Latest AI and machine learning research in medicare for healthcare professionals.
Hyperspectral imaging provides detailed spectral information and holds significant potential for monitoring of greenhouse gases (GHGs). However, its application is constrained by limited spatial coverage and infrequent revisit times. In contrast, multispectral imaging offers broader spatial and temporal coverage but often lacks the spectral detail that can enhance GHG detection. To address these...
A key advantage of Recurrent Neural Networks (RNNs) over Transformers is their linear computational and space complexity enables faster training and inference for long sequences. However, RNNs are fundamentally unable to randomly access historical context, and simply integrating attention mechanisms may undermine their efficiency advantages. To overcome this limitation, we propose \textbf{H}iera...
Recent reasoning models through test-time scaling have demonstrated that long chain-of-thoughts can unlock substantial performance boosts in hard re...
We introduce Eagle 2.5, a family of frontier vision-language models (VLMs) for long-context multimodal learning. Our work addresses the challenges i...
Heterogeneous treatment effect estimation in high-stakes applications demands models that simultaneously optimize precision, interpretability, and c...
A robot navigating an outdoor environment with no prior knowledge of the space must rely on its local sensing to perceive its surroundings and plan....
Testing Android apps effectively requires a systematic exploration of the app's possible states by simulating user interactions and system events. W...
Clinical trials are crucial for assessing new treatments; however, recruitment challenges - such as limited awareness, complex eligibility criteria,...
Accurate, detailed, and high-frequent bathymetry is crucial for shallow seabed areas facing intense climatological and anthropogenic pressures. Curr...
There is a long history of building predictive models in healthcare using tabular data from electronic medical records. However, these models fail t...
Knowledge editing has emerged as an effective approach for updating large language models (LLMs) by modifying their internal knowledge. However, the...
High-definition (HD) maps, particularly those containing lane-level information regarded as ground truth, are crucial for vehicle localization resea...
Many real-world applications of flow-based generative models desire a diverse set of samples that cover multiple modes of the target distribution. H...
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-co...
This work proposes a jointly optimized trajectory generation and camera control approach, enabling an autonomous agent, such as an unmanned aerial v...
Long-range dependencies are critical for understanding genomic structure and function, yet most conventional methods struggle with them. Widely adop...
Robotic weed removal in precision agriculture introduces a repetitive heterogeneous task planning (RHTP) challenge for a mobile manipulator. RHTP ha...
This paper presents an adaptive path planner for object search in agricultural fields using UAVs. The path planner uses a high-altitude coverage fli...
Long-form video processing fundamentally challenges vision-language models (VLMs) due to the high computational costs of handling extended temporal ...
Efficiently understanding long-form videos remains a significant challenge in computer vision. In this work, we revisit temporal search paradigms fo...