Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Scale Deep Networks for Image Compressed Sensing

As a successful deep model applied in image compressed sensing, the Compressed Sensing Network (CSNet) has demonstrated superior performance to the previous handcrafted models in both running speed and reconstruction quality. However, CSNet trains different models for different sampling rates that hinders it from practical usage since too many models need to store. In this paper, we propose multi-scale deep network for image compressed sensing. We still use a sampling network to learn the sampling operator and implement the compressed sampling process. Given the compressed measurements, the reconstruction network directly maps them to the desired reconstructed images. There are three main differences in comparison with CSNet. Firstly, this paper proposes to use an unified deep reconstruction network for all sampling rates that decreases large amount of storage requirements. Secondly, we redesign a better deep reconstruction network using the popular residual learning technology. Finally, we investigate an image local smooth prior based loss function to enhance image structural information. Extensive experimental results show that the proposed multi-scale deep network based image compressed sensing method outperforms many other state-of-the-art methods.
Multilingual Grapheme-to-Phoneme Conversion with Global Character Vectors

Multilingual grapheme-to-phoneme (G2P) models are useful for multilingual speech synthesis because one model simultaneously copes with multilingual words. We propose a G2P model that combines global character vectors (GCVs) with bi-directional recurrent neural networks (BRNNs) and enables the direct conversion of text (as a sequence of characters) to pro-nunciation. GCVs are distributional, real-valued representations of characters and their contextual interactions that can be learned from a large-scale text corpus in an unsupervised manner. With the ﬂexibility of learning GCVs from plain text resources, this method has an advantage: it enables monolingual G2P (MoG2P) and multilingual G2P (MuG2P) conversion. We experiment in four languages (Japanese, Korean, Thai, and Chinese) with learning language-dependent (LD) and language-independent (LI) GCVs and then build MoG2P and MuG2P models with two-hidden-layer BRNNs. Our results show that both LD-and LI-GCV-based MoG2P models, whose performances are equivalent, achieved better than 97.7% syllable accuracy, which is a relative improvement from 27% to 90% depending on the language in comparison with Mecab-based models. As for MuG2P, the accuracy is around 98%, which is a slightly degraded performance compared to MoG2P. The proposed method also has the potential of the G2P conversion of non-normalized words, achieving 80% accuracy in Japanese.
Improving Proposal-Based Object Detection Using Convolutional Context Features

A novel extension to proposal-based detection is proposed in order to learn convolutional context features for determining boundaries of objects better. Objects and their context are aimed to be learned through parallel convolutional stages. The resulting object and context feature maps are combined in such a way that they preserve their spatial relationship. The proposed algorithm is trained and evaluated on PASCAL VOC 2007 detection benchmark dataset and yielded improvements in performance over baseline, for all classes, especially the ones with distinctive context.
Key Parts Context and Scene Geometry in Human Head Detection

Due to the relatively fixed shape and color, head detection is widely used in many computer vision tasks, such as finding people and crowd counting. In this paper, we propose a method to detect human heads including: (1) A pairwise CNN model based on key parts context of human head and shoulder. (2) A fusion algorithm by using the priority of scene geometry structure. (3) To further test our approach, we collect a dataset about the passengers inside the bus with head annotations. This dataset composed of 2316 representative images extracted from a total of twenty hours of video. We evaluate our method on two indoor human head datasets and achieve state-of-the-art performance.
De Novo Malignancies Screening After Liver Transplantation for Alcoholic Liver Disease: A Comparative Opportunistic Study

Patients having received a liver transplantation (LT) for alcoholic liver disease (ALD) have a high risk of de novo malignancies, especially in the upper aerodigestive tract and lungs due to their smoking and alcohol history. The aim of this retrospective study was to compare a group of patients transplanted for ALD who continue to smoke and who were included in an intensive screening program for tobacco‐related cancers implemented at the Grenoble University Hospital and a group of similar patients followed according to usual practice (chest computed tomography [CT] scan every 5 years) at the Edouard Herriot Hospital in Lyon. The intensive screening program consisted of an annual checkup, including a clinical examination by an otorhinolaryngologist, a chest CT scan, and an upper digestive endoscopy. A total of 147 patients were included: 71 patients in Grenoble and 76 patients in Lyon. The cumulative incidence of a first tobacco‐related cancer was 12.3% at 3 years, 20.6% at 5 years, 42.6% at 10 years, and 64.0% at 15 years. A curative treatment was possible in 80.0% of the patients in Grenoble versus 57.9% in Lyon (P = 0.068). The rates of curative treatment were 63.6% versus 26.3% (P = 0.062) for lung cancers, 100.0% versus 87.5% (P = 0.498) for lip‐mouth‐pharynx and larynx cancers, and 66.7% versus 100.0% (P = 1) for esophageal cancers, respectively. In addition, for lung cancers, regardless of study group, 68.7% received a curative treatment when the diagnosis was made by CT scan screening versus 14.3% when it was made because of symptoms (P = 0.008). In conclusion, our study strongly confirms the high rate of tobacco‐related de novo malignancies in LT patients for ALD and suggests that the screening of lung cancer by annual chest CT scan could significantly increase the rate of curative treatment.
A Deep Convolutional Auto-Encoder with Embedded Clustering

In this paper, we propose a clustering approach embedded in a deep convolutional auto-encoder (DCAE). In contrast to conventional clustering approaches, our method simultaneously learns feature representations and cluster assignments through DCAEs. DCAEs have been effective in image processing as it fully utilizes the properties of convolutional neural networks. Our method consists of clustering and reconstruction objective functions. All data points are assigned to their new corresponding cluster centers during the optimization, after that, clustering centers are iteratively updated to obtain a stable performance of clustering. The experimental results on the MNIST dataset show that the proposed method substantially outperforms deep clustering models in term of clustering quality.
EEG-based auditory attention detection: boundary conditions for background noise and speaker positions

Objective. A listener’s neural responses can be decoded to identify the speaker the person is attending to in a cocktail party environment. Such auditory attention detection methods have the potential to provide noise suppression algorithms in hearing devices with information about the listener’s attention. A challenge is the effect of noise and other acoustic conditions that can reduce the attention detection accuracy. Specifically, noise can impact the ability of the person to segregate the sound sources and perform selective attention, as well as the external signal processing necessary to decode the attention effectively. The aim of this work is to systematically analyze the effect of noise level and speaker position on attention decoding accuracy. Approach. 28 subjects participated in the experiment. Auditory stimuli consisted of stories narrated by different speakers from two different locations, along with surrounding multi-talker background babble. EEG signals of the subjects were recorded while they focused on one story and ignored the other. The strength of the babble noise as well as the spatial separation between the two speakers were varied between presentations. Spatio-temporal decoders were trained for each subject, and applied to decode attention of the subjects from every 30 s segment of data. Behavioral speech recognition thresholds were obtained for the different speaker separations. Main results. Both the background noise level and the angular separation between speakers affected attention decoding accuracy. Remarkably, attention decoding performance was seen to increase with the inclusion of moderate background noise (versus no noise), while across the different noise conditions performance dropped significantly with increasing noise level. We also observed that decoding accuracy improved with increasing speaker separation, exhibiting the advantage of spatial release from masking. Furthermore, the effect of speaker separation on the decoding accuracy became stronger when the background noise level increased. A significant correlation between speech intelligibility and attention decoding accuracy was found across conditions. Significance. This work shows how the background noise level and relative positions of competing talkers impact attention decoding accuracy. It indicates in which circumstances a neuro-steered noise suppression system may need to operate, in function of acoustic conditions. It also indicates the boundary conditions for the operation of EEG-based attention detection systems in neuro-steered hearing prostheses.
Detail Preserving Multi-Scale Exposure Fusion

Edge-preserving smoothing based multi-scale exposure fusion is a state-of-the-art method to fuse differently exposed images of a high dynamic range (HDR) scene. However, its complexity could be an issue. In this paper, a novel multiscale exposure fusion algorithm is proposed by adopting an approximation method at the highest layer of the pyramid. Experimental results show that the proposed algorithm can be applied to fuse images with comparable or even better quality with the edge-preserving smoothing based multi-scale fusion algorithms. It simplifies the complexity of the edge-preserving smoothing based multi-scale exposure fusion algorithms significantly.
Relationship between the Crystal Structure and Electrical Properties of Oxide Ion Conducting Ba3W1.2Nb0.8O8.6.

The oxide ionic conductor Ba3W1.2Nb0. 8O8.6 has been synthesized as part of an investigation into the new class of Ba3M'M''O8.5 (M' = W, Mo; M'' = Nb) oxide-ion conducting hexagonal perovskite derivatives. The substitution of W6+ for Nb5+ in Ba3W1+ xNb1- xO8.5+ x/2 leads to an increase in the oxygen content, which enhances the low-temperature ionic conductivity. However, at 400 °C, the ionic conductivity of Ba3W1.2Nb0. 8O8.6 is still significantly lower than the molybdenum compound Ba3MoNbO8.5. Remarkably, at 600 °C the bulk oxide ionic conductivities of Ba3MoNbO8.5, Ba3WNbO8.5, and Ba3W1.2Nb0. 8O8.6 are very similar (σb = 0.0022, 0.0017, and 0.0016 S cm-1, respectively). The variable-temperature neutron diffraction results reported here demonstrate that Ba3W1.2Nb0. 8O8.6 undergoes a similar structural rearrangement to Ba3MoNbO8.5 above 300 °C, but the ratio of (W/Nb)O4 tetrahedra to (W/Nb)O6 octahedra rises at a faster rate upon heating between 300 and 600 °C. There is a clear relationship between the ionic conductivity of Ba3M'1+ xM''1- xO8.5+ x/2 (M' = W, Mo; M'' = Nb) phases and the number of tetrahedrally coordinated M' and M ″ cations present within the crystal structure.
Psychotherapy of adjustment disorders: Current state and future directions

Abstract Objectives: This narrative review article provides an overview of current psychotherapeutic approaches specific for adjustment disorders (ADs) and outlines future directions for theoretically-based treatments for this common mental disorder within a framework of stepped care. Methods: Studies on psychological interventions for ADs were retrieved by using an electronic database search within PubMed and PsycINFO, as well as by scanning the reference lists of relevant articles and previous reviews. Results: The evidence base for psychotherapies specifically targeting the symptoms of AD is currently rather weak, but is evolving given several ongoing trials. Psychological interventions range from self-help approaches, relaxation techniques, e-mental-health interventions, behavioural activation to talking therapies such as psychodynamic and cognitive behavioural therapy. Conclusions: The innovations in DSM-5 and upcoming ICD-11, conceptualising AD as a stress-response syndrome, will hopefully stimulate more research in regard to specific psychotherapeutic interventions for AD. Low intensive psychological interventions such as e-mental-health interventions for ADs may be a promising approach to address the high mental health care needs associated with AD and the limited mental health care resources in most countries around the world.
Mechanisms of change of traditional mindfulness practice in Thai adolescent students: a cross-sectional mediation study

Abstract Given the substantial evidence for the efficacy of secular mindfulness-based interventions (MBIs) for reducing psychological distress and increasing wellbeing, research attention has more recently turned to investigating the mechanisms by which this is achieved and has revealed that MBI works not only directly on reducing distress, but also indirectly through a reduction in mediating problems such as rumination and emotional regulation difficulty. However, to the best of the authors’ knowledge, no studies have yet been undertaken to evaluate the effect on such mediating conditions, of traditional Buddhist mindfulness meditation, as practiced in Eastern cultures and increasingly in the West, from which MBIs have been derived but remain distinctly different. The current study was therefore designed to examine the effect of traditional mindfulness on mediating conditions in participants who were born into and were experienced in this approach. Specifically, the study examines the relationship between traditional mindfulness and distress, and between traditional mindfulness and the mediating conditions of rumination and emotional regulation difficulty, in Thai adolescent students experienced in Buddhist meditation practices. The results resonate well with the findings from MBI studies, showing significant negative correlations between rumination and distress and emotional regulation difficulty and distress, and significant positive correlations between mindfulness and the mediators. The implications of the benefits of traditional mindfulness for reducing non-clinical distress, rumination and difficulties in emotion regulation are discussed.
Exploiting Ontologies for Deep Learning: A Case for Sentiment Mining

We present a practical method for explaining deep learningbased text mining with ontology-based information. Our approach uses the recently proposed OntoSenticNet ontology for sentiment mining, and consists of a composite deep learning classifier for sentiment mining, endowed with an ontology-driven attention module. The attention module analyzes the attention the neural network pays to semantic labels assigned to bigrams in input texts.
Learning Sensitive Images Using Generative Models

The sheer amount of personal data being transmitted to cloud services and the ubiquity of cellphones cameras and various sensors, have provoked a privacy concern among many people. On the other hand, the recent phenomenal growth of deep learning that brings advancements in almost every aspect of human life is heavily dependent on the access to data, including sensitive images, medical records, etc. Therefore, there is a need for a mechanism that transforms sensitive data in such a way as to preserves the privacy of individuals, yet still be useful for deep learning algorithms. This paper proposes the use of Generative Adversarial Networks (GANs) as one such mechanism, and through experimental results, shows its efficacy.
Bags in Bag: Generating Context-Aware Bags for Tracking Emotions from Speech

Whereas systems based on deep learning have been proposed to learn efﬁcient representations of emotional speech data, meth-ods such as Bag-of-Audio-Words (BoAW) have yielded similar or even better performance while providing understandable representations of the data. In those representations, however, context information is overlooked as the BoAW include only local information. In this paper, we propose to learn a novel representation ‘Bag-of-Context-Aware-Words’ that en-capsulates the context with neighbouring frames of BoAW; segment-level BoAW are extracted in the ﬁrst layer which are then utilised to create a ﬁnal instance-level bag. Such a hierarchical structure of BoAW enables the system to learn representations with context information. To evaluate the effectiveness of the method, we perform extensive experiments on a time-and value-continuous spontaneous emotion database: RECOLA. The results show that, the best segment length for valence is twice of that for arousal, suggesting that the prediction of the emotional valence requires more context information than the prediction of arousal, and the performance obtained on RECOLA with the proposed Bag-of-Context-Aware-Words outperforms all previously reported results.
Talker Diarization in the Wild: the Case of Child-centered Daylong Audio-recordings

Speaker diarization (answering ’who spoke when’) is a widely researched subject within speech technology. Numerous experiments have been run on datasets built from broadcast news, meeting data, and call centers—the task sometimes appears close to being solved. Much less work has begun to tackle the hardest diarization task of all: spontaneous conversations in real-world settings. Such diarization would be particularly useful for studies of language acquisition, where researchers inves-tigate the speech children produce and hear in their daily lives. In this paper, we study audio gathered with a recorder worn by small children as they went about their normal days. As a re-sult, each child was exposed to different acoustic environments with a multitude of background noises and a varying number of adults and peers. The inconsistency of speech and noise within and across samples poses a challenging task for speaker diarization systems, which we tackled via retraining and data augmen-tation techniques. We further studied sources of structured vari-ation across raw audio ﬁles, including the impact of speaker type distribution, proportion of speech from children, and child age on diarization performance. We discuss the extent to which these ﬁndings might generalize to other samples of speech in the wild.
Anti-Inflammatory and Antioxidant Actions of Methyl Jasmonate Are Associated with Metabolic Modifications in the Liver of Arthritic Rats

Methyl jasmonate (MeJA) is a fatty acid-derived cyclopentanone which shares structural similarities with prostaglandins and has been under study as a promising anti-inflammatory agent. This study investigated the actions of MeJA on systemic inflammation and oxidative status in rats with adjuvant-induced arthritis, a model for rheumatoid arthritis. MeJA (75 to 300 mg·kg−1) was administrated orally during 18 days after arthritis induction with Freund's adjuvant. Articular and systemic inflammation was greatly increased in arthritic rats, likewise the oxidative stress in plasma and liver. The hepatic glucokinase activity and glycolysis were increased in arthritic rats. MeJA decreased most inflammatory parameters and abolished the increased protein carbonylation in plasma and liver, diminished the increased hepatic ROS content, and restored the hepatic GSH/GSSG ratio in arthritic rats. However, the MeJA treatment decreased the hepatic glucokinase activity and glycolysis and stimulated mitochondrial ROS production in healthy and arthritic rats. Oxygen uptake was increased by MeJA only in livers from treated arthritic rats. This action may bear relation to the increased activity of mitochondrial NADP+-dependent enzymes to provide reducing equivalents for the glutathione cycle. These beneficial effects, however, are associated with a decreased glucose flux through the glycolysis in the liver of arthritic and healthy rats.
Automatic Speech Assessment for People with Aphasia Using TDNN-BLSTM with Multi-Task Learning

This paper describes an investigation on automatic speech assessment for people with aphasia (PWA) using a DNN based automatic speech recognition (ASR) system. The main problems being addressed are the lack of training speech in the intended application domain and the relevant degradation of ASR performance for impaired speech of PWA. We adopt the TDNN-BLSTM structure for acoustic modeling and apply the technique of multi-task learning with large amount of domain-mismatched data. This leads to a signiﬁcant improvement on the recognition accuracy, as compared with a conventional single-task learning DNN system. To facilitate the extraction of robust text features for quantifying language impairment in PWA speech, we propose to incorporate N-best hypotheses and confusion network representation of the ASR output. The severity of impairment is predicted from text features and supra-segmental duration features using different regression models. Experimental results show a high correlation of 0 . 842 between the predicted severity level and the subjective Aphasia Quotient score.
Impact of Front-of-Pack Nutrition Labels on Portion Size Selection: An Experimental Study in a French Cohort

In the European Union (EU) three coloured graded Front-of-Pack labels (FoPLs), two endorsed by governments (Nutri-Score and Multiple Traffic Lights (MTL)) and one designed by industry (Evolved Nutrition Label (ENL)) are currently being discussed. This study aimed to investigate the impact of these FoPLs on portion size selection, specifically for less healthy products. In 2018, participants from the French NutriNet-Santé cohort study (N = 25,772) were exposed through a web-based self-administered questionnaire to products from three food categories (sweet biscuits, cheeses, and sweet spreads), with or without FoPLs, and were invited to select the portion they would consume (in size and number). Kruskall-Wallis tests, and mixed ordinal logistic regression models, were used to investigate the effects of FoPLs on portion size selection. Compared to no label, Nutri-Score consistently lowered portion sizes (OR = 0.76 (0.74–0.76)), followed by MTL (OR = 0.83 (0.82–0.84)). For ENL, the effects differed depending on the food group: It lowered portion size selection for cheeses (OR = 0.84 (0.83–0.87)), and increased it for spreads (OR = 1.19 (1.15–1.22)). Nutri-Score followed by MTL appear efficient tools to encourage consumers to decrease their portion size for less healthy products, while ENL appears to have inconsistent effects depending on the food category.
Online Incremental Learning for Speaker-Adaptive Language Models

Voice control is a prominent interaction method on personal computing devices. While automatic speech recognition (ASR) systems are readily applicable for large audiences, there is room for further adaptation at the edge, ie. locally on devices, targeted for individual users. In this work, we explore improving ASR systems over time through a user’s own interactions. Our on-line learning approach for speaker-adaptive language modeling leverages a user’s most recent utterances to enhance the speaker dependent features and traits. We experiment with the Large-Vocabulary Continuous Speech Recognition corpus Tedlium v2, and demonstrate an average reduction in perplexity (PPL) of 19.18% and average relative reduction in word error rate (WER) of 2.80% compared to a state-of-the-art baseline on Tedlium v2.