Miracle-dz/UniFAR-FTUs huggingface dataset:
Rehabilitation of Traumatic Brain Injured Patients: Patient Mood Analysis from Multimodal Video

Rehabilitation after traumatic brain injury (TBI) is very critical as it is largely unpredictable depending upon the nature of the injury. Rehabilitation process and recovery time also varies, as it takes months and years, depending upon the assessment of treatment, mental and physical conditions and strategies. Due to non-cooperative behaviour of patients, and increase in negative emotional expressions it is very beneficial to evaluate these expressions in a contactless way, and perform a rehabilitation physiotherapy, cognitive or other behavioral activities when the patient is in a positive mood. In this paper we have analyzed the methods for facial features extraction for TBI patients to determine optimal time to have aforementioned rehabilitation process on the basis of positive and negative facial expressions. We have employed a deep learning architecture based on convolutional neural network and long short term memory on RGB and thermal data that were collected in challenging scenarios from real patients. It automatically identifies the patient's facial expressions, and inform experts or trainers that “it is the time” to start rehabilitation session.
Point Spread and Illumination Analysis of Lightfield Cameras

A full detail model of the point spread function (PSF) is presented for the complete optical system of aberration-free lightfield cameras with low to moderate numerical apertures. This serves as an applicable model for both plenoptic 1.0 and 2.0 systems with an arbitrarily defocused main lens. New analytical expressions for the microlens pupil distributions are formulated in terms of the main lens defocus as a function of object depth and microlens array to main lens distance, with further analytical treatment for the computation of the lightfield PSF over state of the art numerical methods. Simulations validate the defocus parameter in full accordance with geometrical optics and the sensor PSF for a circle of confusion centred on the microlenses is shown to be consistent with classical diffraction theory.
Incremental TTS for Japanese Language

Simultaneous lecture translation requires speech to be translated in real time before the speaker has spoken an entire sentence since a long delay will create difficulties for the listeners trying to follow the lecture. The challenge is to construct a full-fledged system with speech recognition, machine translation, and textto-speech synthesis (TTS) components that could produce highquality speech translations on the fly. Specifically for a TTS, this poses problems as a conventional framework commonly requires the language-dependent contextual linguistics of a full sentence to produce a natural-sounding speech waveform. Several studies have proposed ways for an incremental TTS (ITTS), in which it can estimate the target prosody from only partial knowledge of the sentence. However, most investigations are being done only in French, English, and German. French is a syllable-timed language and the others are stress-timed languages. The Japanese language, which is a mora-timed language, has not been investigated so far. In this paper, we evaluate the quality of Japanese synthesized speech based on various linguistic and temporal incremental units. Experimental results reveal that an accent phrase incremental unit (a group of moras) is essential for a Japanese ITTS as a trade-off between quality and synthesis units.
Avoiding Speaker Overfitting in End-to-End DNNs Using Raw Waveform for Text-Independent Speaker Verification

In this research, we propose a novel raw waveform end-to-end DNNs for text-independent speaker veriﬁcation. For speaker veriﬁcation, many studies utilize the speaker embedding scheme, which trains deep neural networks as speaker identiﬁers to extract speaker features. However, this scheme has an intrinsic limitation in which the speaker feature, trained to classify only known speakers, is required to represent the identity of unknown speakers. Owing to this mismatch, speaker embedding systems tend to well generalize towards unseen utterances from known speakers, but are overﬁtted to known speakers. This phenomenon is referred to as speaker overﬁtting . In this paper, we investigated regularization techniques, a multi-step training scheme, and a residual connection with pooling layers in the perspective of mitigating speaker overﬁtting which lead to considerable performance improvements. Technique effectiveness is evaluated using the VoxCeleb dataset, which comprises over 1,200 speakers from various uncontrolled environments. To the best of our knowledge, we are the ﬁrst to verify the success of end-to-end DNNs directly using raw waveforms in text-independent scenario. It shows an equal error rate of 7.4%, which is lower than i-vector/probabilistic linear discriminant analysis and end-to-end DNNs that use spectrograms.
Potential roles of school administrators and community-residing adults in tobacco use prevention in Nigerian college students: a viewpoint

Tobacco use is a key risk factor for the development of non-communicable diseases such as coronary heart disease, stroke, and peripheral vascular disease. On May 31 every year, World No Tobacco Day is celebrated to draw attention to the health risks and other risks related to tobacco use and to campaign for effective policies to minimize tobacco use. In this paper, we address important issues related to tobacco use, and highlight the potential roles of school administrators and community-residing adults in supporting college students in Nigeria in refraining from tobacco use. We argue that various stakeholders, including school administrators and community-residing adults, have important roles to play in providing community- and school-level support to college students to enable them to refrain from tobacco use. However, research is needed to empirically measure whether and how school administrators and community-residing adults have helped to reduce tobacco use in college students in Nigeria.
SHPD: Surveillance Human Pose Dataset and Performance Evaluation for Coarse-Grained Pose Estimation

Pose estimation is highly valued in surveillance systems in the era of big data. However, current human pose datasets are limited in their coverage of the pose estimation challenges in outdoor surveillance scenarios. In this paper, we introduce a novel Surveillance Human Pose Dataset (SHPD). Unlike the existing fine-grained parts or key-points based human pose datasets, SHPD is built for two aims: 1) constructing a more specialized human pose benchmark for surveillance tasks, and 2) focusing on coarse-grained global-pose estimation for small scale human objects, which are the most common targets in practical outdoor surveillance applications. The collected images in SHPD are all from on-using surveillance cameras and capture people from a wide and balanced range of outdoor scenarios. A wide variety of surveillance human global poses and their corresponding rich attributes are also provided. Based on SHPD, performance evaluation of global-pose estimation using a few baseline deep-learning networks indicates that, there are ample room for improvement of the recognition accuracy.
Effectiveness of Generative Adversarial Network for Non-Audible Murmur-to-Whisper Speech Conversion

The murmur produced by the speaker and captured by the Non-Audible Murmur (NAM)-one of the Silent Speech Interface (SSI) technique, suffers from the speech quality degradation. This is due to the lack of radiation effect at the lips and lowpass nature of the soft tissue, which attenuates the high frequency-related information. In this work, a novel method for NAM-to-Whisper (NAM2WHSP) speech conversion incorporating Generative Adversarial Network (GAN) is proposed. The GAN minimizes the distributional divergence between the whispered speech and the generated speech parameters (through adversarial optimization). The objective and subjective evaluation performed on the proposed system, justiﬁes the ability of adversarial optimization over Maximum Likelihood (ML)-based optimization networks, such as a Deep Neural Network (DNN), in preserving and improving the speech quality and intelligibility. The adversarial optimization learns the mapping function with 54.2 % relative improvement in MOS and 29.83 % absolute reduction in % WER w.r. t. the state-of-the-art mapping techniques. Furthermore, we evaluated the proposed framework by analyzing the level of contextual information and the number of training utterances required for optimizing the network parameters, for the given task and database.
Cooperative Hybrid Digital-Analog Video Transmission in D2D Networks

In this paper, we propose a cooperative video transmission scheme in D2D networks. This research is motivated by the growing interests in hybrid digital-analog video transmissions and device-to-device (D2D) communications. The framework of D2D communications can be generally modeled as a three-node network. In this network, coset coding is used to allow the destination to exploit the correlations between the video signals received in two phases. We have done some work of further optimization to improve the video quality at destination in this network. First, we derive a closed form of the reconstruction error at the destination. This provides a theoretical foundation for finding the optimal quantization step size in coset coding. Then, based on the accurate analysis on the coset coding we design a new power allocation algorithm. Experimental results verify that our scheme outperforms the recently proposed WCVC and DCVC.
Numerische Simulation kavitierender Strömungen in hydraulischen Strömungsmaschinen

Es wird ein numerisches Verfahren zur Berechnung kavitierender Stromungen in hydraulischen Stromungsmaschinen vorgestellt. Die Kavitation wird mit einem blasendynamischen Kavitationsmodell beschrieben, das in ein Navier-Stokes-Verfahren integriert wird. Ein implizites 3-Level-Zeitschritt-Verfahren ermoglicht sowohl stationare als auch instationare Simulationen. Das entwickelte Verfahren ist in der Lage, wesentliche Effekte wie das Auftreten des Re-Entrant-Jets und die Wolkenablosung physikalisch richtig zu erfassen. Die Anwendung des entwickelten CFD-Codes erfolgt am Beispiel der kavitierenden Stromung durch das Laufrad einer Kreiselpumpe niedriger spezifischer Drehzahl. Die Kavitationsbedingte Anderung des Betriebsverhaltens der Kreiselpumpe wird dabei genau vorhergesagt. Fur die untersuchten Betriebspunkte liegt die Ubereinstimmung des berechneten Forderhohenabfalls mit gemessenen Werten im Bereich der experimentellen Messunsicherheit.
Normalization in Training Deep Convolutional Neural Networks for 2D Bio-medical Semantic Segmentation

2D bio-medical semantic segmentation is important for surgical robotic vision. Segmentation methods based on Deep Convolutional Neural Network (DCNN) out-perform conventional methods in terms of both the accuracy and automation. One common issue in training DCNN is the internal covariate shift, where the convolutional kernels are trained to fit the distribution change of input feature, hence both the training speed and performance are decreased. Batch Normalization (BN) is the first proposed method for addressing internal covariate shift and is widely used. Later Instance Normalization (IN) and Layer Normalization (LN) were proposed and are used much less than BN. Group Normalization (GN) was proposed very recently and has not been applied into 2D bio-medical semantic segmentation yet. Most DCNN-based bio-medical semantic segmentation adopts BN as the normalization method by default, without reviewing its performance. In this paper, four normalization methods - BN, IN, LN and GN are compared and reviewed in details specifically for 2D bio-medical semantic segmentation. The result proved that GN out-performed the other three normalization methods - BN, IN and LN in 2D bio-medical semantic segmentation regarding both the accuracy and robustness. Unet is adopted as the basic DCNN structure. 37 RVs from both asymptomatic and Hypertrophic Cardiomyopathy (HCM) subjects and 20 aortas from asymptomatic subjects were used for the validation. The code and trained models will be available online.
TRPV1 and BDKRB2 receptor polymorphisms can influence the exercise pressor reflex

The mechanisms responsible for the high inter‐individual variability in blood pressure responses to exercise remain unclear. Common genetic variants of genes related to the vascular transduction of sympathetic outflow have been investigated, but variants influencing skeletal muscle afferent feedback during exercise have not been explored. Single nucleotide polymorphisms in TRPV1 rs222747 and BDKRB2 rs1799722 receptors present in skeletal muscle were associated with differences in the magnitude of the blood pressure response to static handgrip exercise but not mental stress. The combined effects of TRPV1 rs222747 and BDKRB2 rs1799722 on blood pressure and heart rate responses during exercise were additive, and primarily found in men. Genetic differences in skeletal muscle metaboreceptors may be a risk factor for exaggerated blood pressure responses to exercise.
Monaural Multi-Talker Speech Recognition with Attention Mechanism and Gated Convolutional Networks

To improve the speech recognition accuracy under the multi-talker scenario, we propose a novel model architecture that incorporates the attention mechanism and gated convolutional network (GCN) into our previously developed permutation invariant training based multi-talker speech recognition system (PIT-ASR). The new architecture has three components: an encoding transformer, an attention module and a frame-level senone predictor. The encoding transformer ﬁrst transforms a mixed speech sequence into a sequence of embedding vectors. Then the attention mechanism extracts individual context vectors from this embedding sequence for different speaker sources. Finally the predictor generates the senone posteriors for all speaker sources independently with the knowledge from the context vectors. To get better embedding representations we explore gated convolutional networks in the encoding trans-former. The experimental results on the artiﬁcially mixed two-talker WSJ0 corpus show that our proposed model can reduce the word error rate (WER) by more than 15% relatively compared to our previous PIT-ASR system.
Fucoidan Extracted from Undaria pinnatifida: Source for Nutraceuticals/Functional Foods

The importance of fucoidan as a functional ingredient in food, health products, and pharmaceutics is well-recognized due to its beneficial biological effects. Fucoidan is usually extracted from brown seaweeds, including Undaria pinnatifida. Fucoidan exhibits beneficial bio-activity and has antioxidant, anticancer, and anticoagulant properties. This review focuses on the biological activity of U. pinnatifida-derived fucoidan and investigates its structure–activity or fraction–activity relationship. It also describes several fucoidan extracts, along with their claimed anticancer effects. It aims to provide information and thoughts for future research such as the development of fucoidan into functional foods or nutraceuticals.
Multi-View Autoencoder for Image Feature Learning with Structured Nonnegative Low Rank

Multi-view feature learning has garnered much attention recently since many real-world data are comprised of different representations or views. As an important and challenging problem in multi-view feature learning, the key issue is how to explore complementary information and eliminate the inconsistency noise. Motivated by this observation, we propose to encode the multiple feature views to an underlying consensus representation. To enhance the robustness against the noise of disagreement among different feature views and exploit the consistency of the shared structure, we impose the nuclear norm constraint on the consensus representation and enforce the encoded representation to be nonnegative low rank structured. The superior experimental results on three image benchmarks show the effectiveness of our method.
Dietary Cholesterol Contained in Whole Eggs Is Not Well Absorbed and Does Not Acutely Affect Plasma Total Cholesterol Concentration in Men and Women: Results from 2 Randomized Controlled Crossover Studies

Whole egg is a food source of dietary cholesterol and inconsistent research findings exist about the effect of dietary cholesterol from whole egg on blood cholesterol concentration. We assessed the effect of co-consuming cooked whole egg (CWE) on dietary cholesterol absorption from two randomized-crossover studies. For study 1, 16 men consumed raw vegetables with no egg, 75 g CWE, or 150 g CWE. For study 2, 17 women consumed cooked vegetables with no egg or 100 g CWE. Triacylglycerol-rich lipoprotein fractions (TRL) were isolated from collected blood. In study 1, total-cholesterol areas under the curve (AUC)0–10h in TRL were not different but triacylglycerol AUC0–10h in TRL was greater for 150 g CWE vs. 75 g CWE and no egg. Similarly, in study 2, total-cholesterol AUC0–10h in TRL was not different but triacylglycerol AUC0–10h in TRL was greater for 100 g CWE vs. no egg. In both studies, whole egg consumption did not affect plasma total-cholesterol AUC0–10h, while triacylglycerol AUC0–10h was increased. These results suggest that the dietary cholesterol in whole egg was not well absorbed, which may provide mechanistic insight for why it does not acutely influence plasma total-cholesterol concentration and is not associated with longer-term plasma cholesterol control.
Deep Learning Techniques for Koala Activity Detection

Free to read on publisher's website Automatically detecting koalas in the real-life environment from audio recordings will immensely help ecologists, conservation groups, and government departments interested in their preservation and the protection of their habitat. Inspired by the success of deep learning approaches in various audio classification tasks, in this paper, the feasibility of recognizing koalas’ calls using a convolutional recurrent neural network architecture (CNN+RNN) is studied. The benefit of this architecture is twofold: firstly, convolutional layers learn local time-frequency patterns from the audio spectrogram and secondly, recurrent layers model longer temporal dependencies of the extracted features. In our datasets, the performance of CNN+RNN is evaluated and compared with standard convolutional neural networks (CNNs). The experimental results show that hybrid CNN+RNN architecture is beneficial for learning long-term patterns in spectrogram exhibited by koalas’ calls in unseen conditions. The proposed method is also applicable for detecting other animal calls such as bird sound where it achieves 87.46% area under curve score on the bird audio detection challenge evaluation data.
Indoor Dense Depth Map at Drone Hovering

Autonomous Micro Aerial Vehicles (MAVs) gained tremendous attention in recent years. Autonomous flight in indoor requires a dense depth map for navigable space detection which is the fundamental component for autonomous navigation. In this paper, we address the problem of reconstructing dense depth while a drone is hovering (small camera motion) in indoor scenes using already estimated cameras and sparse point cloud obtained from a vSLAM. We start by segmenting the scene based on sudden depth variation using sparse 3D points and introduce a patch-based local plane fitting via energy minimization which combines photometric consistency and co-planarity with neighbouring patches. The method also combines a plane sweep technique for image segments having almost no sparse point for initialization. Experiments show, the proposed method produces better depth for indoor in arti-ficial lighting condition, low-textured environment compared to earlier literature in small motion.
Measuring the Band Importance Function for Mandarin Chinese with a Bayesian Adaptive Procedure

A speech intelligibility index (SII) based band importance function (BIF) for Mandarin monosyllabic words spoken by a female speaker was derived with an adaptive procedure in this work. The adaptive procedure, namely the quick-band-importance-function (qBIF) procedure, optimized the stimulus on each trial according listeners’ performance on proceeding trials in an iterative fashion. This method greatly improved the efficiency of data collection. Test-retest experiments were conducted and confirmed the reliability of this adaptive procedure at a group level. The BIF derived in this work showed generally consistence with the BIF derived with the traditional paradigm with noticeable differences at certain frequencies.
Acoustic Modeling with Densely Connected Residual Network for Multichannel Speech Recognition

Motivated by recent advances in computer vision research, this paper proposes a novel acoustic model called Densely Connected Residual Network (DenseRNet) for multichannel speech recognition. This combines the strength of both DenseNet and ResNet. It adopts the basic "building blocks" of ResNet with different convolutional layers, receptive field sizes and growth rates as basic components that are densely connected to form so-called denseR blocks. By concatenating the feature maps of all preceding layers as inputs, DenseRNet can not only strengthen gradient back-propagation for the vanishing-gradient problem, but also exploit multi-resolution feature maps. Preliminary experimental results on CHiME-3 have shown that DenseRNet achieves a word error rate (WER) of 7.58% on beamforming-enhanced speech with six channel real test data by cross entropy criteria training while WER is 10.23% for the official baseline. Besides, additional experimental results are also presented to demonstrate that DenseRNet exhibits the robustness to beamforming-enhanced speech as well as near and far-field speech.
Pierce's Disease of Grapevines: A Review of Control Strategies and an Outline of an Epidemiological Model

Xylella fastidiosa is a notorious plant pathogenic bacterium that represents a threat to crops worldwide. Its subspecies, Xylella fastidiosa subsp. fastidiosa is the causal agent of Pierce's disease of grapevines. Pierce's disease has presented a serious challenge for the grapevine industry in the United States and turned into an epidemic in Southern California due to the invasion of the insect vector Homalodisca vitripennis. In an attempt to minimize the effects of Xylella fastidiosa subsp. fastidiosa in vineyards, various studies have been developing and testing strategies to prevent the occurrence of Pierce's disease, i.e., prophylactic strategies. Research has also been undertaken to investigate therapeutic strategies to cure vines infected by Xylella fastidiosa subsp. fastidiosa. This report explicitly reviews all the strategies published to date and specifies their current status. Furthermore, an epidemiological model of Xylella fastidiosa subsp. fastidiosa is proposed and key parameters for the spread of Pierce's disease deciphered in a sensitivity analysis of all model parameters. Based on these results, it is concluded that future studies should prioritize therapeutic strategies, while investments should only be made in prophylactic strategies that have demonstrated promising results in vineyards.