Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a compact rat-race coupler using dual transmission lines

In this paper, a new guided-wave structure, the dual transmission line, is exploited to develop a compact rat-race coupler based on the reduced-length coupler circuit configuration. A conventional transmission line can be equivalent to a set of dual transmission lines which are realized by two parallel high-impedance transmission lines. The circuit configuration of the reduced-length rat-race coupler can approximately provide a 50 % circuit size reduction. As the transmission lines of this coupler are replaced by high-impedance-based dual transmission lines, the folding technique can be flexibly applied to earn a further circuit size reduction. The proposed rat-race coupler only occupies a 21.87 % circuit size of the conventional one. The measured results of the developed rat-race coupler are in a good agreement with simulated results by full-wave electromagnetic simulator.
Acceleration of SVRG and Katyusha X by Inexact Preconditioning

Empirical risk minimization is an important class of optimization problems with many popular machine learning applications, and stochastic variance reduction methods are popular choices for solving them. Among these methods, SVRG and Katyusha X (a Nesterov accelerated SVRG) achieve fast convergence without substantial memory requirement. In this paper, we propose to accelerate these two algorithms by \textit{inexact preconditioning}, the proposed methods employ \textit{fixed} preconditioners, although the subproblem in each epoch becomes harder, it suffices to apply \textit{fixed} number of simple subroutines to solve it inexactly, without losing the overall convergence. As a result, this inexact preconditioning strategy gives provably better iteration complexity and gradient complexity over SVRG and Katyusha X. We also allow each function in the finite sum to be nonconvex while the sum is strongly convex. In our numerical experiments, we observe an on average $8\times$ speedup on the number of iterations and $7\times$ speedup on runtime.
Targeted Smooth Bayesian Causal Forests: An analysis of heterogeneous treatment effects for simultaneous vs. interval medical abortion regimens over gestation

This article introduces Targeted Smooth Bayesian Causal Forests, or tsbcf, a semi-parametric Bayesian approach for estimating heterogeneous treatment effects which vary smoothly over a single covariate in the observational data setting. The tsbcf method induces smoothness in estimated treamtent effects over the target covariate by parameterizing each tree's terminal nodes with smooth functions. The model allows for separate regularization of treatement effects versus prognostic effect of control covariates; this approach informatively shrinks towards homogeneity while avoiding biased treatment effect estimates. We provide smoothing parameters for prognostic and treatment effects which can be chosen to reflect prior knowledge or tuned in a data-dependent way. We apply tsbcf to early medical abortion outcomes data from British Pregnancy Advisory Service. Our aim is to assess relative effectiveness of simultaneous versus interval administration of mifepristone and misoprostol over the first nine weeks of gestation, where we define successful outcome as complete abortion requiring neither surgical evacuation nor continuing pregnancy. We expect the relative effectiveness of simultaneous administration to vary smoothly over gestational age, but not necessarily other covariates, and our model reflects this. We demonstrate the performance of the tsbcf method on benchmarking experiments. The R package tsbcf implements our method.
World Antibody Drug Conjugate Summit Europe

The World Antibody Drug Conjugate Summit Europe, organized by Biorbis/ Hanson Wade was held in Frankfurt, Germany on February 21-23, 2011. Antibody drug conjugates (ADCs), also called immunoconjugates, are becoming an increasingly important class of therapeutics as demonstrated by the attendance of nearly 100 delegates at this highly focused meeting. Updates on three ADCs that are in late-stage clinical development, trastuzumab emtansine (T-DM1), brentuximab vedotin (SGN-35) and inotuzumab ozogamicin (CMC-544), were presented by speakers from ImmunoGen, Genentech, Roche, Seattle Genetics and Pfizer. These ADCs have shown encouraging therapeutic effects against solid tumors (T-DM1) and hematological malignancies (SGN-35, CMC-544). The key feature of the new generation of ADCs is the effective combination of the cytotoxicity of natural or synthetic highly potent antineoplastic agents, tumor selective monoclonal antibodies and blood-stable optimized linkers. Early clinical data for ADCs were showcased by Progenics Pharmaceuticals (PSMA ADC), Celldex (CDX-011) and Biotest (BT-062). Takeda, MedImmune and sanofi-aventis outlined their strategies for process development and analytical characterization. In addition, presentations on duocarmycin based-ADCs, alpha emitting immunoconjugates and antibody-conjugated nanoparticles were given by representatives from Syntarga, Algeta and the University of Stuttgart, respectively. For more information, please visit http://www.adc-summit.com.
Constructing a Scene-Based Knowledge System for E-Commerce Industries: Business Analysis and Challenges

Online marketers make efforts to sell more products to their customers to increase turnover. One way to sell more products is to ensure that products belonging to the same scene are offered together. As such, it is beneficial to categorize products into different groups and tag them to particular, defined scenes. The mechanism of building relationships between products and scenes is called “scene-based knowledge system construction”. The construction of a scene-based knowledge system is usually based on business operators’ expert knowledge and past experiences, which often causes product categorization to be inaccurate. Hence, in this paper, we discuss the importance and necessity of constructing a scene-based knowledge system, analyze it from a business perspective, and discuss its challenges.
FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction

Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rate (CTR) prediction plays a central role. There are many proposed models in this field such as logistic regression, tree based models, factorization machine based models and deep learning based CTR models. However, many current works calculate the feature interactions in a simple way such as Hadamard product and inner product and they care less about the importance of features. In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions. On the one hand, the FiBiNET can dynamically learn the importance of features via the Squeeze-Excitation network (SENET) mechanism; on the other hand, it is able to effectively learn the feature interactions via bilinear function. We conduct extensive experiments on two real-world datasets and show that our shallow model outperforms other shallow models such as factorization machine(FM) and field-aware factorization machine(FFM). In order to improve performance further, we combine a classical deep neural network(DNN) component with the shallow model to be a deep model. The deep FiBiNET consistently outperforms the other state-of-the-art deep models such as DeepFM and extreme deep factorization machine(XdeepFM).
Improving Neural Networks by Adopting Amplifying and Attenuating Neurons

In the present study, an amplifying neuron and attenuating neuron, which can be easily implemented into neural networks without any significant additional computational effort, are proposed. The activated output value is squared for the amplifying neuron, while the value becomes its reciprocal for the attenuating one. Theoretically, the order of neural networks increases when the amplifying neuron is placed in the hidden layer. The performance assessments of neural networks were conducted to verify that the amplifying and attenuating neurons enhance the performance of neural networks. From the numerical experiments, it was revealed that the neural networks that contain the amplifying and attenuating neurons yield more accurate results, compared to those without them.
MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants

We introduce MCScript2.0, a machine comprehension corpus for the end-to-end evaluation of script knowledge. MCScript2.0 contains approx. 20,000 questions on approx. 3,500 texts, crowdsourced based on a new collection process that results in challenging questions. Half of the questions cannot be answered from the reading texts, but require the use of commonsense and, in particular, script knowledge. We give a thorough analysis of our corpus and show that while the task is not challenging to humans, existing machine comprehension models fail to perform well on the data, even if they make use of a commonsense knowledge base. The dataset is available at http://www.sfb1102. uni-saarland.de/? page_id=2582
GWU NLP Lab at SemEval-2019 Task 3: EmoContext: Effective Contextual Information in Models for Emotion Detection in Sentence-level in a Multigenre Corpus

In this paper we present an emotion classifier model submitted to the SemEval-2019 Task 3: EmoContext. The task objective is to classify emotion (i.e. happy, sad, angry) in a 3-turn conversational data set. We formulate the task as a classification problem and introduce a Gated Recurrent Neural Network (GRU) model with attention layer, which is bootstrapped with contextual information and trained with a multigenre corpus. We utilize different word embeddings to empirically select the most suited one to represent our features. We train the model with a multigenre emotion corpus to leverage using all available training sets to bootstrap the results. We achieved overall %56.05 f1-score and placed 144.
The interleukin-10 promoter genotype determines clinical immune function in hemodialysis patients.

BACKGROUND Immune dysfunction and the impaired hepatitis B vaccination response are complications of chronic renal failure that are tightly associated with inflammation induced by uremia and blood-membrane contacts. Proinflammatory cytokines, such as interleukin (IL)-6, are counter-regulated by IL-10 with a large interindividual variability. Part of the variability of cytokine production is genetically determined since polymorphisms in the cytokine gene promoters lead to high or low production. The aim of this study was to detect the genetic influence of the IL-10 promoter on immune function of chronic hemodialysis patients. METHODS The IL-10 genotype (polymorphic bases at positions -1082 and -819) was determined in 272 chronic hemodialysis patients using highly specific PCR and related to the patients' response to a triple vaccination against hepatitis B. Secretion of IL-10 and IL-6 by peripheral blood leucocytes in vitro was determined by ELISA. RESULTS The prevalence of the IL-10 genotypes in dialysis patients with well-preserved immune function (vaccination responders) was similar to the general population. In contrast, prevalence of the -1082G* allele (associated with high production of IL-10) was low in the nonresponders. The relative risk of vaccination nonresponse in patients homozygous for the -1082A* allele was 1.394 (95% CI, 1.091 to 1.781, P < 0.05) compared to those homozygous for -1082G*. There was no relationship between the IL-10 genotype and the type of renal disease. CONCLUSIONS The IL-10 genotype determines IL-10 production in dialysis patients, which down-regulates uremia- and dialysis-induced chronic inflammation and helps to preserve immune defense functions.
Incorporating Auxiliary Information in Collaborative Filtering Data Update with Privacy Preservation

Online shopping has become increasingly popular in recent years. More and more people are willing to buy products through Internet instead of physical stores. For promotional purposes, almost all online merchants provide product recommendations to their returning customers. Some of them ask professional recommendation service providers to help develop and maintain recommender systems while others need to share their data with similar shops for better product recommendations. There are two issues, (1) how to protect customers’ privacy while retaining data utility before they release the data to the third parties; (2) based on (1), how to handle data growth efficiently. In this paper, we propose a NMF (Nonnegative Matrix Factorization)-based data update approach in collaborative filtering (CF) that solves the problems. The proposed approach utilizes the intrinsic property of NMF to distort the data for protecting user’s privacy. In addition, the user and item auxiliary information is taken into account in incremental nonnegative matrix tri-factorization to help improve the data utility. Experiments on three different datasets (MovieLens, Sushi and LibimSeTi) are conducted to examine the proposed approach. The results show that our approach can quickly update the new data and provide both high level privacy protection and good data utility.
A Novel MFS-MDR Transporter, MdrP, Employs D223 as a Key Determinant in the Na+ Translocation Coupled to Norfloxacin Efflux

Multidrug resistance (MDR) transporters of the major facilitator superfamily (MFS) were previously believed to drive the extrusion of multiple antimicrobial drugs through the coupling to proton translocation. Here, we present the identification of the first Na+-coupled MFS-MDR transporter, MdrP, which also can achieve H+-coupled drug efflux independently of Na+. Importantly, we propose that MdrP can extrude norfloxacin in a mode of drug/Na+ antiport, which has not yet been reported in any MFS member. On this basis, we further provide the insights into a novel Na+ and H+ coupling mechanism of MFS-MDR transporters, even for all secondary transporters. The most important finding lies in that D223 should mainly act as a key determinant in the Na+ translocation coupled to norfloxacin efflux. Furthermore, our results partially modify the knowledge of the conformational stability-related residues in the motif A of MFS transporters and imply the importance of a new positively charged residue, R361, for the stabilization of outward-facing conformation of MFS transporters. These novel findings positively contribute to the knowledge of MFS-MDR transporters, especially about Na+ and H+ coupling mechanism. This study is based mainly on measurements in intact cells or everted membranes, and a biochemical assay with a reconstituted MdrP protein should be necessary to come to conclusion to be assured.
Comprehensive Analysis of Prognostic Alternative Splicing Signatures in Endometrial Cancer

Background Alternative splicing (AS) is one of the critical post-transcriptional regulatory mechanisms of various cancers and also plays a crucial role in the development of cancers, including endometrial cancer (EC). Methods The splicing data and gene expression profiles of EC were obtained from The Cancer Genome Atlas. The corresponding clinical data were extracted from TCGA-CDR. With univariate Cox regression analysis, least absolute shrinkage and selection operator model, and multivariate Cox regression analysis, the survival-related AS events were selected. Functional enrichment analysis was also performed to investigate the functions of these AS events. Splicing factors and AS regulation network were constructed to understand the correlation among these AS events. Result A total of 1826 AS events were identified as survival-related events. Functional enrichment analysis showed that these AS events were associated with several immune system-related processes. Then, the prognostic signatures were developed based on these survival-related events and acted as an independent prognostic factor for EC. Splicing factors and AS regulation network were also constructed to understand the regulatory mechanisms of AS events in EC. Conclusion This study systematically analyzed the role of AS events in EC and developed the prognostic model for EC.
Collective Bargaining and Faculty Job Satisfaction

Estimates of the impact of union membership on job satisfaction suffer from nonrandom self‐selection of employees into unions. In this paper, we circumvent this problem by examining the impact on satisfaction of collective bargaining representation, rather than of union membership. We use a two‐stage technique that controls for nonrandom selection of faculty into institutions, and apply that to a panel of faculty at repeatedly observed four‐year universities. We find that bargaining agreements increase satisfaction with compensation but reduce satisfaction with faculty workload. Bargaining has no statistically measurable impact on overall job satisfaction or on faculty's satisfaction with their authority to make decisions regarding their instructional duties.
Pbx1 is essential for growth of zebrafish swim bladder

pbx1, a TALE (three–amino acid loop extension) homeodomain transcription factor, is involved in a diverse range of developmental processes. We examined the expression of pbx1 during zebrafish development by in situ hybridization. pbx1 transcripts could be detected in the central nervous system and pharyngeal arches from 24 hpf onwards. In the swim bladder anlage, pbx1 was detected as early as 28 hpf, making it the earliest known marker for this organ. Morpholino‐mediated gene knockdown of pbx1 revealed that the swim bladder failed to inflate, with eventual lethality occurring by 8 dpf. The knockdown of pbx1 did not perturb the expression of prdc and foxA3, with both early swim bladder markers appearing normally at 36 and 48 hpf, respectively. However, the expression of anxa5 was completely abolished by pbx1 knockdown at 60 hpf suggesting that pbx1 may be required during the late stage of swim bladder development. Developmental Dynamics 239:865–874, 2010. © 2010 Wiley‐Liss, Inc.
Sight to touch: 3D diffeomorphic deformation recovery with mixture components for perceiving forces in robotic-assisted surgery

Robotic-assisted minimally invasive surgical systems suffer from one major limitation which is the lack of interaction forces feedback. The restricted sense of touch hinders the surgeons' performance and reduces their dexterity and precision during a procedure. In this work, we present a sensory substitution approach that relies on visual stimuli to transmit the tool-tissue interaction forces to the operating surgeon. Our approach combines a 3D diffeomorphic deformation mapping with a generative model to precisely label the force level. The main highlights of our approach are that the use of diffeomorphic transformation ensures anatomical structure preservation and the label assignment is based on a parametric form of several mixture elements. We performed experimentations on both ex-vivo and in-vivo datasets and offer careful numerical results evaluating our approach. The results show that our solution has an error measure less than 1mm in all directions and an average labeling error of 2.05%. It can also be applicable to other scenarios that require force feedback such as microsurgery, knot tying or needle-based procedures.
Antinociceptive Activity of Chemical Components of Essential Oils That Involves Docking Studies: A Review

Introduction Pain is considered an unpleasant sensory and emotional experience, being considered as one of the most important causes of human suffering. Computational chemistry associated with bioinformatics has stood out in the process of developing new drugs, through natural products, to manage this condition. Objective To analyze, through literature data, recent molecular coupling studies on the antinociceptive activity of essential oils and monoterpenes. Data source Systematic search of the literature considering the years of publications between 2005 and December 2019, in the electronic databases PubMed and Science Direct. Eligibility Criteria Were considered as criteria of 1) Biological activity: non-clinical effects of an OE and/or monoterpenes on antinociceptive activity based on animal models and in silico analysis, 2) studies with plant material: chemically characterized essential oils and/or their constituents isolated, 3) clinical and non-clinical studies with in silico analysis to assess antinociceptive activity, 4) articles published in English. Exclusion criteria were literature review, report or case series, meta-analysis, theses, dissertations, and book chapter. Results Of 16,006 articles, 16 articles fulfilled all the criteria. All selected studies were non-clinical. The most prominent plant families used were Asteraceae, Euphorbiaceae, Verbenaceae, Lamiaceae, and Lauraceae. Among the phytochemicals studied were α-Terpineol, 3-(5-substituted-1,3,4-oxadiazol-2-yl)-N′-[2-oxo-1,2-dihydro-3H-indol-3-ylidene] propane hydrazide, β-cyclodextrin complexed with citronellal, (−)-α-bisabolol, β-cyclodextrin complexed with farnesol, and p-Cymene. The softwares used for docking studies were Molegro Virtual Docker, Sybyl®X, Vlife MDS, AutoDock Vina, Hex Protein Docking, and AutoDock 4.2 in PyRx 0.9. The molecular targets/complexes used were Nitric Oxide Synthase, COX-2, GluR2-S1S2, TRPV1, β-CD complex, CaV1, CaV2.1, CaV2.2, and CaV2.3, 5-HT receptor, delta receptor, kappa receptor, and MU (μ) receptor, alpha adrenergic, opioid, and serotonergic receptors, muscarinic receptors and GABAA opioid and serotonin receptors, 5-HT3 and M2 receptors. Many of the covered studies used molecular coupling to investigate the mechanism of action of various compounds, as well as molecular dynamics to investigate the stability of protein-ligand complexes. Conclusions The studies revealed that through the advancement of more robust computational techniques that complement the experimental studies, they may allow some notes on the identification of a new candidate molecule for therapeutic use.
A Single Bout of Aerobic Exercise Provides an Immediate “Boost” to Cognitive Flexibility

Executive function includes the core components of working memory, inhibitory control, and cognitive flexibility. A wealth of studies demonstrate that working memory and inhibitory control improve following a single bout of exercise; however, a paucity – and equivocal – body of work has demonstrated a similar benefit for cognitive flexibility. Cognitive flexibility underlies switching between different attentional- and motor-related goals, and a potential limitation of previous work examining this component in an exercise context is that they included tasks involving non-executive processes (i.e., numerosity, parity, and letter judgments). To address this issue, Experiment 1 employed a 20-min bout of aerobic exercise and examined pre- and immediate post-exercise cognitive flexibility via stimulus-driven (SD) and minimally delayed (MD) saccades ordered in an AABB task-switching paradigm. Stimulus-driven saccades are a standard task requiring a response at target onset, whereas MD saccades are a non-standard and top-down task requiring a response only after the target is extinguished. Work has shown that RTs for a SD saccade preceded by a MD saccade are longer than when a SD saccade is preceded by its same task-type, whereas the converse switch does not influence performance (i.e., the unidirectional switch-cost). Experiment 1 yielded a 28 ms and 8 ms unidirectional switch-cost pre- and post-exercise, respectively (ps < 0.001); however, the magnitude of the switch-cost was reduced post-exercise (p = 0.005). Experiment 2 involved a non-exercise control condition and yielded a reliable and equivalent magnitude unidirectional switch-cost at a pre- (28 ms) and post-break (26 ms) assessment (ps < 0.001). Accordingly, a single-bout of exercise improved task-switching efficiency and thereby provides convergent evidence that exercise provides a global benefit to the core components of executive function.