Colorectal cancer (CRC) remains a persistent major global health burden, with traditional diagnostic methods like colonoscopy suffering from suboptimal patient compliance rates. This study develops an intelligent diagnostic model based on tongue images to assist in CRC diagnosis, leveraging the integrative potential of traditional tongue diagnosis and modern machine learning. Between June 2023 and July 2024, we collected and processed 1,389 tongue images from CRC patients and 1,543 from non-colorectal cancer (NCRC) participants. Our methodology combines innovative image segmentation using the Segment Anything Model (SAM) with Grounding DINO, extracts both hand-crafted features (color, texture, shape) and deep learning features via Swin-Transformer, and employs feature fusion and selection techniques. The diagnostic model achieves an accuracy of 87.93% (F1-score: 0.9072) in internal validation. In an independent external cohort of 119 CRC patients and 221 NCRC participants, it demonstrates 85.18% precision (recall: 85%, F1-score: 0.8507). This non-invasive, cost-effective approach demonstrates significant potential as a complementary screening tool for CRC, particularly in regions with limited access to conventional diagnostic resources.

Objective: To objectively measure Traditional Chinese Medicine (TCM) constitution by moving beyond local features and Body Mass Index (BMI), which only reflects general obesity, through the analysis of overall body shape features. Methods: A deep learning-based parametric 3D human reconstruction approach (PARE) was employed to extract ten body shape parameters (ß0-ß9) from clinical data of 129 subjects. The associations between these parameters and TCM constitution were analyzed using Shapley decomposition to assess their individual contributions. Results: Significant correlations were identified between six TCM constitutions (Phlegm-dampness, Special, Damp-heat, Qi-deficiency, Yin-deficiency, and Yang-deficiency) and body shape parameters. Shapley decomposition quantified the contribution levels of each morphological parameter to the formation of these constitutions. Conclusions: This study provides preliminary insights into the connections between body shape features and TCM constitution using 3D human reconstruction technology, introducing a novel method and perspective for understanding their interplay and advancing the objectification and automation of TCM constitution identification.

This paper presents a video coding scheme that combines traditional optimization methods with deep learning methods based on the Enhanced Compression Model (ECM). In order to achieve better subjective quality, we adjust the quantization parameter (QP) of every coding tree unit (CTU) adaptively according to the spatial-temporal perception information and block importance. In addition, the QP of key frame is individually adjusted according to the video content characteristics. Meanwhile, the deep learning methods propose a convolutional neural network-based loop filter (CNNLF), which is turned on/off based on the rate-distortion optimization at the CTU and frame level. Besides, intra-prediction using neural networks (NN-intra) is proposed to further improve compression quality, where 8 neural networks are used for predicting blocks of different sizes. On the validation set of the Challenge for Learning Image Compression (CLIC), the proposed method achieves remarkable higher compression quality than the conventional ECM 3.0 in terms of PSNR and subjective evaluation.

This paper investigates the downlink channel state information (CSI) sensing in 5G heterogeneous networks composed of user equipments (UEs) with different feedback capabilities. We aim to enhance the CSI accuracy of UEs only affording the low-resolution Type-I codebook. While existing works have demonstrated that the task can be accomplished by solving a phase retrieval (PR) formulation based on the feedback of precoding matrix indicator (PMI) and channel quality indicator (CQI), they need many feedback rounds. In this paper, we propose a novel CSI sensing scheme that can significantly reduce the feedback overhead. Our scheme involves a novel parameter dimension reduction design by exploiting the spatial consistency of wireless channels among nearby UEs, and a constrained PR (CPR) formulation that characterizes the feasible region of CSI by the PMI information. To address the computational challenge due to the non-convexity and the large number of constraints of CPR, we develop a two-stage algorithm that firstly identifies and removes inactive constraints, followed by a fast first-order algorithm. The study is further extended to multi-carrier systems. Extensive tests over DeepMIMO and QuaDriGa datasets showcase that our designs greatly outperform existing methods and achieve the high-resolution Type-II codebook performance with a few rounds of feedback.

In order to control the air pollution in China, the Chinese government issued an air pollution control action plan in Sep. 2013. The goal of the first phase of the plan is by 2017, the air quality was to improve significantly compared to 2012. In this study, the satellite aerosol product derived from the MODerate-resolution Imaging Spectroradiometer (MODIS) over East China were collected and processed to evaluate the effectiveness of the plan. The results show that the mean aerosol optical depth (AOD) over East China in 2017 has reduced about 23.17% compared to 2012, and the trends of the MODIS AOD during the period from 2010 to 2019 over the domain is -0.012 per year, which indicates that the air pollution plan has been well implemented to achieve the expected goals.

Recently, deep learning technology has made significant progresses in high efficiency video coding (HEVC), especially in in-loop filter. In this paper, we propose a dense inception attention network (DIA_Net) to delve into image information and model capacity. The DIA_Net contains multiple inception blocks which have different size kernels so as to dig out various scales information. Meanwhile, attention mechanism including spatial attention and channel attention is utilized to fully exploit feature information. Further we adopt a dense residual structure to deepen the network. We attach DIA_Net to the end of in-loop filter part in HEVC as a post-processor and apply it to luma components. The experimental results demonstrate the proposed DIA_Net has remarkable improvement over the standard HEVC. With all-intra(AI) and random access(RA) configurations, It achieves 8.2% bd-rate reduction in AI configuration and 5.6% bd-rate reduction in RA configuration.

Password-based authenticated key exchange (PAKE) protocol has been widely used in practice, since it is convenient for users. However, the easy-to-remember property of the password also brings security problem. In this paper, we show there is an off-line dictionary attack in an efficient PAKE protocol when the smart card is lost. In order to resist the attack, we give a countermeasure to improve it. The countermeasure makes a simple change to the original protocol which does not affect the efficiency of the protocol.

A novel algorithm that combines LASSO regression and decision tree is proposed to explore the correlation of adverse events (AE) and genetic polymorphism of CYP2D6*2, *10, *14, CYP1A2*1C, *1F in human subjects in a clinical trial. The genotypes of 30 healthy human subjects in a clinical trial for a natural herbal drug and 53 subjects in the blank group were detected by polymerase chain reaction (PCR) and DNA sequencing. The AEs occurring during the trial were recorded. The correlations of AE and genetic polymorphism are analyzed by the new combined algorithm. 53 AEs are reported in the end of the study. Five gene subtypes are selected as correlative factors to the specific AEs by the new algorithm: wild type of CYP1A2*1F and abnormal platelet counting, homozygous CYP1A2*1C and abnormal fibrinogen, heterozygous CYP1A2*1C and abnormal blood chlorine, heterozygous CYP1A2*1C and abnormal urobilinogen, wild type of CYP2D6*2 and abnormal APTT (activated partial thromboplastin time). The result indicates the novel algorithm is effective and is able to detect the correlation of AEs and genetic polymorphism in clinical trials.