-formula> <tex-math notation="LaTeX">$\text{P}k$ </tex-math></inline-formula>NN sub-classifiers in belief function framework. The BP<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN classifier improves the classification performance thanks to the new distance metrics which provide more flexibility to design the feature weights and the belief function-based combination method which can better address the uncertainty involved in the outputs of the sub-classifiers. Experimental results based on synthetic and real data sets show that the proposed BP<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN can achieve better classification accuracy in comparison with some state-of-the-art methods.
Minimum Connected Dominating Set Algorithms for Ad Hoc Sensor Networks

To achieve effective communication in ad hoc sensor networks, researchers have been working on finding a minimum connected dominating set (MCDS) as a virtual backbone network in practice. Presently, many approximate algorithms have been proposed to construct MCDS, the best among which is adopting the two-stage idea, that is, to construct a maximum independent set (MIS) firstly and then realize the connectivity through the Steiner tree construction algorithm. For the first stage, this paper proposes an improved collaborative coverage algorithm for solving maximum independent set (IC-MIS), which expands the selection of the dominating point from two-hop neighbor to three-hop neighbor. The coverage efficiency has been improved under the condition of complete coverage. For the second stage, this paper respectively proposes an improved Kruskal–Steiner tree construction algorithm (IK–ST) and a maximum leaf nodes Steiner tree construction algorithm (ML-ST), both of which can make the result closer to the optimal solution. Finally, the simulation results show that the algorithm proposed in this paper is a great improvement over the previous algorithm in optimizing the scale of the connected dominating set (CDS).
Quantum Walk Sampling by Growing Seed Sets

This work describes a new algorithm for creating a superposition over the edge set of a graph, encoding a quantum sample of the random walk stationary distribution. The algorithm requires a number of quantum walk steps scaling as $\widetilde{O}(m^{1/3} \delta^{-1/3})$, with $m$ the number of edges and $\delta$ the random walk spectral gap. This improves on existing strategies by initially growing a classical seed set in the graph, from which a quantum walk is then run. The algorithm leads to a number of improvements: (i) it provides a new bound on the setup cost of quantum walk search algorithms, (ii) it yields a new algorithm for $st$-connectivity, and (iii) it allows to create a superposition over the isomorphisms of an $n$-node graph in time $\widetilde{O}(2^{n/3})$, surpassing the $\Omega(2^{n/2})$ barrier set by index erasure.
Automatic Online News Feeding in Student Organization Website Using Word Retrieval System

News is important information to know what events occur every day among the community. The Internet makes news spread wider and faster. So that it can help people read news anywhere and anytime easily. But there are often many news on internet that are still not categorized according to the news content. Grabbing method will takes news' important information from online media. The news that has been taken will be categorized according to the news content using the text mining method. Word weighting or Term Frequency is one text mining method that can be used to categorize news because this method has high accurate, easy, and efficient results. This is proven by the results of research testing reaching an average accuracy of 95.2%.
In vitro Mycobacterial Growth Inhibition in South Korean Adults With Latent TB Infection

Background: It is important to understand the ability to inhibit mycobacterial growth in healthy adults who would have been Bacillus Calmette-Guérin (BCG) vaccinated in childhood as this group will be the potential target population for novel booster TB vaccine trials. In this study we investigated not only the long-term immunity induced by childhood BCG vaccination but also protective immunity in terms of the ability to inhibit mycobacterial growth in those who were BCG vaccinated in childhood, with evidence of recent or remote TB infection. Methods: We measured the baseline immune response using a functional mycobacterial growth inhibition assay (MGIA) as a novel approach and an intracellular cytokine staining (ICS) assay as a reference approach in healthy adults, with different status of Mycobacterium tuberculosis (Mtb) infection. Results: Based on MGIA responses in historically BCG-vaccinated healthy adults, demographical characteristics including age, and gender did not affect mycobacterial growth inhibition in PBMC. However, the uninfected healthy control (HC) group showed a greater ability to inhibit mycobacterial growth compared with the latent TB infection (LTBI) group (P = 0.0005). In terms of the M. tuberculosis antigen-specific T-cell immune response in diluted whole blood quantitated using an ICS assay, the LTBI group had a higher frequency of polyfunctional CD 4+ T cells compared with the HC group (P = 0.0002), although there was no correlation between ICS and the MGIA assay. Conclusion: The Mtb infection status had a significant impact on mycobacterial growth inhibition in PBMC from healthy adults in South Korea, a country with an intermediate burden of tuberculosis, with healthy controls showing the greatest mycobacterial growth inhibition.
Complexity of URLLC Scheduling and Efficient Approximation Schemes

In this paper we address the problem of joint admission control and resource scheduling for Ultra Reliable Low Latency Communications (URLLC). We examine two models: (i) the continuous, where all allocated resource blocks contribute to the success probability, and (ii) a binary, where only resource blocks with strong signal are “active” for each user, and user $k$ needs dkactive resource blocks for a successful URLLC transmission. In situations of congestion, we are interested in finding a subset of users that can be scheduled simultaneously. We show that finding a feasible schedule for at least $m$ URLLC users is NP-complete in the (easier) binary SNR model, hence also in the continuous. Maximizing the reward obtained from a feasible set of URLLC users is NP-hard and inapproximable to within (log2d)2/d of the optimal, where $d$ ≐ maxk dk. On the other hand, we prove that checking a candidate set of users for feasibility and finding the corresponding schedule (when feasible) can be done in polynomial time, which we exploit to design an efficient heuristic algorithm for the general continuous SNR model. We complement our theoretical contributions with a numerical evaluation of our proposed schemes.