Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology clarification by using semantic disambiguation

Semantic Web technology highly depends on the quality of ontology. In order to enhance quality of ontology, a vast amount of research has focused on concept modeling task, but there is one major problem with lexical representation of ontology. Current lexical representation is term which may have different meanings, so as to result in frustrating misunderstanding and ambiguity during the application of ontology. To solve this problem, sense is used to replace term as the lexical representation of concepts and properties for its unique meaning. Ontology clarification is the process of disambiguating terms in ontology by using its surrounding ontology elements and its nearby terms in annotated documents using this ontology. The right sense is assigned to a target term by maximizing the relatedness between the target and its neighbors for semantic relatedness between them. Experiments show our ontology clarification method is valid. Comparing with the best word sense disambiguation method, the concept precision is almost 2 times than the precision of noun, and the property precision is almost 3 times than the precision of verb. Another experiment proves that our method is also effective in a semi-automatic process.
A new interpretation of information rate

If the input symbols to a communication channel represent the outcomes of a chance event on which bets are available at odds consistent with their probabilities (i.e., “fair” odds), a gambler can use the knowledge given him by the received symbols to cause his money to grow exponentially. The maximum exponential rate of growth of the gambler's capital is equal to the rate of transmission of information over the channel. This result is generalized to include the case of arbitrary odds. Thus we find a situation in which the transmission rate is significant even though no coding is contemplated. Previously this quantity was given significance only by a theorem of Shannon's which asserted that, with suitable encoding, binary digits could be transmitted over the channel at this rate with an arbitrarily small probability of error.
A novel fast and effictive video retrieval system for surveillance application

Fast and effective indexing and retrieval from large amount of surveillance videos are very important issues. This paper proposes a novel object-semantic-based surveillance video indexing and retrieval system, which is mainly composed of two modules: video analysis and video retrieval. In the video analysis, the systems first segments video objects (VO) from surveillance videos, and the fundamental semantic information is then extracted and indexed into the database. A normal approach of Gaussian Mixed Model (GMM) is applied in video object extraction (VOE) and video object segmentation (VOS). During retrieval, the query is converted to semantic information without re-processing the surveillance videos. Color, edge orientation histograms and SIFT (Scale Invariant Feature Transforms), as the key features and similarity measurement, are considered together to accurately match the video objects (VOM). The experiment shows that a user can retrieve the required videos effectively.
A unifying conceptual model for the environmental responses of isoprene emissions from plants

Background and Aims Isoprene is the most important volatile organic compound emitted by land plants in terms of abundance and environmental effects. Controls on isoprene emission rates include light, temperature, water supply and CO2 concentration. A need to quantify these controls has long been recognized. There are already models that give realistic results, but they are complex, highly empirical and require separate responses to different drivers. This study sets out to find a simpler, unifying principle. Methods A simple model is presented based on the idea of balancing demands for reducing power (derived from photosynthetic electron transport) in primary metabolism versus the secondary pathway that leads to the synthesis of isoprene. This model's ability to account for key features in a variety of experimental data sets is assessed. Key results The model simultaneously predicts the fundamental responses observed in short-term experiments, namely: (1) the decoupling between carbon assimilation and isoprene emission; (2) a continued increase in isoprene emission with photosynthetically active radiation (PAR) at high PAR, after carbon assimilation has saturated; (3) a maximum of isoprene emission at low internal CO2 concentration (ci) and an asymptotic decline thereafter with increasing ci; (4) maintenance of high isoprene emissions when carbon assimilation is restricted by drought; and (5) a temperature optimum higher than that of photosynthesis, but lower than that of isoprene synthase activity. Conclusions A simple model was used to test the hypothesis that reducing power available to the synthesis pathway for isoprene varies according to the extent to which the needs of carbon assimilation are satisfied. Despite its simplicity the model explains much in terms of the observed response of isoprene to external drivers as well as the observed decoupling between carbon assimilation and isoprene emission. The concept has the potential to improve global-scale modelling of vegetation isoprene emission.
Comprehensive Review of the Genetics of Albinism

Introduction It is important to understand albinism, since it is a disorder associated with visual impairment, predisposition to malignant melanomas, and social stigma. The main objective of this article is to review the genetics and biologic mechanisms of the non-syndromic albinism subtypes and to describe associated clinical manifestations. We also discuss research on its treatments. Methods A review of the published literature on albinism subtypes was performed, spanning basic laboratory research, published case reports, and experiences of people with albinism. Results Clear progress has been made in comprehending the causes of albinism; research has shed light on the complexity of the disorder and has led to the molecular classification of subtypes. Discussion Despite the increase in knowledge with regards to albinism, gaps still exist. It is important to continue the pursuit of unraveling the mechanism of the disorder and to monitor the frequency of the subtypes worldwide in order to aid in the development of treatments. Furthermore, disseminating knowledge of albinism is crucial for future progress. Implications for practitioners Albinism is a disorder characterized by hypopigmentation of the hair, skin, and eyes, with accompanying ocular abnormalities that remain relatively stable throughout life. The disorder is defined by a spectrum of pigmentation where albinism is more evident among individuals of dark complexion than their lighter-pigmented peers. Patients with albinism require protection against sun exposure and special resources to address visual impairments. When albinism patients are diagnosed and properly accommodated, they generally report a positive quality of life.
Dynamic Clustering Algorithm Based on Granular Lattice Matrix Space Model

Traditional clustering algorithm usually adopt uniform granularity. It easily leads to too fine or too coarse in clustering process. The former may divides objects into different classes which should be in one. The latter group objects into one class which should be in different. Due to it, we introduce dynamic granularity into traditional clustering algorithm. Firstly, based on research, we present granular lattice matrix space model. Then we describe problem of clustering by the new model. Finally we provide new clustering algorithm based on the new model. To testify the new algorithm, we present tests to prove its efficiency.
Coexpression and activation of TRPV1 suppress the activity of the KCNQ2/3 channel

Transient receptor potential vanilloid 1 (TRPV1) is a ligand-gated nonselective cation channel expressed predominantly in peripheral nociceptors. By detecting and integrating diverse noxious thermal and chemical stimuli, and as a result of its sensitization by inflammatory mediators, the TRPV1 receptor plays a key role in inflammation-induced pain. Activation of TRPV1 leads to a cascade of pro-nociceptive mechanisms, many of which still remain to be identified. Here, we report a novel effect of TRPV1 on the activity of the potassium channel KCNQ2/3, a negative regulator of neuronal excitability. Using ion influx assays, we revealed that TRPV1 activation can abolish KCNQ2/3 activity, but not vice versa, in human embryonic kidney (HEK)293 cells. Electrophysiological studies showed that coexpression of TRPV1 caused a 7.5-mV depolarizing shift in the voltage dependence of KCNQ2/3 activation compared with control expressing KCNQ2/3 alone. Furthermore, activation of TRPV1 by capsaicin led to a 54% reduction of KCNQ2/3-mediated current amplitude and attenuation of KCNQ2/3 activation. The inhibitory effect of TRPV1 appears to depend on Ca2+ influx through the activated channel followed by Ca2+-sensitive depletion of phosphatidylinositol 4,5-bisphosphate and activation of protein phosphatase calcineurin. We also identified physical interactions between TRPV1 and KCNQ2/3 coexpressed in HEK293 cells and in rat dorsal root ganglia neurons. Mutation studies established that this interaction is mediated predominantly by the membrane-spanning regions of the respective proteins and correlates with the shift of KCNQ2/3 activation. Collectively, these data reveal that TRPV1 activation may deprive neurons from inhibitory control mediated by KCNQ2/3. Such neurons may thus have a lower threshold for activation, which may indirectly facilitate TRPV1 in integrating multiple noxious signals and/or in the establishment or maintenance of chronic pain.
A pedagogic JavaScript program for point location strategies

Point location in triangulations is a classical problem in computational geometry. And walking in a triangulation is often used as the starting point for several nice point location strategies. We present a pedagogic JavaScript program demonstrating some of these strategies, which is available at: www-sop.inria. fr/geometrica/demo/point location strategies/
A macro‐element strategy based upon spectral finite elements and mortar elements for transient wave propagation modeling. Application to ultrasonic testing of laminate composite materials

Proposing efficient numerical modeling tools for high‐frequency wave propagation in realistic configurations, such as the one appearing in ultrasonic testing experiments, is a major challenge, especially in the perspective of inversion loops or parametric studies. We propose a numerical methodology addressing this challenge and based upon the combination of the spectral finite element method and the mortar element method. From a prior decomposition of the scene of interest into “macro‐elements,” we show how one can improve the performances of the standard finite element procedures in terms of memory footprint and computational load. Additionally, using this decomposition, we are able to efficiently reconstruct important modeling features on‐the‐fly, such as orientations of anisotropic materials or splitting directions of perfectly matched layers formulations, altogether in a robust and efficient manner. We believe that this strategy is particularly suitable for parametric studies and sensitivity analysis. We illustrate our strategy by simulating the propagation of an ultrasonic wave into an immersed and curved anisotropic laminate 3D specimen flawed with an internal circular delamination of varying size, thus showing the efficiency and the robustness of our approach.
Robotic path planning using rapidly exploring random trees

Rapidly exploring Random Tree (RRT) path planning methods provide feasible paths between a start and goal point in configuration spaces containing obstacles, sacrificing optimality (eg. Shortest path) for speed. The raw resultant paths are generally jagged and the cost of extending the tree can increase steeply as the number of existing branches grow. This paper provides details of a speed-up method using KD trees and a path smoothing procedure of practical interest.
Multi-objective Optimization of Material Collaborative Delivery for Mixed Model Automotive Assembly Process

To solving the real-time material delivery problems from the warehouse to workstations of the mixed model automotive assembly line in accordance with the production cycle. The material flow of automotive assembly, type of material and the production characteristic were analyzed in order to feed material timely & properly. The material real-time delivery system based on field bus has been developed. The material pull systems based on Kanban technology was developed in the environment of JIT production environment. The network architecture of the material monitor system based on internet of things was established. A multi-objective materials delivery model considering from optimization objectives, constraints and impact factors was proposed. The materials transportation costs, materials transportation time and materials storage on-line are considered as optimize objective function. The hybrid particle swarm algorithm was designed and the coding design, particle generation, fitness function, crossover operation, mutation operation of the algorithm was presented. The validity of the model and algorithm was verified by a case of material collaborative delivery problem for gearbox assembly. The results show that the hybrid particle swarm algorithm is feasible and superior to genetic algorithm.
A survey of algorithms for construction of optimal Heterogeneous Bounding Volume Hierarchies

In this paper we describe algorithms for automatic object hierarchy construction. We also describe a set of properties that hierarchies must possess to be considered optimal. Based on knowledge from existing algorithms we develop and evaluate a Branch and Bound construction algorithm that searches for good hierarchies, but due to computation time, not the globally optimal hierarchy. The algorithm is designed to construct hierarchies used for collision detection. This is controlled by a heuristicand cost function. The hierarchy data structure built by the algorithm is the Approximating Hybrid Bounding Volume Hierarchy. It has nodes with varying branching factors, multiple bounding geometry types and approximating geometries in the leaf nodes.
Load Balancing in Server Consolidation

The growth of server consolidation is due to virtualization technology that enables multiple servers to run on a single platform. However, virtualization may bring the overheads in performance. The prediction of virtualization performance is of especially important. The contribution of our paper is two-fold. First, we propose a general model to predict the performance of consolidation. Second, we study a load balancing problem that arises in server consolidation, where is to assign a number of workloads to a small number of high-performance target servers such that the workloads in each target servers are balancing. We first model the load balancing problem as an integer linear programming. Then, an fully polynomial time approximate scheme (FPTAS) is provided to get the near optimal solution. That is to say, for any given $\varepsilon ≫ 0$, our algorithm achieves ($1+\varepsilon$)-approximation, and its running time is polynomial of both the number of source servers and $1/\varepsilon$ when the number of target servers and the dimensions are constants.
Event-triggered distributed ℋ ∞ state estimation with packet dropouts through sensor networks

This work was supported in part by Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61203139, 61473076, 61374127 and 61422301, the Shanghai Rising-Star Program of China under Grant 13QA1400100, the ShuGuang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 13SG34, the Fundamental Research Funds for the Central Universities, DHU Distinguished Young Professor Program, and the Alexander von Humboldt Foundation of Germany.
Performance of Concrete MRF at Near-Field Earthquakes Compared to Far-Field Earthquakes

The characteristic of near-field earthquake records has been investigated in the previous studies. However, the effects of the near-field earthquakes on the response of the building structures need to be further investigated. Engineering demand parameters like inter-story drift ratio and floor acceleration can provide a good means for comparing the response of structures to the near-field and the far-field earthquakes. The main objective of this paper was to apply these two parameters to compare the behavior of the concrete Moment Resistant Frame (MRF) subjected to near-field and far-field ground motions. In this study, non-linear numerical simulations were performed on concrete MRF office buildings subjected to two sets of 14 near-field records and 14 far-field records. The analytical models simulated 4-story, 8-story, and 16 story buildings. The obtained results indicated that the near-field effects can increase the inter-story drift ratio and floor acceleration at lower stories of low and mid-rise building subjected to high ground motion intensities.
Branding and communities: The normative dimension

Abstract The article offers a Peircean “glimpse” of the normativity of the brand community. The theoretical framework will primarily be Peirceʼs semiotics, while also adding a few insights from his thoughts on (ethical) normativity and the formal conditions of community. Firstly, I will describe the brand community as a semiotic entity based on three inter-related conditions involving “inner” and “outer” processes of branding where “inner process” is where we find the most explicit dynamics of the brand community related to a “common consent of meaning” and a “sense of community. ” Secondly, I will try to describe how values and ideals can be understood within the brand community, including how the values and ideals (semiotically) transfer normative meanings by bringing together brands with consumers and thereby affect their feelings, acts, and thoughts so that they can become members within a brand community.
Elastic spectrum allocation in flexgrid optical networks

In this work it is presented the study and the obtained results when applying elasticity in flexible optical networks using flexgrid technology and with objective to evaluate a future line of work that helps to solve the routing and spectrum allocation within next-generation optical networks. For this project, a set of elasticity schemes is proposed and these schemes are evaluated solving the network planning problem proposed, in which traffic demands vary in time. Moreover, in order to evaluate the schemes in realistic network scenarios, both Mathematical Programming and a heuristic are proposed. Finally, the main conclusions regarding the use of elasticity are presented for concluding this work.
Global Principal Component Analysis for Dimensionality Reduction in Distributed Data Mining

Previous data mining activities have mostly focused on mining a centralized database. One big problem with a centralized database is its limited scalability. Because of the distributed nature of many businesses and the exponentially increasing amount of data generated from numerous sources, a distributed database becomes an attractive alternative. The challenge in distributed data mining is how to learn as much knowledge from distributed databases as we do from the centralized database without costing too much communication bandwidth. Both unsupervised classi£cation (clustering) and supervised classi£cation are common practices in data mining applications, where dimensionality reduction is a necessary step. Principal component analysis is a popular technique used in dimensionality reduction. This paper develops a distributed principal component analysis algorithm which derives the global principal components from distributed databases based on the integration of local covariance matrices. We prove that for homogeneous databases, the algorithm can derive the global principal components that are exactly the same as those calculated based on a centralized database. We also provide quantitative measurement of the error introduced in the recompiled global principal components when the databases are heterogeneous.
Male fertility preservation before gonadotoxic therapies

Background: Recent advances in cancer therapy have resulted in an increased number of long-term cancer survivors. Unfortunately, aggressive chemotherapy, radiotherapy and preparative regimens for bone marrow transplantation can severely affect male germ cells, including spermatogonial stem cells (SSCs), and lead to permanent loss of fertility. Different options for fertility preservation are dependent on the pubertal state of the patient. Methods: Relevant studies were identified by an extensive Medline search of English and French language articles. Results: Sperm cryopreservation prior to gonadotoxic treatment is a well established method after puberty. In case of ejaculation failure by masturbation, assisted ejaculation methods or testicular tissue sampling should be considered. Although no effective gonadoprotective drug is yet available for in vivo spermatogonial stem cell (SSC) protection in humans, current evidence supports the feasibility of immature testicular tissue (ITT) cryopreservation. The different cryopreservation protocols and available fertility restoration options from frozen tissue, i.e. cell suspension transplantation, tissue grafting and in vitro maturation, are presented. Results obtained in humans are discussed in the light of lessons learned from animal studies. Conclusion: Advances in reproductive technology have made fertility preservation a real possibility in young patients whose gonadal function is threatened by gonadotoxic therapies. The putative indications for such techniques, as well as their limitations according to disease, are outlined.
Pain Narratives in Breast Cancer Survivors

In-depth interviews were conducted with French breast cancer survivors 24 month after cancer diagnosis (N = 21 women). We documented their experience of chronic pain, compared their pain narratives with their answers to the WHOQOL-BREF questionnaire, and studied both the meaning they gave to their pain and how they dealt with it in their daily lives. Half of participants reported are suffering from iatrogenic chronic pain. Most of the time, this pain was not captured by the WHOQOL questionnaire and was not medically treated. Patients “normalized” their pain in various ways: they considered it either as a necessary step on the road to recovery, as the proof of treatment efficacy, or as a permanent condition one must learn to live with. They learned to deal with pain by taking precautions, giving up certain activities, and changing the way they performed others. Participants were also prone to compare themselves with other patients suffering worse pain. Breast cancer survivors should be better informed about chronic pain and how to alleviate it. Physicians should contribute to fighting pain-related beliefs which lead patients to conceal their pain. Techniques used by patients to cope with chronic pain in their daily lives should also be promoted.