The effects of an attentional training procedure on the performance of high and low test-anxious children

Abstract. We used task-switching (TS) paradigms to study how cognitive training can compensate age-related cognitive decline. Thirty-nine young (age span: 18–25 years) and 40 older (age span: 60–75 years) women were assigned to training and control groups. The training group received 8 one-hour long cognitive training sessions in which the difficulty level of TS was individually adjusted. The other half of the sample did not receive any intervention. The reference task was an informatively cued TS paradigm with nogo stimuli. Performance was measured on reference, near-transfer, and far-transfer tasks by behavioral indicators and event-related potentials (ERPs) before training, 1 month after pretraining, and in case of older adults, 1 year later. The results showed that young adults had better pretraining performance. The reference task was too difficult for older adults to form appropriate representations as indicated by the behavioral data and the lack of P3b components. But after training older adults reached the level of performance of young participants, and accordingly, P3b emerged after both the cue and the target. Training gain was observed also in near-transfer tasks, and partly in far-transfer tasks; working memory and executive functions did not improve, but we found improvement in alerting and orienting networks, and in the execution of variants of TS paradigms. Behavioral and ERP changes remained preserved even after 1 year. These findings suggest that with an appropriate training procedure older adults can reach the level of performance seen in young adults and these changes persist for a long period. The training also affects the unpracticed tasks, but the transfer depends on the extent of task similarities.

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The effect of a simple training procedure on the judgment of visual number.

PsycEXTRA Dataset ◽

10.1037/e452972004-001 ◽

1951 ◽

Author(s):

E. L. Kaufman ◽

A. L. Minturn ◽

T. W. Reese

Keyword(s):

Training Procedure ◽

Visual Number

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Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

Biomimetics ◽

10.3390/biomimetics5010001 ◽

2019 ◽

Vol 5 (1) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Michelle Gutiérrez-Muñoz ◽

Astryd González-Salazar ◽

Marvin Coto-Jiménez

Keyword(s):

Neural Networks ◽

Short Term Memory ◽

Computational Cost ◽

Real Life ◽

Fixed Number ◽

Training Procedure ◽

Statistical Validation ◽

Significant Drop ◽

Training Time ◽

Important Solution

Speech signals are degraded in real-life environments, as a product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions. To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combinations of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation was made based on quality measurements of the signal’s spectrum, the training time of the networks, and statistical validation of results. In total, 120 artificial neural networks of eight different types were trained and compared. The results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, given that reduction in training time is on the order of 30%, in processes that can normally take several days or weeks, depending on the amount of data. The results also present advantages in efficiency, but without a significant drop in quality.

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Micromagnetic Characterization of Operation-Induced Damage in Charpy Specimens of RPV Steels

Applied Sciences ◽

10.3390/app11072917 ◽

2021 ◽

Vol 11 (7) ◽

pp. 2917

Author(s):

Madalina Rabung ◽

Melanie Kopp ◽

Antal Gasparics ◽

Gábor Vértesy ◽

Ildikó Szenthe ◽

...

Keyword(s):

Neutron Irradiation ◽

Pressure Vessel ◽

Operating Conditions ◽

Magnetic Characteristics ◽

Pressure Vessel Steel ◽

Training Procedure ◽

Vessel Steel ◽

Before And After ◽

The Individual ◽

Magnetic Adaptive Testing

The embrittlement of two types of nuclear pressure vessel steel, 15Kh2NMFA and A508 Cl.2, was studied using two different methods of magnetic nondestructive testing: micromagnetic multiparameter microstructure and stress analysis (3MA-X8) and magnetic adaptive testing (MAT). The microstructure and mechanical properties of reactor pressure vessel (RPV) materials are modified due to neutron irradiation; this material degradation can be characterized using magnetic methods. For the first time, the progressive change in material properties due to neutron irradiation was investigated on the same specimens, before and after neutron irradiation. A correlation was found between magnetic characteristics and neutron-irradiation-induced damage, regardless of the type of material or the applied measurement technique. The results of the individual micromagnetic measurements proved their suitability for characterizing the degradation of RPV steel caused by simulated operating conditions. A calibration/training procedure was applied on the merged outcome of both testing methods, producing excellent results in predicting transition temperature, yield strength, and mechanical hardness for both materials.

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Real-Time Instance Segmentation of Traffic Videos for Embedded Devices

Sensors ◽

10.3390/s21010275 ◽

2021 ◽

Vol 21 (1) ◽

pp. 275

Author(s):

Ruben Panero Martinez ◽

Ionut Schiopu ◽

Bruno Cornelis ◽

Adrian Munteanu

Keyword(s):

Real Time ◽

Network Architecture ◽

Training Procedure ◽

Segmentation Method ◽

Embedded Devices ◽

Network Training ◽

Assignment Algorithm ◽

Ablation Study ◽

Reduced Rate ◽

Instance Segmentation

The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade-off further modifies the two branches and replaces the conventional ResNet-based performance-oriented backbone with a lightweight speed-oriented design. The proposed architectural variations achieve real-time performance when deployed on embedded devices. The experimental results demonstrate that the proposed instance segmentation method for traffic videos outperforms the you only look at coefficients algorithm, the state-of-the-art real-time instance segmentation method. The proposed architecture achieves qualitative results with 31.57 average precision on the COCO dataset, while its speed-oriented variations achieve speeds of up to 66.25 frames per second on the Jetson AGX Xavier module.

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The Challenge of Illusory Perception of Animals: The Impact of Methodological Variability in Cross-species Investigation

Animals ◽

10.3390/ani11061618 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1618

Author(s):

Maria Santacà ◽

Christian Agrillo ◽

Maria Elena Miletto Petrazzini

Keyword(s):

Research Field ◽

Choice Test ◽

Training Procedure ◽

Biologically Relevant ◽

Selective Pressures ◽

Extensive Training ◽

Behavioural Research ◽

Illusory Perception ◽

Methodological Approaches ◽

The Impact

Although we live on the same planet, there are countless different ways of seeing the surroundings that reflect the different individual experiences and selective pressures. In recent decades, visual illusions have been used in behavioural research to compare the perception between different vertebrate species. The studies conducted so far have provided contradictory results, suggesting that the underlying perceptual mechanisms may differ across species. Besides the differentiation of the perceptual mechanisms, another explanation could be taken into account. Indeed, the different studies often used different methodologies that could have potentially introduced confounding factors. In fact, the possibility exists that the illusory perception is influenced by the different methodologies and the test design. Almost every study of this research field has been conducted in laboratories adopting two different methodological approaches: a spontaneous choice test or a training procedure. In the spontaneous choice test, a subject is presented with biologically relevant stimuli in an illusory context, whereas, in the training procedure, a subject has to undergo an extensive training during which neutral stimuli are associated with a biologically relevant reward. Here, we review the literature on this topic, highlighting both the relevance and the potential weaknesses of the different methodological approaches.

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Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Journal of Personalized Medicine ◽

10.3390/jpm11010028 ◽

2021 ◽

Vol 11 (1) ◽

pp. 28

Author(s):

Ivan Lorencin ◽

Sandi Baressi Šegota ◽

Nikola Anđelić ◽

Anđela Blagojević ◽

Tijana Šušteršić ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Clinical Picture ◽

Data Augmentation ◽

Lower Amount ◽

Training Procedure ◽

X Ray ◽

Severe Clinical Picture ◽

Lung Condition

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

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Artifact-Free Single Image Defogging

Atmosphere ◽

10.3390/atmos12050577 ◽

2021 ◽

Vol 12 (5) ◽

pp. 577

Author(s):

Gabriele Graffieti ◽

Davide Maltoni

Keyword(s):

Learning Strategy ◽

Real Data ◽

Training Procedure ◽

Single Image ◽

Paired Data ◽

Manual Inspection ◽

Synthetic Datasets ◽

Visibility Enhancement ◽

Difficult Cases ◽

Good Contrast

In this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear images; this usually provides good results on mildly fogged images but is not effective for difficult cases. On the other hand, the models trained with real data can produce visually impressive results, but unwanted artifacts are often present. We propose a curriculum learning strategy and an enhanced CycleGAN model to reduce the number of produced artifacts, where both synthetic and real data are used in the training procedure. We also introduce a new metric, called HArD (Hazy Artifact Detector), to numerically quantify the number of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. HArD is then combined with other defogging indicators to produce a solid metric that is not deceived by the presence of artifacts. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.

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