scholarly journals Perceived Intensity and Discrimination Ability for Lingual Electrotactile Stimulation Depends on Location and Orientation of Electrodes

2017 ◽  
Vol 11 ◽  
Author(s):  
Joel Moritz Jr. ◽  
Philip Turk ◽  
John D. Williams ◽  
Leslie M. Stone-Roy
Keyword(s):  
Discrimination Ability ◽  
Electrotactile Stimulation ◽  
Perceived Intensity

Perception of Emotional Expressions in Adults

2016 ◽  
Vol 37 (1) ◽  
pp. 16-23 ◽  
Author(s):  
Chit Yuen Yi ◽  
Matthew W. E. Murry ◽  
Amy L. Gentzler
Keyword(s):  
Facial Expressions ◽  
Negative Mood ◽  
Emotion Perception ◽  
State Level ◽  
Past Research ◽  
Negative Emotionality ◽  
Basic Emotions ◽  
Perceived Intensity ◽  
Trait Level ◽  
Facial Expressions Of Emotion

Abstract. Past research suggests that transient mood influences the perception of facial expressions of emotion, but relatively little is known about how trait-level emotionality (i.e., temperament) may influence emotion perception or interact with mood in this process. Consequently, we extended earlier work by examining how temperamental dimensions of negative emotionality and extraversion were associated with the perception accuracy and perceived intensity of three basic emotions and how the trait-level temperamental effect interacted with state-level self-reported mood in a sample of 88 adults (27 men, 18–51 years of age). The results indicated that higher levels of negative mood were associated with higher perception accuracy of angry and sad facial expressions, and higher levels of perceived intensity of anger. For perceived intensity of sadness, negative mood was associated with lower levels of perceived intensity, whereas negative emotionality was associated with higher levels of perceived intensity of sadness. Overall, our findings added to the limited literature on adult temperament and emotion perception.

scholarly journals Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Garvita Agarwal ◽  
Lauren Hay ◽  
Ia Iashvili ◽  
Benjamin Mannix ◽  
Christine McLean ◽  
...  
Keyword(s):  
Network Performance ◽  
Deep Neural Network ◽  
Relevant Information ◽  
Jet Substructure ◽  
Discrimination Ability ◽  
Low Level ◽  
Performance Improvements ◽  
Intermediate Layers ◽  
Explainable Ai ◽  
General Method

Abstract A framework is presented to extract and understand decision-making information from a deep neural network (DNN) classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs (“eXpert AUGmented” variables, or XAUG variables), then apply layerwise relevance propagation (LRP) to networks both with and without XAUG variables. The XAUG variables are concatenated with the intermediate layers after network-specific operations (such as convolution or recurrence), and used in the final layers of the network. The results of comparing networks with and without the addition of XAUG variables show that XAUG variables can be used to interpret classifier behavior, increase discrimination ability when combined with low-level features, and in some cases capture the behavior of the classifier completely. The LRP technique can be used to find relevant information the network is using, and when combined with the XAUG variables, can be used to rank features, allowing one to find a reduced set of features that capture part of the network performance. In the studies presented, adding XAUG variables to low-level DNNs increased the efficiency of classifiers by as much as 30-40%. In addition to performance improvements, an approach to quantify numerical uncertainties in the training of these DNNs is presented.

scholarly journals Amusia and protolanguage impairments in schizophrenia

2014 ◽  
Vol 44 (13) ◽  
pp. 2739-2748 ◽  
Author(s):  
J. T. Kantrowitz ◽  
N. Scaramello ◽  
A. Jakubovitz ◽  
J. M. Lehrfeld ◽  
P. Laukka ◽  
...  
Keyword(s):  
Cognitive Remediation ◽  
Thought Disorder ◽  
Musical Ability ◽  
Group Status ◽  
Treatment Development ◽  
Discrimination Ability ◽  
Syndrome Scale ◽  
Language And Music ◽  
Auditory Tasks ◽  
Negative Syndrome

BackgroundBoth language and music are thought to have evolved from a musical protolanguage that communicated social information, including emotion. Individuals with perceptual music disorders (amusia) show deficits in auditory emotion recognition (AER). Although auditory perceptual deficits have been studied in schizophrenia, their relationship with musical/protolinguistic competence has not previously been assessed.MethodMusical ability was assessed in 31 schizophrenia/schizo-affective patients and 44 healthy controls using the Montreal Battery for Evaluation of Amusia (MBEA). AER was assessed using a novel battery in which actors provided portrayals of five separate emotions. The Disorganization factor of the Positive and Negative Syndrome Scale (PANSS) was used as a proxy for language/thought disorder and the MATRICS Consensus Cognitive Battery (MCCB) was used to assess cognition.ResultsHighly significant deficits were seen between patients and controls across auditory tasks (p < 0.001). Moreover, significant differences were seen in AER between the amusia and intact music-perceiving groups, which remained significant after controlling for group status and education. Correlations with AER were specific to the melody domain, and correlations between protolanguage (melody domain) and language were independent of overall cognition.DiscussionThis is the first study to document a specific relationship between amusia, AER and thought disorder, suggesting a shared linguistic/protolinguistic impairment. Once amusia was considered, other cognitive factors were no longer significant predictors of AER, suggesting that musical ability in general and melodic discrimination ability in particular may be crucial targets for treatment development and cognitive remediation in schizophrenia.

scholarly journals Novel Cs2HfCl6 Crystal Scintillator: Recent Progress and Perspectives

Physics ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 320-351
Author(s):  
Serge Nagorny
Keyword(s):  
Recent Progress ◽  
Raw Materials ◽  
Light Output ◽  
Light Yield ◽  
Thermal Conditions ◽  
Pulse Shape Discrimination ◽  
Main Element ◽  
High Quality ◽  
Crystal Sample ◽  
Discrimination Ability

Recent progress in Cs2HfCl6 (CHC) crystal production achieved within the last five years is presented. Various aspects have been analyzed, including the chemical purity of raw materials, purification methods, optimization of the growth and thermal conditions, crystal characterization, defect structure, and internal radioactive background. Large volume, crack-free, and high quality CHC crystals with an ultimate scintillating performance were produced as a result of such extensive research and development (R & D) program. For example, the CHC crystal sample with dimensions ∅23 × 30 mm3 demonstrates energy resolution of 3.2% FWHM at 662 keV, the relative light output at the level of 30,000 ph/MeV and excellent linearity down to 20 keV. Additionally, this material exhibits excellent pulse shape discrimination ability and low internal background of less than 1 Bq/kg. Furthermore, attempts to produce a high quality CHC crystal resulted in research on this material optimization by constitution of either alkali ions (Cs to Tl), or main element (Hf to Zr), or halogen ions (Cl to Br, I, or their mixture in different ratio), as well as doping with various active ions (Te4+, Ce3+, Eu3+, etc.). This leads to a range of new established scintillating materials, such as Tl2HfCl6, Tl2ZrCl6, Cs2HfCl4Br2, Cs2HfCl3Br3, Cs2ZrCl6, and Cs2HfI6. To exploit the whole potential of these compounds, detailed studies of the material’s fundamental properties, and understanding of the variety of the luminescence mechanisms are required. This will help to understand the origin of the high light yield and possible paths to further extend it. Perspectives of CHC crystals and related materials as detectors for rare nuclear processes are also discussed.

scholarly journals MRI-based radiomic feature analysis of end-stage liver disease for severity stratification

2021 ◽  
Author(s):  
Jennifer Nitsch ◽  
Jordan Sack ◽  
Michael W. Halle ◽  
Jan H. Moltz ◽  
April Wall ◽  
...  
Keyword(s):  
Disease Severity ◽  
Prediction Models ◽  
Blood Parameters ◽  
Meld Score ◽  
Poor Correlation ◽  
Discrimination Ability ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Academic Center ◽  
End Stage

Abstract Purpose We aimed to develop a predictive model of disease severity for cirrhosis using MRI-derived radiomic features of the liver and spleen and compared it to the existing disease severity metrics of MELD score and clinical decompensation. The MELD score is compiled solely by blood parameters, and so far, it was not investigated if extracted image-based features have the potential to reflect severity to potentially complement the calculated score. Methods This was a retrospective study of eligible patients with cirrhosis ($$n=90$$ n = 90 ) who underwent a contrast-enhanced MR screening protocol for hepatocellular carcinoma (HCC) screening at a tertiary academic center from 2015 to 2018. Radiomic feature analyses were used to train four prediction models for assessing the patient’s condition at time of scan: MELD score, MELD score $$\ge $$ ≥ 9 (median score of the cohort), MELD score $$\ge $$ ≥ 15 (the inflection between the risk and benefit of transplant), and clinical decompensation. Liver and spleen segmentations were used for feature extraction, followed by cross-validated random forest classification. Results Radiomic features of the liver and spleen were most predictive of clinical decompensation (AUC 0.84), which the MELD score could predict with an AUC of 0.78. Using liver or spleen features alone had slightly lower discrimination ability (AUC of 0.82 for liver and AUC of 0.78 for spleen features only), although this was not statistically significant on our cohort. When radiomic prediction models were trained to predict continuous MELD scores, there was poor correlation. When stratifying risk by splitting our cohort at the median MELD 9 or at MELD 15, our models achieved AUCs of 0.78 or 0.66, respectively. Conclusions We demonstrated that MRI-based radiomic features of the liver and spleen have the potential to predict the severity of liver cirrhosis, using decompensation or MELD status as imperfect surrogate measures for disease severity.

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