Miracle-dz/UniFAR-FTUs huggingface dataset:
MINTIN: Maxout-Based and Input-Normalized Transformation Invariant Neural Network

Convolutional Neural Network (CNN) is a powerful model for image classification, but it is insufficient to deal with the spatial variance of the input. This paper presents a Maxout-based and input-normalized transformation invariant neural network (MINTIN), which aims at addressing the nuisance variation of images and accumulating transformation invariance. We introduce an innovative module, the Normalization, and combine it with the Maxout operator. While the former focuses on each image itself, the latter pays attention to augmented versions of input, resulting in fully-utilized information. This combination, which can be inserted into existing CNN architectures, enables the network to learn invariance to rotation and scaling. While the authors of TI-POOLING acclaimed that they reached state-of-the-art results, ours reach a maximum decrease of 0.71%, 0.23% and 0.51% in error rate on MNIST-rot-12k, half-rotated MNIST and scaling MNIST, respectively. The size of the network is also significantly reduced, leading to high computational efficiency.
A Joint Model of Conversational Discourse Latent Topics on Microblogs

Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts results in poor message-level word co-occurrence patterns. To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: (1) different roles of conversational discourse, and (2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities of messages with varying discourse roles in containing topical words, our model is able to discover clusters of discourse words that are indicative of topical content. In an automatic evaluation on large-scale microblog corpora, our joint model yields topics with better coherence scores than competitive topic models from previous studies. Qualitative analysis on model outputs indicates that our model induces meaningful representations for both discourse and topics. We further present an empirical study on microblog summarization based on the outputs of our joint model. The results show that the jointly modeled discourse and topic representations can effectively indicate summary-worthy content in microblog conversations.
Role of the CHADS2 Score in the Evaluation of Carotid Atherosclerosis in Patients with Atrial Fibrillation Undergoing Carotid Artery Ultrasonography

Objective This study investigated the characteristics of carotid atherosclerosis in patients with atrial fibrillation (AF) and determined the feasibility and significance of the CHADS2 score in predicting the degree of carotid atherosclerosis. Methods Consecutive patients (n = 109) with nonvalvular AF were registered and classified into two groups, the paroxysmal AF group (n = 59) and persistent AF group (n = 50). Fifty healthy patients, matched by sex and age, were considered the control group. All patients were examined using carotid ultrasound and velocity vector imaging (VVI). Results Compared with the control group, the mean intimal-medial thickness in the paroxysmal AF group (0.56 ± 0.11 versus 0.61 ± 0.10, respectively, P < 0.05) and the persistent AF group (0.56 ± 0.11 versus 0.64 ± 0.13, respectively, P < 0.001) was significantly increased. The plaque index (PI) in the persistent AF group was significantly higher than that observed in the paroxysmal AF group (1.05 ± 1.33 versus 1.42 ± 1.47, respectively, P < 0.001). Regarding the VVI indices, those reflecting the long-axis longitudinal motion function of carotid arteries were significantly decreased in both AF groups. Compared with the control group, a significantly lower total longitudinal displacement (tLoD) index was observed in the persistent AF group (0.73 ± 0.66 versus 0.31 ± 0.23, respectively, P < 0·0001) and the paroxysmal AF group (0.73 ± 0.66 versus 0.34 ± 0.17, P < 0·0001). The CHADS2 score was related to indicators reflecting the structure and function of the carotid artery. Conclusions Carotid arterial structure and function were significantly altered in patients with AF. The degree of carotid atherosclerosis depended on the duration of AF. The CHADS2 score may be useful as a predictor of the extent of carotid atherosclerosis in patients with AF.
Multi- View Fusion for Action Recognition in Child-Robot Interaction

Answering the challenge of leveraging computer vision methods in order to enhance Human Robot Interaction (HRI) experience, this work explores methods that can expand the capabilities of an action recognition system in such tasks. A multi-view action recognition system is proposed for integration in HRI scenarios with special users, such as children, in which there is limited data for training and many state-of-the-art techniques face difficulties. Different feature extraction approaches, encoding methods and fusion techniques are combined and tested in order to create an efficient system that recognizes children pantomime actions. This effort culminates in the integration of a robotic platform and is evaluated under an alluring Children Robot Interaction scenario.
On Regression Losses for Deep Depth Estimation

Depth estimation from a single monocular image has reached great performances thanks to recent works based on deep networks. However, as various choices of losses, architectures and experimental conditions are proposed in the literature, it is difficult to establish their respective influence on the performances. In this paper we propose an in-depth study of various losses and experimental conditions for depth regression, on NYUv2 dataset. From this study we propose a new network for depth estimation combining an encoder-decoder architecture with an adversarial loss. This network reaches top scores in the competitive evaluation of NUYv2 dataset while being simpler to train in a single phase.
Bark of Passiflora edulis Treatment Stimulates Antioxidant Capacity, and Reduces Dyslipidemia and Body Fat in db/db Mice

Obesity is considered an important risk factor for several disorders, such as diabetes mellitus, systemic arterial hypertension, dyslipidemia, and atherosclerosis, which are associated with inflammation and oxidative stress as a trigger factor. Passiflora edulis contains important bioactive compounds, such as phenolics, carotenoids, vitamin C, and polyamines in pulp, leaves, seeds, and bark. Aim: To evaluate the effect of bark of Passiflora edulis (BPe) on body composition, and metabolic and oxidative stress parameters in genetically obese mice. Methods: Obese male db/db mice (n = 14 animals) received normal feeds and water ad libitum for 8 weeks. Then, animals were randomly divided to continue either receiving standard chow (obese, n = 7 (OB)) or feed with standard chow plus bark Passiflora edulis (BPe) (obese + BPe, n = 7 (OB + BPe)) for 8 more weeks, totaling 16 weeks. BPe was added to chow (7 g of BPe/kg of chow corresponding to 1.5 g/kg of body weight). The parameters evaluated in animals included food and caloric intake, body weight, body fat, plasma glucose, triglycerides, and total cholesterol. Malondialdehyde and antioxidant capacity were evaluated in plasma and organs. Groups were compared by Student t-test, with p < 0.05. Results: BPe reduced visceral and subcutaneous fat deposit and adiposity index, cholesterol and triglyceride levels, ameliorated the antioxidant capacity, and reduced malondialdehyde (MDA) levels. Conclusion: the bark of Passiflora edulis was effective in improving body composition, and metabolic and antioxidant parameters in obese mice.
A Generalization of PLDA for Joint Modeling of Speaker Identity and Multiple Nuisance Conditions

Probabilistic linear discriminant analysis (PLDA) is the leading method for computing scores in speaker recognition systems. The method models the vectors representing each audio sample as a sum of three terms: one that depends on the speaker identity, one that models the within-speaker variability, and one that models any remaining variability. The last two terms are assumed to be independent across samples. We recently proposed an extension of the PLDA method, which we termed Joint PLDA (JPLDA), where the second term is considered dependent on the type of nuisance condition present in the data (e.g., the language or channel). The proposed method led to significant gains for multilanguage speaker recognition when taking language as the nuisance condition. In this paper, we present a generalization of this approach that allows for multiple nuisance terms. We show results using language and several nuisance conditions describing the acoustic characteristics of the sample and demonstrate that jointly including all these factors in the model leads to better results than including only language or acoustic condition factors. Overall, we obtain relative improvements in detection cost function between 5% and 47% for various systems and test conditions with respect to standard PLDA approaches. © 2018 International Speech Communication Association. All rights reserved.
Trends and determinants of antimicrobial susceptibility of Neisseria gonorrhoeae in the Netherlands, 2007 to 2015

Background Neisseria gonorrhoeae antibiotic resistance surveillance is important to maintain adequate treatment. We analysed 2007–15 data from the Gonococcal Resistance to Antimicrobials Surveillance (GRAS), which currently includes 19 of 25 sexually transmitted infection (STI) centres in the Netherlands. Methods: From each patient with a gonorrhoea culture, the minimum inhibitory concentration (MIC) for several antibiotics was determined. Time trends were assessed by geometric means and linear regression of logarithmic MIC. Determinants for decreased susceptibility to ceftriaxone (MIC > 0.032 mg/L) and resistance to cefotaxime (MIC > 0.125 mg/L) and azithromycin (MIC > 0.5 mg/L) were assessed using stratified logistic regression. Results: 11,768 isolates were analysed. No ceftriaxone resistance was found. In 2015, 27 of 1,425 isolates (1.9%) were resistant to cefotaxime and 176 of 1,623 (10.9%) to azithromycin. Ceftriaxone susceptibility showed no trend (p = 0.96) during the study period, but cefotaxime MIC decreased (p < 0.0001) and azithromycin MIC increased (p < 0.0001) significantly. Concerning ceftriaxone, isolates of men who have sex with men (MSM) from 2013 (p = 0.0005) and 2014 (p = 0.0004) were significantly associated with decreased susceptibility. Significant determinants for cefotaxime resistance were having ≥ 6 partners for women (p = 0.0006). For azithromycin,isolates from MSM collected in 2012 (p = 0.0035), 2013 (p = 0.012), and 2014 (p = 0.013), or from non-Dutch (p < 0.0001) or older (≥ 35 years; p = 0.01) MSM were significantly associated with susceptibility. Resistance in heterosexual men was significantly associated with being ≥ 25 years-old (p = 0.0049) or having 3–5 partners (p = 0.01). Conclusions: No ceftriaxone resistance was found, but azithromycin MIC increased in 2007–15. Resistance determinants could help with focused intervention strategies.
Hospital-Wide Surveillance of Healthcare-Associated Infections as a Source of Information about Specific Hospital Needs. A 5-Year Observation in a Multiprofile Provincial Hospital in the South of Poland

Healthcare-associated infections (HAIs) are adverse complications of hospitalisation resulting in delayed recovery and increased costs. The aim of this study was an analysis of epidemiological factors obtained in the framework of constant, comprehensive (hospital-wide) infection registration, and identification of priorities and needs in infection control, both with regard to targeted surveillance, as well as preventative actions. The study was carried out according to the methodology recommended by the HAI-Net (Surveillance Network) coordinated by the European Centre for Disease Prevention and Control, in the multiprofile hospital in Southern Poland, between 2012 and 2016. A total of 159,028 patients were under observation and 2184 HAIs were detected. The incidence was 1.4/100 admissions (2.7/1000 patient-das of hospitalisation) and significantly differed depending on the type of the patient care: in intensive care units (ICU) 16.9%; in surgical units, 1.3%; non-surgical units, 1.0%; and paediatric units, 1.8%. The most common HAI was gastrointestinal infections (GIs, 28.9%), followed by surgical site infections (SSIs, 23.0%) and bloodstream infections (BSIs, 16.1%). The vast majority of GIs, BSIs, urinary tract infections, and incidents of pneumonia (PN) were detected in non-ICUs. As many as 33.2% of cases of HAI were not confirmed microbiologically. The most frequently detected etiologic agent of infections was Clostridium difficile—globally and in GI (49%). Comprehensive analysis of the results allowed to identify important elements of surveillance of infections, i.e., surveillance of GI, PN, and BSI not only in ICU, but also in non-ICU wards, indicating a need for implementing rapid actions to improve compliance with HAI prevention procedures.
Image Splicing Detection Through Illumination Inconsistencies and Deep Learning

Fake news and deep fakes have been making social and mainstream media headlines. At the same time, engaged scientists strive for finding ways to detect forgeries and suspicious manipulations using even the subtlest clues. In this vein, this work proposes a new method for detecting photographic splicing by bringing together the high representation power of Illuminant Maps and Convolutional Neural Networks as a way of learning directly from available training data the most important hints of a forgery. This work propose a method that eliminates the laborious feature engineering process, allow locate forgery region and yields a classification accuracy of more than 96%, outperforming state-of-the-art methods in different datasets. The potential uses of the proposed method is further highlighted by analyzing some suspicious real-world photographs that recently broke the news.
Interaction network analysis of the six game complexes in high-level volleyball through the use of Eigenvector Centrality

Social Network Analysis establishes a network system and provides information about the relationships (edges) between system components (nodes). Although nodes usually correspond to actors within the network (e.g., the players), it is possible to stipulate game actions as nodes, thus creating a network of the flow of game actions. In this study, Eigenvector Centrality (a form of weighted centrality that considers n-order connections) was used to identify differences in the centrality of distinct game actions within each of the six game complexes of volleyball. Thirteen matches (46 sets, 2,049 rallies) of the final round of the 2015 FIVB's World Grand Prix (Women) were analyzed. Results showed that analyzing actions as actors (i.e., nodes) offers a clear and comprehensive understanding of game flow and poses an interesting alternative to mainstream research where players are considered nodes. Functional differences between the six game complexes were highlighted, denoting the validity of such division. Out-of-system playing (i.e., having to set the attack under non-ideal conditions, e.g., in KI, KII, KIII and KIV), emerged as a regularity of the game and should be translated into the training process.
Leveraging Native Language Information for Improved Accented Speech Recognition

Recognition of accented speech is a long-standing challenge for automatic speech recognition (ASR) systems, given the increasing worldwide population of bi-lingual speakers with English as their second language. If we consider foreign-accented speech as an interpolation of the native language (L1) and English (L2), using a model that can simultaneously address both languages would perform better at the acoustic level for accented speech. In this study, we explore how an end-to-end recurrent neural network (RNN) trained system with English and native languages (Spanish and Indian languages) could leverage data of native languages to improve performance for accented English speech. To this end, we examine pre-training with native languages, as well as multi-task learning (MTL) in which the main task is trained with native English and the secondary task is trained with Spanish or Indian Languages. We show that the proposed MTL model performs better than the pre-training approach and outperforms a baseline model trained simply with English data. We suggest a new setting for MTL in which the secondary task is trained with both English and the native language, using the same output set. This proposed scenario yields better performance with +11.95% and +17.55% character error rate gains over baseline for Hispanic and Indian accents, respectively.
Chronic fatigue syndrome patients have alterations in their oral microbiome composition and function

Host–microbe interactions have been implicated in the pathogenesis of chronic fatigue syndrome (CFS), but whether the oral microbiome is altered in CFS patients is unknown. We explored alterations of the oral microbiome in Chinese Han CFS patients using 16S rRNA gene sequencing and alterations in the functional potential of the oral microbiome using PICRUSt. We found that Shannon and Simpson diversity indices were not different in CFS patients compared to healthy controls, but the overall oral microbiome composition was different (MANOVA, p < 0.01). CFS patients had a higher relative abundance of Fusobacteria compared with healthy controls. Further, the genera Leptotrichia, Prevotella, and Fusobacterium were enriched and Haemophilus, Veillonella, and Porphyromonas were depleted in CFS patients compared to healthy controls. Functional analysis from inferred metagenomes showed that bacterial genera altered in CFS patients were primarily associated with amino acid and energy metabolism. Our findings demonstrate that the oral microbiome in CFS patients is different from healthy controls, and these differences lead to shifts in functional pathways with implications for CFS pathogenesis. These findings increase our understanding of the relationship between the oral microbiota and CFS, which will advance our understanding of CFS pathogenesis and may contribute to future improvements in treatment and diagnosis.
Differing Approaches to Managing the Chronic Breathlessness Syndrome in Advanced COPD: A Multi-National Survey of Specialists

Abstract This study explored the approaches of respiratory and palliative medicine specialists to managing the chronic breathlessness syndrome in patients with severe chronic obstructive pulmonary disease. A voluntary, online survey was emailed to all specialists and trainees in respiratory medicine in Australia and New Zealand (ANZ), and to all palliative medicine specialists and trainees in ANZ and the United Kingdom (UK). Five hundred and seventy-seven (33.0%) responses were received from 1,749 specialists, with 440 (25.2%) complete questionnaires included from 177 respiratory and 263 palliative medicine doctors. Palliative medicine doctors in ANZ and the UK had similar approaches to managing chronic breathlessness, whereas respiratory and palliative medicine doctors had significantly different approaches (p < 0.0001). Both specialties most commonly recommended a combination of non-pharmacological and pharmacological breathlessness management strategies. Respiratory doctors focussed more on pulmonary rehabilitation, whereas palliative medicine doctors recommended breathing techniques, anxiety management and the handheld fan. Palliative medicine doctors (197 (74.9%)) recommended short acting oral morphine for breathlessness, as compared with 73 (41.2%) respiratory doctors (p < 0.0001). Respiratory doctors cited opioid concerns related to respiratory depression and lack of knowledge. Nineteen (10.7%) respiratory doctors made no specific recommendations for managing chronic breathlessness. Both specialties reported actively managing chronic breathlessness, albeit with differing approaches. Integrated services, which combine the complementary knowledge and approaches of both specialities, may overcome current gaps in care and improve the management of distressing, chronic breathlessness.
Weighted Generalized Mean Pooling for Deep Image Retrieval

Spatial pooling over convolutional activations (e.g., max pooling or sum pooling) has been shown to be successful in learning deep representations for image retrieval. However, most pooling techniques assume that every activation is equally important, and as a result they suffer from the presence of uninformative image regions that play a negative role as regards matching or lead to the confusion of particular visual instances. To address this issue, we propose a trainable building block that steers pooling to local information important to the task at hand. The method formulates pooling as a weighted generalized mean (wGeM), in which weights are learned on activations, reflecting the discriminative power of each activation in image matching. Embedding wGeM in a deep network improves image representation and boosts retrieval performance on standard benchmarks. wGeM does not require any bounding box annotations, but instead learns the latent probabilities of activations from scratch. It even goes beyond objectness, and learns to look at important visual details rather than the whole region of the object of interest.
Multi-Label Action Unit Detection on Multiple Head Poses with Dynamic Region Learning

This paper presents a multi-label Action Unit (AU) detection method applied on multi-pose facial images. Action Unit detection on multiple head poses is an issue that robust AU detectors must deal with, as it is uncommon for a person to maintain always the same pose when displaying facial expressions. To this end, this work proposes a region learning approach, that dynamically creates regions of interest inside a convolutional neural network (CNN) using facial landmark points. The dynamic region learning (DRL) ensures that each AU is in the center of the region, and also follows the head pose movement. The DRL is built on top of the VGG-Face network, and transfer-learning is used to start the training. The experiments were conducted on the Facial Expression Recognition and Analysis Challenge (FERA 2017) database, which contains nine different head poses. The results show that the dynamic region learning is able to adapt to the nine poses in the database, improving the state-of-the-art with an an average F1-score of 0.582.
ConcatNet: A Deep Architecture of Concatenation-Assisted Network for Dense Facial Landmark Alignment

Facial landmark is one of the most basic elements for obtaining facial information such as facial expression and emotion. However, detecting dense landmarks on an image is challenging due to various facial poses. In this paper, a deep architecture for dense facial landmark detection, called ConcatNet, is proposed. In our architecture, we propose a CNN-based dense landmark detector on part regions of a face, which extends a given set of sparse landmarks to more accurate and dense landmarks. By introducing interface layers for coordinate normalization and part region localization, we concatenate a network for sparse landmark detection to ConcatNet in a global-to-local manner and the whole network to operate in an end-to-end manner. The experimental results on LFW and 300W datasets show that ConcatNet not only expands the number of the sparse landmarks but also increases the accuracy of the landmark positions remarkably. Also, ConcatNet shows high accuracy in detecting the dense landmarks with a smaller dataset and without additional data on an image such as 3D position annotations when compared to 3D model-based detection method.