检索结果-内蒙古大学图书馆

Hybridization of colloidal handlebodies with singular defects and topological solitons in chiral liquid crystals

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Lee, Jun-Yong Kumari, Asha Yuan, Ye Tasinkevych, Mykola Smalyukh, Ivan I. International Institute for Sustainability with Knotted Chiral Meta Matter Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Department of Physics and Mathematics School of Science and Technology Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Department of Physics University of Colorado BoulderCO80309 United States Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program and Soft Materials Research Center University of Colorado BoulderCO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado BoulderCO80309 United States

Topology can manifest itself in colloids when quantified by invariants like Euler characteristics of nonzero-genus colloidal surfaces, albeit spherical colloidal particles are most often studied, and colloidal particles with complex topology are rarely considered. On the other hand, singular defects and topological solitons often define the physical behavior of the molecular alignment director fields in liquid crystals. Interestingly, nematic liquid crystalline dispersions of colloidal particles allow for probing the interplay between topologies of surfaces and fields, but only a limited number of such cases have been explored so far. Here, we study the hybridization of topological solitons, singular defects, and topologically nontrivial colloidal particles with the genus of surfaces different from zero in a chiral nematic liquid crystal phase. Hybridization occurs when distortions separately induced by colloidal particles and LC solitons overlap, leading to energy minimization-driven redistribution of director field deformations and defects. As a result, hybrid director configurations emerge, combining topological features from both components. We uncover a host of director field configurations complying with topological theorems, which can be controlled by applying electric fields. Rotational and translational dynamics arise due to the nonreciprocal evolution of the director fields in response to alternating electric fields of different frequencies. These findings help define a platform for controlling topologically hyper-complex colloidal structures and dynamics with electrically reconfigurable singular defects and topological solitons induced by colloidal handlebodies. Copyright © 2025, The Authors. All rights reserved.

关键词： Topology

Analysis of Decision Tree and Smooth Support Vector Machine Methods on Data Mining 1

学校读者我要写书评

暂无评论

Analysis of Decision Tree and Smooth Support Vector Machine ...

1st International Conference on computer Science and Applied Mathematic, ICCSAM 2018

作者： Sitepu, Natalina Br Sawaluddin Zarlis, M. Efendi, Syahril Dhany, Hanna Willa Informatic Engineering Study Program Magister Faculty of Computer Science and Information Technology Universitas Sumatra Utara Medan Indonesia

This research is about smooth support vector machine (SSVM) and Decision Tree in data mining. Many researchers conduct and develop methods to improve the accuracy and classification of data on good results. This research was conducted by conducting an experiment on STMIK Neumann Medan student data. In this study, it was concluded that Decision Tree performance is better than SSVM, Decision Tree gets very good results that promise to help find the best students to get scholarships. This study is better than SSVM. The training process has a difference of 11.04% and the testing process is 10.08% with each Accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

Testing Real-Time Applications on Windows 10 IOT Using the Nyquist Theory 1

学校读者我要写书评

暂无评论

Testing Real-Time Applications on Windows 10 IOT Using the N...

1st International Conference on computer Science and Applied Mathematic, ICCSAM 2018

作者： Syahputa, Zulfahmi Zarlis, M. Nasution, Beny B. Informatics Engineering Study Program Magister Faculty of Computer Science Information Technology Universitas Sumatera Utara Medan Indonesia

Raspberry Pi is a mini-computer that is provided to carry out activities quickly and precisely, but Raspberry Pi was created to not be able to do the real-time system with the support of Windows 10 IoT operating system, so the real-time system can be done on Raspberry Pi. The real-time applied in the application needs to be tested with the Nyquist theory. The purpose of this study was to get real-time system measurements available on Windows 10 IoT. This test is done using the Nyquist theory by calculating the results of measurements on mp3 streaming performed on Windows 10 IoT. © Published under licence by IOP Publishing Ltd.

关键词： Real time systems

Cell2Sentence: teaching large language models the language of biology 24

学校读者我要写书评

暂无评论

Cell2Sentence: teaching large language models the language o...

Proceedings of the 41st International Conference on Machine Learning

作者： Daniel Levine Syed Asad Rizvi Sacha Lévy Nazreen Pallikkavaliyaveetil David Zhang Xingyu Chen Sina Ghadermarzi Ruiming Wu Zihe Zheng Ivan Vrkic Anna Zhong Daphne Raskin Insu Han Antonio Henrique De Oliveira Fonseca Josue Ortega Caro Amin Karbasi Rahul M. Dhodapkar David Van Dijk Department of Computer Science Yale University New Haven CT School of Engineering Applied Science University of Pennsylvania Philadelphia PA School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT and Wu Tsai Institute Yale University New Haven CT Google and Yale Institute for Foundations of Data Science New Haven CT and Department of Computer Science Yale University New Haven CT and Yale School of Engineering and Applied Science New Haven CT Roski Eye Institute University of Southern California Los Angeles CA and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Yale Institute for Foundations of Data Science New Haven CT and Wu Tsai Institute Yale University New Haven CT and Cardiovascular Research Center Yale School of Medicine New Haven CT and Interdepartmental Program in Computational Biology & Bioinformatics Yale University New Haven CT and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

关键词：

Regularity and numerical approximation of fractional elliptic differential equations on compact metric graphs

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Bolin, David Kovács, Mihály Kumar, Vivek Simas, Alexandre B. Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg GothenburgSE-41296 Sweden Department of Differential Equations Budapest University of Technology and Economics Mauegyetem rkp. 3. BudapestH-1111 Hungary Faculty of Information Technology and Bionics Pázmány Péter Catholic University Práter utca 50/a. BudapestH-1083 Hungary Theoretical Statistics and Mathematics Unit Indian Statistical Institute Bangalore Centre 8th Mile Mysore Road Karnataka Bangalore560059 India

The fractional differential equation Lβu = f posed on a compact metric graph is considered, where β > 0 and L = κ2 − ∇(a∇) is a second-order elliptic operator equipped with certain vertex conditions and sufficiently smooth and positive coefficients κ, a. We demonstrate the existence of a unique solution for a general class of vertex conditions and derive the regularity of the solution in the specific case of Kirchhoff vertex conditions. These results are extended to the stochastic setting when f is replaced by Gaussian white noise. For the deterministic and stochastic settings under generalized Kirchhoff vertex conditions, we propose a numerical solution based on a finite element approximation combined with a rational approximation of the fractional power L−β. For the resulting approximation, the strong error is analyzed in the deterministic case, and the strong mean squared error as well as the L2(Γ×Γ)error of the covariance function of the solution are analyzed in the stochastic setting. Explicit rates of convergences are derived for all cases. Numerical experiments for L = κ2 − ∆, κ > 0 are performed to illustrate the *** Codes 35R02, 35A01, 35A02, 60H15, 60H40 Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

Icd: A New Interpretable Cognitive Diagnosis Model for Intelligent Tutor Systems

学校读者我要写书评

暂无评论

SSRN

SSRN 2022年

作者： Qi, Tianlong Ren, Meirui Guo, Longjiang Li, Xiaokun Li, Jin Zhang, Lichen Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi’an710119 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an710119 China School of Computer Science Shaanxi Normal University Xi’an710119 China Xi’an Key Laboratory of Culture Tourism Resources Development and Utilization Xi’an710062 China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd Harbin150090 China

Cognitive diagnosis plays a crucial role in Intelligent Tutor Systems, which aims to diagnose learners' cognitive states according to learners' observable records, such as exercise records. However, this paper observes that the existing cognitive diagnosis models still have rooms for performance improvements: (1) they ignore the interaction between knowledge concepts;(2) they ignore the quantitative relation between exercises and concepts. In order to solve the aforementioned problems, this paper proposes a new interpretable cognitive diagnosis model named ICD based on a novel neural network designed by us, which can conduct answer records with different lengths. The neural network can fully utilize the interaction among concepts, the quantitative relation between exercises and concepts, and considers bias replacement by slip and guess, which makes the ICD more interpretable. The paper has conducted extensive experiments on four real publicity datasets, and the results show that both the performances and the interpretability of ICD are better than the latest state-of-the-art CDMs such as RCD, NCDM, and CDGK, and the classical CDMs such as DINA and MIRT. © 2022, The Authors. All rights reserved.

关键词： Neural networks

TargetNet: Functional microRNA target prediction with deep neural networks

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Min, Seonwoo Lee, Byunghan Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of LG AI Research Seoul07796 Korea Republic of Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Interdisciplinary Program in Artificial Intelligence and Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of

Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions. Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (1) relaxed CTS selection criteria accommodating irregularities in the seed region, (2) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments, and (3) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-theart algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets. © 2021, CC BY-NC-ND.

关键词： RNA

Implementation of Generate and Test Algorithm for Junior High School Zoning System in Malang 4

学校读者我要写书评

暂无评论

Implementation of Generate and Test Algorithm for Junior Hig...

4th International Conference on Vocational Education and Training, ICOVET 2020

作者： Ratnasar, Novia Agustin Mawarni, Inggar Tri Nur Handayani, Anik Wibawa, Aji Prasetya Hammad, Jehad A. H Wibowotomo, Budi Sugandi, MacHmud Universitas Negeri Malang Vocational Education Program Department Indonesia Universitas Negeri Malang Electrical Engineering Dept Indonesia Al-Quds Open University Faculty of Technology and Applied Sciences Department of Computer Information Systems Palestine Universitas Negeri Malang Industrial Technology Department Malang65145 Indonesia Universitas Negeri Malang Mechanical ENgineering Department Malang65145 Indonesia

ISBN: (纸本)9781728181318

Admission of new students in 2019 through zoning still faces many obstacles, one of which is the readiness of the application to determine the distance of a student's house from the nearest recommended school. The purpose of this study is to provide better recommendations regarding the shortest and most accurate distance in the zoning system for admission of junior high school students in Malang by using the Generate and Test algorithm without having to do it manually or using other map applications. The results of this study indicate that the distance value obtained from the haversine formula corresponds to the original distance measured through a zoning application that applies the Generate and Test algorithm to measure the closest distance from the student's location. The recommended distance is given through a process of repetition and comparison of nodes. By using the generate and test algorithm for the search process the shortest distance from the user to the destination school has an average time of 6.2MB, the level of accuracy is the same as the Haversine calculation. © 2020 IEEE.

关键词： Students

No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases.

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Thomas Z. Xu, Kaiwen Krishnan, Aravind Gao, Riqiang Kammer, Michael N. Antic, Sanja Xiao, David Knight, Michael Martinez, Yency Paez, Rafael Lentz, Robert J. Deppen, Stephen Grogan, Eric L. Lasko, Thomas A. Sandler, Kim L. Maldonado, Fabien Landman, Bennett A. Medical Scientist Training Program Vanderbilt University NashvilleTN37235 United States Biomedical Engineering Vanderbilt University NashvilleTN37235 United States Computer Science Vanderbilt University NashvilleTN37235 United States Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Digital Technology and Innovation Siemens Healthineers PrincetonNJ08540 United States Division of Allergy Pulmonary and Critical Care Medicine Department of Medicine Vanderbilt University Medical Center NashvilleTN37232 United States Department of Thoracic Surgery Vanderbilt University Medical Center NashvilleTN37232 United States Biomedical Informatics Vanderbilt University Medical Center NashvilleTN37232 United States Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN37232 United States

Purpose: Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinical settings in which patients would arguably most benefit are scarce. Materials and Methods: This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged 9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions to assess the area under the receiver operating characteristic curve (AUC) of validated models including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence on chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented each model from their published literature, re-training the models if necessary, and curated each cohort from primary data sources. Results: We observed that model performance varied greatly across clinical use cases. No single predictive model emerged as a clear winner across all cohorts, but certain models excelled in specific clinical contexts. Single timepoint chest CT AI performed well in lung screening, but struggled to generalize to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively promising performance on incidentally-detected nodules. However, when applied to nodules that underwent biopsy, all models underperformed. Conclusion: These results underscore the strengths and limitations of 8 validated predictive models and highlight promising directions towards personalized, noninvasive lung cancer diagnosis. © 2024, CC BY-NC-SA.

关键词： Biopsy