Yuzhe Zhang, Feiran Liu, Yi Shan, Xinyi Huang, Xin Yang, Yueqi Zhu, Xuxin Cheng, Cao Liu, Ke Zeng, Terry Jingchen Zhang, Wenyuan Jiang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Yuzhe Zhang, Xianwei Xue, Xingyong Wu, Mengke Chen, Chen Liu, Xinran He, Run Shao, Feiran Liu, Huanmin Xu, Qiutong Pan, Haiwei Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Adverse weather (e.g., rain, snow, and haze) degrades visual content and undermines downstream multimedia retrieval and object recognition. While recent restoration paradigms utilizing vision transformers and diffusion models have achieved remarkable perceptual quality, their prohibitive computational cost and latency render them unsuitable for low-latency large-scale multimedia stream processing. To bridge the gap between restoration quality and system efficiency, we propose the Hybrid State Space Network (HSS-Net), an efficient preprocessing solution tailored for multimedia understanding systems. In this work, we innovatively construct a novel Hybrid State Space Block (HSSB) by integrating Mamba-based State Space Models (SSMs) into a unified restoration framework. This block leverages the linear complexity of SSMs to model long-range degradation contexts, complemented by a parallel Texture-Aware Gated Module (TAGM) for precise local texture recovery. Furthermore, to address scale variations in multimedia objects, we design a Cross-Scale Feature Fusion Module (CFFM) to enhance semantic feature representation. Extensive experiments demonstrate that HSS-Net achieves state-of-the-art restoration performance. More importantly, with only 12.1M parameters, it significantly boosts downstream object detection accuracy while maintaining low-latency inference speeds, positioning it as an optimal solution for efficient, edge-deployed multimedia analysis. The code will be available at: https://github.com/Moonlitwine/HSS-Net.

Artificial histopathological image classification is emerging as a promising approach for assisting early cancer diagnosis and informing treatment planning. Although convolutional neural network (CNN) based methods have achieved significant success, they still struggle to capture the rich multi-scale and high-frequency details inherent in pathological tissues. To address the above issue, we propose WaveConvX, a novel histopathological image classification model that integrates multi-level wavelet decomposition with the ConvNeXt backbone. Our approach applies a two-stage discrete wavelet transform (DWT) to intermediate feature maps, enabling the decomposition of features into multiple frequency sub-bands. High-frequency components are enhanced using Adaptive Power Gabor Convolution (APGConv), while mid-frequency ranges are refined through tailored attention mechanisms. These processed sub-bands are then fused via inverse wavelet transforms, producing feature maps enriched with both global context and local morphological cues essential for accurate diagnosis. Comprehensive experiments are conducted on three public datasets (BreakHis for breast cancer, KBSMC for gastric cancer, and LC25000 for lung and colon cancer). The results demonstrate that WaveConvX consistently outperforms ten state-of-the-art benchmarks, achieving superior accuracy, F1 scores, and robustness across multiple types and magnifications of cancer. Our work demonstrates the significant potential of wavelet-enhanced CNNs in histopathological image analysis.

In this paper, we propose a frequency response masking (FRM)-based wideband channelizer for passive detection and jamming integrated (PDJI) processor to achieve high sensitivity and the ability to simultaneously jam multiple signals. The FRM method is used to design the prototype filter with narrow transition bandwidth (NTB) in order to acquire low complexity. The prototype filter with NTB is decomposed into the polyphase filters of the channelized receiver and channelized transmitter structures by polyphase decomposition. A hardware circuit design of the PDJI processor based on radio frequency system on chip (RFSoC) is presented, which has 5-channel receiving and 1-channel transmitting capability. The proposed FRM-based wideband channelizer structure for PDJI processor is implemented on Xilinx Zynq UltraScale+ XCZU47DR chip. Experimental results demonstrate that, compared to the non-maximally decimated digital channelizer and generalized polyphase digital channelizer, the proposed FRM-based wideband channelizer offers 21.73% and 23.23% reduction in multiplier consumption, and 18.66% and 19.76% reduction in chip power respectively under single channel receiving and single channel transmitting conditions.

With the rise of artificial intelligence and big data applications, the general-purpose Von Neumann architecture is no longer capable of fulfilling the requirements of these application scenarios. The large amount of parallelizable and repeatable multiply-and-accumulate (MAC) operations in deep neural networks provide the possibility for the emergence of storage-computing integrated architectures. Current-based computation and quantization are employed to circumvent signal margin limitations on the power supply voltage of the computing unit, thereby facilitating low-power design. The proposed design is a computing-in-memory (CIM) circuit based on current sampling accumulation and applies a current-sensing analog-to-digital converter design that exhibits reduced sensitivity to parasitic capacitance compared to voltage-based analog-to-digital converters. Its power consumption is proportional to the input current, achieving higher area efficiency and energy efficiency gains. The design of the CIM circuit based on the current sampling in the 22-nm FDSOI process is fabricated with an area efficiency of 264 GOPS/mm2. The peak energy efficiency is 20.81 TOPS/W, and the inference accuracy reaches 92.11% when employed to VGG-16 under CIFAR-10 dataset.

In this paper, we propose a method to design generalized frequency response masking (FRM)-based P-L band multi-channel channelizers for array signal processing system (ASPS), which have sharp transition bandwidth (STB) and low complexity. The order of the prototype filter with STB designed by FRM method is smaller than that of the direct filter design method. The design approach of the proposed generalized FRM-based multi-channel channelizers is presented which solves the problem of high computational complexity of digital channelizer with STB. The proposed generalized FRM-based digital channelizer, which have the characteristics of unification and flexible configuration, is suitable for real signal, complex signal, odd-stacked, even-stacked, maximally decimation and non-maximally decimation structures. The generalized FRM-based P-L band multi-channel channelizers for ASPS is implemented in a Xilinx Kintex UltraScale XCKU115 processor. Experimental results show that the proposed generalized FRM-based digital channelizer offers 84.57% and 76.24% reduction in multipliers complexity and chip power respectively, which is less than conventional polyphase digital channelizer (PDC).