Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloninger's Temperament Dimensions and Longitudinal Alcohol Use in Early Midlife: A Northern Finland Birth Cohort 1966 Study

BACKGROUND Temperament is theorized to be an important factor contributing to the development of alcohol use disorder, but longitudinal studies on how temperament is related to alcohol use among general population in midlife are scarce. Our aims were to investigate potentially reciprocal associations between temperament and changes in alcohol use from age 31 to 46 using prospective birth cohort data. METHOD Within the Northern Finland Birth Cohort 1966, alcohol use and temperament were studied at ages 31 and 46. Participants (N = 5,274) were classified into moderate users, abstainers and heavy users based on their mean alcohol use (g/d). Additionally, participants were categorized as steady users, reducers, or increasers. Multinomial regression analyses were conducted with Cloninger's Temperament and Character Inventory (TCI) scores as factors influencing alcohol use using moderate and stable users as reference groups. Reciprocity of relations was assessed with cross-lagged structural equation modeling. RESULTS Temperament and alcohol use are rather stable in midlife. Novelty seeking (NS) predicted heavy use (OR = 1.4; CI: 1.3 to 1.6 for men, OR = 1.3; CI: 1.1 to 1.5 for women) and increasing use (OR = 1.2; CI: 1.1 to 1.4 for men, OR = 1.1; CI: 1.0 to 1.3 for women), whereas low NS predicted abstaining among women (OR = 0.7; CI: 0.6 to 0.8). High harm avoidance (HA) predicted abstaining (OR = 1.3; CI: 1.1 to 1.5) for men. Low persistence (P) among men predicted both abstaining (OR = 0.9; CI: 0.7 to 0.98) and heavy use (OR = 0.9; CI: 0.8 to 0.98). Among women, low reward dependence (RD) predicted heavy use (OR = 0.8; CI: 0.7 to 0.9). Among TCI scores, only NS predicted increasing use in the cross-lagged models. CONCLUSIONS Temperament has an impact on alcohol use in midlife. Of the TCI dimensions, only NS seems to predispose to increased alcohol use and problem use throughout life. Additionally, RD among women and P among men are significant factors from a life-course perspective. Our results did not support Cloninger's theory on type I alcoholism, as HA showed no relation to problematic alcohol use in midlife.
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data

We present an event extraction framework to detect event mentions and extract events from the document-level financial news. Up to now, methods based on supervised learning paradigm gain the highest performance in public datasets (such as ACE2005, KBP2015). These methods heavily depend on the manually labeled training data. However, in particular areas, such as financial, medical and judicial domains, there is no enough labeled data due to the high cost of data labeling process. Moreover, most of the current methods focus on extracting events from one sentence, but an event is usually expressed by multiple sentences in one document. To solve these problems, we propose a Document-level Chinese Financial Event Extraction (DCFEE) system which can automatically generate a large scaled labeled data and extract events from the whole document. Experimental results demonstrate the effectiveness of it
Deadline-Guaranteed Point-to-Multipoint Bulk Transfers in Inter-Datacenter Networks

Many modern cloud services are operated across geographically distributed datacenters, and they are usually associated with a demand of transferring bulk data among datacenters for achieving a high performance and reliability. These transfers (e.g., data replications and synchronizations) may require the inter-datacenter networks to deliver data from one Point (or datacenter) to Multiple Points (or datacenters) and impose a deadline on the transfer for providing a guaranteed service to end users. However, very little work has been done to manage an efficient point-to- multipoint bulk transfer while considering the deadline requirements. In this paper, we investigate the deadline- aware point-to-multipoint (P2MP) transfer problem and propose a centralized deAdline- Guaranteed transfEr (AGE) approach that can guarantee the deadline for P2MP transfers while efficiently utilizing the inter-datacenter bandwidth resources. For each arriving request, AGE jointly determines the transfer source selections and bandwidth allocations such that the number of deadline-guaranteed transfers can be maximized. Our simulation experiments show that AGE can accommodate up to 53.3% more transfers whose deadline requirements are met. In addition, AGE can achieve 20% higher network utilization than prior bulk transfer approaches.
Ultrasound Findings in Thyroid Nodules: A Radio–Cytopathologic Correlation

Introduction: Ultrasound (USG) can be a good screening tool to identify high-risk nodule requiring fine-needle aspiration cytology (FNAC). The study aimed to assess the association of USG characteristic of thyroid nodule with malignancy. Methods: A cross-sectional study was performed from August 2011 to July 2012 at Tribhuvan University Teaching Hospital. Patients referred for USG of the neck with thyroid nodule more than 10 mm were offered FNAC and included in the study after taking informed consent. USG characteristics were compared with histopathologic diagnosis of benign or malignant nodule. Results: USG characteristics significantly (P < 0.05) associated with malignancy were as follows: size of thyroid nodule more than 30 mm, ill-defined margin, solid echotexture, hypoechoic lesion, microcalcification, and any form of increased vascularity. High sensitivity was seen in microcalcification, hypoechoic echogenicity, and ill-defined margin and high specificity was seen in ill-defined margin and solid echotexture. Relatively high sensitivity and specificity was found in ill-defined margin. Conclusions: Texture, size, margin, echogenicity, and vascularity are important factors for discriminating benign from malignant nodule. Hypoechogenicity, vascularity of any type, ill-defined margin, and microcalcification were independent predictors of malignancy. None of the characteristics were sensitive and specific to be used independently as screening tool to identify high risk of malignancy.
A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking

This paper investigates non-myopic path planning of mobile sensors for multi-target tracking. Such problem has posed a high computational complexity issue and/or the necessity of high-level decision making. Existing works tackle these issues by heuristically assigning targets to each sensing agent and solving the split problem for each agent. However, such heuristic methods reduce the target estimation performance in the absence of considering the changes of target state estimation along time. In this work, we detour the task-assignment problem by reformulating the general non-myopic planning problem to a distributed optimization problem with respect to targets. By combining alternating direction method of multipliers (ADMM) and local trajectory optimization method, we solve the problem and induce consensus (i.e., high-level decisions) automatically among the targets. In addition, we propose a modified receding-horizon control (RHC) scheme and edge-cutting method for efficient real-time operation. The proposed algorithm is validated through simulations in various scenarios.
Pythagorean fuzzy multicriteria group decision making through similarity measure based on point operators

In this paper, a series of similarity measures based on point operators for Pythagorean fuzzy sets are proposed. Using the proposed similarity measures, two new aggregation operators, viz., Pythagorean fuzzy‐dependent averaging operator and Pythagorean fuzzy‐dependent geometric operator, are developed. The advantage of using these operators is that the influence of unfair arguments of aggregated results could be eliminated, since the associated weights are taken from the aggregated Pythagorean fuzzy arguments. Also, the proposed operators have the capability to adjust the degree of aggregated arguments with the controlling parameters. To establish the application potentiality of those operators, a methodology for solving multicriteria group decision‐making problems having Pythagorean fuzzy arguments is developed. A numerical example is provided to demonstrate the proficiency of the proposed method. The achieved results are compared with the results of other existing technique.
Past and Present Perspectives on β-Lactamases

β-Lactamases, the major resistance determinant for β-lactam antibiotics in Gram-negative bacteria, are ancient enzymes whose origins can be traced back millions of years ago. These well-studied enzymes, currently numbering almost 2,800 unique proteins, initially emerged from environmental sources, most likely to protect a producing bacterium from attack by naturally occurring β-lactams. ABSTRACT β-Lactamases, the major resistance determinant for β-lactam antibiotics in Gram-negative bacteria, are ancient enzymes whose origins can be traced back millions of years ago. These well-studied enzymes, currently numbering almost 2,800 unique proteins, initially emerged from environmental sources, most likely to protect a producing bacterium from attack by naturally occurring β-lactams. Their ancestors were presumably penicillin-binding proteins that share sequence homology with β-lactamases possessing an active-site serine. Metallo-β-lactamases also exist, with one or two catalytically functional zinc ions. Although penicillinases in Gram-positive bacteria were reported shortly after penicillin was introduced clinically, transmissible β-lactamases that could hydrolyze recently approved cephalosporins, monobactams, and carbapenems later became important in Gram-negative pathogens. Nomenclature is based on one of two major systems. Originally, functional classifications were used, based on substrate and inhibitor profiles. A later scheme classifies β-lactamases according to amino acid sequences, resulting in class A, B, C, and D enzymes. A more recent nomenclature combines the molecular and biochemical classifications into 17 functional groups that describe most β-lactamases. Some of the most problematic enzymes in the clinical community include extended-spectrum β-lactamases (ESBLs) and the serine and metallo-carbapenemases, all of which are at least partially addressed with new β-lactamase inhibitor combinations. New enzyme variants continue to be described, partly because of the ease of obtaining sequence data from whole-genome sequencing studies. Often, these new enzymes are devoid of any phenotypic descriptions, making it more difficult for clinicians and antibiotic researchers to address new challenges that may be posed by unusual β-lactamases.
Comparing Sentiment Analysis Models to Classify Attitudes of Political Comments on Facebook (November 2016)

—This paper is a preliminary study which compares nine ML methods of sentiment analysis aimed towards classifying a corpus of 5.3 million messages of the public on Facebook pages of incumbent politicians. Two sentiments were examined: the general attitude of a comment and the attitude of the comment towards the content of a political post. Our results show that Logistic Regression outperformed the other eight ML models in terms of accuracy and F-measures. Also, we found that n-gram representation performed best. An interesting finding is a difference in success rate when classifying attitude in general vs. attitude towards the content in the political context.
A Novel Orthogonal Transmission Scheme for Visible Light Communication

The spatially-incoherent radiators in visible light communication (VLC) constrain the optical carrier to be only driven by a real electrical sub-carrier, which cannot be quadrature modulated as in classic RF-based communication systems. This, in turn, severely limits the transmission throughput of VLC systems. To overcome this technical challenge, a novel coherent transmission is proposed. As such, the optical carrier is only treated as a purely amplitude- modulated carrier capable of transmitting the quadrature modulated symbols. The ability of our novel coherent transmission to fully reconstruct the in-phase and quadrature parts is validated through analytical symbol error rate and Matlab simulations. Results also show that the proposed scheme, while remarkable in its simplicity, can improve both the spectral and energy efficiency of VLC systems, i.e. double the spectral efficiency and achieve more than 45% energy efficiency improvement, when compared to its existing counterpart.
Heat Shock Protein 70 Gene Single Nucleotide Polymorphism and Diabetic Foot Ulcer. Is There Any Relationship?

Objective: The study aims to investigate the potential role of C2437T (Met493Thr) single nucleotide polymorphism (SNP) of the heat shock protein (HSP) 70 in diabetic foot ulcer patients. Methods: In this prospective cohort study, SNP of the HSP70 hom gene, also called HSPA1L, was studied among diabetic patients with an ulcer (Group A: n = 50), diabetic patients without an ulcer (Group B: n = 50), and healthy subjects (Group C: n = 50). Results: There was a higher frequency of T/T genotype in group A (76%) as compared to group B (44%) and group C (14%). Moreover, the frequency of T allele was 7.3% in group A, 5.5% in group B, and 3.9% in group C. C allele frequency was 2.6%, 4.4%, and 6.1% in group A, group B, and group C, respectively. In group A, the odds ratio and risk ratio were 19-fold and 5-fold, respectively, for the HSP70 hom T/T homozygous gene compared to B (OR 19.45; RR 5.42; X2 38.8, p < 0.0001). Moreover, 4-fold and 1.75-fold ratios have been compared with group C (OR 4.03; RR 1.72; X2 10.6, p < 0.001). No significant difference in genotype was observed in group B and group C. Conclusions: There is a significant and positive association of hspHSP70 hom polymorphism restricted to T allele in homozygous and heterozygous states among diabetic foot ulcer (DFU) patients.
Crossbar-Aware Neural Network Pruning

Crossbar architecture has been widely adopted in neural network accelerators due to the efficient implementations on vector-matrix multiplication operations. However, in the case of convolutional neural networks (CNNs), the efficiency is compromised dramatically because of the large amounts of data reuse. Although some mapping methods have been designed to achieve a balance between the execution throughput and resource overhead, the resource consumption cost is still huge while maintaining the throughput. Network pruning is a promising and widely studied method to shrink the model size, whereas prior work for CNNs compression rarely considered the crossbar architecture and the corresponding mapping method and cannot be directly utilized by crossbar-based neural network accelerators. This paper proposes a crossbar-aware pruning framework based on a formulated $L_{0}$ -norm constrained optimization problem. Specifically, we design an $L_{0}$ -norm constrained gradient descent with relaxant probabilistic projection to solve this problem. Two types of sparsity are successfully achieved: 1) intuitive crossbar-grain sparsity and 2) column-grain sparsity with output recombination, based on which we further propose an input feature maps reorder method to improve the model accuracy. We evaluate our crossbar-aware pruning framework on the median-scale CIFAR10 data set and the large-scale ImageNet data set with VGG and ResNet models. Our method is able to reduce the crossbar overhead by 44%–72% with insignificant accuracy degradation. This paper significantly reduce the resource overhead and the related energy cost and provides a new co-design solution for mapping CNNs onto various crossbar devices with much better efficiency.
Efficient reflectance capture using an autoencoder

We propose a novel framework that automatically learns the lighting patterns for efficient reflectance acquisition, as well as how to faithfully reconstruct spatially varying anisotropic BRDFs and local frames from measurements under such patterns. The core of our framework is an asymmetric deep autoencoder, consisting of a nonnegative, linear encoder which directly corresponds to the lighting patterns used in physical acquisition, and a stacked, nonlinear decoder which computationally recovers the BRDF information from captured photographs. The autoencoder is trained with a large amount of synthetic reflectance data, and can adapt to various factors, including the geometry of the setup and the properties of appearance. We demonstrate the effectiveness of our framework on a wide range of physical materials, using as few as 16 ~ 32 lighting patterns, which correspond to 12 ~ 25 seconds of acquisition time. We also validate our results with the ground truth data and captured photographs. Our framework is useful for increasing the efficiency in both novel and existing acquisition setups.
Neural Program Synthesis from Diverse Demonstration Videos

Interpreting decision making logic in demonstration videos is key to collaborating with and mimicking humans. To empower machines with this ability, we propose a neural program synthesizer that is able to explicitly synthesize underlying programs from behaviorally diverse and visually complicated demonstration videos. We introduce a summarizer module as part of our model to improve the network’s ability to integrate multiple demonstrations varying in behavior. We also employ a multi-task objective to encourage the model to learn meaningful intermediate representations for end-to-end training. We show that our model is able to reliably synthesize underlying programs as well as capture diverse behaviors exhibited in demonstrations. The code is available at https://shaohua0116.github. io/demo2program.
Retrospective analysis of cases with an ectopic opening of the common bile duct into duodenal bulb.

BACKGROUND Ectopic opening of the common bile duct (EOCBD) is a very rare entity. It has been reported in the 3rd or 4th portion of the duodenum, pyloric canal, duodenal bulb, and the stomach. OBJECTIVES The aim of this study was to evaluate the clinical characteristics, laboratory values and imaging studies of patients with EOCBD into the duodenal bulb retrospectively. MATERIAL AND METHODS The files of patients who underwent endoscopic retrograde cholangiopancreatography (ERCP) between January 2003 and November 2015 were reviewed. The demographic data, presentations, abdominal ultrasonography, computed tomography (CT), magnetic resonance cholangiopancreatography (MRCP), and ERCP findings of patients with EOCBD into the duodenal bulb were evaluated retrospectively. RESULTS Ectopic openings of the CBD into the duodenal bulb were found in 20 out of 3,270 patients who had undergone ERCP. Twenty patients (15 males and 5 females) with a median age of 59 (40-88) years were included in the study. Ectopic opening of the CBD into the duodenal bulb were found in 20 patients (0.61%). Laboratory test abnormalities included: hyperbilirubinemia in 20 (100%) patients, leukocytosis in 14 (70%) patients, an elevated serum alkaline phosphatase and gamma-glutamyl transferase level in 20 (100%) patients. Indications for ERCP were CBD dilatation and extrahepatic cholestasis (n = 20), cholangitis (n = 12), only choledocholithiasis (n = 7), and acute pancreatitis (n = 2). CONCLUSIONS In patients with recurrent duodenal ulcers and/or apical stricture with accompanying CBD dilatation, extrahepatic cholestasis and cholangitis, EOCBD into the duodenal bulb should be considered.
Deep Encoder-Decoder Models for Unsupervised Learning of Controllable Speech Synthesis

Generating versatile and appropriate synthetic speech requires control over the output expression separate from the spoken text. Important non-textual speech variation is seldom annotated, in which case output control must be learned in an unsupervised fashion. In this paper, we perform an in-depth study of methods for unsupervised learning of control in statistical speech synthesis. For example, we show that popular unsupervised training heuristics can be interpreted as variational inference in certain autoencoder models. We additionally connect these models to VQ-VAEs, another, recently-proposed class of deep variational autoencoders, which we show can be derived from a very similar mathematical argument. The implications of these new probabilistic interpretations are discussed. We illustrate the utility of the various approaches with an application to acoustic modelling for emotional speech synthesis, where the unsupervised methods for learning expression control (without access to emotional labels) are found to give results that in many aspects match or surpass the previous best supervised approach.
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages

Object detection has made great progress in the past few years along with the development of deep learning. However, most current object detection methods are resource hungry, which hinders their wide deployment to many resource restricted usages such as usages on always-on devices, battery-powered low-end devices, etc. This paper considers the resource and accuracy trade-off for resource-restricted usages during designing the whole object detection framework. Based on the deeply supervised object detection (DSOD) framework, we propose Tiny-DSOD dedicating to resource-restricted usages. Tiny-DSOD introduces two innovative and ultra-efficient architecture blocks: depthwise dense block (DDB) based backbone and depthwise feature-pyramid-network (D-FPN) based front-end. We conduct extensive experiments on three famous benchmarks (PASCAL VOC 2007, KITTI, and COCO), and compare Tiny-DSOD to the state-of-the-art ultra-efficient object detection solutions such as Tiny-YOLO, MobileNet-SSD (v1 & v2), SqueezeDet, Pelee, etc. Results show that Tiny-DSOD outperforms these solutions in all the three metrics (parameter-size, FLOPs, accuracy) in each comparison. For instance, Tiny-DSOD achieves 72.1% mAP with only 0.95M parameters and 1.06B FLOPs, which is by far the state-of-the-arts result with such a low resource requirement.
Metagenomic Analysis of Bacteria, Fungi, Bacteriophages, and Helminths in the Gut of Giant Pandas

To obtain full details of gut microbiota, including bacteria, fungi, bacteriophages, and helminths, in giant pandas (GPs), we created a comprehensive microbial genome database and used metagenomic sequences to align against the database. We delineated a detailed and different gut microbiota structures of GPs. A total of 680 species of bacteria, 198 fungi, 185 bacteriophages, and 45 helminths were found. Compared with 16S rRNA sequencing, the dominant bacterium phyla not only included Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria but also Cyanobacteria and other eight phyla. Aside from Ascomycota, Basidiomycota, and Glomeromycota, Mucoromycota, and Microsporidia were the dominant fungi phyla. The bacteriophages were predominantly dsDNA Myoviridae, Siphoviridae, Podoviridae, ssDNA Inoviridae, and Microviridae. For helminths, phylum Nematoda was the dominant. In addition to previously described parasites, another 44 species of helminths were found in GPs. Also, differences in abundance of microbiota were found between the captive, semiwild, and wild GPs. A total of 1,739 genes encoding cellulase, β-glucosidase, and cellulose β-1,4-cellobiosidase were responsible for the metabolism of cellulose, and 128,707 putative glycoside hydrolase genes were found in bacteria/fungi. Taken together, the results indicated not only bacteria but also fungi, bacteriophages, and helminths were diverse in gut of giant pandas, which provided basis for the further identification of role of gut microbiota. Besides, metagenomics revealed that the bacteria/fungi in gut of GPs harbor the ability of cellulose and hemicellulose degradation.