Miracle-dz/UniFAR-FTUs huggingface dataset:
360Anywhere

360-degree video is increasingly used to create immersive user experiences; however, it is typically limited to a single user and not interactive. Recent studies have explored the potential of 360 video to support multi-user collaboration in remote settings. These studies identified several challenges with respect to 360 live streams, such as the lack of gaze awareness, out-of-sync views, and missed gestures. To address these challenges, we created 360Anywhere, a framework for 360 video-based multi-user collaboration that, in addition to allowing collaborators to view and annotate a 360 live stream, also supports projection of annotations in the 360 stream back into the real-world environment in real-time. This enables a range of collaborative augmented reality applications not supported with existing tools. We present the 360Anywhere framework and tools that allow users to generate applications tailored to specific collaboration and augmentation needs with support for remote collaboration. In a series of exploratory design sessions with users, we assess 360Anywhere's power and flexibility for three mobile ad-hoc scenarios. Using 360Anywhere, participants were able to set up and use fairly complex remote collaboration systems involving projective augmented reality in less than 10 minutes.
Earnings management research: Italian convergence towards US

Facing the current convergence of Italian accounting research towards American accounting research, with specific reference to the stream of earnings management, the present paper aims at verifying if this convergence can be read as such institutional homogenization theorized in new institutional sociology (DiMaggio & Powell, 1983). Since accounting research can be viewed as an organizational field, immersed in the institutional setting of universities, the paper analyses the homogenization path of Italian EM research towards American one and detects the isomorphic mechanisms that drove such homogenization. To achieve its aims, the paper develops an historical comparison to highlight differences and similarities in the evolution of Italian and American EM research, according to international comparative accounting history. Such a comparison allows detecting those coercive, mimetic and normative pressures that drove the convergence of Italian EM research towards the dominating American EM research.
How Much does integrating this Commit Cost ?-A Position Paper

Integration is a common development activity that fuses multiple pieces of source code together, either developed in-house or acquired from a third-party (e.g., Git commit). Integration essentially combines software that was developed in parallel by different developers in different teams or organization. Thus it requires to iron out inconsistent and conflicting changes. Inconsistencies are costly and can only be identified a posterior. It is very hard to judge if it is worth integrating a code commit, or which of two equivalent commits or libraries to integrate. If there is an indication about the effort the integration will take, it will help the integrators to make dicisions easier. In this paper, we propose the ISOMO model to quantify the projected cost of integrating a code commit. This will help people to evaluate how much effort a commit will cost and decide if it is worth being integrated.
Exploration of parallel graph-processing algorithms on distributed architectures. (Exploration d'algorithmes de traitement parallèle de graphes sur architectures distribuées)

With the advent of ever-increasing graph datasets in a large number of domains, parallel graph-processing applications deployed on distributed architectures are more and more needed to cope with the growing demand for memory and compute resources. Though large-scale distributed architectures are available, notably in the High-Performance Computing (HPC) domain, the programming and deployment complexity of such graphprocessing algorithms, whose parallelization and complexity are highly data-dependent, hamper usability. Moreover, the difficult evaluation of performance behaviors of these applications complexifies the assessment of the relevance of the used architecture. With this in mind, this thesis work deals with the exploration of graph-processing algorithms on distributed architectures, notably using GraphLab, a state of the art graphprocessing framework. Two use-cases are considered. For each, a parallel implementation is proposed and deployed on several distributed architectures of varying scales. This study highlights operating ranges, which can eventually be leveraged to appropriately select a relevant operating point with respect to the datasets processed and used cluster nodes. Further study enables a performance comparison of commodity cluster architectures and higher-end compute servers using the two use-cases previously developed. This study highlights the particular relevance of using clustered commodity workstations, which are considerably cheaper and simpler with respect to node architecture, over higher-end systems in this applicative context. Then, this thesis work explores how performance studies are helpful in cluster design for graph-processing. In particular, studying throughput performances of a graph-processing system gives fruitful insights for further node architecture improvements. Moreover, this work shows that a more in-depth performance analysis can lead to guidelines for the appropriate sizing of a cluster for a given workload, paving the way toward resource allocation for graph-processing. Finally, hardware improvements for next generations of graph-processing servers areproposed and evaluated. A flash-based victim-swap mechanism is proposed for the mitigation of unwanted overloaded operations. Then, the relevance of ARM-based microservers for graph-processing is investigated with a port of GraphLab on a NVIDIA TX2-based architecture.
Semantic Wikis Distributed on Structured Peer-to-Peer Networks

P2P Semantic wikis were initially developed as collaborative editors with two main goals in mind: (1) ensure a massive collaborative editing in semantic wikis with low maintenance cost where thousands of users can participate in the wiki edition and (2) provide fault tolerance for semantic wikis. Their goals were attained through the adoption of an optimistic replication of their data (semantic wiki pages and annotations) on a set of interconnected semantic wiki servers. The edition of the semantic wiki pages and the execution of semantic queries are now distributed on different peers. Two Peer-to-Peer Semantic Wikis were developed, Swooki and DSMW. They are based on unstructured P2P network where each peer knows its neighbors without any knowledge about their contents. In spite of their advantages, Swooki will face scalability problem regarding the size of data that it is dealing with since it uses a total replication of the entire wiki pages and the triples store, it will be only suitable for small wikis, and DSMW semantic queries may return different and incomplete results since every peer has its own semantic wiki pages and annotations. In this paper, we propose to handle the problems of unstructured P2PSW by building Structured P2P semantic wiki (SP2PSW). The research work proposes all the possible approaches to build SP2PSW, evaluates each approach based on many standard evaluation metrics, derives the optimal one and develops the needed algorithms. At the end, a comparison with the existing unstructured P2PSW (Swooki and DSMW) shows the marked improvement of SP2PSW over Unstructured P2PSW.
Fingerprint and visible light communication based indoor positioning method

In this article, a fingerprint and visible light communication based indoor positioning method is proposed. This method recovers the optical wireless channel features and estimates the location of mobile terminal by these features. In order to reduce the computation complexity of the fingerprint-based positioning method, the proposed method combines triangulation and the nearest neighbor fingerprint algorithm. Firstly, triangulation is used to determine the cluster of probable locations of mobile terminal. The most probable location of mobile terminal within the cluster is then estimated by the nearest neighbor fingerprint algorithm. The simulation results show that the proposed method reduces fingerprint computation and achieves high accuracy in indoor localization.
Towards the deployment of cloud robotics at factory shop floors: A prototype for smart material handling

The evolution of industries and their needs towards the implementation of Industry 4.0 based systems has brought both new technological challenges and opportunities. This article proposes the adoption and deployment of cloud robotics at factories to enhance the control and monitoring of processes, such as handling materials multiple assemblies in single cells. The ultimate research objective of this research is the offloading computation and integrating cloud robotics into an industrial scenario. However, the investigation of state of the art techniques, tools and technologies, and the development of functional prototypes is beforehand required. Then, this article presents a small-scale system as a prototype that employs the Google cloud vision API as a resource that, in turn, is used by networked agents for supporting the decision-making in the process of handling material commodities at factory shop floor. The overall concept as well as the interaction between the main actors of the prototype is detailed. Finally, further research directions are discussed.
How Lifestyle Can Improve Health Expenses

Physical activity is important for the prevention of many diseases and to improve the conditions of sick people. “Exercise is medicine” is the new address for prevention and therapy. The boys also benefit of the sport activity for their psychological maturation. In addition, there is the damage from poor nutrition, alcohol dependence and the overuse of pharmacologically active substances, misuse of drugs, even those permitted. It is therefore necessary to increase the quantity/quality of time devoted to physical activity, both at school and outside school, but above all, it is vital the dissemination of knowledge of the problem. A widespread information / training, along with a sport and proper medical supervision into every type of school, would lead to a sharp reduction in public spending, much higher than the 80 million of which we have just spoken, with an almost negligible investment. The ideal would be to set up in School a course of education to health, with the principles of nutrition and physical activity, in order to improve the lifestyles and reduce health spending through health education, proper nutrition and sports activities. 1-2 hours a week would be enough to communicate to children, and indirectly their families and the families of the future, what are the criteria for a healthy life. It is however, necessary to have suitably qualified teachers for the treatment and dissemination of health prevention principles of proper nutrition and the correct approach to sports activities. A specific training should be therefore held in the University. Health education cannot be done by the science teachers or those of motor sciences, alone; they must have a physician specially trained to coordinate, perhaps in co-presence, the interventions.
Biochar as a Soil Amendment: A Review of the Environmental Implications

The term 'biochar' refers to black carbon formed by the pyrolysis of biomass i.e. by heating biomass in an oxygen-free or low oxygen environment such that it does not (or only partially) combusts. Traditional charcoal is one example of biochar produced from wood. The term 'biochar' is much broader than this however, encompassing black carbon produced from any biomass feedstock. The use of biochar as a soil additive has been proposed as a means to simultaneously mitigate anthropogenic climate change whilst improving agricultural soil fertility. This paper provides a review of what is known about both of these claims and also about the wider environmental implications of the adoption of this process. The intention of this review is not just to summarise current knowledge of the subject, but also to identify gaps in knowledge that require further research.
Learning the Parameters of Reinforcement Learning from Data for Adaptive Spoken Dialogue Systems. (Apprentissage automatique des paramètres de l'apprentissage par renforcement pour les systèmes de dialogues adaptatifs)

Cette these s’inscrit dans le cadre de la recherche sur les systemes de dialogue. Ce document propose d’apprendre le comportement d’un systeme a partir d’un ensemble de dialogues annotes. Le systeme apprend un comportement optimal via l’apprentissage par renforcement. Nous montrons qu’il n’est pas necessaire de definir une representation de l’espace d’etat ni une fonction de recompense. En effet, ces deux parametres peuvent etre appris a partir du corpus de dialogues annotes. Nous montrons qu’il est possible pour un developpeur de systemes de dialogue d’optimiser la gestion du dialogue en definissant seulement la logique du dialogue ainsi qu’un critere a maximiser (par exemple, la satisfaction utilisateur). La premiere etape de la methodologie que nous proposons consiste a prendre en compte un certain nombre de parametres de dialogue afin de construire une representation de l’espace d’etat permettant d’optimiser le critere specifie par le developpeur. Par exemple, si le critere choisi est la satisfaction utilisateur, il est alors important d’inclure dans la representation des parametres tels que la duree du dialogue et le score de confiance de la reconnaissance vocale. L’espace d’etat est modelise par une memoire sparse distribuee. Notre modele, Genetic Sparse Distributed Memory for Reinforcement Learning (GSDMRL), permet de prendre en compte de nombreux parametres de dialogue et de selectionner ceux qui sont importants pour l’apprentissage par evolution genetique. L’espace d’etat resultant ainsi que le comportement appris par le systeme sont aisement interpretables. Dans un second temps, les dialogues annotes servent a apprendre une fonction de recompense qui apprend au systeme a optimiser le critere donne par le developpeur. A cet effet, nous proposons deux algorithmes, reward shaping et distance minimisation. Ces deux methodes interpretent le critere a optimiser comme etant la recompense globale pour chaque dialogue. Nous comparons ces deux fonctions sur un ensemble de dialogues simules et nous montrons que l’apprentissage est plus rapide avec ces fonctions qu’en utilisant directement le critere comme recompense finale. Nous avons developpe un systeme de dialogue dedie a la prise de rendez-vous et nous avons collecte un corpus de dialogues annotes avec ce systeme. Ce corpus permet d’illustrer la capacite de mise a l’echelle de la representation de l’espace d’etat GSDMRL et constitue un bon exemple de systeme industriel sur lequel la methodologie que nous proposons pourrait etre appliquee
Unlocking the Potential of Flexible Energy Resources to Help Balance the Power Grid

Flexible energy resources can help balance the power grid by providing ancillary services. However, the balancing potential of most types of resources is restricted by physical constraints such as the size of their energy buffer, power ramp-rate limits, or control delays. Using the example of secondary frequency regulation, this paper shows how the flexibility of energy resources can be exploited more efficiently by considering multiple resources with complementary physical properties and controlling them in a coordinated way. To this end, optimal adjustable control policies are computed based on robust optimization. Our problem formulation takes into account power ramp-rate limits explicitly and accurately models the different timescales and lead times of the energy and reserve markets. Simulations demonstrate that aggregating select resources can lead to a synergy effect that frees up additional regulation capacity in the range of 18%–1190% compared with the amount of reserves the systems could provide individually. This synergy effect can unlock the regulation potential of energy resources with control delays or restrictive energy or ramp-rate constraints.
Effects of RNA interference‐mediated silencing of toll‐like receptor 4 gene on proliferation and apoptosis of human breast cancer MCF‐7 and MDA‐MB‐231 cells: An in vitro study

Breast cancer is known as the most prevalent cancer in women worldwide, and has an undeniable negative impact on public health, both physically, and mentally. This study aims to investigate the effects of toll‐like receptor 4 (TLR4) gene silencing on proliferation and apoptosis of human breast cancer cells to explore for a new theoretical basis for its treatment. TLR4 small interference RNA (siRNA) fragment recombinant plasmids were constructed, including TLR4 siRNA‐1, TLR4 siRNA‐2, and TLR4 siRNA‐3. Human breast cancer MCF‐7 and MDA‐MB‐231 cells were assigned into blank, negative control (NC), TLR4 siRNA‐1, TLR4 siRNA‐2, and TLR4 siRNA‐3 groups. MCF‐7 and MDA‐MB‐231 cell growth was detected by MTT assay. Apoptosis and cell cycle were determined by flow cytometry. Reverse transcription quantitative polymerase chain reaction (RT‐qPCR) and Western blot analysis were conducted to determine the expression of TLR4, CDK4, cyclin D1, Livin, Bcl‐2, p53, c‐FLIP, and caspase‐3. In comparison with the NC and blank groups, the TLR4 siRNA‐1, TLR4 siRNA‐2, and TLR4 siRNA‐3 groups showed decreased the expression of TLR4, inhibited proliferation of MCF‐7 and MDA‐MB‐231 cells and promoted MCF‐7 and MDA‐MB‐231 cell apoptosis, and the cells were blocked in G1 phase. In comparison with the NC and blank groups, in the TLR4 siRNA‐1, TLR4 siRNA‐2, and TLR4 siRNA‐3 groups, siRNA‐TLR4 significantly increased expression of p53 and caspase‐3 in MCF‐7 and MDA‐MB‐231 cells, while it decreased the expressions of CDK4, cyclinD1, Livin, Bal‐2, and c‐FLIP. The study demonstrates that TLR4 gene silencing inhibits proliferation and induces apoptosis of MCF‐7 and MDA‐MB‐231 cells.
Title Simultaneous model discrimination and parameter estimation in dynamic models of cellular systems Permalink

Background: Model development is a key task in systems biology, which typically starts from an initial model candidate and, involving an iterative cycle of hypotheses-driven model modifications, leads to new experimentation and subsequent model identification steps. The final product of this cycle is a satisfactory refined model of the biological phenomena under study. During such iterative model development, researchers frequently propose a set of model candidates from which the best alternative must be selected. Here we consider this problem of model selection and formulate it as a simultaneous model selection and parameter identification problem. More precisely, we consider a general mixed-integer nonlinear programming (MINLP) formulation for model selection and identification, with emphasis on dynamic models consisting of sets of either ODEs (ordinary differential equations) or DAEs (differential algebraic equations). Results: We solved the MINLP formulation for model selection and identification using an algorithm based on Scatter Search (SS). We illustrate the capabilities and efficiency of the proposed strategy with a case study considering the KdpD/KdpE system regulating potassium homeostasis in Escherichia coli. The proposed approach resulted in a final model that presents a better fit to the in silico generated experimental data. Conclusions: The presented MINLP-based optimization approach for nested-model selection and identification is a powerful methodology for model development in systems biology. This strategy can be used to perform model selection and parameter estimation in one single step, thus greatly reducing the number of experiments and computations of traditional modeling approaches.
Curing Behavior and Properties of Rice Husk/Melamine Formaldehyde Composites

Melamine formaldehyde (MF) composites filled with rice husk powder were prepared by compression molding. The curing processes of the composites with different powder contents and powder particle sizes were studied by dynamic mechanical analysis. Gelation temperature and curing time were subjected to optimization of their mechanical and thermal properties. The rice husk powder loading in the MF matrix and the powder particle size were found to be critical factors governing the curing behavior and properties of the composite. Composites with larger content or smaller powder size had higher gelation temperatures and lower viscosities. The curing times of the composites were also influenced by the powder content. Flexural strength and flexural modulus increased with powder loading in the 20 to 90 mesh particle size range, while notched impact strength decreased. The mechanical properties of the composites decreased to a considerable extent when the fibers were too small to achieve strong interfacial adhesion. Morphological (scanning electron microscopy) and thermal studies (heat deflection temperature) were also conducted.
Near-infrared spectroscopy using indocyanine green dye for minimally invasive measurement of respiratory and leg muscle blood flow in patients with COPD.

Reliability of near-infrared spectroscopy, measuring indocyanine green (ICG) for minimally invasive assessment of relative muscle blood flow during exercise has been examined in fit young individuals but not in chronic obstructive pulmonary disease (COPD). Here we ask whether it could be used to evaluate respiratory and locomotor muscle perfusion in COPD patients. Vastus lateralis muscle blood flow (MBF, the reference method calculated from arterial and muscle ICG concentration curves) and a blood flow index [BFI, calculated using only the (same) muscle ICG concentration curves] were compared in 10 patients (forced expiratory volume in 1 s: 51 ± 6% predicted) at rest and during cycling at 25, 50, 75, and 100% of peak work rate (WRpeak). Intercostal muscle MBF and BFI were also compared during isocapnic hyperpnea at rest, reproducing ventilation levels up to those at WRpeak. Intercostal and vastus lateralis BFI increased with increasing ventilation during hyperpnea (from 2.5 ± 0.3 to 4.5 ± 0.7 nM/s) and cycling load (from 1.0 ± 0.2 to 12.8 ± 1.9 nM/s), respectively. There were strong correlations between BFI and MBF for both intercostal ( r = 0.993 group mean data, r = 0.872 individual data) and vastus lateralis ( r = 0.994 group mean data, r = 0.895 individual data). Fold changes from rest in BFI and MBF did not differ for either the intercostal muscles or the vastus lateralis. Group mean BFI data showed strong interrelationships with respiratory and cycling workload, and whole body metabolic demand ( r ranged from 0.913 to 0.989) simultaneously recorded during exercise. We conclude that BFI is a reliable and minimally invasive tool for evaluating relative changes in respiratory and locomotor muscle perfusion from rest to peak exercise in COPD patient groups. NEW & NOTEWORTHY We show that noninvasive near-infrared spectroscopic (NIRS) detection of indocyanine green dye (ICG) after peripheral venous injection adequately reflects intercostal and locomotor muscle perfusion during exercise and hyperpnea in patients with chronic obstructive pulmonary disease (COPD). Mean, individual, and fold change responses from rest to exercise or hyperpnea correlated closely with the reference method, which requires arterial sampling. NIRS-ICG is a reliable, robust, and essentially noninvasive tool for assessing relative changes in intercostal and locomotor muscle perfusion in COPD patient groups.