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High-Fidelity Micro- and Nano-Scale Infrared Spectroscopic Imaging

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 562-562页

作者： Bhargava, Rohit Yeh, Kevin Kenkel, Seth Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana IL United States Departments of Bioengineering Chemical and Biomolecular Engineering Electrical and Computer Engineering Mechanical Science and Engineering University of Illinois at Urbana-Champaign Urbana IL United States Cancer Center at Illinois University of Illinois at Urbana- Champaign Urbana IL United States

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WEAKLY SUPERVISED INDOOR LOCALIZATION VIA MANIFOLD MATCHING

arXiv

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arXiv 2022年

作者： Kevrekidis, Ioannis G. Peterfreund, Erez Jaffe, Ariel Department of Chemical and Biomolecular Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science The Hebrew University of Jerusalem Jerusalem 9190401 Israel Department of Statistics and Data Science The Hebrew University of Jerusalem Jerusalem9190401 Israel

Inferring the location of a mobile device in an indoor setting is an open problem of utmost significance. A leading approach that does not require the deployment of expensive infrastructure is fingerprinting, where a classifier is trained to predict the location of a device based on its captured signal. The main caveat of this approach is that acquiring a sufficiently large and accurate training set may be prohibitively expensive. Here, we propose a weakly supervised method that only requires the location of a small number of devices. The localization is done by matching a low-dimensional spectral representation of the signals to a given sketch of the indoor environment. We test our approach on simulated and real data and show that it yields an accuracy of a few meters, which is on par with fully supervised approaches. The simplicity of our method and its accuracy with minimal supervision makes it ideal for implementation in indoor localization systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Location

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Self-learn to explain siamese networks robustly

arXiv

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arXiv 2021年

作者： Chen, Chao Shen, Yifan Ma, Guixiang Kong, Xiangnan Rangarajan, Srinivas Zhang, Xi Xie, Sihong Computer Science and Engineering Dept Lehigh University BUPT University of Illinois at Chicago Worcester Polytechnic Institute Department of Chemical and Biomolecular Engineering Lehigh University

Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data are scarce and imbalanced. As these applications make high-stake decisions and involve societal values like fairness and transparency, it is critical to explain the learned models. We aim to study post-hoc explanations of Siamese networks (SN) widely used in learning to compare. We characterize the instability of gradient-based explanations due to the additional compared object in SN, in contrast to architectures with a single input instance. We propose an optimization framework that derives global invariance from unlabeled data using self-learning to promote the stability of local explanations tailored for specific query-reference pairs. The optimization problems can be solved using gradient descent-ascent (GDA) for constrained optimization, or SGD for KL-divergence regularized unconstrained optimization, with convergence proofs, especially when the objective functions are nonconvex due to the Siamese architecture. Quantitative results and case studies on tabular and graph data from neuroscience and chemical engineering show that the framework respects the self-learned invariance while robustly optimizing the faithfulness and simplicity of the explanation. We further demonstrate the convergence of GDA experimentally. © 2021, CC BY.

关键词： Gradient methods

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Transformer Based Tissue Classification in Robotic Needle Biopsy

arXiv

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arXiv 2024年

作者： Wang, Fanxin Cheng, Yikun Mukherjee, Sudipta S. Bhargava, Rohit Kesavadas, Thenkurussi Department of Mechatronics and Robotics Xi'an Jiaotong-Liverpool University 215123 China Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign ChampaignIL61801 United States Beckman Institute for Advanced Science and Technology Departments of Bioengineering Electrical & Computer Engineering Mechanical Science & Engineering Chemical and Biomolecular Engineering University of Illinois Urbana-Champaign ChampaignIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign ChampaignIL61801 United States Division of Research and Economic Development University at Albany State University of New York AlbanyNY12222 United States

Image-guided minimally invasive robotic surgery is commonly employed for tasks such as needle biopsies or localized therapies. However, the nonlinear deformation of various tissue types presents difficulties for surgeons in achieving precise needle tip placement, particularly when relying on low-fidelity biopsy imaging systems. In this paper, we introduce a method to classify needle biopsy interventions and identify tissue types based on a comprehensive needle-tissue contact model that incorporates both position and force parameters. We trained a transformer model using a comprehensive dataset collected from a formerly developed robotics platform, which consists of synthetic and porcine tissue from various locations (liver, kidney, heart, belly, hock) marked with interaction phases (pre-puncture, puncture, post-puncture, neutral). This model achieves a significant classification accuracy of 0.93. Our demonstrated method can assist surgeons in identifying transitions to different tissues, aiding surgeons with tissue awareness. © 2024, CC BY.

关键词： Biopsy

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Ultrawide bandgap semiconductor heterojunction p-n diodes with distributed polarization doped p-type AlGaN layers on bulk AlN substrates

arXiv

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arXiv 2023年

作者： Agrawal, Shivali van Deurzen, Len Encomendero, Jimy Dill, Joseph E. Huang, Hsin Wei Protasenko, Vladimir Xing, Huili Jena, Debdeep Department of Chemical and Biomolecular Engineering Cornell University IthacaNY14853 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Department of Electrical and Computer Engineering Cornell University IthacaNY14853 United States Department of Materials Science and Engineering Cornell University IthacaNY14853 United States Kavli Institute at Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

Ultrawide bandgap heterojunction p-n diodes with polarization-induced AlGaN p-type layers are demonstrated using plasma-assisted molecular beam epitaxy on bulk AlN substrates. Current-voltage characteristics show a turn on voltage of Vbi ≈ 5.5 V, a minimum room temperature ideality factor of η ≈ 1.63, and more than 12 orders of current modulation at room temperature. Stable current operation of the ultrawide bandgap semiconductor diode is measured up to a temperature of 300◦C. The one-sided n+-p heterojunction diode design enables a direct measurement of the spatial distribution of polarization-induced mobile hole density in the graded AlGaN layer from the capacitance-voltage profile. The measured average mobile hole density is p ∼ 5.7 × 1017 cm−3, in close agreement with what is theoretically expected from distributed polarization doping. Light emission peaked at 260 nm (4.78 eV) observed in electroluminescence corresponds to interband radiative recombination in the n+ AlGaN layer. A much weaker deep-level emission band observed at 3.4 eV is attributed to cation-vacancy and silicon complexes in the heavily Si-doped AlGaN layer. These results demonstrate that distributed polarization doping enables ultrawide bandgap semiconductor heterojunction p-n diodes that have wide applications ranging from power electronics to deep-ultraviolet photonics. These devices can operate at high temperatures and in harsh environments. © 2023, CC BY-NC-ND.

关键词： Polarization

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Influence of Training Times and Sample Size on Results in Visual Try-On with GAN

Influence of Training Times and Sample Size on Results in Vi...

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2021 International Conference on Applied Mathematics, Modelling and Intelligent Computing, CAMMIC 2021

作者： Jiang, Wenqing Qi, Ruiyu Zheng, Juntao Computer Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai Guangdong519087 China Chemical and Biomolecular Engineering Lehigh University BethlehemPA18015 United States Internet of Things Guangdong Ocean University Zhanjiang Guangdong524088 China

With online shopping becoming more and more trendy these days, Virtual Try-On Approach is proposed. It is a method of Generative Adversarial Network (GAN) composes of two models: Geometry Matching Module (GMM) and Try-On Module (TOM). It is proposed to solve the inconvenience of customers for not being able to see the fitting effect before receiving the goods. This paper is about finding the relationship between dataset and the effect of the experiment in the GAN experiment. After implementing the models and gaining the result images, we do some observations on the GMM one and try to analyze it and improve the effect. Comparison is made on different aspects: training times of the model and sample size of the training input. Similar trends are shown on the decrease of loss value when the training times or sample size rises. However, the visual effect is not necessarily improved all the time. Compared with the times it has been trained, the improvement from training times is far less than that from the sample size. Finally, we draw the conclusion that the effect is gradually improved as the training times or sample size increases at the beginning. Nevertheless, the improvement cannot be recognized evidently when the training times and sample size have grown large enough. Additionally, the sample size seems to be a more significant factor in the improvement of the visual effect than the training times. © Published under licence by IOP Publishing Ltd.

关键词： Generative adversarial networks

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Foundation Models for Atomistic Simulation of Chemistry and Materials

arXiv

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arXiv 2025年

作者： Yuan, Eric C.-Y. Liu, Yunsheng Chen, Junmin Zhong, Peichen Raja, Sanjeev Kreiman, Tobias Vargas, Santiago Xu, Wenbin Head-Gordon, Martin Yang, Chao Blau, Samuel M. Cheng, Bingqing Krishnapriyan, Aditi Head-Gordon, Teresa Kenneth S. Pitzer Theory Center Department of Chemistry United States Department of Chemical and Biomolecular Engineering United States Electrical Engineering and Computer Science United States Bakar Institute of Digital Materials for the Planet University of California BerkeleyCA94720 United States Chemical Sciences Division United States Applied Mathematics and Computational Research Division United States Energy Technologies Area United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pre-training strategies is possible for learned simulations of chemistry and materials. The scaling of large and diverse datasets and highly expressive architectures for chemical and materials sciences should result in a foundation model that is more efficient and broadly transferable, robust to out-of-distribution challenges, and easily fine-tuned to a variety of downstream observables, when compared to specific training from scratch on targeted applications in atomistic simulation. In this Perspective we aim to cover the rapidly advancing field of machine learned interatomic potentials (MLIP), and to illustrate a path to create chemistry and materials MLIP foundation models at larger scale. © 2025, CC BY-SA.

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chemical Property-Guided Neural Networks for Naphtha Composition Prediction

Chemical Property-Guided Neural Networks for Naphtha Composi...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Chonghyo Joo Jeongdong Kim Hyungtae Cho Jaewon Lee Sungho Suh Junghwan Kim Department of Chemical and Biomolecular Engineering Yonsei University Seoul Republic of Korea Green Materials & Processes R&D Group Korea Institute of Industrial Technology Ulsan Republic of Korea Department of Computer Science RPTU Kaiserslautern-Landau Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

The naphtha cracking process heavily relies on the composition of naphtha, which is a complex blend of different hydrocarbons. Predicting the naphtha composition accurately is crucial for efficiently controlling the cracking process and achieving maximum performance. Traditional methods, such as gas chromatography and true boiling curve, are not feasible due to the need for pilot-plant-scale experiments or cost constraints. In this paper, we propose a neural network framework that utilizes chemical property information to improve the performance of naphtha composition prediction. Our proposed framework comprises two parts: a Watson K factor estimation network and a naphtha composition prediction network. Both networks share a feature extraction network based on Convolutional Neural Network (CNN) architecture, while the output layers use Multi-Layer Perceptron (MLP) based networks to generate two different outputs - Watson K factor and naphtha composition. The naphtha composition is expressed in percentages, and its sum should be 100%. To enhance the naphtha composition prediction, we utilize a distillation simulator to obtain the distillation curve from the naphtha composition, which is dependent on its chemical properties. By designing a loss function between the estimated and simulated Watson K factors, we improve the performance of both Watson K estimation and naphtha composition prediction. The experimental results show that our proposed framework can predict the naphtha composition accurately while reflecting real naphtha chemical properties.

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Evolutionary context-integrated deep sequence modeling for protein engineering 24th

Evolutionary context-integrated deep sequence modeling for p...

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24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020

作者： Luo, Yunan Vo, Lam Ding, Hantian Su, Yufeng Liu, Yang Qian, Wesley Wei Zhao, Huimin Peng, Jian Department of Computer Science University of Illinois at Urbana-Champaign Urbana United States Department of Chemical and Biomolecular Engineering University of Illinois at Urbana-Champaign Urbana United States

ISBN: (纸本)9783030452568

Protein engineering seeks to design proteins with improved or novel functions. Compared to rational design and directed evolution approaches, machine learning-guided approaches traverse the fitness landscape more effectively and hold the promise for accelerating engineering and reducing the experimental cost and effort. © Springer Nature Switzerland AG 2020.

关键词： Proteins

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Feedback regulation of crystal growth with monomer buffering 17

Feedback regulation of crystal growth with monomer buffering

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17th Annual Conference on Foundations of Nanoscience: Self-Assembled Architectures and Devices, FNANO 2020

作者： Schaffter, Samuel W. Scalise, Dominic Schulman, Rebecca Johns Hopkins University Chemical & Biomolecular Engineering United States Bioengineering California Institute of Technology United States Johns Hopkins University Computer Science United States

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