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arXiv 2024年

作者： Chatterjee, Avimita Rappaport, Sonny Giri, Anish Johri, Sonika Proctor, Timothy Bernal Neira, David E. Sathe, Pratik Lubinski, Thomas Department of Computer Science & Engineering The Pennsylvania State University United States QED-C managed by SRI International United States Vanderbilt University United States Coherent Computing Inc CupertinoCA United States Quantum Performance Laboratory Sandia National Laboratories LivermoreCA94550 United States Research Institute of Advanced Computer Science Universities Space Research Association Mountain ViewCA United States Davidson School of Chemical Engineering Purdue University West LafayetteIN United States Los Alamos National Laboratory Los AlamosNM87545 United States Quantum Circuits Inc 25 Science Park New HavenCT06511 United States QED-C Technical Advisory Committee on Standards and Performance Metrics United States

Quantum Hamiltonian simulation is one of the most promising applications of quantum computing and forms the basis for many quantum algorithms. Benchmarking them is an important gauge of progress in quantum computing technology. We present a methodology and software framework to evaluate various facets of the performance of gate-based quantum computers on Trotterized quantum Hamiltonian evolution. We propose three distinct modes for benchmarking: (i) comparing simulation on a real device to that on a noiseless classical simulator, (ii) comparing simulation on a real device with exact diagonalization results, and (iii) using scalable mirror circuit techniques to assess hardware performance in scenarios beyond classical simulation methods. We demonstrate this framework on five Hamiltonian models from the HamLib library: the Fermi and Bose-Hubbard models, the transverse field Ising model, the Heisenberg model, and the Max3SAT problem. Experiments were conducted using Qiskit's Aer simulator, BlueQubit's CPU cluster and GPU simulators, and IBM's quantum hardware. Our framework, extendable to other Hamiltonians, provides comprehensive performance profiles that reveal hardware and algorithmic limitations and measure both fidelity and execution times, identifying crossover points where quantum hardware outperforms CPU/GPU simulators. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation 23rd

Context-Aware Refinement Network Incorporating Structural Co...

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23rd International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2020

作者： Wang, Shen Liang, Kongming Li, Yiming Yu, Yizhou Wang, Yizhou Center for Data Science Peking University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China Department of Computer Science Peking University Beijing China Deepwise AI Lab Beijing China Center on Frontiers of Computing Studies Peking University Beijing China

ISBN: (纸本)9783030597276

Brain midline delineation can facilitate the clinical evaluation of brain midline shift, which plays an important role in the diagnosis and prognosis of various brain pathology. Nevertheless, there are still great challenges with brain midline delineation, such as the largely deformed midline caused by the mass effect and the possible morphological failure that the predicted midline is not a connected curve. To address these challenges, we propose a context-aware refinement network (CAR-Net) to refine and integrate the feature pyramid representation generated by the UNet. Consequently, the proposed CAR-Net explores more discriminative contextual features and larger receptive field, which is of great importance to predict largely deformed midline. For keeping the structural connectivity of the brain midline, we introduce a novel connectivity regular loss (CRL) to punish the disconnectivity between adjacent coordinates. Moreover, we address the ignored prerequisite of previous regression-based methods that the brain CT image must be in the standard pose. A simple pose rectification network is presented to align the source input image to the standard pose image. Extensive experimental results on the CQ dataset and one inhouse dataset show that the proposed method requires fewer parameters and outperforms three state-of-the-art methods in terms of four evaluation metrics. Code is available at https://***/ShawnBIT/Brain-Midline-Detection. © 2020, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

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Statistical and methodical analysis for evaluation and nutritional monitoring methods in cirrhosis liver disease

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AIP Conference Proceedings 2022年第1期2519卷

作者： K. Prakash S. Saradha Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai Tamil Nadu India

In cirrhosis disease, the related deficiency is prevalent. Nutritional monitoring methods and nutritious monitoring techniques have been tested in several trials to predict clinical results in this community. To summarize the various meanings of malnutrition through trials, the relevant proof to NSTs, including its potential of NSTs and NATs to determine clinical results in cirrhosis, we conducted a computerized review and epidemiological study of this literature. The key outcome variables are pre-and post-transplantation death with several side effects. Inclusion: over 16 years of age, cirrhosis. Absence: >25 percentage of hepatocellular carcinoma, mainly NATs depending on experimental testing, or lacking sampling, diagnosis, or result requirements. Forty-seven trials had involved 8850 patients. NSTs were only tested in 3 studies. Thirty-two concepts of starvation have been included in the research. In 69 percent of patients measured with a Risk Ratio (RR) of 2.33 (95 percent CI 1, 96-2, 89), NATs expected pre-transplant deaths. Just 28% of the instances they are tested, with such a Risk Ratio of 3.04 (95 percent CI 1.51-6.12), NATs are expected for post- transplant death. The literature on cirrhosis contains restricted nutrition screening evidence and various descriptions of what defines malnutrition through NATs. Given this discordance, it is obvious that malnutrition is a useful measure of pre-transplant death, nearly irrespective of when it is described.

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A meta-analysis and roadmap of Alzheimer’s diseases prediction by machine learning algorithms

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AIP Conference Proceedings 2022年第1期2519卷 030062页

作者： M. Sudharsan G. Thailambal Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai Tamil Nadu India

Precise detection of Alzheimer’s disease provides a significant role in patient care, particularly at the quick stages of the disorder. Risk identification helps patients take protective steps long before permanent brain injury occurs. While several current experiments have been performing based on computerized diagnosed techniques for Alzheimer’s disease, several techniques of system diagnosis or detections are constrained by congenital scrutiny. Alzheimer’s disease should be diagnosed but should not be predicted from the earlier stages. Machine Learning and Deep Learning has been a popular technique used to detection of Alzheimer’s disease. In this research article, we briefly study several related Alzheimer’s disease literature and discuss how Machine Learning and Deep Learning could assist the researcher in detecting the disease in an efficient way at its earlier disease stages. Purpose Develop and verify a deep learning system that predicts the final diagnosis of Alzheimer’s disease (AD), moderate cognitive impairment (MCI), or neither at fluorine 18 (18F) fluoride oxy glucose (FDG) PET of the brain and compares it to radiologic readers’ performance. Materials and Procedures The Alzheimer’s Disease Neuro imaging Initiative’s perspective 18F-FDG PET brain images.

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BR-GAN: Bilateral Residual Generating Adversarial Network for Mammogram Classification 23rd

BR-GAN: Bilateral Residual Generating Adversarial Network fo...

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23rd International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2020

作者： Wang, Chu-ran Zhang, Fandong Yu, Yizhou Wang, Yizhou Center for Data Science Peking University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China Center on Frontiers of Computing Studies Peking University Beijing China Department of Computer Science Peking University Beijing China Deepwise AI Lab Beijing China

ISBN: (纸本)9783030597122

Mammogram malignancy classification with only image-level annotations is challenging due to a lack of lesion annotations. If we can generate the healthy version of the diseased data, we can easily explore the lesion features. An intuitive idea of such generation is to use existing Cycle-GAN based methods. They achieve the healthy generation regarding healthy images as reference domain, while maintaining the original content by cycle consistency mechanism. However, healthy mammogram patterns are diverse which may lead to uncertain generations. Moreover, the back translation from healthy to the original remains an ill-posed problem due to lack of lesion information. To address these problems, we propose a novel model called bilateral residual generating adversarial network(BR-GAN). We use the Cycle-GAN as a basic framework while regarding the contralateral as generation reference based on the bilateral symmetry prior. To address the ill-posed back translation problem, we propose a residual-preserved mechanism to try to preserve the lesion features from the original features. The generated features and the original features are aggregated for further classification. BR-GAN outperforms current state-of-the-art methods on INBreast and in-house datasets. © 2020, Springer Nature Switzerland AG.

关键词： Domain Knowledge

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Chemically Motivated Simulation Problems are Efficiently Solvable by a Quantum computer

arXiv

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arXiv 2024年

作者： Schleich, Philipp Kristensen, Lasse Bjørn Campos-Gonzalez-Angulo, Jorge A. Aldossary, Abdulrahman Avagliano, Davide Bagherimehrab, Mohsen Gorgulla, Christoph Fitzsimons, Joe Aspuru-Guzik, Alán Department of Computer Science University of Toronto TorontoONM5S 3G4 Canada Vector Institute of Artificial Intelligence TorontoONM5G 1M1 Canada Department of Chemistry University of Toronto TorontoONM5S 3H6 Canada Department of Computer Science University of Copenhagen CopenhagenDK-2100 Denmark Paris75005 France Department of Structural Biology St. Jude Children’s Research Hospital MemphisTN38105 United States Department of Physics Harvard University CambridgeMA02138 United States Horizon Quantum Computing Singapore138565 Singapore Department of Chemical Engineering & Applied Chemistry University of Toronto TorontoONM5S 3E5 Canada Department of Materials Science & Engineering University of Toronto TorontoONM5S 3E4 Canada Acceleration Consortium TorontoONM5S 3H6 Canada Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada Canadian Institute for Advanced Research TorontoONM5S 1M1 Canada

Simulating chemical systems is highly sought after and computationally challenging, as the number of degrees of freedom increases exponentially with the size of the system. Quantum computers have been proposed as a computational means to overcome this bottleneck, thanks to their capability of representing this amount of information efficiently. Most efforts so far have been centered around determining the ground states of chemical systems. However, hardness results and the lack of theoretical guarantees for efficient heuristics for initial-state generation shed doubt on the feasibility. Here, we propose a heuristically guided approach that is based on inherently efficient routines to solve chemical simulation problems, requiring quantum circuits of size scaling polynomially in relevant system parameters. If a set of assumptions can be satisfied, our approach finds good initial states for dynamics simulation by assembling them in a scattering tree. In particular, we investigate a scattering-based state preparation approach within the context of mergo-association. We discuss a variety of quantities of chemical interest that can be measured after the quantum simulation of a process, e.g., a reaction, following its corresponding initial state preparation. © 2024, CC BY.

关键词： Association reactions

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MEGADOCK-Web-Mito: Human mitochondrial protein-protein interaction prediction database

arXiv

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arXiv 2021年

作者： Ohue, Masahito Watanabe, Hiroki Akiyama, Yutaka Department of Computer Science School of Computing Tokyo Institute of Technology G3-56 4259 Nagatsutacho Midori-ku Kanagawa Yokohama City226-8503 Japan Department of Computer Science School of Computing Tokyo Institute of Technology W8-76 2-12-1 Ookayama Meguro-ku Tokyo152-8550 Japan RWBC-OIL National Institute of Advanced Industrial Science and Technology 1-1-1 Umezono Ibaraki Tsukuba305-8560 Japan

Mitochondrial diseases are largely caused by dysfunction in mitochondrial proteins. However, annotations of human mitochondrial proteins are scattered across various public databases and individual studies. To facilitate research aimed at elucidating mitochondrial functions, we constructed the MEGADOCK-Web-Mito database as a protein-protein interaction (PPI) prediction data archive, including prediction results for exhaustive protein pairs of 654 mitochondria-related human proteins. MEGADOCK-Web-Mito enables users to search for all PPI prediction results efficiently and comprehensively. In particular, we linked functional annotations to each human mitochondrial protein. The comprehensive and specialized human mitochondrial PPI prediction results and searching function of MEGADOCK-Web-Mito will support further research on mitochondria and mitochondrial diseases. Copyright © 2021, The Authors. All rights reserved.

关键词： Mitochondria

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Mapping 3d Grain and Precipitate Structure During in Situ Mechanical Testing of Open-Cell Metal Foam Using Micro-Computed Tomography and High-Energy X-Ray Diffraction Microscopy

SSRN

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SSRN 2022年

作者： Johnson, Quinton C. Kenesei, Peter Petruzza, Steve Plumb, Jayden Sharma, Hemant Park, Jun-Sang Marsden, Elliott Matheson, Kristoffer Czabaj, Michael W. Spear, Ashley D. Department of Mechanical Engineering University of Utah Salt Lake CityUT84112 United States Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT84112 United States Department of Computer Science Utah State University LoganUT84322 United States Advanced Photon Source Argonne National Laboratory LemontIL60439 United States

Open-cell metal foams are ultra-low-density cellular metals with complex hierarchical structures that span bulk, cell, ligament, and sub-ligament scales and give rise to desirable properties such as high strength-to-weight ratio and excellent energy absorption. Although literature suggests that intrinsic material structures at sub-ligament length scales (e.g., grains and precipitates) play an important role in mechanical behavior of open-cell metal foams, there are very few experimental measurements of such structures in three dimensions and for meaningful volumes of foam. This study seeks to map and track the three-dimensional (3D) grain and precipitate structures of an intact volume of open-cell aluminum foam by advancing microstructural characterization techniques that leverage X-ray micro-computed tomography ( µ CT) and far-field high-energy X-ray diffraction microscopy (FF-HEDM). A 6%-relative-density aluminum foam sample was mechanically tested in compression while µ CT and FF-HEDM measurements were collected at interrupted loading states at beamline 1-ID of the advanced Photon Source. A new scanning strategy and reconstruction algorithm were established to enable characterization of a foam volume with diameter approximately four times wider than the nominal width of the X-ray beam. The result is a set of maps that detail both the 3D grain and precipitate structures throughout the foam volume at successive strain steps. A novel grain tracking procedure was developed to track individual grains within the foam volume by accounting for the large rigid-body motions that individual ligaments can undergo during mechanical loading. The ability to track grains and precipitate structures in three dimensions throughout large bulk deformation of ulta-low-density polycrystalline materials enables new possibilities for validating numerical models and investigating local failure mechanisms. Furthermore, the methods and procedures developed in this study could be applied to

关键词： Synchrotron radiation

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Critical Spatial Clusters for Vaccine Preventable Diseases 13th

Critical Spatial Clusters for Vaccine Preventable Diseases

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13th International Conference on Social computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2020

作者： Cadena, Jose Marathe, Achla Vullikanti, Anil Lawrence Livermore National Laboratory LivermoreCA United States Department of Public Health Sciences University of Virginia Charlottesville United States Department of Computer Science University of Virginia Charlottesville United States Network Systems Science and Advanced Computing Division Biocomplexity Institute University of Virginia CharlottesvilleVA United States

ISBN: (纸本)9783030612542

The standard public health intervention for controlling the spread of highly contagious diseases, such as measles, is to vaccinate a large fraction of the population. However, it has been shown that in some parts of the United States, even though the average vaccination rate is high, geographical clusters of undervaccinated populations are emerging. Given that public health resources for response are limited, identifying and rank-ordering critical clusters can help prioritize and allocate scarce resources for surveillance and quick intervention. We quantify the criticality of a cluster as the additional number of infections caused if the immunization rate in a cluster reduces. This notion of criticality has not been studied before, and, based on clusters identified in prior research, we show that the current underimmunization rate in the cluster, and its criticality are not correlated. We apply our methods to a population model for the state of Minnesota, where we find undervaccinated clusters with significantly higher criticality than those obtained by other natural heuristics. © 2020, Springer Nature Switzerland AG.

关键词： Heuristic methods

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Dynamic Management of Traffic Signals through Social IoT 3

Dynamic Management of Traffic Signals through Social IoT

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3rd International Conference on computing and Network Communications, CoCoNet 2019

作者： Roopa, M.S. Ayesha Siddiq, S. Buyya, Rajkumar Venugopal, K.R. Iyengar, S.S. Patnaik, L.M. IoT Lab University Visvesvaraya College of Engineering Bangalore University Bengaluru India Lab School of Computing and Information Systems University of Melbourne Australia Bangalore University Bengaluru India Department of Computer Science and Engineering Florida International University United States National Institute of Advanced Studies Indian Institute of Science Campus Bengaluru India

Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and pose an eminent challenge to the movement of emergency vehicles. To achieve better driving we proceed towards a trending research field called Social Internet of Vehicles (SIoV). A social network paradigm that permits the establishment of social relationships among every vehicle in the network or with any road infrastructure can be radically helpful. This holds as the aim of SIoV, to be beneficial for the drivers, in improving the road safety, avoiding mishaps, and have a friendly-driving environment. In this paper, we propose a Dynamic congestion control with Throughput Maximization scheme based on Social Aspect (D-TMSA) utilizing the social, behavioral and preference-based relationships. Our proposed scheme along with the various social relationship types allocates green signal to maximize the traffic flow passing through an intersection. Simulation results show that the D-TMSA outperforms the existing work by achieving high throughput, lowering the total traveling time and reducing the average waiting time to better the flow of traffic based on their social attributes with each other. © 2020 The Authors. Published by Elsevier B.V.

关键词： Traffic congestion

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