Miracle-dz/UniFAR-FTUs huggingface dataset:
How responsiveness, group membership and gender affect the feeling of presence in immersive virtual environments populated with virtual crowds

When designing environments in Immersive Virtual Reality, virtual humans are often used to enrich them. In this paper, we research factors arising by the use of virtual crowds that may instigate more user participation in virtual reality scenarios. In particular, we examine whether implementing responsive virtual crowd behaviors toward the participant provides cues that increase the feeling of presence. The second factor we investigate is whether using appearance characteristics to a virtual crowd enable users to not only to identify as being socially related with the virtual characters, but also behave as such in a virtual environment, a factor we refer to as Group membership. We present an experiment in a Virtual Environment (VE) populated with virtual crowd with a violent incident where the user could intervene, aimed to determine how these factors contribute to the enhancement of plausibility and feeling of presence. Our results show that in IVR, virtual crowds with Responsive behavior can increase the feeling of Presence since the user tends to make more interventions when the virtual crowd is responsive towards and when the user is socially related to the incident's victim.
Evaluating Multiple User Interactions for Ranking Personalization Using Ensemble Methods

The variety of interaction paradigms on the Web, such as clicking, commenting or rating are important sources that help recommender systems to gather accurate information about users’ preferences. Ensemble methods can be used to combine all these pieces of information in a post-processing step to generate recommendations that are more relevant. In this paper, we review the application of existing ensemble methods to improve ranking recommendations in the multimodal interactions context. We compared four ensemble strategies, ranging from simple to complex algorithms including Gradient Descent and Genetic Algorithm to find optimal weights. The evaluation using the HetRec 2011 MovieLens 2k dataset with three different types of interactions shows that a considerable 7% improvement in the Mean Average Precision can be achieved using ensembles when compared to the most performant single interaction.
The passive mechanical properties of muscle.

The most remarkable property of muscles is that they can contract. So it is not surprising that most muscle physiologists study the behavior of contracting muscles. The focus on muscle contraction has meant that research into the mechanical properties of relaxed muscle has been relatively neglected. There is, however, much to be gained from studying the mechanical properties of relaxed skeletal muscles. That is because the passive properties of muscles have a central role in a diverse range of physiological and pathophysiological processes. To list just a few: Passive muscle tension resists joint motion so it places fundamental constraints on movement and motor control systems. The capacity of muscles to generate active forces is modulated by passive muscle forces. The way in which muscles deform under both passive and active conditions influences the sensitivity of muscle spindles and Golgi tendon organs to joint movement and muscle force. Passive and active muscle forces influence the behavior of mechanosensitive cells that regulate muscle growth. Excessive passive and active loading of muscles can cause tendinopathies, contusion injuries, and pressure ulcers. Muscle contracture—an abnormal increase in the passive stiffness of muscles—is a major cause of physical disability in people with common conditions such as stroke, spinal cord injury, and cerebral palsy. Interest in these phenomena and others has motivated research into the passive mechanical properties of skeletal muscles. Here we introduce a series of nine minireviews to be published in the Highlighted Topic “Passive Properties of Muscle” in this and upcoming issues of the Journal of Applied Physiology. Each minireview addresses an aspect of the passive mechanical properties of skeletal muscles. Topics include passive changes in muscle length (8), contemporary imagebased methods for measuring passive mechanical properties of skeletal muscles in vivo (1), epimuscular force transmission under passive conditions (15), passive force enhancement (9), properties of titin (6), muscle thixotropy (13), mechanical properties and physiological behavior of tendon (4), adaptations of the passive properties of skeletal muscle to altered patterns of use (2), and muscle contracture in cerebral palsy (14). In the following paragraphs we explain the context from which these topics have emerged and identify important directions for research into the passive mechanical properties of skeletal muscles. Context
Formalizing Time and Causality in Polychronous Polytimed Models. (Formalisation du temps et de la causalité dans les modèles polychrones polytemporisés)

Integrating components into systems turns out to be difficult when these components were designed according to different paradigms or when they rely on different time frames which must be synchronized. This synchronization may be event-driven (an event occurs because another event occurs) or time-driven (an event occurs because it is time for it to occur). Considering that each component admits its own time frame, and that they may not be related, a unique global time line may not exist.We are interested in specifying synchronization patterns for such polychronous and polytimed systems. Our study had led us to design semantic models for a timed discrete-event language, called the TESL language developed by Boulanger et al. This language has been used for coordinating the simulation of composite models and testing system integration.In this thesis, we present a denotational semantics providing an accurate and logic-consistent understanding of the language. Then we propose an operational semantics to derive satisfying runs from TESL specifications. It has been used for testing purposes, through the implementation of a solver, named Heron. To tackle the issue of the consistency and correctness of these semantic rules, we developed a co-inductive intermediate semantics that relates both the denotational and the operational semantics. Then we establish properties over the relation of our semantic models: soundness, completeness and progress, as well as local termination. Finally, our formalization and these proofs have been fully mechanized in the Isabelle/HOL proof assistant.
Molecular analysis of the dual targeting of the epidermal growth factor receptor and the O6-methylguanine-DNA methyltransferase with a double arm hybrid molecule

Disordered expression of the epidermal growth factor receptor (EGFR) has been associated with induction of DNA repair genes (e.g. XRCC1, ERCC1) and resistance to radiation and genotoxic drugs. However, our previous work showed that EGFR inhibition did not affect O6-methylguanine-DNA methyltransferase (MGMT)-mediated resistance. In order to block uncoupled events associated with EGFR and MGMT, we designed MR30, a single molecule termed “combi-molecule” that contains a quinazoline arm targeted to EGFR and an O6-benzylguanine (O6-BG) moiety to block MGMT. Molecular analysis of the mechanism of action of its two arms showed that: (a) it could block EGFR phosphorylation, (b) down-regulate the RAF-MAPK and the PI3K-AKT pathways, and (c) covalently modify MGMT through S-benzylation, as confirmed by MALDI analysis of a direct binding assay with isolated MGMT, (d) it induced a dose-dependent down-regulation of MGMT in lung and melanoma cells. The pleiotropic mechanism of action of MR30 culminated into strong growth inhibition (IC50: 0.018-6.02 μM), with superior activity when compared with an equimolar combination of gefitinib (a clinical EGFR inhibitor) and O6-BG (a known MGMT inhibitor). Pulse exposure experiments were required to attenuate the contribution of EGFR inhibition to the strong potency of MR30, thereby allowing to achieve the dose level required to sensitize cells to temozolomide (TMZ). Indeed, MR30 significantly sensitized EGFR-MGMT co-expressing cells to TMZ (p<0.05-0.0001). The results in toto suggest that MR30 is the first prototype of agents that may be used against tumours addicted to EGFR and to sensitize resistant tumours co-expressing EGFR and MGMT to TMZ.
The Correlation Between Neonatal Intensive Care Unit Safety Culture and Quality of Care

Supplemental digital content is available in the text. Objectives Key validated clinical metrics are being used individually and in aggregate (Baby-MONITOR) to monitor the performance of neonatal intensive care units (NICUs). The degree to which perceptions of key components of safety culture, safety climate, and teamwork are related to aspects of NICU quality of care is poorly understood. The objective of this study was to test whether NICU performance on key clinical metrics correlates with caregiver perceptions of safety culture. Study Design Cross-sectional study of 6253 very low-birth-weight infants in 44 NICUs. We measured clinical quality via the Baby-MONITOR and its nine risk-adjusted and standardized subcomponents (antenatal corticosteroids, hypothermia, pneumothorax, healthcare-associated infection, chronic lung disease, retinopathy screen, discharge on any human milk, growth velocity, and mortality). A voluntary sample of 2073 of 3294 eligible professional caregivers provided ratings of safety and teamwork climate using the Safety Attitudes Questionnaire. We examined NICU-level variation across clinical and safety culture ratings and conducted correlation analysis of these dimensions. Results We found significant variation in clinical and safety culture metrics across NICUs. Neonatal intensive care unit teamwork and safety climate ratings were correlated with absence of healthcare-associated infection (r = 0.39 [P = 0.01] and r = 0.29 [P = 0.05], respectively). None of the other clinical metrics, individual or composite, were significantly correlated with teamwork or safety climate. Conclusions Neonatal intensive care unit teamwork and safety climate were correlated with healthcare-associated infections but not with other quality metrics. Linkages to clinical measures of quality require additional research.
Infrastructure and Algorithms for Information Retrieval Based On Social Network Analysis/Mining. (Infrastructure et Algorithmes pour la Recherche d'Information Basés sur l'Analyse des Réseaux Sociaux)

Nowadays, the Web has evolved from a static Web where users were only able to consume information, to a Web where users are also able to produce information. This evolution is commonly known as Social Web or Web 2.0. Social platforms and networks are certainly the most adopted technologies in this new era. These platforms are commonly used as a means to interact with peers, exchange messages, share resources, etc. Thus, these collaborative tasks that make users more active in generating content are one of the most important factors for the increasingly growing quantity of available data. From the research perspective, this brings important and interesting challenges for many research fields. In such a context, a mostly crucial problem is to enable users to find relevant information with respect to their interests and needs. This task is commonly referred to as Information Retrieval (IR). IR is performed every day in an obvious way over the Web, typically under a search engine. However, classic models of IR don’t consider the social dimension of the Web. They model web pages as a mixture of a static homogeneous terms generated by the same creators. Then, ranking algorithms are often based on: (i) a query and document text similarity and (ii) the existing hypertext links that connect these web pages, e.g. PageRank. Therefore, classic models of IR and even the IR paradigm should be adapted to the socialization of the Web, in order to fully leverage the social context that surround web pages and users. This thesis presents many approaches that go in this direction. In particular, three methods are introduced in this thesis: (i) a Personalized Social Query Expansion (PSQE) framework, which achieves social and personalized expansions of a query with respect to each user, i.e. for the same query, different users will obtain different expanded queries. (ii) a Personalized Social Document Representation (PSDR) framework that uses social information to enhance, improve and provide a personalized social representation of documents to each user. (iii) a Social Personalized Ranking function called SoPRa, which takes into account social features that are related to users and documents. All these approaches have the particularity of being scalable to large-scale datasets, flexible and adaptable according to the high dynamicity of social data, and efficient since they have been intensively evaluated and compared to the closest works. From a practical point of view, this thesis led to the development of an experimental social Web search engine called LAICOS that includes all the algorithms developed throughout this thesis.
Efficient scheduling in a smart building

Smart buildings present challenging opportunities and issues regarding the intelligence we push in the framework that manage the building. Among them the scheduling and allocation of tasks/jobs. Based on the expertises of Qarnot Computing and LIPN Laboratory, we address three relevant matters of concern for smart buildings. First of all, we explain the economic model that the Qarnot middleware implements for managing, in a distributed way, smart buildings. The key idea is to consider the building as a data-center, managed by a cloud middleware. Then we introduce the key properties we consider as important in the context of the construction of a middleware for buildings. At last, this paper presents an efficient scheduling and allocation system according to an innovative economic model. The idea is to presents a new container scheduling system based on SLA (Service Level Agreements) classes and which is used in smart building with cloud computing environment. The novelty of our system is based on the possibility to adapt, dynamically, the scheduling and the resources allocation of containers according to the different SLA classes and the activities peaks of the nodes in the cloud i.e. the building. Experimental results show that our system gives expected results for our scenario and provides with good performance regarding the balance between objectives.
Locally Oriented Scene Complexity Analysis Real-Time Ocean Ship Detection from Optical Remote Sensing Images

Due to strong ocean waves, broken clouds, and extensive cloud cover interferences, ocean ship detection performs poorly when using optical remote sensing images. In addition, it is a challenge to detect small ships on medium resolution optical remote sensing that cover a large area. In this paper, in order to balance the requirements of real-time processing and high accuracy detection, we proposed a novel ship detection framework based on locally oriented scene complexity analysis. First, the proposed method can separate a full image into two types of local scenes (i.e., simple or complex local scenes). Next, simple local scenes would utilize the fast saliency model (FSM) to rapidly complete candidate extraction, and for complex local scenes, the ship feature clustering model (SFCM) will be applied to achieve refined detection against severe background interferences. The FSM considers a fusion enhancement image as an input of the pulse response analysis in the frequency domain to achieve rapid ship detection in simple local scenes. Next, the SFCM builds the descriptive model of the ship feature clustering algorithm to ensure the detection performance on complex local scenes. Extensive experiments on SPOT-5 and GF-2 ocean optical remote sensing images show that the proposed ship detection framework has better performance than the state-of-the-art methods, and it addresses the tricky problem of real-time ocean ship detection under strong waves, broken clouds, extensive cloud cover, and ship fleet interferences. Finally, the proposed ocean ship detection framework is demonstrated on an onboard processing hardware.
Regional Draining Lymph Nodes: Considerations for Medical Device Studies

Lymph nodes and associated lymphatics filter extracellular fluid and lymph to maintain tissue–fluid balance and detect distant tissue injury. Examination of regional draining lymph nodes (RDLs; lymph nodes that drain the route of article dosing) is an important step in detecting immunotoxicity and other associated changes during general toxicology studies. Similarly, evaluation of RDLs is often a key component of evaluating medical devices. Nonclinical medical device studies can present challenges for RDL evaluation, due to the wide variety of tissues and organs that are implanted with devices, the potential for wear debris/degradation products, and the likely disruption of normal lymphatic drainage by surgical procedures. This article discusses concepts for consideration when designing a nonclinical medical device study that includes the macroscopic evaluation, collection, histologic processing, microscopic assessment, and documentation of findings within RDLs. References describing RDLs for common implantation sites are reported, as are considerations for specific tissues and species commonly used in medical device biocompatibility and functional testing.
Low cost tips for tip-enhanced Raman spectroscopy fabricated by two-step electrochemical etching of 125 µm diameter gold wires

Tip-enhanced Raman spectroscopy (TERS) has become a well-applied technique for nanospectroscopy, allowing for single molecule sensitivity with sub-nanometer spatial resolution. The demand for efficient, reproducible and cost-effective probes for TERS is increasing. Here we report on a new electrochemical etching protocol to fabricate TERS tips starting from 125 µm diameter gold wires in a reproducible way. The process is reliable (50% of the tips have radius of curvature <35 nm, 66% <80 nm), fast (less than 2 min) and 2.5 times cheaper than the etching of standard 250 µm diameter wires. The TERS performance of the tips is tested on dyes, pigments and biomolecules and enhancement factors higher than 105 are observed. TERS mapping with a spatial resolution of 5 nm is demonstrated.
From Noise to Feature: Exploiting Intensity Distribution as a Novel Soft Biometric Trait for Finger Vein Recognition

Most finger vein feature extraction algorithms achieve satisfactory performance due to their texture representation abilities, despite simultaneously ignoring the intensity distribution that is formed by the finger tissue, and in some cases, processing it as background noise. In this paper, we exploit this kind of “noise” as a novel soft biometric trait for achieving better finger vein recognition performance. First, a detailed analysis of the finger vein imaging principle and the characteristics of the image are presented to show that the intensity distribution that is formed by the finger tissue in the background can be extracted as a soft biometric trait for recognition. Then, two finger vein background layer extraction algorithms and three soft biometric trait extraction algorithms are proposed for intensity distribution feature extraction. Finally, a hybrid matching strategy is proposed to solve the issue of dimension difference between the primary and soft biometric traits on the score level. A series of rigorous contrast experiments on three open-access databases demonstrate that our proposed method is feasible and effective for finger vein recognition.
Student performance prediction model for early-identification of at-risk students in traditional classroom settings

Student performance prediction is one of the educational data mining tasks that has received great deal of attentions. It enables the educators to improve the quality and effectiveness of classroom management and to help students achieve better academic performance. However, one of the major challenges in developing a prediction model in a traditional classroom setting is the limited amount of information available to be used as student attributes. In this research, we focus on developing a performance prediction models in information-lacking environment for identifying at-risk students who have tendency to receive low grades. To construct the models, five algorithms with different learning paradigms were investigated. The constructed models were evaluated on 5 different subjects. The results have shown that even though the prediction performances for the subjects that less depended on acquirable previous performance information were quite low, the performances for the subjects offered during higher year of the educational program were quite promising.
Cardiac fibrosis: new insights into the pathogenesis

Cardiac fibrosis is defined as the imbalance of extracellular matrix (ECM) production and degradation, thus contributing to cardiac dysfunction in many cardiac pathophysiologic conditions. This review discusses specific markers and origin of cardiac fibroblasts (CFs), and the underlying mechanism involved in the development of cardiac fibrosis. Currently, there are no CFs-specific molecular markers. Most studies use co-labelling with panels of antibodies that can recognize CFs. Origin of fibroblasts is heterogeneous. After fibrotic stimuli, the levels of myocardial pro-fibrotic growth factors and cytokines are increased. These pro-fibrotic growth factors and cytokines bind to its receptors and then trigger the activation of signaling pathway and transcriptional factors via Smad-dependent or Smad independent-manners. These fibrosis-related transcriptional factors regulate gene expression that are involved in the fibrosis to amplify the fibrotic response. Understanding the mechanisms responsible for initiation, progression, and amplification of cardiac fibrosis are of great clinical significance to find drugs that can prevent the progression of cardiac fibrosis.
Helpful or Not? An investigation on the feasibility of identifier splitting via CNN-BiLSTM-CRF

We recently introduced a new technique to handle source code identifier splitting. The proposed technique, denoted as CNN-BiLSTM-CRF[a neural network composed of a convolutional neural network(CNN), bidirectional long short-term memory networks(BiLSTM) and conditional random fields(CRFs)] enables us to obtain a model that splits identifiers correctly and effectively. This technique combines the use of a CNN layer with the mature BiLSTM-CRF model. The experimental results indicate that CNN-BiLSTM-CRF delivers outstanding performance on all four of the evaluation oracles. More importantly, we endeavored to provide insight into the practical feasibility of this technique by considering the aspects of generality, data size in demand and construction cost, etc. Finally, we reasoned out that CNN-BiLSTM-CRF should be helpful and improvable for identifier splitting in practical works in terms of the accuracy and feasibility. This was validated by multifaceted experiments.
Eosinophilic dermatosis of hematologic malignancy: Correlation of molecular characteristics of skin lesions and extracutaneous manifestations of hematologic malignancy

Skin diseases are frequent in patients with chronic lymphocytic leukemia (CLL) and other hematological neoplasias. Eosinophilic dermatosis (ED) of hematologic malignancy has long been considered a nonspecific cutaneous reaction pattern. Recently neoplastic cells have been shown to be present in ED, thus challenging the classification as a nonspecific dermatosis.