Miracle-dz/UniFAR-FTUs huggingface dataset:
DTCMF: Dynamic trust-based context-aware matrix factorization for collaborative filtering

Trust-aware recommender system (TARS) can provide more relevant recommendation and more accurate rating predictions than the traditional recommender system by taking the trust factors into consideration, yet currently only static trust is modeled in these systems. In this paper, we propose to integrate the social network analysis based dynamic trust model with context-aware matrix factorization into a new dynamic trust-based context-aware matrix factorization(DTCMF) to fully capture the dynamic of trust. Evaluations based on a real dataset and three semi-synthetic datasets demonstrate that our approaches can not only ensure the stability of the system, but also leads to more accurate recommendations.
Veriﬁcation of Timed Algorithms: Gurevich Abstract State Machines versus First Order Timed Logic

We give a survey of our recent and current work on the speciﬁcation and veriﬁcation of timed algorithms within a rather complete logical framework where timed Gurevich Abstract State Machines (GASM) are used to specify algorithms. The time we use is continuous as it is usual in speciﬁcations of control systems and often of protocols (discrete time can be treated within the same framework but it is less intuitive and harder to treat when automating the veriﬁcation process). The topics addressed in this survey concern semantics of timed GASM, representation of this semantics in a ﬁrst order logic adapted to veriﬁcation, heuristic considerations that are implied by the used type of GASM and that help to automate the veriﬁcation.
Doublecortin Knockout Mice Show Normal Hippocampal-Dependent Memory Despite CA3 Lamination Defects

Mutations in the human X-linked doublecortin gene (DCX) cause major neocortical disorganization associated with severe intellectual disability and intractable epilepsy. Although Dcx knockout (KO) mice exhibit normal isocortical development and architecture, they show lamination defects of the hippocampal pyramidal cell layer largely restricted to the CA3 region. Dcx-KO mice also exhibit interneuron abnormalities. As well as the interest of testing their general neurocognitive profile, Dcx-KO mice also provide a relatively unique model to assess the effects of a disorganized CA3 region on learning and memory. Based on its prominent anatomical and physiological features, the CA3 region is believed to contribute to rapid encoding of novel information, formation and storage of arbitrary associations, novelty detection, and short-term memory. We report here that Dcx-KO adult males exhibit remarkably preserved hippocampal- and CA3-dependant cognitive processes using a large battery of classical hippocampus related tests such as the Barnes maze, contextual fear conditioning, paired associate learning and object recognition. In addition, we show that hippocampal adult neurogenesis, in terms of proliferation, survival and differentiation of granule cells, is also remarkably preserved in Dcx-KO mice. In contrast, following social deprivation, Dcx-KO mice exhibit impaired social interaction and reduced aggressive behaviors. In addition, Dcx-KO mice show reduced behavioral lateralization. The Dcx-KO model thus reinforces the association of neuropsychiatric behavioral impairments with mouse models of intellectual disability.
A Far Cry from School History: Massive Online Open Courses as a Generative Source for Historical Research

Current research into Massive Online Open Courses (MOOCs) has neglected the potential of using learner comments for discipline-specific analysis. This article explores how MOOCs, within the historical discipline, can be used to generate, investigate, and document personal narratives, and argues that they serve as a rich platform for historical resource generation. Through these narratives, this research explores changing perceptions of learning; from learning history at school to learning about history in a MOOC. This exploration uses a qualitative thematic analysis of learner comments related to personal narratives of learning history at school from the Trinity College Dublin/Futurelearn “Irish Lives in War and Revolution: 1912-1923” MOOC. These personal narratives were generated both directly and indirectly through four pedagogical tools; reflective questions, multimedia resources, external links, and inter-learner interaction. Broad themes emerged from the analysis of personal narratives including attitudes toward history at school, biased and inadequate teaching, and MOOC teaching compared with school experiences. The analysis demonstrated that MOOCs serve as a generative repository for personal and family historical narratives, and described how MOOCs can change perceptions of teaching and learning history. This paper contributes a novel understanding of MOOCs for discipline-specific analysis, provides a framework for MOOC historical resource generation, and describes changing perceptions of learning from the perspective of MOOC learners.
A Relevance-Based Compilation Method for Conformant Probabilistic Planning

Conformant probabilistic planning (CPP) differs from conformant planning (CP) by two key elements: the initial belief state is probabilistic,and the conformant plan must achieve the goal with probability $\geq\theta$, for some $0<\theta\leq 1$. In earlier work we observed that one can reduce CPP to CP by finding a set of initial states whose probability $\geq\theta$, for whicha conformant plan exists. In previous solvers we used the underlying planner to select this set of states and to plan for them simultaneously. Here we suggest an alternative approach: start with relevance analysis to determine a promising set of initial states on which to focus. Then, call an off-the-shelf conformant planner to solve the resulting problem. This approach has a number of advantages. First, instead of depending on the heuristic function to select the set of initial states,we can introduce specific, efficient relevance reasoning techniques. Second, we can benefit from optimizations used by conformant planners that are unsound when applied to the original CPP. Finally, we are free to use any existing (or new) CP solver. Consequently, the new planner dominates previous solvers on almost all domains and scales to instances that were not solved before.
Correlation study of knee joint proprioception test results using common test methods

[Purpose] To study the correlation of the results obtained from different proprioception test methods, namely, the joint angle reset method, the motion minimum threshold measurement method, and the force sense reproduction method, performed on the same subjects’ knees. [Subjects and Methods] Different proprioception test methods, the joint angle reset method, the motion minimum threshold measurement method and the force sense reproduction method were used to test the knees of 30 healthy young men. [Results] Correlations were found in the following descending order from strong to weak: the correlation between the joint angle reset method and the force sense reproduction method (correlation coefficient of 0.41), the correlation between the joint angle reset method and the motion minimum threshold measurement method (correlation coefficient of 0.29), the correlation between the motion minimum threshold measurement method and the force sense reproduce method (correlation coefficient of 0.15). [Conclusion] No correlation was found among the results obtained using the joint angle reset method, the motion minimum threshold measurement method and the force sense reproduction method. Therefore, no correlation was found among the position sense, the motion sense and the force sense represented by these methods. Using the results of only one of the test methods to represent proprioception is one-sided. Force sensation depends more on the sensory input of information from the Golgi tendon organs, motion sense depends more on the input information of the muscle spindles, and position sense relies on the double input information of the muscle spindles and the Golgi tendon organs.
A multitask recovery algorithm for block-sparse signals

The paper considers the problem of jointly reconstructing multiple block-sparse signals with block partition unknown. Based on the framework of block sparse Bayesian learning (BSBL), we develop a new multitask recovery algorithm, called the extension algorithm of multitask block sparse Bayesian learning (EMBSBL). In contrast to existing methods, EMBSBL exploits not only the statistical interrelationships of signals (i.e., a degree of overlap of nonzero elements' positions among different signals), but also signals' intra-block correlation, and does not need a priori information on block partition. Simulations corroborate the theoretical developments.
Study on Virtualization-Based Simulation Grid

Firstly, the paper concisely introduces the research background of a new kind of networked modeling and simulation (M&S) platform based on the virtualization technology namely Virtualization-based Simulation Grid. Then its connotation and the architecture of platform prototype are presented. Furthermore, some solved key techniques and some typical applications developed by authors are introduced, such as simulation resource virtualization technique, then simulation resource discovery and scheduling technique based on virtualization etc. It can strengthen the capabilities of current networked M&S platform on dynamical resources sharing on-demand, supporting multi-user, collaboration, fault tolerant and security mechanism thus anew modeling and simulation mode is established. Finally, the conclusion and some further works are given.
A Situational Resource Rating System

Recommendation technologies are considered a major technological trend in both industrial and academic environments. This growing interest was highlighted by, e.g., the Netflix prize which generated an intense competition. Recommender systems are crucial to support users and help them by suggesting resources relevant at a given instant. On the other hand, these systems are a core piece of e-commerce web sites, since they aim at generating more sales by encouraging users to buy more items. However, recommender systems are often designed to work with very specific types of resources, and they hardly take into account the current user’s situation. In this paper, we present our approach to augment an existing recommender system with a situation model. On top of this model, we define a situational interest measure to estimate a user’s interest for a resource, which we demonstrate with a prototypical implementation.
Treatment with the vascular disruptive agent OXi4503 induces an immediate and widespread epithelial to mesenchymal transition in the surviving tumor

Epithelial to mesenchymal transition (EMT) is considered an important mechanism in tumor resistance to drug treatments; however, in vivo observation of this process has been limited. In this study we demonstrated an immediate and widespread EMT involving all surviving tumor cells following treatment of a mouse model of colorectal liver metastases with the vascular disruptive agent OXi4503. EMT was characterized by significant downregulation of E‐cadherin, relocation and nuclear accumulation of β‐catenin as well as significant upregulation of ZEB1 and vimentin. Concomitantly, significant temporal upregulation in hypoxia and the pro‐angiogenic growth factors hypoxia‐inducible factor 1‐alpha, hepatocyte growth factor, vascular endothelial growth factor and transforming growth factor‐beta were seen within the surviving tumor. The process of EMT was transient and by 5 days after treatment tumor cell reversion to epithelial morphology was evident. This reversal, termed mesenchymal to epithelial transition (MET) is a process implicated in the development of new metastases but has not been observed in vivo histologically. Similar EMT changes were observed in response to other antitumor treatments including chemotherapy, thermal ablation, and antiangiogenic treatments in our mouse colorectal metastasis model and in a murine orthotopic breast cancer model after OXi4503 treatment. These results suggest that EMT may be an early mechanism adopted by tumors in response to injury and hypoxic stress, such that inhibition of EMT in combination with other therapies could play a significant role in future cancer therapy.
QoS support based on a mobile routing backbone for ad hoc wireless networks

Ad hoc wireless networks (AWNs) obey a unique organizational and behavioral logic since they operate without a fixed infrastructure. Mobile nodes must collaborate amongst themselves to interconnect, organize the topology and establish communication. Moreover, their networking characteristics highlight a new breed of unresolved routing and Quality of Service problems. This article presents new approach for QoS support in AWNs. The fundamental idea behind our approach is to identify nodes in the network that can partake in a QoS mobile routing backbone (MRB). Only nodes having routing abilities and sufficient resources can join the backbone. QoS is supported through efficient bandwidth allocation and load balancing. The routing mechanism guides incoming data flows through network regions made up of nodes rich in resources and interconnected through stable links. Results obtained are encouraging and bring to light the ability our mechanisms to support QoS in AWNs.
Programmable policies for data offloading in LTE network

Mobile data offloading on smaller cells such as Wi-Fi comes as a natural solution to boost cellular networks capacity and keep up with the rapid increase of mobile data traffic demand. In this paper, we propose an offloading mechanism through the abstraction of Software-defined Networking (SDN) in the mobile backhaul to provide programmable offloading policy derivation that are aware of users and applications as well as the condition of wireless network. The proposed mechanism considers the real-time network condition to derive the offloading policies and efficiently accommodate the traffic in both LTE and Wi-Fi networks. Numerical results prove that the proposed approach can significantly improves dropping rate of the incoming traffic with using more real-time and dynamic decisions for offloading.
Documents de Travail du Centre d ’ Economie de la Sorbonne Iterating influence between players in a social network

We generalize a yes-no model of influence in a social network with a single step of mutual influence to a framework with iterated influence. Each agent makes an acceptance-rejection decision and has an inclination to say either ‘yes’ or ‘no’. Due to influence by others, an agent’s decision may be different from his original inclination. Such a transformation from the inclinations to the decisions is represented by an influence function. We analyze the decision process in which the mutual influence does not stop after one step but iterates. Any classical influence function can be coded by a stochastic matrix, and a generalization leads to stochastic influence functions. We apply Markov chains theory to the analysis of stochastic binary influence functions. We deliver a general analysis of the convergence of an influence function and then study the convergence of particular influence functions. This model is compared with the Asavathiratham model of influence. We also investigate models based on aggregation functions. In this context, we give a complete description of terminal classes, and show that the only terminal states are the consensus states if all players are weakly essential. JEL Classification: C7, D7
Towards High-Performance Pattern Matching on Multi-Core Network Processing Platforms

With the continual growth of network speed and the increasing sophistication of network applications, keeping network operations efficient and secure becomes more challenging. Pattern matching is one of the key technologies for content-ware network processing, such as traffic classification, application identification and intrusion prevention. In this paper, we propose a hybrid pattern matching algorithm optimized for multi-core network processing platforms. As a system-level solution, our scheme focuses on both performance stability and hardware/software co-design. To verify the effectiveness of our design, the proposed algorithm is implemented on a state-of-art 16-MIPS-core network processing platform and evaluated with real-life data sets. Experimental results show that, when compared with the traditional Aho-Corasick algorithm, our hybrid solution saves 60~95% memory space while guarantees stable performance on large pattern sets and against adverse test traffic.
μOrgano: A Lego®-Like Plug & Play System for Modular Multi-Organ-Chips

Human organ-on-a-chip systems for drug screening have evolved as feasible alternatives to animal models, which are unreliable, expensive, and at times erroneous. While chips featuring single organs can be of great use for both pharmaceutical testing and basic organ-level studies, the huge potential of the organ-on-a-chip technology is revealed by connecting multiple organs on one chip to create a single integrated system for sophisticated fundamental biological studies and devising therapies for disease. Furthermore, since most organ-on-a-chip systems require special protocols with organ-specific media for the differentiation and maturation of the tissues, multi-organ systems will need to be temporally customizable and flexible in terms of the time point of connection of the individual organ units. We present a customizable Lego®-like plug & play system, μOrgano, which enables initial individual culture of single organ-on-a-chip systems and subsequent connection to create integrated multi-organ microphysiological systems. As a proof of concept, the μOrgano system was used to connect multiple heart chips in series with excellent cell viability and spontaneously physiological beat rates.
Provided for Non-commercial Research and Educational Use Only. Not for Reproduction, Distribution or Commercial Use. Biosocial Construction of Sex Differences and Similarities in Behavior

commercial research and educational use including without limitation use in instruction at your institution, sending it to specific colleagues who know you, and providing a copy to your institution's administrator. All other uses, reproduction and distribution, including without limitation commercial reprints, selling or licensing copies or access, or posting on open internet sites, your personal or institution's website or repository, are prohibited. For exceptions, permission may be sought for such use through Elsevier's permissions site at: Contents 1. Introduction 56 2. Divided Labor 59 2.1. Division of labor in foraging societies 60 2.2. Division of labor with socioeconomic developments 61 2.3. Variability in power relationships between the sexes 63 3. Socialization 65 3.1. Socialization as a biosocial process 66 3.2. Socialization mechanisms 67 4. Cultural Beliefs About Gender 70 4.1. Essentialism of beliefs about the sexes 72 4.2. Implications of essentialist beliefs 73 4.3. Actual change in gender stereotypes 75 5. Gender Roles Shape Social Behavior 76 5.1. Social psychological processes 77 5.2. Biological processes 84 5.3. Social psychological and biological processes work together: Stereotype threat 88 6.
Autosub6000: results of its engineering trials and first science missions

In September 2007 on RRS Discovery, the Autosub6000 Autonomous Underwater Vehicle (AUV) completed its first deep water engineering trials, and less than a year later, fitted with a multibeam bathymetric mapping sonar, carried out its first science missions, as part of a geology and geophysics science cruise onboard the RRS James Cook to investigate potential geo-hazards (such as tsunami generating landslides) on the European and North African margin. In the spirit of true AUV autonomy, while the AUV was deployed, we used the ship for seabed coring operations, and once the AUV was recovered, the high resolution bathymetry which it had obtained guided the next coring operations. In this paper we will describe how we are tackling the issues that specifically affect a deep diving AUV capable of operating with true autonomy, and independently of the mother ship: How to carry enough energy for long endurance and range? How to operate safely and efficiently with varying buoyancy? How to maintain accurate navigation throughout missions lasting up to several days?
LMI Approximations for Cones of Positive Semidefinite Forms

An interesting recent trend in optimization is the application of semidefinite programming techniques to new classes of optimization problems. In particular, this trend has been successful in showing that under suitable circumstances, polynomial optimization problems can be approximated via a sequence of semidefinite programs. Similar ideas apply to conic optimization over the cone of copositive matrices and to certain optimization problems involving random variables with some known moment information. We bring together several of these approximation results by studying the approximability of cones of positive semidefinite forms (homogeneous polynomials). Our approach enables us to extend the existing methodology to new approximation schemes. In particular, we derive a novel approximation to the cone of copositive forms, that is, the cone of forms that are positive semidefinite over the nonnegative orthant. The format of our construction can be extended to forms that are positive semidefinite over more general conic domains. We also construct polyhedral approximations to cones of positive semidefinite forms over a polyhedral domain. This opens the possibility of using linear programming technology in optimization problems over these cones.
AIR TRAFFIC ENROUTE CONFLICT DETECTION USING ADAPTIVE RESONANCE THEORY MAP NEURAL NETWORKS (ART1)

Over the next ten years, air traffic management in the world will change dramatically, bringing both challenges and opportunities. The biggest of those challenges is the continuing rise in traffic volumes and will increase the movement of airbuses around the airport due to busy runways. This air traffic congestion will increase the running cost of flights due to unwanted movement of airbuses around the airport and it will also increase the chances of the airbus collision. This paper considers the problem of solving conflicts arising among several aircraft that are assumed to move in a shared airspace. Aircraft can not get closer to each other than a given safety distance in order to avoid possible conflicts between different airplanes. For such system of multiple aircraft, we consider the path planning problem among given waypoints avoiding all possible conflicts. In this paper we have proposed Adaptive Resonance Theory Map Neural networks (ART1) to produce optimize solution. Our result shows that the average expected conflict time of aircrafts using ARTMAP has been increased to more than 20 minutes. Keywordscollision detection, separation distance, ARTMAP neural networks, etc, stc
FROM CONFLICT TO COLLABORATION : A STUDY OF THE ATTEMPT TO ENACT SHARED PRACTICES

This paper studies how the enactment of collaboration unfolds. The empirical case of the paper describes an attempt to enact shared practices between two departments within a Danish public organization, which had a history of no previous shared activities and tensed relations. The paper demonstrates that the attempt to enact collaboration and shared practices demands more than mere top-down reorganizing of structure. The enactment process of collaboration must encompass interaction between several compounds making up social worlds and is thus a multifaceted process founded in the intersecting and negotiations between different kinds of perspectives.