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arXiv 2023年

作者： Maama, Mohamed Jasra, Ajay Kamatani, Kengo Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Data Science The Chinese University of Hong Kong CN Shenzhen China Institute of Statistical Mathematics Tokyo190-0014 Japan

In this paper we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applications, of which we focus upon the case of neuroscience. The data are assumed to be observed regularly in time and driven by the SDE model with unknown parameters. In practice the SDE may not have an analytically tractable solution and this leads naturally to a time-discretization. We adapt the multilevel Markov chain Monte Carlo method of [15], which works with a hierarchy of time discretizations and show empirically and theoretically that this is preferable to using one single time discretization. The improvement is in terms of the computational cost needed to obtain a pre-specified numerical error. Our approach is illustrated on models applied to examples of Bayesian inference problems using both simulated and real observed data from neuroscience. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Performance Disparities between Accents in Automatic Speech Recognition 37

Performance Disparities between Accents in Automatic Speech ...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： DiChristofano, Alex Shuster, Henry Chandra, Shefali Patwari, Neal Division of Computational & Data Sciences Washington University St. Louis United States Department of Computer Science & Engineering Washington University St. Louis United States Department of Women Gender and Sexuality Studies Washington University St. Louis United States Department of History Washington University St. Louis United States Department of Electrical & Systems Engineering Washington University St. Louis United States

ISBN: (纸本)9781577358800

In this work, we expand the discussion of bias in Automatic Speech Recognition (ASR) through a large-scale audit. Using a large and global data set of speech, we perform an audit of some of the most popular English ASR services. We show that, even when controlling for multiple linguistic covariates, ASR service performance has a statistically significant relationship to the political alignment of the speaker's birth country with respect to the United States' geopolitical power. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Speech recognition

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Duck Eggs Hatching Procedure with A Low Cost User Convenient Integrated Device for Developing Countries 9

Duck Eggs Hatching Procedure with A Low Cost User Convenient...

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9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021

作者： Hossain, Eftakhar Zeyad, Mohammad Ahmed, S.M. Masum Tasrif Anubhove, Md Sadik Computational Biology and Data Science Laboratory Bangladesh Energy and Technology Research Division Advanced Bioinformatics Chattogram Bangladesh Erasmus+ Joint MSC In Smart Cities and Communities Department of Thermal Engineering Bilbao Spain Mons Belgium

ISBN: (纸本)9781665432405

Poultry agriculture is a form of livestock farming that involves raising chickens, turkeys, ducks, etc, and raises them to provide eggs or meat. However, this work sheds light on the method of hatching duck eggs which maintains a suitable environment around the eggs through an automated and low cost device by controlling temperature (T) and relative humidity (RH). This paper presents a control strategy for automatic temperature and relative humidity control systems for the egg/s hatching procedure for rural areas people or unprivileged communities in the developing countries. One of the best outcomes of this work is the proper hatching procedure steps shown from the beginning to the end. Moreover, for a better understanding and outcomes, the research work has been done under a poultry farm in Charsita village, Lakshmipur, Bangladesh for 6 consecutive months of 2020. The experiment (egg/s hatching) has been conducted under an ice-box. Furthermore, a microcontroller (ATmega328p) is used to monitor the temperature and relative humidity by receiving data from the sensor DHT22 and manipulating the appropriate temperature and humidity using a logic algorithm code. Egg/s need a constant temperature and relative humidity of 35.6 °C to 37.8 °C and 60% to 80%, respectively. Nonetheless, an LCD (16*2) with a matrix keypad (4*4) has been integrated into the unit, allowing farmers to easily set, run, and monitor the appropriate temperature and relative humidity levels within the ice-box. Finally, the results of sensor testing, system device management, and performance rates are recorded to ensure the research's success. © 2021 IEEE.

关键词： Microcontrollers

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Design of a SiPM-on-Tile ZDC for the future EIC, and its Performance with Graph Neural Networks

arXiv

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arXiv 2024年

作者： Milton, Ryan Paul, Sebouh J. Schmookler, Barak Arratia, Miguel Karande, Piyush Angerami, Aaron Acosta, Fernando Torales Nachman, Benjamin Department of Physics and Astronomy University of California RiversideCA92521 United States Nuclear and Chemical Science Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States

We present a design for a high-granularity zero-degree calorimeter (ZDC) for the upcoming Electron-Ion Collider (EIC). The design uses SiPM-on-tile technology and features a novel staggered-layer arrangement that improves spatial resolution. To fully leverage the design’s high granularity and non-trivial geometry, we employ graph neural networks (GNNs) for energy and angle regression as well as signal classification. The GNN-boosted performance metrics meet, and in some cases, significantly surpass the requirements set in the EIC Yellow Report, laying the groundwork for enhanced measurements that will facilitate a wide physics program. Our studies show that GNNs can significantly enhance the performance of high-granularity CALICE-style calorimeters by automating and optimizing the software compensation algorithms required for these systems. This improvement holds true even in the case of complicated geometries that pose challenges for image-based AI/ML methods. © 2024, CC BY.

关键词： Image enhancement

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BiomedRAG: A Retrieval augmented Large Language Model for Biomedicine

arXiv

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arXiv 2024年

作者： Li, Mingchen Kilicoglu, Halil Xu, Hua Zhang, Rui Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States School of Information Sciences University of Illinois at Urbana-Champaign Champaign United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains;however, these models encounter issues such as generating inaccurate information or hallucinations. Retrieval-augmented generation provided a solution for these models to update knowledge and enhance their performance. In contrast to previous retrieval-augmented LMs, which utilize specialized cross-attention mechanisms to help LLM encode retrieved text, BiomedRAG adopts a simpler approach by directly inputting the retrieved chunk-based documents into the LLM. This straightforward design is easily applicable to existing retrieval and language models, effectively bypassing noise information in retrieved documents, particularly in noise-intensive tasks. Moreover, we demonstrate the potential for utilizing the LLM to supervise the retrieval model in the biomedical domain, enabling it to retrieve the document that assists the LM in improving its predictions. Specifically, BIOMEDRAG retrieves relevant documents from a specifically curated, diverse chunk database through a unique, purpose-built chunk scoring mechanism by a tunable scorer. This retrieval process then integrates the selected information directly into the LLM's input, facilitating the generation of expected output, such as structured knowledge. Our experiments reveal that with the tuned scorer, BIOMEDRAG attains superior performance across 4 biomedical NLP tasks, encompassing information extraction (triple extraction, relation extraction), text classification, and link prediction leveraging over 8 datasets. For instance, in the triple extraction task, BIOMEDRAG outperforms other triple extraction systems with micro-F1 scores of 81.42 and 88.83 on GIT and ChemProt corpora, respectively. The outstanding performance underscores the potential of BIOMEDRAG in constructing effective biomedical intervention systems for various tasks. © 2024, CC BY.

关键词： Classification (of information)

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Exploring the exact limits of the real-time equation-of-motion coupled cluster cumulant Green’s functions

arXiv

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arXiv 2024年

作者： Peng, Bo Pathak, Himadri Panyala, Ajay Vila, Fernando D. Rehr, John J. Kowalski, Karol Physical and Computational Science Directorate Pacific Northwest National Laboratory RichlandWA99354 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Physics University of Washington SeattleWA98195 United States

In this paper, we analyze the properties of the recently proposed real-time equation-of-motion coupled-cluster (RT-EOM-CC) cumulant Green’s function approach [J. Chem. Phys. 2020, 152, 174113]. We specifically focus on identifying the limitations of the original time-dependent coupled cluster (TDCC) ansatz and propose an enhanced double TDCC ansatz ensuring the exactness in the expansion limit. Additionally, we introduce a practical cluster-analysis-based approach for characterizing the peaks in the computed spectral function from the RT-EOM-CC cumulant Green’s function approach, which is particularly useful for the assignments of satellite peaks when many-body effects dominate the spectra. Our preliminary numerical tests focus on reproducing, approximating, and characterizing the exact impurity Green’s function of the three-site and four-site single impurity Anderson models using the RT-EOM-CC cumulant Green’s function approach. The numerical tests allow us to have a direct comparison between the RT-EOM-CC cumulant Green’s function approach and other Green’s function approaches in the numerical exact limit. © 2024, CC BY.

关键词： Green's function

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Feature Extraction and Unsupervised Clustering of Histopathological Images of Pancreatic Cancer Using Information Maximization

Feature Extraction and Unsupervised Clustering of Histopatho...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Mahfujul Islam Rumman Naoaki Ono Kenoki Ohuchida Md. Altaf-Ul-Amin Ming Huang Shigehiko Kanaya Computational Systems Biology Nara Institiute of Science and Technology Ikoma Japan Data Science Center Division of Information Science (of Nara Institiute of Science and Technology) Ikoma Japan Department of Surgery and Oncology (of Graduate School of Medical Sciences) Kyushu University Fukuoka Japan Computational Systems Biology Division of Information Science (of Nara Institiute of Science and Technology) Ikoma Japan Computational Systems Biology Laboratory Nara Institiute of Science and Technology Ikoma Japan Graduate School of Science and Technology Nara Institiute of Science and Technology Ikoma Japan

ISBN: (纸本)9781665492331

In recent years, computer-aided diagnosis based on deep learning concepts has become an attractive research topic in medical imaging. Most of these works utilized supervised learning methods that required prior pathological knowledge. However, it is necessary to extract potential features in images, based on unsupervised learning in order to obtain new pathological findings. For this reason, we implemented unsupervised cluster analysis based on maximization of mutual information to classify pathological images of pancreatic cancer into discrete categories.

关键词： Deep learning Pathology Supervised learning Pancreatic cancer Feature extraction Computer aided diagnosis Unsupervised learning

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Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats

Transferable Graph Neural Fingerprint Models for Quick Respo...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Wei Chen Yihui Ren Ai Kagawa Matthew R. Carbone Samuel Yen-Chi Chen Xiaohui Qu Shinjae Yoo Austin Clyde Arvind Ramanathan Rick L. Stevens Hubertus J. J. van Dam Deyu Lu Center for Functional Nanomaterials Brookhaven National Laboratory Upton NY USA Computational Science Initiative Brookhaven National Laboratory Upton NY USA Data Science and Learning Division Argonne National Laboratory Lemont IL USA Condensed Matter Physics & Materials Science Brookhaven National Laboratory Upton NY USA

Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronavirus protein targets. With this dataset, we trained graph neural fin-gerprint docking models for high-throughput virtual COVID-19 drug screening. The graph neural fingerprint models yield high prediction accuracy on docking scores with the mean squared error lower than 0.21 kcal/mol for most of the docking targets, showing significant improvement over conventional circular fin-gerprint methods. To make the neural fingerprints transferable for unknown targets, we also propose a transferable graph neural fingerprint method trained on multiple targets. With comparable accuracy to target-specific graph neural fingerprint models, the training and data efficiency of the transferable model is several times higher. We highlight that the impact of this study extends beyond COVID-19 dataset, as our approach for fast virtual ligand screening can be easily adapted and integrated into a general machine learning-accelerated pipeline to battle future bio-threats.

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Biomedrag: A Retrieval Augmented Large Language Model for Biomedicine

SSRN

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SSRN 2024年

作者： Li, Mingchen Kilicoglu, Halil Xu, Hua Zhang, Rui Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States School of Information Sciences University of Illinois at Urbana-Champaign Champaign IL United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

Retrieval-augmented generation (RAG) offers a solution by retrieving knowledge from an established database to avoid hallucination of large language models (LLM). However, these models utilize specialized cross-attention mechanisms to help LLMs encode the retrieved text, hindering the application of RAG in closed LLMs (such as GPT-4). Additionally, these models retrieve information at the sentence or paragraph level, potentially introducing noise and affecting the generation quality. To address these issues, we propose a novel BiomedRAG framework that directly feeds automatically retrieved chunk-based documents into the LLM. Our evaluation of BiomedRAG across four biomedical natural language processing tasks using eight datasets demonstrates that our proposed framework not only improves the performance of LLMs 12.12% averagely but also achieves state of-the-art results compared to various baseline models by 5.33%. The BiomedRAG paves the way for more accurate and adaptable LLM applications in the biomedical domain. © 2024, The Authors. All rights reserved.

关键词： computational linguistics

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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