LAMMPS is a widely used molecular dynamics (MD) software package in materials science, computational chemistry, and biophysics, supporting parallel computing from a single CPU core to large supercomputers. The Kunpeng processor features both high memory bandwidth and core density and is therefore an interesting candidate for accelerating compute-intensive workloads. In this paper, we target the Kunpeng multi-core architecture and focus on optimizing LAMMPS for modern ARM-based platforms by using the Lennard-Jones (L-J) and Tersoff potentials as representative case studies. We investigate both common and specific optimization challenges, and present a comprehensive performance analysis addressing four key aspects: neighbor list algorithm design, force computation optimization, efficient vectorization, and multi-thread parallelization. Experimental results show that the optimized potentials achieve speedups of approximately$2 \times$and$5 \times$, reaching$4.55 \times$and$7.04\times$the performance of the original Intel version for L-J and Tersoff, respectively. Both potentials outperform Intel's acceleration library, with a peak performance up to$2.9\times$-$3.5\times$. In terms of parallel efficiency, we evaluate scalability both within a single CPU (small-scale) and across multiple nodes (large-scale). Strong and weak scaling tests within a single CPU show that when the expansion factor is 32 times, parallel efficiency remains above$90\%$. Large-scale weak scaling across multiple nodes achieves up to$86\%$efficiency when the expansion factor is 32. Using 32 nodes (18,432 processes), our implementation enables billion-atom simulations with L-J and Tersoff potentials. This work achieves breakthrough performance and provides critical support for large-scale molecular dynamics in engineering applications.

Structure-based virtual screening confronts a grand challenge in scaling to trillion-ligand libraries for drug discovery. We present SWDOCKP2, a performance-portable virtual screening framework achieving 1.9 trillion ligand-receptor pairs daily across eight targets on the Sunway OceanLight supercomputer with 39-million cores — 10× faster than prior state-of-the-art. Key innovations combine (1) a ligand database optimizer with conformational sorting and merging, (2) multi-receptor grid alignment enabling parallel target screening and SIMD-accelerated trilinear interpolation, and (3) a Sunway architecture emulator for cross-platform efficiency. These advancements bridge computational scalability with novel drug discovery demands, offering a blueprint for next-generation supercomputing in structure-based drug design. Additionally, SWDOCKP2 will generate an unprecedented dataset of predicted protein-ligand interactions, creating a transformative resource for machine learning applications. By addressing experimental data scarcity, this dataset empowers accurate ligand prediction, generative chemistry, and AI-driven drug discovery.

Vision-language alignment learning for video-text retrieval arouses a lot of attention in recent years. Most of the existing methods either transfer the knowledge of image-text pretraining model to video-text retrieval task without fully exploring the multi-modal information of videos, or simply fuse multi-modal features in a brute force manner without explicit guidance. In this paper, we integrate multi-modal information in an explicit manner by tagging, and use the tags as the anchors for better video-text alignment. Various pretrained experts are utilized for extracting the information of multiple modalities, including object, person, motion, audio, etc. To take full advantage of these information, we propose the TABLE (TAgging Before aLignmEnt) network, which consists of a visual encoder, a tag encoder, a text encoder, and a tag-guiding cross-modal encoder for jointly encoding multi-frame visual features and multi-modal tags information. Furthermore, to strengthen the interaction between video and text, we build a joint cross-modal encoder with the triplet input of [vision, tag, text] and perform two additional supervised tasks, Video Text Matching (VTM) and Masked Language Modeling (MLM). Extensive experimental results demonstrate that the TABLE model is capable of achieving State-Of-The-Art (SOTA) performance on various video-text retrieval benchmarks, including MSR-VTT, MSVD, LSMDC and DiDeMo.

In the operation of manufacturing enterprise supply chain, there are lots of conflicts and differences between the node enterprises because of the different demands on the price, quality, cost, and other factors. These conflicts and differences can be effectively solved by negotiation. In this paper, the authors will abstract different entities in manufacturing enterprise supply chain as agents, and present a negotiation mode, and then discuss the negotiation tactics and procedures between the purchasing agent and supplier agent. Next, a practical example will be discussed and simulated for validating the negotiation model. Application of negotiation tactics and models will be helpful for resolving differences and conflicts, and improving negotiation efficiency. That will be used for optimizing the supply chain management, maximizing the benefits, and improving the operational efficiency of manufacturing enterprise supply chain.

Most modern software systems are continuously evolving, with changes frequently taking place in the core components or the execution context. These changes can adversely introduce regression faults, causing previously working functions to fail. Regression testing is essential for maintaining the quality of evolving complex software, but it can be overly time-consuming when the size of the test suite is large, or the execution of the test cases takes a long time. There are extensive research studies on selective regression testing aiming at minimizing the size of the regression test suite while maximizing the detection of the regression faults. However, most of the existing techniques focus on the regression faults caused by the code changes, the impact of the context changes on the non-modified software has barely been explored. This paper presents a context-aware regression test selection (CARTS) approach that not only accounts for the modification of code but also changes in the execution context, including libraries, external APIs, and databases. After a change, CARTS uses the program invariants denoted in the pre- and postconditions of a function to determine if the function is affected by the change and selects all the test cases that executed the modified code as well as the non-modified functions whose preconditions are affected by the change. To evaluate the effectiveness of our approach, we conducted empirical studies on multi-release open-source software and case studies on real-world systems that have ongoing changes in code as well as in the execution context. The results of our controlled experiments show that with an average of 32.5% of the regression test cases, CARTS selected all the fault-revealing test cases. In the case studies, all the fault-revealing test cases were selected by using an average of 25.3% of the regression test suite. These results suggest that CARTS can be effective for selecting fault-revealing test cases for both code and execution context changes.

Facial Expression Recognition (FER) is a challenging task in computer vision as features extracted from expressional images are usually entangled with other facial attributes, e.g., poses or appearance variations, which are adverse to FER. To achieve a better FER performance, we propose a model named Two-branch Disentangled Generative Adversarial Network (TDGAN) for discriminative expression representation learning. Different from previous methods, TDGAN learns to disentangle expressional information from other unrelated facial attributes. To this end, we build the framework with two independent branches, which are specific for facial and expressional information processing respectively. Correspondingly, two discriminators are introduced to conduct identity and expression classification. By adversarial learning, TDGAN is able to transfer an expression to a given face. It simultaneously learns a discriminative representation that is disentangled from other facial attributes for each expression image, which is more effective for FER task. In addition, a self-supervised mechanism is proposed to improve representation learning, which enhances the power of disentangling. Quantitative and qualitative results in both in-the-lab and in-the-wild datasets demonstrate that TDGAN is competitive to the state-of-the-art methods.

Most existing segmentation networks are built upon a “ U -shaped” encoder–decoder structure, where the multi-level features extracted by the encoder are gradually aggregated by the decoder. Although this structure has been proven to be effective in improving segmentation performance, there are two main drawbacks. On the one hand, the introduction of low-level features brings a significant increase in calculations without an obvious performance gain. On the other hand, general strategies of feature aggregation such as addition and concatenation fuse features without considering the usefulness of each feature vector, which mixes the useful information with massive noises. In this article, we abandon the traditional “ U -shaped” architecture and propose Y-Net, a dual-branch joint network for accurate semantic segmentation. Specifically, it only aggregates the high-level features with low-resolution and utilizes the global context guidance generated by the first branch to refine the second branch. The dual branches are effectively connected through a Semantic Enhancing Module, which can be regarded as the combination of spatial attention and channel attention. We also design a novel Channel-Selective Decoder (CSD) to adaptively integrate features from different receptive fields by assigning specific channelwise weights, where the weights are input-dependent. Our Y-Net is capable of breaking through the limit of singe-branch network and attaining higher performance with less computational cost than “ U -shaped” structure. The proposed CSD can better integrate useful information and suppress interference noises. Comprehensive experiments are carried out on three public datasets to evaluate the effectiveness of our method. Eventually, our Y-Net achieves state-of-the-art performance on PASCAL VOC 2012, PASCAL Person-Part, and ADE20K dataset without pre-training on extra datasets.

While automatic online software anomaly detection is crucial for ensuring the quality of production software, current techniques are mostly inefficient and ineffective. For online software, its inputs are usually provided by the users at runtime and the validity of the outputs cannot be automatically verified without a predefined oracle. Furthermore, some online anomalous behavior may be caused by the anomalies in the execution context, rather than by any code defect, which are even more difficult to detect. Existing approaches tackle this problem by identifying certain properties observed from the executions of the software during a training process and using them to monitor online software behavior. However, they may require a large execution overhead for monitoring the properties, which limits the applicability of these approaches for online monitoring. We present a methodology that applies effective algorithms to select a close to optimal set of anomaly-revealing properties, which enables online anomaly detection with minimal execution overhead. Our empirical results show that an average of 76.5% of anomalies were detected by using at most 5.5% of execution overhead.