Miracle-dz/UniFAR-FTUs huggingface dataset:
National Cultural Norms and Soccer Violence

Can some acts of violence be explained by a society’s cultural norms? Scholars have found it hard to empirically disentangle the effects of cultural norms versus legal institutions in driving violence. We address this problem by exploiting a natural experiment offered by the presence of thousands of international soccer (football) players in the European professional leagues. We find a strong relationship between the history of civil conflict in a player’s home country and his propensity to behave violently on the soccer field, as measured by yellow and red cards. This link is robust to region fixed effects, country characteristics (e.g., rule of law, per capita income), player characteristics (e.g., age, field position, quality), outliers, and team fixed effects. We also find a strong positive relationship between civil war history and national attitudes towards violent conduct, as well as between attitudes and both yellow and red cards. Reinforcing our claim that we isolate cultures of violence rather than simple rule-breaking or something else entirely, there is no meaningful correlation between a player’s home country civil war history and soccer performance measures not closely related to violent conduct. * We are grateful to Dan Altman, Ray Fisman, Matias Iaryczower, Dani Rodrik, seminar participants at Stanford, UCSD, UCLA, IPES, and at the 4 Annual HiCN Workshop at Yale, and a host of anonymous bloggers for useful comments, and Dan Hartley, Teferi Mergo, and Melanie Wasserman for excellent research assistance. All errors remain our own.
The contribution of the student voice in classroom research : a case study 1

Gaye Williams and David Clarke University of Melbourne, Melbourne, Australia g . williams@pgrad.unimelb.edu.au d.clarke@unimelb.edu.au 2 This paper addresses issues related to the triangulation of data in classroom video research—in particular the contribution of the student voice to the research methodology. The case of a Year 8 boy—Leon—is used to illustrate the multiple interpretations that may result when the student voice is included. This case draws attention to the benefits of a research methodology that increases validity by providing additional sources of data and raises issues regarding the weighing of evidence where differences in interpretation arise. This case is used to illustrate the use of triangulation and the evaluation strategies that take advantage of the multidimensional data to synthesise the complementary interpretations derived from the different data sources into a coherent portrayal of an individual’s classroom practice.
Eurographics/ Ieee-vgtc Symposium on Visualization (2006) Volume Composition Using Eye Tracking Data

This paper presents a method to automate rendering parameter selection, simplifying tedious user interaction and improving the usability of visualization systems. Our approach acquires regions-of-interest for a dataset with an eye tracker and simple user interaction. Based on this importance information, we then automatically compute reasonable rendering parameters using a set of heuristic rules adapted from visualization experience and psychophysics experiments. While the parameter selections for a specific visualization task are subjective, our approach provides good starting results that can be refined by the user. Our system improves the interactivity of a visualization system by significantly reducing the necessary parameter selection and providing good initial rendering parameters for newly acquired datasets of similar types.
Diffusion Tensor Magnetic Resonance Imaging of the Brain in APP Transgenic Mice: A Cohort Study

Introduction Fast in-vivo high resolution diffusion tensor imaging (DTI) of the mouse brain has recently been shown to enable cohort studies by the combination of appropriate pulse sequences and cryogenically cooled resonators (CCR). The objective of this study was to apply this DTI approach at the group level to β-amyloid precursor protein (APP) transgenic mice. Methods Twelve mice (5 wild type, 7 APP transgenic tg2576) underwent DTI examination at 1562×250 µm3 spatial resolution with a CCR at ultrahigh field (11.7 T). Diffusion images were acquired along 30 gradient directions plus 5 references without diffusion encoding with a total acquisition time of 35 minutes. Fractional anisotropy (FA) maps were statistically compared by whole brain-based spatial statistics (WBSS) at the group level vs. wild type controls. Results FA-map comparison showed characteristic regional patterns of differences between the groups with localizations associated with Alzheimer’s disease in humans, such as the hippocampus, the entorhinal cortex, and the caudoputamen. Conclusion In this proof-of-principle study, regions associated with amyloid-β deposition could be identified by WBSS of FA maps in APP transgenic mice vs. wild type mice. Thus, DTI in the mouse brain acquired at 11.7 T by use of a CCR was demonstrated to be feasible for cohort studies.
Bridging the Lesson Distribution Gap Content Areas : case-based reasoning , AI architectures Tracking number : 649

Many organizations employ lessons learned (LL) processes to collect, analyze, store, and distribute, validated experiential knowledge (lessons) of their members that, when reused, can substantially improve some evaluation measures of targeted organizational decision processes. Unfortunately, deployed LL systems do not facilitate lesson reuse, and thus fail to bridge the lesson distribution gap between the lesson repository and targeted decision processes (i.e., they fail to bring lessons to the attention of users when and where they are needed and applicable). Our approach for solving this problem, named monitored distribution, tightly integrates lesson distribution with these decision processes. We describe a case-based implementation of monitored distribution (ALDS) in a plan authoring tool suite (HICAP) and evaluate its utility in a simulated military planning domain. Our results show that monitoried distribution can significantly improve plan evaluation measures for this domain.
Diverse and Opposing Roles of IL-27 in Immunity~!2009-11-04~!2009-12-16~!2010-07-14~!

IL-27 was originally thought to play a pro-inflammatory role in immunity, however it is now clear that IL-27 also exerts potent anti-inflammatory effects. Here, we discuss some of the key studies that have elucidated the diverse and apparently paradoxical roles of IL-27. While IL-27 can promote early Th1 development there is evidence of suppressive effects in later phases of Th1 responses. IL-27 exerts a direct inhibitory effect in Th2 immunity by blocking Th2 cell dif- ferentiation. The role of IL-27 in Th17 immune responses is also complex as it seems that while IL-27 can block early Th17 development, fully differentiated Th17 cells may become resistant to the inhibitory effects of IL-27. In the field of cancer biology, IL-27 has been shown to have anti-tumourigenic effects however it acts via both immunogenic and non- immunogenic mechanisms. Collectively, the studies discussed in this review have demonstrated multiple biological func- tions and mechanisms of action of IL-27.
Learning from Large-Scale Distributed Health Data : An Approximate Logistic Regression Approach

Research in healthcare is increasingly depending on the access to and analysis of large distributed datasets. Coupled with the exponential rate at which data is being generated, the need for parallel processing is apparent if knowledge is to be efficiently extracted from these large data sets. The HadoopMapReduce framework has evolved into a popular platform for parallelization in many fields, including healthcare. Unfortunately, implementing iterative machine learning algorithms on Hadoop is inefficient, complex and time consuming. To address this problem, this paper presents a computationally efficient method for computing the logistic regression. A simple approximation to the logistic function leads to a single-pass MapReduce algorithm. The method presented has two important virtues: computational simplicity and application. A series of numerical experiments on artificial and clinically relevant data sets showed that the method competes favorably with regards to accuracy, parallel efficiency and scalability compared to classical iterative methods.
Managing Client Dialogues During Information Systems Design to Facilitate Client Learning

It has long been recognized that client learning is an important factor in the successful development of information systems. While there is little question that clients should learn, there is less clarity about how best to facilitate client learning during developer-client meetings. In this study, we suggest that a cooperative learning strategy called collaborative elaboration developed by educational psychologists provides a theoretical and practical basis for stimulating client learning during an IS design process. The problem with assessing the effects of collaborative elaboration, however, is in controlling for the many other factors that might affect client learning and outcomes of an IS design phase. In a unique research opportunity, we were able to measure the use of collaborative elaboration among 85 developers and clients involved in 17 projects over a semester-long IS design process. The projects were homogeneous with respect to key contextual variables. Our PLS analysis suggested that teams using more collaborative elaboration had more client learning and teams with more client learning achieved better IS design-phase outcomes. This suggests that theories about collaborative elaboration have significant potential for helping IS researchers identify new approaches for stimulating client learning early in the IS design process.
Working memory training and transcranial electrical brain stimulation

Working memory training improves performance on trained and untrained working memory tasks, but there is little consistent evidence that these gains benefit everyday tasks that rely on working memory. Evidence has shown that transcranial electrical stimulation (tES) may be an effective tool for enhancing cognitive training and promoting transfer. In the first study, participants completed Cogmed working memory training with either active or sham transcranial random noise stimulation (tRNS). Training was associated with substantial gains on the training activities and on transfer measures of working memory with common processing and storage demands to the training tasks. tRNS did not enhance gains on trained or untrained activities. The second study systematically investigated the boundary conditions to training transfer by testing whether gains following backward digit recall (BDR) training transferred withinand across-paradigm to untrained backward recall and n-back tasks with varying degrees of overlap with the training activity. A further aim was to test whether transcranial direct current stimulation (tDCS) enhanced training and transfer. Participants were allocated to one of three conditions: (i) BDR training with active tDCS, (ii) BDR training with sham tDCS, or (iii) visual search control training with sham tDCS. The results indicated that training transfer is constrained by paradigm, but not by stimulus domain or stimulus materials. There was no evidence that tDCS enhanced performance on the training or transfer tasks. The results of Study 1 and Study 2 provide no evidence that tES enhances the benefits of working memory training. The absence of transfer between backward recall training and n-back in Study 2 suggested the tasks might tap into distinct aspects of working memory. Consequently, the final study used a latent variable approach to explore the degree of overlap between different forms of backward recall and n-back tasks containing digits, letters, or spatial locations as stimuli. The best-fitting factor model included two distinct but related (r = .68) constructs corresponding to backward recall and n-back. Both categories of task were linked to a separate fluid reasoning construct, providing evidence that both are valid measures of higher-order complex cognition. Overall, the experiments in this thesis suggest that working memory tasks tap into separate processes and that training may be targeting and improving these distinct processes, explaining the absence of cross-paradigm transfer.
Outstanding teachers and ICT

This paper presents evidence about expertise in teaching and the use of ICT (Information Communication Technology) to communicate information. Fifty four (54) expert teachers and teaching assistants from infant, primary and secondary schools participated in the study. Participants were nominated by their peers as 'outstanding' in their practice. Semi- structured interviews were conducted, exploring what technology experts have adapted to produce highly effective practice. The current study discusses the results of the demographic data, the technologies expert practitioners use in their teaching and their reasons for using these technologies.
An Analysis of Predator Selection to Affect Aposematic Coloration in a Poison Frog Species

Natural selection is widely noted to drive divergence of phenotypic traits. Predation pressure can facilitate morphological divergence, for example the evolution of both cryptic and conspicuous coloration in animals. In this context Dendrobatid frogs have been used to study evolutionary forces inducing diversity in protective coloration. The polytypic strawberry poison frog (Oophaga pumilio) shows strong divergence in aposematic coloration among populations. To investigate whether predation pressure is important for color divergence among populations of O. pumilio we selected four mainland populations and two island populations from Costa Rica and Panama. Spectrometric measurements of body coloration were used to calculate color and brightness contrasts of frogs as an indicator of conspicuousness for the visual systems of several potential predators (avian, crab and snake) and a conspecific observer. Additionally, we conducted experiments using clay model frogs of different coloration to investigate whether the local coloration of frogs is better protected than non-local color morphs, and if predator communities vary among populations. Overall predation risk differed strongly among populations and interestingly was higher on the two island populations. Imprints on clay models indicated that birds are the main predators while attacks of other predators were rare. Furthermore, clay models of local coloration were equally likely to be attacked as those of non-local coloration. Overall conspicuousness (and brightness contrast) of local frogs was positively correlated with attack rates by birds across populations. Together with results from earlier studies we conclude that conspicuousness honestly indicates toxicity to avian predators. The different coloration patterns among populations of strawberry poison frogs in combination with behavior and toxicity might integrate into equally efficient anti-predator strategies depending on local predation and other ecological factors.
Understanding blazar jets through their multifrequency emission

Being dominated by non-thermal (synchrotron and inverse Compton) emission from a relativistic jet, blazars offer important clues to the structure and radiative processes in extragalactic jets. Crucial information is provided by blazars’ spectral energy distributions from radio to gamma-rays (GeV and TeV energies), their trends with bolometric luminosity, and their correlated variability properties. This review is focussed on recent multiwavelength monitorings of confirmed and candidate TeV blazars and the constraints they provide for the radiative properties of the emitting particles. I also present recent observations of the newly discovered class of “blue quasars” and the implications for current blazars’ unification schemes.
Deciphering the epitranscriptome: A green perspective

Abstract The advent of high‐throughput sequencing technologies coupled with new detection methods of RNA modifications has enabled investigation of a new layer of gene regulation − the epitranscriptome. With over 100 known RNA modifications, understanding the repertoire of RNA modifications is a huge undertaking. This review summarizes what is known about RNA modifications with an emphasis on discoveries in plants. RNA ribose modifications, base methylations and pseudouridylation are required for normal development in Arabidopsis, as mutations in the enzymes modifying them have diverse effects on plant development and stress responses. These modifications can regulate RNA structure, turnover and translation. Transfer RNA and ribosomal RNA modifications have been mapped extensively and their functions investigated in many organisms, including plants. Recent work exploring the locations, functions and targeting of N6‐methyladenosine (m6A), 5‐methylcytosine (m5C), pseudouridine (Ψ), and additional modifications in mRNAs and ncRNAs are highlighted, as well as those previously known on tRNAs and rRNAs. Many questions remain as to the exact mechanisms of targeting and functions of specific modified sites and whether these modifications have distinct functions in the different classes of RNAs.
RASSF1A inhibits gastric cancer cell proliferation by miR-711- mediated downregulation of CDK4 expression

Although interaction with DNA repair proteins has demonstrated that RASSF1A is a tumour suppressor gene, much attention has been directed in recent years towards its roles in regulating the cell cycle. However, the precise mechanism remains unclear. Uncovering how RASSF1A participates in regulating the cell cycle is critical to exploring effective therapeutic targets for gastric cancer. Here we show that RASSF1A could regulate 14 miRNAs’ expression in the typical human gastric cancer line SGC-7901, of which miR-711 was upregulated the most. Moreover, for SGC-7901 cells, miR-711 was found to downregulate CDK4 expression, and to arrest the cell cycle in the G1 phase. Our results suggest that RASSF1A inhibits the proliferation of gastric cancer cells by upregulating the expression of miR-711, which arrested gastric cancer cells in the G1 phase by downregulating expression of CDK4. This finding might provide us with a novel therapeutic target for gastric cancer by increasing RASSF1A expression via miR-711 regulation.
Hierarchical service-oriented routing of queries in wireless sensor networks

Routing queries from a base station to a particular WSN node is preferable when it is done in a content oriented manner. Previously proposed query routing schemes are either not content based or lack to take advantage of a hierarchical organization of the network when they are content based. In this paper, we propose a hierarchical, service oriented routing scheme to achieve data centric routing of queries in wireless sensor networks. The routing scheme works by first forming clusters of the nodes based on various parameters that will eventually be used to form a query. We use an extended version of LEACH to organize the network into hierarchical clusters. The queries are then routed through the cluster heads to eventually reach the specific nodes that can provide the requested service. Our approach saves energy, extends network lifetime, does away with the need to duplicate a routing table on each node and improves the latency in routing queries.
Proposal for a Multiplatform Semantic eGovernment Solution

Web platforms are becoming the main support for eGovernment solutions. Nevertheless, several drawbacks can be pointed out related to interoperability and accessibility. To overcome this issues and extend the support for a wider range of devices, a new modeling tool called LifeEvent is proposed. Using semantics as support, this tool will introduce interesting advantages in tasks related to discovery, invocation and tracking.
Optimized data fusion for K-means Laplacian clustering

Motivation: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically. Results: Three variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix. Availability: The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat. kuleuven.be/~sistawww/bioi/syu/oklc.html. Contact: shiyu@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Genetic and plastic responses of a northern mammal to climate change

Climate change is predicted to be most severe in northern regions and there has been much interest in to what extent organisms can cope with these changes through phenotypic plasticity or microevolutionary processes. A red squirrel population in the southwest Yukon, Canada, faced with increasing spring temperatures and food supply has advanced the timing of breeding by 18 days over the last 10 years (6 days per generation). Longitudinal analysis of females breeding in multiple years suggests that much of this change in parturition date can be explained by a plastic response to increased food abundance (3.7 days per generation). Significant changes in breeding values (0.8 days per generation), were in concordance with predictions from the breeder' equation (0.6 days per generation), and indicated that an evolutionary response to strong selection favouring earlier breeders also contributed to the observed advancement of this heritable trait. The timing of breeding in this population of squirrels, therefore, has advanced as a result of both phenotypic changes within generations, and genetic changes among generations in response to a rapidly changing environment.
Coding for a Noisy Binary Multiple-access Adder Channel

Multip le-access adder channels have been extensively studied for manyyears. They can be classified into several types. The type of channels weconsider in this paper is discrete,memory less, synchronized and noisy. On noisy mu ltip le-access adder channels a receiver must derive a groupof transmitted codewords uniquely fro m a received word garbled by errors. This means that a code suitable for this channel must have both separabilityand error control ability. In this paper we propose a coding method forsuch a channel.Our method utilizes some property of parity check matrices for Reeed-So lo mon codes. We present not only encoding but also decoding methods of our codes. It is also shown that our method can construct some codes with high coding rates.