Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing interactions in online social networks during exceptional events

Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events.
Regression on manifolds using kernel dimension reduction

We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this problem involves extending and uniting two threads of research. On the one hand, the literature on sufficient dimension reduction has focused on methods for finding the best linear subspace for nonlinear regression; we extend this to manifolds. On the other hand, the literature on manifold learning has focused on unsupervised dimensionality reduction; we extend this to the supervised setting. Our approach to solving the problem involves combining the machinery of kernel dimension reduction with Laplacian eigenmaps. Specifically, we optimize cross-covariance operators in kernel feature spaces that are induced by the normalized graph Laplacian. The result is a highly flexible method in which no strong assumptions are made on the regression function or on the distribution of the covariates. We illustrate our methodology on the analysis of global temperature data and image manifolds.
Non-Fourier thermal conduction in nano-scaled electronic structures

When very fast phenomena and small structure dimensions are involved, the classical law of Fourier becomes inaccurate. A more sophisticated model is then needed to describe the thermal conduction mechanisms in a physically acceptable way. In this paper the according diffusion equation is solved for a nano-scaled semiconductor substrate, in order to gain physical insight in the problem. Analytical solutions for the temperature and heat flux distributions are presented. The complex thermal impedance and thermal step response of the structure are discussed. The most remarkable fact is that the temperature inside the substrate can go below the ambient temperature for a short amount of time. The results also clearly demonstrate the wave character of the heat propagation and the analogy with RLC transmission lines.
Linkage and Association Analyses of Schizophrenia with Genetic Variations on Chromosome 22q11 in Koreans

Objective Chromosome 22q11 has been implicated as a susceptibility locus of schizophrenia. It also contains various candidate genes for which evidence of association with schizophrenia has been reported. To determine whether genetic variations in chromosome 22q11 are associated with schizophrenia in Koreans, we performed a linkage analysis and case-control association study. Methods Three microsatellite markers within a region of 4.35 Mb on 22q11 were genotyped for 47 multiplex schizophrenia families, and a non-parametric linkage analysis was applied. The association analysis was done with 227 unrelated patients and 292 normal controls. For 39 single nucleotide polymorphisms (SNPs) spanning a 1.4 Mb region (33 kb interval) containing four candidate schizophrenia genes (DGCR, COMT, PRODH and ZDHHC8), allele frequencies were estimated in pooled DNA samples. Results No significant linkage was found at any of the three microsatellite markers in single and multi-point analyses. Five SNPs showed suggestive evidence of association (p<0.05) and two more SNPs showed a trend for association (p<0.1) in pooled DNA association analysis. Individual genotyping was performed for those seven SNPs and four more intragenic SNPs. In this second analysis, all of the 11 SNPs individually genotyped did not show significant association. Conclusion The present study suggests that genetic variations on chromosome 22q11 may not play a major role in Korean schizophrenia patients. Inadequate sample size, densities of genetic markers and differences between location of genetic markers of linkage and association can contribute to an explanation of the negative results of this study.
A comparison of microsatellite instability in early onset gastric carcinomas from relatively low and high incidence European populations

We have investigated the genetic basis of gastric carcinomas occurring in patients aged under 40 years from a Portuguese population with a relatively high incidence of gastric cancer. We analysed a panel of 12 microsatellite loci in DNA extracted from gastric carcinomas arising in 16 patients aged 24–39 years from Braga, Portugal. Overall, microsatellite instability (MI) in at least 1 locus was detected in 44% (7 of 16) of carcinomas. A single patient demonstrated a mutator phenotype suggestive of the hereditary nonpolyposis colorectal cancer syndrome with instability in 82% of loci. This carcinoma showed loss of expression of the hMLH1 mismatch repair protein. In a previous study, we found no evidence of MI among 10 cases of early onset gastric carcinomas from an English population, which has a relatively low incidence of gastric cancer. Comparing the 2 series, we found that there was a significant difference (p = 0.04) in the prevalence of MI (at least 1 marker affected). This geographical difference in low‐level MI may be related to a significantly higher prevalence of background chronic atrophic gastritis (8 of 16 vs. 0 of 8) and Helicobacter pylori infection (15 of 16 vs. 2 of 8) in Portuguese carcinomas compared with English cases. Genetic mechanisms underlying the hereditary non‐polyposis colorectal cancer syndrome may play a role in a small number of early onset gastric carcinomas. The difference in prevalence of low‐level MI between these relatively high and low incidence European populations requires further investigation. Int. J. Cancer 85:189–191, 2000. ©2000 Wiley‐Liss, Inc.
Autonomous mobile robot with displacements in a vertical plane and applications in cleaning services

Robotic systems have lead to important progress in the vast field of automation. Even if industrial robots prevailed initially, service robots intended to perform various activities, including domestic tasks, have brought a substantial contribution in the latest two decades. Autonomous mobile robots used for vertical surface cleaning involve the most complex mechanical structures from the point of view of displacement kinematics as well as fixing in operating points. Differently than service robots with horizontal plane displacement, this category of robots has to support its own weight, fixing elements most frequently used being vacuum suction cups. The paper presents the construction, modelling and simulation of functioning of a robot with displacements in a vertical plane used for cleaning vertical surfaces. The original robot construction, developed by the authors, covers the main basic functions of cleaning robots.
An automated workflow for parallel processing of large multiview SPIM recordings

Summary: Selective Plane Illumination Microscopy (SPIM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI) applications. The consecutive processing steps can be easily automated and the individual time points can be processed independently, which lends itself to trivial parallelization on a high performance computing (HPC) cluster. Here, we introduce an automated workflow for processing large multiview, multichannel, multiillumination time-lapse SPIM data on a single workstation or in parallel on a HPC cluster. The pipeline relies on snakemake to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing SPIM data in a fraction of the time required to collect it. Availability and implementation: The code is distributed free and open source under the MIT license http://opensource.org/licenses/MIT. The source code can be downloaded from github: https://github.com/mpicbg-scicomp/snakemake-workflows. Documentation can be found here: http://fiji.sc/Automated_workflow_for_parallel_Multiview_Reconstruction. Contact: schmied@mpi-cbg.de Supplementary information: Supplementary data are available at Bioinformatics online.
Interactions Between Organizational Size and Some IT Factors in the Context of ERP Success Assessment: An Exploratory Investigation

The study set sights on Romanian organizations which implemented a SIVECO ERP and BI software. The methodology used is both quantitative and qualitative. The research results were obtained with the use of a questionnaire, and our purpose was to demonstrate some hypothesis concerning the size of the organisation, the management method and the IT&C based decision. The questionnaire was operated with SPSS 17, through a linear regression analysis. The research has revealed how the organizational size and the IT factors interaction in the 2010 Romanian organizations. The research has a high level of originality, such a study has been never conducted before for computer based advanced management methods implementation.
Comparative Cryptanalysis of Simplified-Data Encryption Standard Using Tabu Search and Simulated Annealing Methods

This paper presents an approach for the comparative cryptanalysis of Simple Data Encryption Standard (S-DES) using Tabu Search and Simulated Annealing methods. In this paper, cipher text only attack is adopted and varieties of optimum keys are generated based on the cost function values. The goal of this paper is three fold. First we want to make a study about how evolutionary computation techniques can efficiently solve the NP-Hard combinatorial problem. For achieving this goal we test several evolutionary computation techniques like Tabu Search algorithm and simulated annealing for the cryptanalysis of simplified data encryption standard problem (SDES). Second was a comparison between Tabu Search algorithm and simulated annealing were made in order to investigate the performance for the cryptanalysis on SDES. The methods were tested and extensive computational results show that Tabu Search algorithm performs better than simulated annealing for such type of NP-Hard combinatorial problem. This paper represents our first effort toward efficient Tabu Search algorithm for the cryptanalysis of SDES. And third was cryptanalysis data could be store for long time with help of Self Monitoring Analysis and Reporting Technology (SMART) Copyback technique.
Entrepreneurial Motivation in Developing Countries: What Does 'Necessity' and 'Opportunity' Entrepreneurship Really Mean?

The paper examines the relationship between the state of necessity and entrepreneurial activity, through qualitative case studies from Uganda and Sri Lanka, and a survey of 1006 Ugandan adults. Questions are posed on the tenability of hypothesis that necessity is a primary motive for business start-up in poor countries. The relationship between necessity and business start-up, though significant, is in the opposite direction from that predicted by the "necessity hypothesis". Those with low incomes were much less likely to start a business, because they often became "trapped" by having to work long hours for just enough income to survive. Opportunistic diversification, however, flourished once resources improved. The results question recent attempts to classify countries on the basis of distinctive forms of entrepreneurship based on necessity and opportunity.
Novel nearly tridiagonal commuting matrix and fractionalizations of generalized DFT matrix

Based on discrete Hermite-Gaussian-like functions, a discrete fractional Fourier transform (DFRFT), which provides sample approximations of the continuous fractional Fourier transform, was defined and investigated recently. In this paper, we propose a novel nearly tridiagonal matrix, which commutes with the generalized discrete Fourier transform (GDFT) matrix. It doesn't has repeated eigenvalue. We can determine a unique orthonormal eigenvector set based on block diagonalization of new commuting matrix. The eigenvectors of the new nearly tridiagonal matrix are shown to be GDFT eigenvectors, which are more similar to the continuous Hermite-Gaussian functions than those developed before. The fractional transform of GDFT (GDFRFT) is defined through eigendecomposition. The new version of GDFRFT produce their transform outputs closer to the samples of the continuous fractional Fourier transform through numerical comparison.
INFERENCE IN ADDITIVELY SEPARABLE MODELS WITH A HIGH DIMENSIONAL COMPONENT

This paper provides inference results for series estimators with a high dimensional component. In conditional expectation models that have an additively separable form, a single component can be estimated with rates customary in nonparametric estimation even when then number of series terms for remaining components is large relative to the sample size. This allows, for example, estimation of a nonlinear response of an outcome variable given a treatment variable of interest while accounting for potentially very many confounders. A key condition which makes inference in this setting possible is sparsity; there is a small (unknown) subset of terms which can replace the entire series without inducing significant bias. This paper considers a model selection procedure for choosing series terms that generalizes the post-double selection procedure given in Belloni, Chernozhukov, Hansen (2013) to the nonparametric setting. In one stage, variables are selected if they are relevant for predicting the treatment. In a second stage, variables are selected if they are relevant in predicting the treatment regressor of interest. Rates of convergence and asymptotic normality are derived for series estimators of a component of a conditional expectation in high dimensional models under sparsity conditions. Simulation results demonstrate that the proposed estimator performs favorably in terms of size of tests and risk properties relative to other estimation strategies.
Multiscale inference about a density

We introduce a multiscale test statistic based on local order statistics and spacings that provides simultaneous confidence statements for the existence and location of local increases and decreases of a density or a failure rate. The procedure provides guaranteed finite-sample significance levels, is easy to implement and possesses certain asymptotic optimality and adaptivity properties.
The experience of co-morbidity: an interpretative phenomenological analysis: living with chronic pain and traumatic stress

There are few qualitative studies exploring co-morbidity and specifically the experience of living with chronic pain and traumatic stress. In addition existing research has focused on the relationship between chronic pain and post-traumatic stress disorder (PTSD) but has not considered the impact of complex post-traumatic stress disorder (CTPSD) on co-existing chronic pain. The aim of this study was to explore the individual’s unique experience of living with co-morbid chronic pain and traumatic stress. This study also supports a wider formulation by exploring CPTSD as a construct, acknowledging the diversity of symptoms beyond the diagnostic criteria of PTSD. Five participants were interviewed about their experiences. Interpretative phenomenological analysis (IPA) was used to analyse the data. An IPA analysis revealed six superordinate themes: 1) “Every day is just such a struggle”; 2) Isolation; 3) The relationship between chronic pain and traumatic stress; 4) Chronic pain and traumatic stress change the relationship with the self; 5) Ways of coping with chronic pain and traumatic stress; 6) Moving forward; learning to adjust and live with chronic pain and traumatic stress. The super-ordinate themes highlight the everyday reality of living with this co-morbidity, suggesting that co-morbid chronic pain and traumatic stress do not exist as separate isolated symptoms but are impacted by individual, relational and social factors. The results also highlight the complex interrelationship between chronic pain and traumatic stress. Implications for policy and clinical practice include raising awareness and providing access to effective care pathways for clients who live with this distressing co-occurrence. An integrated treatment approach is required to address the meaning and multi-dimensional nature of living with this co-morbidity. Recommendations are made for further research in this area.
The Shared Shortest Path Problem in Graphs

Our research into the Shared Shortest Path Prob-lem (SSPP) attempts to route a number of paths, called journeys, in a graph where journeys share costs of common edges. There are several diﬀerent ways to view the problem, measured by diﬀering optimum solutions. Our ﬁrst type of solution attempts to minimize total cost, which is known to be NP-Complete. To satisfy this condition, we used an approach based on the Minimum Spanning Tree. Generally, this approach ﬁnds reasonably cost eﬀective solutions, but more eﬀective solutions can be found using other approaches. Our second metric is to treat each journey as an independent agent and attempt to ﬁnd a Nash Equilibrium (NE) for the graph. From previous research on Congestion Games, we learned that NE always exist for our problem, and created an algorithm that ﬁnds them quickly, in practice. Additionally, while attempting to generalize Nash Equilibria, we investigated how Strong Nash Equilibria can also be applied to our problem. While this is one of the more eﬀective optimality conditions, we found that it is likely NP-Complete, both to verify, and to ﬁnd. In addition, we found that Strong Nash Equilibria do not always exist in graphs. As a result, we chose not to study this approach in detail, but have left it as a ﬁeld for further study.
Scheduling of Dynamic Participants in Real-Time Distributed Systems

Access to shared resources can be controlled by schedules or mutual exclusion. Such methods are not practical in an environment with dynamic participants, where nodes requiring access to shared resources can enter or leave the scene at any time. Current scheduling methods are usually centralized, demand that the system has a clear idea of when the resources are required and do not consider communication failures. Current implementations of distributed mutual exclusion use token- or permission-based methods. Dynamic participation amplifies the lost token problem in token-based approaches, while limited knowledge of the number of nodes makes obtaining quora and consensus in permission-based approaches impossible, rendering both mutual exclusion implementations impractical. This paper presents the CwoRIS protocol which enables short-term scheduling in real-time within an environment with dynamic participants. It motivates the need to support dynamic participants by means of a scenario for autonomous vehicle coordination in intersection crossing. The paper shows that the protocol is able to work in an environment with message loss and argues its correctness by showing mutual exclusion: there are no cases in which two nodes have access to the same resources at the same time.
Face Recognition Based on Geodesic Preserving Projection Algorithm with 3D Morphable Model

A novel nonlinear face recognition method named GPPface is proposed in this paper. GPPface is based on nonlinear dimensionality reduction algorithm, geodesic preserving projection (GPP). As face images are regarded to be embedded in a nonlinear space, GPP is presented to nonlinearly map high-dimensional face images to low-dimensional feature space. GPP overcomes the weaknesses of traditional linear and nonlinear dimensionality reduction algorithms, well preserves the intrinsic structure of the manifold and can fast and efficiently map new sample point to feature space. To recover space structure of face images and tackle small sample size problem, 3D morphable model is developed to derive multiple images of a person from a single image. Experimental results on ORL and PIE face databases show that our method makes impressive performance improvement compared with conventional face recognition methods
A Nettree for pattern Matching with flexible wildcard Constraints

In this paper, a new nonlinear structure called Nettree is proposed. A Nettree is different from a tree in that a node may have more than one parent. An algorithm, named Nettree for pattern Matching with flexible wildcard Constraints (NAMEIC), based on Nettree is designed to solve pattern matching with flexible wildcard constraints. The problem is exponential with regard to the pattern length m. We prove the correctness of the algorithm, and illustrate how it works through an example. NAMEIC is W*m times faster than an existing approach because the result can be given after creating the Nettree in one pass, where W is the maximal gap flexibility. Experiments validate the correctness and efficiency of NAMEIC.
Chato, a KRAB zinc-finger protein, regulates convergent extension in the mouse embryo

In Xenopus and zebrafish embryos, elongation of the anterior-posterior body axis depends on convergent extension, a process that involves polarized cell movements and is regulated by non-canonical Wnt signaling. The mechanisms that control axis elongation of the mouse embryo are much less well understood. Here, we characterize the ENU-induced mouse mutation chato, which causes arrest at midgestation and defects characteristic of convergent extension mutants, including a shortened body axis, mediolaterally extended somites and an open neural tube. The chato mutation disrupts Zfp568, a Krüppel-associated box (KRAB) domain zinc-finger protein. Morphometric analysis revealed that the definitive endoderm of mouse wild-type embryos undergoes cell rearrangements that lead to convergent extension during early somite stages, and that these cell rearrangements fail in chato embryos. Although non-canonical Wnt signaling is important for convergent extension in the mouse notochord and neural plate, the results indicate that chato regulates body axis elongation in all embryonic tissues through a process independent of non-canonical Wnt signaling.
Automatic Ontario license plate recognition using local normalization and intelligent character classification

The purpose of this paper is to introduce a robust method to address the problem of license plate recognition of Ontario on the basis of an illumination compensation technique and intelligent character recognition algorithm. To foster our aim, we firstly frame the input video from into stream of images the database. We then apply our pre-processing and local normalization algorithms to remove the noise and variance of lighting conditions. The image registration is utilized for the plate segmentation and feature construction. The improved cross correlation and D-Isomap analysis are used separately for final stage in character recognition. The experimental results demonstrate a significant performance improvement achieved by the proposed methods.