MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis

Coronavirus disease 2019 (COVID-19) has spread all over the world for three years, but medical facilities in many areas still aren't adequate. There is a need for rapid COVID-19 diagnosis to identify high-risk patients and maximize the use of limited medical resources. Motivated by this fact, we proposed the deep learning framework MEDNC for automatic prediction and diagnosis of COVID-19 using computed tomography (CT) images. Our model was trained using two publicly available sets of COVID-19 data. And it was built with the inspiration of transfer learning. Results indicated that the MEDNC greatly enhanced the detection of COVID-19 infections, reaching an accuracy of 98.79% and 99.82% respectively. We tested MEDNC on a brain tumor and a blood cell dataset to show that our model applies to a wide range of problems. The outcomes demonstrated that our proposed models attained an accuracy of 99.39% and 99.28%, respectively. This COVID-19 recognition tool could help optimize healthcare resources and reduce clinicians' workload when screening for the virus

VisorGPT: Learning Visual Prior via Generative Pre-Training

Various stuff and things in visual data possess specific traits, which can be learned by deep neural networks and are implicitly represented as the visual prior, e.g., object location and shape, in the model. Such prior potentially impacts many vision tasks. For example, in conditional image synthesis, spatial conditions failing to adhere to the prior can result in visually inaccurate synthetic results. This work aims to explicitly learn the visual prior and enable the customization of sampling. Inspired by advances in language modeling, we propose to learn Visual prior via Generative Pre-Training, dubbed VisorGPT. By discretizing visual locations of objects, e.g., bounding boxes, human pose, and instance masks, into sequences, VisorGPT can model visual prior through likelihood maximization. Besides, prompt engineering is investigated to unify various visual locations and enable customized sampling of sequential outputs from the learned prior. Experimental results demonstrate that VisorGPT can effectively model the visual prior, which can be employed for many vision tasks, such as customizing accurate human pose for conditional image synthesis models like ControlNet. Code will be released at https://github.com/Sierkinhane/VisorGPT.Comment: Project web-page: https://sierkinhane.github.io/visor-gpt

ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.Comment: To appear in VLDB 2024.Code: https://github.com/17000cyh/IMDiffusion.gi

Laser solid-phase synthesis of single-atom catalysts

From Springer Nature via Jisc Publications RouterHistory: received 2021-01-22, rev-recd 2021-06-25, registration 2021-07-19, accepted 2021-07-19, pub-electronic 2021-08-18, online 2021-08-18, collection 2021-12Publication status: PublishedAbstract: Single-atom catalysts (SACs) with atomically dispersed catalytic sites have shown outstanding catalytic performance in a variety of reactions. However, the development of facile and high-yield techniques for the fabrication of SACs remains challenging. In this paper, we report a laser-induced solid-phase strategy for the synthesis of Pt SACs on graphene support. Simply by rapid laser scanning/irradiation of a freeze-dried electrochemical graphene oxide (EGO) film loaded with chloroplatinic acid (H2PtCl6), we enabled simultaneous pyrolysis of H2PtCl6 into SACs and reduction/graphitization of EGO into graphene. The rapid freezing of EGO hydrogel film infused with H2PtCl6 solution in liquid nitrogen and the subsequent ice sublimation by freeze-drying were essential to achieve the atomically dispersed Pt. Nanosecond pulsed infrared (IR; 1064 nm) and picosecond pulsed ultraviolet (UV; 355 nm) lasers were used to investigate the effects of laser wavelength and pulse duration on the SACs formation mechanism. The atomically dispersed Pt on graphene support exhibited a small overpotential of −42.3 mV at −10 mA cm−2 for hydrogen evolution reaction and a mass activity tenfold higher than that of the commercial Pt/C catalyst. This method is simple, fast and potentially versatile, and scalable for the mass production of SACs