Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissipativity-Based Sampled-Data Reliable Control Design for T-S Fuzzy System Using Limited Bessel-Legendre Inequality

This paper investigates the dissipativity-based reliable control for Takagi–Sugeno (T-S) fuzzy systems under time-varying sampling intervals based on the input delay approach. By constructing a novel Lyapunov–Krasovskii functional and using a limited Bessel–Legendre inequality that makes full use of the available information on the variable sampling pattern, a sufficient stabilization criterion is obtained to guarantee the sampled-data systems to be asymptotically stable and strictly <inline-formula> <tex-math notation="LaTeX">$(\mathcal {Q},\mathcal {S},\mathcal {R})$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-dissipative. Based on the criterion, a desired dissipativity-based sampled-data reliable controller is proposed. Moreover, in the case of no the actuator failure and <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-dissipative, some new stability and stabilization criteria are also obtained. It is shown that the new stability and stabilization criteria can provide a larger upper bound of the sampling interval than some existing works. The effectiveness and superiority of the design methods developed in this paper are demonstrated by two simulation examples.
Joint Implicit and Explicit Neural Networks for Question Recommendation in CQA Services

Community question answering (CQA) services have emerged as a type of popular social platforms. In the social network, experts provide knowledgeable answers to the questions in their domain of expertise, while celebrities publish influential opinions toward the topics led by some questions. Given the large amount of knowledge organized in the format of question–answers, an interesting research problem is to recommend questions to users so as to maximize their engagements with the platform. However, recommending questions in CQA services is a non-trivial task. Data sources in the CQA services are of different types. It is challenging to incorporate heterogeneous information for the recommendation task. Furthermore, data sparsity is an inherent problem in such platforms. In this paper, we propose a model that is able to jointly model both implicit and explicit information for question recommendation. The model integrates multiple data sources and addresses the problem of data heterogeneity. In the proposed model, we dynamically discover latent user groups and incorporate those hierarchical information to bridge the semantic gaps among users in the shared latent space. We evaluate the proposed model on two real-world datasets, and demonstrate that our model outperforms the state-of-the-art alternatives by a large margin. We also investigate different structures of the proposed model to study the effects of different data sources.
SCOPE: A Stochastic Computing Engine for DRAM-Based In-Situ Accelerator

Memory-centric architecture, which bridges the gap between compute and memory, is considered as a promising solution to tackle the memory wall and the power wall. Such architecture integrates the computing logic and the memory resources close to each other, in order to embrace large internal memory bandwidth and reduce the data movement overhead. The closer the compute and memory resources are located, the greater these benefits become. DRAM-based in-situ accelerators [1] tightly couple processing units to every memory bitline, achieving the maximum benefits among various memory-centric architectures. However, the processing units in such architectures are typically limited to simple functions like AND/OR due to strict area and power overhead constraints in DRAMs, making it difficult to accomplish complex tasks while providing high performance. In this paper, we address the challenge by applying stochastic computing arithmetic to the DRAM-based in-situ accelerator, targeting at the acceleration of error-tolerant applications such as deep learning. In stochastic computing, binary numbers are converted into stochastic bitstreams, which turns integer multiplications into simple bitwise AND operations, but at the expense of larger memory capacity/bandwidth demands. Stochastic computing is a perfect match for the DRAM-based in-situ accelerators because it addresses the in-situ accelerator's low performance problem by simplifying the operations, while leveraging the in-situ accelerator's advantage of large memory capacity/bandwidth. To further boost the performance and compensate for the numerical precision loss, we propose a novel Hierarchical and Hybrid Deterministic (H2D) stochastic computing arithmetic. Finally, we consider quantized deep neural network inference and training applications as a case study. The proposed architecture provides 2.3× improvement in performance per unit area compared with the binary arithmetic baseline, and 3.8× improvement over GPU. The proposed H2D arithmetic contributes 11× performance boost and 60% numerical precision improvement.
Laughter detecting model using wearable EEG meter for Japanese comedy content

This paper proposes the use of a simple electroencephalogram (EEG) meter to measure feedback from viewers of comedy content. We propose a system that is a statistical model based on the random forest algorithm that learns the feature value of 40-dimension signal using a non-invasive wearable measuring instrument of EEG signals. Our proposed model generated from the EEG of nine participants and obtained an accuracy rate of 86.5%.
Ka-band modulated metasurface antennas fabricated by metal additive manufacturing

Metasurface (MTS) antennas present an equivalent modulated reactance tensor in the aperture plane, which is designed to gradually radiate a surface-wave (SW). The modulation results in the (-1) indexed Floquet mode entering the visible region, thus becoming a leaky-wave (LW) mode with curvilinear phase-contour. In this paper, we explore the use of metallic MTSs to synthesize the reactance tensor required to obtain an objective radiation pattern, with a given shape and polarization. The proposed structure is particularly well suited to fabrication by metal additive manufacturing. We have designed several metallic MTS antennas, verifying their performance by full-wave simulations. We have also fabricated a prototype, and the measurements have validated the simulation results.
Overview of Intelligent Solution for Power Transmission System

The standard of living is in a direct connection to the availability and quality of the energy delivered, and therefore the electric power system presents the backbone for the development of a modern human society. While balancing between the economical and environmental issues, the transmission system operators are coping to maintain the power system stability and integrity. Furthermore, emerging trends such as the integration of intermittent renewable energy sources, electro mobility and the establishing of energy market introduce additional uncertainties and make the power system operation even more complex. The latter has been manifested though the large power system blackouts that have occurred worldwide. However, there has been a shift of attention and funding towards the development and deployment of technologies which would enable the evolutionary transfer of the existing power system towards the intelligent grid - Smart Grid. The synchronized measurement technology (SMT) has already been recognized as one of the key drivers in the building of modern power systems. The various Wide Area Monitoring, Protection And Control (WAMPAC) applications and System Integrity Protection Schemes (SIPS) are issue of this paper that provides a wide overview of the benefits gained by applying the SMT to monitor the operating conditions in the transmission power systems.
Genome wide association study to identify predictors for severe skin toxicity in colorectal cancer patients treated with cetuximab

EGFR-antibodies are associated with significant skin toxicity, including acneiform rash and folliculitis. It remains impossible to predict the occurrence of severe skin toxicity due to the lack of predictive markers. Here, we present the first genome-wide association study (GWAS) to find single nucleotide polymorphisms (SNPs) associated with EGFR inhibitor-induced skin toxicity using data of the multicentre randomized phase III CAIRO2 trial (clinicaltrials.gov NCT00208546). In this study, advanced or metastatic colorectal cancer patients were treated with capecitabine, oxaliplatin and bevacizumab with or without cetuximab. Germline DNA was available in 282 of the 368 patients in the cetuximab arm. Mild skin toxicity occurred in 195 patients (i.e. CTC grade 1 or 2, respectively 91 and 104 patients) and severe skin toxicity (i.e. grade 3) in 36 patients. Grade 4 skin toxicity did not occur. None of the SNPs reached the formal genome wide threshold for significance of 5x10-8, though SNPs of at least 8 loci did show moderate association (p-value between 5x10-7 and 5x10-5) with the occurrence of grade 3 (severe) skin toxicity. These SNPs did not overlap with SNPs associated with cetuximab efficacy as found in a previous GWAS in the same CAIRO2 cohort. If formally proven by replication, the SNPs associated with severe EGFR induced skin toxicity may be helpful to predict the occurrence and severity of skin toxicity in patients that will receive cetuximab and allow for adequate information on the risk of skin toxicity and prophylactic measurements.
A liquid-like organelle at the root of motile ciliopathy

Motile ciliopathies are characterized by specific defects in cilia beating that result in chronic airway disease, subfertility, ectopic pregnancy, and hydrocephalus. While many patients harbor mutations in the dynein motors that drive cilia beating, the disease also results from mutations in so-called dynein axonemal assembly factors (DNAAFs) that act in the cytoplasm. The mechanisms of DNAAF action remain poorly defined. Here, we show that DNAAFs concentrate together with axonemal dyneins and chaperones into organelles that form specifically in multiciliated cells, which we term DynAPs, for dynein axonemal particles. These organelles display hallmarks of biomolecular condensates, and remarkably, DynAPs are enriched for the stress granule protein G3bp1, but not for other stress granule proteins or P-body proteins. Finally, we show that both the formation and the liquid-like behaviors of DynAPs are disrupted in a model of motile ciliopathy. These findings provide a unifying cell biological framework for a poorly understood class of human disease genes and add motile ciliopathy to the growing roster of human diseases associated with disrupted biological phase separation.
A Local Search Based on Variant Variable Depth Search for the Quadratic Assignment Problem

The quadratic assignment problem (QAP) has practical applications in the electronics domain, such as component placing on circuit boards, minimizing the number of transistors on integrated circuits, optimal placing of letters on touchscreen devices, etc. Since QAP is NP-hard and large problems are not practically solvable to optimality, heuristic methods such as local search are regarded as an efficient algorithm to obtain nearoptimal solutions within a reasonable time. In this paper, we present a new sophisticated local search algorithm, called variant k-opt local search (vKLS), based on a variant of the variable depth search for QAP. Computational results show that vKLS is capable of finding better solutions on average than the standard VDS and typical 2-opt local search algorithms.
New Multidrug-Resistant Salmonella enterica Serovar Anatum Clone, Taiwan, 2015–2017

In 2011, a Salmonella enterica serovar Anatum clone emerged in Taiwan. During 2016–2017, infections increased dramatically, strongly associated with emergence and spread of multidrug-resistant strains with a plasmid carrying 11 resistance genes, including blaDHA-1. Because these resistant strains infect humans and food animals, control measures are urgently needed.
Quantification of Lipids: Model, Reality, and Compromise

Lipids are key molecules in various biological processes, thus their quantification is a crucial point in a lot of studies and should be taken into account in lipidomics development. This family is complex and presents a very large diversity of structures, so analyzing and quantifying all this diversity is a real challenge. In this review, the different techniques to analyze lipids will be presented: from nuclear magnetic resonance (NMR) to mass spectrometry (with and without chromatography) including universal detectors. First of all, the state of the art of quantification, with the definitions of terms and protocol standardization, will be presented with quantitative lipidomics in mind, and then technical considerations and limitations of analytical chemistry’s tools, such as NMR, mass spectrometry and universal detectors, will be discussed, particularly in terms of absolute quantification.
Towards Unsupervised Single-channel Blind Source Separation Using Adversarial Pair Unmix-and-remix

Blind single-channel source separation is a long standing signal processing challenge. Many methods were proposed to solve this task utilizing multiple signal priors such as low rank, sparsity, temporal continuity etc. The recent advance of generative adversarial models presented new opportunities in signal regression tasks. The power of adversarial training however has not yet been realized for blind source separation tasks. In this work, we propose a novel method for blind source separation (BSS) using adversarial methods. We rely on the independence of sources for creating adversarial constraints on pairs of approximately separated sources, which ensure good separation. Experiments are carried out on image sources validating the good performance of our approach, and presenting our method as a promising approach for solving BSS for general signals.
Constrained Optimization with Preferentially Ordered Outcomes

The Conditional Preference Network (CP-net) graphically represents user's qualitative and conditional preference statements under the ceteris paribus (all else being equal) interpretation. Given that the CP-net induces a partial order over the outcomes, the optimization for a constrained CP-net can have a set of Pareto optimal solutions. The existing algorithms for solving the constrained CP-net require dominance testing between the outcomes, which is a very expensive operation. In this paper, we propose an extension of the CP-net model by eliciting additional relative importance statements between variables in order to have a total order over the outcomes. As a result, the constrained optimization using the proposed model involves a single optimal solution, assuming the underlying CSP is consistent. In this regard, we provide an efficient algorithm to find this optimal solution without the need for dominance testing.
Real-Time Lane Detection-Based Line Segment Detection

This paper introduces a robust algorithm for real-time lane detection using the lane markers in urban streets or highway roads. It is based on applying Region of Interest (ROI) on the input image of the road from a calibrated camera in the front of the car, generating the top view of the image using Inverse Perspective Mapping (IPM), applying the core algorithm Line Segment Detection (LSD) which is followed by post-processing steps. Applying curve fitting to the line segments to get the right and left lines or curves. Finally, to get the output stream inverse IPM is applied. The proposed algorithm can detect the road lanes discriminating dashed and solid road lanes, straight and curved road lanes overcoming the shadow effect challenge with real-time performance 70 frames per second.
Estimation of Economic Indicator Announced by Government From Social Big Data

We introduce a systematic method to estimate an economic indicator from the Japanese government by analyzing big Japanese blog data. Explanatory variables are monthly word frequencies. We adopt 1352 words in the section of economics and industry of the Nikkei thesaurus for each candidate word to illustrate the economic index. From this large volume of words, our method automatically selects the words which have strong correlation with the economic indicator and resolves some difficulties in statistics such as the spurious correlation and overfitting. As a result, our model reasonably illustrates the real economy index. The announcement of an economic index from government usually has a time lag, while our proposed method can be real time.
On-the-Go Grapevine Yield Estimation Using Image Analysis and Boolean Model

This paper describes a new methodology for noninvasive, objective, and automated assessment of yield in vineyards using image analysis and Boolean model. Image analysis, as an inexpensive and noninvasive procedure, has been studied for this purpose, but the effect of occlusions from the cluster or other organs of the vine has an impact that diminishes the quality of the results. To reduce the influence of the occlusions in the estimation, the number of berries was assessed using the Boolean model. To evaluate the methodology, three different datasets were studied: cluster images, manually acquired vine images, and vine images captured on-the-go using a quad. The proposed algorithm estimated the number of berries in cluster images with a root mean square error (RMSE) of 20 and a coefficient of determination (R2) of 0.80. Vine images manually taken were evaluated, providing 310 grams of mean error and R2=0.81. Finally, images captured using a quad equipped with artificial light and automatic camera triggering were also analysed. The estimation obtained applying the Boolean model had 610 grams of mean error per segment (three vines) and R2=0.78. The reliability against occlusions and segmentation errors of the Boolean model makes it ideal for vineyard yield estimation. Its application greatly improved the results when compared to a simpler estimator based on the relationship between cluster area and weight.
PREVAIL I Cluster Vaccination Study With rVSV&Dgr;G-ZEBOV-GP as Part of a Public Health Response in Liberia

OBJECTIVE In November 2015, a 15-year-old boy received a diagnosis of Ebola virus disease (EVD) at the John F. Kennedy Medical Center in Monrovia, Liberia. Two additional family members received a diagnosis of EVD. The protocol for a phase 2 placebo-controlled trial of 2 Ebola vaccines was amended and approved; in 4 days, a single-arm cluster vaccination trial using the Merck rVSVΔG-ZEBOV-GP vaccine was initiated. Here, we evaluate the safety and immunogenicity of the vaccine and discuss challenges for its implementation in a small Ebola outbreak. METHOD We conducted a ring vaccination study among contacts and contacts of close contacts of EVD cases a in Monrovia. Participants were evaluated 1 and 6 months after vaccination. RESULTS Among 650 close contacts and contacts of close contacts of EVD cases, 210 (32%) consented and were vaccinated with rVSVΔG-ZEBOV-GP. Of those vaccinated, 189 (90%) attended the month 1 follow-up visit; 166 (79%) attended the month 6 visit. No serious adverse events were reported. Among 88 participants without an elevated antibody level at baseline, 77.3% (95% confidence interval, 68.5-86.1) had an antibody response at 1 month. CONCLUSIONS The Merck rVSVΔG-ZEBOV-GP vaccine appeared to be safe and immunogenic among the vaccinated individuals. However, fewer than one third of eligible individuals consented to vaccination. These data may help guide implementation decisions for of cluster vaccination programs in an Ebola cluster outbreak response situation.
A Distributed Price-based Strategy for Flexible Demand Coordination in Multi-area Systems

This paper presents a novel distributed control strategy for large-scale deployment of price-responsive flexible demand. Differently from previous theoretical studies on the subject, the proposed analysis explicitly models multi-area systems, accounting for transmission lines of limited capacity and different locational marginal prices (LMP) throughout the network. A game-theoretic framework is adopted, designing a demand coordination scheme that converges to a stable market configuration (characterized as an aggregative equilibrium) through iterative price broadcasts. The performance of the proposed control strategy, that also ensures flattened generation profiles and reduced generation costs, is evaluated in simulation on a five-bus power system.
Human Breast Cancer Xenograft Model Implicates Peroxisome Proliferator–activated Receptor Signaling as Driver of Cancer-induced Muscle Fatigue

Purpose: This study tested the hypothesis that a patient-derived orthotopic xenograft (PDOX) model would recapitulate the common clinical phenomenon of breast cancer–induced skeletal muscle (SkM) fatigue in the absence of muscle wasting. This study additionally sought to identify drivers of this condition to facilitate the development of therapeutic agents for patients with breast cancer experiencing muscle fatigue. Experimental Design: Eight female BC-PDOX–bearing mice were produced via transplantation of tumor tissue from 8 female patients with breast cancer. Individual hind limb muscles from BC-PDOX mice were isolated at euthanasia for RNA-sequencing, gene and protein analyses, and an ex vivo muscle contraction protocol to quantify tumor-induced aberrations in SkM function. Differentially expressed genes (DEG) in the BC-PDOX mice relative to control mice were identified using DESeq2, and multiple bioinformatics platforms were employed to contextualize the DEGs. Results: We found that SkM from BC-PDOX–bearing mice showed greater fatigability than control mice, despite no differences in absolute muscle mass. PPAR, mTOR, IL6, IL1, and several other signaling pathways were implicated in the transcriptional changes observed in the BC-PDOX SkM. Moreover, 3 independent in silico analyses identified PPAR signaling as highly dysregulated in the SkM of both BC-PDOX–bearing mice and human patients with early-stage nonmetastatic breast cancer. Conclusions: Collectively, these data demonstrate that the BC-PDOX model recapitulates the expected breast cancer–induced SkM fatigue and further identify aberrant PPAR signaling as an integral factor in the pathology of this condition.
Optimal Siting of BESS in Distribution Networks under High PV Penetration

This paper focuses on the optimal siting of Battery Energy Storage Systems (BESS) in a Distribution Network with installed Photovoltaic Generation, in order to minimize the energy losses of the system. The bus voltage limit, as well as the ampacity level of the lines are taken into consideration as constraints, while the technical constraints of the BESSs have also been taken into account. Unified Particle Swarm Optimization is used as the solving optimization technique. Simulations are being carried out on IEEE-33 bus system regarding different scenarios and the results are presented and compared. A significant improvement in energy losses, voltage and line ampacity profile is achieved by the introduction of BESS units in a Distribution Network with high PV Penetration.