Multimodal Large Language Models struggle to maintain reliable performance under extreme real-world visual degradations, which impede their practical robustness. Existing robust MLLMs predominantly rely on implicit training/adaptation that focuses solely on visual encoder generalization, suffering from limited interpretability and isolated optimization. To overcome these limitations, we propose Robust-R1, a novel framework that explicitly models visual degradations through structured reasoning chains. Our approach integrates: (i) supervised fine-tuning for degradation-aware reasoning foundations, (ii) reward-driven alignment for accurately perceiving degradation parameters, and (iii) dynamic reasoning depth scaling adapted to degradation intensity. To facilitate this approach, we introduce a specialized 11K dataset featuring realistic degradations synthesized across four critical real-world visual processing stages, each annotated with structured chains connecting degradation parameters, perceptual influence, pristine semantic reasoning chain, and conclusion. Comprehensive evaluations demonstrate state-of-theart robustness: Robust-R1 outperforms all general and robust baselines on the real-world degradation benchmark R-Bench, while maintaining superior anti-degradation performance under multi-intensity adversarial degradations on MMMB, MMStar, and RealWorldQA.

Multimodal information is pivotal for e-commerce search ranking. Existing works leverage multimodal data typically by fine-tuning general Multimodal Large Language Models (MLLMs) via collaborative signals, subsequently integrating the derived representations into ranking models as item features. Despite their efficacy, these methods face two primary limitations: (1) they rely on a single collaborative signal for MLLM fine-tuning, failing to exploit the heterogeneous signals essential for multitask ranking; and (2) they treat multimodal representations as regular item features in ranking models, underutilizing their latent potential for user behavior modeling. To address these challenges, we propose the Multiplex Multimodal Representation Model (MMRM), a unified framework that aligns MLLMs with diverse collaborative signals. By employing a shared backbone with task-specific tokens and projection layers, MMRM simultaneously learns from multiple signals and generates comprehensive multiplex item representations in a single inference pass. Furthermore, we introduce a multiplex user representation strategy in ranking models, which derives task-specific user representations via search-based behavior sequence modeling leveraging multiplex item representations. Extensive experiments demonstrate MMRM's superior efficiency and effectiveness. Notably, MMRM has been successfully deployed in the JD e-commerce search engine, yielding significant performance gains for millions of daily users.

In recent years, the deadbeat-control-based MPC algorithm (DBC-MPC) has been widely studied because it can effectively reduce the number of candidate vectors. However, for the LCL-type grid-connected inverter, when calculating the reference value of the inverter output voltage, the control algorithm only addresses the influence of the current tracking error on the reference value of the output voltage of the inverter, and ignores the influence of the filter capacitor voltage, resulting in poor control performance. Therefore, in this paper an improved DBC-MPC is proposed for the LCL-type grid-connected inverter, where both effects of current and voltage tracking errors on the control performance are fully considered. Compared with the traditional DBC-MPC algorithm, the control performance of the system is greatly improved, while still effectively reducing the number of candidate voltage vectors. Finally, a three-phase simulation model is built to verify the control performance of the proposed algorithm.

Modern General-Purpose computation on Graphics Processing Units (GPGPUs) explore parallelism in applications by building massively parallel architecture and apply multithreading technology to hide the instruction and memory latencies. Such architectures become increasingly popular for parallel applications using CUDA/OpenCL programming languages. In this paper, we investigate thread scheduling algorithms on such highly-threaded GPGPUs. The traditional round-robin scheduling schemes are inefficient in handling instruction execution and memory accesses with disparate latencies. We introduce a new GPGPU thread (warp) scheduling algorithm which enables flexible roundrobin distance for efficiently utilizing multithread parallelism and use program-guided priority shift among concurrent threads (warps) to allow more overlaps between short-latency compute instructions and long-latency memory accesses. Performance evaluations demonstrate that the new scheduling algorithm improves a set of kernel execution times by an average of 12% with 52% reduction on scheduler stall cycles over the fine-granularity round-robin scheme. In this paper, we also accomplish a thorough evaluation of various thread scheduling algorithms based on the amount of hardware threads, the scheduling overhead, and the global memory latency.

Modeling and estimating the primary user (PU) behavior is critical to implement dynamic spectrum access in cognitive radio networks. In this paper, we investigate the estimation accuracy of the PU behavior based on the Markov model. Maximum Likelihood (ML) estimation is employed to estimate the transition probabilities of the Markov model based on the sample sequence of PU idle/busy states. An approximate distribution of the ML estimator is derived to evaluate the estimation accuracy specified by the confidence level. To meet the requirement of estimation accuracy while reducing the unnecessary sensing time, we propose a learning algorithm which refines the estimation results iteratively. It dynamically estimates the required length of the sample sequence which is adaptive to the changing PU behavior. Numerical results show that the proposed estimation algorithm well tracks the variation of the PU behavior. Compared to the estimation method using fixed length of sample sequence, the proposed algorithm achieves the same requirement of the estimation accuracy with less number of samples.

Graphics Processing Units (GPUs) have emerged as a promising platform for parallel computation. With a large number of processor cores and abundant memory bandwidth, GPUs deliver substantial computation power. While providing high computation performance, a GPU consumes high power and needs sufficient power supplies and cooling systems. It is essential to institute an efficient mechanism for evaluating and understanding the power consumption when running real applications on high-end GPUs. In this paper, we present a high-level GPU power consumption model using sophisticated tree-based random forest methods which correlate and predict the power consumption using a set of performance variables. We demonstrate that this statistical model not only predicts the GPU runtime power consumption more accurately than existing regression based approaches, but more importantly, it provides sufficient insights into understanding the correlation of the GPU power consumption with individual performance metrics. We use a GPU simulator that can collect more runtime performance metrics than hardware counters. We measure the power consumption of a wide-range of CUDA kernels on an experimental system with GTX 280 GPU to collect statistical samples for power analysis. The proposed method is applicable to other GPUs as well.

We investigate the channel assignment problem in a cluster-based multi-channel cognitive radio sensor network in this paper. Due to the inherent power and resource constraints of sensor networks, energy efficiency is the primary concern for network design. An R-coefficient is developed to estimate the predicted residual energy using sensor information (current residual energy and expected energy consumption) and channel conditions (primary user behavior). We examine three channel assignment approaches: Random pairing, Greedy channel search and Optimization-based channel assignment. The last two exploit R-coefficient to obtain a residual energy aware channel assignment solution. Simulation results show that R-coefficient-based approaches lead to better performance in terms of energy consumption and residual energy balance. Optimization-based channel assignment outperforms the other two approaches with respect to network lifetime.

On NVIDIA's many-core GPUs, there is no synchronization function among parallel thread blocks. When fine-granularity of data communication and synchronization is required for large-scale parallel programs executed by multiple thread blocks, frequent host synchronization are necessary, and they incur a significant overhead. We investigate a class of applications which uses a chaotic version of iterative methods to obtain numerical solutions for partial differential equations (PDE). Such a fast PDE solver is parallelized on GPUs with multiple thread blocks. In this parallel implementation, although frequent data communication is needed between adjacent thread blocks, a precise order of the data communication is not necessary. Separate communication threads are used for periodically exchanging the boundary values with adjacent thread blocks through the global memory. Since a precise order of the data communication is not required, the computation and the communication threads can be overlapped to alleviate the communication overhead. Performance measurements of two popular applications, Poisson image editing from computer graphics and shape from shading from computer vision, on Tesla C1060 show that a speedup of 4–5 times is achievable for both applications in comparison with the solution using host synchronization.

Data privacy in vehicular ad hoc networks (VANETs) is a practical issue currently under investigation. Privacy-preserving anonymity authentication in networks is a challenging topic related to security, anonymity, network, and data privacy. Existing anonymity authentication in VANET is based on a k-anonymity model. An on board unit (OBU) chooses an adaptive anonymity group size in a request to an road site unit (RSU). Upon response from an RSU, an OBU verifies the RSU's work probabilistically. But k-anonymity model group selection may leak information due to absence of diversity in the sensitive attribute, and an RSU lacks a rational strategy to adapt to an OBU's adaptive and probabilistic strategies. In this paper, we address anonymity authentication cooperative issues. Specifically, an RSU uses cryptographic client PUZZLE (CCPUZZLE) to enforce its maximum anonymity group's recommendation, and countermeasure denial-of-service (DoS) attacks. Cooperative design also relaxes restraints and supports anonymity group formation accommodating various anonymity models. In this paper, we clearly demonstrate that CCPUZZLE brings an OBU's cooperation and also efficiently countermeasures DoS attacks.