A novel coexisting passive reconfigurable intelligent surface (RIS) and active decode-and-forward (DF) relay assisted non-orthogonal multiple access (NOMA) transmission framework is proposed. In particular, two communication protocols are conceived, namely Hybrid NOMA (H-NOMA) and Full NOMA (F-NOMA). Based on the proposed two protocols, both the sum rate maximization and max-min rate fairness problems are formulated for jointly optimizing the power allocation at the access point and relay as well as the passive beamforming design at the RIS. To tackle the non-convex problems, an alternating optimization (AO) based algorithm is first developed, where the transmit power and the RIS phase-shift are alternatingly optimized by leveraging the two-dimensional search and rank-relaxed difference-of-convex (DC) programming, respectively. Then, a two-layer penalty based joint optimization (JO) algorithm is developed to jointly optimize the resource allocation coefficients within each iteration. Finally, numerical results demonstrate that: i) the proposed coexisting RIS and relay assisted transmission framework is capable of achieving a significant user performance improvement than conventional schemes without RIS or relay; ii) compared with the AO algorithm, the JO algorithm requires less execution time at the cost of a slight performance loss; and iii) the H-NOMA and F-NOMA protocols are generally preferable for ensuring user rate fairness and enhancing user sum rate, respectively.
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Computer programs containing calls to linear solvers are a known challenge for automatic differentiation. Previous publications advise against differentiating through the low-level solver implementation, and instead advocate for high-level approaches that express the derivative in terms of a modified linear system that can be solved with a separate solver call. Despite this ubiquitous advice, we are not aware of prior work comparing the accuracy of both approaches. With this article we thus empirically study a simple question: What happens if we ignore common wisdom, and differentiate through linear solvers?
A double self-sustainable reconfigurable intelligent surfaces (RISs) assisted multi-user multiple input multiple output (MIMO) system is investigated. Two RISs are equipped with energy harvesting circuit to achieve self-sustainable transmission. The aim is to minimize the transmission power at the base station (BS), while guaranteeing the quality of service (QoS) requirements of the users and meeting the power consumption requirements of the RISs. A block coordinate descent (BCD) algorithm based on the penalty-based method and successive convex approximation (SCA) is employed to alternatively optimize the active beamforming at the BS and the phase shifts, as well as amplitude coefficients of two RISs. Simulation results show that the required power consumption at the BS for the proposed double self-sustainable RISs system is significantly reduced compared to conventional RIS systems.
We devise achievable encoding schemes for distributed source compression for computing inner products, symmetric matrix products, and more generally, square matrix products, which are a class of nonlinear transformations. To that end, our approach relies on devising nonlinear mappings of distributed sources, which are then followed by the structured linear encoding scheme, introduced by K\"orner and Marton. For different computation scenarios, we contrast our findings on the achievable sum rate with the state of the art to demonstrate the possible savings in compression rate. When the sources have special correlation structures, it is possible to achieve unbounded gains, as demonstrated by the analysis and numerical simulations.
We propose a novel pipeline for unknown object grasping in shared robotic autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are typically learning-based approaches optimised for a specific end-effector, that generate grasp poses directly from sensor input. In the domain of assistive robotics, we seek instead to utilise the user's cognitive abilities for enhanced satisfaction, grasping performance, and alignment with their high level task-specific goals. Given a pair of stereo images, we perform unknown object instance segmentation and generate a 3D reconstruction of the object of interest. In shared control, the user then guides the robot end-effector across a virtual hemisphere centered around the object to their desired approach direction. A physics-based grasp planner finds the most stable local grasp on the reconstruction, and finally the user is guided by shared control to this grasp. In experiments on the DLR EDAN platform, we report a grasp success rate of 87% for 10 unknown objects, and demonstrate the method's capability to grasp objects in structured clutter and from shelves.
This study explores the application of deep learning technologies in software development processes, particularly in automating code reviews, error prediction, and test generation to enhance code quality and development efficiency. Through a series of empirical studies, experimental groups using deep learning tools and control groups using traditional methods were compared in terms of code error rates and project completion times. The results demonstrated significant improvements in the experimental group, validating the effectiveness of deep learning technologies. The research also discusses potential optimization points, methodologies, and technical challenges of deep learning in software development, as well as how to integrate these technologies into existing software development workflows.
The recent embrace of machine learning (ML) in the development of autonomous weapons systems (AWS) creates serious risks to geopolitical stability and the free exchange of ideas in AI research. This topic has received comparatively little attention of late compared to risks stemming from superintelligent artificial general intelligence (AGI), but requires fewer assumptions about the course of technological development and is thus a nearer-future issue. ML is already enabling the substitution of AWS for human soldiers in many battlefield roles, reducing the upfront human cost, and thus political cost, of waging offensive war. In the case of peer adversaries, this increases the likelihood of "low intensity" conflicts which risk escalation to broader warfare. In the case of non-peer adversaries, it reduces the domestic blowback to wars of aggression. This effect can occur regardless of other ethical issues around the use of military AI such as the risk of civilian casualties, and does not require any superhuman AI capabilities. Further, the military value of AWS raises the specter of an AI-powered arms race and the misguided imposition of national security restrictions on AI research. Our goal in this paper is to raise awareness among the public and ML researchers on the near-future risks posed by full or near-full autonomy in military technology, and we provide regulatory suggestions to mitigate these risks. We call upon AI policy experts and the defense AI community in particular to embrace transparency and caution in their development and deployment of AWS to avoid the negative effects on global stability and AI research that we highlight here.
Transformer-based large language models (LLMs) exhibit impressive performance in generative tasks but introduce significant challenges in real-world serving due to inefficient use of the expensive, computation-optimized accelerators. This mismatch arises from the autoregressive nature of LLMs, where the generation phase comprises operators with varying resource demands. Specifically, the attention operator is memory-intensive, exhibiting a memory access pattern that clashes with the strengths of modern accelerators, especially as context length increases. To enhance the efficiency and cost-effectiveness of LLM serving, we introduce the concept of attention offloading. This approach leverages a collection of cheap, memory-optimized devices for the attention operator while still utilizing high-end accelerators for other parts of the model. This heterogeneous setup ensures that each component is tailored to its specific workload, maximizing overall performance and cost efficiency. Our comprehensive analysis and experiments confirm the viability of splitting the attention computation over multiple devices. Also, the communication bandwidth required between heterogeneous devices proves to be manageable with prevalent networking technologies. To further validate our theory, we develop Lamina, an LLM inference system that incorporates attention offloading. Experimental results indicate that Lamina can provide 1.48x-12.1x higher estimated throughput per dollar than homogeneous solutions.
Mechanical metamaterials are artificially engineered microstructures that exhibit novel mechanical behavior on the macroscopic scale. Active metamaterials can be externally controlled. Pneumatically actuated metamaterials can change their mechanical, acoustic, or other types of effective behavior in response to applied pressure with possible applications ranging from soft robotic actuators to phononic crystals. To facilitate the design of such pneumatically actuated metamaterials and structures by topology optimization, a robust way of their computational modeling, capturing both pneumatic actuation of internal voids and internal contact, is needed. Since voids in topology optimization are often modeled using a soft material model, the third medium contact formulation lends itself as a suitable stepping stone. We propose a single hyperelastic material model capable of maintaining a prescribed hydrostatic Cauchy stress within a void in the pre-contact phase while simultaneously acting as a third medium to enforce frictionless contact, contrasting existing third medium approaches focused solely on contact. We split the overall third-medium energy density into contact, regularization, and pneumatic pressure contributions, all of which can be individually controlled and tuned. To prevent distortions of the compliant third medium, we include curvature penalization in our model. This improves on existing formulations in terms of compliant third medium behavior, leading ultimately to better numerical stability of the solution. Since our formulation is energetically consistent, we are able to employ more advanced finite element solvers, such as the modified Cholesky algorithm to detect instabilities. We demonstrate the behavior of the proposed formulation on several examples of traditional contact benchmarks, including a standard patch test, and validate it with experimental measurement.
The quest for robust Person re-identification (Re-ID) systems capable of accurately identifying subjects across diverse scenarios remains a formidable challenge in surveillance and security applications. This study presents a novel methodology that significantly enhances Person Re-Identification (Re-ID) by integrating Uncertainty Feature Fusion (UFFM) with Wise Distance Aggregation (WDA). Tested on benchmark datasets - Market-1501, DukeMTMC-ReID, and MSMT17 - our approach demonstrates substantial improvements in Rank-1 accuracy and mean Average Precision (mAP). Specifically, UFFM capitalizes on the power of feature synthesis from multiple images to overcome the limitations imposed by the variability of subject appearances across different views. WDA further refines the process by intelligently aggregating similarity metrics, thereby enhancing the system's ability to discern subtle but critical differences between subjects. The empirical results affirm the superiority of our method over existing approaches, achieving new performance benchmarks across all evaluated datasets. Code is available on Github.
Although several image super-resolution solutions exist, they still face many challenges. CNN-based algorithms, despite the reduction in computational complexity, still need to improve their accuracy. While Transformer-based algorithms have higher accuracy, their ultra-high computational complexity makes them difficult to be accepted in practical applications. To overcome the existing challenges, a novel super-resolution reconstruction algorithm is proposed in this paper. The algorithm achieves a significant increase in accuracy through a unique design while maintaining a low complexity. The core of the algorithm lies in its cleverly designed Global-Local Information Extraction Module and Basic Block Module. By combining global and local information, the Global-Local Information Extraction Module aims to understand the image content more comprehensively so as to recover the global structure and local details in the image more accurately, which provides rich information support for the subsequent reconstruction process. Experimental results show that the comprehensive performance of the algorithm proposed in this paper is optimal, providing an efficient and practical new solution in the field of super-resolution reconstruction.
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