The increasing complexity of distributed robotics has driven the need for platforms that seamlessly integrate edge, fog, and cloud computing layers while meetin...
As multimodal and AI-driven services exchange hundreds of megabytes per request, existing IPC runtimes spend a growing share of CPU cycles on memory copies. Alt...
Local search is a fundamental method in operations research and combinatorial optimisation. It has been widely applied to a variety of challenging problems, inc...
We study the discrete-to-continuum consistency of the training of shallow graph convolutional neural networks (GCNNs) on proximity graphs of sampled point cloud...
Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inferenc...
Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents. However, existing approaches primarily rely on binar...
Automated seizure detection from electroencephalography (EEG) remains difficult due to the large variability of seizure dynamics across patients, recording cond...
We develop a practical framework for distinguishing diffusive stochastic processes from deterministic signals using only a single discrete time series. Our appr...
Recent advancements in Large Language Model (LLM) agents have enabled complex multi-turn agentic tasks requiring extensive tool calling, where conversations can...
Large language models (LLMs) often fail to learn effective long chain-of-thought (Long CoT) reasoning from human or non-Long-CoT LLMs imitation. To understand t...
In safety-critical domains, linguistic ambiguity can have severe consequences; a vague command like 'Pass me the vial' in a surgical setting could lead to catas...
Representing networks as a graph and training a link prediction model using benign connections is an effective method of anomaly-based intrusion detection. Exis...
Deepfake detection systems deployed in real-world environments are subject to adversaries capable of crafting imperceptible perturbations that degrade model per...
The rapid deployment of Internet of Things (IoT) devices has led to large-scale sensor networks that monitor environmental and urban phenomena in real time. Com...
Deformable multi-contrast image registration is a challenging yet crucial task due to the complex, non-linear intensity relationships across different imaging c...
As emerging applications demand higher throughput and lower latencies, operators are increasingly deploying millimeter-wave (mmWave) links within x-haul transpo...
We propose DeePM (Deep Portfolio Manager), a structured deep-learning macro portfolio manager trained end-to-end to maximize a robust, risk-adjusted utility. De...
Recent advances in video generation have been dominated by diffusion and flow-matching models, which produce high-quality results but remain computationally int...
We propose a framework that amortizes the cost of inference-time reasoning by converting transient critiques into retrievable guidelines, through a file-based m...
The constrained combinatorial multi-armed bandit model has been widely employed to solve problems in wireless networking and related areas, including the proble...
Federated Domain Generalization (FDG) aims to collaboratively train a global model across distributed clients that can generalize well on unseen domains. Howeve...
Active learning (AL) plays a critical role in materials science, enabling applications such as the construction of machine-learning interatomic potentials for a...
Domain-generalized retinal vessel segmentation is critical for automated ophthalmic diagnosis, yet faces significant challenges from domain shift induced by non...
Hallucinations, generating responses inconsistent with the visual input, remain a critical limitation of large vision-language models (LVLMs), especially in ope...
