22 accepted papers
As an essential component of fine-tuning, warm-up plays a crucial role in promoting stability and generalization. Many studies have examined its underlying mechanisms from different aspects. However, most of the studies focus on incorporating these insights into optimizers to reduce the reliance on
Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the development of scientific foundation models (FMs). However, applying this capability to experimental particle physics is challeng…
Creative story generation has achieved notable progress with large language models. Current methods construct narratives through hierarchical planning or incremental expansion. These approaches produce structurally complete stories but offer limited support for organic narrative development. Many fi
This paper presents the mechatronic design and implementation of a hybrid-driven disc-shaped autonomous underwater vehicle (HD-AUV). The hybrid-driven system integrates a buoyancy adjustment system and propeller thrusters, enabling the HD-AUV to achieve both high maneuverability motion and energy-ef
Point cloud registration can be categorized into rigid and non-rigid settings depending on the motion characteristics of the underlying objects. Rigid alignment assumes a single global transformation under which corresponding points remain geometrically consistent across scales, whereas non-rigid al…
Optimizers are critical for training deep neural networks. Existing training processes rely on a single static optimizer (e.g., SGD) or a simple hybrid of two optimizers, which miss the opportunity to exploit evolving dynamics in different training states, degrading model quality and convergence. In…
With the accelerated development of Industry 4.0, intelligent manufacturing systems increasingly require efficient task allocation and scheduling in multi-robot systems. However, existing methods rely on domain expertise and face challenges in adapting to dynamic production constraints. Additionally
Ensuring safe and socially compliant driving is essential for autonomous vehicle planning. However, one of the significant challenges remains the performance bottleneck caused by interaction uncertainty in complex traffic scenarios. Traditional planning algorithms typically account for all traffic p
Accurate localization plays an important role in high-level autonomous driving systems. Conventional map matching-based localization methods solve the poses by explicitly matching map elements with sensor observations, generally sensitive to perception noise, therefore requiring costly hyperparamete
Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples. Novelty manifests in HAR as unseen samples, activities, objects, environments, and sensor ch
Scientific innovation relies on detailed workflows, which include critical steps such as contextualizing literature, generating ideas, validating ideas, interpreting results, and planning new research. Scientific publications that document these workflows are extensive and unstructured, making it di…
Recent research leveraging large-scale pretrained diffusion models has demonstrated the potential of using diffusion features to establish semantic correspondences in images. Inspired by advancements in diffusion-based techniques, we propose a novel zero-shot method for refining point cloud registra…
The vehicle dynamics model serves as a vital component of autonomous driving systems, as it describes the temporal changes in vehicle state. Traditional physics-based methods employ mathematical formulae to model vehicle dynamics, but they are unable to adequately describe complex vehicle systems du
Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level abilities beyond mere memorization and the handli…
Scientific literature understanding is crucial for extracting targeted information and garnering insights, thereby significantly advancing scientific discovery. Despite the remarkable success of Large Language Models (LLMs), they face challenges in scientific literature understanding, primarily due…
Electrocardiogram (ECG) signals are crucial indicators of various human physiological states. A thorough analysis of these signals is indispensable for applications such as disease prediction, mental stress assessment, and other medical diagnostics. Despite the rapid progress in large language model…
The prompt-based method has been proven effective in improving the performance of pre-trained language models (PLMs) on sentence-level few-shot tasks. However, when applying prompting to token-level tasks such as Named Entity Recognition (NER), specific templates need to be designed, and all possibl…
High-precision vehicle localization with commercial setups is a crucial technique for high-level autonomous driving tasks. As a newly emerged approach, monocular localization in LiDAR map achieves promising balance between cost and accuracy, but estimating pose by finding correspondences between suc
Depth completion plays a crucial role in autonomous driving, in which cameras and LiDARs are two complementary sensors. Recent approaches attempt to exploit spatial geometric constraints hidden in LiDARs to enhance image-guided depth completion. However, only low efficiency and poor generalization c…
The Shapley value is widely regarded as a trustworthy attribution metric. However, when people use Shapley values to explain the attribution of input variables of a deep neural network (DNN), it usually requires a very high computational cost to approximate relatively accurate Shapley values in real…
This paper introduces a new data augmentation method for neural machine translation that can enforce stronger semantic consistency both within and across languages. Our method is based on Conditional Masked Language Model (CMLM) which is bi-directional and can be conditional on both left and right c…
People used to take a series of nearly identical photos about the same subject, but it is usually a tedious chore to select the reversed ones from them. Despite the remarkable progress, most existing studies on image aesthetics assessment fail to fulfill the task of series photo selection. In this p…