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International Conference on data and Software Engineering (ICODSE)

作者： Matthew Martianus Henry Nur Adhianti Hervanto Mahmud Isnan Dian Kurnianingrum Chyntia Ika Ratnapuri Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506407

ISBN: (纸本)9798331506414

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus.

关键词： Support vector machines Sentiment analysis Logistic regression Social networking (online) Blogs Transformers Encoding Decoding Business intelligence Software engineering

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Energy guided diffusion for generating neurally exciting images 23

Energy guided diffusion for generating neurally exciting ima...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Paweł A. Pierzchlewicz Konstantin F. Willeke Arne F. Nix Pavithra Elumalai Kelli Restivo Tori Shinn Cate Nealley Gabrielle Rodriguez Saumil Patel Katrin Franke Andreas S. Tolias Fabian H. Sinz Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany and Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany aand Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX and Department of Electrical and Computer Engineering Rice University Houston TX Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany and Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany and Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX

In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method for studying tuning properties of biological and artificial visual systems. However, as we move up the visual hierarchy, the complexity of neuronal computations increases. Consequently, it becomes more challenging to model neuronal activity, requiring more complex models. In this study, we introduce a novel readout architecture inspired by the mechanism of visual attention. This new architecture, which we call attention readout, together with a data-driven convolutional core outperforms previous task-driven models in predicting the activity of neurons in macaque area V4. However, as our predictive network becomes deeper and more complex, synthesizing MEIs via straightforward gradient ascent (GA) can struggle to produce qualitatively good results and overfit to idiosyncrasies of a more complex model, potentially decreasing the MEI's model-to-brain transferability. To solve this problem, we propose a diffusion-based method for generating MEIs via Energy Guidance (EGG). We show that for models of macaque V4, EGG generates single neuron MEIs that generalize better across varying model architectures than the state-of-the-art GA, while at the same time reducing computational costs by a factor of 4.7x, facilitating experimentally challenging closed-loop experiments. Furthermore, EGG diffusion can be used to generate other neurally exciting images, like most exciting naturalistic images that are on par with a selection of highly activating natural images, or image reconstructions that generalize better across architectures. Finally, EGG is simple to implement, requires no retraining of the diffusion model, and can easily be generalized to provide other characterizations of the visual system, such as invariances. Thus, EGG provides a general and flexible framework to study the coding properties of the visual system in the context of natural ima

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Root-n consistent semiparametric learning with high-dimensional nuisance parameters under minimal sparsity

arXiv

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arXiv 2023年

作者： Liu, Lin Wang, Xinbo Wang, Yuhao Institute of Natural Sciences MOE-LSC School of Mathematical Sciences CMA-Shanghai SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University China Department of Bioinformatics and Biostatistics School of Life Sciences SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University China Institute for Interdisciplinary Information Sciences Tsinghua University China Shanghai Artificial Intelligence Laboratory Shanghai Qi Zhi Institute China

Treatment effect estimation under unconfoundedness is a fundamental task in causal inference. In response to the challenge of analyzing high-dimensional datasets collected in substantive fields such as epidemiology, genetics, economics, and social sciences, various methods for treatment effect estimation with high-dimensional nuisance parameters (the outcome regression and the propensity score) have been developed in recent years. However, it is still unclear what is the necessary and sufficient sparsity condition on the nuisance parameters such that we can estimate the treatment effect at 1/√n-rate. In this paper, we propose a new Double-Calibration strategy that corrects the estimation bias of the nuisance parameter estimates computed by regularized high-dimensional techniques and demonstrate that the corresponding Doubly-Calibrated estimator achieves 1/√n-rate as long as one of the nuisance parameters is sparse with sparsity below √n/log p, where p denotes the ambient dimension of the covariates, whereas the other nuisance parameter can be arbitrarily complex and completely misspecified. The Double-Calibration strategy can also be applied to settings other than treatment effect estimation, e.g. regression coefficient estimation in the presence of a diverging number of controls in a semiparametric partially linear model, and local average treatment effect estimation with instrumental variables. Copyright © 2023, The Authors. All rights reserved.

关键词： Regression analysis

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia Computer Science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Design,methodology,and baseline of whole city‑million scale children and adolescents myopia survey(CAMS)in Wenzhou,China

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Eye and Vision 2021年第1期8卷 303-316页

作者： Liangde Xu Youyuan Zhuang Guosi Zhang Yunlong Ma Jian Yuan Changseng Tu MiaoMiao Li Wencan Wang Yaru Zhang Xiaoyan Lu Jing Li Xinting Liu Zhengbo Xue Meng Zhou Jie Sun Jinhua Bao Ming Li Fan Lu Hong Wang Jianzhong Su Jia Qu School of Ophthalmology and Optometry and Eye Hospital Wenzhou Medical UniversityWenzhou 325027China State Key Laboratory of Ophthalmology Optometry and Visual ScienceWenzhou 325027China Institute of Biomedical Big Data Wenzhou Medical UniversityWenzhou 325027China National Clinical Research Center for Ocular Disease Wenzhou 325027China College of Bioinformatics Science and Technology Harbin Medical UniversityHarbin 150081People’s Republic of China

Background:Myopia is the most common visual impairment in children and adolescents *** study described an economical and effective population-based screening pipeline and performed the project of a million scale children and adolescents myopia survey(CAMS),which will shed light on the further study of myopia from the level of epidemiology and precision ***:We developed a novel population-based screening pattern,an intelligent screening process and internetbased information transmission and analysis system to carry out the survey consisting of school children in Wenzhou,*** examination items include unaided distance visual acuity,presenting distance visual acuity,and noncycloplegic *** and high myopia were defined as spherical equivalent(SE)≤−1.00 diopters(D)and SE≤−6.00 D,***,the reports of the vision checking were automatically sent to parents and the related *** CAMS project will be done two to four times annually with the support of the *** online eyesight status information management system(OESIMS)was developed to construct comprehensive and efficient electronic vision health records(EVHRs)for myopia information inquiry,risk pre-warning,and further ***:The CAMS completed the first-round of screening within 30 days for 99.41%of Wenzhou students from districts and counties,in June 2019.A total of 1,060,925 participants were eligible for CAMS and 1,054,251(99.37%participation rate)were selected through data quality control,which comprised 1305 schools,and 580,609,251,050 and 170,967 elementary,middle,and high school *** mean age of participants was 12.21±3.32 years(6-20 years),the female-to-male ratio was *** prevalence of myopia in elementary,middle,and high school students was 38.16%,77.52%,and 84.00%,respectively,and the high myopia incidence was 0.95%,6.90%,and 12.98%.Conclusions:The CAMS standardized myopia screening model involves automating large-scal

关键词： Baseline Vision screening Myopia prevention and control Population-based

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rcsb-api: Python Toolkit for Streamlining Access to RCSB Protein data Bank APIs: rcsb-api: Python Toolkit to Access *** APIs

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Journal of Molecular Biology 2025年第15期437卷 168970页

作者： Piehl, Dennis W. Vallat, Brinda Truong, Ivana Morsy, Habiba Bhatt, Rusham Blaumann, Santiago Biswas, Pratyoy Rose, Yana Bittrich, Sebastian Duarte, Jose M. Segura, Joan Bi, Chunxiao Myers-Turnbull, Douglas Hudson, Brian P. Zardecki, Christine Burley, Stephen K. Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States

The Protein data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein data Bank *** research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support *** users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api, that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (***/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences. © 2025 The Author(s)

关键词： bioinformatics FAIR principles RCSB Protein data Bank structural biology

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CCAC: Contrastive Learning with Channel Attention and Contour Loss for Brain Image Registration

CCAC: Contrastive Learning with Channel Attention and Contou...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Ying Zhang Dantong Shi Jiaqi Wang Jinhai Xiang Wen Zhang College of Informatics Huazhong Agricultural University Wuhan China College of Informatics Huazhong Agricultural University Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big Data Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Wuhan China

In medical image analysis, image registration plays a key role in establishing the deformation field between image pairs. A good deformation model is essential for high-quality estimates. However, most existing approaches still face great challenges in extracting better image features and generating detailed information on deformation fields. In this paper, we present a brain image registration model based on contrastive learning with channel attention and contour loss (CCAC). To integrate lower-level features more effectively, we propose a channel attention-based skip connection to obtain weight information of features at different encoder layers. To enhance the feature consistency of semantics in different stages, we then introduce multi-stage contrastive learning to capture semantics at different stages. Moreover, in order to reduce the influence caused by the disparities of inter-image gray value in registered images, contour feature loss is employed. Both qualitative and quantitative experiments on the popular LPBA40 dataset demonstrate that our method can achieve much better performance than the baseline methods.

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING 12

MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Maximum principle

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Genetic effects of Red Lettuce Leaf genes on red coloration in leaf lettuce under artificial lighting conditions

Plant-Environment Interactions

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Plant-Environment Interactions 2022年第4期3卷 179-192页

作者： Wada, Kaede C. Inagaki, Noritoshi Sakai, Hiroaki Yamashita, Hiroto Nakai, Yusuke Fujimoto, Zui Yonemaru, Jun-ichi Itoh, Hironori Breeding Big Data Management and Utilization Group Division of Smart Breeding Research Institute of Crop Science National Agriculture and Food Research Organization Tsukuba Japan Biomacromolecules Research Unit Research Center for Advanced Analysis Core Technology Research Headquarters National Agriculture and Food Research Organization Tsukuba Japan Bioinformatics Unit Research Center for Advanced Analysis Core Technology Research Headquarters National Agriculture and Food Research Organization Tsukuba Japan Greenhouse Vegetable Production Group Division of Field Crop and Vegetable Research Kyushu-Okinawa Agricultural Research Center National Agriculture and Food Research Organization Kurume Japan

Some cultivars of lettuce accumulate anthocyanins, which act as functional food ingredients. Leaf lettuce has been known to be erratic in exhibiting red color when grown under artificial light, and there is a need for cultivars that more stably exhibit red color in artificial light cultivation. In this study, we aimed to dissect the genetic architecture for red coloring in various leaf lettuce cultivars grown under artificial light. We investigated the genotype of Red Lettuce Leaf (RLL) genes in 133 leaf lettuce strains, some of which were obtained from publicly available resequencing data. By studying the allelic combination of RLL genes, we further analyzed the contribution of these genes to producing red coloring in leaf lettuce. From the quantification of phenolic compounds and corresponding transcriptome data, we revealed that gene expression level-dependent regulation of RLL1 (bHLH) and RLL2 (MYB) is the underlying mechanism conferring high anthocyanin accumulation in red leaf lettuce under artificial light cultivation. Our data suggest that different combinations of RLL genotypes cause quantitative differences in anthocyanin accumulation among cultivars, and some genotype combinations are more effective at producing red coloration even under artificial lighting. © 2022 The Authors. Plant-Environment Interactions published by New Phytologist Foundation and John Wiley & Sons Ltd.

关键词： anthocyanin biosynthesis artificial light type plant factories genotyping leaf lettuce (Lactuca sativa) MBW complex RNA-seq transcription factor transcriptome

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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