The interpretability and reliability of pre-stack seismic waveform classification are often hindered by the entanglement of various geological factors in pre-stack seismic waveforms, along with their intrinsic uncertainties. To address the problem, we propose a physical knowledge-guided disentangled representation network for a specific interpretation task of pre-stack seismic waveform classification. Unlike physics-driven deep learning methods that incorporate physical laws directly into network structures or loss functions, our approach uses a physical knowledge constraint network to guide deep feature learning based on physical attributes. This enables the model to capture more interpretable and separable features. First the concept of group supervision and disentangled representation theory is applied to separate specific physical attributes within the pre-stack seismic waveform. Then, physical knowledge about the seismic data is incorporated to impose additional constraints during the disentangling of deep features. Finally, a clustering technique is used to cluster disentangled deep features and generate the clustering result, which can assist geological experts in inferring sedimentary environments and the distribution of reservoir. Testing on synthetic data demonstrates that our method effectively separates and reconstructs geological elements within seismic data, accurately classifies different types of reflection patterns, and shows strong robustness against noise. Furthermore, the application of actual data shows that the classification results of pre-stack seismic waveform using the proposed method have better interpretability.

Prior work applying semiparametric theory to causal inference has primarily focused on deriving estimators that exhibit statistical robustness under a prespecified causal model that permits identification of a desired causal parameter. However, a fundamental challenge is correct specification of such a model, which usually involves making untestable assumptions. Evidence factors is an approach to combining hypothesis tests of a common causal null hypothesis under two or more candidate causal models. Under certain conditions, this yields a test that is valid if at least one of the underlying models is correct, which is a form of causal robustness. We propose a method of combining semiparametric theory with evidence factors. We develop a causal null hypothesis test based on joint asymptotic normality of $K$ asymptotically linear semiparametric estimators, where each estimator is based on a distinct identifying functional derived from each of $K$ candidate causal models. We show that this test provides both statistical and causal robustness in the sense that it is valid if at least one of the $K$ proposed causal models is correct, while also allowing for slower than parametric rates of convergence in estimating nuisance functions. We demonstrate the effectiveness of our method via simulations and applications to the Framingham Heart Study and Wisconsin Longitudinal Study.

In autonomous assistance systems, accurate camera vision is indispensable for driving safety. Featuring this safety-critical scenario, physical adversarial examples are arguably the most threatening. However, existing physical adversarial attacks are either conspicuous or ineffective, leaving a dilemma for balancing between attack effectiveness and stealthiness. In this paper, we put forth a new type of adversarial patch attack, leveraging the behavior characteristic of high-speed shutters in autonomous driving cameras. Instead of deploying static images onto real-world objects, we embed adversarial examples into videos and cast them with projectors. By delicately deciding the frame contents and display frequencies, the adversarial frame contents are almost invisible to humans, but high-speed shutters can capture them (see our demos (https://anonymous.4open.science/r/7003)). We demonstrate the attack feasibility by fooling traffic sign detectors, altering speed limit signs to be mis-detected or undetected, and hence manipulating the driving speed of victims. We conduct extensive experiments under various conditions, confirming that our new attack is effective and robust: It can deceive state-of-the-art detector models with success rates of 99% and over 90% in untargeted and targeted manners, respectively. Our further investigations unravel the transferability of our attack to other detectors in a black-box setting.

Particle swarm optimization (PSO) algorithm is a stochastic and population-based optimization algorithm. Its traditional learning strategy is implemented by updating the best position using the particle’s own historical best experience and its neighborhood’s best experience to find the optimal solution of the problem. However, the learning strategy is ineffective when dealing with highly complex problems. In this paper, a particle swarm optimization algorithm based on a multidimensional mean learning strategy is proposed. In this algorithm, an opposition-based learning strategy is utilized to initialize the population to enhance the exploitation capability. Furthermore, the historical best positions of all the particles are reconstructed in a vertical crossover manner that is based on the mean information of multiple optimal dimensions to generate the guiding particles. Additionally, an improved inertia weight is used to further guide all the particle movements to balance the capability of the proposed algorithm for global exploration and local exploitation. The proposed algorithm is tested on 12 benchmark functions and is compared with some well-known PSO algorithms. The experimental results show that the proposed algorithm obtains more competitive optimal solution compared with other PSO algorithms when solving high-dimensional complex problems.

Abstract Summary: Pan-genome analyses have shed light on the dynamics and evolution of bacterial genome from the point of population. The explosive growth of bacterial genome sequence also brought an extremely big challenge to pan-genome profile analysis. We developed a tool, named PanGP, to complete pan-genome profile analysis for large-scale strains efficiently. PanGP has integrated two sampling algorithms, totally random (TR) and distance guide (DG). The DG algorithm drew sample strain combinations on the basis of genome diversity of bacterial population. The performance of these two algorithms have been evaluated on four bacteria populations with strain numbers varying from 30 to 200, and the DG algorithm exhibited overwhelming advantage on accuracy and stability than the TR algorithm. Availability: PanGP was developed with a user-friendly graphic interface and it was available at http://PanGP.big.ac.cn. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: With the rapid development of DNA sequencing technology, increasing bacteria genome data enable the biologists to dig the evolutionary and genetic information of prokaryotic species from pan-genome sight. Therefore, the high-efficiency pipelines for pan-genome analysis are mostly needed. We have developed a new pan-genome analysis pipeline (PGAP), which can perform five analytic functions with only one command, including cluster analysis of functional genes, pan-genome profile analysis, genetic variation analysis of functional genes, species evolution analysis and function enrichment analysis of gene clusters. PGAP's performance has been evaluated on 11 Streptococcus pyogenes strains. AVAILABILITY: PGAP is developed with Perl script on the Linux Platform and the package is freely available from http://pgap.sf.net. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.