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

TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering

The diffusion model has been proven a powerful generative model in recent years, yet remains a challenge in generating visual text. Several methods alleviated this issue by incorporating explicit text position and content as guidance on where and what text to render. However, these methods still suffer from several drawbacks, such as limited flexibility and automation, constrained capability of layout prediction, and restricted style diversity. In this paper, we present TextDiffuser-2, aiming to unleash the power of language models for text rendering. Firstly, we fine-tune a large language model for layout planning. The large language model is capable of automatically generating keywords for text rendering and also supports layout modification through chatting. Secondly, we utilize the language model within the diffusion model to encode the position and texts at the line level. Unlike previous methods that employed tight character-level guidance, this approach generates more diverse text images. We conduct extensive experiments and incorporate user studies involving human participants as well as GPT-4V, validating TextDiffuser-2's capacity to achieve a more rational text layout and generation with enhanced diversity. The code and model will be available at \url{https://aka.ms/textdiffuser-2}

AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections

Previous animatable 3D-aware GANs for human generation have primarily focused on either the human head or full body. However, head-only videos are relatively uncommon in real life, and full body generation typically does not deal with facial expression control and still has challenges in generating high-quality results. Towards applicable video avatars, we present an animatable 3D-aware GAN that generates portrait images with controllable facial expression, head pose, and shoulder movements. It is a generative model trained on unstructured 2D image collections without using 3D or video data. For the new task, we base our method on the generative radiance manifold representation and equip it with learnable facial and head-shoulder deformations. A dual-camera rendering and adversarial learning scheme is proposed to improve the quality of the generated faces, which is critical for portrait images. A pose deformation processing network is developed to generate plausible deformations for challenging regions such as long hair. Experiments show that our method, trained on unstructured 2D images, can generate diverse and high-quality 3D portraits with desired control over different properties.Comment: SIGGRAPH Asia 2023. Project Page: https://yuewuhkust.github.io/AniPortraitGAN

Blockage of transdifferentiation from fibroblast to myofibroblast in experimental ovarian cancer models

<p>Abstract</p> <p>Background</p> <p>Tumour stromal myofibroblasts can promote tumour invasion. As these cells are genetically more stable than cancer cells, there has been enormous interest in developing targeted molecular therapies against them. Chloride intracellular channel 4 (CLIC4) and reactive oxygen species (ROS) have been linked with promoting stromal cell transdifferentiation in various cancers, but little is known of their roles in ovarian cancer. In this study, we examined the functional roles that both CLIC4 and ROS play in the process of ovarian cancer cell-stimulated or TGF-β1 induced fibroblast-to-myofibroblast transdifferentiation. We also examine whether it is possible to reverse such a process, with the aim of developing novel therapies against ovarian cancer by targeting activated transdifferentiated myofibroblasts.</p> <p>Results</p> <p>We demonstrate that TGF-β1 induced or CM^SKOV3activate transdifferentiated myofibroblasts (fibroblasts). These fibroblasts mimic "reactive" stromal myofibroblasts and demonstrate significant up-regulation of CLIC4 expression and increased level of ROS production. Blocking the production of ROS with an antioxidant consequently reduces the expression of CLIC4, and is accompanied by disappearance of <it>α</it>-smooth-muscle actin (α-SMA), a myofibroblast marker, suggesting ROS acts as a signalling molecule that promotes and enhances CLIC4 activities in the myofibroblast transdifferentiaton process. Down-regulation of CLIC4 with a generic agent or specific siRNA both significantly reduces the expression of factors related to the phenotypes and functions of myofibroblasts, such as α-SMA, hepatocyte growth factor (HGF) and vascular endothelial growth factor (VEGF), thus reversing the myofibroblast phenotype back to fibroblasts. These results convincingly show that ROS and CLIC4 are responsible for TGF-β1 induced fibroblast-to-myofibroblast transdifferentiaton and down-regulation of both is sufficient to block transdifferentiated myofibroblasts.</p> <p>Conclusion</p> <p>Molecular targeting of ROS and CLIC4 has the potential to develop novel therapies for ovarian cancer.</p

Multi-classifier Combination for banks credit risk assessment

Credit risk assessment problem belongs essentially to a classification problem. In this paper, a Multi-classifier Combination algorithm has been developed for banks credit risk assessment. We adopt Back-Propagation (BP) algorithm as the meta-learning algorithm and compared the methods of Bagging and Boosting to construct the Multi-classifier System (MCS). Experimental results on real client's data illustrate the effectiveness of the proposed method

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

Observing the air-sea turbulent heat flux on the trajectory of tropical storm Danas

Tropical cyclones constitute a major risk for coastal communities. To assess their damage potential, accurate predictions of their intensification are needed, which requires a detailed understanding of the evolution of turbulent heat flux (THF). By combining multiple buoy observations along the south-north storm track, we investigated the THF anomalies associated with tropical storm Danas (2019) in the East China Sea (ECS) during its complete life cycle from the intensification stage to the mature stage and finally to its dissipation on land. The storm passage is characterized by strong winds of 10–20 m/s and a sea level pressure below 1 000 hPa, resulting in a substantial enhancement of THF. Latent heat (LH) fluxes are most strongly affected by wind speed, with a gradually increasing contribution of humidity along the trajectory. The relative contributions of wind speed and temperature anomalies to sensible heat (SH) depend on the stability of the boundary layer. Under stable conditions, SH variations are driven by wind speed, while under near-neutral conditions, SH variations are driven by temperature. A comparison of the observed THF and associated variables with outputs from the ERA5 and MERRA2 reanalysis products reveals that the reanalysis products can reproduce the basic evolution and composition of the observed THF. However, under extreme weather conditions, temperature and humidity variations are poorly captured by ERA5 and MERRA2, leading to large LH and SH errors. The differences in the observed and reproduced LH and SH during the passage of Danas amount to 26.1 and 6.6 W/m2 for ERA5, respectively, and to 39.4 and 12.5 W/m2 for MERRA2, respectively. These results demonstrate the need to improve the representation of tropical cyclones in reanalysis products to better predict their intensification process and reduce their damage

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