Group Sense (China)

All people with disabilities should expect equitable access to services and equal opportunities; the UN Convention on the Rights of Persons with Disabilities explicitly recognises the importance of their full and effective participation and inclusion in society. However, particularly in low-income countries, people with disabilities with high support needs compared to other people with disabilities do not always benefit equally from existing policies, development projects, services, or practices. This article reflects on learning gathered from across the Disability Inclusive Development programme. It asserts that strengthening inclusion of people with disabilities with high support needs requires not only practical know-how at project level, but also systemic change to funding practices and project design. We identify key areas where collective focus and systemic change would ensure development programmes more commonly include people with disabilities with high support needs, who are often the hardest to reach and the most marginalised and excluded.

Understanding motion in dynamic environments is critical for autonomous driving, thereby motivating research on class-agnostic motion prediction. In this work, we investigate weakly and self-supervised class-agnostic motion prediction from LiDAR point clouds. Outdoor scenes typically consist of mobile foregrounds and static backgrounds, allowing motion understanding to be associated with scene parsing. Based on this observation, we propose a novel weakly supervised paradigm that replaces motion annotations with fully or partially annotated (1%, 0.1%) foreground/background masks for supervision. To this end, we develop a weakly supervised approach utilizing foreground/background cues to guide the self-supervised learning of motion prediction models. Since foreground motion generally occurs in non-ground regions, non-ground/ground masks can serve as an alternative to foreground/background masks, further reducing annotation effort. Leveraging non-ground/ground cues, we propose two additional approaches: a weakly supervised method requiring fewer (0.01%) foreground/background annotations, and a self-supervised method without annotations. Furthermore, we design a Robust Consistency-aware Chamfer Distance loss that incorporates multi-frame information and robust penalty functions to suppress outliers in self-supervised learning. Experiments show that our weakly and self-supervised models outperform existing self-supervised counterparts, and our weakly supervised models even rival some supervised ones. This demonstrates that our approaches effectively balance annotation effort and performance.

Digital Life Project 2 (DLP2) presents an open-source real-time framework that brings Large Language Models (LLMs) to life through expressive 3D avatars. Users converse naturally by voice, while characters respond on demand with unified audio, whole-body animation, and physics simulation directly in the browser. At its core are: (1) an agentic orchestration of large and small LLMs that governs character behavior, supported by a memory system tracking emotional states and evolving relationships to enable context-dependent reactions; (2) a hybrid real-time pipeline that segments long LLM responses, performs parallel motion retrieval and audio-motion synchronization, and streams efficiently through a custom Protocol Buffers structure for low-latency playback of voice, motion, and expression; and (3) robust mechanisms for user interruption handling, adaptive buffering, and fault tolerance. Characters are fully customizable in both appearance (3D models) and personality (character prompts) and readily adaptable to any LLM or text-to-speech (TTS) service. DLP2 demonstrates how LLMs can be embodied in responsive 3D characters, offering a practical blueprint for real-time, emotionally adaptive digital interactions on the web.

We have developed a novel biosensing device that can detect the real-time, dynamic state of in vivo oxidative status (IVOS) in living systems. Oxidative stress is a well-established condition in CF animal models and humans. Elevated oxidative stress conditions are associated with excessive inflammatory responses from neutrophils that result in fibrotic tissue formation. As such, numerous clinical and preclinical studies suggest that elevated oxidative stress, combined with the heightened pro-inflammatory milieu observed in CF phenotypes, likely increases susceptibility to recurrent infection-inflammation cycles. We aimed to use the IVOS sensor to characterise oxidative environmental differences in wildtype, CF Phe508del and CFTR knockout rat epithelial tissues including the lungs, trachea, nasal mucosa, and oesophagus. Our results revealed no significant differences in the baseline redox balance between wildtype, Phe508del and knockout rat tissues, however when we looked at short term fluctuations in redox status using the ratio of the root mean squared of successive differences (RMSSD) to the normalised mean IVOS arbitrary units, we saw significantly elevated fluctuations in the redox status in the bronchi of Phe508del rats compared to wildtype rats, indicating a lower steady-state oxidative environment combined with large transient oxidative events.

Abstract Large language models (LLMs) have opened up new possibilities for intelligent agents, endowing them with human-like thinking and cognitive abilities. In this work, we delve into the potential of large language models (LLMs) in autonomous driving (AD). We introduce DriveMLM, an LLM-based AD framework that can perform close-loop autonomous driving in realistic simulators. To this end, (1) we bridge the gap between the language decisions and the vehicle control commands by standardizing the decision states according to the off-the-shelf motion planning module. (2) We employ a multimodal LLM (MLLM) to model the behavior planning module of a module AD system, which uses driving rules, user commands, and inputs from various sensors (e.g., camera, LiDAR) as input and makes driving decisions and provide explanations. This model can plug-and-play in existing AD systems such as Autopilot and Apollo for close-loop driving. (3) We design an effective data engine to collect a dataset that includes decision state and corresponding explanation annotation for model training and evaluation. We conduct extensive experiments and show that replacing the decision-making modules of the Autopilot and Apollo with DriveMLM resulted in significant improvements of 3.2 and 4.7 points on the CARLA Town05 Long, respectively, demonstrating the effectiveness of our model. We hope this work can serve as a baseline for autonomous driving with LLMs.

We investigate the regeneration process of UV-written and femtosecond pulseswritten FBGs in standard fused silica glass encapsulated in sealed and unsealed tubes with air and Argon. Then we subject both grating types in unsealed tubes to several high temperature cycling to study the Bragg wavelength drift. We show that the regeneration occurs only in air or unsealed tubes and the RFBGs show less drift than fs-FBGs during the post-annealing process in small diameter tubes (1mm).