Miracle-dz/UniFAR-FTUs huggingface dataset:
A Resource Matching Algorithm for Dynamic Multi-Homing Service Environment

T43 paper investigates the matching problem between radio resources and multi-homing services in a dynamic heterogeneous wireless environment. A mobile terminal (MT) with multi-homing service simultaneously transmits data to multiple radio access networks using multiple air interfaces. We develop an energy efficient resource matching algorithm for statistically guaranteeing the transmission rate of each MT with the minimum power consumption. Firstly, aided with the resource matching conception and the dynamic characteristics of wireless channels, we propose a matching probability metric to construct resource matching model for maximizing energy efficiency of all MTs subject to statistical guarantee requirements, and simplify the dynamic channels characteristic based on matching theory. Secondly, we propose a two-level distributed matching algorithm (TLDMA), with inner minimum power consumption and outer maximum matching probability, which realizes joint optimal allocation of bandwidth, transmission power and rate. Simulation results show that TLDMA outperforms conventional algorithms in terms of wireless resources management.
Alpha Waves as a Neuromarker of Autism Spectrum Disorder: The Challenge of Reproducibility and Heterogeneity

Background: There is no consensus in the literature concerning the presence of abnormal alpha wave profiles in patients with autism spectrum disorder (ASD). This may be due to phenotypic heterogeneity among patients as well as the limited sample sizes utilized. Here we present our results of alpha wave profile analysis based on a sample larger than most of those in the field, performed using a robust processing pipeline. Methods: We compared the alpha waves profiles at rest in children with ASD to those of age-, sex-, and IQ-matched control individuals. We used linear regression and non-parametric normative models using age as covariate forparsing the clinical heterogeneity. We explored the correlation between EEG profiles and the patient’s brain volumes, obtained from structural MRI. We automatized the detection of the alpha peak and visually quality controled our MRI measurements. We assessed the robustness of our results by running the EEG preprocessing with two different versions of Matlab as well as Python. Results: A simple linear regression between peak power or frequency of the alpha waves and the status or age of the participants did not allow to identify any statistically significant relationship. The non-parametric normative model (which took account the non-linear effect of age on the alpha profiles) suggested that participants with ASD displayed more variability than control participants for both frequency and amplitude of the alpha peak (p < 0.05). Independent of the status of the individual, we also observed weak associations (uncorrected p < 0.05) between the alpha frequency, and the volumes of several cortical and subcortical structures (in particular the striatum), but which did not survive correction for multiple testing and changed between analysis pelines. Discussions: Our study did not find evidence for abnormal alpha wave profiles in ASD. We propose, however, an analysis pipeline to perform standardized and automatized EEG analyses on large cohorts. These should help the community to address the challenge of clinical heterogeneity of ASD and to tackle the problems of reproducibility.
Update on functional recovery process for the injured athlete: return to sport continuum redefined

The traditional functional recovery model and return to sport (RTS) decision-making process after long-term injury is insufficient in the real world of sports medicine. There are lower than ideal numbers of athletes returning to competitions after certain injury types.1 2 Additionally, those who do RTS have heightened risk of reinjury,3 may not return to preinjury performance levels,4 or may be unable to sustain the same level of competitive play in the subsequent years after injury.5 This paper provides an update on the RTS process, reflecting the new literature and knowledge in the area as well as applied practice, to support practitioners working with athletes after injuries and in particular after severe injuries (>28 days). 6 A dichotomous conception of functional recovery was common in the past (and often still present) which involved the separation of clinical rehabilitation and RTS. Initially, the patient begins with the medical team before been transferred to the performance team (coaching and fitness staff) for sport-specific training and RTS. There is often little or no overlap in the process, and limited communication and sharing of knowledge during the functional recovery process. When there is a need to prepare the athlete for direct re-entry into sport after injury, it is necessary to consider an overlap of the rehabilitation and RTS processes. This area of overlap …
Non-contact Vital Sign Monitoring Using Universal Software Radio Peripheral

W215 vital sign detection is a promising technique, which allow users to non-invasively detect the respiration of the target through capturing the variation of wireless signals. In particular, respiration detection using received signal strength (RSS) variation has gained more attention since RSS is available in most commercial off-the-shelf (COTS) products, which can remarkably reduce the cost of the system. However, the resolution of RSS measurement provided by those equipment is usually larger than 0.1dB, which is unable to perform reliable respiration detection, especially when the chest wall movement of the target is weak. In this paper, we implement respiration detection system on the universal software radio peripheral (USRP) devices, which can provide more accurate RSS measurement. We use a pair of USRPs where one transmits wireless signals while the other receives the signals and measures the RSS. The experiment results verify that the detection platform can capture the RSS variation smaller than 0.1dB and accurately estimate the respiration rate.
Circadian and photic modulation of daily rhythms in diurnal mammals

The temporal niche that an animal occupies includes a coordinated suite of behavioral and physiological processes that set diurnal and nocturnal animals apart. The daily rhythms of the two chronotypes are regulated by both the circadian system and direct responses to light, a process called masking. Here we review the literature on circadian regulations and masking responses in diurnal mammals, focusing on our work using the diurnal Nile grass rat (Arvicanthis niloticus) and comparing our findings with those derived from other diurnal and nocturnal models. There are certainly similarities between the circadian systems of diurnal and nocturnal mammals, especially in the phase and functioning of the principal circadian oscillator within the hypothalamic suprachiasmatic nucleus (SCN). However, the downstream pathways, direct or indirect from the SCN, lead to drastic differences in the phase of extra‐SCN oscillators, with most showing a complete reversal from the phase seen in nocturnal species. This reversal, however, is not universal and in some cases the phases of extra‐SCN oscillators are only a few hours apart between diurnal and nocturnal species. The behavioral masking responses in general are opposite between diurnal and nocturnal species, and are matched by differential responses to light and dark in several retinorecipient sites in their brain. The available anatomical and functional data suggest that diurnal brains are not simply a phase‐reversed version of nocturnal ones, and work with diurnal models contribute significantly to a better understanding of the circadian and photic modulation of daily rhythms in our own diurnal species.
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet (source code is available at http://github.com/pquochuy/SeqSleepNet). At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modeling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modeling of sequential epochs. The classification is then carried out on the output vectors at every time step of the top recurrent layer to produce the sequence of output labels. Despite being hierarchical, we present a strategy to train the network in an end-to-end fashion. We show that the proposed network outperforms the state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen’s kappa of 87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.
An Ensemble Framework of Voice-Based Emotion Recognition System

Emotion recognition will improve the user experience of artificial intelligence (AI) product. Voice-based emotion recognition system has low hardware requirement and can be easily employed onto current AI product, e.g. Amazon Echo, Google Home and so on. The previous voicebased emotion recognition system mostly focused on the speech collected under controlled environment, e.g. studio environment. However, the practical scenario will be more complicated than controlled environment. Only focusing on acoustic characteristics of speech is not sufficient to model the emotion. In this paper, we propose a ensemble framework which can capture several aspects of characteristics related to emotion. The framework is evaluated on multimodal emotion challenge (MEC) 2017 corpus. The corpus is collected from Chinese films and TV programs, whose scenarios are close to real world. The proposed framework is able to outperform the best baseline system by 29.5% absolutely.
Secrecy Performance Analysis in MISOSE Cognitive Radio Networks Over Correlated Fading Channels

I265 this paper, we investigate the secrecy performance of multi-input single-output and single-eavesdropper (MISOSE) cognitive radio (CR) networks over correlated fading channels. For the security enhancement, the transmit antenna selection (TAS) scheme is used to select the best transmit antenna at Alice based on the main channel. In order to analyze the impact of channel correlation coefficient, interference temperature threshold and antenna numbers on the secrecy performance, we firstly derive the exact closed-form expression of the secrecy outage probability (SOP) for the considered system taking into account the correlation between the main channel and wiretapping channel. Moreover, to extract more deep insights, the asymptotic expression of SOP in high main-to-eavesdropper ratio (MER) regime is also derived, which reveals that the channel correlation does not degrade the secrecy diversity order and even can strengthen the secrecy performance in high MER regime. Finally, numerical results are provided to demonstrate the correctness and accuracy of our analysis.
Complete Calibration of Tri-Axial Magnetometer in Strap-Down Attitude and Heading Reference System * This work is partially supported by National Natural Science Foundation of China Grant #61361006, Guangxi Natural Science Foundation Grant #2015GXNSFBA139253, and Guangxi Key Laboratory of Automatic 

A190 calibration method for tri-axial magnetometer in attitude and heading reference system (AHRS) is presented in this paper. This method utilized two different constraints simultaneously, one is the norm of geomagnetic vector, and the other is the dot product of geomagnetic and gravity vectors. Numerical simulation and experiment results demonstrate that the proposed method shows better performance than the frequently used ellipsoid fitting method, as well as the constant intersection method, since it can eliminate the inter-triad misalignment between magnetometer and other sensors in AHRS, and it is robust to measurement noise.
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery. However, a major challenge is the lack of suitable benchmarks for an objective and quantitative evaluation of learned representations. To address this issue we introduce Morpho-MNIST, a framework that aims to answer: "to what extent has my model learned to represent specific factors of variation in the data? " We extend the popular MNIST dataset by adding a morphometric analysis enabling quantitative comparison of trained models, identification of the roles of latent variables, and characterisation of sample diversity. We further propose a set of quantifiable perturbations to assess the performance of unsupervised and supervised methods on challenging tasks such as outlier detection and domain adaptation. Data and code are available at this https URL.
A Differential Degree Test for Comparing Brain Networks

Recently, graph theory has become a popular method for characterizing brain functional organization. One important goal in graph theoretical analysis of brain networks is to identify network differences across disease types or conditions. Typical approaches include massive univariate testing of each edge or comparisons of local and/or global network metrics to identify deviations in topological organization. Some limitations of these methods include low statistical power due to the large number of comparisons and difficulty attributing overall differences in networks to local variations in brain function. We propose a novel differential degree test (DDT) to identify brain regions incident to a large number of differentially weighted edges across two populations. The proposed test could help detect key brain locations involved in diseases by demonstrating significantly altered neural connections. We achieve this by generating an appropriate set of null networks which are matched on the first and second moments of the observed difference network using the Hirschberger-Qi-Steuer (HQS) algorithm. This formulation permits separation of the network's true topology from the nuisance topology which is induced by the correlation measure and may drive inter-regional connectivity in ways unrelated to the brain function. Simulations indicate that the proposed approach routinely outperforms competing methods in detecting differentially connected regions of interest. Furthermore, we propose a data-adaptive threshold selection procedure which is able to detect differentially weighted edges and is shown to outperform competing methods that perform edge-wise comparisons controlling for the error rate. An application of our method to a major depressive disorder dataset leads to the identification of brain regions in the default mode network commonly implicated in this ruminative disorder.
Role of the SLC26A9 Chloride Channel as Disease Modifier and Potential Therapeutic Target in Cystic Fibrosis

The solute carrier family 26, member 9 (SLC26A9) is an epithelial chloride channel that is expressed in several organs affected in patients with cystic fibrosis (CF) including the lungs, the pancreas, and the intestine. Emerging evidence suggests SLC26A9 as a modulator of wild-type and mutant CFTR function, and as a potential alternative target to circumvent the basic ion transport defect caused by deficient CFTR-mediated chloride transport in CF. In this review, we summarize in vitro studies that revealed multifaceted molecular and functional interactions between SLC26A9 and CFTR that may be implicated in normal transepithelial chloride secretion in health, as well as impaired chloride/fluid transport in CF. Further, we focus on recent genetic association studies and investigations utilizing genetically modified mouse models that identified SLC26A9 as a disease modifier and supported an important role of this alternative chloride channel in the pathophysiology of several organ manifestations in CF, as well as other chronic lung diseases such as asthma and non-CF bronchiectasis. Collectively, these findings and the overlapping endogenous expression with CFTR suggest SLC26A9 an attractive novel therapeutic target that may be exploited to restore epithelial chloride secretion in patients with CF irrespective of their CFTR genotype. In addition, pharmacological activation of SLC26A9 may help to augment the effect of CFTR modulator therapies in patients with CF carrying responsive mutations such as the most common disease-causing mutation F508del-CFTR. However, future research and development including the identification of compounds that activate SLC26A9-mediated chloride transport are needed to explore this alternative chloride channel as a therapeutic target in CF and potentially other muco-obstructive lung diseases.
Long-term follow-up of unresectable medium-large hepatocellular carcinoma nodules treated with radiofrequency ablation using a multiple-electrode switching system.

OBJECTIVE: The purpose of this study was to prospectively evaluate the safety and effectiveness of radiofrequency ablation (RFA) by using a multiple-electrode switching system to treat unresectable medium-large (3.1-6.0 cm) HCC nodules. METHODS: RFA using a multiple-electrode switching system was performed for HCC nodules with size > 3.0 < 6.0 cm in nonsurgical candidates. Two electrodes were consecutively placed for 3.1-4.0 cm tumours, and three electrodes for 4.1-5.9 cm tumours, with a 2.0-2.5 cm spacing. The power was switched from one electrode to the next automatically when the impedance reached 30 Ω above the baseline level. 25 patients (M/F = 9/16; median age 76 years, range 61-84) with liver cirrhosis (20 HCV-positive) in Child's Class A (22 cases) and B (3 cases) and 26 HCC nodules (median diameter 4.0 cm; range 3.2-5.5 cm) underwent treatment in 25 sessions from 2013 and 2018. Therapeutic effectiveness was assessed through CT or MRI exam at 30-40 days post-ablation. RESULTS: No procedure-related death or major complications occurred. Complete ablation was obtained in all nodules (100%). At a median follow up of 30 months, local tumor progression occurred in five out of 26 nodules (19.2%). Overall survival at 4 years was 49%. CONCLUSION: RFA with a multiple-electrode switching system may be a safe, quick and effective therapeutic option for treatment of 3.1-﻿6.0 cm unresectable HCC tumours. ADVANCES IN KNOWLEDGE: RFA with multiple electrodes provides favourable clinical results in patients with medium-large HCC nodules who are not suitable for surgery.
Drug pharmacomicrobiomics and toxicomicrobiomics: from scattered reports to systematic studies of drug–microbiome interactions

ABSTRACT Introduction: Pharmacomicrobiomics and toxicomicrobiomics study how variations within the human microbiome (the combination of human-associated microbial communities and their genomes) affect drug disposition, action, and toxicity. These emerging fields, interconnecting microbiology, bioinformatics, systems pharmacology, and toxicology, complement pharmacogenomics and toxicogenomics, expanding the scope of precision medicine. Areas covered: This article reviews some of the most recently reported pharmacomicrobiomic and toxicomicrobiomic interactions. Examples include the impact of the human gut microbiota on cardiovascular drugs, natural products, and chemotherapeutic agents, including immune checkpoint inhibitors. Although the gut microbiota has been the most extensively studied, some key drug-microbiome interactions involve vaginal, intratumoral, and environmental bacteria, and are briefly discussed here. Additionally, computational resources, moving the field from cataloging to predicting interactions, are introduced. Expert opinion: The rapid pace of discovery triggered by the Human Microbiome Project is moving pharmacomicrobiomic research from scattered observations to systematic studies focusing on screening microbiome variants against different drug classes. Better representation of all human populations will improve such studies by avoiding sampling bias, and the integration of multiomic studies with designed experiments will allow establishing causation. In the near future, pharmacomicrobiomic testing is expected to be a key step in screening novel drugs and designing precision therapeutics.
Serum Tumor Necrosis Factor-α and Interferon-γ Levels in Pediatric Mycoplasma pneumoniae Pneumonia: A Systematic Review and Meta-Analysis

Background Mycoplasma pneumoniae pneumonia (MPP) is one of the most common forms of community-acquired pneumonia in children. The objective of this study was to explore potential changes in levels of serum tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ) associated with pediatric MPP. Methods This protocol has been registered (PROSPERO 2017: CRD42017077979). A literature search was performed in October 2017 using PubMed, Embase, the Cochrane Library, and other Chinese medical databases to identify studies. The meta-analysis was performed using Review Manager 5.3 software. Random-effect models were used to estimate mean differences (MDs) and 95% confidence intervals (CIs) of cytokine levels. Results Twelve studies were included in the meta-analysis, encompassing 2,422 children with MPP and 454 healthy control children. Serum TNF-α levels were significantly higher in children with MPP compared with healthy children (MD = 22.5, 95% CI = 13.78–31.22, P < 0.00001), and there was significant heterogeneity across studies (I2 = 100%, P < 0.00001). Subgroup analyses showed no evidence for a difference in serum TNF-α levels between children with refractory and nonrefractory MPP. Serum IFN-γ levels did not significantly differ in children with MPP compared with healthy children (MD = 4.83, 95% CI = −3.27–12.93, P=0.24). Conclusions Our meta-analysis showed that serum TNF-α and IFN-γ levels were significantly elevated and unchanged, respectively, in pediatric MPP. Because infection by different pathogens has variable effects on serum TNF-α and IFN-γ levels, the finding could be helpful in developing novel diagnostic methods.
Incentive Design, Sizing and Grid Integration of Residential PV Systems with Heat Pumps and Battery Storage Systems

Changing economics and regulatory requirements force investors and distribution network operators to reevaluate interdependencies between new incentives, sizing and operation as well as grid integration of photovoltaic (PV) systems combined with battery storage systems (BSSs) and heat pumps. Thus, this paper presents an optimization model and a case study to assess these interdependencies. It can be concluded that only the combination of market-based prices, peak charges and feed-in limits is an effective measure that provides operational incentives to mitigate power peaks while leading to PV sizes that are significantly larger than PV-only systems.