Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast variation-aware read alignment with deBGA-VARA

Many genetic variants have been reported from sequencing projects due to decreasing experimental costs. Compared to the current typical paradigm, read mapping incorporating existing variants can improve the performance of subsequent analysis. However, storing and indexing various types of variation require costly RAM space. Aligning reads to a graph model-based index including the whole set of variants is ultimately an NP-hard problem in theory. This method is supposed to map sequencing reads efficiently to a graphical index with a reference genome and known variation to increase alignment quality and variant calling accuracy. Herein, we propose a variation-aware read alignment algorithm (VARA), which generates the alignment between read and multiple genomic sequences simultaneously utilizing the schema of the Landau-Vishkin algorithm. VARA dynamically extracts regional variants to construct a pseudo tree-based structure on-the-fly for seed extension without loading the whole genome variation into memory space. We developed the novel high-throughput sequencing read aligner deBGA-VARA by integrating VARA into deBGA. The deBGA-VARA is benchmarked both on simulated reads and the NA12878 sequencing dataset. The experimental results demonstrate that read alignment incorporating genetic variation knowledge can achieve high sensitivity and accuracy. Moreover, due to its efficiency, VARA provides a promising solution for further improvement of variant calling while maintaining small memory footprints. The deBGA-VARA is available at: https://github.com/hitbc/deBGA-VARA.
QoE Aware Resource Allocation for Multi-view Video Flows in LTE

Recently, the popularity of multi-view video flow transmission over cellular networks has received a significant surge. However, due to considerably higher bandwidth demands compared to conventional 2D videos, efficient resource allocation techniques specially oriented towards multi-view video must be designed to allow their efficient transmission. From the perspective of maintaining a minimum acceptable Quality of Experience (QoE) for each active user even during overload situations, adaptive 3D multi-view video streaming is being widely viewed as a promising enabling technology. Leveraging this technology, we first pose the problem of scheduling a set of multi-view videos as an optimization problem which attempts to maximize aggregate QoE. In addition, a heuristic solution to the optimization problem has been designed. Simulation results show that the proposed strategy is able to effectively deliver satisfactory the average playback-quality while simultaneously minimizing bit-rate switching in the video output.
Region level Bi-directional Deep Learning Framework for EEG-based Image Classification

Despite many deep learning models are proposed for content understanding or pattern recognition of brain activities via EEGs, EEG-based object classification still demands efforts for the improvement with respect to its accuracy, generalization, and interpretation, yet some characters of human brains have been relatively unexplored. In this paper, we propose a regionlevel bi-directional deep learning framework for EEG-based object classification. Inspired by the hemispheric lateralization of human brain, we propose to extract additional information at regional level to strengthen and emphasize the differences between two hemispheres. The bi-directional long short-term memory is used to capture the dynamic correlations hidden from both the past and the future to the current state in EEG sequence. Extensive experiments are carried out and our results demonstrate the effectiveness of our proposed framework. Compared with the existing work, our framework achieves outstanding performances in EEG-based object classification task.
Design and Implementation of a 0.3-V Differential Difference Amplifier

A new silicon realization of an ultra-low-voltage and ultra-low-power differential-difference amplifier (DDA) is presented in this paper. The circuit combines the idea of non-tailed bulk-driven differential pairs with a partial positive feedback used for voltage gain boosting. The DDA operates from <inline-formula> <tex-math notation="LaTeX">$V_{\mathbf {DD}}$ </tex-math></inline-formula> ranging from 0.3 to 0.5 V. For a 0.3-V version, the circuit provides measured DC voltage gain larger than 60 dB, the GBW product of 1.85 kHz, PSRR of 57 dB and the average slew-rate of 1.55 V/ms at 20 pF load capacitance, while consuming only 22 nW of power. An instrumentation amplifier based on the proposed DDA showed the THD of 0.5 % for <inline-formula> <tex-math notation="LaTeX">$V_{\mathbf {in}}=50$ </tex-math></inline-formula> mV<inline-formula> <tex-math notation="LaTeX">$_{\mathbf {pp}}$ </tex-math></inline-formula>, and the 3-dB bandwidth of 750 Hz with the voltage gain of 2 V/V. The circuit has been fabricated in a standard n-well <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> CMOS process from TSMC. Chip test results agree with simulations. A special design procedure has also been developed that allows the circuit to be optimized under such extreme supply conditions.
AutoHighlight : Automatic Highlights Detection and Segmentation in Soccer Matches

More and more football fans prefer to watch football highlights because it takes too much time to watch the entire football game. At the same time, many TV stations and sports companies need to store and retrieve fragments of different events in football matches. The traditional way is to analyze and cut the football match manually. This method is time consuming and the process largely depends on personal choice, which results in unsecured accuracy. Therefore, it’s necessary to develop a fast and accurate system for automatic summarization and analysis of football matches video. Automatic event detection and segmentation in football matches is about detecting specific event and extracting the related moments for football viewers and companies. This paper presents a deep learning based event detection and segmentation system for emphasizing specific events during football matches. The proposed system use live text of football matches as auxiliary information to segment the important event segmentation. We firstly use the deep learning model to tag each live text. Second, we merge successive live texts with the same label. Then we go to the football match to segment corresponding segmentation to the merged live text. Finally, if necessary, we run pure video based event detection model to these segmentation for finer grained segmentation. Experiments on real football matches show encouraging results. The proposed system greatly improves the accuracy and speed of segmentation.
Predicting Topic Participation by Jointly Learning User Intrinsic and Extrinsic Preference

Understanding the preferences of social media participants plays a crucial role in many business applications. A specific aspect of interest is to predict which topics a particular user is more likely to be involved in. Existing efforts on such topic participation forecasting mainly focus on learning from historical user-generated texts to infer their preferred topics, or leverage on an information propagation theory on networks to predict the topics of potential interest. However, jointly utilizing both the sources of data to provide a holistic prediction for such a task has not been exploited. We present a novel joint learning framework that takes advantage of both users’ intrinsic (learned from their past social media postings) and extrinsic preference (learned from the social network influence) and that embeds them into low-dimensional space vectors. To facilitate effective learning, we encode latent continuous embedded vectors into binary ones via locality-sensitive hashing. Furthermore, to explain the predictions made by our “black-box” model, we investigate the importance of each training sample on the topic prediction performance of a testing instance to demonstrate its interpretability. The experiments conducted on datasets collected from several popular social media platforms demonstrate the effectiveness of our proposed method when compared with existing baselines.
Fano Resonances and Photoluminescence in Self-Assembled High-Quality-Factor Microbottle Resonators

We demonstrate self-assembly of microbottle resonators (MBRs) that support high-quality-factor (high-<inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>) whispering gallery modes (WGMs). Polymethyl methacrylate (PMMA), a transparent organic polymer, has been used for the self-assembly. Active MBRs have been created by doping PMMA with laser dye rhodamine 6G. WGM-modulated photoluminescence has been observed from the active MBRs. The self-assembled passive MBRs show <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factors of the order of <inline-formula> <tex-math notation="LaTeX">$10^{6}$ </tex-math></inline-formula>. Fano- and electromagnetically induced transparency-type resonances have been observed in the transmission spectra.
Deep Reinforcement Learning Approach for Train Rescheduling Utilizing Graph Theory

Railway disturbances occur every day and train rescheduling is conducted by human experts. Approaches for automating rescheduling have been widely studied (e.g., heuristic approach and mixed integer problem-based approach). However, extant research is still inadequate for employing the approach in practical application in terms of size, run-time, and solution accuracy. In this research, we simulate train dispatching using graph theory and propose a reinforcement learning method (i.e., Deep Q-Network (DQN)) for rescheduling. We also show experimental results of this algorithm. DQN presented positive results for over 50% of test cases, and its train rescheduling decreased approximately 20% of passengers’ dissatisfaction in a certain case. It can be expected that applying a DQN approach to real-world scale cases by will improve methods to handle larger-scale networks.
Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis

Application of machine learning methodology on patient functional and structural data has been shown to improve glaucoma classification accuracy. Intraocular pressure (IOP) and the biomechanical behaviors of the eye are early indicators of glaucoma. Four classifiers: linear logistic regression, support vector machine, random forest classifier and gradient boosting classifier are tested for discrimination using a 52-patient biomechanical data set that includes 20 glaucoma (40 eyes) and 32 healthy subjects (64 eyes). Results show that the 98.3% accuracy from linear logistic regression (LLR) is the highest correlation accuracy amongst the tested methods. The LLR classification accuracy is comparable with classification accuracies attained for classification using functional and structural measurements from image data sets. Since IOP elevation and biomechanical changes often precede imageable symptoms, the new biomechanics diagnostic classifier maybe used as a detection method for early stage glaucoma.
Automatic Segmentation and Quantification of TB Scale Volumetric Murine Brain Vasculature Data

Emerging serial section light microscopy platforms, like Knife-Edge Scanning Microscopy (KESM), generate high-resolution data sets at a rate exceeding 1 TB / cm3 of tissue, and can generate 3D voxel data of an entire mouse organ like a brain or kidney. Not only is this technique much faster than imaging slides manually on a traditional microtome it generates much larger and better statistically sampled data sets. These large datasets require new and innovative infrastructure to support the development and deployment of automated segmentation algorithms. In this paper we briefly describe the KESM, our analysis infrastructure, and our validation procedures for automated tissue segmentation routines. We demonstrate multiparametric quantification of vasculature across large sample volumes and the validation of segmentation of these volumes using both trained pathologists and un-trained workers. This type of validated vascular analysis is useful for understanding tumor angiogenesis, arteriosclerosis, vasculopathies, and neurodegenerative diseases. To that end, recent studies have shown an increase in blood vessel density & reduction in blood vessel diameter in the striatum of mice with Huntington’s Disease [1], [2]. We show that our validated mouse model recapitulate and expand on these findings across larger volumes.
Optimal Design and Performance Analysis for Interior Composite-Rotor Bearingless Permanent Magnet Synchronous Motors

The bearingless permanent magnet synchronous motor (BPMSM) is widely developed due to the advantages such as high speed, high power factor, high power density, and zero friction. In this paper, in order to provide more space for the placement of a permanent magnet (PM), a new type of BPMSM with interior composed magnetic structure is designed. The operation principles of the proposed BPMSM with the Maxwell force are introduced first. Then, by using of the finite element method (FEM), the pole-arc coefficient of BPMSM is optimized to reduce the cogging and increase the levitation force. In addition, the electromagnetic properties including magnetic field, levitation force, electromagnetic torque, loss, and cogging torque are simulated and analyzed. Finally, the static levitation experiment is carried out to verify the performance of levitation force. The research lays a foundation for further research about optimal design of the new type of BPMSM and also provides a mathematical model foundation for research of the control system.
Effects of filler calcination on structure and dielectric properties of polyethylene/silica nanocomposites

This paper reports on an investigation of the effects of calcination on the structure and dielectric properties of polyethylene/silica (SiO2) nanocomposites. Calcination temperatures of 600 and 900 °C have been used in order to modify the surface chemistry and surface structure of the SiO2. The results show that, after calcination, the concentration of surface hydroxyl groups and water molecules around SiO2 and within resulting nanocomposites is reduced. The real permittivity of nanocomposites containing calcined SiO2 decreases compared to nanocomposites based on uncalcined SiO2. The DC breakdown strength of nanocomposites containing calcined SiO2 becomes higher than those containing uncalcined SiO2. In contrast, AC breakdown was found not to be significantly affected by addition of any of the silicas considered here. The use of calcined nanofillers can have positive effects akin to the use of chemically functionalized nanofillers in enhancing the dielectric properties of nanocomposites; both approaches remove polar surface hydroxyl groups.
Scientific Visualization and Reproducibility for "Open" Environmental Science

Practicing reproducible scientific research requires access to appropriate reproducibility methodology and software, as well as open data. Strict reproducibility in complex scientific domains such as environmental science, ecology and medicine, however, is difficult if not impossible. Here, we consider replication as a relaxed but bona fide substitution for strict reproducibility and propose using 3D terrain visualization for replication in environmental science studies that propose causal relationships between one or more driver variables and one or more response variables across complex ecosystem landscapes. We base our contention of the usefulness of visualization for replication on more than ten years observing environmental science modelers who use our 3D terrain visualization software to develop, calibrate, validate, and integrate predictive models. To establish the link between replication and model validation and corroboration, we consider replication as proposed by Munafò, i.e., triangulation. We enumerate features of visualization systems that would enable such triangulation and argue that such systems would render feasible domain-specific, open visualization software for use in replicating environmental science studies.
A Liquid Crystal Tunable Filter-Based Hyperspectral LiDAR System and Its Application on Vegetation Red Edge Detection

In this letter, a hyperspectral light detection and ranging (HSL) with 10-nm spectral resolution was designed and tested using a supercontinuum laser source. The major difference between the prototyped HSL and similar instruments was that a liquid crystal tunable filter (LCTF) was installed before the avalanche photodiode detector and utilized as a spectroscopic device. The design allowed continuous wavelength selection of the backscattered echoes in the time dimension. Moreover, for general accuracy evaluation of range measurement and spectral measurement, laboratory experiments for vegetation red edge detection were performed using the prototyped HSL to assess its feasibility on agriculture application. Yellow and green leaves from aloe and dracaena plants were measured by the LCTF-HSL for detecting the corresponding “red edge” position. Spectral profiles measured by an SVC-HR-1024 spectrometer which is designed by SVC company were used as a reference to evaluate the measurements of HSL. The comparison results showed that the red edge positions extracted from the two individual measurements were similar, thus indicating that the LCTF-based high-resolution HSL was effective for this application.
IoT Devices Recognition Through Network Traffic Analysis

The growing Internet of Things (IoT) market introduces new challenges for network activity monitoring. Legacy network monitoring is not tailored to cope with the huge diversity of smart devices. New network discovery techniques are necessary in order to find out what IoT devices are connected to the network. In this context, data analysis techniques can be leveraged to find out specific patterns that can help to recognize device types. Indeed, contrary to desktop computers, IoT devices perform very specific tasks making their networking behavior very predictable. In this paper, we present a machine learning based approach in order to recognize the type of IoT devices connected to the network by analyzing streams of packets sent and received. We built an experimental smart home network to generate network traffic data. From the generated data, we have designed a model to describe IoT device network behaviors. By leveraging the t-SNE technique to visualize our data, we are able to differentiate the network traffic generated by different IoT devices. The data describing the network behaviors are then used to train six different machine learning classifiers to predict the IoT device that generated the network traffic. The results are promising with an overall accuracy as high as 99.9% on our test set achieved by Random Forest classifier.
Patient-Specific Heartbeat Classification Based on I-Vector Adapted Deep Neural Networks

Automatic heartbeat classification from electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A main challenge in ECG classification is the variability of ECG signals across patients. This paper proposes a patient-specific heartbeat classifier to address the inter-patient variations in ECG signals. Inspired by the success of identity vectors (i-vectors) in speech and speaker recognition, we extracted one i-vector from five minutes of ECG data for each patient and applied it to adapt a patient-independent deep neural network (DNN) to a patient-specific DNN, namely i-vector adapted patient-specific DNN ($iAP$-DNN). Evaluations on the MIT-BIH arrhythmia database show that the $iAP$-DNN is able to classify raw ECG signals of the corresponding patient into normal heartbeats and different types of arrhythmias and that it outperforms existing patient-specific classifiers in terms of sensitivity-vs-specificity and Mathews correlation coefficients.
Attitude Control for Spacecraft with MSGMW based on Sliding Mode Controller and Extended State Observer

Magnetically Suspended Gimbaled Momentum Wheel (MSGMW) can stabilize three-axis attitude of the spacecraft in small angle range, with the advantages of reducing actuator number, minimizing cost and enhancing reliability. In this paper, the attitude control problem of spacecraft with only one MSGMW is investigated. The nonlinear coupled dynamic model of this problem is deduced, as well as its linear simplified model. The control strategy is also discussed. The desired control torque is brought to divide the system into two loops, namely, the outer attitude control loop and the inner gimbaling angle control loop. Sliding Mode Controller (SMC) with Extended State Observer (ESO) for attitude control loop is employed to reject external disturbance and inertial parameter uncertainty. The efficiency and performance of the controller is demonstrated by numerical simulations.
HMNPPID: A Database of Protein-protein Interactions Associated with Human Malignant Neoplasms

Protein-protein interactions (PPIs) play a key role in varies aspects of the structural and functional organization of the cell. Knowledge about them unveils the molecular mechanisms of biological processes. At the same time, the PPIs related to human malignant neoplasms can shed light on the biology behind these neoplasms. However, there is no such PPI database available so far. We constructed a database of protein-protein interactions associated with human malignant neoplasms, named HMNPPID, which contains the PPIs of 171 kinds of human malignant neoplasms. HMNPPID provides healthcare professionals with the PPIs of specific malignant neoplasm without digging into amounts of biomedical literatures, which significantly improves the efficiency of researches on the PPIs of malignant neoplasms. In addition, a visualization program, VisualPPI, is provided to facilitate the analysis of the specific PPI network for a malignant neoplasm.
Visual Manipulation Relationship Network for Autonomous Robotics

Robotic grasping is one of the most important fields in robotics, in which great progress has been made in recent years with the help of convolutional neural network (CNN). However, including multiple objects in one scene can invalidate the existing CNN-based grasp detection algorithms, because manipulation relationships among objects are not considered, which are required to guide the robot to grasp things in the right order. This paper presents a new CNN architecture called Visual Manipulation Relationship Network (VMRN) to help robots detect targets and predict the manipulation relationships in real time, which ensures that the robot can complete tasks in a safe and reliable way. To implement end-to-end training and meet real-time requirements in robot tasks, we propose the Object Pairing Pooling Layer (OP2L) to help to predict all manipulation relationships in one forward process. Moreover, in order to train VMRN, we collect a dataset named Visual Manipulation Relationship Dataset (VMRD) consisting of 5185 images with more than 17000 object instances and the manipulation relationships between all possible pairs of objects in every image, which is labeled by the manipulation relationship tree. The experimental results show that the new network architecture can detect objects and predict manipulation relationships simultaneously and meet the real-time requirements in robot tasks.