54 accepted papers
Microassembly is becoming increasingly critical in modern smart manufacturing, placing higher demands on system performance—particularly for in situ and in vivo applications in biomedicine, photonics, sensors, and microrobotics. Non-contact mechanical microassembly has emerged as a promising solutio…
Peripheral nerve injuries represent a significant clinical challenge in reconstructive surgery, traumatology, and neurosurgery, often leading to permanent sensorimotor deficits and diminished life quality. Thus, achieving precise epineurial suturing without nerve fascicle damage and tension remains …
Miniature adhesive patches (MAPs) are widely used in medicine for tissue repair, wound healing, and biosensing applications. Despite considerable advances in medical robotics, the automated in vivo delivery of MAPs remains a formidable challenge due to the intricate nature of biological environments
Attributed graph clustering has achieved remarkable success by synergistically integrating topological structures and node attributes. While subspace learning has emerged as a dominant paradigm for node partitioning, most existing methods rely on implicit low-rank constraints, which often fail to ca
Within heterogeneous treatment effect (HTE) analysis, various estimands have been proposed to capture the effect of a treatment conditional on covariates. Recently, the conditional quantile comparator (CQC) has emerged as a promising estimand, offering quantile-level summaries akin to the conditiona…
High-precision liquid droplet manipulation is widely used in life science, biomedical engineering, and industry. However, there still lacks a robotic approach supporting the direct in-petri dish manipulation of liquid droplets without the need of additives (such as magnetic beads) or other setup req…
Alerts generated by Security Operations Centers (SOCs) are often numerous and scattered, requiring significant effort from security analysts to manage, which severely slows response times. While recent alert correlation graph methods can effectively reduce alert volume, these graphs are often too co
The high memory demands of the Key-Value (KV) Cache during the inference of Large Language Models (LLMs) severely restrict their deployment in resource-constrained platforms. Quantization can effectively alleviate the memory pressure caused by KV Cache. However, existing methods either rely on stati
Simulation-based inference (SBI) enables amortized Bayesian inference by first training a neural posterior estimator (NPE) on prior-simulator pairs, typically through low-dimensional summary statistics, which can then be cheaply reused for fast inference by querying it on new test observations. Beca…
The non-contact acoustic manipulation of particle clusters has great potential in various fields. However, current acoustic manipulation technologies for particle cluster manipulation primarily focus on aggregating particles to a specific shape, while morphological transformation of aggregated parti
Despite their strong performance on reasoning tasks, large reasoning models (LRMs) often suffer from overthinking, producing unnecessarily long outputs and incurring high end-to-end latency, a significant limitation to their real-world deployment. To address overthinking, early-exit mechanisms have …
Flow-based methods have achieved significant success in various generative modeling tasks, capturing nuanced details within complex data distributions. However, few existing works have exploited this unique capability to resolve fine-grained structural details beyond generation tasks. This paper pre…
Open-set object detection (OSOD) is highly desirable for robotic manipulation in unstructured environments. However, existing OSOD methods often fail to meet the requirements of robotic applications due to their high computational burden and complex deployment. To address this issue, this paper prop
Recent years have witnessed a profound evolution in the abilities of Large Language Model, which has significantly boosted the proliferation of role-playing agents and platforms. Nonetheless, there is a conspicuous absence of systematic and comprehensive evaluations of role-playing abilities which…
We study the problem of likelihood maximization when the likelihood function is intractable but model simulations are readily available. We propose a sequential, gradient-based optimization method that directly models the Fisher score based on a local score matching technique which uses simulations…
Molecular docking is a fundamental technique in structure-based drug discovery, playing a critical role in predicting the binding poses of protein-ligand complexes. While traditional docking methods are generally reliable, they are often computationally expensive. Recent deep learning (DL) approache…
Optimising probabilistic models is a well-studied field in statistics. However, its connection with the training of generative models remains largely under-explored. In this paper, we show that the evolution of time-varying generative models can be projected onto an exponential family manifold, natu
This paper addresses differential inference in time-varying parametric probabilistic models, like graphical models with changing structures. Instead of estimating a high-dimensional model at each time and estimating changes later, we directly learn the differential parameter, i.e., the time derivati…
Noncontact manipulation of soft micro-fibers has great potential in advanced manufacturing, materials science, and biomedical engineering. However, current noncontact manipulation techniques primarily focus on objects with regular shapes, e.g., solid particles, cells, or droplets, with fewer solutio
Deep-learning-based object detection has gained widespread application in surface defect inspection, with anchor-based detectors achieving remarkable success by utilizing dense anchors to align with defects. Determining the optimal anchor configuration, i.e., sizes and aspect ratios of anchor boxes,
This paper introduces a novel iterative method for missing data imputation that sequentially reduces the mutual information between data and the corresponding missingness mask. Inspired by GAN-based approaches that train generators to decrease the predictability of missingness patterns, our method e…
Acoustic tweezers have been a valuable tool across various fields, from nano-microfabrication to biology. Their unique characteristics enable three-dimensional particle manipulation, where acoustic trapping serves as a fundamental requirement. However, traditional methods struggle to maintain steady
Score matching is a vital tool for learning the distribution of data with applications across many areas including diffusion processes, energy based modelling, and graphical model estimation. Despite all these applications, little work explores its use when data is incomplete. We address this by ada…
As an essential component of noncontact manipulation, acoustic manipulation has achieved great success in multidisciplinary research and applications. Although acoustic tweezers have made advancements in manipulating particles in air, handling individual particles with high precision in water remain
Large language models (LLMs) have shown excellent performance on general machine translation. However, LLMs suffer from high deployment cost and unsatisfying quality on low-resource domains. To this end, we explore to build base translation models with LLM-enhanced data augmentation. For data augmen…
Dealing with language heterogeneity has always been one of the challenges in neural machine translation (NMT).The idea of using mixture-of-experts (MoE) naturally excels in addressing this issue by employing different experts to take responsibility for different problems.However, the parameter-ineff…
Knot tying is an essential task for robotic surgery, which is routinely realized by dual-arm robotic manipulation. Despite the well-established protocol and progress at macro scale so far, there remain challenges to further advance robotic knot tying technique, particularly in terms of decreasing sp…
Acoustic holography has emerged as a cutting-edge technique for constructing a micro-robot acoustic end-effector for non-contact manipulation. As one of typical implementations of acoustic holography, Binary Amplitude-Only Hologram (BAOH) featured with a simple structure provides an efficient altern…
The conditional quantile treatment effect (CQTE) can provide insight into the effect of a treatment beyond the conditional average treatment effect (CATE). This ability to provide information over multiple quantiles of the response makes the CQTE especially valuable in cases where the effect of a tr…
Landing on a vertically oscillating platform poses a significant challenge for multi-rotor unmanned aerial vehicle (UAVs) due to the time-varying ground effect (GE). In this work, we formulated a data-driven GE dynamic model that accurately describes the complex interactions between UAVs and both st…
Dynamic model is essential for robust and reliable robotic fish motion control. Despite considerable efforts in robotic fish dynamic modeling, background flow has not been well considered yet, leading to the deterioration of applying robotic fish to practice. In this paper, we propose a novel dynami…
Many machine learning problems can be seen as approximating a *target* distribution using a *particle* distribution by minimizing their statistical discrepancy. Wasserstein Gradient Flow can move particles along a path that minimizes the $f$-divergence between the target and particle distributions.…
The pursuit of 3D reconstruction from 2D images for nanomanipulation under scanning electron microscopy stands as a critical research endeavor. Previous methods either necessitates additional lighting which is difficult in standard SEM devices or relies on feature matching with low resolution and pr…
Non-contact particle cluster manipulation holds significant promise in the realms of advanced manufacturing, chemistry, and pharmacy. However, achieving precise and dynamic control over the spatial kinematics of particle clusters remains a significant challenge, necessitating real-time and accuratel
We introduce Sequential Neural Posterior Score Estimation (SNPSE), a score-based method for Bayesian inference in simulator-based models. Our method, inspired by the remarkable success of score-based methods in generative modelling, leverages conditional score-based diffusion models to generate samp…
Estimating truncated density models is difficult, as these models have intractable normalising constants and hard to satisfy boundary conditions. Score matching can be adapted to solve the truncated density estimation problem, but requires a continuous weighting function which takes zero at the boun…
Density Ratio Estimation (DRE) is an important machine learning technique with many downstream applications. We consider the challenge of DRE with missing not at random (MNAR) data. In this setting, we show that using standard DRE methods leads to biased results while our proposal (M-KLIEP), an adap…
Emotion detection presents challenges to intelligent human-robot interaction (HRI). Foundational deep learning techniques used in emotion detection are limited by information-constrained datasets or models that lack the necessary complexity to learn interactions between input data elements, such as…
The conventional understanding of adversarial training in generative adversarial networks (GANs) is that the discriminator is trained to estimate a divergence, and the generator learns to minimize this divergence. We argue that despite the fact that many variants of GANs were developed following thi…
Noncontact particle manipulation (NPM) shows great application potential than its conventional counterpart particularly in terms of non-invasiveness, and thus has significantly extended robotic manipulation capacity into bio- medical engineering, material science, etc. As NPM by means of electric, m…
Phase-only acoustic holography is a fundamental and promising technique for contactless robotic manipulation. Through independently controlling phase-only hologram (POH) of phase array of transducers (PAT) and simultaneously driving each channel by sophisticated circuits, a certain acoustic field is…
Ultrafast acoustic holography (AH) enabling dynamic contactless micro-nano robotic manipulation has recently attracted wide attention. As an advanced technique, AH encodes specific three-dimensional (3D) acoustic field on a two-dimensional (2D) hologram whereby realizing holographic reconstruction w…
To improve the performance of Automatic Speech Recognition (ASR), it is common to deploy an error correction module at the post-processing stage to correct recognition errors. In this paper, we propose 1) an error correction model, which takes account of both contextual information and phonetic info…
Holographic acoustic field has shown great potential for non-contact robotic manipulations of millimeter or sub-millimeter size objects to effectively deliver acoustic power. The latest technology for generating dynamic holographic acoustic field is through phased transducer array, where relative ph
In this paper, we show the arc length of the optimal ROC curve is an $f$-divergence. By leveraging this result, we express the arc length using a variational objective and estimate it accurately using positive and negative samples. We show this estimator has a non-parametric convergence rate $O_p(n^…
Acoustic holography is a newly emerging and promising technique to dynamically generate arbitrary desired holographic acoustic field in 3D space for contactless robotic manipulation. The latest technology supporting complex dynamic holographic acoustic field reconstruction is through phased transduc…
We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated through the Bayesian framework with a specific pri…
Video-Text Retrieval has been a hot research topic with the growth of multimedia data on the internet. Transformer for video-text learning has attracted increasing attention due to its promising performance. However, existing cross-modal transformer approaches typically suffer from two major limitat…
Fake news brings us panic and misunderstanding against the truth, especially under some unusual circumstances, such as the outbreak of COVID-19. It’s crucial to detect fake news on social media early to avoid further propagation. Previous methods manually label the stances implied in post-reply pair…
Simultaneous assembly of multiple objects is a key technology to form solid connections among objects to get compact structures in precision assembly and micro-assembly. Dramatically different from traditional assembly of two objects, the interaction among multiple objects is more complicated on ana…
Maximum Likelihood Estimators (MLE) has many good properties. For example, the asymptotic variance of MLE solution attains equality of the asymptotic Cram{\'e}r-Rao lower bound (efficiency bound), which is the minimum possible variance for an unbiased estimator. However, obtaining such MLE solution…
Nowadays, many problems require learning a model from data owned by different participants who are restricted to share their examples due to privacy concerns, which is referred to as multiparty learning in the literature. In conventional multiparty learning, a global model is usually trained from sc…
Density ratio estimation is a vital tool in both machine learning and statistical community. However, due to the unbounded nature of density ratio, the estimation proceudre can be vulnerable to corrupted data points, which often pushes the estimated ratio toward infinity. In this paper, we present a…
We learn the structure of a Markov Network between two groups of random variables from joint observations. Since modelling and learning the full MN structure may be hard, learning the links between two groups directly may be a preferable option. We introduce a novel concept called the \emphpartition…