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In this work, we consider the problem of finding a set of tours to a traveling salesperson problem (TSP) instance maximizing diversity, while satisfying a given cost constraint. This study aims to investigate the effectiveness of applying niching to maximize diversity rather than simply maintaining it. To this end, we introduce a 2-stage approach where a simple niching memetic algorithm (NMA), derived from a state-of-the-art for multi-solution TSP, is combined with a baseline diversifying algorithm. The most notable feature of the proposed NMA is the use of randomized improvement-first local search instead of 2-opt. Our experiment on TSPLIB instances shows that while the populations evolved by our NMA tend to contain clusters at tight quality constraints, they frequently occupy distant basins of attraction rather than close-by regions, improving on the baseline diversification in terms of sum-sum diversity. Compared to the original NMA, ours, despite its simplicity, finds more distant solutions of higher quality within less running time, by a large margin.

For scenes such as floods and earthquakes, the disaster area is large, and rescue time is tight. Multi-UAV exploration is more efficient than a single UAV. Existing UAV exploration work is modeled as a Coverage Path Planning (CPP) task to achieve full coverage of the area in the presence of obstacles. However, the endurance capability of UAV is limited, and the rescue time is urgent. Thus, even using multiple UAVs cannot achieve complete disaster area coverage in time. Therefore, in this paper we propose a multi-Agent Endurance-limited CPP (MAEl-CPP) problem based on a priori heatmap of the disaster area, which requires the exploration of more valuable areas under limited energy. Furthermore, we propose a path planning algorithm for the MAEl-CPP problem, by ranking the possible disaster areas according to their importance through satellite or remote aerial images and completing path planning according to the importance level. Experimental results show that our proposed algorithm is at least twice as effective as the existing method in terms of search efficiency.

Serverless computing platforms currently rely on basic pricing schemes that are static and do not reflect customer feedback. This leads to significant inefficiencies from a total utility perspective. As one of the fastest-growing cloud services, serverless computing provides an opportunity to better serve both users and providers through the incorporation of market-based strategies for pricing and resource allocation. With the help of utility functions to model the delay-sensitivity of customers, we propose a novel scheduler to allocate resources for serverless computing. The resulting resource allocation scheme is optimal in the sense that it maximizes the aggregate utility of all users across the system, thus maximizing social welfare. Our approach gives rise to a natural dynamic pricing scheme that is obtained by solving an optimization problem in its dual form. We further develop feedback mechanisms that allow the cloud provider to converge to optimal resource allocation, even when the users' utilities are private and unknown to the service provider. Simulations show that our approach can track market demand and achieve significantly higher social welfare (or, equivalently, cost savings for customers) compared to existing schemes.

State-of-the-art machine learning models are routinely trained on large-scale distributed clusters. Crucially, such systems can be compromised when some of the computing devices exhibit abnormal (Byzantine) behavior and return arbitrary results to the parameter server (PS). This behavior may be attributed to a plethora of reasons, including system failures and orchestrated attacks. Existing work suggests robust aggregation and/or computational redundancy to alleviate the effect of distorted gradients. However, most of these schemes are ineffective when an adversary knows the task assignment and can choose the attacked workers judiciously to induce maximal damage. Our proposed method Aspis assigns gradient computations to worker nodes using a subset-based assignment which allows for multiple consistency checks on the behavior of a worker node. Examination of the calculated gradients and post-processing (clique-finding in an appropriately constructed graph) by the central node allows for efficient detection and subsequent exclusion of adversaries from the training process. We prove the Byzantine resilience and detection guarantees of Aspis under weak and strong attacks and extensively evaluate the system on various large-scale training scenarios. The principal metric for our experiments is the test accuracy, for which we demonstrate a significant improvement of about 30% compared to many state-of-the-art approaches on the CIFAR-10 dataset. The corresponding reduction of the fraction of corrupted gradients ranges from 16% to 99%.

The next generation multibeam satellites open up a new way to design satellite communication channels with the full flexibility in bandwidth, transmit power and beam coverage management. In this paper, we exploit the flexible multibeam satellite capabilities and the geographical distribution of users to improve the performance of satellite-assisted edge caching systems. Our aim is to jointly optimize the bandwidth allocation in multibeam and caching decisions at the edge nodes to address two important problems: i) cache feeding time minimization and ii) cache hits maximization. To tackle the non-convexity of the joint optimization problem, we transform the original problem into a difference-of-convex (DC) form, which is then solved by the proposed iterative algorithm whose convergence to at least a local optimum is theoretically guaranteed. Furthermore, the effectiveness of the proposed design is evaluated under the realistic beams coverage of the satellite SES-14 and Movielens data set. Numerical results show that our proposed joint design can reduce the caching feeding time by 50\% and increase the cache hit ratio (CHR) by 10\% to 20\% compared to existing solutions. Furthermore, we examine the impact of multispot beam and multicarrier wide-beam on the joint design and discuss potential research directions.

Federated learning makes it possible for all parties with data isolation to train the model collaboratively and efficiently while satisfying privacy protection. To obtain a high-quality model, an incentive mechanism is necessary to motivate more high-quality workers with data and computing power. The existing incentive mechanisms are applied in offline scenarios, where the task publisher collects all bids and selects workers before the task. However, it is practical that different workers arrive online in different orders before or during the task. Therefore, we propose a reverse auction-based online incentive mechanism for horizontal federated learning with budget constraint. Workers submit bids when they arrive online. The task publisher with a limited budget leverages the information of the arrived workers to decide on whether to select the new worker. Theoretical analysis proves that our mechanism satisfies budget feasibility, computational efficiency, individual rationality, consumer sovereignty, time truthfulness, and cost truthfulness with a sufficient budget. The experimental results show that our online mechanism is efficient and can obtain high-quality models.

Learned indexes, which use machine learning models to replace traditional index structures, have shown promising results in recent studies. Existing learned indexes use heuristic rules to construct index structures, which are often suboptimal and sensitive to data distribution. In this paper, we argue that upper-level RMI nodes should focus on data partitioning instead of model fitting, and show that it leads to much better results in real-world datasets. We introduce entropy as a metric to quantify and characterize the models in learned indexes, which provides a new theoretical basis for subsequent works. Moreover, we propose a new memory layout design with a fixed node size throughout the tree structure, which allows the type of each node to be flexibly chosen at runtime. We propose CARMI, a new efficient and updatable cache-aware RMI framework. To reduce reliance on the expertise of database administrators, CARMI uses a hybrid construction algorithm to automatically construct the index structures under various datasets and workloads without any manual tuning. Our experimental study shows that CARMI performs better and is more robust compared to baselines, achieving an average of 2.37x/1.98x speedup compared to B+ Tree/ALEX, while using only about 0.70x memory space of B+ Tree. In the SOSD platform, CARMI outperforms all the baselines over the real-world datasets, with an average speedup of 1.21x over the nearest competitor. We believe that our theoretical analysis and proposed framework can help learned indexes to get closer to practical deployment.

As investigations on physical layer security evolve from point-to-point systems to multi-user scenarios, multi-user interference (MUI) is introduced and becomes an unavoidable issue. Different from treating MUI totally as noise in conventional secure communications, in this paper, we propose a rate-splitting multiple access (RSMA)-based secure beamforming design, where user messages are split and encoded into common and private streams. Each user not only decodes the common stream and the intended private stream, but also tries to eavesdrop the private streams of other users. We formulate a weighted sum-rate (WSR) maximization problem subject to the secrecy rate requirements of all users. To tackle the non-convexity of the formulated problem, a successive convex approximation (SCA)-based approach is adopted to convert the original non-convex and intractable problem into a low-complexity suboptimal iterative algorithm. Numerical results demonstrate that the proposed secure beamforming scheme outperforms the conventional multi-user linear precoding (MULP) technique in terms of the WSR performance while ensuring user secrecy rate requirements.

We present Neural A*, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, a machine learning approach to search-based planning is still challenging due to the discrete nature of search algorithms. In this work, we reformulate a canonical A* search algorithm to be differentiable and couple it with a convolutional encoder to form an end-to-end trainable neural network planner. Neural A* solves a path planning problem by encoding a problem instance to a guidance map and then performing the differentiable A* search with the guidance map. By learning to match the search results with ground-truth paths provided by experts, Neural A* can produce a path consistent with the ground truth accurately and efficiently. Our extensive experiments confirmed that Neural A* outperformed state-of-the-art data-driven planners in terms of the search optimality and efficiency trade-off, and furthermore, successfully predicted realistic human trajectories by directly performing search-based planning on natural image inputs.

Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropriately account for heteroscedasticity, even in the bandit setting. Motivated by recent findings that address this issue in bandits, we propose to use Information-Directed Sampling (IDS) for exploration in reinforcement learning. As our main contribution, we build on recent advances in distributional reinforcement learning and propose a novel, tractable approximation of IDS for deep Q-learning. The resulting exploration strategy explicitly accounts for both parametric uncertainty and heteroscedastic observation noise. We evaluate our method on Atari games and demonstrate a significant improvement over alternative approaches.