RTLMarker: Protecting LLM-Generated RTL Copyright via a Hardware Watermarking Framework

Recent advances of large language models in the field of Verilog generation have raised several ethical and security concerns, such as code copyright protection and dissemination of malicious code. Researchers have employed watermarking techniques to identify codes generated by large language models. However, the existing watermarking works fail to protect RTL code copyright due to the significant syntactic and semantic differences between RTL code and software code in languages such as Python. This paper proposes a hardware watermarking framework RTLMarker that embeds watermarks into RTL code and deeper into the synthesized netlist. We propose a set of rule-based Verilog code transformations , ensuring the watermarked RTL code's syntactic and semantic correctness. In addition, we consider an inherent tradeoff between watermark transparency and watermark effectiveness and jointly optimize them. The results demonstrate RTLMarker's superiority over the baseline in RTL code watermarking.

Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing.

Adaptive Self-supervised Learning for Social Recommendations

In recent years, researchers have attempted to exploit social relations to improve the performance in recommendation systems. Generally, most existing social recommendation methods heavily depends on substantial domain knowledge and expertise in primary recommendation tasks for designing useful auxiliary tasks. Meanwhile, Self-Supervised Learning (SSL) recently has received considerable attention in the field of recommendation, since it can provide self-supervision signals in assisting the improvement of target recommendation systems by constructing self-supervised auxiliary tasks from raw data without human-annotated labels. Despite the great success, these SSL-based social recommendations are insufficient to adaptively balance various self-supervised auxiliary tasks, since assigning equal weights on various auxiliary tasks can result in sub-optimal recommendation performance, where different self-supervised auxiliary tasks may contribute differently to improving the primary social recommendation across different datasets. To address this issue, in this work, we propose Adaptive Self-supervised Learning for Social Recommendations (AdasRec) by taking advantage of various self-supervised auxiliary tasks. More specifically, an adaptive weighting mechanism is proposed to learn adaptive weights for various self-supervised auxiliary tasks, so as to balance the contribution of such self-supervised auxiliary tasks for enhancing representation learning in social recommendations. The adaptive weighting mechanism is used to assign different weights on auxiliary tasks to achieve an overall weighting of the entire auxiliary tasks and ultimately assist the primary recommendation task, achieved by a meta learning optimization problem with an adaptive weighting network. Comprehensive experiments on various real-world datasets are constructed to verify the effectiveness of our proposed method.

Efficient Scaling of Diffusion Transformers for Text-to-Image Generation

We empirically study the scaling properties of various Diffusion Transformers (DiTs) for text-to-image generation by performing extensive and rigorous ablations, including training scaled DiTs ranging from 0.3B upto 8B parameters on datasets up to 600M images. We find that U-ViT, a pure self-attention based DiT model provides a simpler design and scales more effectively in comparison with cross-attention based DiT variants, which allows straightforward expansion for extra conditions and other modalities. We identify a 2.3B U-ViT model can get better performance than SDXL UNet and other DiT variants in controlled setting. On the data scaling side, we investigate how increasing dataset size and enhanced long caption improve the text-image alignment performance and the learning efficiency.

BodyMetric: Evaluating the Realism of Human Bodies in Text-to-Image Generation

Accurately generating images of human bodies from text remains a challenging problem for state of the art text-to-image models. Commonly observed body-related artifacts include extra or missing limbs, unrealistic poses, blurred body parts, etc. Currently, evaluation of such artifacts relies heavily on time-consuming human judgments, limiting the ability to benchmark models at scale. We address this by proposing BodyMetric, a learnable metric that predicts body realism in images. BodyMetric is trained on realism labels and multi-modal signals including 3D body representations inferred from the input image, and textual descriptions. In order to facilitate this approach, we design an annotation pipeline to collect expert ratings on human body realism leading to a new dataset for this task, namely, BodyRealism. Ablation studies support our architectural choices for BodyMetric and the importance of leveraging a 3D human body prior in capturing body-related artifacts in 2D images. In comparison to concurrent metrics which evaluate general user preference in images, BodyMetric specifically reflects body-related artifacts. We demonstrate the utility of BodyMetric through applications that were previously infeasible at scale. In particular, we use BodyMetric to benchmark the generation ability of text-to-image models to produce realistic human bodies. We also demonstrate the effectiveness of BodyMetric in ranking generated images based on the predicted realism scores.

Analog-Only Beamforming for Near-Field Multiuser MIMO Communications

For the existing near-field multiuser communications based on hybrid beamforming (HBF) architectures, high-quality effective channel estimation is required to obtain the channel state information (CSI) for the design of the digital beamformer. To simplify the system reconfiguration and eliminate the pilot overhead required by the effective channel estimation, we considered an analog-only beamforming (AoBF) architecture in this study. The AoBF is designed with the aim of maximizing the sum rate, which is then transformed into a problem, maximizing the power transmitted to the target user equipment (UE) and meanwhile minimizing the power leaked to the other UEs. To solve this problem, we used beam focusing and beam nulling and proposed two AoBF schemes based on the majorization-minimization (MM) algorithm. First, the AoBF scheme based on perfect CSI is proposed, with the focus on the beamforming performance and regardless of the CSI acquisition. Then, the AoBF scheme based on imperfect CSI is proposed, where the low-dimensional imperfect CSI is obtained by beam sweeping based on a near-field codebook. Simulation results demonstrate that the two AoBF schemes can approach the sum rate of the HBF schemes but outperform HBF schemes in terms of energy efficiency (EE).

BodyMetric: Evaluating the Realism of HumanBodies in Text-to-Image Generation

Accurately generating images of human bodies from text remains a challenging problem for state of the art text-to-image models. Commonly observed body-related artifacts include extra or missing limbs, unrealistic poses, blurred body parts, etc. Currently, evaluation of such artifacts relies heavily on time-consuming human judgments, limiting the ability to benchmark models at scale. We address this by proposing BodyMetric, a learnable metric that predicts body realism in images. BodyMetric is trained on realism labels and multi-modal signals including 3D body representations inferred from the input image, and textual descriptions. In order to facilitate this approach, we design an annotation pipeline to collect expert ratings on human body realism leading to a new dataset for this task, namely, BodyRealism. Ablation studies support our architectural choices for BodyMetric and the importance of leveraging a 3D human body prior in capturing body-related artifacts in 2D images. In comparison to concurrent metrics which evaluate general user preference in images, BodyMetric specifically reflects body-related artifacts. We demonstrate the utility of BodyMetric through applications that were previously infeasible at scale. In particular, we use BodyMetric to benchmark the generation ability of text-to-image models to produce realistic human bodies. We also demonstrate the effectiveness of BodyMetric in ranking generated images based on the predicted realism scores.

Online Physics-Informed Dynamic Mode Decomposition: Theory and Applications

Dynamic Mode Decomposition (DMD) has received increasing research attention due to its capability to analyze and model complex dynamical systems. However, it faces challenges in computational efficiency, noise sensitivity, and difficulty adhering to physical laws, which negatively affect its performance. Addressing these issues, we present Online Physics-informed DMD (OPIDMD), a novel adaptation of DMD into a convex optimization framework. This approach not only ensures convergence to a unique global optimum, but also enhances the efficiency and accuracy of modeling dynamical systems in an online setting. Leveraging the Bayesian DMD framework, we propose a probabilistic interpretation of Physics-informed DMD (piDMD), examining the impact of physical constraints on the DMD linear operator. Further, we implement online proximal gradient descent and formulate specific algorithms to tackle problems with different physical constraints, enabling real-time solutions across various scenarios. Compared with existing algorithms such as Exact DMD, Online DMD, and piDMD, OPIDMD achieves the best prediction performance in short-term forecasting, e.g. an $R^2$ value of 0.991 for noisy Lorenz system. The proposed method employs a time-varying linear operator, offering a promising solution for the real-time simulation and control of complex dynamical systems.

Dependency-Aware CAV Task Scheduling via Diffusion-Based Reinforcement Learning

In this paper, we propose a novel dependency-aware task scheduling strategy for dynamic unmanned aerial vehicle-assisted connected autonomous vehicles (CAVs). Specifically, different computation tasks of CAVs consisting of multiple dependency subtasks are judiciously assigned to nearby CAVs or the base station for promptly completing tasks. Therefore, we formulate a joint scheduling priority and subtask assignment optimization problem with the objective of minimizing the average task completion time. The problem aims at improving the long-term system performance, which is reformulated as a Markov decision process. To solve the problem, we further propose a diffusion-based reinforcement learning algorithm, named Synthetic DDQN based Subtasks Scheduling, which can make adaptive task scheduling decision in real time. A diffusion model-based synthetic experience replay is integrated into the reinforcement learning framework, which can generate sufficient synthetic data in experience replay buffer, thereby significantly accelerating convergence and improving sample efficiency. Simulation results demonstrate the effectiveness of the proposed algorithm on reducing task completion time, comparing to benchmark schemes.

Fast High-Quality Enhanced Imaging Algorithm for Layered Dielectric Targets Based on MMW MIMO-SAR System

Millimeter-wave (MMW) multiple-input multiple-output synthetic aperture radar (MIMO-SAR) system is a technology that can achieve high resolution, high frame rate, and all-weather imaging and has received extensive attention in the non-destructive testing and internal imaging applications of layered dielectric targets. However, the non-ideal scattering effect caused by dielectric materials can significantly deteriorate the imaging quality when using the existing MIMO-SAR fast algorithms. This paper proposes a rapid, high-quality dielectric target-enhanced imaging algorithm for a new universal non-uniform MIMO-SAR system. The algorithm builds on the existing non-uniform MIMO-SAR dielectric target frequency-domain algorithm (DT-FDA) by constructing a forward sensing operator and incorporating it into the alternating direction method of multipliers (ADMM) framework. This approach avoids large matrix operations while maintaining computational efficiency. By integrating an optimal regularization parameter search, the algorithm enhances the image reconstruction quality of dielectric internal structures or defects. Experimental results show the proposed algorithm outperforms IBP and DT-FDA, achieving better focusing, sidelobe suppression, and 3D imaging accuracy. It yields the lowest image entropy (8.864) and significantly improves efficiency (imaging time: 15.29 s vs. 23295.3 s for IBP).