10 accepted papers
Monolithic agents in deep search often suffer from "cognitive overload," while existing multi-agent approaches mostly rely on frozen models that cannot learn from collaboration failures. To bridge this gap, we propose $\textbf{DECOR}$ ($\textbf{DE}$compose and $\textbf{CO}$llaborate via $\textbf{R}$…
Achieving safe and robust interaction in articulated-soft humanoid robots (ASRs) remains a major challenge due to their compliant joints, high degree of freedom, and highly nonlinear coupled dynamics, which makes them especially sensitive to external disturbances. This paper presents a novel contact…
Dexterous grasp generation is a predominant task that enables robots to perform human-level manipulation. However, a dexterous hand always maintains high-dimensional DoF and actuation space, making existing approaches that rely on holistic latent representations difficult to produce high-quality and
Multi-task learning (MTL) is widely utilized across a variety of real-world applications, including recommendation systems. For instance, in the field of e-commerce, MTL is commonly employed to simultaneously model click, conversion, and user dwelling time. Among a various of MTL models, the Multi-g…
Vision-language pretraining (VLP) enables open-world generalization beyond predefined labels, a critical capability in surgery due to the diversity of procedures, instruments, and patient anatomies. However, applying VLP to ophthalmic surgery presents unique challenges, including limited vision-lang…
This paper aims at unifying simplified models of balancing – based on linear, centre-of-mass (CoM) expressions – and articulated-soft floating-base (ASFB) robot dynamics models, for control design. Two distinct controller variations are introduced: the first operates by mapping gross applied force r
Face aging is a highly complex process that includes intricate aging patterns. Previous works condition aging patterns utilizing one-hot or artificial predefined distributions. Nevertheless, different age groups show different intraclass variation in appearance. This makes it difficult for previous…
Deep learning methods have been widely applied in Attention Deficit Hyperactivity Disorder (ADHD) classification in the past decade due to their effective learned features. However, these features are lack of neurobiological meanings and hard to be biomarkers. Here, we proposed an attention auto-enc…
We revisit multiple hypothesis testing and propose a two-phase test, where each phase is a fixed-length test and the second-phase proceeds only if a reject option is decided in the first phase. We derive achievable error exponents of error probabilities under each hypothesis and show that our two-ph…
We establish fundamental limits of tracking a moving target over the unit cube under the framework of 20 questions with measurement-dependent noise. In this problem, there is an oracle who knows the instantaneous location of a target. Our task is to query the oracle as few times as possible to accur…