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Breast cancer histopathology using infrared spectroscopic imaging: The impact of instrumental configurations

Clinical Spectroscopy 2021年 3卷

作者： Shachi Mittal Tomasz P. Wrobel Michael Walsh Andre Kajdacsy-Balla Rohit Bhargava Department of Bioengineering and Beckman Institute for Advanced Science and Technology University of Illinois at Urbana−Champaign Urbana IL United States Department of Materials Science and Biomedical Engineering University of Wisconsin – Eau Claire WI United States Department of Pathology University of Illinois at Chicago IL United States Departments of Mechanical Science and Engineering Electrical and Computer Engineering Chemical and Biomolecular Engineering and Chemistry University of Illinois at Urbana−Champaign Urbana IL United States Cancer Center at Illinois University of Illinois at Urbana−Champaign Urbana IL United States

Digital analysis of cancer specimens using spectroscopic imaging coupled to machine learning is an emerging area that links spatially localized spectral signatures to tissue structure and disease. In this study, we examine the role of spatial-spectral tradeoffs in infrared spectroscopic imaging configurations for probing tumors and the associated microenvironment profiles at different levels of model complexity. We image breast tissue using standard and high-definition Fourier Transform Infrared (FT-IR) imaging and systematically examine the localization, spectral origins, and utility of data for classification. Results demonstrate that higher spatial detail provides high sensitivity and specificity of tissue segmentation, despite the increased subcellular variability. High definition imaging also allows accurate analysis of complex, multiclass models of breast tissue without compromising accuracy. A comparison of results also highlights the key differences in the data distributions and classification performance across modalities to better guide decision making for acquiring and analyzing IR imaging data for specific histopathological models.

关键词： High definition imaging Standard definition Breast pathology Machine learning Tissue segmentation

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Model-Free Event-Triggered Containment Control of Multi-Agent Systems

Model-Free Event-Triggered Containment Control of Multi-Agen...

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2018 Annual American Control Conference, ACC 2018

作者： Yang, Yongliang Modares, Hamidreza Vamvoudakis, Kyriakos G. Yin, Yixin Wunsch, Donald C. University of Science and Technology Beijing School of Automation Electrical Engineering Beijing100083 China Missouri University of Science Technology Department of Electrical Computer Engineering RollaMO65409 United States Virginia Tech Kevin T. Crofton Department of Aerospace and Ocean Engineering BlacksburgVA24061-0203 United States

ISBN: (纸本)9781538654286

This paper presents a model-free distributed event-triggered containment control scheme for linear multiagent systems. The proposed event-triggered scheme guarantees asymptotic stability of the equilibrium point of the containment error as well the avoidance of the Zeno behavior. To relax the requirement of complete knowledge of the dynamics, we combine an off-policy reinforcement learning algorithm in an actor critic structure with the event-trigger control mechanism to obtain the feedback gain of the distributed containment control protocol. A simulation experiment is conducted to verify the effectiveness of the approach. © 2018 AACC.

关键词： Protocols Decentralized control Multi-agent systems Heuristic algorithms Robots Eigenvalues and eigenfunctions Learning (artificial intelligence)"

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Genetic algorithm optimized support vector machine in noma-based satellite networkswith imperfect csi

arXiv

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arXiv 2020年

作者： Yan, Xiaojuan An, Kang Wang, Cheng-Xiang Zhu, Wei-Ping Li, Yusheng Feng, Zhiqiang National Mobile Commun. Research Lab School of Info. Science and Eng. Southeast University Nanjing210096 China College of Mechanical Naval Architecture and Ocean Engineering Beibu Gulf University Qinzhou535011 China Sixty-third Research Institute National University of Defense Technology Nanjing210007 China Purple Mountain Laboratories Nanjing211111 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada

With the help of a power-domain non-orthogonal multiple access (NOMA) scheme, satellite networks can simultaneously serve multiple users within limited time/spectrum resource block. However, the existence of channel estimation errors inevitably degrade the judgment on users' channel state information (CSI) accuracy, thus affecting the user pairing processing and suppressing the superiority of the NOMA scheme. Inspired by the advantages ofmachine learning (ML) algorithms, we propose an improved support vector machine (SVM) scheme to reduce the inappropriate user pairing risks and enhance the performance of NOMA based satellite networks with imperfect CSI. Particularly, a genetic algorithm (GA) is employed to optimize the regularization and kernel parameters of the SVM, which effectively improves the classification accuracy of the proposed scheme. Simulations are provided to demonstrate that the performance of the proposed method is better than that with random user paring strategy, especially in the scenario with a large number of users. Copyright © 2020, The Authors. All rights reserved.

关键词： Genetic algorithms

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Improved list-decodability of random linear binary codes

arXiv

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arXiv 2018年

作者： Li, Ray Wootters, Mary Department of Computer Science Stanford University Departments of Computer Science and Electrical Engineering Stanford University

There has been a great deal of work establishing that random linear codes are as listdecodable as uniformly random codes, in the sense that a random linear binary code of rate 1-H(p)-ϵ is pp,O(1/ϵ))-list-decodable with high probability. In this work, we show that such codes are (p,H(p)/ϵ + 2)-list-decodable with high probability, for any p ∈ (0, 1/2) and ϵ > 0. In addition to improving the constant in known list-size bounds, our argument - which is quite simple - works simultaneously for all values of p, while previous works obtaining L = O(1/ϵ) patched together different arguments to cover different parameter regimes. Our approach is to strengthen an existential argument of (Guruswami, Håstad, Sudan and Zuckerman, IEEE Trans. IT, 2002) to hold with high probability. To complement our upper bound for random linear codes, we also improve an argument of (Guruswami, Narayanan, IEEE Trans. IT, 2014) to obtain an essentially tight lower bound of 1/ϵ on the list size of uniformly random codes;this implies that random linear codes are in fact more list-decodable than uniformly random codes, in the sense that the list sizes are strictly smaller. To demonstrate the applicability of these techniques, we use them to (a) obtain more information about the distribution of list sizes of random linear codes and (b) to prove a similar result for random linear rank-metric codes. Copyright © 2018, The Authors. All rights reserved.

关键词： Binary codes

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Signal Detection for Full-duplex Cognitive Underwater Acoustic Communications with SIC Using Model-Driven Deep Learning Network

Signal Detection for Full-duplex Cognitive Underwater Acoust...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Junfeng Wang Shexiang Ma Yue Cui Haixin Sun Mingzhang Zhou Biao Wang Jianghui Li Lanjun Liu School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin China College of Computer and Information Engineering Tianjin Normal University Tianjin China School of Information Science and Engineering Xiamen University Xiamen China College of Electronic and Information Engineering Jiangsu University of Science and Technology Zhenjiang China Institute of Sound and Vibration Research University of Southampton Southampton UK College of Engineering Ocean University of China Qingdao China

This paper aims to handle the model-driven deep learning network based signal detection for full-duplex cognitive underwater acoustic communications (FDCUACs) with self-interference cancellation (SIC). The FDCUACs play an important role in underwater wireless communications, which employs the index modulation-orthogonal frequency division multiplexing-spread spectrum (IM-OFDM-SS) that carries subcarrier index bits and symbol bits simultaneously to further enhance data rate. It is shown that the proposed signal detector for the FDCUACs with the SIC can be modelled as a model-driven deep learning network, i.e., an easy index bit recovering processor utilizing the uniqueness behavior of spread codes plus a deep learning network with eight essential layers employed to directly regain the data belonging to the pre-selected carrier index in the transmitter. Compared with the original receiver and signal detection for the IM-OFDM-SS communications, the proposed scheme omits the traditional steps such as channel estimation, equalization and demodulation, and demonstrates the remarkable performance.

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Remote sensing image scene classification based on densely connected multilayer Kernel ELM

Remote sensing image scene classification based on densely c...

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2018 Australian and New Zealand Control Conference, ANZCC 2018

作者： Jiang, X.W. Yan, T.H. Xu, Q. He, B. Du, H.P. Li, W.H. School of Mechanical Electrical Engineering China Jiliang University Hangzhou China School of Information Science Engineering Ocean University of China Qingdao China School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong Australia School of Mechanical Materials Mechatronic and Biomedical Eng. University of Wollongong Wollongong Australia

ISBN: (纸本)9781538666173

The remote sensing images are captured by high-altitude satellites, and the coverage of each image scene is relatively large. It tends to have large intraclass differences and small differences between classes. Using conventional Hierarchical ELM (H-ELM) and multilayer kernel ELM (ML-KELM) to classify remote sensing image scenes, the network structure of the learning model is deep and the parameters are many. This leads to long training time and large memory consumption. In order to solve this problem, based on the ML-KELM, this paper proposes a densely connected kernel ELM (Dense-KELM) learning model, which is used to classify remote sensing image scenes. Experimental results show that at the same model depth, the Dense-KELM model has higher classification accuracy in remote sensing image scenes than the H-ELM and the ML-KELM. Its training time is slightly larger than the ML-KELM but much smaller than the H-ELM. This densely connected learning model can extract high-level features of remote sensing images more effectively, represent the details between remote sensing scenes, and improve the classification accuracy of remote sensing image scenes. Moreover, the densely connected network structure can effectively reduce the number of parameters of the depth model, improve the training speed of the model, and save the storage space of the model. © 2018 IEEE.

关键词： Remote sensing

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Optical imprinting of superlattices in two-dimensional materials

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Physical Review Research 2020年第4期2卷 043004-043004页

作者： Hwanmun Kim Hossein Dehghani Hideo Aoki Ivar Martin Mohammad Hafezi Joint Quantum Institute NIST and University of Maryland College Park Maryland 20742 USA Department of Physics University of Maryland College Park Maryland 20742 USA Departments of Electrical and Computer Engineering and Institute for Research in Electronics and Applied Physics University of Maryland College Park Maryland 20742 USA Department of Physics The University of Tokyo Hongo Tokyo 113-0033 Japan National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan Materials Science Division Argonne National Laboratory Argonne Illinois 60439 USA

We propose an optical method of shining circularly polarized and spatially periodic laser fields to imprint superlattice structures in two-dimensional electronic systems. By changing the configuration of the optical field, we synthesize various lattice structures with different spatial symmetry, periodicity, and strength. We find that the wide optical tunability allows one to tune different properties of the effective band structure, including Chern number, energy bandwidths, and band gaps. The in situ tunability of the superlattice gives rise to unique physics ranging from the topological transitions to the creation of the flat bands through the kagome superlattice, which can allow a realization of strongly correlated phenomena in Floquet systems. We consider the high-frequency regime where the electronic system can remain in the quasiequilibrium phase for an extended amount of time. The spatiotemporal reconfigurability of the present scheme opens up possibilities to control light-matter interaction to generate novel electronic states and optoelectronic devices.

关键词： Optoelectronics 2-dimensional systems Graphene Quasicrystals Topological insulators Bloch-Floquet theorem

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Analysis of a reduced model of epithelialmesenchymal fate determination in cancer metastasis as a singularly-perturbed monotone system

arXiv

引用

arXiv 2019年

作者： Al-Radhawi, M. Ali Sontag, Eduardo D. Departments of Bioengineering and Electrical and Computer Engineering Northeastern University 360 Huntington Avenue BostonMA02115 United States Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School BostonMA02115

Tumor metastasis is one of the main factors responsible for the high fatality rate of cancer. Metastasis can occur after malignant cells transition from the epithelial phenotype to the mesenchymal phenotype. This transformation allows cells to migrate via the circulatory system and subsequently settle in distant organs after undergoing the reverse transition. The core gene regulatory network controlling these transitions consists of a system made up of coupled SNAIL/miRNA-34 and ZEB1/miRNA-200 subsystems. In this work, we formulate a mathematical model and analyze its long-term behavior. We start by developing a detailed reaction network with 24 state variables. Assuming fast promoter and mRNA kinetics, we then show how to reduce our model to a monotone four-dimensional system. For the reduced system, monotone dynamical systems theory can be used to prove generic convergence to the set of equilibria for all bounded trajectories. The theory does not apply to the full model, which is not monotone, but we briefly discuss results for singularly-perturbed monotone systems that provide a tool to extend convergence results from reduced to full systems, under appropriate time separation assumptions. Copyright © 2019, The Authors. All rights reserved.

关键词： Diseases

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Leakage mitigation for quantum error correction using a mixed qubit scheme

arXiv

引用

arXiv 2019年

作者： Brown, Natalie C. Brown, Kenneth R. School of Physics Georgia Institute of Technology AtlantaGA United States Schools of Chemistry and Biochemistry and Computational Science and Engineering Georgia Institute of Technology AtlantaGA United States Departments of Electrical and Computer Engineering Chemistry and Physics Duke University DurhamNC United States

Leakage errors take qubits out of the computational subspace and will accumulate if not addressed. A leaked qubit will reduce the effectiveness of quantum error correction protocols due to the cost of implementing leakage reduction circuits and the harm caused by interacting leaked states with qubit states. Ion trap qubits driven by Raman gates have a natural choice between qubits encoded in magnetically insensitive hyperfine states that can leak and qubits encoded in magnetically sensitive Zeeman states of the electron spin that cannot leak. In our previous work, we compared these two qubits in the context of the toric code with a depolarizing leakage error model and found that for magnetic field noise with a standard deviation less than 32 µG that the 174Yb+ Zeeman qubit outperforms the 171Yb+ hyperfine qubit. Here we examine a physically motivated leakage error model based on ions interacting via the Mølmer-Sørenson gate. We find that this greatly improves the performance of hyperfine qubits but the Zeeman qubits are more effective for magnetic field noise with a standard deviation less than 10 µG. At these low magnetic fields, we find that the best choice is a mixed qubit scheme where the hyperfine qubits are the ancilla and the leakage is handled without the need of an additional leakage reduction circuit. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic moments

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Colossal enhancement of spin transmission through magnon confinement in an antiferromagnet

arXiv

引用

arXiv 2025年

作者： Husain, Sajid Ramesh, Maya Li, Xinyan Prokhorenko, Sergei Ojha, Shashank Kumar Ross, Aiden Das, Koushik Zhao, Boyang Park, Hyeon Woo Meisenheimer, Peter Nahas, Yousra Caretta, Lucas Martin, Lane W. Kim, Se Kwon Yao, Zhi Wen, Haidan Salahuddin, Sayeef Chen, Long-Qing Han, Yimo de Sousa, Rogério Bellaiche, Laurent Bibes, Manuel Schlom, Darrell G. Ramesh, Ramamoorthy Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Rice University Materials Science and NanoEngineering HoustonTX United States Department of Materials Science and Engineering Cornell University IthacaNY14853 United States Rice Advanced Materials Institute Rice University HoustonTX77005 United States Smart Ferroic Materials Center Physics Department Institute for Nanoscience and Engineering University of Arkansas FayettevilleAR United States Department of Materials Science and Engineering Materials Research Institute Pennsylvania State University University ParkPA16802 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Chemistry University of California BerkeleyCA94720 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Department of Physics Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Engineering Brown University ProvidenceRI77005 United States Departments of Chemistry and Physics and Astronomy Rice University HoustonTX77005 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory CA94720 United States Advanced Photon Source Argonne National Laboratory LemontIL60439 United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Rice Advanced Materials Institute Smalley-Curl Institute Ken Kennedy Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy University of Victoria VictoriaBCV8W 2Y2 Canada Centre for Advanced Materials and Related Technology University of Victoria VictoriaBCV8W 2Y2 Canada Department of Materials Science and Engineering Tel Aviv University Ramat Aviv Tel Aviv6997801 Israel Laboratoire Albert Fert CNRS Thales Université Paris-Saclay Pa

Since Felix Bloch’s introduction of the concept of spin waves in 1930, magnons (the quanta of spin waves) have been extensively studied in a range of materials for spintronics, particularly for non-volatile logic-in-memory devices. Controlling magnons in conventional antiferromagnets and harnessing them in practical applications, however, remains a challenge. In this letter, we demonstrate highly efficient magnon transport in an LaFeO3/BiFeO3/LaFeO3 all-antiferromagnetic system which can be controlled electrically, making it highly desirable for energy-efficient computation. Leveraging spin-orbit-driven spin-charge transduction, we demonstrate that this material architecture permits magnon confinement in ultrathin antiferromagnets, enhancing the output voltage generated by magnon transport by several orders of magnitude, which provides a pathway to enable magnetoelectric memory and logic functionalities. Additionally, its non-volatility enables ultralow-power logic-in-memory processing, where magnonic devices can be efficiently reconfigured via electrically controlled magnon spin currents within magnetoelectric channels. © 2025, CC BY.

关键词： Antiferromagnetism

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