Distributionally Adversarial Attack

Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. It is worth noting that the original objective of an attack/defense model relies on a data distribution $p(\mathbf{x})$, typically in the form of risk maximization/minimization, e.g., $\max/\min\mathbb{E}_{p(\mathbf(x))}\mathcal{L}(\mathbf{x})$ with $p(\mathbf{x})$ some unknown data distribution and $\mathcal{L}(\cdot)$ a loss function. However, since PGD generates attack samples independently for each data sample based on $\mathcal{L}(\cdot)$, the procedure does not necessarily lead to good generalization in terms of risk optimization. In this paper, we achieve the goal by proposing distributionally adversarial attack (DAA), a framework to solve an optimal {\em adversarial-data distribution}, a perturbed distribution that satisfies the $L_\infty$ constraint but deviates from the original data distribution to increase the generalization risk maximally. Algorithmically, DAA performs optimization on the space of potential data distributions, which introduces direct dependency between all data points when generating adversarial samples. DAA is evaluated by attacking state-of-the-art defense models, including the adversarially-trained models provided by {\em MIT MadryLab}. Notably, DAA ranks {\em the first place} on MadryLab's white-box leaderboards, reducing the accuracy of their secret MNIST model to $88.79\%$ (with $l_\infty$ perturbations of $\epsilon = 0.3$) and the accuracy of their secret CIFAR model to $44.71\%$ (with $l_\infty$ perturbations of $\epsilon = 8.0$). Code for the experiments is released on \url{https://github.com/tianzheng4/Distributionally-Adversarial-Attack}.Comment: accepted to AAAI-1

Deep imaging inside scattering media through virtual spatiotemporal wavefront shaping

The multiple scattering of light makes materials opaque and obstructs imaging. Optimized wavefronts can overcome scattering to focus but typically require restrictive guidestars and only work within an isoplanatic patch. Focusing by lenses and wavefront shaping by spatial light modulators also limit the imaging volume and update speed. Here, we introduce scattering matrix tomography (SMT): use the measured scattering matrix of the sample to construct its volumetric image by scanning a confocal spatiotemporal focus with input and output wavefront correction for every isoplanatic patch, dispersion compensation, and index-mismatch correction--all performed digitally during post-processing without a physical guidestar. The digital focusing offers a large depth of field without constraint by the focal plane's Rayleigh range, and the digital wavefront correction enables image optimization with fast updates unrestricted by the speed of the hardware. We demonstrate SMT with sub-micron diffraction-limited lateral resolution and one-micron bandwidth-limited axial resolution at one millimeter beneath ex vivo mouse brain tissue and inside a dense colloid, where conventional imaging methods fail due to the overwhelming multiple scattering. SMT translates deep-tissue imaging into a computational reconstruction and optimization problem. It is noninvasive and label-free, with prospective applications in medical diagnosis, biological science, colloidal physics, and device inspection

Effects of wave parameters on load reduction performance for amphibious aircraft with V-hydrofoil

An investigation of the influence of the hydrofoil on load reduction performance during an amphibious aircraft landing on still and wavy water is conducted by solving the Unsteady Reynolds-Averaged Navier-Stokes equations coupled with the standard $k-\omega$ turbulence model in this paper. During the simulations, the numerical wave tank is realized by using the velocity-inlet boundary wave maker coupled with damping wave elimination technique on the outlet, while the volume of fluid model is employed to track the water-air interface. Subsequently, the effects of geometric parameters of hydrofoil have been first discussed on still water, which indicates the primary factor influencing the load reduction is the static load coefficient of hydrofoil. Furthermore, the effects of descent velocity, wave length and wave height on load reduction are comprehensively investigated. The results show that the vertical load reduces more than 55$\%$ at the early stage of landing on the still water through assembling the hydrofoil for different descent velocity cases. Meanwhile, for the amphibious aircraft with high forward velocity, the bottom of the fuselage will come into close contact with the first wave when landing on crest position, and then the forebody will impact the next wave surface with extreme force. In this circumstance, the load reduction rate decreases to around 30$\%$, which will entail a further decline with the increase of wave length or wave height

Parametric study on the water impacting of a free-falling symmetric wedge based on the extended von Karman's momentum theory

The present study is concerned with the peak acceleration azmax occurring during the water impact of a symmetric wedge. This aspect can be important for design considerations of safe marine vehicles. The water-entry problem is firstly studied numerically using the finite-volume discretization of the incompressible Navier-Stokes equations and the volume-of-fluid method to capture the air-water interface. The choice of the mesh size and time-step is validated by comparison with experimental data of a free fall water-entry of a wedge. The key original contribution of the article concerns the derivation of a relationship for azmax (as well as the correlated parameters when azmax occurs), the initial velocity, the deadrise angle and the mass of the wedge based on the transformation of von Karman momentum theory which is extended with the inclusion of the pile-up effect. The pile-up coefficient, which has been proven dependent on the deadrise angle in the case of water-entry with a constant velocity, is then investigated for the free fall motion and the dependence law derived from Dobrovol'skaya is still valid for varying deadrise angle. Reasonable good theoretical estimates of the kinematic parameters are provided for a relatively wide range of initial velocity, deadrise angle and mass using the extended von Karman momentum theory which is the combination of the original von Karman method and Dobrovol'skaya's solution and this theoretical approach can be extended to predict the kinematic parameters during the whole impacting phase.Comment: arXiv admin note: text overlap with arXiv:2207.1041

CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation

We study the task of weakly-supervised point cloud semantic segmentation with sparse annotations (e.g., less than 0.1% points are labeled), aiming to reduce the expensive cost of dense annotations. Unfortunately, with extremely sparse annotated points, it is very difficult to extract both contextual and object information for scene understanding such as semantic segmentation. Motivated by masked modeling (e.g., MAE) in image and video representation learning, we seek to endow the power of masked modeling to learn contextual information from sparsely-annotated points. However, directly applying MAE to 3D point clouds with sparse annotations may fail to work. First, it is nontrivial to effectively mask out the informative visual context from 3D point clouds. Second, how to fully exploit the sparse annotations for context modeling remains an open question. In this paper, we propose a simple yet effective Contextual Point Cloud Modeling (CPCM) method that consists of two parts: a region-wise masking (RegionMask) strategy and a contextual masked training (CMT) method. Specifically, RegionMask masks the point cloud continuously in geometric space to construct a meaningful masked prediction task for subsequent context learning. CMT disentangles the learning of supervised segmentation and unsupervised masked context prediction for effectively learning the very limited labeled points and mass unlabeled points, respectively. Extensive experiments on the widely-tested ScanNet V2 and S3DIS benchmarks demonstrate the superiority of CPCM over the state-of-the-art.Comment: Accepted by ICCV 202

Experimental investigation on the impact of coal fines generation and migration on coal permeability

Measurements of the coal fines production and the impact of these fines on the permeability of two coals from the Bowen Basin, Australia, were performed at different flow conditions (single-phase water or gas, two-phase water and gas) and pressure conditions. The fines collected from each coal samples ranged in size from 1 mu m to 14 mu m. For both coal samples, during the first 50 h, the permeability decreases from 0.005 mD and 0.048 mD by 60.9% and 85%, respectively, followed by gradual decline with fluctuations. By the end of water injection, the permeability drops by 88% and 89%, respectively. This phenomenon is attributed to the counteraction between formation damage (cleats plugging and coal fines settlement) and breakthrough of coal fines from the samples (widened cleats). It was found that coal fines volumetric production is proportional to the third power of flow velocity once the flow paths for coal fines are established. The critical flow velocities of coal fines production for both samples were also obtained. For hydrophobic coal, water-drive-gas two-phase flow introduces abrupt permeability loss due to coal fines generation and migration. Furthermore, pauses (well shut-in) in the experiments cause slight permeability drops. A comparison between the two samples indicates that narrower and less connected cleating system results in more frequent coal fines generation and migration, resulting in significant permeability fluctuations with general decreasing trend. Tortuosity of the cleats can enhance the deterioration in permeability by coal fines behaviours. This study delivers fundamental understandings of coal fines generation and migration during the CSG production process, and useful guidelines are suggested to be implemented in the field to minimize production loss induced by coal fines behaviours

Learning A Foundation Language Model for Geoscience Knowledge Understanding and Utilization

Large language models (LLMs)have achieved great success in general domains of natural language processing. In this paper, we bring LLMs to the realm of geoscience, with the objective of advancing research and applications in this field. To this end, we present the first-ever LLM in geoscience, K2, alongside a suite of resources developed to further promote LLM research within geoscience. For instance, we have curated the first geoscience instruction tuning dataset, GeoSignal, which aims to align LLM responses to geoscience-related user queries. Additionally, we have established the first geoscience benchmark, GeoBenchmark, to evaluate LLMs in the context of geoscience. In this work, we experiment with a complete recipe to adapt a pretrained general-domain LLM to the geoscience domain. Specifically, we further train the LLaMA-7B model on over 1 million pieces of geoscience literature and utilize GeoSignal's supervised data to fine-tune the model. Moreover, we share a protocol that can efficiently gather domain-specific data and construct domain-supervised data, even in situations where manpower is scarce. Experiments conducted on the GeoBenchmark demonstrate the the effectiveness of our approach and datasets