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The demand for unprecedented performance in the upcoming 6G wireless networks is fomenting the research on THz communications empowered by Reconfigurable Inteligent Surfaces (RISs). A wide range of use cases have been proposed, most of them, assuming high-level RIS models that overlook some of the hardware impairments that this technology faces. The expectation is that the emergent reconfigurable THz technologies will eventually overcome its current limitations. This disassociation from the hardware may mask nonphysical assumptions, perceived as hardware limitations. In this paper, a top-down approach bounded by physical constraints is presented, distilling from system-level specifications, hardware requirements, and upper bounds for the RIS-aided system performance. We consider D-band indoor and outdoor scenarios where a more realistic assessment of the state-of-the-art solution can be made. The goal is to highlight the intricacies of the design procedure based on sound assumptions for the RIS performance. For a given signal range and angular coverage, we quantify the required RIS size, number of switching elements, and maximum achievable bandwidth and capacity.

Community detection is the problem of identifying densely connected clusters of nodes within a network. The Louvain algorithm is a widely used method for this task, but it can produce communities that are internally disconnected. To address this, the Leiden algorithm was introduced. However, our analysis and empirical observations indicate that the Leiden algorithm still identifies disconnected communities, albeit to a lesser extent. To mitigate this issue, we propose two new parallel algorithms: GSP-Leiden and GSP-Louvain, based on the Leiden and Louvain algorithms, respectively. On a system with two 16-core Intel Xeon Gold 6226R processors, we demonstrate that GSP-Leiden/GSP-Louvain not only address this issue, but also outperform the original Leiden, igraph Leiden, and NetworKit Leiden by 190x/341x, 46x/83x, and 3.4x/6.1x respectively - achieving a processing rate of 195M/328M edges/s on a 3.8B edge graph. Furthermore, GSP-Leiden/GSP-Louvain improve performance at a rate of 1.6x/1.5x for every doubling of threads.

Graph neural networks (GNNs) have demonstrated significant success in various applications, such as node classification, link prediction, and graph classification. Active learning for GNNs aims to query the valuable samples from the unlabeled data for annotation to maximize the GNNs' performance at a lower cost. However, most existing algorithms for reinforced active learning in GNNs may lead to a highly imbalanced class distribution, especially in highly skewed class scenarios. GNNs trained with class-imbalanced labeled data are susceptible to bias toward majority classes, and the lower performance of minority classes may lead to a decline in overall performance. To tackle this issue, we propose a novel class-balanced and reinforced active learning framework for GNNs, namely, GCBR. It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes. GCBR designs class-balance-aware states, as well as a reward function that achieves trade-off between model performance and class balance. The reinforcement learning algorithm Advantage Actor-Critic (A2C) is employed to learn an optimal policy stably and efficiently. We further upgrade GCBR to GCBR++ by introducing a punishment mechanism to obtain a more class-balanced labeled set. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed approaches, achieving superior performance over state-of-the-art baselines.

Deep unfolding network (DUN) that unfolds the optimization algorithm into a deep neural network has achieved great success in compressive sensing (CS) due to its good interpretability and high performance. Each stage in DUN corresponds to one iteration in optimization. At the test time, all the sampling images generally need to be processed by all stages, which comes at a price of computation burden and is also unnecessary for the images whose contents are easier to restore. In this paper, we focus on CS reconstruction and propose a novel Dynamic Path-Controllable Deep Unfolding Network (DPC-DUN). DPC-DUN with our designed path-controllable selector can dynamically select a rapid and appropriate route for each image and is slimmable by regulating different performance-complexity tradeoffs. Extensive experiments show that our DPC-DUN is highly flexible and can provide excellent performance and dynamic adjustment to get a suitable tradeoff, thus addressing the main requirements to become appealing in practice. Codes are available at //github.com/songjiechong/DPC-DUN.

With the recent growth in demand for large-scale deep neural networks, compute in-memory (CiM) has come up as a prominent solution to alleviate bandwidth and on-chip interconnect bottlenecks that constrain Von-Neuman architectures. However, the construction of CiM hardware poses a challenge as any specific memory hierarchy in terms of cache sizes and memory bandwidth at different interfaces may not be ideally matched to any neural network's attributes such as tensor dimension and arithmetic intensity, thus leading to suboptimal and under-performing systems. Despite the success of neural architecture search (NAS) techniques in yielding efficient sub-networks for a given hardware metric budget (e.g., DNN execution time or latency), it assumes the hardware configuration to be frozen, often yielding sub-optimal sub-networks for a given budget. In this paper, we present CiMNet, a framework that jointly searches for optimal sub-networks and hardware configurations for CiM architectures creating a Pareto optimal frontier of downstream task accuracy and execution metrics (e.g., latency). The proposed framework can comprehend the complex interplay between a sub-network's performance and the CiM hardware configuration choices including bandwidth, processing element size, and memory size. Exhaustive experiments on different model architectures from both CNN and Transformer families demonstrate the efficacy of the CiMNet in finding co-optimized sub-networks and CiM hardware configurations. Specifically, for similar ImageNet classification accuracy as baseline ViT-B, optimizing only the model architecture increases performance (or reduces workload execution time) by 1.7x while optimizing for both the model architecture and hardware configuration increases it by 3.1x.

The internet of things (IoT) based wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore, this paper proposes a probabilistic on-demand charging scheduling for integrated sensing and communication (ISAC)-assisted WRSNs with multiple mobile charging vehicles (MCVs) that addresses three parts. First, it considers the four attributes with their probability distributions to balance the charging load on each MCV. The distributions are residual energy of charging node, distance from MCV to charging node, degree of charging node, and charging node betweenness centrality. Second, it considers the efficient charging factor strategy to partially charge network nodes. Finally, it employs the ISAC concept to efficiently utilize the wireless resources to reduce the traveling cost of each MCV and to avoid the charging conflicts between them. The simulation results show that the proposed protocol outperforms cutting-edge protocols in terms of energy usage efficiency, charging delay, survival rate, and travel distance.

While neural networks (NNs) have a large potential as goal-oriented controllers for Cyber-Physical Systems, verifying the safety of neural network based control systems (NNCSs) poses significant challenges for the practical use of NNs -- especially when safety is needed for unbounded time horizons. One reason for this is the intractability of NN and hybrid system analysis. We introduce VerSAILLE (Verifiably Safe AI via Logically Linked Envelopes): The first approach for the combination of differential dynamic logic (dL) and NN verification. By joining forces, we can exploit the efficiency of NN verification tools while retaining the rigor of dL. We reflect a safety proof for a controller envelope in an NN to prove the safety of concrete NNCS on an infinite-time horizon. The NN verification properties resulting from VerSAILLE typically require nonlinear arithmetic while efficient NN verification tools merely support linear arithmetic. To overcome this divide, we present Mosaic: The first sound and complete verification approach for polynomial real arithmetic properties on piece-wise linear NNs. Mosaic lifts off-the-shelf tools for linear properties to the nonlinear setting. An evaluation on case studies, including adaptive cruise control and airborne collision avoidance, demonstrates the versatility of VerSAILLE and Mosaic: It supports the certification of infinite-time horizon safety and the exhaustive enumeration of counterexample regions while significantly outperforming State-of-the-Art tools in closed-loop NNV.

We present a method for global motion planning of robotic systems that interact with the environment through contacts. Our method directly handles the hybrid nature of such tasks using tools from convex optimization. We formulate the motion-planning problem as a shortest-path problem in a graph of convex sets, where a path in the graph corresponds to a contact sequence and a convex set models the quasi-static dynamics within a fixed contact mode. For each contact mode, we use semidefinite programming to relax the nonconvex dynamics that results from the simultaneous optimization of the object's pose, contact locations, and contact forces. The result is a tight convex relaxation of the overall planning problem, that can be efficiently solved and quickly rounded to find a feasible contact-rich trajectory. As a first application of this technique, we focus on the task of planar pushing. Exhaustive experiments show that our convex-optimization method generates plans that are consistently within a small percentage of the global optimum. We demonstrate the quality of these plans on a real robotic system.

Ensembles over neural network weights trained from different random initialization, known as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently introduced batch ensembles provide a drop-in replacement that is more parameter efficient. In this paper, we design ensembles not only over weights, but over hyperparameters to improve the state of the art in both settings. For best performance independent of budget, we propose hyper-deep ensembles, a simple procedure that involves a random search over different hyperparameters, themselves stratified across multiple random initializations. Its strong performance highlights the benefit of combining models with both weight and hyperparameter diversity. We further propose a parameter efficient version, hyper-batch ensembles, which builds on the layer structure of batch ensembles and self-tuning networks. The computational and memory costs of our method are notably lower than typical ensembles. On image classification tasks, with MLP, LeNet, and Wide ResNet 28-10 architectures, our methodology improves upon both deep and batch ensembles.

Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research.