Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Supervised Multimodal Deep Learning Model for Polarity Detection in Arguments

Deep learning has been successfully applied to many tasks such as image classification, feature learning, Text classification (sentiments analysis or opinion mining) etc. However, little research has focused on extracting polarity of sentiments expressed in text using a multimodal architecture. In other words, no researches take in consideration the multimodal nature of human behaviors before classifying sentiments. The representation of a person (also call User Modeling in some domains such as Intelligent Tutoring Systems) is an important feature to take in consideration if one wants to extract subjective information such as the polarity of sentiments expressed by the person. To design an effective representation of a user, it is important to consider all sources of data informing about its current state. We present a usersensitive deep multimodal architecture which takes advantage of deep learning and user data to extract a rich latent representation of a user. This rich latent representation mainly helps in text classification tasks. The architecture consists of the combination of a Long Short-Term Memory (LSTM), LSTM-Auto-Encoder, Convolutional Neural Networks and multiple Deep Neural Networks, in order to support the multimodality of data. The resulting model has been tested on a public multimodal dataset and is able to achieve best results compared to state-of-the-art algorithms for a similar task: detection of opinion polarity. The results suggest that the latent representation learnt from multimodal data helps in the discrimination of polarity of opinion.
A Biologically Plausible Speech Recognition Framework Based on Spiking Neural Networks

Humans perform remarkably well for speech recognition using sparse and asynchronous events carried by electrical impulses. Motivated by the observations that human brains primarily learn features from environmental stimuli in an unsupervised manner and consume extremely low power for complex cognitive tasks, we propose a biologically plausible speech recognition mechanism using unsupervised self-organizing map (SOM) for feature representation and event-driven spiking neural network (SNN) for spatiotemporal pattern classification. Moreover, we improve the biological realism of the proposed framework by using mel-scaled filter bank as the front-end, so as to mimic the human auditory system. Our experiments on the TIDIGITS dataset achieve speech recognition accuracy surpassing those of other bio-inspired systems. The proposed SOM-SNN framework can be implemented using the artificial silicon cochlear and neuromorphic processor, so as to fully exploit the potential of event-based speech recognition system.
Why Layering in Recurrent Neural Networks? A DeepESN Survey

The extension of Recurrent Neural Networks (RNNs) in the direction of deep learning is a topic that is gaining more and more attention in the neural networks community. The study of deep RNNs opened a number of intriguing research questions on the actual role played by layering in the architectural design of RNNs. Recently, the introduction of the Deep Echo State Network (DeepESN) model allowed to start addressing such open issues in literature, contributing to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions of recurrent layers. This contribution intends to present a unified view over the major advancements in the study of DeepESNs, enabling to directly point out the natural advantages of a layered construction of recurrent networks for temporal data processing.
Relative patterns of sexual activity and fertility among HIV positive and negative women—Evidence from 46 DHS

Objectives Projections of fertility of HIV positive women as ART scales up are needed to plan prevention of mother-to-child transmission (PMTCT) services. We describe differences in exposure to pregnancy between HIV positive and HIV negative women by age, region and national ART coverage to evaluate the extent to which behavioural differences explain lower fertility among HIV positive women and assess whether exposure to pregnancy has changed with antiretroviral treatment (ART) scale-up. Methods We analysed 46 nationally representative household surveys in sub-Saharan Africa conducted between 2003 and 2015 to estimate risk of exposure to recent sex and pregnancy of HIV positive and HIV negative women by age using a log binomial model. We tested for regional and urban/rural differences and associations with national ART coverage. We estimated an adjusted fertility rate ratio of HIV positive to HIV negative women adjusting for differences in exposure to pregnancy. Results Exposure to pregnancy differs significantly between HIV positive and negative women by age, modified by region. Younger HIV positive women have a higher exposure to pregnancy than HIV negative women and the opposite is true at older ages. The switch occurs at 25–29 for rural women and 30–34 for urban women. There was no evidence that exposure to pregnancy of HIV positive women have changed as national ART coverage increased. The inferred rate of fecundity of HIV positive women when adjusted for differences in exposure to pregnancy were lower than unadjusted fertility rate ratios in women aged 20–29 and 20–24 in urban and rural areas respectively varying between 0.6 and 0.9 over regions. Discussion The direct effects of HIV on fertility are broadly similar across ages, while the dramatic age gradient that has frequently been observed is largely attributable to variation in relative sexual exposure by age.
Introgression across Hybrid Zones Is Not Mediated by Large X-Effects in Green Toads with Undifferentiated Sex Chromosomes

Divergence between incipient species remains an incompletely understood process. Hybrid zones provide great research potential, reflecting natural organismal genomic interactions and gene evolution in a variety of recombinants over generations. While sex chromosomes are known evolutionary drivers of reproductive isolation, empirical population genetics has mostly examined species with heteromorphic sex chromosomes. We recently reported restricted introgression at sex-linked markers in an amphibian system with homomorphic sex chromosomes (Hyla), consistent with a large X-effect, designating a greater role of sex chromosomes in driving hybrid incompatibilities. Here, using a similar approach, we examined two hybrid zones of Palearctic green toads (Bufo viridis subgroup), involving several lineages that arose at different times and form secondary contacts. We find no evidence for differential introgression of sex-linked versus autosomal markers across both zones. This absence of large X-effects in Bufo indicates that, unlike in Hyla, hybrid incompatibilities may not result from the faster-heterogametic sex and faster-male aspects of Haldane’s rule. The recent suppression of XY recombination in Hyla but not in Bufo may have driven greater divergence between Hyla sex chromosomes, causing stronger reproductive isolation. Alternatively, stronger linkage among Hyla’s sex-linked markers could restrict introgression. We hypothesize that the degree of sex-specific recombination may condition the importance of homomorphic sex chromosomes in speciation.
Top-Down Person Re-Identification With Siamese Convolutional Neural Networks

Automated person re-identification is a challenging research problem that has many real-world applications, especially in video surveillance. While many recent studies have been focusing on solving the person re-identification problem using full-scale images or video footages, little work has been done to solve the person re-identification problem in a top-down context. In this work, we propose a solution to the top-down reidentification problem that uses the Siamese architecture in conjunction with Convolutional Neural Networks. In our approach, a pair of top-down images is distinguished by a single Siamese network, which is trained to predict the similarity, or a distance between two input images. Experiments have shown that once the model is properly trained, it is able to achieve one-shot, top-down re-identification by learning unseen classes of person in real-time.
Interception by two predatory fly species is explained by a proportional navigation feedback controller

When aiming to capture a fast-moving target, animals can follow it until they catch up, or try to intercept it. In principle, interception is the more complicated strategy, but also more energy efficient. To study whether simple feedback controllers can explain interception behaviours by animals with miniature brains, we have reconstructed and studied the predatory flights of the robber fly Holcocephala fusca and killer fly Coenosia attenuata. Although both species catch other aerial arthropods out of the air, Holcocephala contrasts prey against the open sky, while Coenosia hunts against clutter and at much closer range. Thus, their solutions to this target catching task may differ significantly. We reconstructed in three dimensions the flight trajectories of these two species and those of the presented targets they were attempting to intercept. We then tested their recorded performances against simulations. We found that both species intercept targets on near time-optimal courses. To investigate the guidance laws that could underlie this behaviour, we tested three alternative control systems (pure pursuit, deviated pursuit and proportional navigation). Only proportional navigation explains the timing and magnitude of fly steering responses, but with differing gain constants and delays for each fly species. Holcocephala uses a dimensionless navigational constant of N ≈ 3 with a time delay of ≈28 ms to intercept targets over a comparatively long range. This constant is optimal, as it minimizes the control effort required to hit the target. In contrast, Coenosia uses a constant of N ≈ 1.5 with a time delay of ≈18 ms, this setting may allow Coenosia to cope with the extremely high line-of-sight rotation rates, which are due to close target proximity, and thus prevent overcompensation of steering. This is the first clear evidence of interception supported by proportional navigation in insects. This work also demonstrates how by setting different gains and delays, the same simple feedback controller can yield the necessary performance in two different environments.
Ameliorating Effect of Herbal Antistressor Products on the Growth and Carcass Parameters of Japanese Quails Reared Under Heat Stress

The performance of the birds tends to decline during summer due to heat stress. In order to address the problem of heat stress, an experiment was conducted to investigate the effect of different herbal antistressor products on the growth performance of quails. For this, 120 day-old Japanese quails were randomly divided into four treatment groups with six replicates of five birds each. The experimental design consisted of T1: control, T2: Stressroak liquid, T3: AV/ESL/16 liquid, T4: Geriforte liquid. The results indicated that control group had significantly (P0.05). The feed conversion ratio improved significantly (P>0.05) in herbal antistressor supplemented groups compared to the control. There was no significant (P>0.05) effect on various carcass parameters studied, except for abdominal fat percentage which was significantly (P
SGX-Tor: A Secure and Practical Tor Anonymity Network With SGX Enclaves

With Tor being a popular anonymity network, many attacks have been proposed to break its anonymity or leak information of a private communication on Tor. However, guaranteeing complete privacy in the face of an adversary on Tor is especially difficult, because Tor relays are under complete control of world-wide volunteers. Currently, one can gain private information, such as circuit identifiers and hidden service identifiers, by running Tor relays and can even modify their behaviors with malicious intent. This paper presents a practical approach to effectively enhancing the security and privacy of Tor by utilizing Intel SGX, a commodity trusted execution environment. We present a design and implementation of Tor, called SGX-Tor, that prevents code modification and limits the information exposed to untrusted parties. We demonstrate that our approach is practical and effectively reduces the power of an adversary to a traditional network-level adversary. Finally, SGX-Tor incurs moderate performance overhead; the end-to-end latency and throughput overheads for HTTP connections are 3.9% and 11.9%, respectively.
Accurate Sparse Feature Regression Forest Learning for Real-Time Camera Relocalization

Camera relocalization is needed in several applications such as augmented reality or robot navigation. However, it is still challenging to have a both real-time and accurate method. In this paper, we present our hybrid method combing machine learning approach and geometric approach for real-time camera relocalization from a single RGB image. We introduce our sparse feature regression forest to improve the machine learning part. In our regression forest, we propose a novel split function, that uses a whole feature vector instead of classical binary test function to improve the accuracy of 2D-3D point correspondences. Moreover, we use sparse feature extraction (SURF features) to reduce time processing. The results indicate that our method is the only real-time hybrid method (50ms per frame). We also achieve results as accurate as the best state-of-the-art methods (hybrid methods) and outperform machine learning based and sparse feature based methods.
Repeated exposure and conditioning strategies for increasing vegetable liking and intake: systematic review and meta-analyses of the published literature

Abstract Background Vegetable intakes are typically lower than recommended for health. Although repeated exposure has been advocated to increase vegetable liking and consumption, no combination of the evidence yet provides a measure of benefit from repeated exposure or alternative conditioning strategies. Objective This work aimed to identify and synthesize the current evidence for the use of repeated exposure and conditioning strategies for increasing vegetable liking and consumption. Design Three academic databases were searched over all years of records using prespecified search terms. Published data from all suitable articles were tabulated in relation to 3 research questions and combined via meta-analyses. Results Forty-three articles detailing 117 comparisons investigating the use of repeated exposure and conditioning strategies for increasing liking and intakes of vegetables were found. Our analyses demonstrate: 1) increased liking and intakes of the exposed vegetable after repeated exposure compared with no exposure; 2) increased liking for the exposed vegetable after conditioning compared with repeated exposure, increased intakes after the use of rewards, and some suggestion of decreased intakes after flavor-nutrient conditioning; and 3) increased liking and intakes of a novel vegetable after repeated exposure to a variety of other vegetables compared with no exposure or repeated exposure to one other vegetable. Effect sizes, however, are small, and limited evidence suggests long-term benefits. Our analyses, furthermore, are limited by limitations in study design, compliance, and/or reporting. Conclusions Based on our findings, we recommend the use of repeated exposure to one and a variety of vegetables, and the use of rewards, for increasing vegetable liking and consumption. Confirmation from further large, well-conducted studies that use realistic scenarios, however, is also required. This study was registered at PROSPERO as CRD42017056919.
Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction

—Spatiotemporal sequence prediction is an important problem in deep learning. We study next-frame(s) video prediction using a deep-learning-based predictive coding framework that uses convolutional, long short-term memory (convLSTM) modules. We introduce a novel reduced-gate convolutional LSTM (rgcLSTM) architecture that requires a signiﬁcantly lower parameter budget than a comparable convLSTM. Our reduced-gate model achieves equal or better next-frame(s) prediction accuracy than the original convolutional LSTM while using a smaller parameter budget, thereby reducing training time. We tested our reduced gate modules within a predictive coding architecture on the moving MNIST and KITTI datasets. We found that our reduced-gate model has a signiﬁcant reduction of approximately 40 percent of the total number of training parameters and a 25 percent redution in elapsed training time in comparison with the standard convolutional LSTM model. This makes our model more attractive for hardware implementation especially on small devices.
Dietary Buglossoides arvensis Oil as a Potential Candidate to Substitute Fish Oil in Rainbow Trout Diets.

The utilization of vegetable oils in salmonid diets substantially decreased the body content of omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA), and thus the product quality for human consumption. Therefore, new ingredients for aquaculture feeds are needed that maximize the deposition of health-promoting n-3 LC-PUFA. This study investigated Buglossoides arvensis (Ahiflower) oil, a plant oil rich in alpha-linolenic acid (18:3n-3, ALA) and stearidonic acid (18:4n-3, SDA), as a source of n-3 fatty acids in rainbow trout (Oncorhynchus mykiss) nutrition. Rainbow trout (87.4 ± 0.6 g) were fed for 56 days. The oils of the control diet (FV) were substituted by Ahiflower oil at 33%, 66%, and 100% (A33, A66, A100). Dietary Ahiflower oil increased the final body weights of fish. mRNA steady state levels of fatty acyl desaturase 2a (delta-6) (fads2a(d6)) and 2b (delta-5) (fads2b(d5)) as well as carnitine palmitoyl transferase 1 a (cpt1a) were not altered by dietary treatments. In contrast, cpt1c mRNA steady state levels were significantly downregulated in samples of fish fed A66 and A100. Significantly higher eicosapentaenoic acid (20:5n-3, EPA) and docosahexaenoic acid (22:6n-3, DHA) levels were found in the liver and significantly higher EPA levels in the fillet of rainbow trout of A66 and A100 compared to FV. The content of DHA in fillets of fish fed Ahiflower oil was not significantly different to fish fed FV. Thus, high dietary amounts of Ahiflower oil can compensate for reduced dietary EPA and DHA levels.
Hierarchical Laplacian Score for unsupervised feature selection

In this paper, we address the problem of unsupervised feature selection. This is an important challenge due to the absence of class labels that would guide the search for relevant information. Motivated by this challenge, we define the new method named Hierarchical Laplacian Score (HLS) that constrains the Laplacian Score using a tree topology structure. The purpose of using this structure is to automatically discover local data structure and local nearest neighbors for each data object. Experimental results on various datasets have demon- strated the effectiveness of the proposed algorithm in clustering and classification applications.
Apprenticeship Bootstrapping

Apprenticeship learning is a learning scheme based on the direct imitation of humans. Inverse reinforcement learning is used to learn a reward function from human data. Coupling Inverse reinforcement learning with reinforcement learning has demonstrated production of human-competitive policies. However, obtaining human subjects with the right level of skills for complex tasks can be a challenge. We propose a new learning scheme called Apprenticeship Bootstrapping to learn a composite task using human demonstrations on sub-tasks. The scenario is a ground-air interaction task with an Unmanned Aerial Vehicle that needs to maintain 3 autonomous Unmanned Ground Vehicles within range of an imaging sensor. For validation, we show that the bootstrapped policy performs as good as a policy learnt from a human performing the composite task. The method offers a clear advantage when skilled humans are available for simpler tasks that form the building blocks for a more complex task, where availability of experts is limited.
Association between echo intensity and attenuation of skeletal muscle in young and older adults: a comparison between ultrasonography and computed tomography

Background Skeletal muscle experiences both quantitative and qualitative changes with aging. Echo intensity (EI) of the skeletal muscle obtained by ultrasonography (US) has been considered to reflect muscle quality. This technique is noninvasive, easily accessible, and is relatively inexpensive than that of other imaging techniques such as computed tomography (CT). Previous studies have reported that EI is related to several physical performances. However, few studies have investigated the validity of EI against other imaging methods in case of imaging of the skeletal muscle. We compared quantitative and qualitative indices evaluated by the US and CT imaging systems to strengthen their validity. Participants and methods A total of 40 adults participated in this study: 19 young (10 men and 9 women; their mean (±SD) age was 22.7±1.5 years) and 21 older adults (13 men and 8 women; their mean age was 70.6±4.8 years). Both thighs of each participant were evaluated using US and CT imaging systems. Results With respect to the US indices, the young group had significantly higher muscle thickness (MT) of the front thigh (P<0.001) and lower EI (P=0.001) than that of the older group. With respect to the CT indices, the cross-sectional area (CSA) and mean CT value (Hounsfield unit [HU]) of the muscle were found to be significantly higher in the young group (P<0.001) than that of the older group, whereas the percentage of low-density muscle area (%LDMA) was found to be significantly higher in the older group (P<0.001) than that of the young group. A significant, strong, and positive correlation was observed between MT and CSA, which reflects the muscle quantity (r=0.774; P<0.001). With respect to the indices of muscle quality, we found significant and moderate correlations between EI and CT values (r=−0.502; P<0.001) and between EI and %LDMA (r=0.441; P<0.001). However, these correlations were found to be decreased in the older group [between EI and CT value (r=−0.363; P=0.018) and between EI and %LDMA (r=0.257; P=0.100)]. Conclusion Results of this study indicate that the EI is moderately associated with muscle attenuation as assessed by CT, which means that higher EI at least partly reflects intramuscular lipid infiltration.
Epidemiology of Buruli Ulcer Infections, Victoria, Australia, 2011–2016

Buruli ulcer (BU) is a destructive soft-tissue infection caused by the environmental pathogen Mycobacterium ulcerans. In response to rising BU notifications in the state of Victoria, Australia, we reviewed all cases that occurred during 2011–2016 to precisely map the time and likely place of M. ulcerans acquisition. We found that 600 cases of BU had been notified; just over half were in residents and the remainder in visitors to defined BU-endemic areas. During the study period, notifications increased almost 3-fold, from 66 in 2013 to 182 in 2016. We identified 4 BU-endemic areas: Bellarine Peninsula, Mornington Peninsula, Frankston region, and the southeastern Bayside suburbs of Melbourne. We observed a decline in cases on the Bellarine Peninsula but a progressive increase elsewhere. Acquisitions peaked in late summer. The appearance of new BU-endemic areas and the decline in established areas probably correlate with changes in the level of local environmental contamination with M. ulcerans.