Kinds Of Tasks Research Articles

Impaired Motor Skill Acquisition Using Mirror Visual Feedback Improved by Transcranial Direct Current Stimulation (tDCS) in Patients With Parkinson's Disease.

Recent non-invasive brain stimulation techniques in combination with motor training can enhance neuroplasticity and learning. It is reasonable to assume that such neuroplasticity-based interventions constitute a useful rehabilitative tool for patients with Parkinson’s Disease (PD). Regarding motor skill training, many kinds of tasks that do not involve real motor movements have been applied to PD patients. The purpose of this study is to elucidate whether motor skill training using mirror visual feedback (MVF) is useful to patients with PD in order to improve untrained hand performance dependent on the time course of training; and whether MVF combined with anodal transcranial direct current stimulation (tDCS) over primary motor cortex (M1) causes an additional effect based on increased motor cortical excitability. Eighteen right-handed patients with PD in the off-medication state and 10 age-matched healthy subjects (HS) performed four sessions of right-hand ball rotation using MVF (intervention) on two separate days, 1 week apart (day 1 and day 2). HS subjects received only sham stimulation. The intervention included four sessions of motor-skill training using MVF for 20 min comprised of four sets of training for 30 s each. PD patients were randomly divided into two intervention groups without or with anodal tDCS over the right M1 contralateral to the untrained hand. As the behavior evaluation, the number of ball rotations of the left hand was counted before (pre) and immediately after (post) intervention on both days (pre day 1, post day 1, pre day 2, and post day 2). Motor evoked potential (MEP), input-output function, and cortical silent period were recorded to evaluate the motor cortical excitatory and inhibitory system in M1 pre day 1 and post day 2. The number of ball rotations of the left hand and the facilitation of MEP by intervention were significantly impaired in patients with PD compared to HS. In contrast, if anodal tDCS was applied to right M1 of patients with PD, the number of ball rotations in accordance with I-O function at 150% intensity was significantly increased after day 1 and retained until day 2. This finding may help provide a new strategy for neurorehabilitation improving task-specific motor memory without real motor movements in PD.

Solving Linear Diophantine Systems on Parallel Architectures

Solving linear Diophantine systems of equations is applied in discrete-event systems, model checking, formal languages and automata, logic programming, cryptography, networking, signal processing, and chemistry. For modeling discrete systems with Petri nets, a solution in non-negative integer numbers is required, which represents an intractable problem. For this reason, solving such kinds of tasks with significant speedup is highly appreciated. In this paper we design a new solver of linear Diophantine systems based on the parallel-sequential composition of the system clans. The solver is studied and implemented to run on parallel architectures using a two-level parallelization concept based on MPI and OpenMP. A decomposable system is usually represented by a sparse matrix; a minimal clan size of the decomposition restricts the granulation of the technique. MPI is applied for solving systems for clans using a parallel-sequential composition on distributed-memory computing nodes, while OpenMP is applied in solving a single indecomposable system on a single node using multiple cores. A dynamic task-dispatching subsystem is developed for distributing systems on nodes in the process of compositional solution. Computational speedups are obtained on a series of test examples, e.g., illustrating that the best value constitutes up to 45 times speedup obtained on 5 nodes with 20 cores each.

Effectiveness of mental footnotes in the cognitive therapy for panic attack: two case-studies.

New findings in the area of experimental, cognitive psychology point out that the implicit meanings determine the information that is being processed. The so-called mental footnotes can even modify the path trajectory in a driving task. This influence has been proposed to go beyond this kind of task. Mental footnotes have been proposed as determining factors in the information processing in specific psychopatological syndromes. The objective of this study is to evaluate the adequacy of applying these implicit pieces of information, in form of mental footnotes, to stop the series of catastrophic thoughts in the panic attack syndrome. Following the theory of mental models and the iconic representation of the real world, two cases of panic attack have been treated. By using the negation illusory-reality technique, a mental footnote of this does not have to happen was added to the catastrophic representation of the world in patients with this syndrome. As a result, these two patients have not reported panic attacks in, at least, 2 year after the last session of therapy. A deeper study of the efficacy of this technique is encouraged in order to be recommended for the treatment of patients who suffer from panic attacks.

Mathematics Competence Level: The Contribution of Non-symbolic and Spatial Magnitude Comparison Skills.

Magnitude comparison skills have been related to mathematics competence, although results in this area vary. The current study aimed to describe the performance of 75 children (aged 4–5 years) in two comparison tasks; and examine the strength of the relationship between each of the two tasks and mathematics competence level (MCL). Participants were assessed with the Early Numeracy Test which provides a global MCL score. Magnitude comparison skills were assessed with two tasks: a non-symbolic number comparison task and a spatial comparison task. Results of the Pearson correlation analysis showed a relationship between the two tasks with better performance in the spatial comparison task. Regression analysis with the stepwise method showed that only the non-symbolic number comparison task had a significant value in the prediction of the MCL pointing to the need to take these kinds of tasks into account in the first years of school.

EAEA16 Keynote Address: The Future of Globalization

The ‘old’ globalization was about things we make. The future globalization will be quite different: it will also be about things we do. This future is coming incredibly fast and in ways few people expect. Digital technology is making it possible for ‘tele‐migrants’ sitting in one country to do things in another country, and for AI to become incredibly effective in certain kinds of tasks. We need to prepare ourselves for the future of globalization by building up skills that allow us to avoid direct competition with tele‐migrants and by realizing that humanity is an edge.

Radio frequency radiation measurement for base tower station safety compliances: a case study in Pulau Pinang Malaysia

The residence of Pulau Pinang and Malaysia generally are worried with the possible health effects due to Base Tower Station (BTS) radiation. Particularly, the residents of Pulau Pinang are utilizing their mobile phones for multiple kind of tasks including communications, browsing the internet and other applications. With the recent advances in mobile communication technologies, the end user demanded a better coverage, great communication services, and faster speed for internet browsing. To fulfill the demand, service provider and communication companies are providing plenty of communication base tower leading to the beliefs of that the tower emitted radiation and cause harmful effect to human health and voiced out and complain to the municipal councils in Malaysia. In this paper, a measurement was conducted to study electromagnetic fields (EMF) radiation level in Pulau Pinang. The measurement is compared with the international standard provided by International Commission of Non-Ionizing Radiation Protection (ICNIRP). Far field measurement of different values of long term evolution (LTE) exposure was demonstrated in radiofrequency (RF) shielded environment. LTE850, LTE1800 and LTE2600 field exposure was compared in term of its’ electrical field and power density that adhere to the standard provided by ICNIRP.

Predicting program properties from 'big code'

We present a new approach for predicting program properties from large codebases (aka "Big Code"). Our approach learns a probabilistic model from "Big Code" and uses this model to predict properties of new, unseen programs. The key idea of our work is to transform the program into a representation that allows us to formulate the problem of inferring program properties as structured prediction in machine learning. This enables us to leverage powerful probabilistic models such as Conditional Random Fields (CRFs) and perform joint prediction of program properties. As an example of our approach, we built a scalable prediction engine called JSNICE for solving two kinds of tasks in the context of JavaScript: predicting (syntactic) names of identifiers and predicting (semantic) type annotations of variables. Experimentally, JSNICE predicts correct names for 63% of name identifiers and its type annotation predictions are correct in 81% of cases. Since its public release at http://jsnice.org, JSNice has become a popular system with hundreds of thousands of uses. By formulating the problem of inferring program properties as structured prediction, our work opens up the possibility for a range of new "Big Code" applications such as de-obfuscators, decompilers, invariant generators, and others.

The Use of a Diagnostic Competence Model About Children’s Operation Sense for Criterion-Referenced Individual Feedback in a Large-Scale Formative Assessment

We provide evidence of validity for a newly developed diagnostic competence model of operation sense, by both (a) describing the theoretically substantiated development of the competence model in close association with its use within a large-scale formative assessment and (b) providing empirical evidence for the theoretically described cognitive levels of competences. The competence model describes students’ operation sense on four distinct levels. On each level, the model elaborates on the characteristics of tasks that students on this level are able to answer correctly. Moreover, the model explains this by referring to two kinds of cognitive processes that are supposed to be necessary to respond to these kinds of tasks successfully. In a validation study, about 85% of the variance in the item difficulties was explained by the four, a priori allocated, levels of operation sense. We discuss the relevance of the validation of the diagnostic competence model for the provision of criterion-referenced feedback in a large-scale formative assessment, including suggestions for teachers’ subsequent support activities, and the contributions of the model to the state of research about operation sense.

Study on muscle activity during pseudo‐haptics occurrence by electromyogram measurement

AbstractBased on electromyogram measurement, the influence of task contents on muscle activity during pseudo‐haptics occurrence is basically examined. As a result of experiments with two kinds of tasks, it is considered that motivation and immersion to tasks improved by setting goals, which is effective for pseudo‐haptics occurrence. It was suggested that the contents of the task and the setting goals influence the continuous occurrence of the pseudo‐haptics.

Overt social interaction and resting state in young adult males with autism: core and contextual neural features.

Conversation is an important and ubiquitous social behaviour. Individuals with autism spectrum disorder (autism) without intellectual disability often have normal structural language abilities but deficits in social aspects of communication like pragmatics, prosody, and eye contact. Previous studies of resting state activity suggest that intrinsic connections among neural circuits involved with social processing are disrupted in autism, but to date no neuroimaging study has examined neural activity during the most commonplace yet challenging social task: spontaneous conversation. Here we used functional MRI to scan autistic males (n = 19) without intellectual disability and age- and IQ-matched typically developing control subjects (n = 20) while they engaged in a total of 193 face-to-face interactions. Participants completed two kinds of tasks: conversation, which had high social demand, and repetition, which had low social demand. Autistic individuals showed abnormally increased task-driven interregional temporal correlation relative to controls, especially among social processing regions and during high social demand. Furthermore, these increased correlations were associated with parent ratings of participants' social impairments. These results were then compared with previously-acquired resting state data (56 autism, 62 control subjects). While some interregional correlation levels varied by task or rest context, others were strikingly similar across both task and rest, namely increased correlation among the thalamus, dorsal and ventral striatum, somatomotor, temporal and prefrontal cortex in the autistic individuals, relative to the control groups. These results suggest a basic distinction. Autistic cortico-cortical interactions vary by context, tending to increase relative to controls during task and decrease during test. In contrast, striato- and thalamocortical relationships with socially engaged brain regions are increased in both task and rest, and may be core to the condition of autism.

Handling negative mentions on social media channels using deep learning

ABSTRACTSocial media channels such as social networks, forum or online blogs have been emerging as major sources from which brands can gather user opinions about their products, especially the negative mentions. This kind of task, popular known as sentiment analysis, has been addressed recently by many deep learning approaches. However, negative mentions on social media have their own language characteristics which require certain adaptation and improvement from existing works for better performance. In this paper, we propose a new architecture for handling negative mentions on social media channels. As compared to the architecture published in our previous work, we expose substantial change in the combination manner of deep neural network layers for better training and classification performance on social-oriented messages. We also propose the way to re-train the pre-trained embedded words for better reflect sentiment terms, introducing the resultant sentimentally-embedded word vectors. Finally, we introduce the concept of a penalty matrix which incurs more reasonable loss function when handling negative mentions. Our experiments on real datasets demonstrated significant improvement.

Children and adults successfully comprehend subject-only sentences online.

In many offline studies, children show selectively better comprehension of sentences with the focus particle only when it modifies the object argument (Jane only ate an apple) than they do when it modifies the subject argument (Only Jane ate an apple). Here we explore the nature of this asymmetry by examining performance in a different kind of task: the moment-to-moment comprehension of unambiguous sentences. If past errors reflect a fundamental difference in representation or complexity of computation, we would expect the same asymmetry in this task. We observed that adults were able to successfully predict the target referent for both types of only-sentences, as indicated by anticipatory looks, while 6- to 8-year-old children could do so only for subject-modifying only-sentences. These findings suggest that much of the asymmetry in past work may be due to task demands. We discuss the implications of these results for children’s syntactic and pragmatic development.

Entrer dans un genre procédural : l’écriture d’un programme de construction en géométrie

The discovery of a procedural genre: the writing of a program of construction in geometry. The writing of a program of geometrical construction is a particular kind of task in so far as it requires the respect of some strict expectations in addition to mathematical work. Thanks to analysis of in-class sessions in primary school, we wonder how three pupils with difficulties appropriate these expectations and succeed in (or do not succeed in) overcoming various obstacles in order to enter in this specific genre.

Natural parallelisation paradigm on accelerator physics example

In this paper the problems of using the existing methods and algorithms for applied science with the help of high performance techniques is described. The idea is to provide the scientists with an easy to use tools for constructing the implementation of their research working on heterogeneous systems. Without a doubt the problem-solving environment for these purposes must give the exact solution in a reasonable time. The concept which is described in the article was devised specifically for such kind of tasks. The application of it in terms of high energy physics is given. To illustrate the work of the concept the analysis of the distribution of the particles in the beam under the influence of the space charge is done.

Natural parallelisation paradigm on accelerator physics example

In this paper the problems of using the existing methods and algorithms for applied science with the help of high performance techniques is described. The idea is to provide the scientists with an easy to use tools for constructing the implementation of their research working on heterogeneous systems. Without a doubt the problem-solving environment for these purposes must give the exact solution in a reasonable time. The concept which is described in the article was devised specifically for such kind of tasks. The application of it in terms of high energy physics is given. To illustrate the work of the concept the analysis of the distribution of the particles in the beam under the influence of the space charge is done.

Advanced neural network solution for detection of lung pathology and foreign body on chest plain radiographs

Objective : An approach was suggested to detect whether patient has any lung pathology or not. Materials and Methods : The approach was based on neural networks-aided analysis of chest X-ray frontal images. The neural network ensemble included 15 neural networks. Some of them were trained to analyze different parts of chest area, e.i. heart, diaphragm, lungs and related parts. And the other set of networks were trained to describe another meta-data of X-rays, such as patient position (laying or standing), quality of image, etc. The set of outputs of every model was aggregated with boosting model then. Result of model prediction was presented as probability of lung pathology on radiograph. Another 2 models were described in this article as parts of suggested approach. One of these model was trained to detect if there any foreign body within chest area on X-ray image or not. Another one was trained to classify which kind of foreign body was visualized (after first model gave positive prediction). To train models 9093 frontal X-ray images were used. Those images were labeled by group of radiologist. Results : The study showed that both foreign bodies detection and classification models demonstrated satisfactory results. The weak part of both models was precision for negative classes (those classes are “non-medical artefact” for one model and “foreign body is not visualized” for the other one). But it was not so critical for such kind of tasks. Conclusions : the model may be used to help a practitioner make decision whether a patient needs additional diagnostics or not.

Deep Haar Scattering Networks in Pattern Recognition: A Promising Approach

The aim of this paper is to discuss the use of Haar scattering networks, which is a very simple architecture that naturally supports a large number of stacked layers, yet with very few parameters, in a relatively broad set of pattern recognition problems, including regression and classification tasks. This architecture, basically, consists of stacking convolutional filters, that can be thought as a generalization of Haar wavelets, followed by non-linear operators which aim to extract symmetries and invariances that are later fed in a classification/regression algorithm. We show that good results can be obtained with the proposed method for both kind of tasks. We have outperformed the best available algorithms in 4 out of 18 important data classification problems, and have obtained a more robust performance than ARIMA and ETS time series methods in regression problems for data with strong periodicities.

Optimization model and online task interleaving scheduling algorithm for MIMO radar

Abstract As the preemptive single-machine scheduling problem, a variety of tasks, each of which consists of three phases: the transmit interval, the wait interval and the receive interval, to be performed by a single multiple input multiple output (MIMO) radar within a finite time horizon needs the efficient scheduling algorithm. However, due to the special signal processing characteristics, traditional task scheduling algorithms for the phased array radar can’t be applied to the MIMO radar to release its full multifunctional potential. Additionally, the real-time demand of the radar system must be taken into consideration when designing scheduling algorithms. Therefore, the optimization task scheduling model is established and a novel heuristic task interleaving scheduling algorithm is proposed. The full MIMO radar task structure is considered in a way that the wait interval can be utilized to transmit or receive subtasks. In addition, in order to sufficiently make full use of each subtask, different transmit intervals are incorporated into a whole dwell and different receive intervals are overlapped. The proposed algorithm decomposes the task schedulability analysis into the time constraint analysis and the energy constraint analysis, and adaptively interleaves different kinds of tasks. The simulation results show that the proposed algorithm can provide better performance compared with the four existing scheduling algorithms while maintaining a reasonable runtime.

New Directions in Self-Regulation: The Role of Metamotivational Beliefs

Research on self-regulation has primarily focused on how people exert control over their thoughts, emotions, and behavior. Less attention has been paid to the ways in which people manage their motivational states in the service of achieving valued goals. In this article, we explore an emerging line of research that focuses on people’s beliefs about their own motivation (i.e., their metamotivational knowledge), as well as the influence these beliefs have on their selection of regulatory strategies. In particular, we review evidence showing that people are often quite sensitive to the fact that distinct motivational states (e.g., eagerness vs. vigilance) are adaptive for different kinds of tasks. We also discuss how other metamotivational beliefs are inaccurate on average (e.g., beliefs about how rewards affect intrinsic motivation). Finally, we consider the implications of metamotivation research for the field of self-regulation and discuss future directions.

口吃者加工汉语歧义短语的神经过程

口吃者加工汉语歧义短语的神经过程