Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Approach for Routing in Ad-Hoc Wireless Local Area Networks

An ad-hoc wireless network is a cooperative set of mobile hosts which can communicate with each other over the wireless links (direct or indirect) without any static network interaction. This report presents a "cluster-based" approach to routing in ad-hoc networks. A k-cluster is defined by a subset of nodes which are ''reachable'' to each other by a path of length at most k for some fixed k. This report deals with clusters of k = 1, i.e., 1-clusters (or simply clusters). Our approach is motivated by our study of the existence of clusters (size greater than 2) in random graphs. The basic idea behind the protocol is to divide the graph into a number of overlapping clusters. A change in the network topology corresponds to a change in the cluster membership. Performance of the proposed routing protocol (reconvergence and update overhead) will hence be determined by the average cluster size in the network graph. The effectiveness of this approach lies in the fact that existing routing protocols can be directly applied to the network - replacing the nodes by clusters. When the average cluster size is less than 2, the proposed approach does not perform any worse than the existing routing protocols.
A cloud computing architecture for spectrum sensing as a service

The Internet of Things concept has revolutionized the way of using sensors and produced data. The interconnection of sensors to computing systems and to both storage infrastructures and processing facilities in a cloud fashion enables the paradigm of Sensing as a Service (S2aaS). In this paper, we propose a system architecture compliant with the S2aaS model, and detail it for a specific use case, the Spectrum Sensing as a Service (S3aaS). We illustrate the system components, including heterogeneous spectrum sensors, a distributed messaging system, a scheduler, a scalable database with a relevant SQL interface tool, and a user interface tool used to interact with the S3aaS system. Finally, we show the implementation of a proof-of-concept prototype used for assessing its effectiveness in operation.
Immunomodulatory Effects of Mesenchymal Stromal Cells in Crohn's Disease

The ability of mesenchymal stromal cells (MSCs) to suppress immune responses combined with their potential to actively participate in tissue repair provides a strong rationale for the use of MSCs as a new treatment option in diseases characterized by inflammation and severe tissue damage, such as Crohn's disease (CD) and perianal fistulas. Multiple studies have shown that MSCs suppress a range of immune cells, such as dendritic cells (DC), naïve and effector T cells, and natural killer (NK) cells. Recently published papers attribute the immunosuppressive capacity of MSCs to soluble factors produced by MSCs, such as prostaglandin E2 (PGE2), inducible nitric oxide synthase (iNOS), and indoleamine 2,3-dioxygenase (IDO). Promising results are obtained from phase I and II clinical trials with autologous and allogeneic MSCs as treatment for refractory CD and perianal fistulas; however the question remains: what are the molecular mechanisms underlying the immunomodulating properties of MSCs? This paper highlights the present knowledge on the immunosuppressive effects of MSCs and its complexity in relation to CD and perianal fistulas.
Neuroeconomics and Game Theory

Many of our decisions are made in the context of highly complex social interactions. By combining neuroscientific measures with formal models of game theory, researchers have gained insight into the mechanisms underlying social decision making. Early findings from this interdisciplinary effort have already begun to show how reasoning about the intentions of others is important for interactive behavior, how the reward system of the brain is directly involved in cooperation and social exchange, and how the experience of emotions plays a vital role in successful decision making. The neuroeconomic approach to social decision making offers exciting potential to move beyond speculation about the processes that determine our decisions in interactive contexts, and to extend our knowledge about the functioning of the human brain. Keywords: game theory; neuroeconomics; social reward; emotions; theory of mind
Distinct features of fast oscillations in phasic and tonic rapid eye movement sleep

Spatiotemporal activity patterns of neurones are organized by different types of coherent network oscillations. Frequency content and cross‐frequency coupling of cortical oscillations are strongly state‐dependent, indicating that different patterns of wakefulness or sleep, respectively, support different cognitive or mnestic processes. It is therefore crucial to analyse specific sleep patterns with respect to their oscillations, including interaction between fast and slow rhythms. Here we report the oscillation profile of phasic rapid eye movement (REM), a form of REM sleep which has been implicated in hippocampus‐dependent memory processing. In all analysed frequency bands (theta, gamma and fast gamma, respectively) we find higher frequencies and higher power in phasic REM compared to tonic REM or wakefulness. Theta‐phase coupling of fast oscillations, however, was highest in tonic REM, followed by phasic REM and wakefulness. Our data suggest different roles of phasic and tonic REM for information processing or memory formation during sleep.
Dynamic Binding of Parameters

Marvel is a rule-based development environment (RBDE) that assists in the development of software projects. Marvel encapsulates each software development activity in a rule that speciies the condition for invoking the activity and its eeects on the components of the project under development. These components are abstracted as objects and stored in a persistent object database. Each rule applies to a speciic class of objects, which is speciied as the parameter of the rule. Firing a rule entails binding its formal parameter to a speciic object. If the rule changes the object in such a way that the conditions of other rules become satissed, these other rules are automatically red. A problem arises in this forward chaining model when the classes of the objects manipulated by the rules are diierent. Marvel has to determine which object to bind to the parameter of each rule in the chain, based on the object manipulated by the original rule that initiated the chain. We describe a heuristic approach for solving this problem in the current Marvel implementation and introduce an algorithmic approach that does better.
Geometry of the Weil-Petersson completion of Teichm\

We present a view of the current understanding of the geometry of Weil-Petersson (WP) geodesics on the completion of the Teichm\"uller space. We sketch a collection of results by other authors and then proceed to develop the properties of the WP CAT(0) geometry. Our approach includes a simplified proof of the Masur-Wolf theorem, a classification of flats and of geodesic limits.
Using Mutual Information and Models of Evolution for Improved Pattern Detection

Many uses of information theory have recently been discovered in the field of bioinformatics - clustering and classification of data, sequence alignment scoring, discovering dependencies between sites in amino acid alignments, etc. Mutual Information has proven itself to be a very convenient metric for determining the dependency between two sets of data, and has advantages over other common statistical methods such as correlation. Models of evolution, or substitution matrices, have always been at the very heart of bioinformatics, with a large variety of applications based on PAM, BLOSUM, JTT or other matrices. In this paper we describe a novel algorithm that incorporates substitution rates from a given matrix when calculating the mutual information between sites in an amino acid alignment. We formally describe this algorithm in detail as well as some experimental results. As a result of this work we demonstrate that the incorporation of substitution matrices in the calculation leads to an improved detection of patterns of similarity between sites within a multiple sequence alignment.
Stable transformation and actin visualization in callus cultures of dodder (Cuscuta europaea)

Agrobacterium tumefaciens-mediated transformation of callus culture, combined with a visual selection of GFP-tagged fimbrin actin binding domain (FABD2) expression is described for parasitic species (Cuscuta europaea). The conditions for callus induction from 1 cm-long explants from the basal part of 7-day-old dodder seedlings were defined. We obtained light-green calli, which were transformed with A. tumefaciens bacterial strain GV3101 carrying plasmid pCB302 (35S::ABD2:gfp) with neomycin phosphotransferase (nptII) gene. The limitations of selection procedures based on antibiotics were avoided using green fluorescent protein (GFP) detection, as a visual selection marker subcellularly targeted to the actin cytoskeleton. Fluorescence microscopy analyses demonstrated a network of nucleus-associated actin arrays and dense cortical actin arrangements in stably transformed Cuscuta callus cells. RT-PCR analyses confirmed gfp expression in transformed calli 7, 14 and 21 days after transformation. Although the GFP fluorescence associated with the actin cytoskeleton has retained for at least six months without silencing, no shoot regeneration was observed. It can be concluded that, C. europaea callus cells are competent for transformation, but under given conditions, these cells failed to realize their morphogenic and regeneration potentials.
Dynamic behavior of quantum cellular automata

We examine the dynamic behavior of quantum cellular automata, arrays of artificial quantum‐dot cells that can be used to perform useful computations. The dynamics of the array can be solved directly, retaining the full many‐electron degrees of freedom only for small array sizes. For larger arrays, we develop several approximate techniques for reducing the size of the basis set required. We examine the effect of intercellular quantum correlations on the switching response. Several important examples of switching behavior are solved using the techniques developed.
A Case Study in Knowledge Acquisition for Insurance Risk Assessment using a KDD Methodology

We describe some initial experiences in dealing with the task of acquiring knowledge where a very large collection of case histories is available. A Knowledge Discovery in Databases (KDD) approach is taken. KDD is the process of extracting novel information and knowledge from large databases, consisting of many interacting stages performing specific data manipulation and transformation operations with an information flow from one stage onto the next (and usually with feedback into previous stages). We characterise our experiences of this process for the task of acquiring knowledge for the domain of motor vehicle insurance premium setting for NRMA Insurance Limited.
Improved Detection of Remote Homologues Using Cascade PSI-BLAST: Influence of Neighbouring Protein Families on Sequence Coverage

Background Development of sensitive sequence search procedures for the detection of distant relationships between proteins at superfamily/fold level is still a big challenge. The intermediate sequence search approach is the most frequently employed manner of identifying remote homologues effectively. In this study, examination of serine proteases of prolyl oligopeptidase, rhomboid and subtilisin protein families were carried out using plant serine proteases as queries from two genomes including A. thaliana and O. sativa and 13 other families of unrelated folds to identify the distant homologues which could not be obtained using PSI-BLAST. Methodology/Principal Findings We have proposed to start with multiple queries of classical serine protease members to identify remote homologues in families, using a rigorous approach like Cascade PSI-BLAST. We found that classical sequence based approaches, like PSI-BLAST, showed very low sequence coverage in identifying plant serine proteases. The algorithm was applied on enriched sequence database of homologous domains and we obtained overall average coverage of 88% at family, 77% at superfamily or fold level along with specificity of ∼100% and Mathew’s correlation coefficient of 0.91. Similar approach was also implemented on 13 other protein families representing every structural class in SCOP database. Further investigation with statistical tests, like jackknifing, helped us to better understand the influence of neighbouring protein families. Conclusions/Significance Our study suggests that employment of multiple queries of a family for the Cascade PSI-BLAST searches is useful for predicting distant relationships effectively even at superfamily level. We have proposed a generalized strategy to cover all the distant members of a particular family using multiple query sequences. Our findings reveal that prior selection of sequences as query and the presence of neighbouring families can be important for covering the search space effectively in minimal computational time. This study also provides an understanding of the ‘bridging’ role of related families.
β1 Integrins in Muscle, But Not in Motor Neurons, Are Required for Skeletal Muscle Innervation

In vitro studies have provided evidence thatβ1 integrins in motor neurons promote neurite outgrowth, whereasβ1 integrins in myotubes regulate acetylcholine receptor (AChR) clustering. Surprisingly, using genetic studies in mice, we show here that motor axon outgrowth and neuromuscular junction (NMJ) formation in large part are unaffected when the integrin β1 gene (Itgb1) is inactivated in motor neurons. In the absence of Itgb1 expression in skeletal muscle, interactions between motor neurons and muscle are defective, preventing normal presynaptic differentiation. Motor neurons fail to terminate their growth at the muscle midline, branch excessively, and develop abnormal nerve terminals. These defects resemble the phenotype of agrin-null mice, suggesting that signaling molecules such as agrin, which coordinate presynaptic and postsynaptic differentiation, are not presented properly to nerve terminals. We conclude that Itgb1 expression in muscle, but not in motor neurons, is critical for NMJ development.
Joven con Síndrome de Down y natación recreativa: posibilidades hacia la inclusión

El proposito del estudio fue analizar las percepciones de los agentes participantes y los factores que inciden en la inclusion de un nino con sindrome de Down (SD) en un programa de natacion recreativa. Los participantes fueron: un joven con SD, nueve jovenes sin discapacidad diagnosticada, la monitora de natacion, el coordinador de la actividad y la madre del nino con SD. El presente estudio, basado en una metodologia cualitativa, recogio la informacion a traves de entrevistas semi-estructuradas, observacion sistematica y diario de la investigadora. La utilizacion de un proceso inductivo permitio categorizar la informacion y obtener los siguientes resultados: es necesaria una mayor coordinacion entre los agentes implicados, adaptar el programa en funcion de las caracteristicas de todos los participantes, mejorar la formacion especifica de los tecnicos responsables y la fluidez de la comunicacion entre los agentes implicados para posibilitar la inclusion del nino con SD a traves de la natacion recreativa. PALABRAS CLAVES : inclusion, natacion recreativa, sindrome de Down. ABSTRACT The purpose of this study was to analyse the perceptions of the participant agents and factors affecting the inclusion of a child with Down syndrome (DS) in a recreational swimming activity program. Program participants included: a child with DS, nine children without diagnosed disabilities, the swimming instructor, the mother of a child with DS and the coordinator of the activity. This study was based on qualitative research approach and data were collected through semi-structured interviews, systematic observation and research diary. The following results were obtained after the inductive process: the need of greater coordination among those involved, designs appropriate programs based on the characteristics of all participants, improve specific training of technicians responsible as well as improve the communication of the agents participating in order to enable the inclusion of a child with DS in a recreational swimming activity. KEYWORDS : inclusion, recreational swimming, Down syndrome.
SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression

Abstract Single-cell mRNA sequencing (RNA-seq) methods have undergone rapid development in recent years, and transcriptome analysis of relevant cell populations at single-cell resolution has become a key research area of biomedical sciences. We here present single-cell mRNA 3-prime end sequencing (SC3-seq), a practical methodology based on PCR amplification followed by 3-prime-end enrichment for highly quantitative, parallel and cost-effective measurement of gene expression in single cells. The SC3-seq allows excellent quantitative measurement of mRNAs ranging from the 10,000-cell to 1-cell level, and accordingly, allows an accurate estimate of the transcript levels by a regression of the read counts of spike-in RNAs with defined copy numbers. The SC3-seq has clear advantages over other typical single-cell RNA-seq methodologies for the quantitative measurement of transcript levels and at a sequence depth required for the saturation of transcript detection. The SC3-seq distinguishes four distinct cell types in the peri-implantation mouse blastocysts. Furthermore, the SC3-seq reveals the heterogeneity in human-induced pluripotent stem cells (hiPSCs) cultured under on-feeder as well as feeder-free conditions, demonstrating a more homogeneous property of the feeder-free hiPSCs. We propose that SC3-seq might be used as a powerful strategy for single-cell transcriptome analysis in a broad range of investigations in biomedical sciences.
Dynamic Item Weighting and Selection for Collaborative Filtering

User-to-user correlation is a fundamental component of Collaborative Filtering (CF) recommender systems. In user-to-user correlation the importance assigned to each single item rating can be adapted by using item dependent weights. In CF, the item ratings used to make a prediction play the role of features in classical instance-based learning. This paper focuses on item weighting and item selection methods aimed at improving the recommendation accuracy by tuning the user-to-user correlation metric. In fact, item selection is a complex problem in CF, as standard feature selection methods cannot be applied. The huge amount of features/items and the extreme sparsity of data make common feature selection techniques not effective for CF systems. In this paper we introduce methods aimed at overcoming these problems. The proposed methods are based on the idea of dynamically selecting the highest weighted items, which appear in the user profiles of the active and neighbor users, and to use only them in the rating prediction. We have compared these methods using a range of error measures and we show that the proposed dynamic item selection performs better than standard item weighting and can significantly improve the recommendation accuracy.
Self-synchronizing properties of CSMA wireless multi-hop networks

We show that CSMA is able to spontaneously synchronize transmissions in a wireless network with constant-size packets, and that this property can be used to devise efficient synchronized CSMA scheduling mechanisms without message passing. Using tools from queuing theory, we prove that for any connected wireless networks with arbitrary interference constraints, it is possible to implement self-synchronizing TDMA schedules without any explicit message passing or clock synchronization besides transmitting the original data packets, and the interaction can be fully local in that each node decides when to transmit next only by overhearing its neighbors' transmissions. We also provide a necessary and sufficient condition on the emergence of self-synchronization for a given TDMA schedule, and prove that such conditions for self-synchronization can be checked in a finite number of steps for a finite network topology.
On the Optimization of a Duplicate Document Detection Algorithm Based on SIMD and Document Statistics

although considerable effort has been devoted to duplicate document detection (DDD) and its applications, there is very limited study on the optimization of its time-consuming functions. An experimental analysis which is conducted on a million Grant Proposal documents from the nsfc.gov. cn shows that even by using the clustering and the sampling methods, the speed of DDD is still quite slow. By analyzing the performance of our system with Intel VTune Performance Analyzer, we find out that the shingle comparison is the most time-consuming part in our system, occupying 58% CPU usage. Based on the analysis of the whole algorithm and the data statistics, we propose and implement an optimized shingle comparison algorithm using Intel SIMD technology. Experiments demonstrate that the proposed optimization technique brings 11.6%-38.5% performance gain with various instruction sets and parameters settings. Further performance gain could be achieved base on the accuracy and speed tradeoff.
An admissions OSCE: the multiple mini‐interview

Context Although health sciences programmes continue to value non‐cognitive variables such as interpersonal skills and professionalism, it is not clear that current admissions tools like the personal interview are capable of assessing ability in these domains. Hypothesising that many of the problems with the personal interview might be explained, at least in part, by it being yet another measurement tool that is plagued by context specificity, we have attempted to develop a multiple sample approach to the personal interview.
Automated Generation of Assessment Test Items from Text: Some Quality Aspects

This paper overviews the problem of automated generation of assessment test items from natural-language text. In a previously published article, an experimental system aimed at generating fill-in-theblank test items from Russian text was decribed. In this paper, some aspects of the system’s quality are analyzed. Main directions for future work are defined, including evaluation of the system and development of methods for filtering text fragments and selecting words to blank out.
Data Mining for Direct Marketing: Problems and Solutions

Direct marketing is a process of identifying likely buyers of certain products and promoting the products accordingly. It is increasingly used by banks, insurance companies, and the retail industry. Data mining can provide an effective tool for direct marketing. During data mining, several specific problems arise. For example, the class distribution is extremely unbalanced (the response rate is about 1%), the predictive accuracy is no longer suitable for evaluating learning methods, and the number of examples can be too large. In this paper, we discuss methods of coping with these problems based on our experience on direct-marketing projects using data mining.
Color-guided depth refinement based on edge alignment

Depth maps captured by consumer-level depth cameras such as Kinect usually suffer from the problem of corrupted edges and missing depth values. In this paper, an effective approach with the support of guided color images is proposed to tackle this problem. Firstly, an effective two-pass alignment algorithm is used to reliably align the depth edges with color image edges. Then, a new depth map with refined edges is generated based on interpolated drift vectors. Finally, a constrained maximal bilateral filter is proposed to fill the holes. Compared with existing methods, our approach can better refine the depth edges and avoid blurred depths in areas of depth discontinuities, as demonstrated by experiments on real Kinect data.