Liquid-Metal-Based Multichannel Strain Sensor for Sign Language Gesture Classification Using Machine Learning.

Liquid metals are highly conductive like metallic materials and have excellent deformability due to their liquid state, making them rather promising for flexible and stretchable wearable sensors. However, patterning liquid metals on soft substrates has been a challenge due to high surface tension. In this paper, a new method is proposed to overcome the difficulties in fabricating liquid-state strain sensors.

Dense Paraphrasing for multimodal dialogue interpretation.

Multimodal dialogue involving multiple participants presents complex computational challenges, primarily due to the rich interplay of diverse communicative modalities including speech, gesture, action, and gaze. These modalities interact in complex ways that traditional dialogue systems often struggle to accurately track and interpret. To address these challenges, we extend the textual enrichment strategy of Dense Paraphrasing (DP), by translating each nonverbal modality into linguistic expressions.

Cure the Incurable: Update of Treatment in Inherited Neuromuscular Disorders.

Originally thought to be incurable, huge therapeutic progress has been made in recent years in the field of inherited neuromuscular disorders. Approaches aiming to rescue the underlying pathophysiology, i.e.

Deep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording.

The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole-cell recordings. The framework integrates machine learning for anomaly detection and deep learning for multi-class classification.

Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans.

Alzheimer's disease (AD) is a neurodegenerative ailment that is becoming increasingly common, making it a major worldwide health concern. Effective care depends on an early and correct diagnosis, but traditional diagnostic techniques are frequently constrained by subjectivity and expensive costs. This study proposes a novel Vision Transformer-equipped Convolutional Neural Networks (VECNN) that uses three-dimensional magnetic resonance imaging to improve diagnosis accuracy.