Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the Magnetic Response of a Nanocrystalline High-Frequency Magnetic Link With Multi-Input Excitations

With the technological advancement of magnetic materials and semiconductor devices, the use of high-frequency magnetic link made of high-saturation induction and low specific core loss nanocrystalline alloy has been gaining popularity to replace the common dc link or ac link specially in integrating multiple renewable sources to the grid. The magnetic integration of multiple renewable sources through a common magnetic link has a number of advantages including galvanic isolation compared with the common dc or ac link. However, the magnetic response of the common magnetic link under multi-input excitations highly affects the energy conversion efficiency. To the best of the authors’ knowledge, the magnetic response of an advanced magnetic-material-based magnetic link under multi-input excitations has not been well investigated in the literature. In this paper, a systematic investigation is carried out to interconnect multiple sources through a common magnetic link. The magnetic response and the procedure used in the investigation are also presented and discussed in this paper.
Saliency Detection with Side Information on Railway Transportation Dataset

with the development of intelligent railway transportation, accurate location of wayside track assets like traffic signals, mileposts, switches, crossings make essential sense. In this paper, we propose a novel saliency detection model with deep learning and side information aimed at railway transportation dataset. We firstly construct a new railway transportation dataset, which includes 1282 images from the real videos taken by the camera on moving trains and collected from the Internet. Extensive experiments show the effectiveness of our model which can accurately locate the salient object in the dataset.
Network Formation Game for Routing in Unmanned Aerial Vehicle Networks

With the development of electronics, sensors, and communications, the widespread deployment of unmanned aerial vehicles (UAVs) is rapidly growing in various applications, such as collecting and transmitting data to the ground station, which may be located far from the operational area. The wireless channels may generally degrade due to long distance or line-of-sight obstacles. In this paper, based on the network formation game, a distributed routing algorithm is proposed to maximize considering the achievable data rate, wireless latency and energy consumption comprehensively. To solve this game, each UAV plays its best response in a decentralized manner that a stable network can be achieved. Finally, some comparisons are provided to illustrate the efficiency of the proposed algorithm.
Accurate Classification of Seizure and Seizure-Free Intervals of Intracranial EEG Signals From Epileptic Patients

Electroencephalogram (EEG) signals are widely used to detect epileptic seizures in a patient’s neuronal activity. Since visual inspection and interpretation of EEG signal are time-consuming and prone to errors, various computer-aided diagnostic (CAD) tools have been proposed. In this paper, we present a novel automated detection system to distinguish between intracranial EEG time courses with seizures and those that are seizure-free based on complexity measures. Specifically, the features used to characterize the EEG signals are estimates of multiscaling properties over a large spectrum measured by using the generalized Hurst exponent. We tested the capacity of these estimates to correctly classify seizure intervals using a publicly available data set. Using the k-nearest neighbor classifier and testing with tenfold cross validation, we achieved 100% accurate classification. Our proposed CAD system outperformed the existing state-of-the-art models. Moreover, our CAD system is not only accurate but also fast and simple to implement. Therefore, it can be used as an expert system to support a decision in clinical applications.
High-Degree Feature for Deep Neural Network Based Acoustic Model

In this paper, we propose to use high-degree features to improve the discrimination performance of Deep Neural Network (DNN) based acoustic model. Thanks to the successful posterior probability estimation of DNNs for high-dimensional features, high-dimensional acoustic features are commonly considered in DNN-based acoustic models.Even though it is not clear how DNN-based acoustic models estimate the posterior probability robustly, the use of high-dimensional features is based on a theorem that it helps separability of patters. There is another well-known knowledge that high-degree features increase linear separability of nonlinear input features. However, there is little work to exploit high-degree features explicitly in a DNN-based acoustic model. Therefore, in this work, we investigate high-degree features to improve the performance further.In this work, the proposed approach was evaluated on a Wall Street Journal (WSJ) speech recognition domain. The proposed method achieved up to 21.8% error reduction rate for the Eval92 test set by reducing the word error rate from 4.82% to 3.77% when using degree-2 polynomial expansion.
Research Challenges and Opportunities of UAV Millimeter-Wave Communications

Millimeter-wave (mmWave) communication with multi-gigahertz bandwidth availability enables much higher capacity and transmission rate than conventional microwave communications. However, mmWave signals suffer from propagation-related shortcomings. The flexibility of UAV communication can compensate most of the deficiencies of mmWave signals, preserve its advantages, and provide more opportunities. In this article, we present the current state of the art, research opportunities, and challenges for UAV mmWave communication. We present channel characteristics and show its modeling issues. We further analyze how to achieve UAV mmWave channel acquisition and precoder design. Moreover, we discuss the challenges and possible solutions for UAV mmWave cellular networks, including UAV-to-base-station and UAV-to-user communications and spectrum sharing.
Secrecy Performance Analysis for Hybrid Wiretapping Systems Using Random Matrix Theory

In this paper, we study the secrecy performance in a hybrid wiretapping wireless system, where the half-duplex (HD) or full-duplex (FD) eavesdroppers may wiretap the confidential signal and/or transmit a jamming signal. To evaluate the secrecy performance, we derive the approximate closed-form results for the secrecy outage probability and mean secrecy rate by means of the random matrix theory (RMT). The RMT method can greatly simplify the complicated mathematical analysis with high accuracy, and can provide a useful analytical framework for other researches. The Monte Carlo simulations and numerical results are provided to validate the theoretical analysis and demonstrate the impacts of the system parameters. From the perspective of BS transmission, increasing BS transmission power can greatly improve the secrecy performance in the low transmit power region, but the secrecy performance is constant in the high BS transmit power region. Moreover, in terms of adversaries, FD eavesdroppers have better wiretapping performance than HD eavesdroppers when the jamming power is relatively low; otherwise, this result is reversed.
From the Bottom-Up: Toward Area-Selective Atomic Layer Deposition with High Selectivity†

Bottom-up nanofabrication by area-selective atomic layer deposition (ALD) is currently gaining momentum in semiconductor processing, because of the increasing need for eliminating the edge placement errors of top-down processing. Moreover, area-selective ALD offers new opportunities in many other areas such as the synthesis of catalysts with atomic-level control. This Perspective provides an overview of the current developments in the field of area-selective ALD, discusses the challenge of achieving a high selectivity, and provides a vision for how area-selective ALD processes can be improved. A general cause for the loss of selectivity during deposition is that the character of surfaces on which no deposition should take place changes when it is exposed to the ALD chemistry. A solution is to implement correction steps during ALD involving for example surface functionalization or selective etching. This leads to the development of advanced ALD cycles by combining conventional two-step ALD cycles with correction steps in multistep cycle and/or supercycle recipes.
Intestine‐Specific Overexpression of Carboxylesterase 2c Protects Mice From Diet‐Induced Liver Steatosis and Obesity

Murine hepatic carboxylesterase 2c (Ces2c) and the presumed human ortholog carboxylesterase 2 (CES2) have been implicated in the development of nonalcoholic fatty liver disease (NAFLD) in mice and obese humans. These studies demonstrated that Ces2c hydrolyzes triglycerides (TGs) in hepatocytes. Interestingly, Ces2c/CES2 is most abundantly expressed in the intestine, indicating a role of Ces2c/CES2 in intestinal TG metabolism. Here we show that Ces2c is an important enzyme in intestinal lipid metabolism in mice. Intestine‐specific Ces2c overexpression (Ces2cint) provoked increased fatty acid oxidation (FAO) in the small intestine accompanied by enhanced chylomicron clearance from the circulation. As a consequence, high‐fat diet–fed Ces2cint mice were resistant to excessive diet‐induced weight gain and adipose tissue expansion. Notably, intestinal Ces2c overexpression increased hepatic insulin sensitivity and protected mice from NAFLD development. Although lipid absorption was not affected in Ces2cint mice, fecal energy content was significantly increased. Mechanistically, we demonstrate that Ces2c is a potent neutral lipase, which efficiently hydrolyzes TGs and diglycerides (DGs) in the small intestine, thereby generating fatty acids (FAs) for FAO and monoglycerides (MGs) and DGs for potential re‐esterification. Consequently, the increased availability of MGs and DGs for re‐esterification and primordial apolipoprotein B48 particle lipidation may increase chylomicron size, ultimately mediating more efficient chylomicron clearance from the circulation. Conclusion: This study suggests a critical role for Ces2c in intestinal lipid metabolism and highlights the importance of intestinal lipolysis to protect mice from the development of hepatic insulin resistance, NAFLD, and excessive diet‐induced weight gain during metabolic stress.
Compact C-band Rectenna for Satellite Applications

This paper addresses the design and the characterization of a compact C-band rectenna for satellite applications. The proposed flat dipole rectenna achieves an efficiency of 58.2% at 3.25 GHz when illuminated with an electromagnetic power density of only $33.9 \mu \mathrm{W} /$cm $^{2}$. The proposed rectenna is also compact; its size is 1334 $mm^{2}$ that is only 16% of the square wavelength at 3.25 GHz.
Impact of Small Cells Overlapping on Mobility Management

The mobility management will be more complex and will have a great impact on the quality of service in the future cellular networks, as these networks will have to handle a huge number of user equipments (UEs) and their frequent hand offs due to very dense short-footage small cells. This paper presents a framework to model and derive the coverage of small cells, the cell sojourn time, and the hand off rate in multi-tier small cell networks. The distribution of the small cells around a reference UE’s path is studied by taking into consideration the overlaps among the small cells. Two types of hand off rates are introduced to estimate the load managed by different cells, where inter-frequency hand off rate and intra-frequency hand off rate represent the fraction of hand offs managed by the first tier and the other tiers, respectively. Our analysis shows that ignoring the overlaps among the small cells affects the accuracy of the results significantly. The simulation results validate the accuracy of the analytical results and also show the impact of different parameters, such as the small cell density, the number of tiers and the size of the small cells on the small cell sojourn time, the macro cell sojourn time, and the hand off rate.
Multichannel ASR with Knowledge Distillation and Generalized Cross Correlation Feature

Multi-channel signal processing techniques have played an important role in the far-field automatic speech recognition (ASR) as the separate front-end enhancement part. However, they often meet the mismatch problem. In this paper, we proposed a novel architecture of acoustic model, in which the multi-channel speech without preprocessing was utilized directly. Besides the strategy of knowledge distillation and the generalized cross correlation (GCC) adaptation were employed. We use knowledge distillation to transfer knowledge from a well-trained close-talking model to distant-talking scenarios in every frame of the multichannel distant speech. Moreover, the GCC between microphones, which contains the spatial information, is supplied as an auxiliary input to the neural network. We observe good compensation of those two techniques. Evaluated with the AMI and ICSI meeting corpora, the proposed methods achieve relative WER improvement of 7.7% and 7.5% over the model trained directly on the concatenated multi-channel speech.
Generating Semantic Similarity Atlas for Natural Languages

Cross-lingual studies attract a growing interest in natural language processing (NLP) research, and several studies showed that similar languages are more advantageous to work with than fundamentally different languages in transferring knowledge. Different similarity measures for the languages are proposed by researchers from different domains. However, a similarity measure focusing on semantic structures of languages can be useful for selecting pairs or groups of languages to work with, especially for the tasks requiring semantic knowledge such as sentiment analysis or word sense disambiguation. For this purpose, in this work, we leverage a recently proposed word embedding based method to generate a language similarity atlas for 76 different languages around the world. This atlas can help researchers select similar language pairs or groups in cross-lingual applications. Our findings suggest that semantic similarity between two languages is strongly correlated with the geographic proximity of the countries in which they are used.
Improved Auto-Marking Confidence for Spoken Language Assessment

Automatic assessment of spoken language proficiency is a sought-after technology. These systems often need to handle the operating scenario where candidates have a skill level or first language which was not encountered during the training stage. For high stakes tests it is necessary for those systems to have good grading performance when the candidate is from the same population as those contained in the training set, and they should know when they are likely to perform badly in the case when the candidate is not from the same population as the ones contained in training set. This paper focuses on using Deep Density Networks to yield auto-marking confidence. Firstly, we explore the benefits of parametrising either a predictive distribution or a posterior distribution over the parameters of the model likelihood and obtaining the predictive distribution via marginalisation. Secondly, we investigate how it is possible to act on the parametrised density in order to explicitly teach the model to have low confidence in areas of the observation space where there is no training data by assigning confidence scores to artificially generated data. Lastly, we compare the capabilities of Factor Analysis, Variational Auto-Encodes, and Wasserstein Generative Adversarial Networks to generate artificial data.
Distributed constrained consensus of multi-vehicle systems with external disturbances

In this paper, the nonconvex constrained consensus problem for a second-order multi-vehicle system with external disturbances is addressed by proposing a distributed control algorithm based on local information. First, the initial closed-loop system is turned into a reduced-order system which is convenient for the subsequent calculation. In the presence of external disturbances, it is proved that the nonconvex constrained consensus can be achieved if the presented sufficient condition is satisfied.
Investigating the Downstream Impact of Grapheme-Based Acoustic Modeling on Spoken Utterance Classification

Automatic speech recognition (ASR) and natural language understanding are critical components of spoken language understanding (SLU) systems. One obstacle to providing services with SLU systems in multiple languages is the cost associated with acquiring all of the language-specific resources required for ASR in each language. Modeling graphemes eliminates the need to obtain a pronunciation dictionary which maps from speech sounds to words and is one way to reduce ASR resource dependencies when rapidly developing ASR in new languages. However, little is known about the downstream impact on SLU task performance when selecting graphemes as the acoustic modeling unit. This work investigates acoustic modeling for the ASR component of an SLU system using grapheme-based approaches together with convolutional and recurrent neural network architectures. We evaluate both ASR word accuracy and spoken utterance classification (SUC) accuracy for English, Italian and Spanish language tasks and find that it is possible to achieve SUC accuracy that is comparable to conventional phoneme-based systems which leverage a pronunciation dictionary.
Attribute-based Person Retrieval and Search in Video Sequences

The search for persons based on their visual appearance is an important task of modern surveillance systems which can be supported by automatic person re-identification approaches. However, such approaches are generally image-based and thus require a query image as input. In cases where only a witness description is available the task turns into a cross-modal text-to-image search problem which requires specialized approaches. In this work we describe an approach for person search in video data based purely on attribute witness descriptions. We first develop an ensemble of classifiers for robust attribute classification. We then extend the approach to full person search by combining it with a person detector. Given an initial high-confidence match in the video we use temporal information to explore and return full person tracks. We evaluate our approach on the AVSS 2018 Soft Biometric Retrieval Challenge dataset. Our approach manages to find the correct person at rank-1 in 71.79% of all cases.
Can Machines Learn to Comprehend Scientific Literature?

To measure the ability of a machine to understand professional-level scientific articles, we construct a scientific question answering task called PaperQA. The PaperQA task is based on more than 80 000 “fill-in-the-blank” type questions on articles from reputed scientific journals such as Nature and Science. We perform fine-grained linguistic analysis and evaluation to compare PaperQA and other conventional question and answering (QA) tasks on general literature (e.g., books, news articles, and Wikipedia texts). The results indicate that the PaperQA task is the most difficult QA task for both humans (lay people) and machines (deep-learning models). Moreover, humans generally outperform machines in conventional QA tasks, but we found that advanced deep-learning models outperform humans by 3%–13% on average in the PaperQA task. The PaperQA dataset used in this paper is publicly available at http://dmis.korea. ac.kr/downloads? id=PaperQA.
Performance of LTE-A Heterogeneous Network using NHFR Method

To overcome the interference problem in LTE/ LTE-A networks and in cellular networks in general, the frequency reuse technique is adopted. Interference is a common and big problem in cellular networks, especially in LTE-A due to the use of heterogeneous networks. Different frequency reuse methods such as soft frequency reuse, fractional frequency reuse and Reuse 3 are examples of the frequency reuse technique. In this paper, the NHFR (New Hybrid Frequency Reuse) method is used to mitigate interference in heterogeneous networks and furthermore the performance of such networks is investigated. Analytical solutions are obtained and different performance metrics are calculated. Also, simulations are performed by using the Vienna simulator and comparison with other methods is done. Both analytical and simulation results show that the NHFR method outperforms all the other methods.
An Experimental Study on Audio Replay Attack Detection Using Deep Neural Networks

Automatic speaker verification (ASV) systems can be easily spoofed by previously recorded speech, synthesized speech and speech signal that artificially generated by voice conversion techniques. In order to increase the reliability of the ASV systems, detecting spoofing attacks whether a given speech signal is genuine or spoofed plays an important role. In this paper, we consider the detection of replay attacks which is the most accessible attack type against ASV systems. To this end, we utilize a deep neural network (DNN) based classifier using features extracted from the long-term average spectrum. The experiments are conducted on the latest edition of Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017) database. The results are compared with the ASVspoof 2017 baseline system which consists of Gaussian mixture model (GMM) classifier with constant-Q transform cepstral coefficients (CQCC) front-end as well as the GMM with standard mel-frequency cepstrum coefficients (MFCC) features. Experimental results reveal that DNN considerably outperforms the well-known and successful GMM classifier. It is found that long term average spectrum (LTAS) based features are superior to CQCC and MFCC in terms of equal error rate (EER). Finally, we find that high-frequency components convey much more discriminative information for replay attack detection independent of features and classifiers.
Measurement of 2D:4D Ratio and Neck Circumference in Adolescents: Sexual Dimorphism and its Implications in Obesity – A Cross Sectional Study

Introduction: A bidirectional relationship between testosterone and obesity is explained by the hypogonadal obesity cycle and evidence from reports stating that weight loss leads to increased testosterone levels. There is an alarming rise in the prevalence of obesity among children and adolescents. The objectives of the present study were to measure the 2D:4D ratio of adolescent students and study its association with neck circumference (NC). Materials and Methods: After obtaining ethical clearance, the study was conducted on 168 adolescents pursuing their undergraduate education in a South Indian university. 2D:4D ratio and NC were measured using Digital Vernier Calipers and plastic inch tape, respectively. All the participants were divided into three groups (normal, overweight, and obese) on the basis of their BMI. Mean 2D:4D ratio and NC were compared between the three groups using one-way ANOVA. Results: Mean right and left hand ratios of the study population were 0.973 ± 0.030 and 0.975 ± 0.069, respectively. Comparison of 2D:4D ratios between the sexes revealed statistical significance (males = 0.966, females = 0.977, and P value = 0.019). There was no significant correlation between 2D:4D ratios and BMI. There was a significant negative correlation between NC and 2D:4D ratios of the individuals with normal BMI. However, no statistically significant correlation between NC and 2D:4D ratios was observed in overweight and obese individuals. Conclusion: 2D:4D ratio and NC could be used as simple measures for screening of people at higher risk for heart disease and metabolic syndrome. However, studies on a larger sample might help us reveal the association between NC and 2D:4D ratios.