17 accepted papers
Differentially Private Stochastic Gradient Descent (DPSGD) is widely used to protect sensitive data during the training of machine learning models, but its privacy guarantee often comes at a large cost of model performance due to the lack of tight theoretical bounds quantifying privacy loss. While r
Continuous ECG monitoring is clinically valuable, but scaling it beyond electrodes to comfortable long-term use motivates contactless mmWave sensing. In practice, mmWave-to-ECG reconstruction is severely constrained by the scarcity of high-quality synchronized recordings and poor cross-subject gener…
Clear and high-quality underwater images are essential for marine applications, including autonomous navigation, ecological monitoring, and infrastructure inspection. However, underwater images typically suffer from severe colour distortion, low contrast, and diminished structural visibility due to
As a vital task in healthcare, combinatorial medication recommendation aims to generate drug combinations tailored to patient health status. Precisely capturing the rich semantic information within clinical narratives is crucial for achieving this goal. However, existing approaches primarily rely on
Accurate and interpretable multi-disease diagnosis remains a critical challenge in medical research, particularly when leveraging heterogeneous multimodal medical data. Current approaches often rely on single-modal data, limiting their ability to comprehensively understand complex diseases. To addre
EEG foundation models aim to learn transferable representations, yet EEG recordings are dominated by high-frequency noise and large cross-subject variability. Existing pretraining strategies such as masked autoencoding or autoregressive modeling often treat waveform reconstruction as the learning si…
Nowcasting forms the first line of defense against rapidly evolving weather hazards, where even minutes of delay can lead to severe societal impacts. However, existing systems predominantly extrapolate 2D radar reflectivity, which struggles under rapid intensification regimes. We introduce \N, a mul…
Continuous cardiac monitoring during sleep is vital for detecting silent arrhythmia and other nocturnal cardiac events. While electrocardiogram (ECG) is the clinical gold standard, its reliance on electrodes and physical contact makes it intrusive for daily long-term use. Millimeter-wave (mmWave) ra
Large language models (LLMs) have received lots of attention for their impressive performance in in-context dialogues and their potential to revolutionize service industries with a new business model, Model-as-a-Service (MaaS). Automated data labeling is a natural and promising service. However, lab…
In federated learning (FL), models must \emph{converge quickly} under tight communication budgets while \emph{generalizing} across non-IID client distributions. These twin requirements have naturally led to two widely used techniques: client/server \emph{momentum} to accelerate progress, and \emph{s…
Global aging necessitates tele-monitoring systems to provide real-time tracking and timely assistance for older adults living independently. While pervasive wireless devices (e.g., CSI, IMU, UWB) enable cost-effective, non-intrusive monitoring, existing systems lack flexibility, limiting their adapt
Cross-Document Multi-entity question answering (MEQA) demands the integration of scattered information across documents to resolve complex queries involving entities, relationships, and contextual dependencies. Although Large Language Models (LLMs) and Retrieval-augmented Generation (RAG) systems sh
Periodic or quasi-periodic phenomena reveal intrinsic characteristics in various natural processes, such as weather patterns, movement behaviors, traffic flows, and biological signals. Given that these phenomena span multiple modalities, the capabilities of Multimodal Large Language Models (MLLMs) o…
Robust and unobtrusive in-vehicle physiological monitoring is crucial for ensuring driving safety and user experience. While remote physiological measurement (RPM) offers a promising non-invasive solution, its translation to real-world driving scenarios is critically constrained by the scarcity of c…
Remote Photoplethysmography (rPPG) enables non-contact extraction of physiological signals, providing significant advantages in medical monitoring, emotion recognition, and face anti-spoofing. However, the extraction of reliable rPPG signals is hindered by motion variations in real-world environment…
Electrocardiography (ECG) for objective cognitive load estimation gained increasing attention, and offers a more feasible and non-invasive alternative to traditional methods such as electroencephalography (EEG). Despite the promise of ECG signal, application in real-world scenarios is hampered by th…
Goal-oriented dialog policy learning algorithms aim to learn a dialog policy for selecting language actions based on the current dialog state. Deep reinforcement learning methods have been used for dialog policy learning. This work is motivated by the observation that, although dialog is a domain wi…