All-optical wavelength-tunable narrow-linewidth fiber laser

Parameter regulations of narrow-linewidth fiber lasers in frequency domain has drawn considerable interests for widespread applications in the light quantum computing, precise coherent detection, and generation of micro-waves. All-optical methods provide compact, precise and fast accesses to achieving these lasers with wavelength-tunability. Here, the optical-thermal effects of graphene is utilized to precisely control operations of free-running lasers with a tuning speed of 140 MHz/ms. Assisted by the single-longitude-mode operation and linewidth suppression of stimulated Brillouin backscattering, we obtain an optical-controllable ~750 Hz fiber laser with a wavelength-tuning range of 3.7 nm

Reinforcement Learning with Human Feedback for Realistic Traffic Simulation

In light of the challenges and costs of real-world testing, autonomous vehicle developers often rely on testing in simulation for the creation of reliable systems. A key element of effective simulation is the incorporation of realistic traffic models that align with human knowledge, an aspect that has proven challenging due to the need to balance realism and diversity. This works aims to address this by developing a framework that employs reinforcement learning with human preference (RLHF) to enhance the realism of existing traffic models. This study also identifies two main challenges: capturing the nuances of human preferences on realism and the unification of diverse traffic simulation models. To tackle these issues, we propose using human feedback for alignment and employ RLHF due to its sample efficiency. We also introduce the first dataset for realism alignment in traffic modeling to support such research. Our framework, named TrafficRLHF, demonstrates its proficiency in generating realistic traffic scenarios that are well-aligned with human preferences, as corroborated by comprehensive evaluations on the nuScenes dataset.Comment: 9 pages, 4 figure

ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency

Deep neural networks (DNNs) are increasingly integrated into LiDAR (Light Detection and Ranging)-based perception systems for autonomous vehicles (AVs), requiring robust performance under adversarial conditions. We aim to address the challenge of LiDAR spoofing attacks, where attackers inject fake objects into LiDAR data and fool AVs to misinterpret their environment and make erroneous decisions. However, current defense algorithms predominantly depend on perception outputs (i.e., bounding boxes) thus face limitations in detecting attackers given the bounding boxes are generated by imperfect perception models processing limited points, acquired based on the ego vehicle's viewpoint. To overcome these limitations, we propose a novel framework, named ADoPT (Anomaly Detection based on Point-level Temporal consistency), which quantitatively measures temporal consistency across consecutive frames and identifies abnormal objects based on the coherency of point clusters. In our evaluation using the nuScenes dataset, our algorithm effectively counters various LiDAR spoofing attacks, achieving a low ( 85%) true positive ratio (TPR), outperforming existing state-of-the-art defense methods, CARLO and 3D-TC2. Furthermore, our evaluation demonstrates the promising potential for accurate attack detection across various road environments.Comment: BMVC 2023 (17 pages, 13 figures, and 1 table

AdvDO: Realistic Adversarial Attacks for Trajectory Prediction

Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe driving behaviors. While many prior works aim to achieve higher prediction accuracy, few study the adversarial robustness of their methods. To bridge this gap, we propose to study the adversarial robustness of data-driven trajectory prediction systems. We devise an optimization-based adversarial attack framework that leverages a carefully-designed differentiable dynamic model to generate realistic adversarial trajectories. Empirically, we benchmark the adversarial robustness of state-of-the-art prediction models and show that our attack increases the prediction error for both general metrics and planning-aware metrics by more than 50% and 37%. We also show that our attack can lead an AV to drive off road or collide into other vehicles in simulation. Finally, we demonstrate how to mitigate the adversarial attacks using an adversarial training scheme.Comment: To appear in ECCV 202

Graph Analysis in Decentralized Online Social Networks with Fine-Grained Privacy Protection

Graph analysts cannot directly obtain the global structure in decentralized social networks, and analyzing such a network requires collecting local views of the social graph from individual users. Since the edges between users may reveal sensitive social interactions in the local view, applying differential privacy in the data collection process is often desirable, which provides strong and rigorous privacy guarantees. In practical decentralized social graphs, different edges have different privacy requirements due to the distinct sensitivity levels. However, the existing differentially private analysis of social graphs provide the same protection for all edges. To address this issue, this work proposes a fine-grained privacy notion as well as novel algorithms for private graph analysis. We first design a fine-grained relationship differential privacy (FGR-DP) notion for social graph analysis, which enforces different protections for the edges with distinct privacy requirements. Then, we design algorithms for triangle counting and k-stars counting, respectively, which can accurately estimate subgraph counts given fine-grained protection for social edges. We also analyze upper bounds on the estimation error, including k-stars and triangle counts, and show their superior performance compared with the state-of-the-arts. Finally, we perform extensive experiments on two real social graph datasets and demonstrate that the proposed mechanisms satisfying FGR-DP have better utility than the state-of-the-art mechanisms due to the finer-grained protection

Optical polarization rogue waves from supercontinuum generation in zero dispersion fiber pumped by dissipative soliton

Optical rogue waves emerge in nonlinear optical systems with extremely large amplitudes, and leave without a trace. In this work, we reveal the emergence of optical polarization rogue waves in supercontinuum generation from a zero-dispersion fiber, pumped by a dissipative soliton laser. Flat spectral broadening is achieved by modulation instability, followed by cascaded four-wave-mixing. In this process, we identify the emergence of optical polarization rogue waves, based on the probability density function of the relative distance among polarization states. Experimental results show that optical polarization rogue waves originate from vector multi-wave-mixing. Besides, we observe double peaks, and even triple peaks in the histogram of the state of polarization. This is a new and intriguing property, never observed so far in optical rogue waves, for example those emerging in the statistics of pulse intensities. Our polarization domain statistical analysis provides a new insight into the still debated topic of the mechanism for rogue wave generation in optical supercontinuum