Miracle-dz/UniFAR-FTUs huggingface dataset:
Knot Magnify Loss for Face Recognition

Deep Convolutional Neural Netowrks (DCNN) have significantly improved the performance of face recognition in recent years. Softmax loss is the most widely used loss function for training the DCNN-based face recognition system. It gives the same weights to easy and hard samples in one batch, which would leads to performance gap on the quality imbalanced data. In this paper, we discover that the rare hard samples in the training dataset has become a main obstacle for training a robust face recognition model. We propose to address this problem by a new supervisor signal that pays more attention to the rare hard samples and reduces the effects of the easy samples relatively. Our proposed novel Knot Magnify (KM) loss modulates the classical softmax loss to suppress the influence of easy samples and up-weight the loss of hard samples during training. Our results show that after training with KM loss, face recognition model is able to get competing accuracy on the well-known face recognition benchmark LFW dataset and the challenging CFP dataset.
LOS MIMO Design Based on Multiple Optimum Antenna Separations

The use of multiple antennas in a transmit and receive antenna array for MIMO wireless communication allows the spatial degrees of freedom in rich scattering environments to be exploited. However, for line-of-sight (LOS) MIMO channels with uniform linear arrays (ULAs) at the transmitter and receiver, the antenna separations at the transmit and receive array need to be optimized to maximize the spatial degrees of freedom and the channel capacity. In this paper, we first revisit the derivation of the optimum antenna separation at the transmit and receive ULAs in a LOS MIMO system, and provide the general expression for the optimum antenna separation product, which consists of multiple solutions. Although only the solution corresponding to the smallest antenna separation product is usually considered in the literature, we exploit the multiple solutions for a LOS MIMO design over a range of distances between the transmitter and receiver. In particular, we consider the LOS MIMO design in a vehicle-to-vehicle (V2V) communication scenario, over a range of distances between the transmit and receive vehicle.
Selenium-Related Transcriptional Regulation of Gene Expression

The selenium content of the body is known to control the expression levels of numerous genes, both so-called selenoproteins and non-selenoproteins. Selenium is a trace element essential to human health, and its deficiency is related to, for instance, cardiovascular and myodegenerative diseases, infertility and osteochondropathy called Kashin–Beck disease. It is incorporated as selenocysteine to the selenoproteins, which protect against reactive oxygen and nitrogen species. They also participate in the activation of the thyroid hormone, and play a role in immune system functioning. The synthesis and incorporation of selenocysteine occurs via a special mechanism, which differs from the one used for standard amino acids. The codon for selenocysteine is a regular in-frame stop codon, which can be passed by a specific complex machinery participating in translation elongation and termination. This includes a presence of selenocysteine insertion sequence (SECIS) in the 3′-untranslated part of the selenoprotein mRNAs. Nonsense-mediated decay is involved in the regulation of the selenoprotein mRNA levels, but other mechanisms are also possible. Recent transcriptional analyses of messenger RNAs, microRNAs and long non-coding RNAs combined with proteomic data of samples from Keshan and Kashin–Beck disease patients have identified new possible cellular pathways related to transcriptional regulation by selenium.
A Heartbeat Away From Consciousness: Heart Rate Variability Entropy Can Discriminate Disorders of Consciousness and Is Correlated With Resting-State fMRI Brain Connectivity of the Central Autonomic Network

Background: Disorders of consciousness are challenging to diagnose, with inconsistent behavioral responses, motor and cognitive disabilities, leading to approximately 40% misdiagnoses. Heart rate variability (HRV) reflects the complexity of the heart-brain two-way dynamic interactions. HRV entropy analysis quantifies the unpredictability and complexity of the heart rate beats intervals. We here investigate the complexity index (CI), a score of HRV complexity by aggregating the non-linear multi-scale entropies over a range of time scales, and its discriminative power in chronic patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), and its relation to brain functional connectivity. Methods: We investigated the CI in short (CIs) and long (CIl) time scales in 14 UWS and 16 MCS sedated. CI for MCS and UWS groups were compared using a Mann-Whitney exact test. Spearman's correlation tests were conducted between the Coma Recovery Scale-revised (CRS-R) and both CI. Discriminative power of both CI was assessed with One-R machine learning model. Correlation between CI and brain connectivity (detected with functional magnetic resonance imagery using seed-based and hypothesis-free intrinsic connectivity) was investigated using a linear regression in a subgroup of 10 UWS and 11 MCS patients with sufficient image quality. Results: Higher CIs and CIl values were observed in MCS compared to UWS. Positive correlations were found between CRS-R and both CI. The One-R classifier selected CIl as the best discriminator between UWS and MCS with 90% accuracy, 7% false positive and 13% false negative rates after a 10-fold cross-validation test. Positive correlations were observed between both CI and the recovery of functional connectivity of brain areas belonging to the central autonomic networks (CAN). Conclusion: CI of MCS compared to UWS patients has high discriminative power and low false negative rate at one third of the estimated human assessors' misdiagnosis, providing an easy, inexpensive and non-invasive diagnostic tool. CI reflects functional connectivity changes in the CAN, suggesting that CI can provide an indirect way to screen and monitor connectivity changes in this neural system. Future studies should assess the extent of CI's predictive power in a larger cohort of patients and prognostic power in acute patients.
Frequency Domain Linear Prediction Features for Replay Spoofing Attack Detection

Automatic speaker verification (ASV) systems are vulnerable to various types of spoofing attacks such as speech synthesis, voice conversion and replay attacks. Recent research has highlighted the need for more effective countermeasures for replay attacks, which can be very challenging to detect, however replayed speech has previously shown frequency band-specific differences when compared with genuine speech. In this paper, we propose the use of long-term temporal envelopes of subband signals using a frequency domain linear prediction (FDLP) framework. This flexible framework makes use of temporal envelope information, which has not previously been investigated for replay spoofing detection. Evaluations of the proposed system and its fusion with other subsystems were carried out on the ASVspoof 2017 database. Interestingly, smoother temporal envelopes, based on very long windows of up to 1 second, seem to be most successful and show good prospects for performance improvements via fusion.
Convolutional Neural Networks for Visual Information Analysis with Limited Computing Resources

Over the past decade, Deep Convolutional Neural Networks with heavy architectures and large numbers of parameters have achieved state-of-the-art results and eclipsed other methods in multiple visual analysis tasks, including object detection. However, the real-time requirements of such tasks directly conflict with the restricted computational capabilities of embedded systems, prohibiting the immediate deployment of bulky models, and necessitating their optimization for inference. Parameter pruning techniques reduce the number of parameters while reducing the input size leads to smaller internal representations, leading by extension to fewer computational operations. Furthermore, inference optimization schemes provided by Deep Learning frameworks can yield significant speed ups, for example by allowing half-precision floating point operations. We investigate the behavior of various model configurations in object detection tasks and perform a comparative study on inference optimization methods which aim to reduce the computational cost of Convolutional Neural Networks, while examining the effect of such methods on their performance, and propose architecture modifications for this purpose.
Depth Human Action Recognition Based on Convolution Neural Networks and Principal Component Analysis

In this work, we address human action recognition problem under viewpoint variation. The proposed model is formulated by wisely combining convolution neural network (CNN) model with principle component analysis (PCA). In this context, we pass real depth videos through a CNN model in a frame-wise manner. The view invariant features are extracted by employing convolution layers as mid-outputs and considered as 3D nonnegative tensors. The PCA algorithm is separately imposed on view-invariant high-level space of image and video groups to seek both local and holistic hidden dynamics information. To deal with noisy data and temporal misalignment, we utilize the Fourier temporal pyramid to encode temporal and obtain the final descriptors. Our proposed framework supplies a robust discriminative representation with low dimension and computational requirements. We evaluate the proposed method on two standard multiview depth video datasets. The experimental results show that our method has superior performance compared to other approaches.
Computational Studies in Drug Design against Cancer.

BACKGROUND The application of in silico tools in the development of anti cancer drugs. Objective: The summing of different computer aided drug design approaches that have been applied in the development of anti cancer drugs. Method: Structure based, ligand based, hybrid protein-ligand pharmacophore methods, Homology modeling, molecular docking aids in different steps of drug discovery pipeline with considerable saving in time and expenditure. In silico tools also finds applications in the domain of cancer drug development. Results: Structure-based pharmacophore modeling aided in the identification of PUMA inhibitors, structure based approach with high throughput screening for the development of Bcl-2 inhibitors, to derive the most relevant protein-protein interactions, anti mitotic agents; I-Kappa-B Kinase β (IKK- β) inhibitor, screening of new class of aromatase inhibitors that can be important targets in cancer therapy. Conclusion: Application of computational methods in the design of anti cancer drugs was found to be effective.
Crowd Counting with Fully Convolutional Neural Network

Crowd counting estimation is an extremely challenging task due to various crowded scenarios. In this paper, we present a deep learning framework for crowd counting from a single static image with different number of people and arbitrary perspective. In the design of convolutional neural network structure, we employ the VGG16 model but drop the fully connected layers. Meanwhile, high-level features are combined with low-level features through laterally connected feature pyramid network by element-wise addition to ensure higher resolution and more context information. Extensive experiments are conducted on ShanghaiTech and UCF_CC_50 datasets. The results show that our model achieves the lowest mean absolute error (MAE) and comparable mean square error (MSE), and outperforms the current state-of-the-art methods.
Total Flavonoids in Caragana (TFC) Promotes Angiogenesis and Enhances Cerebral Perfusion in a Rat Model of Ischemic Stroke

Previous studies have demonstrated that total flavonoid extracts from Caragana sinica (TFC) exert multiple therapeutic effects, promote blood flow, and exhibit anti-inflammatory and antioxidant properties. The present study aimed to investigate whether TFC promotes angiogenesis and exerts neuroprotective effects in a rat model of transient middle cerebral artery occlusion (tMCAO). Male Wistar rats were subjected to tMCAO for 1.5 h, followed by 24 h of reperfusion. TFC (15, 30, 60 mg/kg) was administered for 14 days. Evaluations of neurological function were performed following reperfusion, and infarct volumes were assessed in brain slices stained with 2,3,5-triphenyltetrazolium chloride (TTC). Our results indicated that TFC significantly attenuated cerebral infarct volume and neurological deficits following tMCAO. Laser Doppler, micro-PET/CT, and MRI analyses further demonstrated that TFC reduced infarct volume and enhanced cerebral blood flow in a dose-dependent manner, with the most significant effects occurring at a concentration of 60 mg/kg. Significant up-regulation of CD31, VEGF, Ang-1, HIF-1α, delta-like 4 (Dll4), and Notch1 expression was also observed in the experimental groups, relative to that in the vehicle group. In summary, the results of the present study indicate that TFC (15, 30, 60 mg/kg) attenuates neurological deficits, reduces infarct volume, and promotes angiogenesis following MCAO in a concentration-dependent manner, likely via increases in the expression of CD31, VEGF, Ang-1, HIF-1α, Dll4, and Notch1. Further studies are required to determine the clinical usefulness and potential mechanisms of TFC in patients with cerebral focal ischemic stroke.
Strategies to improve adherence to antiretroviral therapy and retention in care for people living with HIV in high-income countries: a protocol for an overview of systematic reviews

Introduction While access to antiretroviral therapy (ART) for people living with HIV has expanded in recent years, additional efforts are required to support adherence to medication and retention in care. Interventions should be applicable in real-world settings and amenable to widespread use. The objectives of this overview are to identify effective pragmatic interventions that increase adherence to ART and retention in care for people living with HIV at high risk for suboptimal adherence and retention in high-income countries. Methods and analysis We will conduct an overview of systematic reviews of studies on interventions which target improved adherence to medication and retention in care among high-risk people living with HIV in high-income countries (men who have sex with men, African, Caribbean and black people, sex workers, people who inject drugs, indigenous people and other socially marginalised groups). We will search the following databases: PubMed, EMBASE (Exerpta Medica Database), CINAHL (Cumulative Index to Nursing and Allied Health Literature), PsycINFO, Web of Science and the Cochrane Library. We will conduct screening, data extraction and assessment of methodological quality of the systematic reviews. Analysis will be narrative. Our findings will be interpreted in light of the certainty of the evidence, level of pragmatism, setting and population of interest. Ethics and dissemination Only published secondary data will be used in this study, and therefore ethics approval is not required. Our findings will be disseminated as peer-reviewed manuscripts, conference abstracts and through community activities. The findings from this overview will inform a mixed-methods study among people living with HIV and health workers in Ontario, Canada.
Gradient Based Evolution to Optimize the Structure of Convolutional Neural Networks

Due to decreasing hardware prices, machine learning is becoming increasingly interesting for industrial applications such as automatic visual inspection (AVI). This paper presents a metaheuristic approach to the automatic generation of a well suited convolutional neural network (CNN) based on differential evolution. This makes it possible to find a suitable architecture of a CNN for a given task with little prior knowledge. Another aim is to reduce the resources needed in the inference as much as possible. Therefore, we choose a function that considers both the accuracy and the resources used to measure the fitness of a CNN. For typical industrial datasets, we obtain CNNs with an accuracy of more than 98 % on average within relatively short processing time.
Exploiting Speaker and Phonetic Diversity of Mismatched Language Resources for Unsupervised Subword Modeling

This study addresses the problem of learning robust frame-level feature representation for unsupervised subword modeling in the zero-resource scenario. Robustness of the learned features is achieved through effective speaker adaptation and exploiting cross-lingual phonetic knowledge. For speaker adaptation, an out-of-domain automatic speech recognition (ASR) system is used to estimate fMLLR features for untranscribed speech of target zero-resource languages. The fMLLR features are applied in multi-task learning of a deep neural network (DNN) to further obtain phonetically discriminative and speaker-invariant bottleneck features (BNFs). Frame-level labels for DNN training can be acquired based on two approaches: Dirichlet process Gaussian mixture model (DPGMM) clustering, and out-of-domain ASR decoding. Moreover, system fusion is performed by concatenating BNFs extracted by different DNNs. Our methods are evaluated by ZeroSpeech 2017 Track one, where the performance is evaluated by ABX minimal pair discriminability. Experimental results demonstrate that: (1) Using an out-of-domain ASR system to perform speaker adaptation of zero-resource speech is effective and efﬁcient; (2) Our system achieves highly competitive performance to state of the art; (3) System fusion could improve feature representation capability.
Emotional Prosody Perception in Mandarin-speaking Congenital Amusics

Congenital amusia, which is a neurogenetic disorder affecting musical pitch processing, was found recently to affect not only human speech perception, but also emotional perception. Since previous studies only examined participants with non-tonal languages, they cannot easily generalize the finding to people with tonal language background, due to the fact that those people utilize pitch cues much more heavily in daily communication compared with others. To make clear the doubt, this paper investigates emotional prosody perception of Mandarin speakers with congenital amusia. We tried to recruit 19 amusics and matched control group of similar number of normal speakers, and carried out emotional perception experiments in which speech and non-speech stimuli with six kinds of emotions were used, including happy, sad, fear, angry, surprise, and neutral. Results showed that the amusics performed significantly worse than matched controls. This indicated that tone-language expertise cannot compensate for pitch deficits in amusia for emotional perception. Further analyses demonstrated that there was a positive correlation between emotion prosody performance and pitch perceptional ability. These findings further support previous hypothesis that music and language share cognitive and neural resources, and provide a new perspective on the proposition of the relation between music and language.
Social Signal Processing

Social Signal Processing (SSP) is the new cross-disciplinary research domain that aims at understanding and modelling social interactions (research in human-sciences) and at providing computers with similar abilities (research in computer science). SSP is still in its formative phase, and the journey towards artificial social intelligence and socially-aware computing is still long. This chapter surveys the current state of the art and summarizes issues that the researchers in this field face.
Resveratrol: A Double-Edged Sword in Health Benefits

Resveratrol (3,5,4′-trihydroxy-trans-stilbene) belongs to polyphenols’ stilbenoids group, possessing two phenol rings linked to each other by an ethylene bridge. This natural polyphenol has been detected in more than 70 plant species, especially in grapes’ skin and seeds, and was found in discrete amounts in red wines and various human foods. It is a phytoalexin that acts against pathogens, including bacteria and fungi. As a natural food ingredient, numerous studies have demonstrated that resveratrol possesses a very high antioxidant potential. Resveratrol also exhibit antitumor activity, and is considered a potential candidate for prevention and treatment of several types of cancer. Indeed, resveratrol anticancer properties have been confirmed by many in vitro and in vivo studies, which shows that resveratrol is able to inhibit all carcinogenesis stages (e.g., initiation, promotion and progression). Even more, other bioactive effects, namely as anti-inflammatory, anticarcinogenic, cardioprotective, vasorelaxant, phytoestrogenic and neuroprotective have also been reported. Nonetheless, resveratrol application is still being a major challenge for pharmaceutical industry, due to its poor solubility and bioavailability, as well as adverse effects. In this sense, this review summarized current data on resveratrol pharmacological effects.
Alzheimer’s Disease and Rheumatoid Arthritis: A Mendelian Randomization Study

Alzheimer’s disease (AD) is the most common neurodegenerative disease. In recent years, multiple pathway analyses of AD genome-wide association studies (GWAS) have been conducted, and provided strong support for immune pathways in AD. Rheumatoid arthritis (RA) is a chronic autoimmune disease. It is reported that antirheumatic drugs had protective effect on dementia in RA patients. However, observational studies have reported a controversial inverse relationship between AD and RA. In addition, Mendelian randomization studies have also been performed to evaluate the association of RA with AD. However, these studies reported inconsistent association of RA with AD. Until now, it is still unclear that AD is a causally associated with RA. Here, we performed a Mendelian randomization study to investigate the causal association of AD with RA. We analyzed the large-scale AD GWAS dataset (74,046 individuals) and RA GWAS dataset (58,284 individuals) from the European descent. However, we did not identify any significant association of AD with RA using inverse-variance weighted meta-analysis (IVW), weighted median regression and MR-Egger regression.
Dereverberation and Beamforming in Robust Far-Field Speaker Recognition

This paper deals with robust speaker veriﬁcation (SV) in far-ﬁeld sensing. The robustness is veriﬁed on a subset of NIST SRE 2010 corpus retransmitted in multiple real rooms of different acoustics and captured with multiple microphones. We experimented with various data preprocessing steps including different approaches to dereverberation and beamforming applied to ad-hoc microphone arrays. We found that signiﬁcant improvements in accuracy can be achieved with neural network based generalized eigenvalue beamformer preceded by weighted prediction error dereverberation. We also explored the effect of data augmentation by adding various real or simulated room acoustic properties to the Probabilistic Linear Discriminant Analysis (PLDA) training dataset. As a result, we developed a speaker recognition system whose performance is stable across different room acoustic conditions. It yields 41.4% relative improvement in performance over the system without multi-channel processing tested on the cleanest micro-phone data. With the best combination of data preprocessing and augmentation, we obtained a performance close to the one we achieved with the original clean test data.