The emergence of back-side interconnects aims to sustain the continued scaling of semiconductor technology. To extend existing back-end tools, netlist planning has been introduced to transform single-sided netlists into double-sided ones, thereby exploring the potential of utilizing bridging cells for double-sided signal routing. However, the lack of a native double-sided routing approach that fully leverages both front-side and back-side resources hinders the effective handling of complex systematic requirements. In light of this, we propose a native double-sided signal routing approach DSR for the first time, which realizes efficient cross-layer path selection in 3D routing space by unified modeling of front-side and back-side resources. We develop a native double-sided global routing algorithm that jointly considers resource allocation and bridging cell insertion, guided by delay models for performance optimization. Under the guidance of global routing, we further extend the double-sided routing graph and incorporate delay-aware mechanisms to enhance resource allocation and routing quality in detailed routing. Experimental results demonstrate that, compared with existing works, the proposed approach achieves significant improvements in delay and runtime, while maintaining wirelength and eliminating Design Rule Violations (DRVs).

Cancer is a highly heterogeneous disease underpinned by complex molecular alterations. Accurate subtyping is critical for guiding personalized treatment and improving clinical outcomes. However, multi-omics data are high-dimensional, noisy, and heterogeneous across platforms, posing major challenges for reliable subtyping. To address this, dimensionality reduction is necessary to capture underlying molecular patterns in a low-dimensional space, facilitating both computational efficiency and biological interpretation. We present Consensus subtyping method with Ensemble Dimensionality Reduction for multi-omics data integration (CEDR), a consensus subtyping framework that integrates complementary linear and nonlinear dimensionality reduction methods with robust clustering and probabilistic ensemble modeling. Different from existing dimensionality reduction techniques, our framework adopts an ensemble learning framework that integrates multiple dimensionality reduction techniques with robust clustering to achieve reliable consensus cancer subtyping. We apply Optimally Tuned Robust Improper Maximum Likelihood Estimator to the concatenated low-dimensional matrix for robust subtyping, and ensemble the result with the Mixture Model for Clustering Ensembles to identify stable subtypes. Across extensive simulations, CEDR consistently outperformed conventional dimensionality reduction-based clustering, the Cluster Of Clusters Analysis (COCA) ensemble strategy, and state-of-the-art multi-omics integration algorithms (SNF and CIMLR) in both accuracy and robustness. Application to clear cell renal cell carcinoma and lower-grade glioma revealed biologically interpretable subtypes characterized by distinctive survival outcomes, pathway activities, and immune infiltration patterns. These findings demonstrate that CEDR provides a powerful and reliable strategy for multi-omics data integration and cancer subtyping, with strong potential for broader applications in high-dimensional multimodal data analysis.

As AI technologies become increasingly integrated into online medical consultations, understanding patient responses to different forms of AI involvement is crucial. However, limited research has examined how patients’ perceptions of AI involvement affect their intention to comply with medical advice. Drawing on the heuristic–systematic model, this study examines how agent identity (human, AI, or AI-assisted human) influences patients’ intention to comply with medical advice. Across four scenario-based experiments, results show that patients are more likely to comply with advice from AI-assisted human agents than from AI agents, but less likely than with human agents. Perceived decision-maker autonomy mediates this effect. Moreover, we identify two boundary conditions: the effect of perceived decision-maker autonomy is weakened when decision transparency is high but strengthened when disease severity is high. These findings advance understanding of human–AI collaboration and offer practical insights to enhance patient acceptance of AI in online consultations.

Deep learning has emerged as the predominant technique for predicting routability in Very-Large-Scale-Integrated (VLSI) circuits. However, it often struggles to generalize to various tasks and performs poorly when addressing inherent data imbalance issues in electronic design automation. Overcoming these challenges typically requires retraining or fine-tuning models, which poses significant difficulties for chip engineers who lack resources and expertise in neural network training. In light of this, we propose and address the universal problem of routability prediction for the first time. By framing this issue as a meta-learning scenario, we propose a Few-Shot Learning (FSL)-based approach, URoute, which adapts flexibly to new tasks by utilizing features of the query chip and labeled examples without additional training. To tackle the data imbalance problem, we further propose a meta-learning strategy based on importance sampling to optimize the model training process. To validate the generality and adaptability of URoute, we construct an FSL dataset based on CircuitNet and ISPD2015 datasets. Experimental results demonstrate that URoute exhibits greater robustness and flexibility compared to existing methods when handling unseen routability prediction tasks, achieving competitive results.

The Ethereum platform is booming with growing richness and variety in decentralized finance (DeFi) products. However, this progress comes with sophisticated threats, such as sandwich attacks, where attackers exploit the openness and certainty of blockchain technology to manipulate market prices and secure illegal financial rewards through a strategically planned series of transactions. The existing sandwich attack detection methods are ineffective at detecting multi-token transactions and fail to identify multi-token sandwich attacks. To tackle this challenge, this study improves the original detector’s algorithm to identify both traditional single-token and multi-token sandwich attacks. The enhanced system is not only responsive and accurate but also capable of detecting and alerting potential multi-token sandwich attacks. It has been successfully integrated with the go-Ethereum client (Geth). The system is performance-optimized with an average processing time of 0.81 seconds per block and an accuracy rate of 96.17%. The response time for detecting new blocks in real-time is usually no more than 4 seconds, with most between 2 and 3 seconds, which meets practical application requirements. By carefully analyzing the transaction data flow, this system is not only able to identify the traditional front-running attack and sandwich attack, but also extends to multi-currency complex attack strategies. The core innovation lies in the system’s ability to accurately detect and provide early warnings of multi-token sandwich attacks through real-time analysis of in-block transactions, all while maintaining the overall operational efficiency of the node.

As decentralized finance (DeFi) grows and decentralized exchanges (DEXs) expand, the security of Ethereum smart contracts and blockchain transactions is receiving increasing scholarly attention. The ERC20 token standard has facilitated the emergence of numerous honeypot contracts, which deceive traders by allowing token purchases but blocking withdrawals. This study proposes a lightweight honeypot contract detection system integrated into the go-Ethereum client (Geth). Unlike previous work, our detector does not rely on contract interaction records or source code provided by contract creators. Instead, our approach performs static data flow analysis on contract bytecode to identify honeypot mechanisms. By focusing exclusively on the control flow of the ERC20 Transfer method, our system achieves faster detection than full-contract analysis methods, with an average processing time of 9.74 milliseconds per contract. Experiments on both known honeypot contracts and real-world token contracts demonstrate the effectiveness of our approach in detecting malicious ERC20 contracts.

In the computing-in-memory (CIM) field, floating-point (FP) CIM is afflicted with high computing latency and energy consumption due to the intricate procedures involved in exponent computation and processing. In this work, an 8Kb FP time-domain (TD) static-random-access-memory (SRAM) CIM macro is presented. Fabricated with a 180nm process, this macro exhibits low computational latency and high energy efficiency. A novel FP computing architecture is proposed, which is capable of concurrently executing exponent summation, maximum value finding, difference generation, and mantissa shifting. This architecture effectively reduces the overall delay in exponent computation and processing, thereby enhancing the throughput. Furthermore, a bit-partitioned computing concept and an exponent sparsity scheme are introduced. In this scheme, sparsity judgment is made solely by processing the high 4 bits of the exponent, which significantly reduces power consumption in the remaining exponent computation and processing steps. Additionally, based on the bit-partitioned concept, a dual-edge TD exponent summation and mantissa multiplication-and-accumulation (MAC) circuit is devised. This circuit not only suppresses nonlinear errors during multi-bit computation but also exploits both the rising and falling edges of pulses for computation, thus accelerating the macro’s operation speed. Compared to previous approaches, an extra 24% power reduction is achieved. At a sparsity level of 90%, a normalized energy efficiency of 14.418 TFLOPS/W and a normalized area efficiency of 0.041 TFLOPS/mm2are attained. When this work is applied to the ResNet-18 model with BF16 format for input, weight, and output, the accuracy loss on the CIFAR-100 dataset is merely −0.16%.

In this article, we introduce a dual-mode CMOS image sensor designed for pixel-level motion detection. A compact computational pixel structure is designed with a pitch of 8 μm and a fill factor of 22.1%. We have implemented a current-mode motion detection module to generate motion signals and control the operation of the column-level analog-todigital converter (ADC) to optimize power efficiency. The data rate can be adjusted by tuning the threshold currents. A 128 × 128 image sensor is fabricated in a 0.18-μm CMOS process. Test results indicate that the imager can output full image at 60 frames per second (fps). Of significant note are the substantial power consumption reductions achieved in both of the two motion detection modes, amounting to 30.7% and 60%, respectively. In data compression mode, a 3.02 pJ/pixel/frame Figure of Merit (FoM) is achieved.

In computing networks, the management center quickly schedules storage and computing resources of cloud-edge-device heterogeneous system. As the complexity of the network increases and resource nodes continue to be accessed, the burden on management center becomes heavier and heavier. We focus on the distributed autonomous management capabilities of resources in computing networks, and the correlation between resource parameters of a node with its network location. This paper proposes a computing resource management method in computing networks, which leverages parameter translation strategy for converting resource parameters into part of the IPv6 address of the resource node, SRv6 packets for network configuration. In the process of resource node search and resource node parameter update, part of the application layer’s services is settled on the network layer, which enhances performance and enables autonomous management of resources in computing networks.

Abstract With the rapid development of the Ethereum ecosystem and the increasing applications of decentralized finance (DeFi), the security research of smart contracts and blockchain transactions has attracted more and more attention. In particular, front-running attacks on the Ethereum platform have become a major security concern. These attack strategies exploit the transparency and certainty of the blockchain, enabling attackers to gain unfair economic benefits by manipulating the transaction order. This study proposes a sandwich attack detection system integrated into the go-Ethereum client (Geth). This system, by analyzing transaction data streams, effectively detects and defends against front-running and sandwich attacks. It achieves real-time analysis of transactions within blocks, quickly and effectively identifying abnormal patterns and potential attack behaviors. The system has been optimized for performance, with an average processing time of 0.442 s per block and an accuracy rate of 83%. Response time for real-time detection new blocks is within 5 s, with the majority occurring between 1 and 2 s, which is considered acceptable. Research findings indicate that as a part of the go-Ethereum client, this detection system helps enhance the security of the Ethereum blockchain, contributing to the protection of DeFi users’ private funds and the safety of smart contracts. The primary contribution of this study lies in offering an efficient blockchain transaction monitoring system, capable of accurately detecting sandwich attack transactions within blocks while maintaining normal operation speeds as a full node.

In practice, it is of paramount importance that firms make joint decisions in production planning, pricing and retailer selection while considering emission regulation. This is because the joint decisions can ensure firms to obtain higher profits while contributing to sustainable environments. However, due to the problem complexity, no models facilitating such decision making are available. This study aims to develop a model to help firms make optimal joint decisions. To model the situations where a manufacturer is the leader and the retailers are followers, we adopt the Stackelberg game theory and develop a 0–1 mixed nonlinear bilevel program to maximize the profits of both the manufacturer and his retailers. We further develop a nested genetic algorithm to solve the game model. Numerical examples demonstrate (i) the applicability of the game model and the algorithm and (ii) the robustness of the algorithm. Managerial insights are obtained, suggesting that (i) manufacturers need to identify the capacity ranges (called capacity traps ) where capacity increases result in reduced profits when making decisions to optimize profits; (ii) retailers should make suitable, e.g., pricing decisions so that the manufacturers can include them in the supply chains; (iii) both manufacturers and retailers may not need to consider the carbon emission buying (or selling) price when making decisions.

Cloud computing raises new challenges for how to protect user privacy. Fully homomorphic encryption is one way to solve the problem. In this paper, we show a useful property of Dual-LWE assumption to construct Key-Switching procedure compared with LWE assumption without extra error term. Hence, we propose to construct "Errorless Key Switching" fully homomorphic encryption (FHE) scheme based on dual learning with errors (Dual-LWE) assumption. Specifically, we compile the Dual-LWE problem proposed by Gentry et.al. at STOC2008 and First-is-errorless LWE (Ferr.LWE) problem proposed by Brakerski et al. at STOC2013 into Dual-First-is-errorless LWE (Dual-Ferr.LWE) problem. Then, utilizing Dual-Ferr.LWE assumption to construct a various GPV(vGPV) scheme, and we use vGPV scheme as the fundamental building block to construct FHE with errorless key switching scheme. Lastly, under the assumption of decisional learning with errors(DLWE), we prove that our scheme is CPA secure.