Miracle-dz/UniFAR-FTUs huggingface dataset:
Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline CDH1 sequence variants

The variant curation guidelines published in 2015 by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) provided the genetics community with a framework to assess variant pathogenicity; however, these rules are not gene specific. Germline pathogenic variants in the CDH1 gene cause hereditary diffuse gastric cancer and lobular breast cancer, a clinically challenging cancer predisposition syndrome that often requires a multidisciplinary team of experts to be properly managed. Given this challenge, the Clinical Genome Resource (ClinGen) Hereditary Cancer Domain prioritized the development of the CDH1 variant curation expert panel (VCEP) to develop and implement rules for CDH1 variant classifications. Here, we describe the CDH1 specifications of the ACMG/AMP guidelines, which were developed and validated after a systematic evaluation of variants obtained from a cohort of clinical laboratory data encompassing ∼827,000 CDH1 sequenced alleles. Comparing previously reported germline variants that were classified using the 2015 ACMG/AMP guidelines to the CDH1 VCEP recommendations resulted in reduced variants of uncertain significance and facilitated resolution of variants with conflicted assertions in ClinVar. The ClinGen CDH1 VCEP recommends the use of these CDH1‐specific guidelines for the assessment and classification of variants identified in this clinically actionable gene.
Design of Dual Band SIW and HMSIW Cavity Backed U-Shaped Slot Antennas

A dual band substrate integrated waveguide (SIW) and half-mode substrate integrated waveguide (HMSIW) with cavity backed planar slot antenna are proposed. The presented antennas adopt a combination of two SIW cavities of different size through coupling window. The placement of U-shaped slots helps to generate multiple hybrid modes which can be tuned by changing slot dimensions. Two SIW cavities loaded with U-shaped slots are used to excite four closely arranged hybrid modes in C band (4–8 GHz) to realize dual band response. The compact HMSIW structure antenna even shows a better performance while reducing nearly half size of the SIW antenna.
Simultaneous Realization of Multiple Music Video Applications Based on Heterogeneous Network Analysis Via Latent Link Estimation

To help users seek desired music videos and create attractive music videos, many methods that realize applications such as music video recommendation, captioning and generation have been proposed. In this paper, a novel method that realizes these applications simultaneously on the basis of heterogeneous network analysis via latent link estimation is proposed. To the best of our knowledge, this work is the first attempt to realize music video recommendation, captioning and generation simultaneously. The proposed method enables latent link estimation with consideration of multimodal information and multiple social metadata obtained from music videos via Laplacian multiset canonical correlation analysis. Thus, it becomes feasible to construct a heterogeneous network that enables direct comparison of audio, visual and textual information of music videos and user information on the same feature space. Furthermore, link prediction on the obtained heterogeneous network enables association with (i) user information and their desired audio information; (ii) audio information and textual information that describes contents of musical pieces; and (iii) audio information and visual information that represents contents of musical pieces visually. As a result, support for (i) music video recommendation; (ii) captioning; and (iii) generation becomes feasible, respectively. Experimental results for a real-world dataset constructed by using YouTube-8M show the effectiveness of the proposed method.
Optical plasma boundary reconstruction based on least squares for EAST Tokamak

Reconstructing the shape and position of plasma is an important issue in Tokamaks. Equilibrium and fitting (EFIT) code is generally used for plasma boundary reconstruction in some Tokamaks. However, this magnetic method still has some inevitable disadvantages. In this paper, we present an optical plasma boundary reconstruction algorithm. This method uses EFIT reconstruction results as the standard to create the optimally optical reconstruction. Traditional edge detection methods cannot extract a clear plasma boundary for reconstruction. Based on global contrast, we propose an edge detection algorithm to extract the plasma boundary in the image plane. Illumination in this method is robust. The extracted boundary and the boundary reconstructed by EFIT are fitted by same-order polynomials and the transformation matrix exists. To acquire this matrix without camera calibration, the extracted plasma boundary is transformed from the image plane to the Tokamak poloidal plane by a mathematical model, which is optimally resolved by using least squares to minimize the error between the optically reconstructed result and the EFIT result. Once the transform matrix is acquired, we can optically reconstruct the plasma boundary with only an arbitrary image captured. The error between the method and EFIT is presented and the experimental results of different polynomial orders are discussed.
Compressive Sensing-Based Multiuser Detection via Iterative Reweighed Approach in M2M Communications

Massive machine-to-machine (M2M) is an important application for Internet of Things in 5G. In this letter, we focus on solving the multiuser detection problem supported by low-activity code division multiple access for M2M communications. To address the user activity factor unknown issue in the optimal maximum a posterior probability and improve the signal reconstruction ability, we propose iterative reweighed and minimum mean-square-error iterative reweighed algorithms based on compressive sensing theory. The simulation results demonstrate that the proposed algorithms achieve substantial performance gain over traditional detectors.
Online Learning based Uplink Scheduling in HetNets with Limited Backhaul Capacity

Heterogeneous cellular networks (HetNets) can significantly improve the spectrum efficiency, where low-power low-complexity base stations (Pico-BSs) are deployed inside the coverage of macro base stations (Macro-BSs). Due to cross-tier interference, joint detection of the uplink signals is widely adopted so that a Pico-BS can either detect the uplink signals locally or forward them to the Macro-BS for processing. The latter can achieve increased throughput at the cost of additional backhaul transmission. However, in existing literature the delay of the backhaul links was often neglected. In this paper, we study the delay-optimal uplink scheduling problem in HetNets with limited backhaul capacity. Local signal detection or joint signal detection is scheduled in a unified delay-optimal framework. Specifically, we first prove that the problem is NP-hard and then formulate it as a Markov Decision Process problem. We propose an efficient and effective algorithm, called OLIUS, that can deal with the exponentially growing state and action spaces. Furthermore, OLIUS is online learning based which does not require any prior statistical knowledge on user behavior or channel characteristics. We prove the convergence of OLIUS and derive an upper bound on its approximation error. Extensive experiments in various scenarios show that our algorithm outperforms existing methods in reducing delay and power consumption.
EERA-ASR: An Energy-Efficient Reconfigurable Architecture for Automatic Speech Recognition With Hybrid DNN and Approximate Computing

This paper proposes a hybrid deep neural network (DNN) for automatic speech recognition and an energy-efficient reconfigurable architecture with approximate computing for accelerating the DNN. To accelerate the hybrid DNN and reduce the energy consumption, we propose a digital–analog mixed reconfigurable architecture with approximate computing units, including a binary weight network accelerator with analog multi-chain delay-addition units for bit-wise approximate computing and a recurrent neural network accelerator with approximate multiplication units for different calculation accuracy requirements. Implemented under TSMC 28nm HPC+ process technology, the proposed architecture can achieve the energy efficiency of 163.8TOPS/W for 20 keywords recognition and 3.3TOPS/W for common speech recognition.
Storage-Aware Network Stack for NVM-Assisted Key-Value Store

This paper describes the design of a new software zero-copy network framework for NVM-assisted key-value stores, which directly stores and persists transactions from network into raw non-volatile memory used as write-ahead cache for data consistency. NVM is fast and bit-addressable which makes it the perfect choice for transient transaction log persistency than hard disks or even Flash drives, but its limited write cycle requires wear-leveling during direct access. However, popular RDMA-based zero-copy transmission normally needs to have the remote memory address beforehand and cannot cope with the address changing caused by wear-leveling easily. The software zero-copy solution proposed in this paper is designed with the awareness of NVM wear-leveling and log metadata management. Simulation results show that the new network framework improves performance by over 200× in throughput and decreases latency by more than 20× comparing to the traditional socket and hard disk based solution. When both equipped with NVM, the zero-copy network stack improves performance by 18 to 62% in throughput and 40 to 81% in latency comparing to the standard socket and with the lowest CPU consumption.
Multiple Ornstein–Uhlenbeck Processes for Maritime Traffic Graph Representation

We propose an unsupervised procedure to automatically extract a graph-based model of commercial maritime traffic routes from historical Automatic Identification System (AIS) data. In the proposed representation, the main elements of maritime traffic patterns, such as maneuvering regions and sea-lanes, are represented, respectively, with graph vertices and edges. Vessel motion dynamics are defined by multiple Ornstein–Uhlenbeck processes with different long-run mean parameters, which in our approach can be estimated with a change detection procedure based on Page's test, aimed to reveal the spatial points representative of velocity changes. A density-based clustering algorithm is then applied to aggregate the detected changes into groups of similar elements and reject outliers. To validate the proposed graph-based representation of the maritime traffic, two performance criteria are tested against a real-world trajectory dataset collected off the Iberian Coast and the English Channel. Results show the effectiveness of the proposed approach, which is suitable to be integrated at any level of a JDL system.
Human Action Monitoring for Healthcare Based on Deep Learning

Human action monitoring can be advantageous to remotely monitor the status of patients or elderly person for intelligent healthcare. Human action recognition enables efficient and accurate monitoring of human behaviors, which can exhibit multifaceted complexity attributed to disparities in viewpoints, personality, resolution and motion speed of individuals, etc. The spatial-temporal information plays an important role in the human action recognition. In this paper, we proposed a novel deep learning architecture named as recurrent 3D convolutional neural network (R3D) to extract effective and discriminative spatial-temporal features to be used for action recognition, which enables the capturing of long-range temporal information by aggregating the 3D convolutional network entries to serve as an input to the LSTM (Long Short-Term Memory) architecture. The 3D convolutional network and LSTM are two effective methods for extracting the temporal information. The proposed R3D network integrated these two methods by sharing a shared 3D convolutional network in sliding windows on video streaming to capturing short-term spatial-temporal features into the LSTM. The output features of LSTM encapsulate the long-range spatial-temporal information representing high-level abstraction of the human actions. The proposed algorithm is compared to traditional and the-state-of-the-art and deep learning algorithms. The experimental results demonstrated the effectiveness of the proposed system, which can be used as smart monitoring for remote healthcare.
Machine Learning Based Transportation Modes Recognition Using Mobile Communication Quality

In order to recognize the transportation modes without any additional sensor devices, we propose a recognition method by using communication quality factors. In the proposed method, instead of Global Positioning System (GPS) and accelerometer sensors, we collect mobile TCP throughputs, Received Signal Strength Indicators (RSSIs), and cellular base station IDs (Cell IDs) through in-line network measurement when the user enjoys mobile services, such as video streaming service. In accuracy evaluations, we conduct two different field experiments to collect the data in five typical transportation modes (static, walking, riding a bicycle, a bus and a train,) and then construct the classifiers by applying Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Random Forest (RF). Results conclude that these transportation modes can be recognized by using communication quality factors with high accuracy as well as the use of accelerometer sensors.
Should phosphatidylethanol be currently analysed using whole blood, dried blood spots or both?

Abstract Phosphatidylethanol (PEth) are phospholipids produced through non-oxidative ethanol metabolism. They accumulate in red blood cells and have been traditionally analysed in whole blood as potential biomarkers for moderate to long-term alcohol consumption. More recently, their analysis in dried blood spots has been gaining favour, namely, due to the ease in sampling, transport and storage conditions required. This paper aims at providing a short comparative review between analysing PEth in whole blood and dried blood spots and the potential pitfalls that researchers may face when setting up PEth testing for clinical use.
Stator flux based MRAS speed and stator resistance estimator for sensorless PMSM drive

In this paper, a simple and robust speed and stator resistance estimator is proposed for sensorless permanent magnet synchronous motor drive to improve its performance at standstill and low speed regions. The speed and stator resistance estimator is formed using stator flux based model reference adaptive system, which yields fast transient response, reduces steady state error for low rotor speed variation and load disturbances. The proposed method improves the performance of the drive at low speed regions. The simulations are carried out through MATLAB/Simulink environment and results show the effectiveness of the sensorless PMSM drive. The maximum estimated rotor speed and position error are found to be are ±3 rad/s, ±1 °respectively, during low speed operation at transient and steady state conditions.
QRS residual removal in atrial activity signals extracted from single lead: a new perspective based on signal extrapolation

Atrial activity (AA) signal must first be extracted from atrial fibrillation electrocardiogram (AF ECG) before it is used to characterise AF. However, extracting AA signal is not an easy task, especially from single-lead ECG recording. The AA signals within QRS intervals extracted by the existing single-lead extraction methods are often heavily distorted due to the existence of large QRS residuals. In this study, the authors focus on reducing the QRS residuals in the extracted AA signals, and propose a novel signal extrapolation based method. AA signal is assumed to be band-limited, and a dedicated extrapolation formula is derived. Based on this extrapolation formula, the AA samples within QRS interval are reconstructed by using the ones in the adjacent SQ segments. The experiments with simulated AF ECGs showed that, after using the proposed method, the normalised mean square error of the AA signal extracted by average beat subtraction method decreased by 26–50%, 15–36%, 12–40 and 42–63% for simulated AF ECGs in lead I, II, V 1 and V 6, respectively. Experiments with real AF ECG also proved that the proposed method is able to greatly reduce the ventricular residuals of the extracted AA signal.
Comparing physical activity estimates in children from hip-worn Actigraph GT3X+ accelerometers using raw and counts based processing methods

ABSTRACT This study examined differences in physical activity (PA) estimates provided from raw and counts processing methods. One hundred and sixty-five children (87 girls) wore a hip-mounted ActiGraph GT3X+ accelerometer for 7 days. Data were available for 129 participants. Time in moderate PA (MPA), vigorous PA (VPA) and moderate-vigorous PA (MVPA) were calculated using R-package GGIR and ActiLife. Participants meeting the wear time criteria for both processing methods were included in the analysis. Time spent in MPA (−21.4 min.d−1, 95%CI −21 to −20) and VPA (−36 min.d−1, 95%CI −40 to −33) from count data were higher (P < 0.001) than raw data. Time spent in MVPA between the two processing methods revealed significant differences (All P < 0.001). Bland-Altman plots suggest that the mean bias for time spent in MPA, VPA and MVPA were large when comparing raw and count methods. Equivalence tests showed that estimates from raw and count processing methods across all activity intensities lacked equivalence. Lack of equivalence and poor agreement between raw and count processing methods suggest the two approaches to estimate PA are not comparable. Further work to facilitate the comparison of findings between studies that process and report raw and count physical activity data may be necessary.
Anatomical analysis of the distribution patterns of occipital cutaneous nerves and the clinical implications for pain management

Purpose Establishing the distribution patterns of occipital cutaneous nerves may help us understand their contribution to various occipital pain patterns and ensure that a proper local injection method for treatment is employed. The aim of this study was to demonstrate the detailed distribution patterns of the greater occipital nerve (GON), lesser occipital nerve (LON), and third occipital nerve (TON) using the modified Sihler’s staining technique. Methods Ten human cadavers were manually dissected to determine the nerve distributions. Specimens from eight human cadavers were treated using the modified Sihler’s staining. Results In all cases, distinct GON branches proceeded laterally and were intensively distributed in the superolateral area from their emerging point. Very thin twigs were observed at the middle-trisected area, which had a fan-like shape, in the middle-upper occipital region. Conclusion The LON and TON distribution areas were biased to the lateral side below the superior nuchal line, although these nerves exhibited multiple interconnections or overlapping areas with the GON. Furthermore, a nerve rarified zone in the shape of an inverted triangle was identified in the middle occipital area. Our findings improve our understanding of the occipital nerve anatomy and will aid in the management of occipital pain in clinical practice.
IoTAegis: A Scalable Framework to Secure the Internet of Things

The infamous Mirai attack which hijacked nearly half a million Internet connected devices demonstrated the widespread security vulnerabilities of the Internet-of-Things (IoT). This study employs a set of active and passive observation methods to discover the security vulnerabilities of IoT devices within a university campus. We show that (a) the number of non-compute devices dominates the number of compute devices with open ports in a campus network; (b) 58.9% or more devices do not keep up-to-date firmware and 51.3% or more do not have a user defined password; and (c) the number of devices together with the diversity of device ages and vendors make the protection of IoT devices a difficult problem. We further develop IoTAegis framework which offers device-level protection to automatically manage device configurations and security updates. Our solution is shown to be effective, scalable, lightweight, and deployable in different forms and network types.
Probability Distribution of the Re-Healing Delay in a One-Way Highway VANET

This letter proposes a closed-form expression for the probability distribution of the re-healing delay (time taken in the store-and-forward strategy to send a packet from a cluster head to the tail of the next cluster) conditioned on the gap distance between those two clusters on a one-way highway. Moreover, a closed-form expression is derived for the unconditional probability distribution of the re-healing delay. Using the derived probability distribution, one can straightforwardly study the impact of VANET parameters on the re-healing delay. Also, the probability distribution of the end-to-end delay in VANETs can be derived from the results in this letter. The accuracy of the proposed analysis is validated using simulations.
Finding the Stars in the Fireworks: Deep Understanding of Motion Sensor Fingerprint

With the proliferation of mobile devices and various sensors (e.g., GPS, magnetometer, accelerometers, gyroscopes) equipped, richer services, e.g. location based services, are provided to users. A series of methods have been proposed to protect the users' privacy, especially the trajectory privacy. Hardware fingerprinting has been demonstrated to be a surprising and effective source for identifying/authenticating devices. In this work, we show that a few data samples collected from the motion sensors are enough to uniquely identify the source mobile device, i.e., the raw motion sensor data serves as a fingerprint of the mobile device. Specifically, we first analytically understand the fingerprinting capacity using features extracted from hardware data. To capture the essential device feature automatically, we design a multi-LSTM neural network to fingerprint mobile device sensor in real-life uses, instead of using handcrafted features by existing work. Using data collected over 6 months, for arbitrary user movements, our fingerprinting model achieves 93 % F -score given one second data, while the state-of-the-art work achieves 79% F-score. Given ten seconds randomly sampled data, our model can achieve 98.8% accuracy. We also propose a novel generative model to modify the original sensor data and yield anonymized data with little fingerprint information while retain good data utility.
Novel methods for Implementation of Efficient Median Filter

In this paper, we propose two new methodologies to implement the median filter based on threshold decomposition and stacking properties. In the first method, we obtain binary median filter output using a closed form polynomial expression from the binary signal/image. In the second method, we derive a positive boolean expression to obtain the binary median value. We compare time complexities of the proposed methods with existing method. Two proposed methods take less time to compute the median filter response compared to sorting based median filter. Using these two methods to obtain binary median value, we provide an efficient VLSI implementation.