pp-Poisson surface reconstruction in curl-free flow from point clouds

The aim of this paper is the reconstruction of a smooth surface from an unorganized point cloud sampled by a closed surface, with the preservation of geometric shapes, without any further information other than the point cloud. Implicit neural representations (INRs) have recently emerged as a promising approach to surface reconstruction. However, the reconstruction quality of existing methods relies on ground truth implicit function values or surface normal vectors. In this paper, we show that proper supervision of partial differential equations and fundamental properties of differential vector fields are sufficient to robustly reconstruct high-quality surfaces. We cast the $p$-Poisson equation to learn a signed distance function (SDF) and the reconstructed surface is implicitly represented by the zero-level set of the SDF. For efficient training, we develop a variable splitting structure by introducing a gradient of the SDF as an auxiliary variable and impose the $p$-Poisson equation directly on the auxiliary variable as a hard constraint. Based on the curl-free property of the gradient field, we impose a curl-free constraint on the auxiliary variable, which leads to a more faithful reconstruction. Experiments on standard benchmark datasets show that the proposed INR provides a superior and robust reconstruction. The code is available at \url{https://github.com/Yebbi/PINC}.Comment: 21 pages, accepted for Advances in Neural Information Processing Systems, 202

Authenticated Key Exchange Secure under the Computational Diffie-Hellman Assumption

In this paper, we present a new authenticated key exchange(AKE) protocol and prove its security under the random oracle assumption and the computational Diffie-Hellman(CDH) assumption. In the extended Canetti-Krawczyk model, there has been no known AKE protocol based on the CDH assumption. Our protocol, called NAXOS+, is obtained by slightly modifying the NAXOS protocol proposed by LaMacchia, Lauter and Mityagin. We establish a formal security proof of NAXOS+ in the extended Canetti-Krawczyk model using as a main tool the trapdoor test presented by Cash, Kiltz and Shoup

ControlDreamer: Stylized 3D Generation with Multi-View ControlNet

Recent advancements in text-to-3D generation have significantly contributed to the automation and democratization of 3D content creation. Building upon these developments, we aim to address the limitations of current methods in generating 3D models with creative geometry and styles. We introduce multi-view ControlNet, a novel depth-aware multi-view diffusion model trained on generated datasets from a carefully curated text corpus. Our multi-view ControlNet is then integrated into our two-stage pipeline, ControlDreamer, enabling text-guided generation of stylized 3D models. Additionally, we present a comprehensive benchmark for 3D style editing, encompassing a broad range of subjects, including objects, animals, and characters, to further facilitate research on diverse 3D generation. Our comparative analysis reveals that this new pipeline outperforms existing text-to-3D methods as evidenced by human evaluations and CLIP score metrics.Comment: Project page: https://controldreamer.github.io

KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services

With the growth of online services, the need for advanced text classification algorithms, such as sentiment analysis and biased text detection, has become increasingly evident. The anonymous nature of online services often leads to the presence of biased and harmful language, posing challenges to maintaining the health of online communities. This phenomenon is especially relevant in South Korea, where large-scale hate speech detection algorithms have not yet been broadly explored. In this paper, we introduce "KoMultiText", a new comprehensive, large-scale dataset collected from a well-known South Korean SNS platform. Our proposed dataset provides annotations including (1) Preferences, (2) Profanities, and (3) Nine types of Bias for the text samples, enabling multi-task learning for simultaneous classification of user-generated texts. Leveraging state-of-the-art BERT-based language models, our approach surpasses human-level accuracy across diverse classification tasks, as measured by various metrics. Beyond academic contributions, our work can provide practical solutions for real-world hate speech and bias mitigation, contributing directly to the improvement of online community health. Our work provides a robust foundation for future research aiming to improve the quality of online discourse and foster societal well-being. All source codes and datasets are publicly accessible at https://github.com/Dasol-Choi/KoMultiText.Comment: Accepted to the NeurIPS 2023 Workshop on Socially Responsible Language Modelling Research (SoLaR

Hydrodynamic Study on the “Stop-and-Acceleration” Pattern of Refilling Flow at Perforation Plates by Using a Xylem-Inspired Channel

Porous structures, such as perforation plates and pit membranes, have attracted considerable attention due to their hydraulic regulation of water flow through vascular plant networks. However, limited information is available regarding the hydraulic functions of such structures during water-refilling and embolism repair because of difficulties in simultaneous in vivo measurements of refilling flow and pressure variations in xylem vessels. In this study, we developed a xylem-inspired microchannel with a porous mesh for systematic investigation on the hydraulic contribution of perforation plates on water-refilling. In particular, the “stop-and-acceleration” phenomenon of the water meniscus at the porous mesh structure was carefully examined in macroscopic and microscopic views. This distinctive phenomenon usually occurs in the xylem vessels of vascular plants during embolism repair. Based on the experimental results, we established a theoretical model of the flow characteristics and pressure variations around the porous structure inside the microchannel. Perforation plates could be speculated to be a pressure-modulated flow controller that facilitates embolism recovery. Furthermore, the proposed xylem-inspired channel can be used to investigate the hydraulic functions of porous structures for water management in plants

Mindfully Aware and Open: Mitigating Subjective and Objective Financial Vulnerability via Mindfulness Practices

Our research presents mindfulness as a potential intervention to mitigate financial vulnerability, defined as the ability to handle unexpected future financial setbacks. As potential interventions to mitigate consumer financial vulnerability, we provide a conceptual framework on how two types of mindfulness practices (i.e., non-judgmental awareness and openness to experience) can mitigate the subjective and objective financial vulnerability differently. We suggest ways to manipulate the two types of mindfulness and discuss the results of our initial pilot study, focusing on lower-income consumers. In addition, we propose fruitful avenues for future research and provide recommendations for managers and policymakers to better address consumer financial vulnerability and enhance consumer welfare via mindfulness practiceThis research has been supported by Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of "Fostering Young Doctors Research" (SOCANET-CM-UC3M) and in the context of the V PRICIT Regional Programme of Research and Technological Innovation. Proyectos Interdisciplinares Jóvenes Doctores (2020/00031/002), and Ministerio de Ciencia, Innovación y Universidades, España (2019/00405/001)