Miracle-dz/UniFAR-FTUs huggingface dataset:
The neurocognitive consequences of the wandering mind: a mechanistic account of sensory-motor decoupling

A unique human characteristic is our ability to mind wander – a state in which we are free to engage in thoughts that are not directly tied to sensations and perceptions from our immediate physical environment. From a neurocognitive perspective, it has been proposed that during mind wandering, our executive resources are decoupled from the external environment and directed to these internal thoughts. In this review, we examine an underappreciated aspect of this phenomenon – attenuation of sensory-motor processing – from two perspectives. First, we describe the range of widespread sensory, cognitive and motor processes attenuated during mind wandering states, and how this impacts our neurocognitive processing of external events. We then consider sensory-motor attenuation in a class of clinical neurocognitive disorders that have ties to pathological patterns of decoupling, reviews suggesting that mind wandering and these clinical states may share a common mechanism of sensory-motor attenuation. Taken together, these observations suggest the sensory-motor consequences of decoupled thinking are integral to normal and pathological neurocognitive states.
An automated time-window selection algorithm for seismic tomography

We present FLEXWIN, an open source algorithm for the automated selection of time windows on pairs of observed and synthetic seismograms. The algorithm was designed specifically to accommodate synthetic seismograms produced from 3-D wavefield simulations, which capture complex phases that do not necessarily exist in 1-D simulations or traditional traveltime curves. Relying on signal processing tools and several user-tuned parameters, the algorithm is able to include these new phases and to maximize the number of measurements made on each seismic record, while avoiding seismic noise. Our motivation is to use the algorithm for iterative tomographic inversions, in which the synthetic seismograms change from one iteration to the next. Hence, automation is needed to handle the volume of measurements and to allow for an increasing number of windows at each model iteration. The algorithm is sufficiently flexible to be adapted to many tomographic applications and seismological scenarios, including those based on synthetics generated from 1-D models. We illustrate the algorithm using data sets from three distinct regions: the entire globe, the Japan subduction zone, and southern California.
Investigating Self-Similarity and Heavy-Tailed Distributions on a Large-Scale Experimental Facility

After the seminal work by Taqqu relating self-similarity to heavy-tailed distributions, a number of research articles verified that aggregated Internet traffic time series show self-similarity and that Internet attributes, like Web file sizes and flow lengths, were heavy-tailed. However, the validation of the theoretical prediction relating self-similarity and heavy tails remains unsatisfactorily addressed, being investigated using either numerical or network simulations, or from uncontrolled Web traffic data. Notably, this prediction has never been conclusively verified on real networks using controlled and stationary scenarios, prescribing specific heavy-tailed distributions, and estimating confidence intervals. With this goal in mind, we use the potential and facilities offered by the large-scale, deeply reconfigurable and fully controllable experimental Grid5000 instrument, combined with state-of-the-art estimators, to investigate the prediction's observability on real networks. To this end, we organize a large number of controlled traffic circulation sessions on a nationwide real network involving 200 independent hosts. We use a FPGA-based measurement system to collect the corresponding traffic at packet level. We then estimate both the self-similarity exponent of the aggregated time series and the heavy-tail index of flow-size distributions, independently. Not only do our results complement and validate, with a striking accuracy, some conclusions drawn from a series of pioneering studies, but they also bring in new insights on the controversial role of certain components of real networks.
Robust estimation approach for blind denoising

This work develops a new robust statistical framework for blind image denoising. Robust statistics addresses the problem of estimation when the idealized assumptions about a system are occasionally violated. The contaminating noise in an image is considered as a violation of the assumption of spatial coherence of the image intensities and is treated as an outlier random variable. A denoised image is estimated by fitting a spatially coherent stationary image model to the available noisy data using a robust estimator-based regression method within an optimal-size adaptive window. The robust formulation aims at eliminating the noise outliers while preserving the edge structures in the restored image. Several examples demonstrating the effectiveness of this robust denoising technique are reported and a comparison with other standard denoising filters is presented.
Stochastic non-determinism and effectivity functions

This paper investigates stochastic nondeterminism on continuous state spaces by relating nondeterministic kernels and stochastic effectivity functions to each other. Nondeterministic kernels are functions assigning each state a set o subprobability measures, and effectivity functions assign to each state an upper-closed set of subsets of measures. Both concepts are generalizations of Markov kernels used for defining two different models: Nondeterministic labelled Markov processes and stochastic game models, respectively. We show that an effectivity function that maps into principal filters is given by an image-countable nondeterministic kernel, and that image-finite kernels give rise to effectivity functions. We define state bisimilarity for the latter, considering its connection to morphisms. We provide a logical characterization of bisimilarity in the finitary case. A generalization of congruences (event bisimulations) to effectivity functions and its relation to the categorical presentation of bisimulation are also studied.
Data Mining Techniques Using Decision Tree Model in Materialised Projection and Selection

With the availability of very large data storage today, redundant data structures are no longer a big issue. However, an intelligent way of managing materialised projection and selection views that can lead to fast access of data is the central issue dealt with in this paper. A set of implementation steps for the data warehouse administrators or decision makers to improve the response time of queries is also defined. The study concludes that both attributes and tuples, are important factors to be considered to improve the response time of a query. The adoption of data mining techniques in the physical design of data warehouses has been shown to be useful in practice.
Estimating selection on non-synonymous mutations

distribution of mutational effects deleterious mutations advantageous mutations Drosophila pseudoobscura Drosophila miranda Drosophila affinis 3 ABSTRACT The distribution of mutational effects on fitness is of fundamental importance for many aspects of evolution. We develop two methods for characterizing the fitness effects of deleterious, non-synonymous mutations, using polymorphism data from two related species. These methods also provide estimates of the proportion of amino-acid substitutions that are selectively favorable, when combined with data on between-species sequence divergence. The methods are applicable to species with different effective population sizes, but which share the same distribution of mutational effects. The first, simpler, method assumes that diversity for all non-neutral mutations is given by the value under mutation-selection balance, while the second method allows for stronger effects of genetic drift and yields estimates of the parameters of the probability distribution of mutational effects. We apply these methods to data on populations of Drosophila miranda and D. pseudoobscura, and find evidence for the presence of deleterious non-synonymous mutations, mostly with small heterozygous selection coefficients (a mean of the order of 10-5 for segregating variants). A leptokurtic gamma distribution of mutational effects with a shape parameter between 0.1 and 1 can explain observed diversities, in the absence of a separate class of completely neutral non-synonymous mutations. We also describe a simple approximate method for estimating the harmonic mean selection coefficient from diversity data on a single species.
From Face-to-Face to Cyberspace : Examining the Impact of Instant-Messaging Within Already-Established Relationships

Although numerous studies have investigated the formation of relationships through computermediated communication (CMC), their focus has remained on new relationships, formed anonymously through the Internet. This article covers new ground, looking at relationships that are first established face-to-face (FtF), then evolve to include CMC, specifically through the use of America Online’s Instant Messenger service (AIM). Because CMC differs inherently from FtF communication, it seems important to determine if AIM users are able to harmoniously combine online interaction with offline conceptions of their conversational partners. This theoretical model explains various characteristics of CMC and their influences on the specific characteristics of messages that occur over AIM. Further analysis will combine these factors using several perspectives in interpersonal communication, and finally attempt to associate AIM use with positive or negative relational outcomes, specifically the degree of conflict within the
A Unifying Approach to Learning by Doing and Learning by AnalogyUte Schmid and

We present an approach to learning cognitive skills from problem solving experience { addressing some phenomena well known from human learning but seldom covered together in machine learning. The core of our approach is the acquisition of (recursive) program schemes (RPSs) by generalization-ton over plans, using an inductive program technique. RPSs represent domain speciic control knowledge, that is problem solving strategies for classes of problems. Because RPSs are abstract schemes, representing the sub-goal structure of a domain, they are suitable for analogical problem solving and learning.
Inter-transcriber reliability for two systems of prosodic annotation: ToBI (Tones and Break Indices) and RaP (Rhythm and Pitch)

Abstract Speech researchers often rely on human annotation of prosody to generate data to test hypotheses and generate models. We present an overview of two prosodic annotation systems: ToBI (Tones and Break Indices) (Silverman et al., 1992), and RaP (Rhythm and Pitch) (Dilley & Brown, 2005), which was designed to address several limitations of ToBI. The paper reports two large-scale studies of inter-transcriber reliability for ToBI and RaP. Comparable reliability for both systems was obtained for a variety of prominence- and boundary-related agreement categories. These results help to establish RaP as an alternative to ToBI for research and technology applications.
Effect of unexpected stressor duration on the expression of stress response habituation

While habituation develops to repeated predicable psychological stress, manipulating certain parameters of the stress experience may lead to disruption of a stressor’s predictability and subsequent dishabituation of the stress response. In this experiment, we investigated whether behavioral, endocrine, and neural responses (indicators of activity of the Limbic-Hypothalamic-Pituitary-Adrenal-, or LHPA-, axis) to psychological stress (restraint) differ when the duration of stress given on test day violates expectations based on prior stress experience. Rats experienced 10 min of daily restraint on Days 1-4 followed by either the same duration (10 min) or a longer duration (30 min) of restraint on Day 5. Video recordings were collected for each restraint episode (Days 1-5) and trunk blood and brains (for immediate early gene expression analysis [c-fos mRNA]) were collected following restraint on Day 5. Struggling behavior was manually scored as active attempts to escape the restraint device. Rats experiencing the same duration of repeated restraint showed decreased plasma corticosterone (CORT) compared to 10 min acute restraint group (habituation). In addition, these rats showed decreased active struggling over repeated restraint trials. Conversely, the rats experiencing a longer duration of restraint on Day 5 showed an increased CORT response (dishabituation). These rats showed a habituated behavioral iv response during the first 10 min of restraint, however struggling behavior increased once the duration of restraint exceeded the expected duration (with a peak at 12 min). This peak in struggling behavior did not occur during 30 min acute restraint, indicating that the effect was related to memory of previous restraint experience and not simply due to a longer duration of restraint. In contrast, the animals still showed habituated cfos mRNA expression in the paraventricular nucleus (PVN), lateral septum (LS), and medial prefrontal cortex (mPFC) in response to the increased stressor duration. Thus, there is dissociation between c-fos mRNA expression in key stress responsive brain regions and the behavioral and endocrine response to increased stressor duration. In conclusion, habituation of the endocrine and behavioral stress responses occurred when the duration of the stressor matches previous experience, while dishabituation occurred (with remarkable temporal precision) following an unpredicted increase in stress duration.
A Methodology for Neural Spatial Interaction Modeling

This paper attempts to develop a mathematically rigid and unified framework for neural spatial interaction modeling. Families of classical neural network models, but also less classical ones such as product unit neural network ones are considered for the cases of unconstrained and singly constrained spatial interaction flows. Current practice appears to suffer from least squares and normality assumptions that ignore the true integer nature of the flows and approximate a discrete-valued process by an almost certainly misrepresentative continuous distribution. To overcome this deficiency we suggest a more suitable estimation approach, maximum likelihood estimation under more realistic distributional assumptions of Poisson processes, and utilize a global search procedure, called Alopex, to solve the maximum likelihood estimation problem. To identify the transition from underfitting to overfitting we split the data into training, internal validation, and test sets. The bootstrapping pairs approach with replacement is adopted to combine the purity of data splitting with the power of a resampling procedure to overcome the generally neglected issue of fixed data splitting and the problem of scarce data. In addition, the approach has power to provide a better statistical picture of the prediction variability. Finally, a benchmark comparison against the classical gravity models illustrates the superiority of both, the unconstrained and the origin constrained neural network model versions in terms of generalization performance measured by Kullback and Leiblerís information criterion.
The Effect of Reminiscence Intervention on the Elderly Population: a Systematic Review

The purpose of this study was to investigate the effect of reminiscence therapy on cognitive disorder in elders. We established the PICO-SD, reviewed 1761studies from database of RISS, NDSL, DBpia, National Assembly Library, KoreaMed, KMbase, KSI KISS, KCI, and finally selected 18 studies based on the inclusion and exclusion criteria. With regard to research places were elderly care center, seniors welfare center etc and intervention periods ranged from 4 to 30 weeks. In addition, outcome variables were cognitive function, depression, quality of life etc. Reminiscence intervention appears to be effective in improving health for elders.
The development and testing of PSYCHLOPS Kids: a new child‐centred outcome measure

BACKGROUND There are currently no client generated measures able to capture a child's perspective of the value of generic therapeutic mental health interventions. We have developed a new measure called 'PSYCHLOPS Kids'. It measures areas of individual importance to the respondent and contains both quantitative and qualitative elements. We aimed to pilot this new outcome measure and determine its psychometric properties. METHODS PSYCHLOPS Kids was adapted from the adult PSYCHLOPS questionnaire, a validated and reliable client-generated measure used in primary care mental health. Development of PSYCHLOPS Kids involved an expert group, pilot testing with dramatherapists followed by psychometric testing with children receiving dramatherapy aged 7-13 years. RESULTS One hundred and thirty-two children completed pre- and postintervention questionnaires. Mean initial PSYCHLOPS Kids scores (scale of 0-12) were 4.98 (SD: 3.42); mean post-therapy, 3.24 (SD: 3.03); mean effect size of change, 0.51. The Strengths and Difficulties Questionnaire (SDQ) was used as a comparator instrument completed by parent/carers of 32 children; mean effect size, 0.39. The effect size difference between both instruments was not significant (t = 1.05; p = .30); the PSYCHLOPS Kids Problem domain effect size (mean, 0.68) was significantly greater than for the SDQ (t = 2.06; p = .04). Concurrent validity was demonstrated by strong predictive power of change scores for the self-assessment of change item in PSYCHLOPS Kids; therapist-assessment of change was not a significant predictor of change scores. PSYCHLOPS Kids and SDQ change scores were not significantly correlated. CONCLUSIONS PSYCHLOPS Kids is the first client generated mental health outcome measure focussing on problems for generic use in children. It has demonstrated moderate responsiveness to change and satisfactory testing for measured aspects of validity and reliability. PSYCHLOPS Kids now requires further validity, reliability and qualitative testing.
A negative-correlation redundancy design model for automated synthesis of analog circuits

The negative-correlation redundancy approach is a novel way for fault-tolerant design of analog circuits under uncertain faults. One of its key factors is the design of negative-correlation redundancy evolutionary model. In this paper, we propose a novel negative-correlation redundancy evolutionary design model. In order to improve the scalability of this model, DE_SR algorithm is proposed. Experimental results on analog low-pass filters show clearly that this novel model is effective and feasible. Compared with the existing negative-correlation redundancy evolutionary model named ENCF, our model has a lower computational complexity and higher search efficiency, and it have better on large-scale problems of circuit synthesis.
Influence Of Task-role Mental Models On Human Interpretation Of Robot Motion Behavior

The transition in robotics from tools to teammates has begun. However, the benefit autonomous robots provide will be diminished if human teammates misinterpret robot behaviors. Applying mental model theory as the organizing framework for human understanding of robots, the current empirical study examined the influence of task-role mental models of robots on the interpretation of robot motion behaviors, and the resulting impact on subjective ratings of robots. Observers (N = 120) were exposed to robot behaviors that were either congruent or incongruent with their task-role mental model, by experimental manipulation of preparatory robot task-role information to influence mental models (i.e., security guard, groundskeeper, or no information), the robot’s actual task-role behaviors (i.e., security guard or groundskeeper), and the order in which these robot behaviors were presented. The results of the research supported the hypothesis that observers with congruent mental models were significantly more accurate in interpreting the motion behaviors of the robot than observers without a specific mental model. Additionally, an incongruent mental model, under certain circumstances, significantly hindered an observer’s interpretation accuracy, resulting in subjective sureness of inaccurate interpretations. The strength of the effects that mental models had on the interpretation and assessment of robot behaviors was thought to have been moderated by the ease with which a particular mental model could reasonably explain the robot’s behavior, termed mental model applicability. Finally, positive associations were found between differences in observers’ interpretation accuracy and differences in subjective ratings of robot intelligence, safety, and trustworthiness. The current research offers implications for the relationships between mental model components, as well as implications for designing robot behaviors to appear more transparent, or opaque, to humans.
An Application-Level solution to TCP''s Satellite Inefficiencies

In several experiments using NASA's Advanced Communications Technology Satellite (ACTS), investigators have reported disappointing throughput using the TCP/IP protocol suite over T1 satellite circuits. A detailed analysis of FTP le transfers reveals that the TCP receive window size, the TCP \Slow Start" algorithm, and the TCP acknowledgment mechanism contribute to the observed limits in throughput. To further explore TCP's limitations over satellite circuits, we developed a modi ed version of FTP (XFTP) that uses multiple TCP connections. By using multiple TCP connections, we have been able to simulate a large, virtual TCP receive window. Our experiences with XFTP over both actual satellite circuits and a software satellite emulator show that a utilization of better than 90% is possible. Our results also indicate the bene t of introducing congestion avoidance at the application level.
Secure Rank-Ordered Search of Multi-keyword Trapdoor over Encrypted Cloud Data

Advances in cloud computing and Internet technologies have pushed more and more data owners to outsource their data to remote cloud servers to enjoy with huge data management services in an efficient cost. However, despite its technical advances, cloud computing introduces many new security challenges that need to be addressed well. This is because, data owners, under such new setting, loss the control over their sensitive data. To keep the confidentiality of their sensitive data, data owners usually outsource the encrypted format of their data to the untrusted cloud servers. Several approaches have been provided to enable searching the encrypted data. However, the majority of these approaches are limited to handle either a single keyword search or a Boolean search but not a multikeyword ranked search, a more efficient model to retrieve the top documents corresponding to the provided keywords. In this paper, we propose a secure multi-keyword ranked search scheme over the encrypted cloud data. Such scheme allows an authorized user to retrieve the most relevant documents in a descending order, while preserving the privacy of his search request and the contents of documents he retrieved. To do so, data owner builds his searchable index, and associates with each term document with a relevance score, which facilitates document ranking. The proposed scheme uses two distinct cloud servers, one for storing the secure index, while the other is used to store the encrypted document collection. Such new setting prevents leaking the search result, i.e. the document identifiers, to the adversary cloud servers. We have conducted several empirical analyses on a real dataset to demonstrate the performance of our proposed scheme.