A novel transversely isotropic strength criterion for soils based on a mobilized plane approach

The peak shear strength rules of transversely isotropic soils are stress state dependent and dependent on relative orientation between bedding plane and principal stress. Accordingly, the shear strength of transversely isotropic soils exhibits two primary characteristics: (i) the strength curve on the deviatoric plane is asymmetrical with respect to three principal stress axes; (ii) the shear strength changes with the direction angle of the bedding plane when the intermediate principal stress coefficient is a constant. In this paper, the mobilized plane is introduced and used to reveal the failure mechanism of soils. By projecting the microstructure tensor of transversely isotropic soils onto the normal of the mobilized plane, the directionality of the transversely isotropic soils is introduced into the friction rules on the mobilized plane, and a transversely isotropic strength parameter is proposed. The proposed strength parameter can extend isotropic strength criteria into transversely isotropic strength criteria. This mobilized plane approach is used to establish a novel transversely isotropic nonlinear unified strength criterion (TI-NUSC). The difficulty to establish a unified description of the asymmetrical strength curve and its evolution with direction angle is overcome by the established criterion. Comparisons between available test results and the TI-NUSC shows that the TI-NUSC can successfully describe these two primary peak strength characteristics

Cryptanalysis of a Type of White-Box Implementations of the SM4 Block Cipher

The SM4 block cipher was first released in 2006 as SMS4 used in the Chinese national standard WAPI, and became a Chinese national standard in 2016 and an ISO international standard in 2021. White-box cryptography aims primarily to protect the secret key used in a cryptographic software implementation in the white-box scenario that assumes an attacker to have full access to the execution environment and execution details of an implementation. Since white-box cryptography has many real-life applications nowadays, a few white-box implementations of the SM4 block cipher has been proposed with its increasingly wide use, among which a type of constructions is dominated, that use an affine diagonal block encoding to protect the original XOR sum of the three branches entering the S-box layer of a round and use its inverse to protect the original input of the S-box layer, such as Xiao and Lai\u27s implementation in 2009, Shang\u27s implementation in 2016 and Yao and Chen\u27s implementation in 2020. In this paper, we show that this type of white-box SM4 constructions can be somewhat equivalent to a plain implementation mostly with Boolean masks from a security viewpoint, by devising collision-based attacks on Xiao and Lai\u27s, Shang\u27s and Yao and Chen\u27s implementations with a time complexity of respectively about $2^{22}$, $2^{39}$ and $2^{22}$ to peel off most white-box operations until only Boolean masks remain. Besides, we present a collision-based attack on a white-box SM4 implementation with a time complexity of about $2^{17.1}$ to recover an original round key, which uses a linear diagonal block encoding instead of an affine diagonal block encoding. Our results show that generating such a white-box SM4 implementation with affine encodings can be simplified into generating a plain implementation with Boolean masks (if its security expectation is beyond the above-mentioned complexity), and the effect of an affine encoding is significantly better than the effect of a linear encoding in the sense of our cryptanalysis results

Fractional elastoplastic constitutive model for soils based on a novel 3D fractional plastic flow rule

A novel three-dimensional (3D) fractional plastic flow rule that is not limited by the coordinate basis of the differentiable function is proposed based on the fractional derivative and the coordinate transformation. By introducing the 3D fractional plastic flow rule into the characteristic stress space, a 3D fractional elastoplastic model for soil is established for the first time. Only five material parameters with clear physical significance are required in the proposed model. The capability of the model in capturing the strength and deformation behaviour of soils under true 3D stress conditions is verified by comparing model predictions with test results

SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models

Object-centric learning aims to represent visual data with a set of object entities (a.k.a. slots), providing structured representations that enable systematic generalization. Leveraging advanced architectures like Transformers, recent approaches have made significant progress in unsupervised object discovery. In addition, slot-based representations hold great potential for generative modeling, such as controllable image generation and object manipulation in image editing. However, current slot-based methods often produce blurry images and distorted objects, exhibiting poor generative modeling capabilities. In this paper, we focus on improving slot-to-image decoding, a crucial aspect for high-quality visual generation. We introduce SlotDiffusion -- an object-centric Latent Diffusion Model (LDM) designed for both image and video data. Thanks to the powerful modeling capacity of LDMs, SlotDiffusion surpasses previous slot models in unsupervised object segmentation and visual generation across six datasets. Furthermore, our learned object features can be utilized by existing object-centric dynamics models, improving video prediction quality and downstream temporal reasoning tasks. Finally, we demonstrate the scalability of SlotDiffusion to unconstrained real-world datasets such as PASCAL VOC and COCO, when integrated with self-supervised pre-trained image encoders.Comment: Project page: https://slotdiffusion.github.io/ . An earlier version of this work appeared at the ICLR 2023 Workshop on Neurosymbolic Generative Models: https://nesygems.github.io/assets/pdf/papers/SlotDiffusion.pd

An Efficient Multi-solution Solver for the Inverse Kinematics of 3-Section Constant-Curvature Robots

Piecewise constant curvature is a popular kinematics framework for continuum robots. Computing the model parameters from the desired end pose, known as the inverse kinematics problem, is fundamental in manipulation, tracking and planning tasks. In this paper, we propose an efficient multi-solution solver to address the inverse kinematics problem of 3-section constant-curvature robots by bridging both the theoretical reduction and numerical correction. We derive analytical conditions to simplify the original problem into a one-dimensional problem. Further, the equivalence of the two problems is formalised. In addition, we introduce an approximation with bounded error so that the one dimension becomes traversable while the remaining parameters analytically solvable. With the theoretical results, the global search and numerical correction are employed to implement the solver. The experiments validate the better efficiency and higher success rate of our solver than the numerical methods when one solution is required, and demonstrate the ability of obtaining multiple solutions with optimal path planning in a space with obstacles.Comment: Robotics: Science and Systems 202

rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement

Remote photoplethysmography (rPPG) is an important technique for perceiving human vital signs, which has received extensive attention. For a long time, researchers have focused on supervised methods that rely on large amounts of labeled data. These methods are limited by the requirement for large amounts of data and the difficulty of acquiring ground truth physiological signals. To address these issues, several self-supervised methods based on contrastive learning have been proposed. However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior in physiological signals and seem to have a limited ability to cope with noisy. In this paper, a linear self-supervised reconstruction task was designed for extracting the inherent self-similar prior in physiological signals. Besides, a specific noise-insensitive strategy was explored for reducing the interference of motion and illumination. The proposed framework in this paper, namely rPPG-MAE, demonstrates excellent performance even on the challenging VIPL-HR dataset. We also evaluate the proposed method on two public datasets, namely PURE and UBFC-rPPG. The results show that our method not only outperforms existing self-supervised methods but also exceeds the state-of-the-art (SOTA) supervised methods. One important observation is that the quality of the dataset seems more important than the size in self-supervised pre-training of rPPG. The source code is released at https://github.com/linuxsino/rPPG-MAE

Human performance analysis of processes for retrieving Beidou satellite navigation system during breakdown

Satellite navigation systems provide continuous, timely, and accurate signals of location, speed, and time to users all over the world. Although the running of these systems has become highly automated, the human operator is still vital for its continued operation, especially when certain equipment failures occur. In this paper, we examined 180 incidents of one particular type of equipment failure and the whole recovery process as recorded in the log files from a ground control center of the Beidou satellite navigation system. We extracted the information, including the technical description of the failure, the time when the fault occurred, the full recovery time, and the demographic information of the team members on the shift responsible for responding to the failure. We then transformed these information into the cognitive complexity of the task, time of day, shift handover period, and team skill composition. Multiple regression analysis showed that task complexity and shift handover were key predictors of recovery time. Time of day also influenced the recovery time, during midnight to 4 a.m., operators made longer responses. We also found that the fault handling processes could be improved if the team’s most adept member is more skillful at that role than in other teams. We discussed the theoretical and practical implication of this study

Combined signature of N7-methylguanosine regulators with their related genes and the tumor microenvironment: a prognostic and therapeutic biomarker for breast cancer

BackgroundIdentifying predictive markers for breast cancer (BC) prognosis and immunotherapeutic responses remains challenging. Recent findings indicate that N7-methylguanosine (m7G) modification and the tumor microenvironment (TME) are critical for BC tumorigenesis and metastasis, suggesting that integrating m7G modifications and TME cell characteristics could improve the predictive accuracy for prognosis and immunotherapeutic responses.MethodsWe utilized bulk RNA-sequencing data from The Cancer Genome Atlas Breast Cancer Cohort and the GSE42568 and GSE146558 datasets to identify BC-specific m7G-modification regulators and associated genes. We used multiple m7G databases and RNA interference to validate the relationships between BC-specific m7G-modification regulators (METTL1 and WDR4) and related genes. Single-cell RNA-sequencing data from GSE176078 confirmed the association between m7G modifications and TME cells. We constructed an m7G-TME classifier, validated the results using an independent BC cohort (GSE20685; n = 327), investigated the clinical significance of BC-specific m7G-modifying regulators by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, and performed tissue-microarray assays on 192 BC samples.ResultsImmunohistochemistry and RT-qPCR results indicated that METTL1 and WDR4 overexpression in BC correlated with poor patient prognosis. Moreover, single-cell analysis revealed relationships between m7G modification and TME cells, indicating their potential as indicators of BC prognosis and treatment responses. The m7G-TME classifier enabled patient subgrouping and revealed significantly better survival and treatment responses in the m7Glow+TMEhigh group. Significant differences in tumor biological functions and immunophenotypes occurred among the different subgroups.ConclusionsThe m7G-TME classifier offers a promising tool for predicting prognosis and immunotherapeutic responses in BC, which could support personalized therapeutic strategies

Learning to reconstruct and understand indoor scenes from sparse views

This paper proposes a new method for simultaneous 3D reconstruction and semantic segmentation for indoor scenes. Unlike existing methods that require recording a video using a color camera and/or a depth camera, our method only needs a small number of (e.g., 3~5) color images from uncalibrated sparse views, which significantly simplifies data acquisition and broadens applicable scenarios. To achieve promising 3D reconstruction from sparse views with limited overlap, our method first recovers the depth map and semantic information for each view, and then fuses the depth maps into a 3D scene. To this end, we design an iterative deep architecture, named IterNet, to estimate the depth map and semantic segmentation alternately. To obtain accurate alignment between views with limited overlap, we further propose a joint global and local registration method to reconstruct a 3D scene with semantic information. We also make available a new indoor synthetic dataset, containing photorealistic high-resolution RGB images, accurate depth maps and pixel-level semantic labels for thousands of complex layouts. Experimental results on public datasets and our dataset demonstrate that our method achieves more accurate depth estimation, smaller semantic segmentation errors, and better 3D reconstruction results over state-of-the-art methods