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arXiv 2023年

作者： Nguyen, Thanh-Huy Kha, Quang Hien Truong, Thai Ngoc Toan Lam, Ba Thinh Ngo, Ba Hung Dinh, Quang Vinh Le, Nguyen Quoc Khanh Department of Biomedical Engineering National Cheng Kung University Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taiwan Viet Nam Ho Chi Minh City International University Viet Nam Graduate School of Data Science Chonnam National University Korea Republic of Vietnamese-German University Viet Nam Research Center for Artificial Intelligence in Medicine Taipei Medical University Taiwan Professional Master Program in Artificial Intelligence in Medicine Taiwan

In recent years, many mammographic image analysis methods have been introduced for improving cancer classification tasks. Two major issues of mammogram classification tasks are leveraging multi-view mammographic information and class-imbalance handling. In the first problem, many multi-view methods have been released for concatenating features of two or more views for the training and inference stage. Having said that, most multi-view existing methods are not explainable in the meaning of feature fusion, and treat many views equally for diagnosing. Our work aims to propose a simple but novel method for enhancing examined view (main view) by leveraging low-level feature information from the auxiliary view (ipsilateral view) before learning the high-level feature that contains the cancerous features. For the second issue, we also propose a simple but novel malignant mammogram synthesis framework for upsampling minor class samples. Our easy-to-implement and no-training framework has eliminated the current limitation of the CutMix algorithm which are unreliable synthesized images with random pasted patches, hard-contour problems, and domain shift problems. Our results on VinDr-Mammo and CMMD datasets show the effectiveness of our two new frameworks for both multi-view training and synthesizing mammographic images, outperforming the previous conventional methods in our experimental settings. © 2023, CC BY-NC-SA.

关键词： Mammography

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Effects of sodium alginate-poly(acrylic acid) cross-linked hydrogel beads on soil conditioner in the absence and presence of phosphate and carbonate ions

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Case Studies in Chemical and Environmental Engineering 2024年 9卷

作者： Hidayat, Endar Sarbani, Nur Maisarah Mohamad Lahiri, Sudip Kumar Samitsu, Sadaki Yonemura, Seiichiro Mitoma, Yoshiharu Harada, Hiroyuki Graduate School of Comprehensive Scientific Research Program in Biological System Sciences Prefectural University of Hiroshima Shobara 727-0023 Japan Department of Life System Science Faculty of Bioresources Science Prefectural University of Hiroshima Shobara 727-0023 Japan Data-driven Polymer Design Group Research Center for Macromolecules and Biomaterials National Institute for Materials Science 1−2−1 Sengen Tsukuba 305-0047 Japan Department of Mechanical & Industrial Engineering University of Toronto Toronto M5S 3G8 ON Canada Faculty of Allied Health Sciences Chettinad Hospital and Research Institute Chettinad Academy of Research and Education Tamil Nadu Kelambakkam 603103 India

Hydrogel beads, composed of polymers, possess the ability to absorb substantial quantities of water simultaneously and gradually release it in dry conditions. In this work, hydrogel beads were synthesized using sodium alginate (S-Alg) and poly (acrylic acid) (P-Acc), either in the absence or presence of phosphate (PO₄ ³⁻) (S-Alg/P-Acc@PO₄ ³⁻) and carbonate (CO₃ ²⁻) anionic ions (S-Alg/P-Acc@CO₃ ²⁻). Subsequently, these beads were crosslinked with Ca²⁺ ions. The scanning electron microscopy (SEM) images demonstrated that the presence of anionic ions has an impact on the structure of the hydrogel beads and enhances porosity expansion while concurrently reducing the concentration of carboxyl (COOH) groups. The hydrogel beads exhibited a great swelling behavior, with S-Alg/P-Acc@CO₃ ²⁻ capable of absorbing up to 73.5 % of water in acidic conditions, but decreased under neutral and basic conditions. Soil water loss (SWL) experiments confirmed the ability of hydrogel beads to retain water in the soil. Additionally, observations of soil characteristics after a 14-day treatment revealed no significant difference in pH and C/N ratio (P-value <0.05). However, soil's available Fe and N–NO₃ ^- significantly reduced (P-value <0.05) while substantially promoting soil N–NH₄ ⁺, available phosphorus (P), cation exchange capacity (CEC), soil organic carbon (SOC), and exchangeable cation (Ca²⁺, K⁺ and Mg²⁺). The presence of hydroxyl (-OH) and –COOH groups was found to be crucial for enhancing soil properties. © 2024 The Authors

关键词： Anionic ions Hydrogel beads Poly (acrylic acid) Sodium alginate Soil characteristics Soil water retention Swelling

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Toward spatially unbiased generative models

arXiv

引用

arXiv 2021年

作者： Choi, Jooyoung Lee, Jungbeom Jeong, Yonghyun Yoon, Sungroh Data Science and AI Laboratory Seoul National University Korea Republic of ASRI INMC Interdisciplinary Program in AI Seoul National University Korea Republic of AI Research Team Samsung SDS

Recent image generation models show remarkable generation performance. However, they mirror strong location preference in datasets, which we call spatial bias. Therefore, generators render poor samples at unseen locations and scales. We argue that the generators rely on their implicit positional encoding to render spatial content. From our observations, the generator's implicit positional encoding is translation-variant, making the generator spatially biased. To address this issue, we propose injecting explicit positional encoding at each scale of the generator. By learning the spatially unbiased generator, we facilitate the robust use of generators in multiple tasks, such as GAN inversion, multi-scale generation, generation of arbitrary sizes and aspect ratios. Furthermore, we show that our method can also be applied to denoising diffusion probabilistic models. Our code is available at: https://***/jychoi118/toward_spatial_unbiased. © 2021, CC BY-NC-SA.

关键词： Signal encoding

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A holistic exploration of the potentially recoverable redshift information of Stage IV galaxy surveys

arXiv

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arXiv 2024年

作者： Scott, Bryan R. Malz, Alex I. Sorba, Robert Northwestern University 1800 Sherman Ave 8th Floor EvanstonIL60201 United States LSST Discovery Alliance Data Science Fellowship Program United States McWilliams Center for Cosmology and Astrophysics Department of Physics Carnegie Mellon University United States Institute for Computational Astrophysics Saint Mary’s University 923 Robie Street HalifaxNSB3H 3C3 Canada

Extragalactic science and cosmology with Stage IV galaxy surveys will rely almost exclusively on redshift measurements derived solely from photometry, which are subject to systematic and statistical uncertainties with numerous analysis choices, such as that of an estimator and prior information and no universal solution. Single-survey photometric redshift estimates ought to be improved by combining data from multiple surveys, with common wisdom asserting that optical data benefits from additional infrared coverage but not from additional ultraviolet coverage. The degree of improvement for either case is not well-characterized and attempts to quantify it necessitate assumptions of a chosen estimator and its prior information. We apply an information theoretic metric of potentially recoverable redshift information to assess the impact of multi-survey photometry combinations without assuming an estimator nor priors in the context of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) in the optical, Roman and Euclid Surveys in the infrared, and Cosmological Advanced Survey Telescope for Optical-UV Research (CASTOR) in the ultraviolet. We conclude that the addition of UV photometry can benefit redshift determination of certain galaxy populations, but that gain is tempered by their decreased chance of meeting detection criteria at higher wavelengths. We explore the spectral energy distribution (SEDs) of galaxies whose potentially recoverable redshift information is most impacted by additional photometry to provide a basis for future evaluation of which science cases may motivate which combinations. The holistic assessment approach we develop here is generic and may be applied to quantify the impact of combining photometric datasets, changing experimental design choices, including observing strategy optimization, and evaluating systematics mitigation procedures. © 2024, CC BY.

关键词： Cosmology

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AligNART: Non-autoregressive neural machine translation by jointly learning to estimate alignment and translate

arXiv

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arXiv 2021年

作者： Song, Jongyoon Kim, Sungwon Yoon, Sungroh Data Science and AI Laboratory Seoul National University Korea Republic of Kakao Enterprise Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Kakao Enterprise

Non-autoregressive neural machine translation (NART) models suffer from the multi-modality problem which causes translation inconsistency such as token repetition. Most recent approaches have attempted to solve this problem by implicitly modeling dependencies between outputs. In this paper, we introduce AligNART, which leverages full alignment information to explicitly reduce the modality of the target distribution. AligNART divides the machine translation task into (i) alignment estimation and (ii) translation with aligned decoder inputs, guiding the decoder to focus on simplified one-to-one translation. To alleviate the alignment estimation problem, we further propose a novel alignment decomposition method. Our experiments show that AligNART outperforms previous non-iterative NART models that focus on explicit modality reduction on WMT14 En↔De and WMT16 Ro→En. Furthermore, AligNART achieves BLEU scores comparable to those of the state-of-the-art connectionist temporal classification based models on WMT14 En↔De. We also observe that AligNART effectively addresses the token repetition problem even without sequence-level knowledge distillation. Copyright © 2021, The Authors. All rights reserved.

关键词： Neural machine translation

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

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ILVR: Conditioning method for denoising diffusion probabilistic models

arXiv

引用

arXiv 2021年

作者： Choi, Jooyoung Kim, Sungwon Jeong, Yonghyun Gwon, Youngjune Yoon, Sungroh Data Science and AI Laboratory Seoul National University Korea Republic of ASRI INMC Interdisciplinary Program in AI Seoul National University Korea Republic of AI Research Team Samsung SDS

Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation. However, due to the stochasticity of the generative process in DDPM, it is challenging to generate images with the desired semantics. In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image. Here, the refinement of the generative process in DDPM enables a single DDPM to sample images from various sets directed by the reference image. The proposed ILVR method generates high-quality images while controlling the generation. The controllability of our method allows adaptation of a single DDPM without any additional learning in various image generation tasks, such as generation from various downsampling factors, multi-domain image translation, paint-to-image, and editing with scribbles. © 2021, CC BY-NC-SA.

关键词： Iterative methods

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Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error

Guiding Brain-to-Vocalization Decoder Design Using Structure...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jingya Huang Pablo Tostado-Marcos Sowmya Manojna Narasimha Aashish N. Patel Ezequiel. M. Arneodo Timothy Q. Gentner Gal Mishne Vikash Gilja Department of Electrical and Computer Engineering University of California San Diego CA Department of Bioengineering University of California San Diego CA Institute for Neural Computation University of California San Diego CA Neuroscience Graduate Program University of California San Diego CA Department of Physics University of California San Diego CA Department of Psychology University of California San Diego CA Halıcıoğlu Data Science Institute University of California San Diego CA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

State-of-the-art intracortical neuroprostheses currently enable communication at 60+ words per minute for anarthric individuals by training on over 10K sentences to account for phoneme variability in different word contexts. There is limited understanding about whether this performance can be maintained in decoding naturalistic speech with 40K+ word vocabularies across elicited, spontaneous, and conversational speech contexts. We introduce a vocal-unit-level generalization test to explicitly evaluate neural decoder performance with an expanded and more diverse behavioral repertoire. Tested on neural decoders modeling zebra finch vocalization, an analog to human vocal production, we compare three decoders with different input types: spike trains, neural factors, and firing rates. The factors and rates are latent neural features inferred using trained Latent Factor Analysis via Dynamical Systems (LFADS) models that capture the population neural dynamics during vocal production. While the conventional random holdout generalization error measure is similar for all three decoders, factor- and rate-based decoders outperform spike-based decoders when testing vocal-unit-holdout generalization error. These results suggest the later models better adapt to flexible vocalization inference when trained with partial observation of data variation, motivating further exploration of decoders incorporating latent neural and vocalization dynamics.

关键词： Training Adaptation models Vocabulary Firing Measurement uncertainty Neuroprostheses Production Decoding Engineering in medicine and biology Testing

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APPLICATION OF THE DIGITAL ANNEALER UNIT IN OPTIMIZING CHEMICAL REACTION CONDITIONS FOR ENHANCED PRODUCTION YIELDS

arXiv

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arXiv 2024年

作者： Li, Shih-Cheng Wang, Pei-Hwa Su, Jheng-Wei Chiang, Wei-Yin Huang, Shih-Hsien Lin, Yen-Chu Ou, Chia-Ho Chen, Chih-Yu Insilico Medicine Taiwan Ltd. Taipei City110208 Taiwan Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Quantum Information Center Chung Yuan Christian University Taoyuan City320314 Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City320314 Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung City900392 Taiwan

Finding appropriate reaction conditions that yield high product rates in chemical synthesis is crucial for the chemical and pharmaceutical industries. However, due to the vast chemical space, conducting experiments for each possible reaction condition is impractical. Consequently, models such as QSAR (Quantitative Structure-Activity Relationship) or ML (Machine Learning) have been developed to predict the outcomes of reactions and illustrate how reaction conditions affect product yield. Despite these advancements, inferring all possible combinations remains computationally prohibitive when using a conventional CPU. In this work, we explore using a Digital Annealing Unit (DAU) to tackle these large-scale optimization problems more efficiently by solving Quadratic Unconstrained Binary Optimization (QUBO). Two types of QUBO models are constructed in this work: one using quantum annealing and the other using ML. Both models are built and tested on four high-throughput experimentation (HTE) datasets and selected Reaxys datasets. Our results suggest that the performance of models is comparable to classical ML methods (i.e., Random Forest and Multilayer Perceptron (MLP)), while the inference time of our models requires only seconds with a DAU. Additionally, in campaigns involving active learning and autonomous design of reaction conditions to achieve higher reaction yield, our model demonstrates significant improvements by adding new data, showing promise of adopting our method in the iterative nature of such problem settings. Our method can also accelerate the screening of billions of reaction conditions, achieving speeds millions of times faster than traditional computing units in identifying superior conditions. Therefore, leveraging the DAU with our developed QUBO models has the potential to be a valuable tool for innovative chemical synthesis. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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