arXiv subjects

Wenhai Zhang

Publications and source records attributed to Wenhai Zhang.

4 recordsLinked to original sources

arXiv2026

EdgeFM: Efficient Edge Inference for Vision-Language Models

Vision-language models (VLMs) have demonstrated strong applicability in edge industrial applications, yet their deployment remains severely constrained by requirements for deterministic low latency and stable execution under resource limitations. Existing frameworks either rely on bloated general-purpose designs or force developers into opaque, hardware-specific closed-source ecosystems, leading to hardware lock-in limitation and poor cross-platform adaptability. Observing that modern AI agents can efficiently search and tune configurations to generate highly optimized low-level kernels for standard LLM operators, we propose EdgeFM, a lightweight, agent-driven VLM/LLM inference framework tailored for cross-platform industrial edge deployment. EdgeFM removes non-essential features to reduce single-request latency, and encapsulates agent-tuned kernel optimizations as a modular library of reusable skills. By allowing direct invocation of these skills rather than waiting for closed-source implementations, it effectively closes the performance gap long dominated by proprietary toolchains. The framework natively supports mainstream platforms including x86 and NVIDIA Orin SoCs, and represents the first end-to-end VLA deployment on the domestic Horizon Journey platform, enhancing cross-platform portability. In most cases, it yields clearly better inference performance than conventional vendor-specific toolchains, achieving up to 1.49 times speedup over TensorRT-Edge-LLM on the NVIDIA Orin platform. Experimental results show that EdgeFM delivers favorable end-to-end inference performance, providing an open-source, production-grade solution for diverse edge industrial scenarios.

cs.CV↗

arXiv2023

Guidelines in Wastewater-based Epidemiology of SARS-CoV-2 with Diagnosis

With the global spread and increasing transmission rate of SARS-CoV-2, more and more laboratories and researchers are turning their attention to wastewater-based epidemiology (WBE), hoping it can become an effective tool for large-scale testing and provide more ac-curate predictions of the number of infected individuals. Based on the cases of sewage sampling and testing in some regions such as Hong Kong, Brazil, and the United States, the feasibility of detecting the novel coronavirus in sewage is extremely high. This study re-views domestic and international achievements in detecting SARS-CoV-2 through WBE and summarizes four aspects of COVID-19, including sampling methods, virus decay rate cal-culation, standardized population coverage of the watershed, algorithm prediction, and provides ideas for combining field modeling with epidemic prevention and control. Moreover, we highlighted some diagnostic techniques for detection of the virus from sew-age sample. Our review is a new approach in identification of the research gaps in waste water-based epidemiology and diagnosis and we also predict the future prospect of our analysis.

q-bio.QM↗

arXiv2020

Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might convert to the severe stage, for designing effective treatment plan and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time, and if yes, predict the possible conversion time that the patient would spend to convert to the severe stage. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of high-dimensional data and learn the shared information across the classification task and the regression task. To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 422 chest computed tomography (CT) scans, where 52 cases were converted to severe on average 5.64 days and 34 cases were severe at admission. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.

eess.IV↗

arXiv2014

A novel hohlraum with ultrathin depleted-uranium-nitride coating layer for low hard x-ray emission and high radiation temperature

An ultra-thin layer of uranium nitrides (UN) has been coated on the inner surface of the depleted uranium hohlraum (DUH), which has been proved by our experiment can prevent the oxidization of Uranium (U) effectively. Comparative experiments between the novel depleted uranium hohlraum and pure golden (Au) hohlraum are implemented on Shenguang III prototype laser facility. Under the laser intensity of 6*10^14 W/cm2, we observe that, the hard x-ray (> 1.8 keV) fraction of this uranium hohlraum decreases by 61% and the peak intensity of total x-ray flux (0.1 keV ~ 5.0 keV) increases by 5%. Radiation hydrodynamic code LARED is used to interpret the above observations. Our result for the first time indicates the advantage of the UN-coated DUH in generating the uniform x-ray field with a quasi Planckian spectrum and thus has important implications in optimizing the ignition hohlraum design.

physics.plasm-ph↗