Black-box Targeted Adversarial Attack on Segment Anything (SAM)

Deep recognition models are widely vulnerable to adversarial examples, which change the model output by adding quasi-imperceptible perturbation to the image input. Recently, Segment Anything Model (SAM) has emerged to become a popular foundation model in computer vision due to its impressive generalization to unseen data and tasks. Realizing flexible attacks on SAM is beneficial for understanding the robustness of SAM in the adversarial context. To this end, this work aims to achieve a targeted adversarial attack (TAA) on SAM. Specifically, under a certain prompt, the goal is to make the predicted mask of an adversarial example resemble that of a given target image. The task of TAA on SAM has been realized in a recent arXiv work in the white-box setup by assuming access to prompt and model, which is thus less practical. To address the issue of prompt dependence, we propose a simple yet effective approach by only attacking the image encoder. Moreover, we propose a novel regularization loss to enhance the cross-model transferability by increasing the feature dominance of adversarial images over random natural images. Extensive experiments verify the effectiveness of our proposed simple techniques to conduct a successful black-box TAA on SAM

TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving

In autonomous driving, an accurate understanding of environment, e.g., the vehicle-to-vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks such as trajectory prediction and motion planning. Environment information comes from high-definition (HD) map and historical trajectories of vehicles. Due to the heterogeneity of the map data and trajectory data, many data-driven models for trajectory prediction and motion planning extract vehicle-to-vehicle and vehicle-to-lane interactions in a separate and sequential manner. However, such a manner may capture biased interpretation of interactions, causing lower prediction and planning accuracy. Moreover, separate extraction leads to a complicated model structure and hence the overall efficiency and scalability are sacrificed. To address the above issues, we propose an environment representation, Temporal Occupancy Flow Graph (TOFG). Specifically, the occupancy flow-based representation unifies the map information and vehicle trajectories into a homogeneous data format and enables a consistent prediction. The temporal dependencies among vehicles can help capture the change of occupancy flow timely to further promote model performance. To demonstrate that TOFG is capable of simplifying the model architecture, we incorporate TOFG with a simple graph attention (GAT) based neural network and propose TOFG-GAT, which can be used for both trajectory prediction and motion planning. Experiment results show that TOFG-GAT achieves better or competitive performance than all the SOTA baselines with less training time.Comment: Accepted by ICRA 202

Estimation of N2 and N2O ebullition from eutrophic water using an improved bubble trap device

AbstractEbullition pathway of N2 and N2O emission and its importance on nitrogen loss were quantified during a survey of a eutrophic pond located at the subtropical climate zone in China. Using an improved bubble trap device, in situ collection of N2 bubbles was achieved by avoiding the contamination of N2 in the air. Measurements using the device indicated very high ebullition rates (36.3–366.7mlm−2h−1) and N2 ebullition flux (0.025–0.297gm−2h−1) at warmer months of September and October. The ebullition rates and N2 ebullition fluxes dropped sharply in colder months of December and January, ranged 2.5–15.9mlm−2h−1 and 0.002–0.016gm−2h−1, respectively. Distinct spatial variation of ebullition rates, and N2 and N2O ebullition fluxes were observed, with the highest rate at the heavy sediment location. Ebullition of N2O was a very minor fraction of total gaseous nitrogen released to air. The data demonstrated that ebullition could contribute greatly to biogenic N2 fluxes in eutrophic waters with significant bubble emission

VEGF Is Involved in the Increase of Dermal Microvascular Permeability Induced by Tryptase

Tryptases are predominantly mast cell-specific serine proteases with pleiotropic biological activities and play a critical role in skin allergic reactions, which are manifested with rapid edema and increases of vascular permeability. The exact mechanisms of mast cell tryptase promoting vascular permeability, however, are unclear and, therefore, we investigated the effect and mechanism of tryptase or human mast cells (HMC-1) supernatant on the permeability of human dermal microvascular endothelial cells (HDMECs). Both tryptase and HMC-1 supernatant increased permeability of HDMECs significantly, which was resisted by tryptase inhibitor APC366 and partially reversed by anti-VEGF antibody and SU5614 (catalytic inhibitor of VEGFR). Furthermore, addition of tryptase to HDMECs caused a significant increase of mRNA and protein levels of VEGF and its receptors (Flt-1 and Flk-1) by Real-time RT-PCR and Western blot, respectively. These results strongly suggest an important role of VEGF on the permeability enhancement induced by tryptase, which may lead to novel means of controlling allergic reaction in skin

Crack initiation and early propagation plane orientation of 2A12-T4 aluminum alloy under tension-torsion fatigue loading including mean tensile stress

The specimen fractures appearances are analyzed to investigate the effects of mean tensile stress on the fatigue crack initiation and early propagation plane orientation under axial-torsion fatigue loading for 2A12T4 aluminum alloy. The fatigue crack initiation and early propagation plane orientations are measured by optical microscope, the results of macro-analysis show that both the maximum shear stress amplitude and normal mean stress have effects on the orientations of crack initiation and propagation plane orientation, which are close to the plane of the maximum shear stress amplitude plane. With increasing the mean tensile stress, more cracks are inclined to initial and propagate on or near the maximum shear stress amplitude plane with larger normal mean stress, and the angle of deviation from the plane of maximum shear stress amplitude increases. The predicted plane orientations based on critical plane methods are compared with experimental measured results

Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

This paper presents a comprehensive evaluation of the Optical Character Recognition (OCR) capabilities of the recently released GPT-4V(ision), a Large Multimodal Model (LMM). We assess the model's performance across a range of OCR tasks, including scene text recognition, handwritten text recognition, handwritten mathematical expression recognition, table structure recognition, and information extraction from visually-rich document. The evaluation reveals that GPT-4V performs well in recognizing and understanding Latin contents, but struggles with multilingual scenarios and complex tasks. Specifically, it showed limitations when dealing with non-Latin languages and complex tasks such as handwriting mathematical expression recognition, table structure recognition, and end-to-end semantic entity recognition and pair extraction from document image. Based on these observations, we affirm the necessity and continued research value of specialized OCR models. In general, despite its versatility in handling diverse OCR tasks, GPT-4V does not outperform existing state-of-the-art OCR models. How to fully utilize pre-trained general-purpose LMMs such as GPT-4V for OCR downstream tasks remains an open problem. The study offers a critical reference for future research in OCR with LMMs. Evaluation pipeline and results are available at https://github.com/SCUT-DLVCLab/GPT-4V_OCR