Mo Li

Qitai Tan, Yiyun Chen, Mo Li et al.

Recent advances in deep learning have driven rapid progress in time series forecasting, yet many state-of-the-art models continue to struggle with robust performance in real-world applications, even when they achieve strong results on standard benchmark datasets. This persistent gap can be attributed to the black-box nature of deep learning architectures and the inherent limitations of current evaluation frameworks, which frequently lack the capacity to provide clear, quantitative insights into the specific strengths and weaknesses of different models, thereby complicating the selection of appropriate models for particular forecasting scenarios. To address these issues, we propose a synthetic data-driven evaluation paradigm, SynTSBench, that systematically assesses fundamental modeling capabilities of time series forecasting models through programmable feature configuration. Our framework isolates confounding factors and establishes an interpretable evaluation system with three core analytical dimensions: (1) temporal feature decomposition and capability mapping, which enables systematic evaluation of model capacities to learn specific pattern types; (2) robustness analysis under data irregularities, which quantifies noise tolerance thresholds and anomaly recovery capabilities; and (3) theoretical optimum benchmarking, which establishes performance boundaries for each pattern type-enabling direct comparison between model predictions and mathematical optima. Our experiments show that current deep learning models do not universally approach optimal baselines across all types of temporal features.The code is available at https://github.com/TanQitai/SynTSBench

Xuesong Wang, Mo Li, Xingyan Shi et al.

Semantic communication enhances transmission efficiency by conveying semantic information rather than raw input symbol sequences. Task-oriented semantic communication is a variant that tries to retains only task-specific information, thus achieving greater bandwidth savings. However, these neural-based communication systems are vulnerable to model inversion attacks, where adversaries try to infer sensitive input information from eavesdropped transmitted data. The key challenge, therefore, lies in preserving privacy while ensuring transmission correctness and robustness. While prior studies typically assume that adversaries aim to fully reconstruct the raw input in task-oriented settings, there exist scenarios where pixel-level metrics such as PSNR or SSIM are low, yet the adversary's outputs still suffice to accomplish the downstream task, indicating leakage of sensitive information. We therefore adopt the attacker's task accuracy as a more appropriate metric for evaluating attack effectiveness. To optimize the gap between the legitimate receiver's accuracy and the adversary's accuracy, we propose DiffSem, a diffusion-aided framework for task-oriented semantic communication. DiffSem integrates a transmitter-side self-noising mechanism that adaptively regulates semantic content while compensating for channel noise, and a receiver-side diffusion U-Net that enhances task performance and can be optionally strengthened by self-referential label embeddings. Our experiments demonstrate that DiffSem enables the legitimate receiver to achieve higher accuracy, thereby validating the superior performance of the proposed framework.

Xiaomin Zhou, Chen Li, Md Mamunur Rahaman et al.

Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.