ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09801

Shangyu Xing, Fei Zhao, Zhen Wu, Tuo An, Weihao Chen, Chunhui Li, Jianbing Zhang, Xinyu Dai

引用次数: 0

How Flawed is ECE? An Analysis via Logit Smoothing 欧洲经委会有多大缺陷？对数平滑分析

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10046

Muthu Chidambaram, Holden Lee, Colin McSwiggen, Semon Rezchikov

引用次数: 0

A System-Level Dynamic Binary Translator using Automatically-Learned Translation Rules 使用自动学习翻译规则的系统级动态二进制翻译器

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09688

Jinhu Jiang, Chaoyi Liang, Rongchao Dong, Zhaohui Yang, Zhongjun Zhou, Wenwen Wang, P. Yew, Weihua Zhang

{"title":"A System-Level Dynamic Binary Translator using Automatically-Learned Translation Rules","authors":"Jinhu Jiang, Chaoyi Liang, Rongchao Dong, Zhaohui Yang, Zhongjun Zhou, Wenwen Wang, P. Yew, Weihua Zhang","doi":"10.48550/arXiv.2402.09688","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09688","url":null,"abstract":"System-level emulators have been used extensively for system design, debugging and evaluation. They work by providing a system-level virtual machine to support a guest operating system (OS) running on a platform with the same or different native OS that uses the same or different instruction-set architecture. For such system-level emulation, dynamic binary translation (DBT) is one of the core technologies. A recently proposed learning-based DBT approach has shown a significantly improved performance with a higher quality of translated code using automatically learned translation rules. However, it has only been applied to user-level emulation, and not yet to system-level emulation. In this paper, we explore the feasibility of applying this approach to improve system-level emulation, and use QEMU to build a prototype. ... To achieve better performance, we leverage several optimizations that include coordination overhead reduction to reduce the overhead of each coordination, and coordination elimination and code scheduling to reduce the coordination frequency. Experimental results show that it can achieve an average of 1.36X speedup over QEMU 6.1 with negligible coordination overhead in the system emulation mode using SPEC CINT2006 as application benchmarks and 1.15X on real-world applications.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963222","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

Textual Localization: Decomposing Multi-concept Images for Subject-Driven Text-to-Image Generation 文本定位：分解多概念图像，实现主题驱动的文本到图像生成

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09966

Junjie Shentu, Matthew Watson, N. A. Moubayed

{"title":"Textual Localization: Decomposing Multi-concept Images for Subject-Driven Text-to-Image Generation","authors":"Junjie Shentu, Matthew Watson, N. A. Moubayed","doi":"10.48550/arXiv.2402.09966","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09966","url":null,"abstract":"Subject-driven text-to-image diffusion models empower users to tailor the model to new concepts absent in the pre-training dataset using a few sample images. However, prevalent subject-driven models primarily rely on single-concept input images, facing challenges in specifying the target concept when dealing with multi-concept input images. To this end, we introduce a textual localized text-to-image model (Texual Localization) to handle multi-concept input images. During fine-tuning, our method incorporates a novel cross-attention guidance to decompose multiple concepts, establishing distinct connections between the visual representation of the target concept and the identifier token in the text prompt. Experimental results reveal that our method outperforms or performs comparably to the baseline models in terms of image fidelity and image-text alignment on multi-concept input images. In comparison to Custom Diffusion, our method with hard guidance achieves CLIP-I scores that are 7.04%, 8.13% higher and CLIP-T scores that are 2.22%, 5.85% higher in single-concept and multi-concept generation, respectively. Notably, our method generates cross-attention maps consistent with the target concept in the generated images, a capability absent in existing models.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963273","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

TEXTRON: Weakly Supervised Multilingual Text Detection through Data Programming TEXTRON：通过数据编程进行弱监督多语言文本检测

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09811

Dhruv Kudale, Badri Vishal Kasuba, Venkatapathy Subramanian, P. Chaudhuri, Ganesh Ramakrishnan

{"title":"TEXTRON: Weakly Supervised Multilingual Text Detection through Data Programming","authors":"Dhruv Kudale, Badri Vishal Kasuba, Venkatapathy Subramanian, P. Chaudhuri, Ganesh Ramakrishnan","doi":"10.48550/arXiv.2402.09811","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09811","url":null,"abstract":"Several recent deep learning (DL) based techniques perform considerably well on image-based multilingual text detection. However, their performance relies heavily on the availability and quality of training data. There are numerous types of page-level document images consisting of information in several modalities, languages, fonts, and layouts. This makes text detection a challenging problem in the field of computer vision (CV), especially for low-resource or handwritten languages. Furthermore, there is a scarcity of word-level labeled data for text detection, especially for multilingual settings and Indian scripts that incorporate both printed and handwritten text. Conventionally, Indian script text detection requires training a DL model on plenty of labeled data, but to the best of our knowledge, no relevant datasets are available. Manual annotation of such data requires a lot of time, effort, and expertise. In order to solve this problem, we propose TEXTRON, a Data Programming-based approach, where users can plug various text detection methods into a weak supervision-based learning framework. One can view this approach to multilingual text detection as an ensemble of different CV-based techniques and DL approaches. TEXTRON can leverage the predictions of DL models pre-trained on a significant amount of language data in conjunction with CV-based methods to improve text detection in other languages. We demonstrate that TEXTRON can improve the detection performance for documents written in Indian languages, despite the absence of corresponding labeled data. Further, through extensive experimentation, we show improvement brought about by our approach over the current State-of-the-art (SOTA) models, especially for handwritten Devanagari text. Code and dataset has been made available at https://github.com/IITB-LEAP-OCR/TEXTRON","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963308","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

Strategic Vote Timing in Online Elections With Public Tallies 有公开计票的在线选举中的战略性投票时间安排

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09776

Aviv Yaish, S. Abramova, Rainer Bohme

{"title":"Strategic Vote Timing in Online Elections With Public Tallies","authors":"Aviv Yaish, S. Abramova, Rainer Bohme","doi":"10.48550/arXiv.2402.09776","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09776","url":null,"abstract":"We study the effect of public tallies on online elections, in a setting where voting is costly and voters are allowed to strategically time their votes. The strategic importance of choosing emph{when} to vote arises when votes are public, such as in online event scheduling polls (e.g., Doodle), or in blockchain governance mechanisms. In particular, there is a tension between voting early to influence future votes and waiting to observe interim results and avoid voting costs if the outcome has already been decided. Our study draws on empirical findings showing that\"temporal\"bandwagon effects occur when interim results are revealed to the electorate: late voters are more likely to vote for leading candidates. To capture this phenomenon, we analyze a novel model where the electorate consists of informed voters who have a preferred candidate, and uninformed swing voters who can be swayed according to the interim outcome at the time of voting. In our main results, we prove the existence of equilibria where both early and late voting occur with a positive probability, and we characterize conditions that lead to the appearance of\"last minute\"voting behavior, where all informed voters vote late.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963343","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

GeoBotsVR: A Robotics Learning Game for Beginners with Hands-on Learning Simulation GeoBotsVR：面向初学者的机器人学习游戏，提供动手操作学习模拟

ArXiv Pub Date : 2024-02-15 DOI: 10.1145/3613905.3648111

Syed Tanzim, Mubarrat

引用次数: 0

Performative Reinforcement Learning in Gradually Shifting Environments 渐变环境中的表演强化学习

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09838

Ben Rank, Stelios Triantafyllou, Debmalya Mandal, Goran Radanovic

{"title":"Performative Reinforcement Learning in Gradually Shifting Environments","authors":"Ben Rank, Stelios Triantafyllou, Debmalya Mandal, Goran Radanovic","doi":"10.48550/arXiv.2402.09838","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09838","url":null,"abstract":"When Reinforcement Learning (RL) agents are deployed in practice, they might impact their environment and change its dynamics. Ongoing research attempts to formally model this phenomenon and to analyze learning algorithms in these models. To this end, we propose a framework where the current environment depends on the deployed policy as well as its previous dynamics. This is a generalization of Performative RL (PRL) [Mandal et al., 2023]. Unlike PRL, our framework allows to model scenarios where the environment gradually adjusts to a deployed policy. We adapt two algorithms from the performative prediction literature to our setting and propose a novel algorithm called Mixed Delayed Repeated Retraining (MDRR). We provide conditions under which these algorithms converge and compare them using three metrics: number of retrainings, approximation guarantee, and number of samples per deployment. Unlike previous approaches, MDRR combines samples from multiple deployments in its training. This makes MDRR particularly suitable for scenarios where the environment's response strongly depends on its previous dynamics, which are common in practice. We experimentally compare the algorithms using a simulation-based testbed and our results show that MDRR converges significantly faster than previous approaches.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963741","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

Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence 生成式人工智能时代大型语言模型基准的不足之处

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09880

Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul Watters, M. Halgamuge

{"title":"Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence","authors":"Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul Watters, M. Halgamuge","doi":"10.48550/arXiv.2402.09880","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09880","url":null,"abstract":"The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities has spurred public curiosity to evaluate and compare different LLMs, leading many researchers to propose their LLM benchmarks. Noticing preliminary inadequacies in those benchmarks, we embarked on a study to critically assess 23 state-of-the-art LLM benchmarks, using our novel unified evaluation framework through the lenses of people, process, and technology, under the pillars of functionality and security. Our research uncovered significant limitations, including biases, difficulties in measuring genuine reasoning, adaptability, implementation inconsistencies, prompt engineering complexity, evaluator diversity, and the overlooking of cultural and ideological norms in one comprehensive assessment. Our discussions emphasized the urgent need for standardized methodologies, regulatory certainties, and ethical guidelines in light of Artificial Intelligence (AI) advancements, including advocating for an evolution from static benchmarks to dynamic behavioral profiling to accurately capture LLMs' complex behaviors and potential risks. Our study highlighted the necessity for a paradigm shift in LLM evaluation methodologies, underlining the importance of collaborative efforts for the development of universally accepted benchmarks and the enhancement of AI systems' integration into society.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963773","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

Why are Sensitive Functions Hard for Transformers? 为什么变压器难以实现敏感功能？

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09963

Michael Hahn, Mark Rofin

引用次数: 0

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