Miracle-dz/UniFAR-FTUs huggingface dataset:
Architecture and Noise Analysis for Block-Based Compressive Imaging

This paper addresses two main issues present in compressive imaging: namely, the issue of long acquisition time by using a single-pixel sensor and that of high computational complexity for high-resolution image recovery. We show that these issues are resolved in a new architecture, which is referred to as block-based compressive imaging. The new architecture consists of a lens, a programmable aperture assembly, and multiple single-pixel sensors. The aperture assembly equally applies to all single-pixel sensors, which make the compressive measurements of a scene with seamless block views. The combined measurements from the sensors can be used to reconstruct a unified image with a large number of pixels.
Improved Acoustic Modelling for Automatic Literacy Assessment of Children

Automatic literacy assessment of children is a complex task that normally requires carefully annotated data. This paper focuses on a system for the assessment of reading skills, aiming to detection of a range of fluency and pronunciation errors. Naturally, reading is a prompted task, and thereby the acquisition of training data for acoustic modelling should be straightforward. However, given the prominence of errors in the training set and the importance of labelling them in the transcription, a lightly supervised approach to acoustic modelling has better chances of success. A method based on weighted finite state transducers is proposed, to model specific prompt corrections, such as repetitions, substitutions, and deletions, as observed in real recordings. Iterative cycles of lightly-supervised training are performed in which decoding improves the transcriptions and the derived models. Improvements are due to increasing accuracy in phone-to-sound alignment and in the training data selection. The effectiveness of the proposed methods for rela-belling and acoustic modelling is assessed through experiemnts on the CHOREC corpus, in terms of sequence error rate and alignment accuracy. Improvements over the baseline of up to 60% and 23.3% respectively are observed.
Do Deep-Learning Saliency Models Really Model Saliency?

Visual attention allows the human visual system to effectively deal with the huge flow of visual information acquired by the retina. Since the years 2000, the human visual system began to be modelled in computer vision to predict abnormal, rare and surprising data. Attention is a product of the continuous interaction between bottom-up (mainly feature-based) and top-down (mainly learning-based) information. Deep-learning (DNN) is now well established in visual attention modelling with very effective models. The goal of this paper is to investigate the importance of bottom-up versus top-down attention. First, we enrich with top-down information classical bottom-up models of attention. Then, the results are compared with DNN-based models. Our provocative question is: “do deep-learning saliency models really predict saliency or they simply detect interesting objects? ”. We found that if DNN saliency models very accurately detect top-down features, they neglect a lot of bottom-up information which is surprising and rare, thus by definition difficult to learn.
Automatic Segmentation of Shoulder Joint in MRI Using Patch-Based and Fully Convolutional Networks

Two deep learning networks, patch-based and fully convolutional networks, are employed for automated detection and segmentation of shoulder joint structure on MRI. First, four segmentation models are build including three U-Net based models (glenoid segmentation model, humeral head segmentation model, glenoid and humeral head as a whole segmentation model) and one patch-based adjusted AlexNet (AANet) segmentation model. Then the four segmentation models are used to get the candidate bone regions from which the correct locations and regions of glenoid and humeral head are obtained by voting. Last, AANet model is further used to segment the edge of the bone with accuracy at the pixel level. From the experimental results, Dice Coefficient, Positive Predicted Value (PPV) and Sensitivity average accuracy are 0.92±0.02, 0.96±0.03 and 0.94±0.02 respectively. Note that our framework is also generic enough to be applied to the precise segmentation of specific organs and tissues in CT and MRI under small sample data.
Speaker Activity Detection and Minimum Variance Beamforming for Source Separation

This work proposes a framework that renders minimum variance beamforming blind allowing for source separation in real world environments with an ad-hoc multi-microphone setup us-ing no assumptions other than knowing the number of speakers. The framework allows for multiple active speakers at the same time and estimates the activity of every single speaker at ﬂexible time resolution. These estimated speaker activities are subsequently used for the calibration of the beamforming algorithm. This framework is tested with three different speaker activity detection (SAD) methods, two of which use classical algorithms and one that is event-driven. Our methods, when tested in real world reverberant scenarios, can achieve very high signal-to-interference ratio (SIR) of around 20 dB and sound quality of 0 . 85 in short-time objective intelligibility (STOI) close to optimal beamforming results of 22 dB SIR and 0 . 89 in STOI.
Animatable 3D Model Generation from 2D Monocular Visual Data

In this paper, we present an approach for creating animatable 3D models from temporal monocular image acquisitions of non-rigid objects. During deformation, the object of interest is captured with only a single camera under full perspective projection. The aim of the presented framework is to obtain a shape deformation model in terms of joints and skinning weights that can finally be used for animating the model vertices. First, the monocular rigid shape estimation problem is solved by computing a template model of the object in rest pose from an image sequence. Next, the unknown external camera parameters and the deformation for each vertex are estimated alternately in a sequential approach. The resulting consistent non-rigid shape geometries are used to compute a kinematic skeleton control structure including skinning weights and optimized shape. For that, a completely data-driven optimization scheme is used, which iterates over three steps: (a) optimization of pose for each frame as well as joint parameters consistent over the entire sequence, (b) optimization of rest pose vertices to enhance the shape and (c) optimization of skinning weights for improved deformation characteristics. With experimental results on publicly available synthetic as well as real-world datasets, we demonstrate the quality of the proposed approach. The resulting models with fixed topology and rigged with skeleton and skinning weights can be animated in existing render engines.
Mindfulness-Based Stress Reduction and Mindfulness-Based Cognitive Therapy with Older Adults: A Qualitative Review of Randomized Controlled Outcome Research

ABSTRACT Objective: Many older adults cope with various chronic physical health conditions, and in some cases, with mental health and/or cognitive difficulties. Mindfulness-based interventions offer an evidence-based, mind-body complementary treatment approach for a wide range of comorbidities, yet most investigations were conducted with young or middle-aged adults. The purpose of this review was to identify randomized controlled trials (RCTs) of two leading mindfulness-based interventions conducted with older adults. Methods: Our search of five databases identified seven RCT investigations of either Mindfulness-Based Stress Reduction (MBSR) or Mindfulness-Based Cognitive Therapy (MBCT) conducted exclusively with older adults. Results: Results generally supported the use of MBSR for chronic low back pain, chronic insomnia, improved sleep quality, enhanced positive affect, reduced symptoms of anxiety and depression, and improved memory and executive functioning. In a sample of older adults exhibiting elevated anxiety in the absence of elevated depression, MBCT effectively reduced symptoms of anxiety. Conclusions: This review highlights the feasibility and possible benefits of MBSR and MBCT for older adults. Additional large scale RCTs conducted with older adults coping with the range of physical, behavioral, and cognitive challenges older adults commonly face still are needed. Clinical Implications: MBSR may be a promising intervention for older adults experiencing a variety of health concerns and possibly even cognitive decline. MBCT may reduce geriatric anxiety, although its effects on geriatric depression were not measured.
Experiments with Training Corpora for Statistical Text-to-speech Systems

Common text-to-speech (TTS) systems rely on training data for modelling human speech. The quality of this data can range from professional voice actors recording hand-curated sentences in high-quality studio conditions, to found voice data representing arbitrary domains. For years, the unit selection technology dominant in the ﬁeld required many hours of data that was expensive and time-consuming to collect. With the advancement of statistical methods of waveform generation, there have been experiments with more noisy and often much larger datasets, testing the inherent ﬂexibility of such systems. In this paper we examine the relationship between training data and speech synthesis quality. We then hypothesise that statistical text-to-speech beneﬁts from high acoustic quality corpora with high level of prosodic variation, but that beyond the ﬁrst few hours of training data we do not observe quality gains. We then describe how we engineered a training dataset containing optimized distribution of features, and how these features were de-ﬁned. Lastly, we present results from a series of evaluation tests. These conﬁrm our hypothesis and show how a carefully engineered training corpus of a smaller size yields the same speech quality as much larger datasets, particularly for voices that use WaveNet.
Light Field Image Compression Based on Convolutional Neural Networks and Linear Approximation

Computer vision applications such as refocusing, segmentation and classification become one of the most advanced imaging services. Light Field (LF) imaging systems provide a rich semantic information of the scene. Using a dense set of cameras and microlens arrays (Plenoptic camera), the direction of each ray coming from the scene toward the LF capture system can be extracted and represented by spatial and angular coordinates. However, such imaging system induces many drawbacks including the large amount of data produced and complexity increase for scene representation. In this paper, we propose an efficient LF image coding scheme. This scheme first encodes a sparse set of views using the latest hybrid video encoder (JEM). Then, it estimates a second sparse set of views using a linear approximation. At the decoder side, we use a Deep Learning (DL) approach to estimate the whole LF image from the reconstructed sparse sets of views. Experimental results show that the proposed scheme provides higher visual quality and overcomes the state of the art LF image compression solution by 30 % bitrate gain.
CFD: A Collaborative Feature Difference Method for Spontaneous Micro-Expression Spotting

Micro-expression (ME) is a special type of human expression which can reveal the real emotion that people want to conceal. Spontaneous ME (SME) spotting is to identify the subsequences containing SMEs from a long facial video. The study of SME spotting has a significant importance, but is also very challenging due to the fact that in real-world scenarios, SMEs may occur along with normal facial expressions and other prominent motions such as head movements. In this paper, we improve a state-of-the-art SME spotting method called feature difference analysis (FD) in the following two aspects. First, FD relies on a partitioning of facial area into uniform regions of interest (ROIs) and computing features of a selected sequence. We propose a novel evaluation method by utilizing the Fisher linear discriminant to assign a weight for each ROI, leading to more semantically meaningful ROIs. Second, FD only considers two features (LBP and HOOF) independently. We introduce a state-of-the-art MDMO feature into FD and propose a simple yet efficient collaborative strategy to work with two complementary features, i.e., LBP characterizing texture information and MDMO characterizing motion information. We call our improved FD method collaborative feature difference (CFD). Experimental results on two well-established SME datasets SMIC-E and CASME II show that CFD significantly improves the performance of the original FD.
Shockwave therapy in selected soft tissue diseases: a literature review.

OBJECTIVE The aim of our study is to present the current state of knowledge on the use of shockwave therapy (SWT) in the treatment of soft tissue wounds, by reviewing the available literature. METHOD Medical databases were searched for articles using the keywords: 'shockwave AND wound healing', 'shockwave AND ulcers', 'shockwave AND burns', 'shockwave AND bedsores', 'shockwave AND diabetic foot ulcer', 'ESWT AND wound healing', 'shock wave AND diabetic gangrene'. RESULTS A total of 14 scientific articles were included in the study which described the methodology of treatments and list the type of generator, physical parameters used during the procedure, number of treatments and the type of treated wounds. From these articles, 191 soft tissue wounds were analysed. CONCLUSION Evidence from the articles analysed in this study suggests a beneficial effect of SWT to treat diabetic foot ulcers, venous leg ulcers, pressure ulcers and burns. SWT can be used in combination with standard treatment in soft tissue wounds.
Sensitization to Fungi in Atopic Dermatitis Patients 14 Year and Older – Association with Other Atopic Diseases and Parameters

Background: Fungi as a source of allergen are still largely neglected in basic research as well as in clinical practice. This study aimed to highlight the impact of fungal allergens in a group of patients suffering from atopic dermatitis (AD). Aims and Objectives: The evaluation of the sensitization to fungi in AD patients aged 14 year and older and the evaluation of the relation of fungal sensitization to the occurrence of other atopic diseases and parameters. Materials and Methods: All patients satisfying inclusion criteria attending our hospital between 2008 and 2017 were included in the study. The complete dermatological and allergological examinations were performed in all included patients including examination of specific immunoglobulin E, skin prick test to mixture of fungi and inhalant allergens, evaluation of asthma bronchiale, rhinitis, onset of AD, family history about atopy, duration of eczematous lesions, severity of AD. Pairs of these categories were entered in the contingency tables and the Chi-square test for relationship of these variables was performed with the level of significance set below 5%. Results: Three hundred and thirty-one patients were included in the study; the average age was 26.8 years. The sensitization to fungi was recorded in 100 patients (30%). In these patients, the occurrence of asthma bronchiale, rhinitis, family history about atopy, sensitization to grass and trees was significantly higher than in patients without sensitization to fungi. We did not find any significant relation between the sensitization to fungi and the severity of AD, no relation was also found between the sensitization to fungi and sensitization to mites, animal dander, bird feather, and dust. Conclusion: The occurrence of asthma bronchiale, rhinitis, family history about atopy, sensitization to grass and trees was significantly higher in AD patients with sensitization to fungi. No relation was found between the severity of AD and the sensitization to fungi.
Video Codec Forensics Based on Convolutional Neural Networks

The recent development of multimedia has made video editing accessible to everyone. Unfortunately, forensic analysis tools capable of detecting traces left by video processing operations in a blind fashion are still at their beginnings. One of the reasons is that videos are customary stored and distributed in a compressed format, and codec-related traces tends to mask previous processing operations. In this paper, we propose to capture video codec traces through convolutional neural networks (CNNs) and exploit them as an asset. Specifically, we train two CNN s to extract information about the used video codec and coding quality, respectively. Building upon these CNN s, we propose a system to detect and localize temporal splicing for video sequences generated from the concatenation of different video segments, which are characterized by inconsistent coding schemes and/or parameters (e.g., video compilations from different sources or broadcasting channels). The proposed solution is validated using videos at different resolutions (i.e., CIF, 4CIF, PAL and 720p) encoded with four common codecs (i.e., MPEG2, MPEG4, H264 and H265) at different qualities (i.e., different constant and variable bitrates, as well as constant quantization parameters).
Acute physical exercise increases leptin‐induced hypothalamic extracellular signal‐regulated kinase1/2 phosphorylation and thermogenesis of obese mice

The obesity is a result of energy imbalance and the increase in thermogenesis seems an interesting alternative for the treatment of this disease. The mechanism of energy expenditure through thermogenesis is tightly articulated in the hypothalamus by leptin. The hypothalamic extracellular signal‐regulated kinase‐1/2 (ERK1/2) is a key mediator of the thermoregulatory effect of leptin and mediates the sympathetic signal to the brown adipose tissue (BAT). In this context, physical exercise is one of the main interventions for the treatment of obesity. Thus, this study aimed to verify the effects of acute physical exercise on leptin‐induced hypothalamic ERK1/2 phosphorylation and thermogenesis in obese mice. Here we showed that acute physical exercise reduced the fasting glucose of obese mice and increased leptin‐induced hypothalamic p‐ERK1/2 and uncoupling protein 1 (UCP1) content in BAT ( P < 0.05). These molecular changes are accompanied by an increased oxygen uptake (VO 2) and heat production in obese exercised mice ( P < 0.05). The increased energy expenditure in the obese exercised animals occurred independently of changes in spontaneous activity. Thus, this is the first study demonstrating that acute physical exercise can increase leptin‐induced hypothalamic ERK1/2 phosphorylation and energy expenditure of obese mice.
Radiomic analysis of pulmonary ground-glass opacity nodules for distinction of preinvasive lesions, invasive pulmonary adenocarcinoma and minimally invasive adenocarcinoma based on quantitative texture analysis of CT

Objective To identify the differences among preinvasive lesions, minimally invasive adenocarcinomas (MIAs) and invasive pulmonary adenocarcinomas (IPAs) based on radiomic feature analysis with computed tomography (CT). Methods A total of 109 patients with ground-glass opacity lesions (GGOs) in the lungs determined by CT examinations were enrolled, all of whom had received a pathologic diagnosis. After the manual delineation and segmentation of the GGOs as regions of interest (ROIs), the patients were subdivided into three groups based on pathologic analyses: the preinvasive lesions (including atypical adenomatous hyperplasia and adenocarcinoma in situ) subgroup, the MIA subgroup and the IPA subgroup. Next, we obtained the texture features of the GGOs. The data analysis was aimed at finding both the differences between each pair of the groups and predictors to distinguish any two pathologic subtypes using logistic regression. Finally, a receiver operating characteristic (ROC) curve was applied to accurately evaluate the performances of the regression models. Results We found that the voxel count feature (P<0.001) could be used as a predictor for distinguishing IPAs from preinvasive lesions. However, the surface area feature (P=0.040) and the extruded surface area feature (P=0.013) could be predictors of IPAs compared with MIAs. In addition, the correlation feature (P=0.046) could distinguish preinvasive lesions from MIAs better. Conclusions Preinvasive lesions, MIAs and IPAs can be discriminated based on texture features within CT images, although the three diseases could all appear as GGOs on CT images. The diagnoses of these three diseases are very important for clinical surgery.
Joint Learning of Facial Expression and Head Pose from Speech

Natural movement plays a signiﬁcant role in realistic speech animation, and numerous studies have demonstrated the contribution visual cues make to the degree human observers ﬁnd an animation acceptable. Natural, expressive, emotive, and prosodic speech exhibits motion patterns that are difﬁcult to predict with considerable variation in visual modalities. Recently, there have been some impressive demonstrations of face animation derived in some way from the speech signal. Each of these methods have taken unique approaches, but none have included rigid head pose in their predicted output. We observe a high degree of correspondence with facial activity and rigid head pose during speech, and exploit this observation to jointly learn full face animation and head pose rotation and translation combined. From our own corpus, we train Deep Bi-Directional LSTMs (BLSTM) capable of learning long-term structure in language to model the relationship that speech has with the complex activity of the face. We deﬁne a model architecture to encourage learning of rigid head motion via the latent space of the speaker’s facial activity. The result is a model that can predict lip sync and other facial motion along with rigid head motion directly from audible speech.
Development of Large Vocabulary Speech Recognition System with Keyword Search for Manipuri

Research in Automatic Speech Recognition (ASR) has witnessed a steep improvement in the past decade (especially for English language) where the variety and amount of training data available is huge. In this work, we develop an ASR and Keyword Search (KWS) system for Manipuri, a low-resource Indian Language. Manipuri (also known as Meitei), is a Tibeto-Burman language spoken predominantly in Manipur (a northeastern state of India). We collect and transcribe tele-phonic read speech data of 90+ hours from 300+ speakers for the ASR task. Both state-of-the-art Gaussian Mixture-Hidden Markov Model (GMM-HMM) and Deep Neural Network-Hidden Markov Model (DNN-HMM) based architectures are developed as a baseline. Using the collected data, we achieve better performance using DNN-HMM systems, i.e., 13.57% WER for ASR and 7.64% EER for KWS. The KALDI speech recognition tool-kit is used for developing the systems. The Manipuri ASR system along with KWS is integrated as a visual interface for demonstration purpose. Future systems will be improved with more amount of training data and advanced forms of acoustic models and language models.