Miracle-dz/UniFAR-FTUs huggingface dataset:
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery

We propose a primal-dual based framework for analyzing the global optimality of nonconvex lowrank matrix recovery. Our analysis are based on the restricted strongly convex and smooth conditions, which can be verified for a broad family of loss functions. In addition, our analytic framework can directly handle the widely-used incoherence constraints through the lens of duality. We illustrate the applicability of the proposed framework to matrix completion and one-bit matrix completion, and prove that all these problems have no spurious local minima. Our results not only improve the sample complexity required for characterizing the global optimality of matrix completion, but also resolve an open problem in Ge et al. (2017) regarding one-bit matrix completion. Numerical experiments show that primaldual based algorithm can successfully recover the global optimum for various low-rank problems.
Risk factors and seroprevalence of hepatitis E evaluated in frozen‐serum samples (2002‐2003) of pregnant women compared with female blood donors in a Southern region of Brazil

Hepatitis E has always been related to morbidity in pregnant women. Its epidemiology is not well understood in Brazil. Therefore, we tested sera from 209 pregnant women and 199 female blood donors, collected at a single center in Curitiba, Brazil. The Wantai assay was used for testing the anti‐hepatitis E virus (anti‐HEV), immunoglobulin G (IgG), and an in‐house polymerase chain reaction process for testing HEV RNA. Anti‐HEV was detected in 22.5% of the total group, 19% in the pregnant women group, and 26% in the blood donor group (P = 0.11), a much higher prevalence when compared with other studies in Brazil. Demographical analysis showed that 92.4% were born in the South Region of Brazil, 4.9% in the Southeast, and 2.7% were distributed over other regions of the country. With respect to their origin, 99% were from the South, 0.7% from the Southeast, and 0.2% from the Central‐West regions. Income, education, race, number of pregnancies, and abortion did differ significantly when comparing both the groups (P < 0.001). Age >30 (P = 0.012) and the number (>3) of pregnancies (P = 0.008) were related to anti‐HEV positivity. All anti‐HEV IgG–positive females were HEV RNA negative. In conclusion, HEV positivity was found in one out of five young women, which showed an urgent need for further epidemiological studies in Brazil.
Enabling Efficient Updates in KV Storage via Hashing

Persistent key-value (KV) stores mostly build on the Log-Structured Merge (LSM) tree for high write performance, yet the LSM-tree suffers from the inherently high I/O amplification. KV separation mitigates I/O amplification by storing only keys in the LSM-tree and values in separate storage. However, the current KV separation design remains inefficient under update-intensive workloads due to its high garbage collection (GC) overhead in value storage. We propose HashKV, which aims for high update performance atop KV separation under update-intensive workloads. HashKV uses hash-based data grouping, which deterministically maps values to storage space to make both updates and GC efficient. We further relax the restriction of such deterministic mappings via simple but useful design extensions. We extensively evaluate various design aspects of HashKV. We show that HashKV achieves 4.6× update throughput and 53.4% less write traffic compared to the current KV separation design. In addition, we demonstrate that we can integrate the design of HashKV with state-of-the-art KV stores and improve their respective performance.
A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection

Hate speech detection in social media texts is an important Natural language Processing task, which has several crucial applications like sentiment analysis, investigating cyberbullying and examining socio-political controversies. While relevant research has been done independently on code-mixed social media texts and hate speech detection, our work is the first attempt in detecting hate speech in Hindi-English code-mixed social media text. In this paper, we analyze the problem of hate speech detection in code-mixed texts and present a Hindi-English code-mixed dataset consisting of tweets posted online on Twitter. The tweets are annotated with the language at word level and the class they belong to (Hate Speech or Normal Speech). We also propose a supervised classification system for detecting hate speech in the text using various character level, word level, and lexicon based features.
Millimeter-Wave-Based Fiber-Wireless Bridge System for 8K UHD Video Streaming and Gigabit Ethernet Data Connectivity

In this paper, we introduce an end-to-end and real-time seamless fiber-wireless (Fi-Wi) bridge system, for gigabit Ethernet standard data connectivity and 8K standard video streaming. The proposed end-to-end Fi-Wi bridge system consists of seamless integration of 2 × 20-km intermediate frequency-over-fiber (IFoF) and 96-GHz millimeter-wave (mmWave) wireless links. Using our system, zero packet loss of 36 compressed 8K standard videos streaming is successfully achieved for different values of mmWave attenuation levels, ranging from 35 to 55 dB. We also demonstrate the transmission of Ethernet frames at 5-Gbps capacity on the Fi-Wi bridge with 20 meters mmWave wireless link, where 8% error vector magnitude can be attained. We then investigate and evaluate the degradation caused by the IFoF and mmWave links characteristics on the overall system performance. Furthermore, we estimate analytically the attainable mmWave transmission range for different weather conditions. The obtained results demonstrate the potentials of our proposed Fi-Wi bridge systems to provide high data and reliable transmission for areas lacking fiber infrastructure and for live broadcasting relay of 8K based TV programs.
Quality of Sleep in CKD Patients on Chronic Hemodialysis and the Effect of Dialysis Shift.

OBJECTIVE To determine the frequency of poor sleep quality in chronic kidney disease (CKD) patients on chronic haemodialysis and compare the quality of sleep amongst patients undergoing chronic haemodialysis in morning shift to other shifts (including afternoon, evening and night). STUDY DESIGN Cross-sectional, descriptive study. PLACE AND DURATION OF STUDY Dialysis Unit, Department of Nephrology, Shifa International Hospital Islamabad, Pakistan, from May to November 2016. METHODOLOGY CKD patients who were dialysis dependant for a minimum of three months between ages of 20 years to 70 years were included. Participants were interviewed using a questionnaire measuring age, gender, time on haemodialysis, duration of each session and shift in which they were dialysed. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Using patient's responses to all questions in the PSQI, sleep quality score for each patient was calculated and they were classified as poor or good sleepers. RESULTS A total of 113 patients were included. Out of these, 82 patients (72.6%) had poor quality of sleep and 31 (27.4%) patients had good sleep quality. Thirty three out of the 48 morning shift patients had poor sleep quality and 49 out of 65 patients in other shifts had poor sleep quality. This relationship was not statistically significant. CONCLUSION The frequency of poor sleep quality amongst CKD patients receiving chronic haemodialysis is high. However, dialysis shift does not seem to have any statistically significant relationship with the occurrence of poor sleep quality in these patients.
Understanding variation in prevalence estimates of polycystic ovary syndrome: a systematic review and meta-analysis

BACKGROUND Polycystic ovary syndrome (PCOS) prevalence estimates vary when different diagnostic criteria are applied. Lack of standardization of individual elements within these criteria may contribute to prevalence differences. OBJECTIVE AND RATIONALE A systematic review of studies reporting prevalence of PCOS, using at least one of the National Institutes of Health (NIH), Rotterdam or Androgen Excess Society (AE-PCOS) criteria, was conducted. The aim was to investigate the impact on prevalence reporting of different definitions of the clinical elements for PCOS diagnosis. SEARCH METHODS A systematic search of Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL), Emcare and BIOSIS was conducted. The search was limited to English language and studies published between January 1990 and January 2018. Included articles needed to define PCOS by at least one of the NIH, Rotterdam or AE-PCOS criteria, be of an unselected population and be published as a full text article. Risk-of-bias was assessed. OUTCOMES A total of 21 studies met the inclusion criteria. The random-effects pooled prevalence of PCOS in studies that used the NIH criteria (7% [95% CI: 6-7%]), was significantly different from that identified in studies that used the Rotterdam criteria (12% [95% CI: 10-15%], P < 0.0001) but not studies that used the AE-PCOS criteria (10% [95% CI: 6-13%], P = 0.075). The pooled estimates for Rotterdam and AE-PCOS were not significantly different from each other (P = 0.201). Pooled prevalence estimates were compared between studies separated on the basis of: oligo-amenorrhoea vs oligo-amenorrhoea plus short cycles, clinical androgen excess requiring hirsutism vs any clinical androgen excess, use of different versions and cut-offs for the Ferriman-Gallwey (F-G) score, and inclusion vs non-inclusion of oral contraceptive users. There were no statistically significant differences for any of these comparisons. There was insufficient information to allow subgroup analyses of definitions of polycystic ovaries. WIDER IMPLICATIONS Inclusion of ovarian morphology results in statistically significantly higher pooled prevalence estimates for PCOS. Heterogeneity in prevalence estimates for PCOS reflect the broad clinical spectrum of the condition, lack of standardization of the elements within each set of diagnostic criteria and the use of a range of diagnostic cut-offs, as well as potential differences between study populations. The use of different definitions for anovulation and clinical androgen excess did not appear to contribute to differences in the estimated prevalence of PCOS in this study. However, as the number of studies in most of the comparison groups was small, real differences may have been missed. Uncertainty surrounding the diagnosis of PCOS urgently needs to be addressed in order to provide clinicians and their patients with greater diagnostic certainty, and hence reduce inappropriate labelling and the potential psychological harm that may accompany misdiagnosis.
Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction

Fine-grained opinion analysis aims to extract aspect and opinion terms from each sentence for opinion summarization. Supervised learning methods have proven to be effective for this task. However, in many domains, the lack of labeled data hinders the learning of a precise extraction model. In this case, unsupervised domain adaptation methods are desired to transfer knowledge from the source domain to any unlabeled target domain. In this paper, we develop a novel recursive neural network that could reduce domain shift effectively in word level through syntactic relations. We treat these relations as invariant “pivot information” across domains to build structural correspondences and generate an auxiliary task to predict the relation between any two adjacent words in the dependency tree. In the end, we demonstrate state-of-the-art results on three benchmark datasets.
Role of the Default Mode Network in Cognitive Transitions

A frequently repeated finding is that the default mode network (DMN) shows activation decreases during externally-focused tasks. This finding has led to an emphasis in DMN research on internally-focused self-relevant thought processes. A recent study, in contrast, implicates the DMN in substantial externally-focused task switches. Using functional magnetic resonance imaging, we scanned 24 participants performing a task switch experiment. Whilst replicating previous DMN task switch effects, we also found large DMN increases for brief rests as well as task restarts after rest. Our findings are difficult to explain using theories strictly linked to internal or self-directed cognition. In line with principal results from the literature, we suggest that the DMN encodes scene, episode or context, by integrating spatial, self-referential and temporal information. Context representations are strong at rest, but re-reference to context also occurs at major cognitive transitions.
A Helping Hand: Transfer Learning for Deep Sentiment Analysis

Deep convolutional neural networks excel at sentiment polarity classification, but tend to require substantial amounts of training data, which moreover differs quite significantly between domains. In this work, we present an approach to feed generic cues into the training process of such networks, leading to better generalization abilities given limited training data. We propose to induce sentiment embeddings via supervision on extrinsic data, which are then fed into the model via a dedicated memory-based component. We observe significant gains in effectiveness on a range of different datasets in seven different languages.
Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems

Stream-processing workloads and modern shared cluster environments exhibit high variability and unpredictability. Combined with the large parameter space and the diverse set of user SLOs, this makes modern streaming systems very challenging to statically configure and tune. To address these issues, in this paper we investigate a novel control-plane design, Chi, which supports continuous monitoring and feedback, and enables dynamic re-configuration. Chi leverages the key insight of embedding control-plane messages in the data-plane channels to achieve a low-latency and flexible control plane for stream-processing systems. Chi introduces a new reactive programming model and design mechanisms to asynchronously execute control policies, thus avoiding global synchronization. We show how this allows us to easily implement a wide spectrum of control policies targeting different use cases observed in production. Large-scale experiments using production workloads from a popular cloud provider demonstrate the flexibility and efficiency of our approach.
Polish Cross-Cultural Adaptation and Validation of the Knee Outcome Survey Activities of Daily Living Scale (KOS-ADLS) in Patients Undergoing Total Knee Arthroplasty

Background The Knee Outcome Survey Activities of Daily Living Scale (KOS-ADLS) is a self-reported questionnaire used for the evaluation of the overall health of patients with knee dysfunctions. The purpose of this study was to perform a cross-cultural adaptation of the Polish version of KOS-ADLS and to evaluate its psychometric properties in patients at the end-stage of knee osteoarthritis who were qualified for a total knee replacement (TKR). Material/Methods Seventy patients consecutively qualified for surgical TKR procedure participated in this study. To adapt the KOS-ADLS, the following scales and reference tests were used: Knee injury and Osteoarthritis Outcome Score (KOOS), Visual Analogue Scale (VAS), Time Up and Go test (TUG), and Five Times Sit to Stand test (5×STS). The studies were conducted 3 times: 2 weeks before surgery (first study), 6 to 13 days later (retest), and 6 months after surgery. Results The Polish version of KOS-ADLS showed excellent reliability (ICC=0.89 SEM=2.68, MDC=7.43) and high responsiveness (ES=4.76, SRM=3.18). The internal consistency was poor in the first assessment (Cronbach’s alpha=0.68), but acceptable in the post-surgery evaluation (Cronbach’s alpha=0.86). There were fair and moderate correlations found between KOS-ADLS and VAS scales in the first examination, TUG, and 5×STS. Stronger correlations were observed between the results obtained in KOS-ADLS and KOOS. Conclusions The Polish version of the KOS-ADLS demonstrated good reliability, validity, and responsiveness for use in patients who have undergone TKR surgery.
Feasible Arm Identification

We introduce the feasible arm identification problem, a pure exploration multi-armed bandit problem where the agent is given a set of Ddimensional arms and a polyhedron P “ tx : Ax ď bu Ă R. Pulling an arm gives a random vector and the goal is to determine, using a fixed budget of T pulls, which of the arms have means belonging to P . We propose three algorithms MD-UCBE, MD-SAR, and MD-APT and provide a unified analysis establishing upper bounds for each of them. We also establish a lower bound that matches up to constants the upper bounds of MDUCBE and MD-APT. Finally, we demonstrate the effectiveness of our algorithms on synthetic and real-world datasets.
Exploiting Uplink NOMA to Improve Sum Secrecy Throughput in IoT Networks

This paper exploits nonorthogonal multiple access (NOMA) to enhance the uplink secure transmission in Internet of Things (IoT) networks. Considering the different intercept ability of eavesdroppers (Eve), secrecy performances of both strong and weak Eve wiretap scenarios have been investigated. In strong Eve wiretap scenario (SWS), Eve is assumed to be powerful enough to decode message without interference and, in weak Eve wiretap scenario (WWS), Eve is assumed to have significant demodulation capability constraint. The new closed-form expressions of joint connection outage probability (JCOP), joint secrecy outage probability (JSOP), and sum secrecy throughput (SST) are derived in these two scenarios to indicate the impact of parameters, i.e., transmit power, codeword rate, and the placement of devices, on security performance. In order to demonstrate the superiority of NOMA, we also investigate the secrecy performance of orthogonal multiple access (OMA) system as a benchmark. Analysis results show that the performance in WWS is always better than that in SWS and, in low signal-to-noise ratio (SNR) or high codeword rate region, the performances of these two scenarios are close. In addition, we present the condition that NOMA outperforms OMA in terms of SST. Moreover, the placements of devices are significant to the SST performance of NOMA system. The suboptimal device placement scheme has been designed to maximize SST. Analysis results demonstrate that when Eve is far away from legal users, the suboptimal results tend to optimal.
Linking Empowering Leadership and Employee Work Engagement: The Effects of Person-Job Fit, Person-Group Fit, and Proactive Personality

Based on person-environment fit theory, this study examined the effects of empowering leadership on employee work engagement. We also investigated the mediating mechanism of person-job fit and person-group fit. In addition, we explored employee proactive personality’s moderating role between empowering leadership and the above two kinds of fit, and then the set of indirect effects. Using a survey sample of 6179 employees from a technology company in China, we found that empowering leadership has a positively indirect influence on employees work engagement though person-job fit and person-group fit. Further, moderated mediation analysis revealed proactive personality augmented empowering leadership direct effect on person-job fit and person-group fit and indirect effect on work engagement. Theoretical and practical implications were also discussed.
A Document Descriptor using Covariance of Word Vectors

In this paper, we address the problem of finding a novel document descriptor based on the covariance matrix of the word vectors of a document. Our descriptor has a fixed length, which makes it easy to use in many supervised and unsupervised applications. We tested our novel descriptor in different tasks including supervised and unsupervised settings. Our evaluation shows that our document covariance descriptor fits different tasks with competitive performance against state-of-the-art methods.
Aspects of the Microglia Transcriptome

Gene expression analyses of microglia, the tissue-resident macrophages of the central nervous system (CNS), led to the identification of homeostatic as well as neurological disease-specific gene signatures of microglia phenotypes. Upon alterations in the neural microenvironment, either caused by local insults from within the CNS (during neurodegenerative diseases) or by macroenvironmental incidents, such as social stress, microglia can switch phenotypes-generally referred to as “microglial activation”. The interplay between the microenvironment and its influence on microglia phenotypes, regulated by (epi)genetic mechanisms, can be imagined as the different colorful crystal formations (microglia phenotypes) that change upon rotation (microenvironmental changes) of a kaleidoscope. Here, microglia phenotypes in relation to neurodevelopment, homeostasis, aging, and neurodegenerative diseases, based on transcriptome studies, will be discussed. By overlaying these disease-specific microglia signatures, recent publications have identified a specific set of genes that are differentially expressed in all investigated diseases, called a microglia core gene signature with multiple diseases. This chapter is concluded with a discussion about the complexity of this associated microglia core gene signature.
Data Communication and Analytics for Smart Grid Systems

With the popularity of smart electrical appliances and home energy management systems, there has been a massive amount of data generated by the power consumption. This data can be beneficial for the utility as it provides the behavior patterns of customers, and thus useful decisions can be made to optimize the load on the grid. In this work, we establish a bidirectional communication system between some homes through the customer agents (CAs), which are installed at home, and the transformer agent (TA) which is installed at the local transformer. Once data is collected at the TA, it is sent to the cloud through LTE. We then use IBM Cloud services to filter and analyze this data to forecast energy consumption and make recommendations to different customers based on their real-time changing behaviors. To this end, we use six different machine learning models predicting the energy consumption: support vector regression (SVR) using linear kernel, SVR using Gaussian kernel, SVR using the polynomial kernel, linear regression, polynomial regression, and feed- forward neural networks (FFNN). To measure the accuracy of these models, we compute three different error metrics, the normalized mean absolute percentage error (NMAPE), the normalized root mean square error (NRMSE), and R² also known as the coefficient of determination. Based on these results, we observe that the performance of the forecasting model depends on the dataset properties including the size and variations. For example, while linear and polynomial regressions perform well for small-scale datasets, FFNN gives higher accuracy for large-scale datasets.