As the dimensionality of the decision space in multi-objective optimization problems increases, the decision space expands exponentially, presenting significant challenges to the search efficiency of traditional multi-objective evolutionary algorithms in large-scale multi-objective optimization problems. To quickly locate promising search regions in the vast decision space, this paper proposes utilizing denoising diffusion probabilistic models to generate promising solutions, based on which a novel elite-guided large-scale multi-objective evolutionary algorithm is introduced. Specifically, in our proposed method, the population is divided into elite and poor solutions, with each poor solution paired with an elite solution. The elite solutions serve as generation targets, and their paired poor solutions act as conditions during the training of the generative model. Our approach allows the model to not only capture the distribution of elite solutions but also effectively model the evolutionary trajectory from poor solutions to elite solutions. The entire population is used as conditions, and the trained generative model generates ideal positions, which are then updated to produce offspring solutions. Experimental results on large-scale multi-objective benchmark functions demonstrate that the proposed algorithm outperforms four state-of-the-art large-scale multi-objective evolutionary algorithms.

This paper addresses the challenge of identifying similarities between different optimization tasks, which is crucial for enhancing transfer learning and automated optimization systems. Traditional rule-based methods often fail to capture the complexity of problems, while existing data-driven approaches lack comprehensive feature representation and generalization across domains. Moreover, there is a severe lack of training data when applying deep learning strategies. To overcome these limitations, we propose a novel model based on contrastive learning for optimization task similarity recognition. Our approach integrates information from the decision space, objective space, and derivative space, creating a unified representation framework inspired by image data formats. We employ a convolutional neural network to extract task features and utilize contrastive learning to measure task similarity. Experimental results demonstrate the model’s effectiveness in generalizing to new optimization tasks and its sensitivity to task differences. We conducted experiments on 40- and 60-dimensional problems, where sampling only 5 times the dimensionality of data points was sufficient for distinction. The proposed method not only provides a comprehensive representation of optimization tasks but also enhances the model’s generalization performance.

Yi Sun, Han Wang, Jiaqiang Li, Jiacheng Liu, Xiangyu Li, Hao Wen, Yizhen Yuan, Huiwen Zheng, Yan Liang, Yuanchun Li, Yunxin Liu. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

SAR ship detection faces critical deployment challenges: existing models exhibit excessive computational requirements and inadequate small-target performance for practical maritime surveillance. We propose LightSAR-Net, a lightweight multi-scale framework addressing these limitations through systematic architectural optimization.The network integrates three innovations: DEF-Stem employs dual-branch processing with Sobel edge enhancement to suppress sea clutter while preserving ship contours; VoV-GSCSP utilizes group-hybridized convolution with dynamic weighting for parameter-efficient feature fusion; DAT-DH decouples classification-regression tasks through spatial probability mapping and deformable convolution for enhanced dense target discrimination.SSDD evaluation achieves 97.1% mAP0.5(+1.0% over baseline) while reducing parameters by 29.9% and computation by 19.1%, demonstrating effective optimization for satellite edge computing platforms.

Small open reading frames (smORFs) have been acknowledged to play various roles on essential biological pathways and affect human beings from diabetes to tumorigenesis. Predicting smORFs in silico is quite a prerequisite for processing the omics data. Here, we proposed the smORF-coding-potential-predicting framework, sOCP, which provides functions to construct a model for predicting novel smORFs in some species. The sOCP model constructed in human was based on in-frame features and the nucleotide bias around the start codon, and the small feature subset was proved to be competent enough and avoid overfitting problems for complicated models. It showed more advanced prediction metrics than previous methods and could correlate closely with experimental evidence in a heterogeneous dataset. The model was applied to Rattus norvegicus and exhibited satisfactory performance. We then scanned smORFs with ATG and non-ATG start codons from the human genome and generated a database containing about a million novel smORFs with coding potential. Around 72 000 smORFs are located on the lncRNA regions of the genome. The smORF-encoded peptides may be involved in biological pathways rare for canonical proteins, including glucocorticoid catabolic process and the prokaryotic defense system. Our work provides a model and database for human smORF investigation and a convenient tool for further smORF prediction in other species.

In the last decade, a number of Single Error Correction Double Adjacent Error Correction (SEC-DAEC) codes have been proposed to protect memories against Multiple Cell Upsets (MCUs). These codes are able to correct errors that affect two adjacent bits that is one of the most common MCU patterns. However, soft errors can also affect the encoder and decoder circuitry creating data corruption. An alternative to protect the encoders is to use parity prediction Concurrent Error Detection (CED) to detect errors and avoid writing erroneous words in the memory. This approach has been previously studied for Orthogonal Latin Square (OLS) codes and for matrix codes. In this paper, the implementation of parity prediction Concurrent Error Detection (CED) for SEC-DAEC codes is considered. To that end, first it is shown that CED has a significant cost for the existing SEC-DAEC codes. This is because they are odd weight codes and parity prediction is much simpler for even weight codes. Based on that observation, even weight SEC-DAEC codes are designed and evaluated. The results show that CED can be efficiently implemented in the proposed codes that achieve a significant reduction in encoder circuit complexity compared to previously proposed SEC-DAEC codes.

Individual tree detection is critical in forest monitoring and inventory. In this paper, we propose a novel method to use multi-view satellite images to detect individual trees and delineate their crowns. As compared to previous methods that only use image information, we generate the DSM from the multi-view high-resolution satellite images and combine it with the spectral information to detect the trees. Firstly, the vegetation areas are extracted to remove the non-vegetation objects while terrain areas are extracted to help estimate the tree height. Then, we utilize top-hat morphological operation to efficiently find the local maximal points as treetops and further refine them by checking their heights and doing non-maximum suppression. Finally, we use a revised superpixel segmentation algorithm to delineate the tree crowns which considered both 2D spectral and 3D structure similarities. To effectively assess the performance, we rigorously match and evaluate the detected and reference trees in a one-to-one relationship. A quantitative evaluation at three different sites shows that the proposed method is able to detect individual trees at different regions with high accuracy.

Soft errors caused by particle strike in combinational circuits are a major concern in the design of reliable circuits. Particle strike induced single event transient (SET), especially the evolutional single event multiple transients (SEMTs) in nanoscale CMOS technologies, has been the non-negligible reliability issue for hardening design of combinational circuits. This paper presents a low overhead method to protect combinational circuits against particle strike. This method is made up of a combination of two sub-method: (1) a soft error sensitivity estimation method, called Layout-Based Multiple Event Probability Propagation (LBMEPP) and (2) a protection method based on gate sizing, called Intelligent optimization-Based Gate Sizing (IOBGS). Unlike the previous techniques that either overlook the SEMTs event or exploit fault injection. LBMEPP can provide the sensitivity estimation of combinational circuits in the presence of SET and SEMTs. The SEMTs adjacent cells are identified by the cell's layout and Geant4 Monte Carlo simulation. Therefore, the SEMTs event can be considered in the sensitivity estimation. Using the estimation result of LBMEPP, IOBGS adopts multi-objective particle swarm optimization algorithm to dynamically allocate and adjust each logical cells. In IOBGS, SER, circuit area and longest path delay of the circuit are selected as the optimization goals. The experiments conducted on several typical circuits show that the proposed optimization method can evidently decrease the SER with a limited overhead.

The use of error-correction codes (ECCs) with advanced correction capability is a common system-level strategy to harden the memory against multiple bit upsets (MBUs). Therefore, the construction of ECCs with advanced error correction and low redundancy has become an important problem, especially for adjacent ECCs. Existing codes for mitigating MBUs mainly focus on the correction of up to 3-bit burst errors. As the technology scales and cell interval distance decrease, the number of affected bits can easily extend to more than 3 bit. The previous methods are therefore not enough to satisfy the reliability requirement of the applications in harsh environments. In this paper, a technique to extend 3-bit burst error-correction (BEC) codes with quadruple adjacent error correction (QAEC) is presented. First, the design rules are specified and then a searching algorithm is developed to find the codes that comply with those rules. The ${H}$ matrices of the 3-bit BEC with QAEC obtained are presented. They do not require additional parity check bits compared with a 3-bit BEC code. By applying the new algorithm to previous 3-bit BEC codes, the performance of 3-bit BEC is also remarkably improved. The encoding and decoding procedure of the proposed codes is illustrated with an example. Then, the encoders and decoders are implemented using a 65-nm library and the results show that our codes have moderate total area and delay overhead to achieve the correction ability extension.