Ensemble representation for multiple facial expressions : evidence for a capacity limited perceptual process

We tested the processing capacity of establishing ensemble representation for multiple facial expressions using the simultaneous-sequential paradigm. Each set consisted of 16 faces conveying a variable amount of happy and angry expressions. Participants judged on a continuous scale the perceived average emotion from each face set (Experiment 1). In the simultaneous condition, the 16 faces were presented concurrently; in the sequential condition, two sets, each containing eight faces, were presented successively. Results showed that judgments varied depending on the number of happy versus angry faces contained in the sets and were sensitive at the single trial level to the perceived mean emotion intensity (based on postexperiment ratings), providing evidence of a genuine mean representation rather than the mere use of a single face or enumeration. Experiments 2 and 3 replicated Experiment 1, but implemented a different response format (binary choices) and added masks following each display, respectively. Importantly, in all three experiments, performance was consistently better in the sequential than in the simultaneous condition, revealing a limited-capacity process. A set of control analyses ruled out the use of enumeration or mere subsampling by the participants to perform the task. Collectively, these results indicate that participants could readily extract mean emotion from multiple faces shown concurrently in a set, but this process is best conceived as being capacity limited.</p

The Performance Analysis for Embedded Systems using Statistics Methods

Performance comparison for the computer system under different hardware platform &amp; system structure is of vital importance in the study of the performance evaluation. The Performance Analysis for Embedded Systems by using statistics methods based on the randomized complete block designs was proposed. Using the randomized block design, the differences between conditions can be separated from the difference in the processing, and be separated from the experimental bias. A case study of automatic gate machines used in the automatic fare collection system of Shanghai Metro is presented. The obtained assessment results show that our approach is helpful and effective. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2864

Increasing Interest in Inclusive Education in the Context of Action Plan for the Development and Enhancement of Special Education during the Fourteenth Five-Year Period in China: An Analysis of Baidu Index Data

Objective: Current evidence shows that public interest in inclusive education has been rising since the implementation of Action Plan for the development and enhancement of special education during the Fourteenth Five-Year period in China. The aim of this study was to quantify recent trends in public interest and related online search behavior for inclusive education in the context of this Action Plan. Methods: Baidu Index, a database of search engines with massive information, was employed. By searching for the keyword inclusive education, and using content analysis to understand the data information related to inclusive education. This study also extracted the search trend data of Chinese netizens on the related terms "Law on the Protection of Persons with Disabilities " and "Regulation on the Education of Persons with Disabilities" from January 1, 2022 to October 27, 2022. Finally, it compares the search trend of public search interests of inclusive education with related terms. Results: The public's interest in "inclusive education" and the related terms "Law on the Protection of Persons with Disabilities " and "Regulation on the Education of Persons with Disabilities" has been on the rise since the implementation of the Action Plan. The search trend reached its peak in February and May 2022, the valley in January 2022, and the search volume in other time periods tended to be stable. Conclusion: Baidu Index can understand the public's interest in inclusive education. The study shows that the rising search trend of inclusive education is closely related to the implementation of the Action Plan. The search volume of the "Law on the Protection of Persons with Disabilities " and "Regulation on the Education of Persons with Disabilities" is basically the same as that of "inclusive education", but the average search volume daily of "inclusive education" is slightly higher than that of "Regulation on the Education of Persons with Disabilities"

Significantly Increased Public Interest in Sleep Disorder during the COVID-19 Pandemic: An Analysis of Google Trends Data

Background: The COVID-19 pandemic has had a profound impact on sleep disorders. Previous studies have shown that people's sleep time is delayed. Methods: Analyze the public's interest to the keyword "sleep disorder" during the COVID-19 pandemic from December 2019 to December 2022 using Google Trends. The study also focused on the search trend data of the top three countries with high to low overall search frequency for this keyword. Results: It has been observed that during the peak period of the COVID-19 pandemic, the public's search interest for "sleep disorder" has significantly increased. Analyzing Google Trends from December 2019 to December 2022, the top three countries with high interest in searching for "sleep disorder" are the United States, the Philippines, and Canada. It shows that search interest in the United States is an increasing trend year by year, and the overall trend is relatively stable. The search trend for "sleep disorder" among Filipino netizens fluctuates greatly and is generally on a downward trend. The search trend for "sleep disorder" among Canadian netizens is moderate, and the overall trend is like that of Filipinos. It is worth noting that the Google Trends for "sleep disorder" among Filipino and Canadian netizens has changed from an overall increase to an overall decrease in 2020 as the watershed. This indicates a temporal correlation between the surge in COVID-19 cases and online search for "sleep disorder". Conclusion: It shows that public interest in "sleep disorder" has significantly increased during the COVID-19 pandemic, and there may be a certain correlation between the COVID-19 pandemic and sleep disorders. These are worthy of further exploration by researchers, especially the changes in people's daily routines caused by the COVID-19 pandemic, which in turn affect people's sleep quality

Consumers\u27 Repurchase Probability in Online Marketplace: A Belief Updating Perspective

It is commonly recognized that online transactions are “one-shot” transactions. However, a contemporary dataset from a dominant online marketplace in China reveals that averagely 24.3% transactions are repurchase transactions. Given that consumers already have purchase experience with a specific seller, their repurchase behavior may be influenced by both the seller’s reputation and their perceived seller performance. A consequent research question is: Whether and how do these two streams of information jointly affect consumers’ repurchase behavior? We adopt a belief update model, and also collect actual transaction data to examine this research question. Our findings include: (1) both seller reputation and consumers’ perceived seller performance have positive effects on consumers’ repurchase probability; (2) the effect of seller reputation is positively moderated by performance ambiguity; (3) consumers’ perceived seller performance has stronger effects on their repurchase probability when the seller has low reputation (vs. high reputation)

ANALYZING THE SPATIO-TEMPORAL CHARACTERISTICS OF PUBLIC INTEREST IN GENDER DISCRIMINATION THROUGH BAIDU INDEX DATA

Gender equality has always been a hot topic of global concern. This study aims to understand the temporal and spatial characteristics of public interest towards gender discrimination in China, providing information support for eliminating gender discrimination and promoting social equity in China to achieve the United Nations Sustainable Development Goal 5. It uses Baidu Index as the research tool, selecting gender discrimination as the keyword, taking July 1, 2013, to October 13, 2022, as the research interval to analyze the trend research, demand map, and population portrait of gender discrimination. It has been found that the search trend of gender discrimination has been slowly rising year by year, from the overall trend of personal computers (PC) and mobile. The peak appears periodically during the Spring Festival and summer vacation each year; “Gender discrimination” has a significant peak in early March 2017 and mid-June 2022. Additionally, the public also shows great concern for discrimination and racial discrimination. Most netizens who pay attention to gender discrimination come from East China. Females pay more attention to gender discrimination than males, and most of the females are under 19 years old and 20-29 years old. Finally, it contrastively analyzes the search trend of terms such as “discrimination” and “gender discrimination” and finds that the search index of “discrimination” is like that of “gender discrimination.

The value of surgery in the patients with de novo stage Ⅳ breast cancer

Breast cancer is one of the common malignant tumors in the world. About 6% of the patients are de novo stage Ⅳ breast cancer, which is incurable. Traditionally, the role of surgery has been confined to relieving symptoms, improving quality of life and reducing tumor load. Nowadays, a multidisciplinary team is a prerequisite for optimal management, and patients with oligometastatic lesion always live for a long time. R0 resection of the primary and metastatic foci can extend the time of progression-free survival (PFS), which may bring survival benefits. Therefore, whether to perform surgery has become a hot clinical issue. This article deeply discussed extensively the surgical value in de novo stage Ⅳ breast cancer patients

Generalized Category Discovery with Large Language Models in the Loop

Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only known categories. Due to the lack of supervision and category information, current methods usually perform poorly on novel categories and struggle to reveal semantic meanings of the discovered clusters, which limits their applications in the real world. To mitigate the above issues, we propose Loop, an end-to-end active-learning framework that introduces Large Language Models (LLMs) into the training loop, which can boost model performance and generate category names without relying on any human efforts. Specifically, we first propose Local Inconsistent Sampling (LIS) to select samples that have a higher probability of falling to wrong clusters, based on neighborhood prediction consistency and entropy of cluster assignment probabilities. Then we propose a Scalable Query strategy to allow LLMs to choose true neighbors of the selected samples from multiple candidate samples. Based on the feedback from LLMs, we perform Refined Neighborhood Contrastive Learning (RNCL) to pull samples and their neighbors closer to learn clustering-friendly representations. Finally, we select representative samples from clusters corresponding to novel categories to allow LLMs to generate category names for them. Extensive experiments on three benchmark datasets show that Loop outperforms SOTA models by a large margin and generates accurate category names for the discovered clusters. Code and data are available at https://github.com/Lackel/LOOP.Comment: Accepted by ACL 2024 Findings, code and data are available at https://github.com/Lackel/LOO

Overall Survival Time Prediction for High-grade Glioma Patients based on Large-scale Brain Functional Networks

High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning