Exploring Artistic Visualization of Physiological Signals for Mindfulness and Relaxation: A Pilot Study

Mindfulness and relaxation techniques for mental health are increasingly being explored in the human-computer interaction community. Physiological signals and their visualization have often been exploited together in a form of biofeedback with other intervention methods. Here, we aim to contribute to the body of existing work on biofeedback interfaces for mindfulness, with a particular focus on incorporating artistic effects into physiological signal visualization. With an implemented artistic biofeedback interface, we conduct a pilot study where 10 participants attend stress-induction sessions followed by two biofeedback mindfulness sessions: classic biofeedback and artistic visualization. The result demonstrates that artistic visualization-driven biofeedback significantly improves the effectiveness of biofeedback in helping users feel relaxed in comparison with a classic graphical form of biofeedback. Also, it shows that the artistic effect makes it easy to understand what biofeedback represents. Future work includes exploring how advanced physiological computing methods can improve its efficiency and performance

Nanoscale Au-ZnO heterostructure developed by atomic layer deposition towards amperometric H2O2 detection

Nanoscale Au-ZnO heterostructures were fabricated on 4-in. SiO2/Si wafers by the atomic layer deposition (ALD) technique. Developed Au-ZnO heterostructures after post-deposition annealing at 250 degrees C were tested for amperometric hydrogen peroxide (H2O2) detection. The surface morphology and nanostructure of Au-ZnO heterostructures were examined by field emission scanning electron microscopy (FE-SEM), Raman spectroscopy, atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), etc. Additionally, the electrochemical behavior of Au-ZnO heterostructures towards H2O2 sensing under various conditions is assessed by chronoamperometry and electrochemical impedance spectroscopy (EIS). The results showed that ALD-fabricated Au-ZnO heterostructures exhibited one of the highest sensitivities of 0.53 mu A mu M(-1)cm(-2), the widest linear H2O2 detection range of 1.0 mu M-120mM, a low limit of detection (LOD) of 0.78 mu M, excellent selectivity under the normal operation conditions, and great long-term stability. Utilization of the ALD deposition method opens up a unique opportunity for the improvement of the various capabilities of the devices based on Au-ZnO heterostructures for amperometric detection of different chemicals

DAD vision: opto-electronic co-designed computer vision with division adjoint method

The miniaturization and mobility of computer vision systems are limited by the heavy computational burden and the size of optical lenses. Here, we propose to use a ultra-thin diffractive optical element to implement passive optical convolution. A division adjoint opto-electronic co-design method is also proposed. In our simulation experiments, the first few convolutional layers of the neural network can be replaced by optical convolution in a classification task on the CIFAR-10 dataset with no power consumption, while similar performance can be obtained

Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-configurable social tree and five basic multi-agent reward schemes. Finally, we provide Python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https://sites.google.com/view/arena-unity/

Machine Unlearning: Solutions and Challenges

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy violations, security breaches, and performance deterioration. To address these issues, machine unlearning has emerged as a critical technique to selectively remove specific training data points' influence on trained models. This paper provides a comprehensive taxonomy and analysis of machine unlearning research. We categorize existing research into exact unlearning that algorithmically removes data influence entirely and approximate unlearning that efficiently minimizes influence through limited parameter updates. By reviewing the state-of-the-art solutions, we critically discuss their advantages and limitations. Furthermore, we propose future directions to advance machine unlearning and establish it as an essential capability for trustworthy and adaptive machine learning. This paper provides researchers with a roadmap of open problems, encouraging impactful contributions to address real-world needs for selective data removal

Devil in the Number: Towards Robust Multi-modality Data Filter

In order to appropriately filter multi-modality data sets on a web-scale, it becomes crucial to employ suitable filtering methods to boost performance and reduce training costs. For instance, LAION papers employs the CLIP score filter to select data with CLIP scores surpassing a certain threshold. On the other hand, T-MARS achieves high-quality data filtering by detecting and masking text within images and then filtering by CLIP score. Through analyzing the dataset, we observe a significant proportion of redundant information, such as numbers, present in the textual content. Our experiments on a subset of the data unveil the profound impact of these redundant elements on the CLIP scores. A logical approach would involve reevaluating the CLIP scores after eliminating these influences. Experimentally, our text-based CLIP filter outperforms the top-ranked method on the ``small scale" of DataComp (a data filtering benchmark) on ImageNet distribution shifts, achieving a 3.6% performance improvement. The results also demonstrate that our proposed text-masked filter outperforms the original CLIP score filter when selecting the top 40% of the data. The impact of numbers on CLIP and their handling provide valuable insights for improving the effectiveness of CLIP training, including language rewrite techniques.Comment: ICCV 2023 Workshop: TNGCV-DataCom