Blackstone has closed its latest fund – the $6.3bn BXLS VI – saying it is the largest ever private fund for the life sciences sector.
Insulet hires Stryker vet, reinstating commercial chief role as C-suite overhaul continues
Insulet has reinstated the chief commercial officer position three years after eliminating the role to pursue more focused regional and local market execution.
Thematic landscapes and temporal trends of disability technology adoption: insights from Structural Topic Modelling
IntroductionIn recent years, the importance of accessible and inclusive technologies has increasingly supported people with disabilities. However, prior studies on the adoption of technology remain fragmented, often focusing on specific disabilities or tools without exploring broader connections. Addressing this, the current study addresses the gaps by identifying core topics, examining temporal variations, and analyzing interrelations […]
Validating an AI-assisted comentoring model for identifying at-risk students and for academic mentoring: a study protocol
BackgroundAcademic mentoring plays a critical role in monitoring student progress, maintaining academic integrity, identifying early signs of risk, and delivering personalized guidance to improve learning outcomes. Traditionally, this has relied on face-to-face interactions; however, advancements in artificial intelligence (AI) have introduced new opportunities for AI-assisted mentoring. While promising, many existing AI models for student monitoring […]
Depression subtype classification from social media posts: few-shot prompting vs. fine-tuning of large language models
BackgroundSocial media provides timely proxy signals of mental health, but reliable tweet-level classification of depression subtypes remains challenging due to short, noisy text, overlapping symptomatology, and labeling bias. Large language models (LLMs) are increasingly used in mental health for tasks such as symptom extraction, risk screening, and triage, yet their reliability for fine-grained depression subtype […]
MOSS-VoiceGenerator: Create Realistic Voices with Natural Language Descriptions
arXiv:2603.28086v1 Announce Type: cross Abstract: Voice design from natural language aims to generate speaker timbres directly from free-form textual descriptions, allowing users to create voices tailored to specific roles, personalities, and emotions. Such controllable voice creation benefits a wide range of downstream applications-including storytelling, game dubbing, role-play agents, and conversational assistants, making it a significant […]
Pre-Deployment Complexity Estimation for Federated Perception Systems
arXiv:2603.28282v1 Announce Type: cross Abstract: Edge AI systems increasingly rely on federated learning to train perception models in distributed, privacy-preserving, and resource-constrained environments. Yet, before training begins, practitioners often lack practical tools to estimate how difficult a federated learning task will be in terms of achievable accuracy and communication cost. This paper presents a classifier-agnostic, […]
Robust Batch-Level Query Routing for Large Language Models under Cost and Capacity Constraints
arXiv:2603.26796v1 Announce Type: cross Abstract: We study the problem of routing queries to large language models (LLMs) under cost, GPU resources, and concurrency constraints. Prior per-query routing methods often fail to control batch-level cost, especially under non-uniform or adversarial batching. To address this, we propose a batch-level, resource-aware routing framework that jointly optimizes model assignment […]
PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning
arXiv:2603.26816v1 Announce Type: cross Abstract: High-dimensional low-sample-size (HDLSS) datasets constrain reliable environmental model development, where labeled data remain sparse. Reinforcement learning (RL)-based adaptive sensing methods can learn optimal sampling policies, yet their application is severely limited in HDLSS contexts. In this work, we present PiCSRL (Physics-Informed Contextual Spectral Reinforcement Learning), where embeddings are designed using […]
Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts
arXiv:2601.10079v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has become essential for eliciting complex reasoning capabilities in Large Language Models (LLMs). However, the substantial memory overhead of storing Key-Value (KV) caches during long-horizon rollouts acts as a critical bottleneck, often prohibiting efficient training on limited hardware. While existing KV compression techniques offer a remedy for […]
Exploring Collatz Dynamics with Human-LLM Collaboration
arXiv:2603.11066v4 Announce Type: replace-cross Abstract: We develop a structural framework for the Collatz map based on odd-to-odd dynamics, modular return structure, and a decomposition of trajectories into bursts and gaps. On the unconditional side, we prove several exact results. The fiber-57 branch q = 7 (mod 8) returns in exactly two odd-to-odd steps with uniform […]
A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning
arXiv:2502.18535v2 Announce Type: replace-cross Abstract: Machine learning is increasingly deployed through outsourced and cloud-based pipelines, which improve accessibility but also raise concerns about computational integrity, data privacy, and model confidentiality. Zero-knowledge proofs (ZKPs) provide a compelling foundation for verifiable machine learning because they allow one party to certify that a training, testing, or inference result […]