A generalized antiplane singularity within a semi-infinite wedge of arbitrary angle

The state of stress arising within an elastic wedge generated by an antiplane singularity present within it is studied. An analytical solution is derived with a unified generic approach. The singularity may represent either an anti-plane concentrated force or a screw dislocation. To validate the general solution, two degenerate cases are presented. Further, the image force present on the screw dislocation due to the wedge free boundary is obtained. It is found that when the screw dislocations are placed in the vicinity of the wedge surface, the image force will drive dislocations to the free boundary where they will be annihilated. This implies that a dislocation-free zone may exist along the free surface of the wedge. To demonstrate the application of the fundamental solutions, a formulation for a slip band under anti-plane loading with Green’s function method is provided. The solutions developed in this study may be used as building blocks to model the damage of material near a V-notch under versatile anti-plane load conditions or torsional loading

Group sparse optimization for learning predictive state representations

Predictive state representations (PSRs) are a commonly used approach for agents to summarize the information from history generated during their interaction with a dynamical environment and the agents may use PSRs to predict the future observation. Existing works have shown the benefits of PSRs for modelling partially observable dynamical systems. One of the key issues in PSRs is to discover a set of tests for representing states, which is called core tests. However, there is no very efficient technique to find the core tests for a large and complex problem in practice. In this paper, we formulate the discovering of the set of core tests as an optimization problem and exploit a group sparsity of the decision-making matrix to solve the problem. Then the PSR parameters can be obtained simultaneously. Hence, the model of the underlying system can be built immediately. The new learning approach doesn’t require the specification of the number of core tests. Furthermore, the embedded optimization method for solving the considered group Lasso problem, called alternating direction method of multipliers (ADMM), can achieve a global convergence. We conduct experiments on three problem domains including one extremely large problem domain and show promising performances of the new approach

Light Field Diffusion for Single-View Novel View Synthesis

Single-view novel view synthesis, the task of generating images from new viewpoints based on a single reference image, is an important but challenging task in computer vision. Recently, Denoising Diffusion Probabilistic Model (DDPM) has become popular in this area due to its strong ability to generate high-fidelity images. However, current diffusion-based methods directly rely on camera pose matrices as viewing conditions, globally and implicitly introducing 3D constraints. These methods may suffer from inconsistency among generated images from different perspectives, especially in regions with intricate textures and structures. In this work, we present Light Field Diffusion (LFD), a conditional diffusion-based model for single-view novel view synthesis. Unlike previous methods that employ camera pose matrices, LFD transforms the camera view information into light field encoding and combines it with the reference image. This design introduces local pixel-wise constraints within the diffusion models, thereby encouraging better multi-view consistency. Experiments on several datasets show that our LFD can efficiently generate high-fidelity images and maintain better 3D consistency even in intricate regions. Our method can generate images with higher quality than NeRF-based models, and we obtain sample quality similar to other diffusion-based models but with only one-third of the model size

DreamTalk: When Expressive Talking Head Generation Meets Diffusion Probabilistic Models

Diffusion models have shown remarkable success in a variety of downstream generative tasks, yet remain under-explored in the important and challenging expressive talking head generation. In this work, we propose a DreamTalk framework to fulfill this gap, which employs meticulous design to unlock the potential of diffusion models in generating expressive talking heads. Specifically, DreamTalk consists of three crucial components: a denoising network, a style-aware lip expert, and a style predictor. The diffusion-based denoising network is able to consistently synthesize high-quality audio-driven face motions across diverse expressions. To enhance the expressiveness and accuracy of lip motions, we introduce a style-aware lip expert that can guide lip-sync while being mindful of the speaking styles. To eliminate the need for expression reference video or text, an extra diffusion-based style predictor is utilized to predict the target expression directly from the audio. By this means, DreamTalk can harness powerful diffusion models to generate expressive faces effectively and reduce the reliance on expensive style references. Experimental results demonstrate that DreamTalk is capable of generating photo-realistic talking faces with diverse speaking styles and achieving accurate lip motions, surpassing existing state-of-the-art counterparts.Comment: Project Page: https://dreamtalk-project.github.i

Sodium 5-amino-1,3,4-thia­diazole-2-thiol­ate dihydrate

There are two 5-amino-1,3,4-thia­diazole-2(3H)-thiolate anions in the asymmetric unit of the title compound, Na+·C2H2N3S2 −·2H2O, which are almost perpendicular to each other [dihedral angle = 84.64 (6)°]. The two Na+ cations are in distorted fourfold coordinations by O atoms of the water molecules. The crystal structure is stabilized by N—H⋯S, O—H⋯N and O—H⋯S hydrogen bonds

Addax: A fast, private, and accountable ad exchange infrastructure

This paper proposes Addax, a fast, verifiable, and private online ad exchange. When a user visits an ad-supported site, Addax runs an auction similar to those of leading exchanges; Addax requests bids, selects the winner, collects payment, and displays the ad to the user. A key distinction is that bids in Addax’s auctions are kept private and the outcome of the auction is publicly verifiable. Addax achieves these properties by adding public verifiability to the affine aggregatable encodings in Prio (NSDI’17) and by building an auction protocol out of them. Our implementation of Addax over WAN with hundreds of bidders can run roughly half the auctions per second as a non-private and non-verifiable exchange, while delivering ads to users in under 600 ms with little additional bandwidth requirements. This efficiency makes Addax the first architecture capable of bringing transparency to this otherwise opaque ecosystem