Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluate the Impact of Mutual Friends and Load Distribution on Epidemic Routing in Delay Tolerant Networks

Routing in Delay Tolerant Networks (DTN) requires nodes to work in a cooperative way. Due to the selfishness nature, the nodes are not willing to use their energy to help others. But two nodes with some relations (friends) are more willing to help each other. In addition, two nodes (not friends) having mutual friends can get help from each other through these mutual friends. This paper proposes a theoretic framework to evaluate the impact of mutual friends on Epidemic Routing (ER). Because the limited energy of nodes will influence their delivery decisions, we establish a model to evaluate the load distribution of ER. Simulations based on real motion traces are made and results show the accuracy of these two models. Further more, for the load distribution, numerical simulations are also considered and results show that most nodes in ER algorithm forward fewer times and only fewer nodes forward more than 5 times.
cAMP controls rod photoreceptor sensitivity via multiple targets in the phototransduction cascade

In early studies, both cyclic AMP (cAMP) and cGMP were considered as potential secondary messengers regulating the conductivity of the vertebrate photoreceptor plasma membrane. Later discovery of the cGMP specificity of cyclic nucleotide–gated channels has shifted attention to cGMP as the only secondary messenger in the phototransduction cascade, and cAMP is not considered in modern schemes of phototransduction. Here, we report evidence that cAMP may also be involved in regulation of the phototransduction cascade. Using a suction pipette technique, we recorded light responses of isolated solitary rods from the frog retina in normal solution and in the medium containing 2 µM of adenylate cyclase activator forskolin. Under forskolin action, flash sensitivity rose more than twofold because of a retarded photoresponse turn-off. The same concentration of forskolin lead to a 2.5-fold increase in the rod outer segment cAMP, which is close to earlier reported natural day/night cAMP variations. Detailed analysis of cAMP action on the phototransduction cascade suggests that several targets are affected by cAMP increase: (a) basal dark phosphodiesterase (PDE) activity decreases; (b) at the same intensity of light background, steady background-induced PDE activity increases; (c) at light backgrounds, guanylate cyclase activity at a given fraction of open channels is reduced; and (d) the magnitude of the Ca2+ exchanger current rises 1.6-fold, which would correspond to a 1.6-fold elevation of [Ca2+]in. Analysis by a complete model of rod phototransduction suggests that an increase of [Ca2+]in might also explain effects (b) and (c). The mechanism(s) by which cAMP could regulate [Ca2+]in and PDE basal activity is unclear. We suggest that these regulations may have adaptive significance and improve the performance of the visual system when it switches between day and night light conditions.
Probabilistic Modeling in Community-based Question Answering Services

Community-based Question Answering (CQA) services enable members to ask questions and have them answered by the community. These services have the potential of rapidly creating large archives of questions and answers. However, their information is rarely exploited. This thesis presents a new statistical topic model for modeling Question-Answering archives. The model explicitly captures topic dependency and correlation between questions and answers, and models differences in their vocabulary. The proposed model is applied for the task of Question Answering and its performance is evaluated using a dataset extracted from the programming website Stack Overflow. Experimental results show that it achieves improved performance in retrieving the correct answer for a query question compared to the LDA model. The model has also been applied for Automatic Tagging and comparisons with LDA show that the new model achieves better clustering performance for larger numbers of topics. x List of Abbreviations Used CQA Community-based question answering DCoDQ Common Words over Distinct Query Words JS Jensen-Shannon KL Kullback-Leibler LCoLQ Length of Common Words over Length of Query Words LDA Latent Dirichlet Allocation LSI Latent Semantic Indexing MAP Mean Average of Precision MRR Mean Reciprocal Rank PLSI Probabilistic Latent Semantic Indexing QATM Question Answering Topic Model SWB Special Words with Background TFIDF Term Frequency Inverse Document Frequency xi Acknowledgements I would like to thank my adviser, Dr Evangelos Milios, whose knowledge, support and encouragement allowed me to develop and pursue this thesis. I would also like to express my gratitude to Dr Mahdi Shafiei, for his guidance, help and support. Last but not least, I would like to thank my family, who have loved and supported me throughout my life.
Multi-modal constraint propagation for heterogeneous image clustering

This paper presents a multi-modal constraint propagation approach to exploiting pairwise constraints for constrained clustering tasks on multi-modal datasets. Pairwise constraint propagation methods have previously been designed primarily for single modality data and cannot be directly applied to multi-modal data or a dataset with multiple representations. In this paper, we provide an effective solution to the multi-modal constraint propagation problem by decomposing it into a set of independent multi-graph based two-class label propagation subproblems which are then merged into a unified problem and solved by quadratic optimization. We also show that such a formulation yields a closed-form solution. Our approach allows the initial pairwise constraints to be propagated throughout the entire multi-modal dataset. The propagated constraints are further used to refine the similarities between the objects for subsequent clustering tasks. The proposed method has been tested in constrained clustering tasks on two real-life multi-modal image datasets and shown to achieve significant improvements with respect to the single modality methods.
Service Level Checking in the Cloud Computing Context

Cloud Computing raises numerous questions about the implementation and deployment of Service-Oriented Architecture (SOA) and virtualization. It also raises questions related to services contracts, their management and verification, independently of services migration within the Cloud(s). Issues and solutions related to the verification of contracts compliance are referred as Service Level Checking (SLC). Implementing SLC in the Cloud Computing context requires an innovating and functional architecture, in order to separate monitoring information collection concerns from contracts compliance verification concerns. Such contracts or Service Level Agreements (SLAs) apply to the target systems and services. In order to solve the separation of concerns issue, we have used the mediation approach. Our architecture and prototype have been implemented and validated in the context of the French ANR SemEUsE and European Celtic SERVERY cooperative research projects.
EXECUTING PARALLEL PROGRAMS WITH SYNCHRONIZATION BOTTLENECKS EFFICIENTLY

We propose a scheme within which parallel programs with potential synchronization bottlenecks run e ciently. In the straightforward implementations which use basic locking schemes, the execution time for the program parts with bottlenecks increases signi cantly when the number of processors increases. Our scheme makes the parallel performance for the bottleneck parts of programs close to the sequential performance while maintaining the e ciency with which the nonbottleneck parts run. Experiments with a 64-processor SMP and a 128-processor DSM machine conrmed that parallel programs implemented with our scheme perform much better than parallel programs implemented with other widely-used locking schemes.
Detecting and Quantifying Biomolecular Interactions of a Dendritic Polyglycerol Sulfate Nanoparticle Using Fluorescence Lifetime Measurements

Interactions of nanoparticles with biomaterials determine the biological activity that is key for the physiological response. Dendritic polyglycerol sulfates (dPGS) were found recently to act as an inhibitor of inflammation by blocking selectins. Systemic application of dPGS would present this nanoparticle to various biological molecules that rapidly adsorb to the nanoparticle surface or lead to adsorption of the nanoparticle to cellular structures such as lipid membranes. In the past, fluorescence lifetime measurements of fluorescently tagged nanoparticles at a molecular and cellular/tissue level have been proven to reveal valuable information on the local nanoparticle environment via characteristic fluorescent lifetime signatures of the nanoparticle bound dye. Here, we established fluorescence lifetime measurements as a tool to determine the binding affinity to fluorescently tagged dPGS (dPGS-ICC; ICC: indocarbocyanine). The binding to a cell adhesion molecule (L-selectin) and a human complement protein (C1q) to dPGS-ICC was evaluated by the concentration dependent change in the unique fluorescence lifetime signature of dPGS-ICC. The apparent binding affinity was found to be in the nanomolar range for both proteins (L-selectin: 87 ± 4 nM and C1q: 42 ± 12 nM). Furthermore, the effect of human serum on the unique fluorescence lifetime signature of dPGS-ICC was measured and found to be different from the interactions with the two proteins and lipid membranes. A comparison between the unique lifetime signatures of dPGS-ICC in different biological environments shows that fluorescence lifetime measurements of unique dPGS-ICC fluorescence lifetime signatures are a versatile tool to probe the microenvironment of dPGS in cells and tissue.
Geometric registration for zoomable camera using epipolar constraint and pre-calibrated intrinsic camera parameter change

In general, video see-through based augmented reality (AR) cannot change the magnification of camera zooming parameter due to the difficulty of dealing with changes in intrinsic camera parameters. To realize the usage of camera zooming in AR, we propose a novel simultaneous intrinsic and extrinsic camera parameter estimation method based on an energy minimization framework. Our method is composed of the online and offline stages. An intrinsic camera parameter change depending on the zoom values is calibrated in the offline stage. Intrinsic and extrinsic camera parameters are then estimated based on the energy minimization framework in the online stage. In our method, two energy terms are added to the conventional marker-based camera parameter estimation method. One is reprojection errors based on the epipolar constraint. The other is the constraint of continuity of zoom values. By using a novel energy function, our method can estimate accurate intrinsic and extrinsic camera parameters. In an experiment, we confirmed that the proposed method can achieve accurate camera parameter estimation during camera zooming.
Application of Individualized Speed Thresholds to Interpret Position Specific Running Demands in Elite Professional Rugby Union: A GPS Study

A number of studies have used GPS technology to categorise rugby union locomotive demands. However, the utility of the results of these studies is confounded by small sample sizes, sub-elite player status and the global application of absolute speed thresholds to all player positions. Furthermore, many of these studies have used GPS units with low sampling frequencies. The aim of the present study was to compare and contrast the high speed running (HSR) demands of professional rugby union when utilizing micro-technology units sampling at 10 Hz and applying relative or individualised speed zones. The results of this study indicate that application of individualised speed zones results in a significant shift in the interpretation of the HSR demands of both forwards and backs and positional sub-categories therein. When considering the use of an absolute in comparison to an individualised HSR threshold, there was a significant underestimation for forwards of HSR distance (HSRD) (absolute = 269 ± 172.02, individualised = 354.72 ± 99.22, p < 0.001), HSR% (absolute = 5.15 ± 3.18, individualised = 7.06 ± 2.48, p < 0.001) and HSR efforts (HSRE) (absolute = 18.81 ± 12.25; individualised = 24.78 ± 8.30, p < 0.001). In contrast, there was a significant overestimation of the same HSR metrics for backs with the use of an absolute threshold (HSRD absolute = 697.79 ± 198.11, individualised = 570.02 ± 171.14, p < 0.001; HSR% absolute = 10.85 ± 2.82, individualised = 8.95 ± 2.76, p < 0.001; HSRE absolute = 41.55 ± 11.25; individualised = 34.54 ± 9.24, p < 0.001). This under- or overestimation associated with an absolute speed zone applies to varying degrees across the ten positional sub-categories analyzed and also to individuals within the same positional sub-category. The results of the present study indicated that although use of an individulised HSR threshold improves the interpretation of the HSR demands on a positional basis, inter-individual variability in maximum velocity within positional sub-categories means that players need to be considered on an individual basis to accurately gauge the HSR demands of rugby union.
Processing Top-k Monitoring Queries in Wireless Sensor Networks

Top-k monitoring queries are useful in many wireless sensor network applications. There is a well-known approach called FILA to process this kind of queries. Its basic idea is to install a filter at each sensor node to avoid unnecessary transmissions of sensor readings. FILA uses two algorithms to ensure the correctness and efficiency of the approach: a query reevaluation algorithm and a filter setting algorithm. In this paper, we propose improvements to each of these two algorithms. First, we propose a decentralized query reevaluation algorithm to reduce the communication cost of sending probe messages. Second, we propose a linear regression-based filter setting algorithm to improve the effectiveness of filters. Experimental results on real data traces show that our proposed improvements further enhance the performance of FILA in terms of network lifetime.
BUAP: N-gram based Feature Evaluation for the Cross-Lingual Textual Entailment Task

This paper describes the evaluation of different kinds of textual features for the CrossLingual Textual Entailment Task of SemEval 2013. We have counted the number of N grams for three types of textual entities (character, word and PoS tags) that exist in the pair of sentences from which we are interested in determining the judgment of textual entailment. Difference, intersection and distance (Euclidian, Manhattan and Jaccard) of N -grams were considered for constructing a feature vector which is further introduced in a support vector machine classifier which allows to construct a classification model. Five different runs were submitted, one of them considering voting system of the previous four approaches. The results obtained show a performance below the median of six teams that have participated in the competition.
Search in Financial Markets, and Monetary Policy

We extend a standard monetary quantitative model to provide a richer role for financial intermediaries and to generate greater persistence in the effects of monetary policy shocks. We first assume that existing clients of banks operate a diminishing returns to scale technology. Second, we assume that banks enter into contact with suitable new clients via a search-and-matching mechanism similar to that used to study labour markets; here, the nominal interest rate is the instrument that implements the sharing of the net surplus associated with a bank-entrepreneur match. Operating in such a world, banks have an incentive to set aside a fraction of any unexpected liquidity injection to search for new clients, delaying the entry of the injection in the economic system. Further, the initial liquidity injection generates persistent changes in the relative bargaining weights of banks and entrepreneurs, affecting the sharing of net surpluses and thus the nominal interest rate that implements it. The combination of these two effects is shown to add persistence to the decrease in nominal interest rates that follows monetary policy easings and to create hump-shaped responses in output and inflation
Interleukin 13 and Serotonin: Linking the Immune and Endocrine Systems in Murine Models of Intestinal Inflammation

Objective Infiltration of activated immune cells and increased cytokine production define the immunophenotype of gastrointestinal (GI) inflammation. In addition, intestinal inflammation is accompanied by alteration in the numbers of serotonin (5-hydroxytryptamine; 5-HT) synthesizing enterochromaffin (EC) cells and in 5-HT amount. It has been established that EC cells express interleukin (IL)-13 receptor, additionally IL-13 has been implicated in the pathogenesis of ulcerative colitis. In this study, we investigated the role of IL-13 mediated 5-HT signaling in pathogenesis of colitis. Methodology Colitis was induced in IL-13 deficient (IL-13−/−) and wild-type (WT) mice with dextran sulfate sodium (DSS) and dinitrobenzene sulfonic acid (DNBS), as well as in IL-13−/− mice given recombinant mouse IL-13 (rmIL-13) and 5-hydroxytryptamine (5-HTP), the direct precursor of 5-HT. Principal Findings and Conclusion Elevated colonic IL-13 levels were observed in WT mice receiving DSS in comparison to control. IL-13−/− mice administered DSS exhibited significantly reduced severity of colitis compared to WT mice as reflected by macroscopic and histological damage assessments. Following DSS administration, significantly lower pro-inflammatory cytokine production and fewer infiltrating macrophages were observed in IL-13−/− mice compared to WT. The reduced severity of colitis observed in IL-13−/− mice was also accompanied by down-regulation of EC cell numbers and colonic 5-HT content. In addition, increasing colonic 5-HT content by administration of rmIL-13 or 5-HTP exacerbated severity of DSS colitis in IL-13−/− mice. IL-13−/− mice also exhibited reduced severity of DNBS-induced colitis. These results demonstrate that IL-13 plays a critical role in the pathogenesis of experimental colitis and 5-HT is an important mediator of IL-13 driven intestinal inflammation. This study revealed important information on immune-endocrine axis in gut in relation to inflammation which may ultimately lead to better strategy in managing various intestinal inflammatory conditions including inflammatory bowel disease.
A Generalized Stationary Point Convergence Theory for Evolutionary Algorithms

This paper presents a convergence theory for evolutionary pattern search algorithms (EPSAs). EPSAs are self-adapting evolutionary algorithms that modify the step size of the mutation operator in response to the success of previous optimization steps. Previously, the authors have proven a stationary point convergence theory for EPSAs for which the step size is not allowed to increase. The present analysis generalizes this analysis to prove a convergence theory for EPSAs that are allowed to both increase and decrease the step size. This convergence theory is based on an extension of the convergence theory for generalized pattern search methods.
Neural network based model for visual-motor integration learning of robot's drawing behavior: Association of a drawing motion from a drawn image

In this study, we propose a neural network based model for learning a robot's drawing sequences in an unsupervised manner. We focus on the ability to learn visual-motor relationships, which can work as a reusable memory in association of drawing motion from a picture image. Assuming that a humanoid robot can draw a shape on a pen tablet, the proposed model learns drawing sequences, which comprises drawing motion and drawn picture image frames. To learn raw pixel data without any given specific features, we utilized a deep neural network for compressing large dimensional picture images and a continuous time recurrent neural network for integration of motion and picture images. To confirm the ability of the proposed model, we performed an experiment for learning 15 sequences comprising three types of shapes. The model successfully learns all the sequences and can associate a drawing motion from a not trained picture image and a trained picture with similar success. We also show that the proposed model self-organizes its behavior according to types shapes.
New insights into the genetics of glioblastoma multiforme by familial exome sequencing

Glioblastoma multiforme (GBM) is the most aggressive and malignant subtype of human brain tumors. While a family clustering of GBM has long been acknowledged, relevant hereditary factors still remained elusive. Exome sequencing of families offers the option to discover respective genetic factors. We sequenced blood samples of one of the rare affected families: while both parents were healthy, both children were diagnosed with GBM. We report 85 homozygous non-synonymous single nucleotide variations (SNVs) in both siblings that were heterozygous in the parents. Beyond known key players for GBM such as ERBB2, PMS2, or CHI3L1, we identified over 50 genes that have not been associated to GBM so far. We also discovered three accumulative effects potentially adding to the tumorigenesis in the siblings: a clustering of multiple variants in single genes (e.g. PTPRB, CROCC), the aggregation of affected genes on specific molecular pathways (e.g. Focal adhesion or ECM receptor interaction) and genomic proximity (e.g. chr22.q12.2, chr1.p36.33). We found a striking accumulation of SNVs in specific genes for the daughter, who developed not only a GBM at the age of 12 years but was subsequently diagnosed with a pilocytic astrocytoma, a common acute lymphatic leukemia and a diffuse pontine glioma. The reported variants underline the relevance of genetic predisposition and cancer development in this family and demonstrate that GBM has a complex and heterogeneous genetic background. Sequencing of other affected families will help to further narrow down the driving genetic causes for this disease.
Nussinov Multiple Docking for Protein Structure Prediction

Protein structure prediction and protein docking prediction are two related problems in molecular biology. We suggest the use of multiple docking in the process of protein structure prediction. Once reliable structural models are predicted to disjoint fragments of the protein target sequence, a combinatorial assembly may be used to predict their native arrangement. Here, we present CombDock, a combinatorial docking algorithm for the structural units assembly problem. We have tested the algorithm on various examples using both domains and domain substructures as input. Inaccurate models of the structural units were also used, to test the robustness of the algorithm. The algorithm was able to predict a near-native arrangement of the input structural units in almost all of the cases, showing that the combinatorial approach succeeds in overcoming the inexact shape complementarity caused by the inaccuracy of the models. KEY WORDS—protein structure prediction, multiple docking, combinatorial assembly, self-reconfigurable chain-type system