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arXiv 2024年

作者： Takayama, Masayuki Okuda, Tadahisa Pham, Thong Ikenoue, Tatsuyoshi Fukuma, Shingo Shimizu, Shohei Sannai, Akiyoshi Data Science and AI Innovation Research Promotion Center Shiga University Japan Department of Health Data Science Tokyo Medical University Graduate School of Medicine Human Health Sciences Kyoto University Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Center for Advanced Intelligence Project RIKEN Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Japan Graduate School of Medicine Human Health Sciences Kyoto University Department of Epidemiology Infectious Disease Control and Prevention Hiroshima University Graduate school of Biomedical and Health Sciences Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Institute for the Advanced Study of Human Biology Kyoto University Center for Advanced Intelligence Project RIKEN Japan Department of Physics Kyoto University Data Science and AI Innovation Research Promotion Center Shiga University Graduate School of Engineering The University of Tokyo Center for Advanced Intelligence Project RIKEN Research Development Center for Large Language Models National Institute of Informatics Japan

In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is significant for creating consistent meaningful causal models, despite the challenges in systematic acquisition of the background knowledge. To overcome these challenges, this paper proposes a novel methodology for causal inference, in which SCD methods and knowledge based causal inference (KBCI) with a large language model (LLM) are synthesized through "statistical causal prompting (SCP)" for LLMs and prior knowledge augmentation for SCD. Experiments have revealed that GPT-4 can cause the output of the LLM-KBCI and the SCD result with prior knowledge from LLM-KBCI to approach the ground truth, and that the SCD result can be further improved, if GPT-4 undergoes SCP. Furthermore, by using an unpublished real-world dataset, we have demonstrated that the background knowledge provided by the LLM can improve SCD on this dataset, even if this dataset has never been included in the training data of the LLM. The proposed approach can thus address challenges such as dataset biases and limitations, illustrating the potential of LLMs to improve data-driven causal inference across diverse scientific domains. © 2024, CC BY.

关键词： Computational linguistics

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Skin Lesion Correspondence Localization in Total Body Photography

arXiv

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arXiv 2023年

作者： Huang, Wei-Lun Tashayyod, Davood Kang, Jun Gandjbakhche, Amir Kazhdan, Michael Armand, Mehran Johns Hopkins University Department of Computer Science BaltimoreMD United States Johns Hopkins University Department of Orthopaedic Surgery BaltimoreMD United States Johns Hopkins School of Medicine Department of Dermatology BaltimoreMD United States Eunice Kennedy Shriver National Institute of Child Health and Human Development BethesdaMD United States Lumo Imaging RockvilleMD United States

Longitudinal tracking of skin lesions - finding correspondence, changes in morphology, and texture - is beneficial to the early detection of melanoma. However, it has not been well investigated in the context of full-body imaging. We propose a novel framework combining geometric and texture information to localize skin lesion correspondence from a source scan to a target scan in total body photography (TBP). Body landmarks or sparse correspondence are first created on the source and target 3D textured meshes. Every vertex on each of the meshes is then mapped to a feature vector characterizing the geodesic distances to the landmarks on that mesh. Then, for each lesion of interest (LOI) on the source, its corresponding location on the target is first coarsely estimated using the geometric information encoded in the feature vectors and then refined using the texture information. We evaluated the framework quantitatively on both a public and a private dataset, for which our success rates (at 10 mm criterion) are comparable to the only reported longitudinal study. As full-body 3D capture becomes more prevalent and has higher quality, we expect the proposed method to constitute a valuable step in the longitudinal tracking of skin lesions. Copyright © 2023, The Authors. All rights reserved.

关键词： Photography

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Invariant inter-subject relational structures in the human visual cortex

arXiv

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arXiv 2024年

作者： Lipman, Ofer Grossman, Shany Friedman, Doron Hel-Or, Yacov Malach, Rafael Efi Arazi School of Computer Science Reichman University Herzliya Israel Max Planck Institute for Human Development Berlin Germany Max Planck UCL Centre for Computational Psychiatry and Ageing Research Berlin Germany Institute of Psychology Universitsät Hamburg Germany Sammy Ofer School of Communications Reichman University Herzliya Israel Department of Brain Sciences Weizmann Institute of Science Rehovot Israel

It is a fundamental of behavior that different individuals see the world in a largely similar manner. This is an essential basis for humans’ ability to cooperate and communicate. However, what are the neuronal properties that underlie these inter-subject commonalities of our visual world? Finding out what aspects of neuronal coding remain invariant across individuals’ brains will shed light not only on this fundamental question but will also point to the neuronal coding scheme at the basis of visual perception. Here we address this question by obtaining intracranial recordings from three cohorts of patients taking part in a different visual recognition task (overall 19 patients and 244 high-order visual contacts included in the analyses) and examining the neuronal coding scheme that was most consistent across individuals’ visual cortex. Our results highlight relational coding – expressed by the set of similarity distances between profiles of pattern activations – as the most consistent representation across individuals. Alternative coding schemes, such as population vector coding or linear coding, failed to achieve similar inter-subject consistency. Our results thus support relational coding as the central neuronal code underlying individuals’ shared perceptual content in the human brain. © 2024, CC0.

关键词： Neurons

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Blockchain Technology for Enhanced Transparency, Efficiency, and Security in Supply Chain Management

Blockchain Technology for Enhanced Transparency, Efficiency,...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Santhosh Krishna B V M. Vanitha Lakshmi S Sathyanarayanan Balaji L Harshith C M S. Kaliappan Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of ECE School of Engineering Presidency University Bengaluru Sri Sathya Sai University for Human Excellence Gulbarga Karnataka Dept of ECE School of Electrical and Communication Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Division of Research and Development Lovely Professional University Punjab

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Though innovation in barcodes, GPS, and IoT sensors has enhanced the scope of modern supply chains to track assets to a larger extent, it raises a critical challenge for centralized systems in terms of data breaches, unauthorized access, and manipulation. Blockchain technology serves as an optimum decentralized and tamper-proof solution for further improvement of data integrity, security, and transparency. This paper discusses the integration of blockchain into supply chains its benefits and challenges and its potential to change global supply chain operations at its very core. In a nutshell, the system proposed outlines a blockchain-based framework using IoT, GPS, and smart contracts that guarantee traceability, security, and efficiency throughout the supply chain in real time. The results are showing high improvement in transparency, security, and operational efficiency, which will certainly lead to its usage cases in varied sectors.

关键词： Technological innovation Supply chain management Data integrity Supply chains Smart contracts Transforms Blockchains Security Stakeholders Global Positioning System

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Librispeech transducer model with internal language model prior correction

arXiv

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arXiv 2021年

作者： Zeyer, Albert Merboldt, André Michel, Wilfried Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52062 Germany AppTek GmbH Aachen52062 Germany

We present our transducer model on Librispeech. We study variants to include an external language model (LM) with shallow fusion and subtract an estimated internal LM. This is justified by a Bayesian interpretation where the transducer model prior is given by the estimated internal LM. The subtraction of the internal LM gives us over 14% relative improvement over normal shallow fusion. Our transducer has a separate probability distribution for the non-blank labels which allows for easier combination with the external LM, and easier estimation of the internal LM. We additionally take care of including the end-of-sentence (EOS) probability of the external LM in the last blank probability which further improves the performance. All our code and setups are published. Copyright © 2021, The Authors. All rights reserved.

关键词： Transducers

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Investigating methods to improve language model integration for attention-based encoder-decoder ASR models

arXiv

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arXiv 2021年

作者： Zeineldeen, Mohammad Glushko, Aleksandr Michel, Wilfried Zeyer, Albert Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

Attention-based encoder-decoder (AED) models learn an implicit internal language model (ILM) from the training transcriptions. The integration with an external LM trained on much more unpaired text usually leads to better performance. A Bayesian interpretation as in the hybrid autoregressive transducer (HAT) suggests dividing by the prior of the discriminative acoustic model, which corresponds to this implicit LM, similarly as in the hybrid hidden Markov model approach. The implicit LM cannot be calculated efficiently in general and it is yet unclear what are the best methods to estimate it. In this work, we compare different approaches from the literature and propose several novel methods to estimate the ILM directly from the AED model. Our proposed methods outperform all previous approaches. We also investigate other methods to suppress the ILM mainly by decreasing the capacity of the AED model, limiting the label context, and also by training the AED model together with a pre-existing LM. Copyright © 2021, The Authors. All rights reserved.

关键词： Speech recognition

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EFFICIENT SEQUENCE TRAINING OF ATTENTION MODELS USING APPROXIMATIVE RECOMBINATION

arXiv

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arXiv 2021年

作者： Wynands, Nils-Philipp Michel, Wilfried Rosendahl, Jan Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52062 Germany AppTek GmbH Aachen52062 Germany

Sequence discriminative training is a great tool to improve the performance of an automatic speech recognition system. It does, however, necessitate a sum over all possible word sequences, which is intractable to compute in practice. Current state-of-the-art systems with unlimited label context circumvent this problem by limiting the summation to an n-best list of relevant competing hypotheses obtained from beam search. This work proposes to perform (approximative) recombinations of hypotheses during beam search, if they share a common local history. The error that is incurred by the approximation is analyzed and it is shown that using this technique the effective beam size can be increased by several orders of magnitude without significantly increasing the computational requirements. Lastly, it is shown that this technique can be used to effectively perform sequence discriminative training for attention-based encoder-decoder acoustic models on the LibriSpeech task. Copyright © 2021, The Authors. All rights reserved.

关键词： Speech recognition

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On language model integration for RNN transducer based speech recognition

arXiv

引用

arXiv 2021年

作者： Zhou, Wei Zheng, Zuoyun Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

The mismatch between an external language model (LM) and the implicitly learned internal LM (ILM) of RNN-Transducer (RNN-T) can limit the performance of LM integration such as simple shallow fusion. A Bayesian interpretation suggests to remove this sequence prior as ILM correction. In this work, we study various ILM correction-based LM integration methods formulated in a common RNN-T framework. We provide a decoding interpretation on two major reasons for performance improvement with ILM correction, which is further experimentally verified with detailed analysis. We also propose an exact-ILM training framework by extending the proof given in the hybrid autoregressive transducer, which enables a theoretical justification for other ILM approaches. Systematic comparison is conducted for both in-domain and cross-domain evaluation on the Librispeech and TED-LIUM Release 2 corpora, respectively. Our proposed exact-ILM training can further improve the best ILM method. Copyright © 2021, The Authors. All rights reserved.

关键词： Transducers

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Automatic learning of subword dependent model scales

arXiv

引用

arXiv 2021年

作者： Meyer, Felix Michel, Wilfried Zeineldeen, Mohammad Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

To improve the performance of state-of-the-art automatic speech recognition systems it is common practice to include external knowledge sources such as language models or prior corrections. This is usually done via log-linear model combination using separate scaling parameters for each model. Typically these parameters are manually optimized on some held-out data. In this work we propose to optimize these scaling parameters via automatic differentiation and stochastic gradient decent similar to the neural network model parameters. We show on the LibriSpeech (LBS) and Switchboard (SWB) corpora that the model scales for a combination of attention-based encoder-decoder acoustic model and language model can be learned as effectively as with manual tuning. We further extend this approach to subword dependent model scales which could not be tuned manually which leads to 7% improvement on LBS and 3% on SWB. We also show that joint training of scales and model parameters is possible and gives additional 6% improvement on LBS. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic models

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How COVID-19 has changed crowdfunding: Evidence from GoFundMe

arXiv

引用

arXiv 2021年

作者： Wang, Junda Zhang, Xupin Luo, Jiebo The Department of Computer Science University of Rochester RochesterNY14627 United States The Department of Human Development University of Rochester RochesterNY14627 United States

While the long-term effects of COVID-19 are yet to be determined, its immediate impact on crowdfunding is nonetheless significant. This study takes a computational approach to more deeply comprehend this change. Using a unique data set of all the campaigns published over the past two years on GoFundMe, we explore the factors that have led to the successful funding of a crowdfunding project. In particular, we study a corpus of crowdfunded projects, analyzing cover images and other variables commonly present on crowdfunding sites. Furthermore, we construct a classifier and a regression model to assess the significance of features based on XGBoost. In addition, we employ counterfactual analysis to investigate the causality between features and the success of crowdfunding. More importantly, sentiment analysis and the paired sample t-test are performed to examine the differences in crowdfunding campaigns before and after the COVID-19 outbreak that started in March 2020. First, we note that there is significant racial disparity in crowdfunding success. Second, we find that sad emotion expressed through the campaign's description became significant after the COVID-19 outbreak. Considering all these factors, our findings shed light on the impact of COVID-19 on crowdfunding campaigns. Copyright © 2021, The Authors. All rights reserved.

关键词： Crowdsourcing

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