As drone-based applications proliferate, paramount contactless sensing of airborne drones from the ground becomes indispensable. This work demonstrates concentrating on propeller rotational speed will substantially improve drone sensing performance and proposes an event-camera-based solution, EventPro. EventPro features two components: Count Every Rotation achieves accurate, real-time propeller speed estimation by mitigating ultra-high sensitivity of event cameras to environmental noise. Every Rotation Counts leverages these speeds to infer both internal and external drone dynamics. Extensive evaluations in real-world drone delivery scenarios show that EventPro achieves a sensing latency of 3 ms and a rotational speed estimation error of merely 0.23%. Additionally, EventPro infers drone flight commands with 96.5% precision and improves drone tracking accuracy by over 22% when combined with other sensing modalities. Demo: https://eventpro25.github.io/EventPro/.

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we upgrade traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for precise drone landings. To fully exploit thetemporal consistencyandspatial complementaritybetween these two modalities, we propose two innovative modules:(i)the Consistency-instructed Collaborative Tracking module, which further leverages the drone's physical knowledge of periodic micro-motions and structure for accurate measurements extraction, and(ii)the Graph-informed Adaptive Joint Optimization module, which integrates drone motion information for efficient sensor fusion and drone localization. Extensive experiments (30+ hours) demonstrate that mmE-Loc attains 0.083$m$localization accuracy and 5.12$ms$end-to-end latency, outperforming four state-of-the-art methods by over 48% and 62%, respectively.

Aligning large language models (LLMs) with human preferences has become a critical step in their development.Recent research has increasingly focused on test-time alignment, where additional compute is allocated during inference to enhance LLM safety and reasoning capabilities.However, these test-time alignment techniques often incur substantial inference costs, limiting their practical application.We are inspired by the speculative sampling acceleration, which leverages a small draft model to efficiently predict future tokens, to address the efficiency bottleneck of test-time alignment.We introduce the reward-Shifted Speculative Sampling (SSS) algorithm, in which the draft model is aligned with human preferences, while the target model remains unchanged.We theoretically demonstrate that the distributional shift between the aligned draft model and the unaligned target model can be exploited to recover the RLHF optimal solution without actually obtaining it, by modifying the acceptance criterion and bonus token distribution.Our algorithm achieves superior gold reward scores at a significantly reduced inference cost in test-time weak-to-strong alignment experiments, thereby validating both its effectiveness and efficiency. 1

Foundation medical segmentation models, with MedSAM being the most popular, have achieved promising performance across organs and lesions. However, MedSAM still suffers from compromised performance on specific lesions with intricate structures and appearance, as well as bounding box prompt-induced perturbations. Although current test-time adaptation (TTA) methods for medical image segmentation may tackle this issue, partial (e.g., batch normalization) or whole parametric updates restrict their effectiveness due to limited update signals or catastrophic forgetting in large models. Meanwhile, these approaches ignore the computational complexity during adaptation, which is particularly significant for modern foundation models. To this end, our theoretical analyses reveal that directly refining image embeddings is feasible to approach the same goal as parametric updates under the MedSAM architecture, which enables us to realize high computational efficiency and segmentation performance without the risk of catastrophic forgetting. Under this framework, we propose to encourage maximizing factorized conditional probabilities of the posterior prediction probability using a proposed distribution-approximated latent conditional random field loss combined with an entropy minimization loss. Experiments show that we achieve about 3\% Dice score improvements across three datasets while reducing computational complexity by over 7 times.

Multi-robot clusters are increasingly deployed in indoor environments, where effective communication and 3D perception are critical for coordinated operations. Monocular cameras, known for their lightweight design, cost-effectiveness, and versatility, present a promising solution for these tasks. However, relying solely on monocular cameras for comprehensive perception and communication presents significant challenges. To address this, we introduce MonoLDP, a novel system that leverages monocular cameras for depth estimation, mutual pose estimation, and visible light communication in indoor environments, providing an integrated framework to overcome these limitations. MonoLDP features a two-stage network: (1) a depth estimation module that infers depth from monocular images, and (2) a depth-guided 3D object recognition network for agent-relative localization and pose estimation. We created a custom dataset to validate the accuracy of MonoLDP. On our indoor dataset, MonoLDP outperforms the baseline by 43.39% in 3D detection and 42.39% in bird's-eye view detection, with an average localization error of 0.104 m and an orientation error of 1.66 degrees. Moreover, the depth estimation network demonstrates excellent performance on the NYU v2 dataset. Additionally, the system achieves a communication rate of 1.2 Kbps with a bit error rate below 10-2at a distance of up to 4 m using LED arrays. Our code will be released at https://github.com/RavenLiang1005/MonoLDP.git.

Drones operating in complex environments face a significant threat from thin obstacles, such as steel wires and kite strings at the submillimeter level, which are notoriously difficult for conventional sensors like RGB cameras, LiDAR, and depth cameras to detect. This paper introduces SkyShield, an event-driven, end-to-end framework designed for the perception of submillimeter scale obstacles. Drawing upon the unique features that thin obstacles present in the event stream, our method employs a lightweight U-Net architecture and an innovative Dice-Contour Regularization Loss to ensure precise detection. Experimental results demonstrate that our event-based approach achieves mean F1 Score of 0.7088 with a low latency of 21.2 ms, making it ideal for deployment on edge and mobile platforms.

Spiking Neural Networks (SNNs), as a biologically plausible alternative to Artificial Neural Networks (ANNs), have demonstrated advantages in terms of energy efficiency, temporal processing, and biological plausibility. However, SNNs are highly sensitive to distribution shifts, which can significantly degrade their performance in real-world scenarios. Traditional test-time adaptation (TTA) methods designed for ANNs often fail to address the unique computational dynamics of SNNs, such as sparsity and temporal spiking behavior. To address these challenges, we propose SPike-Aware Consistency Enhancement (SPACE), the first source-free and single-instance TTA method specifically designed for SNNs. SPACE leverages the inherent spike dynamics of SNNs to maximize the consistency of spike-behavior-based local feature maps across augmented versions of a single test sample, enabling robust adaptation without requiring source data. We evaluate SPACE on multiple datasets. Furthermore, SPACE exhibits robust generalization across diverse network architectures, consistently enhancing the performance of SNNs on CNNs, Transformer, and ConvLSTM architectures. Experimental results show that SPACE outperforms state-of-the-art ANN methods while maintaining lower computational cost, highlighting its effectiveness and robustness for SNNs in real-world settings. The code will be available at https://github.com/ethanxyluo/SPACE.

Accurately extracting protocol-state machines (PSMs) from the long, densely written Request-for-Comments (RFC) standards that govern Internet‐scale communication remains a bottleneck for automated security analysis and protocol testing. In this paper, we introduce RFC2PSM, the first large-scale dataset that pairs 1,580 pages of cleaned RFC text with 108 manually validated states and 297 transitions covering 14 widely deployed protocols spanning the data-link, transport, session, and application layers. Built on this corpus, we propose PsmBench, a benchmark that (i) feeds chunked RFC to an LLM, (ii) prompts the model to emit a machine-readable PSM, and (iii) scores the output with structure-aware, semantic fuzzy-matching metrics that reward partially correct graphs.A comprehensive baseline study of nine state-of-the-art open and commercial LLMs reveals a persistent state–transition gap: models identify many individual states (up to $0.82$ F1) but struggle to assemble coherent transition graphs ($\leq 0.38$ F1), highlighting challenges in long-context reasoning, alias resolution, and action/event disambiguation. We release the dataset, evaluation code, and all model outputs as open-sourced, providing a fully reproducible starting point for future work on reasoning over technical prose and generating executable graph structures. RFC2PSM and PsmBench aim to catalyze cross-disciplinary progress toward LLMs that can interpret and verify the protocols that keep the Internet safe.

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we replace traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for drone landings. To fully leverage the temporal consistency and spatial complementarity between these modalities, we propose two innovative modules, consistency-instructed collaborative tracking and graph-informed adaptive joint optimization, for accurate drone measurement extraction and efficient sensor fusion. Real-world experiments in landing scenarios from a drone delivery company demonstrate that mmE-Loc outperforms SOTA methods in both accuracy and latency.

Accurately localizing high-speed dynamic objects in 3D space with low latency is crucial for various robotic applications. Current methods face challenges due to extended exposure times and limited sensor resolution, hindering precise object detection and localization. Event cameras, known for their high temporal resolution and asynchronous nature, offer a promising solution. To leverage the potential of the event camera, we propose EventTracker, a novel framework that integrates event and depth measurements for precise and low-latency 3D localization and tracking of the high-speed dynamic object. EventTracker incorporates a collaborative object detection and tracking algorithm optimized for both event and depth data, overcoming detection and registration challenges. Additionally, a graph-instructed optimization algorithm enhances accuracy by fusing heterogeneous sensor data effectively. Experimental evaluation in dynamic environments demonstrates significant improvements in localization performance compared to baseline methods.

Event cameras excel in high-speed and high-dynamic-range scenarios but are highly sensitive to rain, which introduces significant noise while also revealing detailed rain features. This paper introduces a novel Event-based Rain-Background Decomposition Network that integrates Spiking Neural Networks (SNNs) and Convolutional Neural Networks (CNNs). By "Distilling Rain," we reconstruct a rain-free background for downstream tasks, and by "Collecting Rain," we extract the physical characteristics of rain. Experimental evaluations demonstrate the network's effectiveness in both background reconstruction and rain modeling. This work extends the capabilities of event cameras by mitigating the adverse effects of rain while also leveraging rain-induced noise to extract valuable environmental data, enhancing their utility in both challenging weather conditions and detailed environmental analysis.

Hyperspectral image (HSI) change detection (CD) has gained significant attention in the field of remote sensing. Current CD methods typically extract features based on spatial or spectral correlations between bitemporal HSIs, which often overlook the difference information, leading to a decrease in CD accuracy. Furthermore, these algorithms do not fully consider the alignment of features between images across different channel and spatial dimensions. To tackle these issues, we propose a novel approach called the differential information-enhanced feature exchange network (DIEFEN) for HSI CD, which leverages the difference information between images and enhances the alignment of bitemporal image features to improve CD accuracy. Specifically, an enhanced differential multihead attention (EDMA) module is proposed to utilize difference information to guide the feature aggregation of bitemporal images, effectively highlighting changing pixels and suppressing unchanging pixels. A feature focus and long-range attention (FFLA) module is designed to extract local and global features, and a channel-spatial interaction (CSI) module is constructed to align features and mitigate the impact of noise. Experimental results on three HSI CD datasets demonstrate that the proposed DIEFEN method outperforms several state-of-the-art methods. The source code of the proposed DIEFEN is released athttps://github.com/creativeXin/DIEFEN.

Finding the mode of a high dimensional probability distribution $\mathcal{D}$ is a fundamental algorithmic problem in statistics and data analysis. There has been particular interest in efficient methods for solving the problem when $\mathcal{D}$ is represented as a mixture model or kernel density estimate, although few algorithmic results with worst-case approximation and runtime guarantees are known. In this work, we significantly generalize a result of (LeeLiMusco:2021) on mode approximation for Gaussian mixture models. We develop randomized dimensionality reduction methods for mixtures involving a broader class of kernels, including the popular logistic, sigmoid, and generalized Gaussian kernels. As in Lee et al.'s work, our dimensionality reduction results yield quasi-polynomial algorithms for mode finding with multiplicative accuracy $(1-\epsilon)$ for any $\epsilon > 0$. Moreover, when combined with gradient descent, they yield efficient practical heuristics for the problem. In addition to our positive results, we prove a hardness result for box kernels, showing that there is no polynomial time algorithm for finding the mode of a kernel density estimate, unless $\mathit{P} = \mathit{NP}$. Obtaining similar hardness results for kernels used in practice (like Gaussian or logistic kernels) is an interesting future direction.

Time series classification and class imbalance problem are two common issues in a multitude of real-life scenarios. This paper simultaneously explores both issues with deep convolution neural networks (CNNs). Because standard networks treat the majority and minority classes with same class weights, most CNN-based networks fail to classify imbalanced time series. Until recently, there is very little work applying deep learning to imbalanced time series classification (ITSC). Thus, we propose an adaptive cost-sensitive learning strategy to address the ITSC problem. The standard CNN is modified to a cost-sensitive network (CS-CNN), which is able to punish the misclassified samples using a class-dependent cost matrix. Moreover, this cost matrix is automatically updated based on overall class distribution and the CS-CNN’s training performance. The proposed method is extended to FCN, LSTM-FCN and ResNet. It is experimentally tested on five public benchmark UCR datasets and a real-life large volume dataset. Four cost-sensitive CNN-based networks are compared with several data samplers and two traditional ITSC methods. The modified networks are superior in all metrics. Results show that cost-sensitive networks successfully complete the ITSC tasks.