Miracle-dz/UniFAR-FTUs huggingface dataset:
Delay asymptotics and bounds for multitask parallel jobs

We study delay of jobs that consist of multiple parallel tasks, which is a critical performance metric in a wide range of applications such as data file retrieval in coded storage systems and parallel computing. In this problem, each job is completed only when all of its tasks are completed, so the delay of a job is the maximum of the delays of its tasks. Despite the wide attention this problem has received, tight analysis is still largely unknown since analyzing job delay requires characterizing the complicated correlation among task delays, which is hard to do. We first consider an asymptotic regime where the number of servers, n, goes to infinity, and the number of tasks in a job, k(n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k^{(n)}$$\end{document}, is allowed to increase with n. We establish the asymptotic independence of any k(n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k^{(n)}$$\end{document} queues under the condition k(n)=o(n1/4)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k^{(n)}= o(n^{1/4})$$\end{document}. This greatly generalizes the asymptotic independence type of results in the literature, where asymptotic independence is shown only for a fixed constant number of queues. As a consequence of our independence result, the job delay converges to the maximum of independent task delays. We next consider the non-asymptotic regime. Here, we prove that independence yields a stochastic upper bound on job delay for any n and any k(n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k^{(n)}$$\end{document} with k(n)≤n\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k^{(n)}\le n$$\end{document}. The key component of our proof is a new technique we develop, called “Poisson oversampling. ” Our approach converts the job delay problem into a corresponding balls-and-bins problem. However, in contrast with typical balls-and-bins problems where there is a negative correlation among bins, we prove that our variant exhibits positive correlation.
Unobtrusive Monitoring of Speech Impairments of Parkinson'S Disease Patients Through Mobile Devices

Parkinson's disease (PD) produces several speech impairments in the patients. Automatic classification of PD patients is performed considering speech recordings collected in noncontrolled acoustic conditions during normal phone calls in a unobtrusive way. A speech enhancement algorithm is applied to improve the quality of the signals. Two different classification approaches are considered: the classification of PD patients and healthy speakers and a multi -class experiment to classify patients in several stages of the disease. According to the results it is possible to classify PD patients and healthy controls with a AUe of up to 0.87. This work is a step forward to the development of telemonitoring systems to assess the speech of the patients.
Macronutrient Intake in Relation to Migraine and Non-Migraine Headaches

We investigated the association of mean daily macronutrient intake with migraine and non-migraine headaches. This cross-sectional study included 8042 men and 23,728 women from the ongoing population-based NutriNet-Santé e-cohort. Headache status was assessed via an online self-report questionnaire (2013–2016). Migraine was defined using established criteria and dietary macronutrient intake was estimated via ≥3 24 h dietary records. Mean daily intake (g/day) of carbohydrates (simple, complex, and total), protein, and fat (saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, and total) were the main exposure variables. Adjusted gender-specific analysis of variance (ANOVA) models were fit. Presence of migraines was noted in 9.2% of men (mean age = 54.3 ± 13.3 years) and 25.7% of women (mean age = 49.6 ± 12.8 years). In adjusted models, we observed (1) somewhat lower protein (p < 0.02) and higher total fat (p < 0.01) intake among male migraineurs compared with males without headaches and those with non-migraine headaches; (2) somewhat higher total fat (p < 0.0001) and total carbohydrate intake (p < 0.05) among female migraineurs compared with females without headaches and those with non-migraine headaches. The findings, which provide preliminary support for modest gender-specific differences in macronutrient intake by migraine status, merit confirmation in different population-based settings, as well as longitudinally, and could help to inform future dietary interventions in headache prevention.
ON the Use of Wavenet as a Statistical Vocoder

In this paper, we explore the possibility of using the WaveNet architecture as a statistical vocoder. In that case, the generation of speech waveforms is locally conditioned only by acoustic features. Focusing on the single speaker case at the moment, we investigate the impact of the local conditions as well as that of the amount of data available for training. Furthermore, variations of the WaveNet architecture are considered and discussed in the context of our work. We compare our work against a very recent work which also used WaveNet architecture as a speech vocoder using the same speech data. More specifically, we used two female and two male speakers from the CMU-ARCTIC database to contrast the use of cepstrum coefficients and filter-bank features as local conditioners with the goal to improve the overall quality for both male and female speakers. In the paper we also discuss the impact of the size of the training data. Objective metrics for quality and intelligibility of the generated by the WaveNet speech as well as subjective tests support our suggestions.
Effects of Self‐Conditioning Techniques (Self‐Hypnosis) in Promoting Weight Loss in Patients with Severe Obesity: A Randomized Controlled Trial

The usefulness of the rapid‐induction techniques of hypnosis as an adjunctive weight‐loss treatment has not been defined. This randomized controlled trial evaluated whether self‐conditioning techniques (self‐hypnosis) added to lifestyle interventions contributed to weight loss (primary outcome), changes in metabolic and inflammatory variables, and quality of life (QoL) improvement (secondary outcomes) in severe obesity.
SpiNNaker: Event-Based Simulation—Quantitative Behavior

SpiNNaker (Spiking Neural Network Architecture) is a specialized computing engine, intended for real-time simulation of neural systems. It consists of a mesh of 240x240 nodes, each containing 18 ARM9 processors: over a million cores, communicating via a bespoke network. Ultimately, the machine will support the simulation of up to a billion neurons in real time, allowing simulation experiments to be taken to hitherto unattainable scales. The architecture achieves this by ignoring three of the axioms of computer design: the communication fabric is non-deterministic; there is no global core synchronisation, and the system state—held in distributed memory—is not coherent. Time models itself: there is no notion of computed simulation time—wallclock time is simulation time. Whilst these design decisions are orthogonal to conventional wisdom, they bring the engine behavior closer to its intended simulation target—neural systems. We describe how SpiNNaker simulates large neural ensembles; we provide performance figures and outline some failure mechanisms. SpiNNaker simulation time scales 1:1 with wallclock time at least up to nine million synaptic connections on a 768 core subsystem (∼1400th of the full system) to accurately produce logically predicted results.
Investigating the Components of Signals Produced by the Pockels CCD Imaging Technique in CdZnTe

Several groups have demonstrated the potential of the Pockels effect in cadmium zinc telluride as a means to detect ionizing radiation. Migrating charge carriers are believed to generate the signal detected via the Pockels effect due to the distortions they create within the electric field; however, trapped space charge beneath the cathode has been regularly observed, which suggests field changes away from the charge generation area. Consequently, the Pockels signal amplitude contains a charge integrated, i.e., dose element. In this paper, the effects of electric field collapse at the location of charge carrier generation are demonstrated. This opens the potential to apply the technique for imaging dose rate distributions. Charge-coupled device images representing the changes in an electric field within the crystal were taken, and the response to illumination from a collimated 1550-nm, 4.5-mW IR laser and irradiation from 150-kVp X-rays was measured. The data demonstrate that the signal acquired is a combination of both the local change in the electric field at the location where the carriers are being released/generated and an element caused by carriers becoming trapped, leading to electric field enhancement due to space charge near the cathode and field reduction toward the anode.
LieNet: Real-time Monocular Object Instance 6D Pose Estimation

In this work, we present, LieNet, a novel deep learning framework that simultaneously detects, segments multiple object instances, and estimates their 6D poses from a single RGB image without requiring additional post-processing. Our system is accurate and fast ( ∼ 10 fps), which is well suited for real-time applications. In particular, LieNet detects and segments object instances in the image analogous to modern instance segmentation networks such as Mask R-CNN, but contains a novel additional sub-network for 6D pose estimation. LieNet estimates the rotation matrix of an object by regressing a Lie algebra based rotation representation, and estimates the translation vector by predicting the distance of the object to the camera center. The experiments on two standard pose benchmarking datasets show that LieNet greatly outperforms other recent CNN based pose prediction methods when they are used with monocular images and without post-reﬁnements.
Effects of corticosterone on mild auditory fear conditioning and extinction; role of sex and training paradigm

Multiple lines of evidence suggest that glucocorticoid hormones enhance memory consolidation of fearful events. However, most of these studies involve male individuals. Since anxiety, fear, and fear-associated disorders present differently in male and female subjects we investigated in mice whether male and female mice perform differently in a mild, auditory fear conditioning task and tested the modulatory role of glucocorticoid hormones. Using an auditory fear conditioning paradigm with different footshock intensities (0.1, 0.2, and 0.4 mA) and frequencies (1× or 3×), we find that intraperitoneal injections with corticosterone (2 mg/kg) immediately after training, altered freezing behavior when repeated footshocks were applied, and that the direction of the effects were opposite in male and female mice. Effects were independent of footshock intensity. In male mice, corticosterone consistently increased freezing behavior in response to the tone, whereas in female mice, corticosterone reduced freezing behavior 24 h after training. These effects were not related to the phase of the oestrous cycle. In addition, corticosterone enhanced extinction learning for all tones, in both male and female mice. These results emphasize that glucocorticoid hormones influence memory consolidation and retrieval, and underscore sex-specific effects of glucocorticoid hormones in modulating conditioned fear responses.
Critically-Sampled Graph Filter Banks with Spectral Domain Sampling

This paper presents a framework for perfect reconstruction two-channel critically-sampled graph filter banks with spectral domain sampling. Graph signals have a unique characteristic: sampling in the vertex and graph spectral domains are generally different, in contrast to classical signal processing. Conventional graph filter banks are designed using vertex domain sampling, whereas the proposed approach utilizes a novel spectral domain sampling. Our proposed technique leads to perfect reconstruction transforms for any type of undirected graphs and can be applied both to combinatorial and symmetric normalized graph Laplacians. Some filter bank designs and an experiment on nonlinear approximation are shown to validate their effectiveness.
Bi-stream Region Ensemble Network: Promoting Accuracy in Fingertip Localization from Stereo Images

Accurate ﬁngertip localization is an important and challenging problem in Human Computer Interaction (HCI). Previous research on hand pose estimation are mainly based on depth images. However, due to the noise and missing values in depth images, it is difﬁcult to improve the performance of ﬁngertip localization. In this paper, we propose a novel scheme named Bi-stream Region Ensemble Network (Bi-REN) to estimate the locations of ﬁngertips and the wrist from stereo images directly, without converting them into depth images. It ﬁrst extracts feature maps using DenseNet structure in two streams from left and right images separately, and then regions of feature maps are cropped. After corresponding feature regions of the two streams are concatenated, they are fused by fully connected layers to predict the ﬁnal joint positions. Our method achieves the highest accuracy in a publicly available dataset ThuHand17, with the mean error of the six joints (the wrist and ﬁve ﬁngertips) as 8.98mm, reduced around 18% compared with the state-of-the-art method.
Optimum Exact Histogram Specification

Exact histogram specification (EHS) is a classic image processing problem which generalises histogram equalisation. Over the years, no optimum solution to the EHS problem has been given with respect to any similarity criterion. An analytic and efficient solution to the optimum EHS problem, according to the mean squared error (MSE) criterion, is presented here. The inverse problem is also examined, and closed-form performance analyses are given in both cases.
Impaired histaminergic neurotransmission in children with narcolepsy type 1

Narcolepsy is a sleep disorder characterized in humans by excessive daytime sleepiness and cataplexy. Greater than fifty percent of narcoleptic patients have an onset of symptoms prior to the age of 18. Current general agreement considers the loss of hypothalamic hypocretin (orexin) neurons as the direct cause of narcolepsy notably cataplexy. To assess whether brain histamine (HA) is also involved, we quantified the cerebrospinal fluid (CSF) levels of HA and tele‐methylhistamine (t‐MeHA), the direct metabolite of HA between children with orexin‐deficient narcolepsy type 1 (NT1) and controls.
Expert Review on Big Data Analytics Implementation Model in Data-driven Decision-Making

Data-driven decision-making can offer improved insights for information value and create new business opportunity. The purpose of this paper is to present the findings of expert opinion in verifying the influencing factors in big data analytics (BDA) implementation that are beneficial in developing the BDA implementation assessment model. The study was carried out by conducting face-to-face approach sessions with three academicians and four industry experts who have vast experience in big data research and its implementation. The findings from these exercises has confirmed and verified the content of the ten factors that include organization dimension (such as big data strategy, top management support, resource commitment, organizational relationship), people dimension (such as analytics skills, managerial skills and analytics culture) and technology dimension (includes data infrastructures, information processing and quality) were appropriate for the research model. It was described using descriptive analysis such as frequency, mean and standard deviation. Once the verification process is complete, the research model will be validated through survey in the future work.
Viral Sepsis in Children

Sepsis in children is typically presumed to be bacterial in origin until proven otherwise, but frequently bacterial cultures ultimately return negative. Although viruses may be important causative agents of culture-negative sepsis worldwide, the incidence, disease burden and mortality of viral-induced sepsis is poorly elucidated. Consideration of viral sepsis is critical as its recognition carries implications on appropriate use of antibacterial agents, infection control measures, and, in some cases, specific, time-sensitive antiviral therapies. This review outlines our current understanding of viral sepsis in children and addresses its epidemiology and pathophysiology, including pathogen-host interaction during active infection. Clinical manifestation, diagnostic testing, and management options unique to viral infections will be outlined.
Deep Network for Simultaneous Stereo Matching and Dehazing

Unveiling the image structure and dense correspondence under the haze layer remains a challenging task, since the scattering effects cause image features to be less distinctive. In this paper, we introduce a deep network that simultaneously estimates a clear latent image and disparity from a hazy stereo image pair. To this end, inspired by a physical model of hazy image acquisition, we propose a dehazing loss function which serves as an additional cue for establishing dense correspondence. We show that stereo matching and dehazing can be synergistically formulated by incorporating depth information from haze transmission into the stereo matching process, and vice versa . As a result, our method estimates high-quality disparity for scenes in scattering media, and produces appearance images with enhanced visibility. We quantitatively evaluate the proposed method on synthetic datasets and provide an extensive ablation study. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods on both dehazing and stereo matching tasks.
Leveraging LSTM Networks for Attack Detection in Fog-to-Things Communications

The evolution and sophistication of cyber-attacks need resilient and evolving cybersecurity schemes. As an emerging technology, the Internet of Things (IoT) inherits cyber-attacks and threats from the IT environment despite the existence of a layered defensive security mechanism. The extension of the digital world to the physical environment of IoT brings unseen attacks that require a novel lightweight and distributed attack detection mechanism due to their architecture and resource constraints. Architecturally, fog nodes can be leveraged to offload security functions from IoT and the cloud to mitigate the resource limitation issues of IoT and scalability bottlenecks of the cloud. Classical machine learning algorithms have been extensively used for intrusion detection, although scalability, feature engineering efforts, and accuracy have hindered their penetration into the security market. These shortcomings could be mitigated using the deep learning approach as it has been successful in big data fields. Apart from eliminating the need to craft features manually, deep learning is resilient against morphing attacks with high detection accuracy. This article proposes an LSTM network for distributed cyber-attack detection in fog-to-things communication. We identify and analyze critical attacks and threats targeting IoT devices, especially attacks exploiting vulnerabilities of wireless communications. The conducted experiments on two scenarios demonstrate the effectiveness and efficiency of deeper models over traditional machine learning models.
Second language acquisition effects of a primary physical education intervention: A pilot study with young refugees

Background A physical education (PE) intervention for young refugees was designed combining physical activity within the context of primary PE games with second language learning activities in German. The intervention was based on theoretical implications from the field of second language acquisition and evidence for positive effects of physical activity on cognitive outcomes (e.g. language acquisition). The aim of this study was to analyze short term effects on second language acquisition. Methods Sixty-one young refugees were included in the study (age: 8.5 ± 1.4 years). The intervention group participated in language-enriched PE lessons based on an elaborated approach to second language learning acquisition. The control group did not receive any treatment. Both groups were pre-and post-tested in domain specific vocabulary, listening comprehension and use of local prepositions within the context of primary PE games. Results Results from linear mixed-effect modelling suggest that the intervention group significantly improved domain specific vocabulary and listening comprehension in comparison to the control group. Conclusions The intervention was successful since the PE lessons contributed to the second language acquisition of young refugees. Therefore, this learning approach might also be useful for physical activity based second language learning activities in other PE contexts for early second language learners in primary school.
Interactional Stancetaking in Online Forums

Language is shaped by the relationships between the speaker/writer and the audience, the object of discussion, and the talk itself. In turn, language is used to reshape these relationships over the course of an interaction. Computational researchers have succeeded in operationalizing sentiment, formality, and politeness, but each of these constructs captures only some aspects of social and relational meaning. Theories of interactional stancetaking have been put forward as holistic accounts, but until now, these theories have been applied only through detailed qualitative analysis of (portions of) a few individual conversations. In this article, we propose a new computational operationalization of interpersonal stancetaking. We begin with annotations of three linked stance dimensions—affect, investment, and alignment—on 68 conversation threads from the online platform Reddit. Using these annotations, we investigate thread structure and linguistic properties of stancetaking in online conversations. We identify lexical features that characterize the extremes along each stancetaking dimension, and show that these stancetaking properties can be predicted with moderate accuracy from bag-of-words features, even with a relatively small labeled training set. These quantitative analyses are supplemented by extensive qualitative analysis, highlighting the compatibility of computational and qualitative methods in synthesizing evidence about the creation of interactional meaning.