Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the Dual Implant Reverse-Conducting SuperJunction Insulated-Gate Bipolar Transistor

This letter presents the dual implant superjunction (SJ) trench reverse-conducting (RC) insulated-gate bipolar transistor (IGBT) concept with two implanted SJ pillars in the drift region: one from the cathode side and another from the anode side. The proposed device is compatible with current manufacturing processes and enables a full SJ structure to be achieved in a 1.2-kV device, as alignment between the pillars is not required. Extensive technology computer aided design (TCAD) simulations have been performed and demonstrated that utilizing this dual implantation technique can result in a 77% reduction in turn-off losses for the full SJ structure, compared to a conventional RC-IGBT. The results show that any snapback in the ON-state waveform significantly increases the turn-off losses and only a deep SJ device (pillar $\text {gap} < 10~\mu \text{m}$ ) warrants the additional processing expense.
Estrogen receptor alpha activates MAPK signaling pathway to promote the development of endometrial cancer

Endometrial cancer (EC) is a common malignant tumor of the female reproductive system in the world. For most of the treated patients, although the survival rate is improved, most patients still have a poor prognosis. The pathogenesis of EC has always been a strong scientific focus, but there is no clear conclusion. Therefore, in view of modularization, this study is to conduct an in‐depth analysis on the effects of estrogen receptor alpha (ERα) regarding EC. The purpose is to identify the molecular course of EC. We obtained 10 co‐expression modules, in which ANO2, EMP3, and other genes are significantly differentially expressed in patients with EC. Additionally, there are active regulatory effects in dysfunction modules, thus genes such as ANO2 and EMP3 would be identified as key genes, which are associated with the development of EC. Enrichment results showed that the module genes were significantly involved in RNA splicing, covalent chromatin modification, histone modification, and organelle fission, and other biological processes, as well as significantly regulated mitogen‐activated protein kinases (MAPK) signaling pathway, Endocytosis, Rap1 signaling pathway, and viral carcinogenesis, and other signaling pathways. Finally, we identified noncoding RNA pivot including FENDRR, miR‐520c‐3p. Besides, transcription factors pivot including NFKB1, E2F1, and RELA which significantly regulate dysfunction module genes. Overall, our work deciphered a co‐expression network involving differential gene regulation in ERα‐associated EC. It helps reveal the core modules and potential regulatory factors of the diseases and enhances our understanding of the pathogenesis. More importantly, we revealed that ERα activates the MAPK signaling pathway to promote the development of EC. It helps to provide a new reference for later research.
Analysis and Design of Hybrid Inductive and Capacitive Wireless Power Transfer System

IPT system needs capacitor to compensate the system, while CPT system requires inductor to tune the system. Therefore, a hybrid system is one of the possible solutions to compensate the IPT coupler with CPT ones and vice versa. In this paper, an Inductive and capacitive hybrid wireless power transfer (HWPT) system is proposed to improve the system coupler anti-misalignment ability. The couplers of IPT and CPT are employed together to compensate each other and transfer power together. Superposition theory is used to analyze the system working principle in detail. With the analysis results, a scaled-down system is built to validate the performance of the proposed approach. Experimental results show that the proposed HWPT system can achieve 655 W output power with 85.82% DC-DC efficiency at the well-aligned condition, and the maximum variation of output power is only 10% with the coupler misalignment from 0-270 mm, which agree well with analysis results.
Datacenter Workload Classification and Characterization: An Empirical Approach

Datacenter traffic has increased significantly due to rising number of web applications on Internet. These applications have diverse Quality of Service (QoS) requirements making datacenter management a complex task. For a datacenter the amount of resources required for a given resource type (computing, memory, network and storage) is termed as workload. In cloud datacenters, workload classification and characterization is used for resource management, application performance management, capacity sizing, and for estimating the future resource demand. An accurate estimation of future resource demand helps in meeting QoS requirements and ensure efficient resource utilization. Thus modeling and characterization of datacenter workloads becomes necessary to meet performance requirements of applications in a cost-efficient manner. In this paper, a methodology to classify datacenter workloads and characterize them based on resource usage is proposed. Two different workloads have been used, one is Google Cluster Trace (GCT) dataset and other is Bit Brains Trace (BBT) dataset. Seven different machine learning algorithms for workload classification have been used. Workload distribution is estimated in a mix of heterogeneous applications for both GCT and BBT. The seven machine learning algorithms have been compared on the basis of their classification accuracy. Finally, an algorithm to estimate the importance of different attributes for classification is proposed in this paper.
Characterization of a Maize [beta]-Amylase cDNA Clone and Its Expression during Seed Germination

A maize (Zea mays L.) cDNA clone (pZMB2) encoding [beta]-amylase was isolated from a cDNA library prepared from the aleurone RNA of germinating kernels. The cDNA encodes a predicted product of 488 amino acids with significant similarity to known [beta]-amylases from barley (Hordeum vulgare), rye (Secale cereale), and rice (Oryza sativa). Glycine-rich repeats found in the carboxyl terminus of the endosperm-specific [beta]-amylase of barley and rye are absent from the maize gene product. The N-terminal sequence of the first 20 amino acids of a [beta]-amylase peptide derived from purified protein is identical to the 5th through 24th amino acids of the predicted cDNA product, indicating the absence of a conventional signal peptide in the maize protein. Recombinant inbred mapping data indicate that the cDNA clone is single-copy gene that maps to chromosome 7L at position 83 centimorgans. Northern blot analysis and in vitro translation-immunoprecipitation data indicate that the maize [beta]-amylase is synthesized de novo in the aleurone cells but not in the scutellum during seed germination.
Classification of Contractual Conflicts via Learning of Semantic Representations

Contracts are the main medium through which parties formalize their trade relations, be they the exchange of goods or the specification of mutual obligations. While electronic contracts allow automated processes to verify their correctness, most agreements in the real world are still written in natural language, which need substantial human revision effort to eliminate possible conflicting statements in long and complex contracts. In this paper, we formalize a typology of conflict types between clauses suitable for machine learning and develop techniques to review contracts by learning to identify and classify such conflicts, facilitating the task of contract revision. We evaluate the effectiveness of our techniques using a manually annotated contract conflict corpus with results close to the current state-of-the-art for conflict identification, while introducing a more complex classification task of such conflicts for which our method surpasses the state-of-the art method.
Input Fusion of MFCC and SCMC Features for Acoustic Scene Classification using DNN

In this paper, we propose a feature set by concatenating Mel-Frequency Cepstral Coefficients (MFCC) and Spectral Centroid Magnitude Coefficients (SCMC) features for Acoustic Scene Classification (ASC) using Deep Neural Networks (DNN). MFCC features are used to hold the acoustic characteristics such as spectral envelope of an acoustic scene in each frame. It also carries the sub-band average energy as a single dimension. SCMC features are used to hold the distribution of energy in a sub-band effectively. A test is carried out on Tampere University of Technology (TUT) Acoustic Scenes 2017 Dataset. The DNN architecture for utterance level classification has been used. The proposed system’s performance on a 4-fold cross-validation setup is 80.2% and it gives 5.4% relative improvement in performance when compared to the baseline system that uses log-Mel band energies with Multi-Layer Perceptron model.
Training Cooperative Agents for Multi-Agent Reinforcement Learning

Deep Learning and back-propagation has been successfully used to perform centralized training with communication protocols among multiple agents in a cooperative environment. In this paper we present techniques for centralized training of Multi-Agent (Deep) Reinforcement Learning (MARL) using the model-free Deep Q-Network as the baseline model and message sharing between agents. We present a novel, scalable, centralized MARL training technique, which separates the message learning module from the policy module. The separation of these modules helps in faster convergence in complex domains like autonomous driving simulators. A second contribution uses the centrally trained model to bootstrap training of distributed, independent, cooperative agent policies for execution and thus addresses the challenges of noise and communication bottlenecks in real-time communication channels. This paper theoretically and empirically compares our centralized training algorithms to current research in the field of MARL. We also present and release a new OpenAI-Gym environment which can be used for multi-agent research as it simulates multiple autonomous cars driving cooperatively on a highway.
Growth Factor Signaling Regulates Mechanical Nociception in Flies and Vertebrates

Mechanical sensitization is one of the most difficult clinical pain problems to treat. However, the molecular and genetic bases of mechanical nociception are unclear. Here we develop a Drosophila model of mechanical nociception to investigate the ion channels and signaling pathways that regulate mechanical nociception. We fabricated von Frey filaments that span the subthreshold to high noxious range for Drosophila larvae. Using these, we discovered that pressure (force/area), rather than force per se, is the main determinant of aversive rolling responses to noxious mechanical stimuli. We demonstrated that the RTK PDGF/VEGF receptor (Pvr) and its ligands (Pvfs 2 and 3) are required for mechanical nociception and normal dendritic branching. Pvr is expressed and functions in class IV sensory neurons, whereas Pvf2 and Pvf3 are produced by multiple tissues. Constitutive overexpression of Pvr and its ligands or inducible overexpression of Pvr led to mechanical hypersensitivity that could be partially separated from morphological effects. Genetic analyses revealed that the Piezo and Pain ion channels are required for mechanical hypersensitivity observed upon ectopic activation of Pvr signaling. PDGF, but not VEGF, peptides caused mechanical hypersensitivity in rats. Pharmacological inhibition of VEGF receptor Type 2 (VEGFR-2) signaling attenuated mechanical nociception in rats, suggesting a conserved role for PDGF and VEGFR-2 signaling in regulating mechanical nociception. VEGFR-2 inhibition also attenuated morphine analgesic tolerance in rats. Our results reveal that a conserved RTK signaling pathway regulates baseline mechanical nociception in flies and rats. SIGNIFICANCE STATEMENT Hypersensitivity to touch is poorly understood and extremely difficult to treat. Using a refined Drosophila model of mechanical nociception, we discovered a conserved VEGF-related receptor tyrosine kinase signaling pathway that regulates mechanical nociception in flies. Importantly, pharmacological inhibition of VEGF receptor Type 2 signaling in rats causes analgesia and blocks opioid tolerance. We have thus established a robust, genetically tractable system for the rapid identification and functional analysis of conserved genes underlying mechanical pain sensitivity.
Truncated Peroxisome Proliferator-Activated Receptor-γ Coactivator 1α Splice Variant Is Severely Altered in Huntington’s Disease

Background: Reduced peroxisome proliferator-activated receptor-γ coactivator 1α (PGC1α) gene expression has been observed in striatal cell lines, transgenic mouse models of Huntington’s disease (HD), and brain tissue from HD patients. As this protein is a key transcription regulator of the expression of many mitochondrial proteins, these observations strongly support the role of aberrant mitochondrial function in the pathogenesis of HD. The PGC1α protein undergoes posttranslational modifications that affect its transcriptional activity. The N-truncated splice variant of PGC1α (NT-PGC1α) is produced in tissues, but the role of truncated splice variants of PGC1α in HD and in the regulation of mitochondrial gene expression has not been elucidated. Objective: To examine the expression and modulation of expression of NT-PGC1α levels in HD. Methods and Results: We found that the NT-PGC1α protein, a splice variant of ∼38 kDa, but not full-length PGC1α is severely and consistently altered in human HD brain, human HD myoblasts, mouse HD models, and HD striatal cells. NT-PGC1α levels were significantly upregulated in HD cells and mouse brown fat by physiologically relevant stimuli that are known to upregulate PGC1α gene expression. This resulted in an increase in mitochondrial gene expression and cytochrome c content. Conclusion: Our data suggest that NT-PGC1α is an important component of the PGC1α transcriptional network, which plays a significant role in the pathogenesis of HD.
Touch-Stroke Dynamics Authentication Using Temporal Regression Forest

Touch-stroke dynamics is a relatively recent behavioral biometrics. It authenticates an individual by observing his behavior when swiping a “stroke” on a smartphone or tablet. Several studies have attempted to determine the optimum authentication accuracy of classifiers, but none of them has used time series or temporal machine learning techniques. We postulate that when a user performs a series of touch strokes in a continuous manner, it can be perceived as a temporal behavior characteristic of the person. In this letter, we propose the use of a temporal regression forest to unearth this hidden but vital temporal information. By incorporating this temporal information in the authentication process, the proposed model is able to achieve average equal error rates of ∼4.0% and ∼2.5% on the Serwadda dataset and Frank dataset, respectively.
Gait Generation with Smooth Transition Using Neural Oscillator Network Based Locomotion Control for Snake-Like Robot

Nature snakes move efficiently and transit gait flexibly in complex environment, that is enlightening to the research of snake-like robots. In order to improve flexibility and adaptability of snake-like robots, we design a neural oscillator network connected by two chains of Hopf oscillators to generate serpenoid curves of different locomotion. To avoid stiff transition of control signals, a smooth transition approach is combined with the neural oscillator network. The proposed control method is capable of generating lateral undulation, rectilinear motion, sinus-lifting and sidewinding locomotion for snake-like robots and switching among these locomotion smoothly by adjusting several parameters of the neural oscillator network. Results of simulations and experiments demonstrate the effectiveness of the proposed neural oscillator network.
The Simultaneous Estimation Method of Terrain Parameter and Vehicle Dynamics Variables for Agricultural Vehicle

This study proposes the simultaneous estimation method of terrain parameter and vehicle dynamics variables: cornering stiffness, side slip angle and disturbance moment. This method can be used to estimate and improve the dynamics of vehicles traveling on rough terrain. This estimation method consists of observer for nonlinear plant and the switching method by the degree of observability. Cornering stiffness, side slip angle, and disturbance moment can be estimated from measurement values, which are yaw rate, lateral distance and vehicle orientation with an accelerometer, a gyro sensor and GPS sensor by this method.
Cognition-Aware Summarization of Photos Representing Events

The use of photo summarization technology to summarize a photo collection is often oriented to users who own the photo collection. However, people’s interest in sharing photos with others highlights the importance of cognition-aware summarization of photos by which viewers can easily recognize the exact event those photos represent. In this research, we address the problem of cognition-aware summarization of photos representing events, and propose to solve this problem and to improve the perceptual quality of a photo set by proactively preventing misrecognization that a photo set might bring. Three types of neighbor events that can possibly cause misrecognizations are discussed in this paper, namely sub-events, super-events and sibling-events. We analyze the reasons for these misrecognitions and then propose three criteria to prevent from them. A combination of the criteria is used to generate summarization of photos that can represent an event with several photos. Our approach was empirically demonstrated with photos from Flickr by utilizing their visual features and related tags. The results indicated the effectiveness of our proposed methods in comparison with a baseline method. key words: cognition-aware, photo summarization, event
Phoneme Embedding and its Application to Speech Driven Talking Avatar Synthesis

Word embedding has made great achievements in many natural language processing tasks. However, the attempt to apply word embedding to the field of speech got few breakthroughs. The reason is that word vectors mainly contain semantic and syntactic information. Such high level features are difficult to be directly incorporated in speech related tasks compared to acoustic or phoneme related features. In this paper, we investigate the method for phoneme embedding to generate phoneme vectors carrying acoustic information for speech related tasks. One-hot representations of phoneme labels are fed into embedding layer to generate phoneme vectors that are then passed through bidirectional long short-term memory (BLSTM) recurrent neural network to predict acoustic features. Weights in embedding layer are updated through backpropagation during training. Analyses indicate that phonemes with similar acoustic pronunciations are close to each other in cosine distance in the generated phoneme vector space, and tend to be in the same category after k-means clustering. We evaluate the phoneme embedding by applying the generated phoneme vector into speech driven talking avatar synthesis. Experimental results indicate that adding phoneme vector as features can achieve 10.2% relative improvement in objective test.
Vers une approche utilisant l'apprentissage de métrique pour du clustering semi-supervisé interactif d'images

The problem of unsupervised and semi-supervised clustering is extensively studied in machine learning. In order to involve user in image data clustering, (Lai et al., 2014) proposed a new approache for interactive semi-supervised clustering that translates the feedback of user (expressed at the level of individual images) into pairwise constraints between groups of images, these groupes being constituted thanks to the underlying hierarchical clustering solution and user feedback. Recently, the need for appropriate measures of distance or similarity between data led to the emergence of distance metric learning approaches. In this paper, we propose a method incorporating metric learning in the existing system of (Lai et al., 2014) to improve performance and reduce the computational time. Our experimental results obtained on the image database Wang show that the metric learning methods fit well into the existing system improve its performances as well as its computational time. MOTS-CLÉS : clustering semi-supervisé interactif, contraintes par paires, apprentissage de métrique, analyse d’images.
Effects of Secondary Leakage Inductance on the LLC Resonant Converter—Part II: Frequency Control Bandwidth with respect to Load Variation

Resonant converters rely on a precise knowledge of leakage inductance of the equipped transformers. Resonant circuit topologies such as LLC usually utilize the transformer leakage as an inductive component in the resonant tank, allowing for a drastic reduction in the converter weight, size and volume. The existence of the secondary leakage inductance affects the whole operation of the LLC resonant converter. This paper reveals that placing the secondary winding near the air gap would increase the resonant tank input impedance, vertically shrink the voltage-gain curve of the converter, and consequently minimize the frequency range (i.e frequency bandwidth with respect to load variation). On contrary, placing the secondary winding in a close contact with the magnetic core would decrease the resonant tank input impedance, vertically stretches the voltage-gain curve of the converter, and widen the frequency variation range. It has been reported that the winding location with respect to the air gap has an impact on the leakage inductance value. In other words, placing the secondary winding in a close contact with the magnetic core (zero mmf position) would maximize the leakage energy storage originated from the secondary winding, and hence maximize the secondary leakage inductance and vice versa. The theoretical discussion is presented which is merely based on Ampere’s law and Dowell’s model. Furthermore, transformer prototypes had been constructed and tested in a 390V/12V-220W LLC converter prototype to evaluate the proposed analysis.
Chronic Diseases and Employment: Which Interventions Support the Maintenance of Work and Return to Work among Workers with Chronic Illnesses? A Systematic Review

The increase of chronic diseases worldwide impact quality of life, cause economic and medical costs, and make it necessary to look for strategies and solutions that allow people with chronic diseases (PwCDs) to lead an active working life. As part of the CHRODIS Plus Joint European Action project, a systematic review was conducted to identify studies of interventions that support the maintenance of work and return to work (RTW) among workers with chronic illnesses. These interventions should target employees with the following conditions: diabetes, cardiovascular diseases, metabolic vascular syndrome, respiratory diseases, musculoskeletal disorders, mental disorders, and neurological disorders. An extensive search was performed in PubMed, EMBASE, and PsycINFO for English language studies. Included in this review were 15 randomized controlled trials (RCT) for adult employees (aged 18+). We found that workplace-oriented and multidisciplinary programs are the most supportive to RTW and reducing the absence due to illness. In addition, cognitive behavioral therapies achieve positive results on RTW and sick leave. Finally, coaching is effective for the self-management of chronic disease and significantly improved perceptions of working capacity and fatigue.
Evaluation of covariate effects on pharmacokinetics of monoclonal antibodies in oncology

The development of monoclonal antibodies (mAbs) requires an understanding of the interindividual variability (IIV) in pharmacokinetics (PK) at the population level facilitated by population PK (PopPK) modelling. However, there is no clear rationale for selecting which covariates to screen during PopPK model development. Here, we compare the effect of covariates on PK parameters for mAbs in oncology and identify the most commonly used covariates affecting PK parameters.