Miracle-dz/UniFAR-FTUs huggingface dataset:
Examination of the Effects of Methamphetamine Use Severity on Executive Functioning and Impulsivity

Background: Abuse of psychostimulants, including methamphetamine (MA), has been linked to cognitive deficits. However, there is ongoing debate that evidence for the relationship between MA and cognitive deficits is limited. While previous research has demonstrated neurocognitive differences between MA users and non-substance users, less is known about variability in cognitive functioning within MA users and whether the severity of MA-related problems predicts cognitive functioning within individuals who regularly use MA. Objective: This study aims to elucidate neurocognitive deficits, specifically impulsivity and executive functioning, as a function of MA use severity. This study used a multivariate approach that simultaneously accounts for clinical variables (e.g., frequency of use, craving, and diagnostic symptoms) as well as outcome variables related to impulsivity and executive function.Method: Non-treatment seeking individuals who reported regular MA use (n = 177) completed an impulsivity battery comprised of self-report (Barratt Impulsivity Scale-II (BIS)) and behavioral measures (Stop Signal Task (SST), Delay Discounting Task (DDT)) and a sub-sample (n = 63) completed an in-depth neuropsychological battery. A structural equation modeling (SEM) approach was used to (1) test the relationship between the MA-use problem severity and measures of impulsivity; and (2) examine the association between MA-use problem severity and tests of executive functioning.Results: The final SEM model of impulsivity and MA use problems revealed that greater MA use severity was associated with greater self-reported impulsiveness (? = 0.51 and ? = 0.42), but no relationship was found between MA use severity and the behavioral measures of impulsivity (SST and DDT). Analyses of neuropsychological functioning suggested that MA use severity was not significantly associated with either executive functioning factor; however follow-up analyses revealed that gender and frequency of MA use may moderate the relationship between MA use severity and measures of executive functioning. Conclusions: The current findings extend previous research by providing additional evidence that MA use is associated with greater self-reported impulsivity and highlights the importance of evaluating impulsivity as a multidimensional construct. The lack of relationship between MA use severity and executive functioning suggest that individual differences in MA use may not be directly related to neuropsychological functioning, although previous literature has shown performance on neuropsychological tests can differentiate between MA users and non-substance users. Future studies should expand on the current findings by comparing neurocognition across substances and by utilizing the MA Use Severity factor in longitudinal and biologically informative approaches, such as genetic and neuroimaging studies.
Can Tweets Kill a Movie ? An Empirical Evaluation of the Bruno Effect

Copyright is held by the author/owner(s). CHI 2010, April 10–15, 2010. Atlanta, Georgia, USA ACM 978-1-60558-930-5/10/04. Abstract On Friday, July 10th 2009, the movie Bruno was number one at the box office and took in over $18.8 million in revenue. Based on this initial performance, analysts predicted the movie would rake in over $50 million in its opening weekend. By Saturday, however, the movie experienced an unusually sharp 38% decline in box office receipts. Several prominent journalists speculated that comments on the social media site Twitter.com may have amplified negative wordof-mouth about the movie and caused the dramatic fall-off in revenue. We investigate this “Bruno effect” and, contrary to popular accounts, find that neither positive nor negative comments on Twitter are associated with changes in box office performance. We do find suggestive evidence, however, that the volume of tweets, while not necessarily altering consumer behavior, may provide useful information for predicting opening weekend box office performance.
Using haptics to extract object shape from rotational manipulations

Increasingly widespread available haptic sensors mounted on articulated hands offer new sensory channels that can complement shape extraction from vision to enable a more robust handling of objects in cases when vision is restricted or even unavailable. However, to estimate object shape from haptic interaction data is a difficult challenge due to the complexity of the contact interaction between the movable object and sensor surfaces, leading to a coupled estimation problem of shape and object pose. While for vision efficient solutions to the underlying SLAM problem are known, the available information is much sparser in the tactile case, posing great difficulties for a straightforward adoption of standard SLAM algorithms. In the present paper, we thus explore whether a biologically inspired model based on dynamic neural fields can offer a route towards a practical algorithm for tactile SLAM. Our study is focused on a restricted scenario where a two-fingered robot hand manipulates an n-gon with a fixed rotational axis. We demonstrate that our model can accumulate shape information from reasonably short interaction sequences and autonomously build a representation despite significant ambiguity of the tactile data due to the rotational periodicity of the object. We conclude that the presented framework may be a suitable basis to solve the tactile SLAM problem also in more general settings which will be the focus of subsequent work.
Who Needs Microtubules? Myogenic Reorganization of MTOC, Golgi Complex and ER Exit Sites Persists Despite Lack of Normal Microtubule Tracks

A wave of structural reorganization involving centrosomes, microtubules, Golgi complex and ER exit sites takes place early during skeletal muscle differentiation and completely remodels the secretory pathway. The mechanism of these changes and their functional implications are still poorly understood, in large part because all changes occur seemingly simultaneously. In an effort to uncouple the reorganizations, we have used taxol, nocodazole, and the specific GSK3-β inhibitor DW12, to disrupt the dynamic microtubule network of differentiating cultures of the mouse skeletal muscle cell line C2. Despite strong effects on microtubules, cell shape and cell fusion, none of the treatments prevented early differentiation. Redistribution of centrosomal proteins, conditional on differentiation, was in fact increased by taxol and nocodazole and normal in DW12. Redistributions of Golgi complex and ER exit sites were incomplete but remained tightly linked under all circumstances, and conditional on centrosomal reorganization. We were therefore able to uncouple microtubule reorganization from the other events and to determine that centrosomal proteins lead the reorganization hierarchy. In addition, we have gained new insight into structural and functional aspects of the reorganization of microtubule nucleation during myogenesis.
Graphene-Based Nanomaterials as Efficient Peroxidase Mimetic Catalysts for Biosensing Applications: An Overview

“Artificial enzymes”, a term coined by Breslow for enzyme mimics is an exciting and promising branch of biomimetic chemistry aiming to imitate the general and essential principles of natural enzymes using a variety of alternative materials including heterogeneous catalysts. Peroxidase enzymes represent a large family of oxidoreductases that typically catalyze biological reactions with high substrate affinity and specificity under relatively mild conditions and thus offer a wide range of practical applications in many areas of science. The increasing understanding of general principles as well as intrinsic drawbacks such as low operational stability, high cost, difficulty in purification and storage, and sensitivity of catalytic activity towards atmospheric conditions of peroxidases has triggered a dynamic field in nanotechnology, biochemical, and material science that aims at joining the better of three worlds by combining the concept adapted from nature with the processability of catalytically active graphene-based nanomaterials (G-NMs) as excellent peroxidase mimetic catalysts. This comprehensive review discusses an up-to-date synthesis, kinetics, mechanisms, and biosensing applications of a variety of G-NMs that have been explored as promising catalysts to mimic natural peroxidases.
Face Super-resolution based-on Non-negative Matrix Factorization

Principal Component Analysis (PCA) is a classical method which is commonly used for human face images representation in face super-resolution. But the features extracted by PCA are holistic and difficult to have semantic interpretation. In order to synthesize a high-resolution face image with structural details, we propose a face super-resolution algorithm based on non-negative matrix factorization (NMF). This algorithm uses the NMF to obtain structural information representation of face images, and then the target image is regularized by Markov random fields, with maximum a posteriori probability approach. Finally, the steepest descent method is used to optimize NMF coefficient of high-resolution image. Experiment results demonstrate that the NMF-based face super-resolution algorithm performs better than PCA-based algorithms, in the subjective and objective quality.
UML-Based Approach to Generate Polymorphic Testing Sequence and its Implementation

Polymorphism is one of the three most important characters of object-oriented technology. Not only it enhances the flexibility of programming and reused of codes, but also it brings new challenge for the software testing. After analyzing the software UML model document, we represent it in XML form. And then, under the guidance of Control-Flow Graph, we integrated class diagram’s information with collaboration diagram, extended traditional Interprocedural Restricted Control-Flow Graph(IRCFG) with polymorphic information and class diagram information. Also the approach to general testing sequence is given in this paper. Finally we designed an architecture of software testing and used Java API to implement some modules.
Modeling the Length of Distal Radioulnar Ligaments

The distal radioulnar ligaments are essential contributors to wrist joint stability. We present a CT-based model for ligament displacement, in which ligaments are approximated by shortest paths in a 3D space with bone obstacles. This first model allows for the study of distal ligaments biomechanics in vivo and non-invasively. We show that our simplified model gives surprising insight into distal ligament and wrist biomechanics. We apply our ligament model to 6 patients with recalcitrant limitation of forearm rotation and present results. As byproducts, a better bone surface reconstruction via manifolds is achieved, and a novel method for automated alignment/comparison of the same bone across different patients
The Effectiveness of Internet Cognitive Behavioural Therapy (iCBT) for Depression in Primary Care: A Quality Assurance Study

Background Depression is a common, recurrent, and debilitating problem and Internet delivered cognitive behaviour therapy (iCBT) could offer one solution. There are at least 25 controlled trials that demonstrate the efficacy of iCBT. The aim of the current paper was to evaluate the effectiveness of an iCBT Program in primary care that had been demonstrated to be efficacious in two randomized controlled trials (RCTs). Method Quality assurance data from 359 patients prescribed the Sadness Program in Australia from October 2010 to November 2011 were included. Results Intent-to-treat marginal model analyses demonstrated significant reductions in depressive symptoms (PHQ9), distress (K10), and impairment (WHODAS-II) with medium-large effect sizes (Cohen's d = .51–1.13.), even in severe and/or suicidal patients (Cohen's d = .50–1.49.) Secondary analyses on patients who completed all 6 lessons showed levels of clinically significant change as indexed by established criteria for remission, recovery, and reliable change. Conclusions The Sadness Program is effective when prescribed by primary care practitioners and is consistent with a cost-effective stepped-care framework.
MuSeeCol: A See-through Multi-touch Surface for Face-to-face Musical Collaboration

MuSeeCol is a collaborative music creation system that allows two users to jointly and playfully compose melodic or percussive music. Collaborative music is rendered when two face-to-face users interact with their respective side of a see-through multi-touch sensitive acrylic panel. We present user study findings from nine pairs of individuals who have used the MuSeeCol system collaboratively and summarize some general observations of what types of interaction designs make for effective face-to-face collaboration.
Hypothesis testing under maximal leakage privacy constraints

The problem of publishing privacy-guaranteed data for hypothesis testing is studied using the maximal leakage (ML) as a metric for privacy and the type-II error exponent as the utility metric. The optimal mechanism (random mapping) that maximizes utility for a bounded leakage guarantee is determined for the entire leakage range for binary datasets. For non-binary datasets, approximations in the high privacy and high utility regimes are developed. The results show that, for any desired leakage level, maximizing utility forces the ML privacy mechanism to reveal partial to complete knowledge about a subset of the source alphabet. The results developed on maximizing a convex function over a polytope may also of an independent interest.
A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers

Today's data centers offer IT services mostly hosted on dedicated physical servers. Server virtualization provides a technical means for server consolidation. Thus, multiple virtual servers can be hosted on a single server. Server consolidation describes the process of combining the workloads of several different servers on a set of target servers. We focus on server consolidation with dozens or hundreds of servers, which can be regularly found in enterprise data centers. Cost saving is among the key drivers for such projects. This paper presents decision models to optimally allocate source servers to physical target servers while considering real-world constraints. Our central model is proven to be an NP-hard problem. Therefore, besides an exact solution method, a heuristic is presented to address large-scale server consolidation projects. In addition, a preprocessing method for server load data is introduced allowing for the consideration of quality-of-service levels. Extensive experiments were conducted based on a large set of server load data from a data center provider focusing on managerial concerns over what types of problems can be solved. Results show that, on average, server savings of 31 percent can be achieved only by taking cycles in the server workload into account.
Exploring the Impacts of Anthropogenic Disturbance on Seawater and Sediment Microbial Communities in Korean Coastal Waters Using Metagenomics Analysis

The coastal ecosystems are considered as one of the most dynamic and vulnerable environments under various anthropogenic developments and the effects of climate change. Variations in the composition and diversity of microbial communities may be a good indicator for determining whether the marine ecosystems are affected by complex forcing stressors. DNA sequence-based metagenomics has recently emerged as a promising tool for analyzing the structure and diversity of microbial communities based on environmental DNA (eDNA). However, few studies have so far been performed using this approach to assess the impacts of human activities on the microbial communities in marine systems. In this study, using metagenomic DNA sequencing (16S ribosomal RNA gene), we analyzed and compared seawater and sediment communities between sand mining and control (natural) sites in southern coastal waters of Korea to assess whether anthropogenic activities have significantly affected the microbial communities. The sand mining sites harbored considerably lower levels of microbial diversities in the surface seawater community during spring compared with control sites. Moreover, the sand mining areas had distinct microbial taxonomic group compositions, particularly during spring season. The microbial groups detected solely in the sediment load/dredging areas (e.g., Marinobacter, Alcanivorax, Novosphingobium) are known to be involved in degradation of toxic chemicals such as hydrocarbon, oil, and aromatic compounds, and they also contain potential pathogens. This study highlights the versatility of metagenomics in monitoring and diagnosing the impacts of human disturbance on the environmental health of marine ecosystems from eDNA.
A Computational Approach to the Automation of Creative Naming

In this paper, we propose a computational approach to generate neologisms consisting of homophonic puns and metaphors based on the category of the service to be named and the properties to be underlined. We describe all the linguistic resources and natural language processing techniques that we have exploited for this task. Then, we analyze the performance of the system that we have developed. The empirical results show that our approach is generally effective and it constitutes a solid starting point for the automation of the naming process.
Reconstructing propagation networks with temporal similarity metrics

Node similarity is a significant property driving the growth of real networks. In this paper, based on the observed spreading results we apply the node similarity metrics to reconstruct propagation networks. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops to nearly zero. In order to improve the similarity-based reconstruction method, we finally propose a temporal similarity metric to take into account the time information of the spreading. The reconstruction results are remarkably improved with the new method.
Learning Modulation of Odor-Induced Oscillatory Responses in the Rat Olfactory Bulb: A Correlate of Odor Recognition?

In the first relay of information processing, the olfactory bulb (OB), odors are known to generate specific spatial patterns of activity. Recently, in freely behaving rats, we demonstrated that learning modulated oscillatory activity in local field potential (LFP), in response to odors, in both β (15-40 Hz) and γ (60-90 Hz) bands. The present study further characterized this odor-induced oscillatory activity with emphasis on its spatiotemporal distribution over the olfactory bulb and on its relationship with improvement of behavioral performances along training. For that purpose, LFPs were simultaneously recorded from four locations in the OB in freely moving rats performing an olfactory discrimination task. Electrodes were chronically implanted near relay neurons in the mitral cell body layer. Time-frequency methods were used to extract signal characteristics (amplitude, frequency, and time course) in the two frequency bands. Before training, odor presentation produced, on each site, a power decrease in γ oscillations and a weak but significant increase in power of β oscillations (∼25 Hz). When the training was achieved, these two phenomena were amplified. Interestingly, the β oscillatory response showed several significant differences between the anterodorsal and posteroventral regions of the OB. In addition, clear-cut β responses occurred in the signal as soon as animals began to master the task. As a whole, our results point to the possible functional importance of β oscillatory activity in the mammalian OB, particularly in the context of olfactory learning.
A language-independent probabilistic model for automatic conversion between graphemic and phonemic transcription of words

In this paper we present a novel language-independent probabilistic model for automatic grapheme-to-phoneme and phoneme-to-grapheme conversion of words. In a fully unsupervised training procedure, two processes are applied; the transformation rules, which usually fail to provide the correct symbols, are eliminated, and new variable-length string transformation rules are defined improving the string transformation accuracy in the training data. In an iterative process the probabilistic transformation rules are updated in the direction of reducing the error rate of the transformed symbols. Long-term dependencies are defined automatically. Training and testing of the model was carried out on lexicon and natural language corpora of six European Languages. Accurate generalisations have been achieved in all experiments for both transformation directions using a relative small number of defined rules in the training procedure. It is demonstrated that the variable-length probabilistic rules are sufficiently effective for describing bi-directional transcription.
Toward Generalizing the Unification with Statistical Outliers

In this work, we introduce a novel definition of outlier, namely the Gradient Outlier Factor (or GOF), with the aim to provide a definition that unifies with the statistical one on some standard distributions but has a different behavior in the presence of mixture distributions. Intuitively, the GOF score measures the probability to stay in the neighborhood of a certain object. It is directly proportional to the density and inversely proportional to the variation of the density. We derive formal properties under which the GOF definition unifies the statistical outlier definition and show that the unification holds for some standard distributions, while the GOF is able to capture tails in the presence of different distributions even if their densities sensibly differ. Moreover, we provide a probabilistic interpretation of the GOF score, by means of the notion of density of the data density. Experimental results confirm that there are scenarios in which the novel definition can be profitably employed. To the best of our knowledge, except for distance-based outlier, no other data mining outlier definition has a so clearly established relationship with statistical outliers.
Hybrid bulk synchronous parallelism library for clustered smp architectures

This paper presents the design and implementation of BSP++, a C++ parallel programming library based on the Bulk Synchronous Parallelism model to perform high performance computing on both SMP and SPMD architectures using OpenMPI and MPI. We show how C++ support for genericity provides a functional and intuitive user interface which still delivers a large fraction of performance compared to hand written code. We show how the library structure and programming models allow simple hybrid programming by composing BSP super-steps and letting BSP++ handling the middleware interface. The performance and scalability of this approach are then assessed by various benchmarks of classic HPC application kernels and distributed algorithms on various hybrid machines including a subset of the GRID5000 grid.