ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09722

Stephen Hausler, David Hall, Sutharsan Mahendren, Peyman Moghadam

{"title":"Reg-NF: Efficient Registration of Implicit Surfaces within Neural Fields","authors":"Stephen Hausler, David Hall, Sutharsan Mahendren, Peyman Moghadam","doi":"10.48550/arXiv.2402.09722","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09722","url":null,"abstract":"Neural fields, coordinate-based neural networks, have recently gained popularity for implicitly representing a scene. In contrast to classical methods that are based on explicit representations such as point clouds, neural fields provide a continuous scene representation able to represent 3D geometry and appearance in a way which is compact and ideal for robotics applications. However, limited prior methods have investigated registering multiple neural fields by directly utilising these continuous implicit representations. In this paper, we present Reg-NF, a neural fields-based registration that optimises for the relative 6-DoF transformation between two arbitrary neural fields, even if those two fields have different scale factors. Key components of Reg-NF include a bidirectional registration loss, multi-view surface sampling, and utilisation of volumetric signed distance functions (SDFs). We showcase our approach on a new neural field dataset for evaluating registration problems. We provide an exhaustive set of experiments and ablation studies to identify the performance of our approach, while also discussing limitations to provide future direction to the research community on open challenges in utilizing neural fields in unconstrained environments.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"18 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition LLMs as Bridges：重构基础多模态命名实体识别

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09989

Jinyuan Li, Han Li, Di Sun, Jiahao Wang, Wenkun Zhang, Zan Wang, Gang Pan

{"title":"LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition","authors":"Jinyuan Li, Han Li, Di Sun, Jiahao Wang, Wenkun Zhang, Zan Wang, Gang Pan","doi":"10.48550/arXiv.2402.09989","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09989","url":null,"abstract":"Grounded Multimodal Named Entity Recognition (GMNER) is a nascent multimodal task that aims to identify named entities, entity types and their corresponding visual regions. GMNER task exhibits two challenging properties: 1) The weak correlation between image-text pairs in social media results in a significant portion of named entities being ungroundable. 2) There exists a distinction between coarse-grained referring expressions commonly used in similar tasks (e.g., phrase localization, referring expression comprehension) and fine-grained named entities. In this paper, we propose RiVEG, a unified framework that reformulates GMNER into a joint MNER-VE-VG task by leveraging large language models (LLMs) as a connecting bridge. This reformulation brings two benefits: 1) It maintains the optimal MNER performance and eliminates the need for employing object detection methods to pre-extract regional features, thereby naturally addressing two major limitations of existing GMNER methods. 2) The introduction of entity expansion expression and Visual Entailment (VE) Module unifies Visual Grounding (VG) and Entity Grounding (EG). It enables RiVEG to effortlessly inherit the Visual Entailment and Visual Grounding capabilities of any current or prospective multimodal pretraining models. Extensive experiments demonstrate that RiVEG outperforms state-of-the-art methods on the existing GMNER dataset and achieves absolute leads of 10.65%, 6.21%, and 8.83% in all three subtasks.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"26 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Symmetry-Breaking Augmentations for Ad Hoc Teamwork 用于临时团队协作的对称性破坏增强技术

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09984

Ravi Hammond, Dustin Craggs, Mingyu Guo, Jakob Foerster, Ian Reid

引用次数: 0

Validation of homogenized finite element models of human metastatic vertebrae using digital volume correlation 利用数字体积相关性验证人体转移椎体的均质化有限元模型

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09828

Chiara Garavelli, A. Aldieri, M. Palanca, Enrico Dall'Ara, M. Viceconti

{"title":"Validation of homogenized finite element models of human metastatic vertebrae using digital volume correlation","authors":"Chiara Garavelli, A. Aldieri, M. Palanca, Enrico Dall'Ara, M. Viceconti","doi":"10.48550/arXiv.2402.09828","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09828","url":null,"abstract":"The incidence of vertebral fragility fracture is increased by the presence of preexisting pathologies such as metastatic disease. Computational tools could support the fracture prediction and consequently the decision of the best medical treatment. Anyway, validation is required to use these tools in clinical practice. To address this necessity, in this study subject-specific homogenized finite element models of single vertebrae were generated from micro CT images for both healthy and metastatic vertebrae and validated against experimental data. More in detail, spine segments were tested under compression and imaged with micro CT. The displacements field could be extracted for each vertebra singularly using the digital volume correlation full-field technique. Homogenized finite element models of each vertebra could hence be built from the micro CT images, applying boundary conditions consistent with the experimental displacements at the endplates. Numerical and experimental displacements and strains fields were eventually compared. In addition, the outcomes of a micro CT based homogenized model were compared to the ones of a clinical-CT based model. Good agreement between experimental and computational displacement fields, both for healthy and metastatic vertebrae, was found. Comparison between micro CT based and clinical-CT based outcomes showed strong correlations. Furthermore, models were able to qualitatively identify the regions which experimentally showed the highest strain concentration. In conclusion, the combination of experimental full-field technique and the in-silico modelling allowed the development of a promising pipeline for validation of fracture risk predictors, although further improvements in both fields are needed to better analyse quantitatively the post-yield behaviour of the vertebra.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"26 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community 社交奖励：通过来自在线创意社区的百万用户反馈评估和改进生成式人工智能

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09872

Arman Isajanyan, Artur Shatveryan, David Kocharyan, Zhangyang Wang, Humphrey Shi

{"title":"Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community","authors":"Arman Isajanyan, Artur Shatveryan, David Kocharyan, Zhangyang Wang, Humphrey Shi","doi":"10.48550/arXiv.2402.09872","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09872","url":null,"abstract":"Social reward as a form of community recognition provides a strong source of motivation for users of online platforms to engage and contribute with content. The recent progress of text-conditioned image synthesis has ushered in a collaborative era where AI empowers users to craft original visual artworks seeking community validation. Nevertheless, assessing these models in the context of collective community preference introduces distinct challenges. Existing evaluation methods predominantly center on limited size user studies guided by image quality and prompt alignment. This work pioneers a paradigm shift, unveiling Social Reward - an innovative reward modeling framework that leverages implicit feedback from social network users engaged in creative editing of generated images. We embark on an extensive journey of dataset curation and refinement, drawing from Picsart: an online visual creation and editing platform, yielding a first million-user-scale dataset of implicit human preferences for user-generated visual art named Picsart Image-Social. Our analysis exposes the shortcomings of current metrics in modeling community creative preference of text-to-image models' outputs, compelling us to introduce a novel predictive model explicitly tailored to address these limitations. Rigorous quantitative experiments and user study show that our Social Reward model aligns better with social popularity than existing metrics. Furthermore, we utilize Social Reward to fine-tune text-to-image models, yielding images that are more favored by not only Social Reward, but also other established metrics. These findings highlight the relevance and effectiveness of Social Reward in assessing community appreciation for AI-generated artworks, establishing a closer alignment with users' creative goals: creating popular visual art. Codes can be accessed at https://github.com/Picsart-AI-Research/Social-Reward","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"24 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Potential of Large Language Models in Artistic Creation: Collaboration and Reflection on Creative Programming 探索大型语言模型在艺术创作中的潜力：创意编程的合作与反思

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09750

Anqi Wang, Zhizhuo Yin, Yulu Hu, Yuanyuan Mao, Pan Hui

引用次数: 0

DE-COP: Detecting Copyrighted Content in Language Models Training Data DE-COP：检测语言模型训练数据中的版权内容

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09910

Andr'e V. Duarte, Xuandong Zhao, Arlindo L. Oliveira, Lei Li

引用次数: 0

Examining Pathological Bias in a Generative Adversarial Network Discriminator: A Case Study on a StyleGAN3 Model 检查生成式对抗网络判别器中的病态偏差：StyleGAN3 模型案例研究

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09786

Alvin Grissom II, Ryan F. Lei, Jeova Farias Sales Rocha Neto, Bailey Lin, Ryan Trotter

引用次数: 0

Effective and Scalable Math Support: Evidence on the Impact of an AI- Tutor on Math Achievement in Ghana 有效和可扩展的数学支持：人工智能辅导员对加纳数学成绩影响的证据

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09809

Owen Henkel, Hannah Horne-Robinson, Nessie Kozhakhmetova, Amanda Lee

{"title":"Effective and Scalable Math Support: Evidence on the Impact of an AI- Tutor on Math Achievement in Ghana","authors":"Owen Henkel, Hannah Horne-Robinson, Nessie Kozhakhmetova, Amanda Lee","doi":"10.48550/arXiv.2402.09809","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09809","url":null,"abstract":"This study evaluates the impact of Rori, an AI powered conversational math tutor accessible via WhatsApp, on the math performance of approximately 1,000 students in grades 3-9 across 11 schools in Ghana. Each school was assigned to a treatment group or control group; the students in the control group continued their regular math instruction, while students in the treatment group engaged with Rori, for two 30-minute sessions per week over 8 months in addition to regular math instruction. We find that the math growth scores were substantially higher for the treatment group with an effect size of 0.37, and that the results were statistically significant (p<0.001). The fact that Rori works with basic mobile devices on low-bandwidth data networks gives the intervention strong potential to support personalized learning on other low-and-middle-income countries (LMICs), where laptop ownership and high-speed internet - prerequisite for many video-centered learning platforms - remain extremely limited. While the results should be interpreted judiciously, as they only report on year 1 of the intervention, and future research is necessary to better understand which conditions are necessary for successful implementation, they do suggest that chat-based tutoring solutions leveraging artificial intelligence could offer a costeffective approach to enhancing learning outcomes for millions of students globally.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"17 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Partial synchrony for free? New bounds for Byzantine agreement via a generic transformation across network models 部分同步免费？通过跨网络模型的通用转换实现拜占庭协议的新界限

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10059

P. Civit, M. A. Dzulfikar, S. Gilbert, R. Guerraoui, J. Komatovic, M. Vidigueira, I. Zablotchi

{"title":"Partial synchrony for free? New bounds for Byzantine agreement via a generic transformation across network models","authors":"P. Civit, M. A. Dzulfikar, S. Gilbert, R. Guerraoui, J. Komatovic, M. Vidigueira, I. Zablotchi","doi":"10.48550/arXiv.2402.10059","DOIUrl":"https://doi.org/10.48550/arXiv.2402.10059","url":null,"abstract":"Byzantine consensus allows n processes to decide on a common value, in spite of arbitrary failures. The seminal Dolev-Reischuk bound states that any deterministic solution to Byzantine consensus exchanges Omega(n^2) bits. In recent years, great advances have been made in deterministic Byzantine agreement for partially synchronous networks, with state-of-the-art cryptographic solutions achieving O(n^2 kappa) bits (where $kappa$ is the security parameter) and nearly matching the lower bound. In contrast, for synchronous networks, optimal solutions with O(n^2) bits, with no cryptography and the same failure tolerance, have been known for more than three decades. Can this gap in network models be closed? In this paper, we present Repeater, the first generic transformation of Byzantine agreement algorithms from synchrony to partial synchrony. Repeater is modular, relying on existing and novel algorithms for its sub-modules. With the right choice of modules, Repeater requires no additional cryptography, is optimally resilient (n = 3t+1, where t is the maximum number of failures) and, for constant-size inputs, preserves the worst-case per-process bit complexity of the transformed synchronous algorithm. Leveraging Repeater, we present the first partially synchronous algorithm that (1) achieves optimal bit complexity (O(n^2) bits), (2) resists a computationally unbounded adversary (no cryptography), and (3) is optimally-resilient (n = 3t+1), thus showing that the Dolev-Reischuk bound is tight in partial synchrony. Moreover, we adapt Repeater for long inputs, introducing several new algorithms with improved complexity and weaker (or completely absent) cryptographic assumptions.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"12 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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