Tootfinder

To Reach the Unreachable: Exploring the Potential of VR Hand Redirection for Upper Limb Rehabilitation
Peixuan Xiong, Yukai Zhang, Nandi Zhang, Shihan Fu, Xin Li, Yadan Zheng, Jinni Zhou, Xiquan Hu, Mingming Fan
https://arxiv.org/abs/2403.05264

To Reach the Unreachable: Exploring the Potential of VR Hand Redirection for Upper Limb Rehabilitation
Rehabilitation therapies are widely employed to assist people with motor impairments in regaining control over their affected body parts. Nevertheless, factors such as fatigue and low self-efficacy can hinder patient compliance during extensive rehabilitation processes. Utilizing hand redirection in virtual reality (VR) enables patients to accomplish seemingly more challenging tasks, thereby bolstering their motivation and confidence. While previous research has investigated user experience and…

This https://arxiv.org/abs/2310.05446 has been replaced.
link: https://scholar.google.com/scholar?q=a

RetSeg: Retention-based Colorectal Polyps Segmentation Network
Vision Transformers (ViTs) have revolutionized medical imaging analysis, showcasing superior efficacy compared to conventional Convolutional Neural Networks (CNNs) in vital tasks such as polyp classification, detection, and segmentation. Leveraging attention mechanisms to focus on specific image regions, ViTs exhibit contextual awareness in processing visual data, culminating in robust and precise predictions, even for intricate medical images. Moreover, the inherent self-attention mechanism in…

This https://arxiv.org/abs/2310.19041 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

On Linear Separation Capacity of Self-Supervised Representation Learning
Recent advances in self-supervised learning have highlighted the efficacy of data augmentation in learning data representation from unlabeled data. Training a linear model atop these enhanced representations can yield an adept classifier. Despite the remarkable empirical performance, the underlying mechanisms that enable data augmentation to unravel nonlinear data structures into linearly separable representations remain elusive. This paper seeks to bridge this gap by investigating under what c…

Student self-management, academic achievement: Exploring the mediating role of self-efficacy and the moderating influence of gender insights from a survey conducted in 3 universities in America
Zhiqiang Zhao, Ping Ren, Qian Yang
https://arxiv.org/abs/2404.11029

Student self-management, academic achievement: Exploring the mediating role of self-efficacy and the moderating influence of gender insights from a survey conducted in 3 universities in America
Excellent students are not only those who master more effective and efficient learning techniques to acquire and apply information. Even in the absence of correct learning, they are able to self-motivate, evaluate, and adjust their behavior. This study aims to explore the relationship between student self-management and academic achievement, with a focus on investigating the mediating role of self-efficacy and the moderating influence of gender in this relationship. A total of 289 students from…

Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
Yuanguo Lin, Fan Lin, Guorong Cai, Hong Chen, Lixin Zou, Pengcheng Wu
https://arxiv.org/abs/2402.13296 https://arxiv.org/pdf/2402.13296
arXiv:2402.13296v1 Announce Type: new
Abstract: In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution. EvoRL integrates EAs and reinforcement learning, presenting a promising avenue for training intelligent agents. This systematic review firstly navigates through the technological background of EvoRL, examining the symbiotic relationship between EAs and reinforcement learning algorithms. We then delve into the challenges faced by both EAs and reinforcement learning, exploring their interplay and impact on the efficacy of EvoRL. Furthermore, the review underscores the need for addressing open issues related to scalability, adaptability, sample efficiency, adversarial robustness, ethic and fairness within the current landscape of EvoRL. Finally, we propose future directions for EvoRL, emphasizing research avenues that strive to enhance self-adaptation and self-improvement, generalization, interpretability, explainability, and so on. Serving as a comprehensive resource for researchers and practitioners, this systematic review provides insights into the current state of EvoRL and offers a guide for advancing its capabilities in the ever-evolving landscape of artificial intelligence.

On the locomotion of the slider within a self-adaptive beam-slider system
Florian M\"uller, Malte Krack
https://arxiv.org/abs/2403.07423 https://

On the locomotion of the slider within a self-adaptive beam-slider system
A beam-slider system is considered whose passive self-adaption relies on an intricate locomotion process involving both frictional and unilateral contact. The system also exploits geometric nonlinearity to achieve broadband efficacy. The dynamics of the system take place on three distinct time scales: On the fast time scale of the harmonic base excitation are the vibrations and the locomotion cycle. On the slow time scale, the slider changes its position along the beam, and the overall vibratio…

The Effects of Group Discussion and Role-playing Training on Self-efficacy, Support-seeking, and Reporting Phishing Emails: Evidence from a Mixed-design Experiment
Xiaowei Chen, Margault Sacr\'e, Gabriele Lenzini, Samuel Greiff, Verena Distler, Anastasia Sergeeva
https://arxiv.org/abs/2402.11862

The Effects of Group Discussion and Role-playing Training on Self-efficacy, Support-seeking, and Reporting Phishing Emails: Evidence from a Mixed-design Experiment
Organizations rely on phishing interventions to enhance employees' vigilance and safe responses to phishing emails that bypass technical solutions. While various resources are available to counteract phishing, studies emphasize the need for interactive and practical training approaches. To investigate the effectiveness of such an approach, we developed and delivered two anti-phishing trainings, group discussion and role-playing, at a European university. We conducted a pre-registered experiment…

This https://arxiv.org/abs/2312.14378 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Multimodal Attention Merging for Improved Speech Recognition and Audio Event Classification
Training large foundation models using self-supervised objectives on unlabeled data, followed by fine-tuning on downstream tasks, has emerged as a standard procedure. Unfortunately, the efficacy of this approach is often constrained by both limited fine-tuning compute and scarcity in labeled downstream data. We introduce Multimodal Attention Merging (MAM), an attempt that facilitates direct knowledge transfer from attention matrices of models rooted in high resource modalities, text and images,…

This https://arxiv.org/abs/2309.10837 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Improving Opioid Use Disorder Risk Modelling through Behavioral and Genetic Feature Integration
Opioids are an effective analgesic for acute and chronic pain, but also carry a considerable risk of addiction leading to millions of opioid use disorder (OUD) cases and tens of thousands of premature deaths in the United States yearly. Estimating OUD risk prior to prescription could improve the efficacy of treatment regimens, monitoring programs, and intervention strategies, but risk estimation is typically based on self-reported data or questionnaires. We develop an experimental design and co…

Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
Yuanguo Lin, Fan Lin, Guorong Cai, Hong Chen, Lixin Zou, Pengcheng Wu
https://arxiv.org/abs/2402.13296 https://arxiv.org/pdf/2402.13296
arXiv:2402.13296v1 Announce Type: new
Abstract: In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution. EvoRL integrates EAs and reinforcement learning, presenting a promising avenue for training intelligent agents. This systematic review firstly navigates through the technological background of EvoRL, examining the symbiotic relationship between EAs and reinforcement learning algorithms. We then delve into the challenges faced by both EAs and reinforcement learning, exploring their interplay and impact on the efficacy of EvoRL. Furthermore, the review underscores the need for addressing open issues related to scalability, adaptability, sample efficiency, adversarial robustness, ethic and fairness within the current landscape of EvoRL. Finally, we propose future directions for EvoRL, emphasizing research avenues that strive to enhance self-adaptation and self-improvement, generalization, interpretability, explainability, and so on. Serving as a comprehensive resource for researchers and practitioners, this systematic review provides insights into the current state of EvoRL and offers a guide for advancing its capabilities in the ever-evolving landscape of artificial intelligence.

An MBE-CASSCF Approach for the Accurate Treatment of Large Active Spaces
Jonas Greiner, Ivan Gianni, Tommaso Nottoli, Filippo Lipparini, Janus J. Eriksen, J\"urgen Gauss
https://arxiv.org/abs/2403.17836

An MBE-CASSCF Approach for the Accurate Treatment of Large Active Spaces
We present a novel implementation of the complete active space self-consistent field (CASSCF) method that makes use of the many-body expanded full configuration interaction (MBE-FCI) method to incrementally approximate electronic structures within large active spaces. On the basis of a hybrid first-order algorithm employing both Super-CI and quasi-Newton strategies for the optimization of molecular orbitals, we demonstrate both computational efficacy and high accuracy of the resulting MBE-CASSC…

This https://arxiv.org/abs/2403.05574 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy
Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, therapist skill variability, and resource scarcity. Previous LLMs in cognitive reframing mainly converted negative emotions to positive ones, but these approaches have limited efficacy, often not promoting clients' self-discovery of alternative perspectives. In this paper, we unveil the Helping and Empowering through A…