With the rapid development of unmanned aerial vehicle (UAV) technology, UAVs have provided an innovative solution for floating debris monitoring. However, object detection in UAV images remains challenging due to high miss rates for small objects, insufficient low-level feature extraction and computational redundancy. This letter proposes an Efficient Floating Debris detection model based on YOLOv8n, named EFD-YOLO, to address these issues. First, the Edge Fusion Stem (EFStem) module is proposed to enhance low-level feature extraction through an integrated gate-attention mechanism. Second, the Multi-Branch Efficient Reparameterization Block (MBERB) is designed to achieve efficient cross-layer feature fusion. Experimental results demonstrate that compared to YOLOv8n, our model achieves a 6.3% improvement in mean Average Precision (mAP) on the UAV Floating Debris Dataset, while simultaneously reducing parameters by 26.7% and improving small object recall by 21.9%. The inference time of EFD-YOLO on the RK3588 edge device is as low as 30.5 ms, demonstrating real-time capability.

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.

We revisit the problem of low-memory robust simulation of interactive protocols over noisy channels. Haeupler [FOCS 2014] considered robust simulation of two-party interactive protocols over oblivious, as well as adaptive, noisy channels. Since the simulation does not need to have fixed communication pattern, the achieved communication rates can circumvent the lower bound proved by Kol and Raz [STOC 2013]. However, a drawback of this approach is that each party needs to remember the whole history of the simulated transcript. In a subsequent manuscript, Haeupler and Resch considered low-memory simulation. The idea was to view the original protocol as a computational DAG and only the identities of the nodes are saved (as opposed to the whole transcript history) for backtracking to reduce memory usage. In this paper, we consider low-memory robust simulation of more general client-server interactive protocols, in which a leader communicates with other members/servers, who do not communicate among themselves; this setting can be applied to information-theoretic multi-server Private Information Retrieval (PIR) schemes. We propose an information-theoretic technique that converts any correct PIR protocol that assumes reliable channels, into a protocol which is both correct and private in the presence of a noisy channel while keeping the space complexity to a minimum. Despite the huge attention that PIR protocols have received in the literature, the existing works assume that the parties communicate using noiseless channels. Moreover, we observe that the approach of Haeupler and Resch to just save the nodes in the aforementioned DAG without taking the transcript history into account will lead to a correctness issue even for oblivious corruptions. We resolve this issue by saving hashes of prefixes of past transcripts. Departing from the DAG representation also allows us to accommodate scenarios where a party can simulate its part of the protocol without any extra knowledge (such as the DAG representation of the whole protocol). In the the two-party setting, our simulation has the same dependence on the error rate as in the work of Haeupler, and in the client-server setting it also depends on the number of servers. Furthermore, since our approach does not remember the complete transcript history, our current technique can defend only against oblivious corruptions.

We revisit the opinion susceptibility problem that was proposed by Abebe et al. [1], in which agents influence one another's opinions through an iterative process. Each agent has some fixed innate opinion. In each step, the opinion of an agent is updated to some convex combination between its innate opinion and the weighted average of its neighbors' opinions in the previous step. The resistance of an agent measures the importance it places on its innate opinion in the above convex combination. Under non-trivial conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to select the resistance of each agent (from some given range) such that the sum of the equilibrium opinions is minimized.

Designing energy-efficient Digital Signal Processor (DSP) cores has become a key concern in embedded systems development. This paper proposes an energy-proportional computing scheme for Very Long Instruction Word (VLIW) architectures. To make the processor power scales with adapted parallelism, we propose incorporating distributed Power-Gated Register Files (PGRF) into VLIW to achieve a PGRF-VLIW architecture. For energy efficiency, we also propose an instruction scheduling algorithm called the Deadline-Constrained Clustered Scheduling (DCCS) algorithm. The algorithm clusters the data dependence graph to reduce data transfer energy and makes optimal use of low-powered local registers for tree-structured data dependence graphs. The results of evaluations conducted using the MiBench and DSPstone benchmark suites substantiate the expected power saving and scaling effects.