Experimental investigation and numerical simulation on the crack initiation and propagation of rock with pre-existing cracks

Rock mass behavior is determined not only by the properties of the rock matrix but also mostly by the pre-existing cracks in the rock mass. Before the overall failure of rock, the crack initiation and propagation around the tip of pre-existing cracks (i.e., pre-crack) will occur and contribute to rock failure. In this paper, the deep granite from a gold mine is taken and made to specimens with the pre-crack of 0.3 mm thickness. Uniaxial compression tests are carried out on the pre-cracked specimens. The acoustic emission (AE) sensors and digital image correlation (DIC) system are employed to record the failure characteristics of the specimens. The extended finite element method (XFEM) with the non-local stress field calculation is used to simulate the crack initiation and propagation of pre-cracks. The crack patterns, opening and shearing displacements of the cracked surface, and the crack length development are obtained from numerical simulations. Finally, the effects of friction of crack surface on the crack pattern and crack propagation are investigated and discussed. It has been found that, for pre-cracked specimens, crack initiation and propagation will occur when the stress is much smaller than the rock compressive strength. And in the range of pre-crack angle 30-60°, the larger the pre-crack angle is, the larger the compressive strength is. The crack patterns from numerical simulations have a good agreement with those from experimented DIC results. Moreover, the order of crack propagation speed is consistent with the order of the compressive strength. The crack pattern and crack propagation are affected by the friction coefficient of the cracked surface

Continual Learning with Dirichlet Generative-based Rehearsal

Recent advancements in data-driven task-oriented dialogue systems (ToDs) struggle with incremental learning due to computational constraints and time-consuming issues. Continual Learning (CL) attempts to solve this by avoiding intensive pre-training, but it faces the problem of catastrophic forgetting (CF). While generative-based rehearsal CL methods have made significant strides, generating pseudo samples that accurately reflect the underlying task-specific distribution is still a challenge. In this paper, we present Dirichlet Continual Learning (DCL), a novel generative-based rehearsal strategy for CL. Unlike the traditionally used Gaussian latent variable in the Conditional Variational Autoencoder (CVAE), DCL leverages the flexibility and versatility of the Dirichlet distribution to model the latent prior variable. This enables it to efficiently capture sentence-level features of previous tasks and effectively guide the generation of pseudo samples. In addition, we introduce Jensen-Shannon Knowledge Distillation (JSKD), a robust logit-based knowledge distillation method that enhances knowledge transfer during pseudo sample generation. Our experiments confirm the efficacy of our approach in both intent detection and slot-filling tasks, outperforming state-of-the-art methods

Uniaxial experimental study of the deformation behavior and energy evolution of conjugate jointed rock based on AE and DIC methods

Conjugate joint is one of the most common joint forms in natural rock mass, which is produced by different tectonic movements. To better understand the preexisting flaws, it is necessary to investigate joint development and its effect on the deformation and strength of the rock. In this study, uniaxial compression tests of granite specimens with different conjugate joints distribution were performed using the GAW-2000 compression-testing machine system. The PCI-2 acoustic emission (AE) testing system was used to monitor the acoustic signal characteristics of the jointed specimens during the entire loading process. At the same time, a 3D digital image correlation (DIC) technique was used to study the evolution of stress field before the peak strength at different loading times. Based on the experimental results, the deformation and strength characteristics, AE parameters, damage evolution processes, and energy accumulation and dissipation properties of the conjugate jointed specimens were analyzed. It is considered that these changes were closely related to the angle between the primary and secondary joints. The results show that the AE counts can be used to characterize the damage and failure of the specimen during uniaxial compression. The local stress field evolution process obtained by the DIC can be used to analyze the crack initiation and propagation in the specimen. As the included angle increases from 0° to 90°, the elastic modulus first decreases and then increases, and the accumulative AE counts of the peak first increase and then decrease, while the peak strength does not change distinctly. The cumulative AE counts of the specimen with an included angle of 45° rise in a ladder-like manner, and the granite retains a certain degree of brittle failure characteristics under the axial loading. The total energy, elastic energy, and dissipation energy of the jointed specimens under uniaxial compression failure were significantly reduced. These findings can be regarded as a reference for future studies on the failure mechanism of granite with conjugate joints

Effect of Heterogeneity on the Failure of Rock with an Initial Crack under Uniaxial Compressions: A Numerical Study

AbstractFailure mechanisms of rock are intrinsically intertwined with heterogeneity and natural fracture. However, the effects of heterogeneity on the failure of rock with natural cracks are still far from clear. By simultaneously considering rock heterogeneity and natural fractures, this paper investigated the effects of heterogeneity on the failure of rock with a single initial crack under uniaxial compressions. The RFPA method with consideration of materials properties heterogeneity was employed, and numerical models with different crack angles were developed. The stress-strain curve, crack development, failure pattern, and AE characteristics were obtained. The numerical results were also compared with experimental results. Further, the effects of initial crack angle and heterogeneity on the strength, failure pattern, and acoustic emission (AE) characteristics were investigated by parametric studies. It has been found that, for a small homogeneity, rock failure is dominated by numerous microcracks within the crack bands that are smeared from the initial crack tips to the loading ends. Rock failure is dominated by macrocracks propagated from the initial crack tips to the loading ends for a large homogeneity. A logarithmic function is proposed to describe the relationship between the uniaxial compressive strength and the homogeneity. The AE characteristics and overall damage evolution are also significantly affected by the heterogeneity

Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion

This paper presents a 3D generative model that uses diffusion models to automatically generate 3D digital avatars represented as neural radiance fields. A significant challenge in generating such avatars is that the memory and processing costs in 3D are prohibitive for producing the rich details required for high-quality avatars. To tackle this problem we propose the roll-out diffusion network (Rodin), which represents a neural radiance field as multiple 2D feature maps and rolls out these maps into a single 2D feature plane within which we perform 3D-aware diffusion. The Rodin model brings the much-needed computational efficiency while preserving the integrity of diffusion in 3D by using 3D-aware convolution that attends to projected features in the 2D feature plane according to their original relationship in 3D. We also use latent conditioning to orchestrate the feature generation for global coherence, leading to high-fidelity avatars and enabling their semantic editing based on text prompts. Finally, we use hierarchical synthesis to further enhance details. The 3D avatars generated by our model compare favorably with those produced by existing generative techniques. We can generate highly detailed avatars with realistic hairstyles and facial hair like beards. We also demonstrate 3D avatar generation from image or text as well as text-guided editability.Comment: Project Webpage: https://3d-avatar-diffusion.microsoft.com

Sulfate diffusion in coal pillar : experimental data and prediction model

The stability of coal pillar dams is crucial for the long-term service of underground reservoirs storing water or heat. Chemical damage of coal dams induced by ions-attacking in coal is one of the main reasons for the premature failure of coal dams. However, the diffusion process of harmful ions in coal is far from clear, limiting the reliability and durability of coal dam designs. This paper investigates sulfate diffusion in coal pillar through experimental and analytical methods. Coal specimens are prepared and exposed to sulfate solutions with different concentrations. The sulfate concentrations at different locations and time are measured. Based on experimental data and Fick's law, the time-dependent surface concentration of sulfate and diffusion coefficient are determined and formulated. Further, an analytical model for predicting sulfate diffusion in coal pillar is developed by considering dual time-dependent characteristics and Laplace transformations. Through comparisons with experimental data, the accuracy of the analytical model for predicting sulfate diffusion is verified. Further, sulfate diffusions in coal dams for different concentrations of sulfate in mine water are investigated. It has been found that the sulfate concentration of exposure surface and diffusion coefficient in coal are both time-dependent and increase with time. Conventional Fick's law is not able to predict the sulfate diffusion in coal pillar due to the dual time-dependent characteristics. The sulfate attacking makes the coal dam a typical heterogeneous gradient structure. For sulfate concentrations 0.01–0.20 mol/L in mine water, it takes almost 1.5 and 4 years for sulfate ions to diffuse 9.46 and 18.92 m, respectively. The experimental data and developed model provide a practical method for predicting sulfate diffusion in coal pillar, which helps the service life design of coal dams

Pathway to Future Symbiotic Creativity

This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation. We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist (Turing Artists) to a Machine artist in its own right. We begin with an overview of the limitations of the Turing Artists then focus on the top two-level systems, Machine Artists, emphasizing machine-human communication in art creation. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. The rapid development of immersive environment and further evolution into the new concept of metaverse enable symbiotic art creation through unprecedented flexibility of bi-directional communication between artists and art manifestation environments. By examining the latest sensor and XR technologies, we illustrate the novel way for art data collection to constitute the base of a new form of human-machine bidirectional communication and understanding in art creation. Based on such communication and understanding mechanisms, we propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle rather than the traditional "end-to-end" dogma. By proposing a new form of inverse reinforcement learning model, we outline the platform design of machine artists, demonstrate its functions and showcase some examples of technologies we have developed. We also provide a systematic exposition of the ecosystem for AI-based symbiotic art form and community with an economic model built on NFT technology. Ethical issues for the development of machine artists are also discussed

Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans

Deep learning empowers the mainstream medical image segmentation methods. Nevertheless current deep segmentation approaches are not capable of efficiently and effectively adapting and updating the trained models when new incremental segmentation classes (along with new training datasets or not) are required to be added. In real clinical environment, it can be preferred that segmentation models could be dynamically extended to segment new organs/tumors without the (re-)access to previous training datasets due to obstacles of patient privacy and data storage. This process can be viewed as a continual semantic segmentation (CSS) problem, being understudied for multi-organ segmentation. In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs. Using the encoder/decoder network structure, we demonstrate that a continually-trained then frozen encoder coupled with incrementally-added decoders can extract and preserve sufficiently representative image features for new classes to be subsequently and validly segmented. To maintain a single network model complexity, we trim each decoder progressively using neural architecture search and teacher-student based knowledge distillation. To incorporate with both healthy and pathological organs appearing in different datasets, a novel anomaly-aware and confidence learning module is proposed to merge the overlapped organ predictions, originated from different decoders. Trained and validated on 3D CT scans of 2500+ patients from four datasets, our single network can segment total 143 whole-body organs with very high accuracy, closely reaching the upper bound performance level by training four separate segmentation models (i.e., one model per dataset/task)

Make a Cheap Scaling: A Self-Cascade Diffusion Model for Higher-Resolution Adaptation

Diffusion models have proven to be highly effective in image and video generation; however, they still face composition challenges when generating images of varying sizes due to single-scale training data. Adapting large pre-trained diffusion models for higher resolution demands substantial computational and optimization resources, yet achieving a generation capability comparable to low-resolution models remains elusive. This paper proposes a novel self-cascade diffusion model that leverages the rich knowledge gained from a well-trained low-resolution model for rapid adaptation to higher-resolution image and video generation, employing either tuning-free or cheap upsampler tuning paradigms. Integrating a sequence of multi-scale upsampler modules, the self-cascade diffusion model can efficiently adapt to a higher resolution, preserving the original composition and generation capabilities. We further propose a pivot-guided noise re-schedule strategy to speed up the inference process and improve local structural details. Compared to full fine-tuning, our approach achieves a 5X training speed-up and requires only an additional 0.002M tuning parameters. Extensive experiments demonstrate that our approach can quickly adapt to higher resolution image and video synthesis by fine-tuning for just 10k steps, with virtually no additional inference time.Comment: Project Page: https://guolanqing.github.io/Self-Cascade