Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling Salient Features as Directions in Fine-Tuned Semantic Spaces

In this paper we consider semantic spaces consisting of objects from some particular domain (e.g. IMDB movie reviews). Various authors have observed that such semantic spaces often model salient features (e.g. how scary a movie is) as directions. These feature directions allow us to rank objects according to how much they have the corresponding feature, and can thus play an important role in interpretable classifiers, recommendation systems, or entity-oriented search engines, among others. Methods for learning semantic spaces, however, are mostly aimed at modelling similarity. In this paper, we argue that there is an inherent trade-off between capturing similarity and faithfully modelling features as directions. Following this observation, we propose a simple method to fine-tune existing semantic spaces, with the aim of improving the quality of their feature directions. Crucially, our method is fully unsupervised, requiring only a bag-of-words representation of the objects as input.
Pirate Dice: An Ethnomethodological Analysis of Paired Playing

Paired playing is a variant of the think aloud method that has potential value in understanding the moment-to-moment work that players perform as they play computer games. Compared with traditional think aloud approaches, the use of two participants creates an experience that can be less awkward and confronting, though at the cost of managing and maintaining a social interaction. This paper reports on the results obtained from an ethnomethodologically-informed analysis of paired playing sessions of a custom computer game, the Pirate Dice game, which was aimed at understanding the kind of work that players performed in order to function as a pair, with implications for understanding some of the limits of this method. It reveals that players were involved in a skilful and intricate "dance" involving talk, gestures, the mouse and the game itself; also identifying some of the variability in the playing experience of these pairs and that collaborative play is its own mode of play. Overall it suggests that paired playing may be complementary to other forms of think aloud but is no replacement.
Association Between Renal Dysfunction and Major Adverse Cardiac Events After Liver Transplantation: Evidence from an International Randomized Trial of Everolimus-Based Immunosuppression

Background Prospective evidence is lacking regarding the association between renal dysfunction and cardiovascular events after liver transplantation. Material/Methods Data were analyzed post hoc regarding renal function and major adverse cardiac events in a two-year prospective trial of de novo liver transplant recipients randomized at 30 days post-transplant to (i) everolimus [EVR]/reduced tacrolimus [EVR/rTAC] (ii) EVR with tacrolimus discontinued [TAC Elimination] or (iii) standard tacrolimus [TAC Control]. Results By month 24 post-transplant, 32/716 patients had experienced a first major cardiac event (4.5%): 4.1% (10/245), 2.2% (5/229) and 7.0% (17/242) of patients in the EVR/rTAC, TAC Elimination and TAC Control groups, respectively (p=0.043). The cumulative eGFR area under the curve (AUC) from randomization to month 24 was 119 706, 123 082, and 105 946 mL in the EVR/rTAC, TAC Elimination, and TAC Control groups, respectively, corresponding to a mean eGFR AUC of 82.4, 83.0, and 71.9 mL/min/1.73 m2. Cox regression modeling showed that mean eGFR AUC was inversely associated with time to first major cardiac event: the hazard ratio per mL/min/1.73 m2 was −0.0000015 [95% CI −0.00000078; −0.0000024] (p<0.001). Conclusions These findings confirm retrospective evidence that the risk of major cardiac events increases with deteriorating renal function after liver transplantation and demonstrate the need for careful cardiovascular risk management in patients with renal impairment. Immunosuppression based on everolimus with tacrolimus withdrawal, or to a lesser extent tacrolimus reduction, improves both renal function and the risk of major cardiac events compared to standard tacrolimus therapy in liver transplant recipients.
Pose-Invariant Face Detection by Replacing Deep Neurons with Capsules for Thermal Imagery in Telemedicine

The aim of this work was to examine the potential of thermal imaging as a cost-effective tool for convenient, nonintrusive remote monitoring of elderly people in different possible head orientations, without imposing specific behavior on users, e.g., looking toward the camera. Illumination and pose invariant head tracking is important for many medical applications as it can provide information, e.g., about vital signs, sensory experiences, injuries, wellbeing. In the performed experiments, we investigated the influence of different modifications of images (rotation, displacement of facial features, and displacement of facial quarters) on the prediction accuracy. Specifically, two models were tested on the set of collected low-resolution thermal images: Inception V3 Convolutional Neural Network (CNN) and Hinton’s Capsule Network. The preliminary results confirm that the prediction ability of the model based on capsules can deal with different head orientations much better than CNN (for the 45° head rotation Capsule Network achieved ~% accuracy while CNN only 9.5%).
Nonlinear Control of an Industrial Charger

The charging of electric cars is an open field of innovation for power converters control. Different hardware (e.g active filters) and software solutions (e.g. non-linear control law) are used to mitigate on the one hand the constrains of the connection to the electric distribution network and on the other hand the power demands of the battery. In this paper a non-linear back-stepping based control is proposed for an industrial charger. A comparative study of the performances for the proposed control architecture is presented in respect with the existing control strategy currently integrated in the electric cars. The comparison is realised for imperfect charging conditions: presence of inductive line impedance and harmonic disturbed network voltage.
Improving Collaborative Filtering's Rating Prediction Coverage in Sparse Datasets by Exploiting User Dissimilarity

Collaborative filtering systems analyze the ratings databases to identify users with similar likings and preferences, termed as near neighbors, and then generate rating predictions for a user by examining the ratings of his near neighbors for items that the user has not yet rated; based on rating predictions, recommendations are then formulated. However, these systems are known to exhibit the "gray sheep" problem, i.e. the situation where no near neighbors can be identified for a number of users, and hence no recommendation can be formulated for them. This problem is more intense in sparse datasets, i.e. datasets with relatively small number of ratings, compared to the number of users and items. In this work, we propose a method for alleviating this problem by exploiting user dissimilarity, under the assumption that if some users have exhibited opposing preferences in the past, they are likely to do so in the future. The proposed method has been evaluated against seven widely used datasets and has been proven to be particularly effective in increasing the percentage of users for which personalized recommendations can be formulated in the context of sparse datasets, while at the same time maintaining or slightly improving rating prediction quality.
Player Types and Player Behaviors: Analyzing Correlations in an On-the-field Gamified System

When the promotion of a positive behavioral change is the goal, most often persuasion techniques are used. To maximize the effectiveness of these techniques designers need to tailor the employed persuasion strategies to each individual. Many studies faced the problem of modeling players' profile by designing taxonomies. However, none of them verified if this approach works in practice. In this paper we investigate whether using a well-known player types categorization based on a questionnaire is an effective mean to represent the way players actually behave in an on-the-field gamified system.
Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach

Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: 1) avoiding causing harmful interference to primary users (PUs) and 2) conducting effective interference coordination with other SUs. These two problems become even more challenging for a distributed DSA network where there is no centralized controllers for SUs. In this paper, we investigate communication strategies of a distributive DSA network under the presence of spectrum sensing errors. To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn “appropriate” spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics. Furthermore, a special type of recurrent neural network, called the reservoir computing (RC), is utilized to realize DRL by taking advantage of the underlying temporal correlation of the DSA network. Using the introduced machine learning-based strategy, SUs could make spectrum access decisions distributedly relying only on their own current and past spectrum sensing outcomes. Through extensive experiments, our results suggest that the RC-based spectrum access strategy can help the SU to significantly reduce the chances of collision with PUs and other SUs. We also show that our scheme outperforms the myopic method which assumes the knowledge of system statistics, and converges faster than the Q-learning method when the number of channels is large.
Anomalous Sound Detection Using Deep Audio Representation and a BLSTM Network for Audio Surveillance of Roads

Surveillance systems based on image analysis can automatically detect road accidents to ensure a quick intervention by rescue teams. However, in some situations, the visual information is insufficiently reliable, whereas the use of a sound detector can greatly improve the overall reliability of the surveillance system. In this paper, we focus on detecting two classes of anomalous sounds for audio surveillance on roads, i.e., tire skidding and car crash, whose occurrences are an evidently acoustic indication of road accidents or disruptions. In the proposed method, we extract a feature of deep audio representation (DAR) and then use a classifier of a bidirectional long short-term memory network to determine the class of the sound to which each test audio segment belongs. We propose a framework based on multiple-stage deep autoencoder network (DAN) to extract the DAR, which fuses complementary information from several input features and thus can be more discriminative and robust than those input features. In the experiments, we discuss the influences of the parameter settings of the DAN’s hidden layers on the performance of DAR and compare the DAR with other features. Furthermore, the proposed method is compared to the state-of-the-art methods. In evaluating the data with various signal-to-noise ratios, the results show that the DAR outperforms other features, and the proposed method is superior to the state-of-the-art methods for detecting anomalous sounds on roads.
Role of P38 mitogen-activated protein kinase on Cx43 phosphorylation in cerebral vasospasm after subarachnoid hemorrhage

Abstract Objectives: Cx43 phosphorylation is involved in the pathogenesis of cerebral vasospasm (CVS) after subarachnoid hemorrhage (SAH). However, the exact phosphorylation mechanism of Cx43 in CVS is not fully elucidated. Thus, we examined the role of P38MAPK in CVS and Cx43 phosphorylation, using a double hemorrhage rat model. Methods: Sprague–Dawley rats weighing 300–350 g were grouped into sham, SAH, vehicle, and SAH + SB203580. SAH was induced by double injecting blood into the prechiasmatic cisterns. Neurological score was measured with the Garcia scoring system, and the diameters of basilar arteries and the expression of pCx43, pP38MAPK, and P38MAPK proteins were measured through pressure myograph measurement and Western blot analysis, respectively. Results: The neurological scores remarkably decreased after SAH but remarkably improved after SB203580 was used. The results of pressure myograph analysis on the SAH and vehicle groups showed the considerable narrowing of the basilar arteries in comparison with that of the sham group. By contrast, the arterial diameters in the SAH + SB203580 group were much larger than those observed in the SAH and vehicle groups. Moreover, the P38MAPK expression in the sham group had no substantial change in contrast to the SAH and vehicle groups, and pCx43 and pP38MAPK increased in the SAH and vehicle groups. Meanwhile, the SAH + SB203580 group showed marked decrease in Cx43 and P38MAPK phosphorylation levels relative to the SAH and vehicle groups. Conclusions: P38MAPK pathway facilitates the development of CVS through the upregulation of Cx43 phosphorylation.
Left ventricular dysfunction in relation with systemic inflammation in chronic obstructive pulmonary disease patients

Background/Aims Most important cause of mortality in chronic obstructive pulmonary disease (COPD) patients is known to be cardiovascular disease (CVD). The objective of the present study was to evaluate the echocardiographic parameters in COPD patients with or without pre-diagnosed CVD and to investigate the relationship between echocardiographic parameters and systemic inflammation markers. Methods A total of 60 stable COPD patients (23 patients with CVD, group 1; 37 patients without CVD, group 2) and 21 healthy controls (group 3) were included in the study. Six-minute walking test (6MWT), COPD assessment test (CAT), and Body mass index, airflow Obstruction, Dyspnea, and Exercise (BODE) index results were recorded. High-sensitivity C-reactive protein (HsCRP), interleukin 8 (IL-8), fetuin-A, Clara cell protein (CCL-16), N-terminal pro-brain natriuretic peptide levels were studied in serum. Parameters of left and right ventricular systolic and diastolic function were measured by echocardiography. Results Patients with COPD had higher levels of systemic inflammation markers and lower level of inflammation inhibitor fetuin-A. When three groups were compared, group 1 had lower 6MWT result. HsCRP was highest in group 2 while other inflammatory markers were similar in groups 1 and 2. Regarding echocardiographic parameters, left ventricular ejection fraction (LVEF) was lower and left ventricle end-diastolic diameter (LVED), left ventricle end-systolic diameter (LVES) diameters were higher in group 1. The aortic diameter was higher in COPD patients. Fetuin-A was correlated with diameter of aorta and LVES. LVEF, LVED, and LVES were found to be correlated with functional parameters of COPD cases. Conclusions In COPD, left ventricular functions are affected as well as right ventricle before prominent clinical findings of cardiac disease and these echocardiographic parameters correlate with functional parameters of COPD patients.
Infection of Wildlife by Mycobacterium bovis in France Assessment Through a National Surveillance System, Sylvatub

Mycobacterium bovis infection was first described in free-ranging wildlife in France in 2001, with subsequent detection in hunter-harvested ungulates and badgers in areas where outbreaks of bovine tuberculosis (TB) were also detected in cattle. Increasing concerns regarding TB in wildlife led the French General Directorate for Food (DGAL) and the main institutions involved in animal health and wildlife management, to establish a national surveillance system for TB in free-ranging wildlife. This surveillance system is known as “Sylvatub. ” The system coordinates the activities of various national and local partners. The main goal of Sylvatub is to detect and monitor M. bovis infection in wildlife through a combination of passive and active surveillance protocols adapted to the estimated risk level in each area of the country. Event-base surveillance relies on M. bovis identification (molecular detection) (i) in gross lesions detected in hunter-harvested ungulates, (ii) in ungulates that are found dead or dying, and (iii) in road-killed badgers. Additional targeted surveillance in badgers, wild boars and red deer is implemented on samples from trapped or hunted animals in at-risk areas. With the exception of one unexplained case in a wild boar, M. bovis infection in free-living wildlife has always been detected in the vicinity of cattle TB outbreaks with the same genotype of the infectious M. bovis strains. Since 2012, M. bovis was actively monitored in these infected areas and detected mainly in badgers and wild boars with apparent infection rates of 4.57–5.14% and 2.37–3.04%, respectively depending of the diagnostic test used (culture or PCR), the period and according to areas. Sporadic infection has also been detected in red deer and roe deer. This surveillance has demonstrated that M. bovis infection, in different areas of France, involves a multi-host system including cattle and wildlife. However, infection rates are lower than those observed in badgers in the United Kingdom or in wild boars in Spain.
Multi-Agent Common Knowledge Reinforcement Learning

Cooperative multi-agent reinforcement learning often requires decentralised policies, which severely limit the agents' ability to coordinate their behaviour. In this paper, we show that common knowledge between agents allows for complex decentralised coordination. Common knowledge arises naturally in a large number of decentralised cooperative multi-agent tasks, for example, when agents can reconstruct parts of each others' observations. Since agents an independently agree on their common knowledge, they can execute complex coordinated policies that condition on this knowledge in a fully decentralised fashion. We propose multi-agent common knowledge reinforcement learning (MACKRL), a novel stochastic actor-critic algorithm that learns a hierarchical policy tree. Higher levels in the hierarchy coordinate groups of agents by conditioning on their common knowledge, or delegate to lower levels with smaller subgroups but potentially richer common knowledge. The entire policy tree can be executed in a fully decentralised fashion. As the lowest policy tree level consists of independent policies for each agent, MACKRL reduces to independently learnt decentralised policies as a special case. We demonstrate that our method can exploit common knowledge for superior performance on complex decentralised coordination tasks, including a stochastic matrix game and challenging problems in StarCraft II unit micromanagement.
First-order Affective Theory of Mind in Persons with Alzheimer’s Disease and in Healthy Older Adults

RÉSUMÉ La théorie de l’esprit (TdeE) est définie comme la capacité de reconnaître les états cognitifs et émotionnels d’autres personnes que soi. Les études évaluant la TdeE chez les personnes âgées en santé et celles atteintes de la maladie d’Alzheimer font état de résultats contradictoires. Cependant, il est reconnu que l’âge avancé (Ruffman, Henry, Livingstone, & Phillips, 2008) et la maladie d’Alzheimer (Elferink, Tilborg, & Kessels, 2015) n’affectent pas de manière uniforme la capacité d’identifier ou d’inférer différentes émotions. On a demandé à 17 personnes avec maladie d’Alzheimer, 21 personnes âgées et 25 jeunes adultes d’inférer les états émotionnels de personnages présentés sans détails faciaux dans des contextes émotionnels. Nous avons observé des résultats similaires chez les jeunes adultes et les adultes plus âgés, mais les performances des personnes atteintes de la maladie d’Alzheimer étaient plus faibles pour les émotions de dégoût, de tristesse et de surprise, sans qu’une différence soit notée pour les cas de colère, de peur ou de joie. Ces résultats suggèrent que les personnes avec maladie d’Alzheimer éprouvent des difficultés à inférer plusieurs états émotionnels, mais pas tous, lorsque de l’information contextuelle est fournie en l’absence d’indices faciaux. ABSTRACT “Theory of Mind” (ToM) is the capacity to deduce other persons’ cognitive and emotional states. Studies investigating affective ToM in healthy older adults and in persons with Alzheimer’s disease have reported contradictory results, although evidence indicates that advanced age (Ruffman, Henry, Livingstone, & Phillips, 2008) and Alzheimer’s disease (Elferink, van Tilborg, & Kessels, 2015) do not affect the ability to identify or infer different emotions to the same extent. To evaluate affective ToM abilities in these populations, we asked 63 individuals (17 with Alzheimer’s disease) to infer the emotional states of characters presented without facial details in emotional situations. We observed similar results in healthy younger and older adults, but poorer performance in persons with Alzheimer’s disease for disgust, sadness, and surprise, but not for anger, fear, and joy. Results suggest that persons with Alzheimer’s disease have difficulties in inferring several emotional states from contextual information without facial cues.
Efficient Privacy-Preserving Outsourcing of Large-Scale Geometric Programming

Nowadays industries are collecting a massive and exponentially growing amount of data that can potentially promote business innovations. However, it is challenging for resourcelimited clients to analyze their data in a cost-effective and timely way as the data volume keeps growing. With cloud computing, one feasible solution is to analyze the massive data by outsourcing them to the cloud. Nonetheless, clients’ data may contain private information that needs to be kept secret. In this paper, we design a secure, efficient, and verifiable outsourcing protocol specifically for geometric programming, which is one of the most fundamental problems in data analysis with many applications. In particular, a secure and efficient transformation scheme is used to encrypt the original geometric programming problem at the client side and protect its privacy before offloading it, and the gradient projection method is employed to solve the encrypted geometric programming problem in the cloud side. Experiments are conducted on both Amazon Elastic Compute Cloud (EC2) and a laptop to evaluate performance of the designed outsourcing protocol, and the results show the feasibility and efficiency of the protocol.
Controlling the Ćuk Converter Using Polytopic Lyapunov Functions

In this brief, the Ćuk dc/dc converter is controlled by a switching sequence based on the values of a polytopic Lyapunov function. In particular, the switching control signal is computed using a Lyapunov function which has polytopic level sets and it is not everywhere differentiable. The problem of not everywhere differentiability is handled using the Filippov’s approach for discontinuous differential equations. The main advantage of the presented control design approach is that it provides exact computation of the state variables’ ripple values which could not be obtained with previously used quadratic Lyapunov functions. The implementation particularities of the approach are illustrated and verified using a number of simulations.