Miracle-dz/UniFAR-FTUs huggingface dataset:
Rad52 function prevents chromosome loss and truncation in Candida albicans

RAD52 is required for almost all recombination events in Saccharomyces cerevisiae. We took advantage of the heterozygosity of HIS4 in the Candida albicans SC5314 lineage to study the role of Rad52 in the genomic stability of this important fungal pathogen. The rate of loss of heterozygosity (LOH) at HIS4 in rad52‐ΔΔ strains was ∼10−3, at least 100‐fold higher than in Rad52+ strains. LOH of whole chromosome 4 or truncation of the homologue that carries the functional HIS4 allele was detected in all 80 rad52‐ΔΔ His auxotrophs (GLH –GL lab His‐) obtained from six independent experiments. Isolates that had undergone whole chromosome LOH, presumably due to loss of chromosome, carried two copies of the remaining homologue. Isolates with truncations carried centric fragments of broken chromosomes healed by de novo telomere addition. GLH strains exhibited variable degrees of LOH across the genome, including two strains that became homozygous for all the heterozygous markers tested. In addition, GLH strains exhibited increased chromosomal instability (CIN), which was abolished by reintroduction of RAD52. CIN of GLH isolates is reminiscent of genomic alterations leading to cancer in human cells, and support the mutator hypothesis in which a mutator mutation or CIN phenotype facilitate more mutations/aneuploidies.
Pedestrian Detection Using Image Fusion and Stereo Vision in Autonomous Vehicles

Pedestrian detection is an important topic in different areas in particular Advance Driver Assistant Systems (ADAS) and Autonomous vehicles (AVs). Because of some intrinsic problems like intensity and light variation and different people’s clothing pedestrian detection is a challenging subject. In autonomous driving to avoid collisions a robust and reliable detection algorithm is essential. In recent years, vision becomes an effective part in autonomous driving that could provide a comprehensive cognition from surrounding that widely used in modern vehicles in different quantity and installation position. Image quality, faster detection algorithm with an optimum detection rate and providing more data for decision level are other challenges in this area. By using two cameras as a stereo vision system and extracting depth map and finding objects in Area of interest we get a stack of ROIs (region of interest) then by enhancing Image in selected ROIs by HIS image fusion technique better image quality for the detection level achieves.
The DevOps Lab Platform for Managing Diversified Projects in Educating Agile Software Engineering

This Research Work-in-Progress paper presents the design of a Software Engineering (SE) course to support project-based practical training. Group projects, especially projects from industry partners, are deemed to be necessary for students to gain hands-on experiences. With projects from the real world, students learn not only practical engineering solutions, but also the context, constraints, and social aspects of SE. For a course having over 100 students with different interests and experiences, it is desired to provide diversified choices of projects to stimulate enthusiasm for learning. However, management and evaluation of diversified projects are challenging. Following the Agile principles, we need to continuously track progress and activities of each group, to provide quick feedback of deliveries, and to periodically evaluate students’ performance. Therefore, we built a DevOps platform based on GitLab version control and continuous integration framework. Commits to GitLab code repositories automatically trigger build, testing, and analysis functions (which provide both qualitative and quantitative feedback to the students). This system has been in operations since 2014 for an undergraduate SE course, with over 500 students participating in over 130 project teams in total. The preliminary research showed promising results in improving SE education.
Feature Pyramid Deep Matching and Localization Network for Image Forensics

To advance the state of the art of image forensics technologies, a new formulation of splicing localization is proposed, which aims to obtain the masks for both the query and donor images for a pair of query(probe) image and potential donor image if a region of the donor image was spliced into the probe. The former Deep Matching and Validation Network(DMVN) addresses the problem with a novel end-to-end learning based solution. Inheriting the deep dense matching layer, we propose Feature Pyramid Deep Matching and Localization Network(FPLN), whose contributions are three folds. Firstly, instead of using just one feature map as in DMVN, FPLN utilizes a pyramid of feature maps with different resolutions w.r. t. the input image to achieve better localization performance, especially for small objects. Secondly, we add a fusion layer that fuses together all the features after deep dense matching layer, which not only takes full advantage of the correlation information between those features, but is also able to integrate two pathways in DMVN into just one simple pathway, simplifying the subsequent architecture. Lastly, we employ focal loss to address the imbalance problem, as the foreground area is usually much smaller than the background area. The experiments demonstrate the superior performance of our proposed method in detection accuracy and in localizing small tempered regions.
Multimodal Speech Driven Facial Shape Animation Using Deep Neural Networks

In this paper we present a deep learning multimodal approach for speech driven generation of face animations. Training a speaker independent model, capable of generating different emotions of the speaker, is crucial for realistic animations. Unlike the previous approaches which either use acoustic features or phoneme label features to estimate the facial movements, we utilize both modalities to generate natural looking speaker independent lip animations synchronized with affective speech. A phoneme-based model qualifies generation of speaker independent animation, whereas an acoustic feature-based model enables capturing affective variation during the animation generation. We show that our multimodal approach not only performs significantly better on affective data, but improves performance over neutral data as well. We evaluate the proposed multimodal speech-driven animation model using two large scale datasets, GRID and SAVEE, by reporting the mean squared error (MSE) over various network structures.
Tab Design and Failures in Cylindrical Li-ion Batteries

Lithium-ion (Li-ion) batteries play a vital role in today’s portable and rechargeable products, and the cylindrical format is used in applications ranging from e-cigarettes to electric vehicles due to their high density and power. The tabs that connect the electrodes (current collectors) to the external circuits are one aspect of the cylindrical battery design that plays a role in reliability and safety. This paper overviews various tab materials, structures, and welding methods and then discusses failures in commercial 18650-type Li-ion batteries due to the tab defects. The recommendations for tab design and manufacturing are given.
A Multiphase Digital Pulsewidth Modulated Polar Transmitter Architecture With Reactive Combiner for Improved Efficiency

This paper presents a new method of reactive-combiner structure to be used as part of a multiphase digital pulsewidth modulated polar transmitter (DPWMPT). It overcomes the limitations of previous implementations of the multiphase pulse-modulated polar transmitter related to terminating pulsewidth modulation (PWM) harmonics located outside the passband, thereby improving both PWM harmonic suppression by using multiphase aliasing-free digital PWM technique and increasing overall efficiency by using reactive combining. For validation, a prototype transmitter was implemented and tested with a 64-QAM 20-MHz bandwidth long-term evolution (LTE) downlink signal with a 7.5-dB peak-to-average power ratio at 2140 MHz. The prototype dual-phase DPWMPT architecture achieved an output power level of 37 dBm and power-added efficiency of 33.5% while passing the stringent spectral requirements of the LTE standard for small-cell base stations using only simply memoryless predistortion correction.
STGRFT for Detection of Maneuvering Weak Target With Multiple Motion Models

Long time coherent integration (LTCI) technique enhances radar detection of maneuvering weak targets. Unfortunately, range migration (RM) and Doppler frequency migration (DFM) would occur during the integration period. Existing LTCI methods are all based on the assumption that the target is of single motion model (i.e., the target's motion model is fixed) during the long integration time. However, with increasing target's maneuverability and long observation time (needed to detect weak targets), the target's motion model is often changing (i.e., target is of multiple motion models) during the integration time, which would reduce the performance of existing LTCI methods significantly. In this paper, we address the LTCI problem for detecting a maneuvering weak target with multiple motion models, where the target signal model is first established. Thereafter, the short-time generalized Radon Fourier transform (STGRFT) based LTCI algorithm is proposed. The STGRFT method could not only eliminate RM and DFM but also estimate the model-changing point (i.e., the time when target's motion model changes) and accumulate the target energy distributed in different motion stages (corresponding to different motion models). Finally, numerical and real-data experiments are carried out to demonstrate its effectiveness. The results confirm that in terms of integration gain, input–output signal-to-noise ratio performance and detection ability, STGRFT-based method is superior to the existing popular LTCI algorithms.
A Slot Array Antenna With Single-Layered Corporate-Feed Based on Ridge Gap Waveguide in the 60 GHz Band

This paper presents an <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula>-element slot array antenna with single-layered corporate-feed based on the ridge gap waveguide (RGW) technology in the 60 GHz band. As is well known, a corporate-feed slot array antenna usually has backed cavities to increase the bandwidth and provides a space for its distribution network, and therefore three layers in total: one layer for radiating slots and two layers for feed network with one layer of back cavities and the other one of power dividers. The antenna in this paper is designed by utilizing only two separate metallic layers–a corporate-feed network layer and a radiating slot layer. Compared with the conventional three-layered slot array antennas, the proposed antenna avoids the utilization of the backed cavity layer so that its complexity and manufacture cost decrease. In order to solve the problem of the narrow bandwidth caused by taking away the backed cavities, we utilize double-ridged radiating slots instead of the conventional rectangular ones. A compact transition power divider from standard waveguide WR-15 to the RGW is introduced to excite the proposed array antenna. The <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula>-element slot array antenna has been fabricated by computerized numerical control machining technique. The measured results demonstrate that the −10 dB reflection coefficient has around 17% bandwidth covering 56.5–67 GHz frequency range, and the measured gain is better than 26 dBi with more than 70% antenna efficiency over 58–66 GHz.
Controllability Analysis of a Standing Bipedal Robot with Active Toe-Joints

In this paper capability of a simplified bipedal robot model having active toe-joint to keep standing balance is investigated. The model consists of a rigid link as the upper body considering the ankle strategy for standing balance maintenance, and a foot, which contains two segments as heel link and toe link, hinged together at a toe joint. To ensure contact between foot and ground, COP constraints are defined and exerted to the dynamical equations. Applying constraints to the model, imposes bounds on the control torques of the joints. In this work, the joint torque bounds, which are important for control, are determined by numerical solution of equations. It is found that angular orientation and velocity of the upper body play a significant role in such allowed control torques. Accordingly, there exists a region in phase plane named controllable region beyond which constraints cannot be satisfied regardless of the control torques. The controllable regions are determined and plotted in phase plane.
Model Free Control of Electrically Driven Manipulator by Optimized Linear Extended State Observer, based on Voltage Control Strategy

This paper presents a new approach for robot manipulator control, based on voltage control strategy. The novelty of this paper is providing a model-free control system with optimized parameters. In this approach, it is not necessary to have dynamic equations and parameters of robot and its motors. Uncertainties and disturbances of each joint of the robot manipulator is estimated by a Linear Extended State Observer (LESO), and the convergence of estimated variables to actual variables has been analyzed. After estimating the disturbances, a proportional controller is used to tracking the reference trajectory. LESO and controller design parameters have been determined using the PSO algorithm. Simulation results depicted that the proposed model-free controller has a satisfactory performance for reducing the position tracking error.
Integrating the Advantages of Hardware and Software Tools into An Induction Machine Education

This paper develops exercises on hardware and software tools for enhancing an induction machine (IM) education. Firstly, the hardware tools mainly consisting of a wound rotor induction motor and a digital oscilloscope are used to demonstrate the operating principles of the IM through an open-circuit, a no-load, and an on-load test. Typically, the teaching of the relationship between stator frequency and rotor frequency is taught by a pure theorem. In this paper, it is directly measured through various exercises. Secondly, the software tools using MATLAB are used to build the simulation tools for IM analysis. Typically, the IM block in Simulink is used to evaluate its performances. In this paper, the simpler simulation IM block is presented for a steady state analysis. It has been proposed with the goals of making it as versatile as possible for students to learn independently about topics such as the effect of voltage input/slip on the performance of the IM. The uses of the proposed exercises on the hardware and software tools have led to a real breakthrough for an IM education. The proposed methods have been successfully integrated into the IM education at Kasetsart University Sriracha Campus.
Token-based Approach for Real-time Plagiarism Detection in Digital Designs

This Research to Practice Work in Progress Paper presents a token-based approach to detecting plagiarism in university courses with hardware programming assignments. Detecting plagiarism manually is a difficult and time-consuming work. In the last two decades, various of plagiarism detection tools have been developed. These techniques could be mainly divided into the following categories: Textual Match, Program Dependence Graph Comparison, Abstract Syntax Tree Analysis and Low-Level Form Code Comparison. Although there had been a lot of researches on detecting code clones in software programming languages (e.g. Basic, C/C++, Java, Python, etc.), research that focused on hardware description languages is still lacking. Based on the effective of the locality sensitive hash function (simhash), which was usually used in detecting near duplicates for web crawling, we proposed an improved real-time plagiarism detection approach for Verilog HDL (hardware description language) programming assignments. The core detecting steps are extracting weighted tokens from source code as high-dimensional feature, and mapping it to a f-bit fingerprints with simhash technique. On account of the syntax characteristics of Verilog HDL, a token extraction strategy was designed to maximize the valid information that a fixed length hash value could represent. Experiments over real course data sets were conducted to evaluate the performance of token-based approach comparing with an existing plagiarism detection tool (Moss). The result shows that our token-based approach does qualify the plagiarism detecting job for both online-query and batch-query in digital designs. Furthermore, token-based plagiarism detection approach could enable conduct incremental plagiarism detection for a single submission without excessive overhead. Finally, we also give a discussion of current way limitations and future research directions.
Shoreline rotation analysis of embayed beaches in the Central Thyrrenian Sea

Coastal geomorphology requires increasingly accurate topographic information of the beach systems to perform reliable simulation of coastal erosion and flooding phenomena, in order to ensure beach erosion management. In the case of the natural beaches of Serapo and S. Agostino, located in the municipality of Gaeta, on the Tyrrhenian Sea, beach rotation was found to be a response to natural forcing from waves of various incident directions. In this paper we analyzed the shoreline variability of these beaches in order to investigate if shoreline oscillations are mostly due to cross-shore sediment exchange or shoreline rotations due to alongshore exchange between the beaches extremities. The final aim was to give some quantitative coastal indicators which can be used to support future coastal management interventions.
Speech Intelligibility Enhancement in Noisy Environments via Voice Conversion with Glimpse Proportion Measure

In this study, we propose a new method for improving speech intelligibility in a public space such as station concourse that focuses on the difference in ease of listening due to the individuality of a speaker's voice. The proposed method evaluates the speech intelligibility caused by voice characteristic difference, and it converts the input voice to improve intelligibility measure by using voice-quality control based on the Gaussian mixture model. In the proposed method, glimpse proportion measure, which can estimate independent listening position, is used as the evaluation index of speech intelligibility. Speech that was converted by using the proposed method had larger energy than the background noise in some frequency bands. Results of subjective evaluation reveal that the proposed method can improve the speech intelligibility under a noisy environment.
Structural Insights into Mechanisms of Catalysis and Inhibition in Norwalk Virus Polymerase*

Crystal structures of Norwalk virus polymerase bound to an RNA primer-template duplex and either the natural substrate CTP or the inhibitor 5-nitrocytidine triphosphate have been determined to 1.8Å resolution. These structures reveal a closed conformation of the polymerase that differs significantly from previously determined open structures of calicivirus and picornavirus polymerases. These closed complexes are trapped immediately prior to the nucleotidyl transfer reaction, with the triphosphate group of the nucleotide bound to two manganese ions at the active site, poised for reaction to the 3′-hydroxyl group of the RNA primer. The positioning of the 5-nitrocytidine triphosphate nitro group between the α-phosphate and the 3′-hydroxyl group of the primer suggests a novel, general approach for the design of antiviral compounds mimicking natural nucleosides and nucleotides.
Analysis of VNS Effect on EEG Connectivity with Granger Causality and Graph Theory

Vagus Nerve Stimulation (VNS) is treatment of refractory epilepsy; however, its physiological mechanism has not been fully understood. The effectiveness of VNS for each patient cannot be predicted preoperatively. Thus, the mechanism of VNS needs to be investigated in order to avoid ineffective operations. Because an epileptic seizure is caused by the spread of excessive discharge from neurons in the cerebrum, analyzing effects of VNS on electroencephalogram (EEG) would be useful for VNS mechanism investigation. In the present work, the EEG data of epileptic patients with VNS were analyzed by using Granger Causality (GC) and the graph theory. Since GC is an index which expresses the intensity of a causal relation between two time series, it may illustrate information interactions between EEG channels. In addition, a directed graph constructed from those GC values would express neural connection. The analysis was carried out with the EEG data of two patients with frontal lobe epilepsy receiving the VNS therapy. The result supported the existing hypothesis indicating the bilateral asymmetry of the VNS effect on the brain, and furthermore, it suggested that VNS would increase neural connection between the frontal lobe and other brain regions, and that should control epileptic seizures by keeping patients awake.
9.8 A 28GHz 20.3%-Transmitter-Efficiency 1.5°-Phase-Error Beamforming Front-End IC with Embedded Switches and Dual-Vector Variable-Gain Phase Shifters

Millimeter-wave beamforming front-end ICs have been studied intensively as the service of 5G wireless communication is scheduled to begin in the near future [1–4]. The ICs include circuit elements such as PAs, LNAs, phase shifters, variable gain blocks, and switches to support antenna arrays for RF/hybrid beamforming. Due to the large number of antennas required for beamforming, the beamforming IC should include as many circuit elements as possible in a chip. The IC also needs high phase- and gain-control resolutions not only for controlling the beams precisely but also for error corrections and calibrations [1]. However, higher-bit controls of the phase and gain as well as high transmitting power increase the chip size in conventional structures, posing a trade-off between them. The front-end IC structure proposed here increases both transmitting power and gain/phase resolutions without increasing either the chip size or the power consumption.
24.4 Sandwich-RAM: An Energy-Efficient In-Memory BWN Architecture with Pulse-Width Modulation

Convolutional neural networks (CNN) achieve state-of-the-art results in the field of visual perception, drastically changing the traditional computer-vision framework. However, the movement of massive amounts of data prevents CNN’s from being integrated into low-power IoT devices. The recently proposed binaryweight network (BWN) reduces the complexity of computation and amount of memory access. A conventional digital implementation, which is composed of separate feature/weight memories and a multiply-and-accumulate (MAC) unit, requires large amounts of data to be moved [3]. To reduce power the weight memory and the computations are integrated together, into an in-memory computation architecture [1, 2, 5]. However, feature data is still stored externally, so data movement has only been partially addressed, especially for BWN. This paper blends feature and partial-weight memory with a computing circuit together, like a sandwich, that achieves significantly less data access (Fig. 24.4.1). It also uses a reconfigurable analog-computation engine, based on pulse-width modulation, that is small and flexible enough to be inserted into the memory.