Tactile sensing offers rich and complementary information to vision and language, enabling robots to perceive fine-grained object properties. However, existing tactile sensors lack standardization, leading to redundant features that hinder cross-sensor generalization. Moreover, existing methods fail to fully integrate the intermediate communication among tactile, language, and vision modalities. To address this, we propose TLV-CoRe, a CLIP-based Tactile-Language-Vision Collaborative Representation learning method. TLV-CoRe introduces a Sensor-Aware Modulator to unify tactile features across different sensors and employs tactile-irrelevant decoupled learning to disentangle irrelevant tactile features. Additionally, a Unified Bridging Adapter is introduced to enhance tri-modal interaction within the shared representation space. To fairly evaluate the effectiveness of tactile models, we further propose the RSS evaluation framework, focusing on Robustness, Synergy, and Stability across different methods. Experimental results demonstrate that TLV-CoRe significantly improves sensor-agnostic representation learning and cross-modal alignment, offering a new direction for multimodal tactile representation.

The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases.However, existing benchmarks often lack coverage for subtle corner cases, allowing incorrect solutions to pass.To bridge this gap, we propose CodeHacker, an automated agent framework dedicated to generating targeted adversarial test cases that expose latent vulnerabilities in program submissions.Mimicking the hack mechanism in competitive programming, CodeHacker employs a multistrategy approach, including stress testing, anti-hash attacks, and logic-specific targeting to break specific code submissions.To ensure the validity and reliability of these attacks, we introduce a Calibration Phase, where the agent iteratively refines its own Validator and Checker via self-generated adversarial probes before evaluating contestant code.Experiments demonstrate that CodeHacker significantly improves the True Negative Rate (TNR) of existing datasets, effectively filtering out incorrect solutions that were previously accepted.Furthermore, generated adversarial cases prove to be superior training data, boosting the performance of RL-trained models on benchmarks like LiveCodeBench.Our code is available at https://github.com/shi0712/ CodeHacker.

This paper proposes a novel Fractional Folding (FF) architecture for digital signal processing integrated circuits. With this new structure, a Fractional Folding based enhanced Parallel Inversionless Berlekamp-Massey (FF-ePIBM) Reed-Solomon Decoder is presented of which the number of processing element (PE) can be reduced to only one, resulting in ultra-low hardware complexity. The FF-ePIBM VLSI architecture can greatly reduce the hardware cost by about 60% compare to the fully expanded parallel ePIBM architecture. A polyphase clock signal is needed in order to achieve fractional folding function according to specific fractional-factor. It is generated from Delay Locked Loop (DLL) commonly embedded in almost all VLSI circuit and system. To maximize the throughput of decoder, pipelined architecture is adopted to optimize critical path delay. The RS (255, 239) decoder with FF-ePIBM architecture is finally implemented with 40nm CMOS technology. The synthesized results demonstrate that the decoder has a gate count of 12.6k and can operate at 3.2GHz to achieve a highest throughput of 25.6Gb/s and a best TSNT FoM value of 623 to this date.

Along with the increasing popularity of cloud computing, the requirement to efficiently migrate enterprises' legacy applications to cloud has become extremely intensive. Existing solutions have many limitations in use, such as the source code of legacy application is always required, or the migrated application cannot be customized or mashed up. What's more, it always overbalances the budget to perform these solutions. In this paper, we proposed a brand new Application Migration Solution (AMS) to efficiently migrate legacy applications to web applications by GUI recognition and reconstruction technology. The core technologies are presented and evaluation results are given for technology validation. Based on AMS, enterprises can deploy their legacy applications to cloud easily. This solution also fulfills advanced enterprise requirements, including application customization requirement or mashup with other applications to compose more integrated and powerful applications.