Reconstructing high-fidelity MR images from undersampled k-space data remains a challenging problem in MRI. While Mamba variants for vision tasks offer promising long-range modeling capabilities with linear-time complexity, their direct application to MRI reconstruction inherits two key limitations: (1) insensitivity to high-frequency anatomical details; and (2) reliance on redundant multi-directional scanning. To address these limitations, we introduce High-Fidelity Mamba (HiFi-Mamba), a novel dual-stream Mamba-based architecture comprising stacked ?-Laplacian (WL) and HiFi-Mamba blocks. Specifically, the WL block performs fidelity-preserving spectral decoupling, producing complementary low- and high-frequency streams. This separation enables the HiFi-Mamba block to focus on low-frequency structures, enhancing global feature modeling. Concurrently, the HiFi-Mamba block selectively integrates high-frequency features through adaptive state-space modulation, preserving comprehensive spectral details. To eliminate the scanning redundancy, the HiFi-Mamba block adopts a streamlined unidirectional traversal strategy that preserves long-range modeling capability with improved computational efficiency. Extensive experiments on standard MRI reconstruction benchmarks demonstrate that HiFi-Mamba consistently outperforms state-of-the-art CNN-based, Transformer-based, and other Mamba-based models in reconstruction accuracy while maintaining a compact and efficient model design.

Recent advances in transformer-based text-to-motion generation have significantly improved motion quality. However, achieving both real-time performance and long-horizon scalability remains an open challenge. In this paper, we present MOGO (Motion Generation with One-pass), a novel autoregressive framework for efficient and scalable 3D human motion generation. MOGO consists of two key components. First, we introduce MoSA-VQ, a motion scale-adaptive residual vector quantization module that hierarchically discretizes motion sequences through learnable scaling parameters, enabling dynamic allocation of representation capacity and producing compact yet expressive multi-level representations. Second, we design the RQHC-Transformer, a residual quantized hierarchical causal transformer that decodes motion tokens in a single forward pass. Each transformer block aligns with one quantization level, allowing hierarchical abstraction and temporally coherent generation with strong semantic flow. Compared to diffusion- and LLM-based approaches, MOGO achieves lower inference latency while preserving high motion fidelity. Moreover, its hierarchical latent design enables seamless and controllable infinite-length motion generation, with stable transitions and the ability to adaptively incorporate updated control signals at arbitrary points in time. To further enhance generalization and interpretability, we introduce Textual Condition Alignment (TCA), which leverages large language models with Chain-of-Thought reasoning to bridge the gap between real-world prompts and training data. TCA not only improves zero-shot performance on unseen datasets but also enriches motion comprehension for in-distribution prompts through explicit intent decomposition. Extensive experiments on HumanML3D, KIT-ML, and the unseen CMP dataset demonstrate that MOGO outperforms prior methods in generation quality, inference efficiency, and temporal scalability.

The increased adoption of smart contracts in many industries has made them an attractive target for cybercriminals, leading to millions of dollars in losses. Thus, continuously fixing newly found vulnerabilities of smart contracts becomes a routine software maintenance task for running smart contracts. However, fixing the vulnerabilities that are specific to the smart contract domain requires security knowledge that many developers lack. Without effective tool support, this task can be very costly in terms of manual labor. To fill this critical need, in this paper, we propose VFIX, which automatically generates security patches for vulnerable smart contracts. In particular, VFIX provides a novel program analysis framework that can incorporate different fix patterns for fixing various types of vulnerabilities. To address the unique challenges in accurately fixing smart contract vulnerabilities, VFIX innovatively combines template-based repair with a set of static program analysis techniques specially designed for smart contracts. Specifically, given an input smart contract, VFIX conducts ensemble identification based on multiple static verification tools to identify vulnerabilities for an automatic fix. Then, VFIX generates patches using template-based fix patterns, and conducts static program analysis (e.g., program dependency computation, pointer analysis) for smart contracts to accurately infer and populate the parameter values for the fix templates. Finally, VFIX performs static verification to ensure that the patched contract is free of vulnerabilities. Our evaluations on 144 real smart contracts containing different types of vulnerabilities show that VFIX can successfully fix 94% of the vulnerabilities and preserve the expected normal behaviors of the smart contracts.

Embracing software-driven smart contracts to fulfill legal agreements is a promising direction for digital transformation in the legal sector. Existing solutions mostly consider smart contracts as simple add-ons, without leveraging the programmability of smart contracts to realize complex semantics of legal agreements. In this paper, we propose iSyn, the first end-to-end system that synthesizes smart contracts to fulfill the semantics of financial legal agreements, with minimal human interventions. The design of iSyn centers around a novel intermediate representation (SmartIR) that closes the gap between the natural language sentences and smart contract statements. Specifically, iSyn includes a synergistic pipeline that unifies multiple NLP-techniques to accurately construct SmartIR instances given legal agreements, and performs template-based synthesis based on the SmartIR instances to synthesize smart contracts. We also design a validation framework to verify the correctness and detect known vulnerabilities of the synthesized smart contracts.We evaluate iSyn using legal agreements centering around financial transactions. The results show that iSyn-synthesized smart contracts are syntactically similar and semantically correct (or within a few edits), compared with the “ground truth” smart contracts manually developed by inspecting the legal agreements.

Causality analysis generates a dependency graph from system audit logs, which has emerged as an important solution for attack investigation. In the dependency graph, nodes represent system entities (e.g., processes and files) and edges represent dependencies among entities (e.g., a process writing to a file). Despite the promising early results, causality analysis often produces a large graph (> 100,000 edges) and it is a daunting task for security analysts to inspect such a large graph for attack investigation. To address challenges in attack investigation, we propose DEPCOMM, a graph summarization approach that generates a summary graph from a dependency graph by partitioning a large graph into process-centric communities and presenting summaries for each community. Specifically, each community consists of a set of intimate processes that cooperate with each other to accomplish certain system activities (e.g., file compression), and the resources (e.g., files) accessed by these processes. Within a community, DEPCOMM further identifies redundant edges caused by less-important and repetitive system activities, and perform compression on these edges. Finally, DEPCOMM generates the summary for each community using the InfoPaths that represent the information flows across communities. These InfoPaths are more likely to capture a set of attack-related processes that work together to achieve certain malicious goals. Our evaluations on real attacks ($\sim 150$ million events) demonstrate that DEPCOMM generates 18.4 communities on average for a dependency graph, which is $\sim 70 \times$ smaller than the original graph. Our compression further reduces the edges in each community to 32.1 on average. Compared with the 9 state-of-the-art community detection algorithms, on average, DEPCOMM achieves a $2.29\times$ better F1-score than these algorithms in detecting communities. Through cooperating with the automatic techniques HOLMES, DEPCOMM can identify attack-related communities by a recall of 96.2%. Our case studies on the real attacks also demonstrate DEPCOMM’s effectiveness in facilitating attack investigation.

With the rapid improvement of Industrial Internet of Things and artificial intelligence, predictive maintenance (PdM) has attracted great attention from both academia and industrial practitioners. When equipment is running, the electrical attributes have intrinsic relations. Meanwhile, they are changing over time. However, existing PdM models are often limited as they lack considering both attribute interactions and temporal dependence of the dynamic working system. To address the problem, in this article, we propose an electrical spatio-temporal graph convolutional network (Electrical-STGCN) for PdM. First, it takes a sequence of electrical records as input. Next, both attribute interactions and temporal dependence are established to extract features. Then, the extracted features are fed into a prediction component. Finally, the output of the Electrical-STGCN (i.e., remaining useful life) can help the workers decide whether to carry out equipment maintenance. The effectiveness of the proposed method is verified in real-world cases. Our method achieves 85.2% Accuracy and 0.9 F1-Score, which are better than the other approaches.

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.

In a paced aircraft final assembly line, some disturbances can be collected timely on the basis of the cyber-physical production system. In order to reduce the execution deviation, some workers need to switch among stations after a fixed period. Thus, a worker allocation problem with the multistage workstation is introduced first. Then, an integer programming formulation is presented to formulate the problem with the objective of shortest workstation cycle and the workload balance of both stations and workers. Moreover, a modified nondominated sorting genetic algorithm (NSGA-IV) is proposed to solve it, which tradeoff the convergence and the population diversity in the decision space. Finally, the NSGA-IV algorithm compares with five multiobjective evolutionary algorithms in a real-world case. Compared with manual allocation, the takt time of an aircraft final assembly line is reduced by 20.86% by using the NSGA-IV algorithm.