Recently, Mamba has gained widespread attention due to its ability to model long-range dependencies with linear computational complexity. To explore the application of Mamba in 2D human pose estimation, we propose MatPose, a Mamba-Transformer hybrid model specifically designed for efficient 2D human pose estimation. The model aims to combine Mamba’s efficient modeling of long-range dependencies with the powerful global context modeling capabilities of the Transformer to effectively extract human pose keypoints. Initially, to address the lack of local features when Mamba is applied to computer vision tasks, we design a Cross-Stage Multi-Scale Convolution (CSMSC) module by integrating multi-scale convolution, cross-stage feature fusion, and spatial attention mechanisms to effectively extract local features. Then, to mitigate the long-range forgetting issue inherent in Mamba, we shorten the sequence length using the Conv-Reduce operation. In addition, we design a Channel Selection Attention (CSA) mechanism to compensate for the feature loss caused by the Conv-Reduce operation. Finally, to explore a suitable integration method for the Mamba-Transformer hybrid model in 2D human pose estimation, we conduct a comprehensive ablation study on the feasibility of integrating Mamba and Transformer models. Experimental results show that the proposed method, compared to the baseline model, improves performance while reducing computational overhead. On the COCO val2017 dataset, MatPose achieves an AP of 74.6 with only 5.18 GFLOPs, outperforming most existing human pose estimation models.

The optimal power flow (OPF) problem can be rapidly and reliably solved by employing responsive online solvers based on neural networks. The dynamic nature of renewable energy generation and the variability of power grid conditions necessitate frequent neural network updates with new data instances. To address this need and reduce the time required for data preparation time, we propose a semisupervised learning framework aided by data augmentation. In this context, ridge regression replaces the traditional solver, facilitating swift prediction of optimal solutions for the given input load demands. In addition, to accelerate the backpropagation during training, we develop novel batch-mean gradient estimation approaches along with a reduced branch set to alleviate the complexity of gradient computation. Numerical simulations demonstrate that our neural network, equipped with the proposed gradient estimators, consistently achieves feasible and near-optimal solutions. These results underline the effectiveness of our approach for practical implementation in real-time OPF applications.

The increasing complexity of System-on-Chip (SoC) designs and the rise of third-party vendors in the semiconductor industry have led to unprecedented security concerns. Traditional formal methods struggle to address software-exploited hardware bugs, and existing solutions for hardware-software co-verification often fall short. This paper presents Microscope, a novel framework for inferring software instruction patterns that can trigger hardware vulnerabilities in SoC designs. Microscope enhances the Structural Causal Model (SCM) with hardware features, creating a scalable Hardware Structural Causal Model (HW-SCM). A domain-specific language (DSL) in SMT-LIB represents the HW-SCM and predefined security properties, with incremental SMT solving deducing possible instructions. Microscope identifies causality to determine whether a hardware threat could result from any software events, providing a valuable resource for patching hardware bugs and generating test input. Extensive experimentation demonstrates Microscope’s capability to infer the causality of a wide range of vulnerabilities and bugs located in SoC-level benchmarks.

Abstract Online health communities (OHCs) function as significant platforms that people use to obtain information and emotional support. Despite many studies on user behavior and relationships, little attention has been paid to user identities and how different layers of identities are interwoven. To address this potential research gap, this study examined users' relationship‐layer identities and their evolution by elaborating on the communication theory of identity (CTI) and social support theory. Additionally, based on our previous study on users' personal‐layer identities in OHCs, we investigated how users' relationship‐layer identities interacted with their personal‐layer identities. This study classified users' posts and replies into providing informational support, seeking informational support, providing emotional support, seeking emotional support, and companionship using the bidirectional encoder representation from transformers (BERT), with F1‐scores above 0.848. Through social network analysis, this study found that users of OHCs constructed their relationship‐layer identities more through informational interactions than through emotional interactions. Users with various personal‐layer identities presented different relationship‐layer identities. Users' relationships were more initiated by information exchange, and users with more interactions had more companionship activities. OHCs provided efficient communication channels for people to exchange social support.

The complexity of modern system-on-chip (SoC) designs and the ever shortened time-to-market (TTM) makes the third-party intellectual property (3PIP) a cornerstone in the modern SoC supply chain. Various 3PIPs are involved in modern SoCs, performing functionality ranging from computation accelerating to sensitive data processing. The wide use of 3PIPs also raises security concerns, e.g., hardware Trojans inserted in 3PIPs may compromise the security of the whole system. While SoC integrators carefully evaluate the functionality of the acquired 3PIPs, there lack effective and low-cost solutions for third-party IP security validation in the SoC environment. Exacerbating the issue, Trojans may be located in multiple IPs and will only perform malicious tasks collaboratively. To address these limitations and to protect modern SoCs, we propose a runtime 3PIP Trojan detection framework. The new framework, named IP-Tag, is a tag-based structure to track the requests on SoC and enforce fine-grained access control in individual IPs. The proposed framework can detect and prevent illegal access and sensitive data leakage on IPs within the SoC environment. The proposed IP-Tag framework was demonstrated on an RISC-V-based SoC and also implemented on an FPGA platform for security and performance analysis. Our experimental results show that the developed IP-Tag can detect and prevent illegal access and sensitive data leakage in SoC with malicious IPs. The hardware overhead is 7.9% LUTs and 7.8% Flip-Flops and a performance overhead is 2.2%.

Non-convex AC optimal power flow (AC-OPF) is a fundamental optimization problem in power system analysis. The computational complexity of conventional solvers is typically high and not suitable for large-scale networks in real-time operation. Hence, deep learning based approaches have gained intensive attention to conduct the time-consuming training process offline. Supervised learning methods may yield a feasible AC-OPF solution with a small optimality gap. However, they often need conventional solvers to generate the training dataset. This paper proposes an end-to-end unsupervised learning based framework for AC-OPF. We develop a deep neural network to output a partial set of decision variables while the remaining variables are recovered by solving AC power flow equations. The fast decoupled power flow solver is adopted to further reduce the computational time. In addition, we propose using a modified augmented Lagrangian function as the training loss. The multipliers are adjusted dynamically based on the degree of constraint violation. Extensive numerical test results corroborate the advantages of our proposed approach over some existing methods.

Dynamic Information Flow Tracking (DIFT) is a technique that facilitates run-time data-flow analysis on a running process, allowing a system to overcome the limitations of finding data dependencies statically at compilation time. DIFT serves as the backbone for applications including data-flow integrity (DFI). However, previous uses of DIFT towards DFI often have large overhead in terms of hardware, software or both, and often cannot provide fine-granularity tracking for software object, such as variables. To address these limitations, we present FineDIFT as a DFI framework which utilizes DIFT to generate a live data-flow graph of a running process and perform hardware-based assisted analysis at fine-granularity, thus being able to enforce the application’s Data-Flow Graph (DFG). We provide a sample implementation on a RISC-V core with a performance overhead of 5.03% for BEEBS benchmarks and hardware overhead of 6% LUTs and 8% Flip-Flops in the FPGA implementation, if excluding the Content-Addressable Memory (CAM) like structure used for metadata storage. With CAM-like structure being synthesized using FPGA logic, the total hardware overhead is$\approx 2 \times $LUTs and 33% Flip-Flops compared to the original RISC-V core. We also use the real-world application and customized vulnerable application to demonstrate the effectiveness of the proposed framework in protecting computing systems.