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Quantum 2.0, QUANTUM 2022

作者： Wang, Imbert Ramesh, Anirudh Kramnik, Danielius Fargas Cabanillas, Josep M. Stojanović, Vladimir Kumar, Prem Popović, Miloš A. Department of Electrical and Computer Engineering Photonics Center Boston University BostonMA02215 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA94720 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We present progress towards realizing electronic-photonic quantum systems on-chip;particularly, entangled photon-pair sources, placing them in the context of previous work, and outlining our vision for mass-producible... 详细信息

ISBN: (纸本)9781957171111

关键词： Quantum entanglement

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Performance Optimization and Scalability Analysis of the MGB Hydrological Model

Performance Optimization and Scalability Analysis of the MGB...

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International Conference on High Performance computing

作者： Henrique R. A. Freitas Celso L. Mendes Aleksandar Ilic Graduate Program in Applied Computing National Institute for Space Research São José dos Campos Brazil Associate Laboratory of Computing National Institute for Space Research São José dos Campos Brazil INESC-ID Instituto Superior Técnico University of Lisbon Lisbon Portugal

Hydrological models are extensively used in applications such as water resources, climate change, land use, and forecast systems. The focus of this paper is performance optimization of the MGB hydrological model, which is widely employed to simulate water flows in large-scale watersheds. The optimization strategies that we selected include AVX-512 vectorization, thread-parallelism on multi-core CPUs (OpenMP), and data-parallelism on many-core GPUs (CUDA). We conducted experiments for real-world input datasets on state-of-the-art HPC systems based on Intel's Skylake CPUs and NVIDIA GPUs. In addition, a Roofline model characterization for these datasets confirmed performance improvements of up to 37.5x on the most time-consuming part of the code and 8.6x on the full MGB model. The work proposed herein shows that careful optimizations are needed for hydrological models to achieve a significant fraction of the performance potential in modern processors.

关键词： Analytical models Computational modeling Scalability High performance computing Conferences Graphics processing units Predictive models

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Tomographic reconstruction of time-bin-entangled qudits

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Physical Review A 2016年第4期94卷 042328-042328页

作者： Samantha J. Nowierski Neal N. Oza Prem Kumar Gregory S. Kanter Center for Photonic Communication and Computing EECS Department Northwestern University 2145 Sheridan Road Evanston Illinois 60208-3118 USA Graduate Program in Applied Physics Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University 2145 Sheridan Road Evanston Illinois 60208-3112 USA

We describe an experimental implementation to generate and measure high-dimensional time-bin-entangled qudits. Two-photon time-bin entanglement is generated via spontaneous four-wave mixing in single-mode fiber. Unbalanced Mach-Zehnder interferometers transform selected time bins to polarization entanglement, allowing standard polarization-projective measurements to be used for complete quantum state tomographic reconstruction. Here we generate maximally entangled qubits (d=2), qutrits (d=3), and ququarts (d=4), as well as other phase-modulated nonmaximally entangled qubits and qutrits. We reconstruct and verify all generated states using maximum-likelihood estimation tomography.

关键词： Entanglement detection Entanglement manipulation Quantum entanglement Quantum tomography

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Exploring cloud elasticity on developing an EPCGlobal-compliant middleware

Exploring cloud elasticity on developing an EPCGlobal-compli...

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IEEE International Conference on RFID

作者： Rodrigo da Rosa Righi Eduardo Souza dos Reis Gustavo Rostirolla Cristiano André da Costa Antonio Marcos Alberti Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos (Unisinos) São Leopoldo RC Brazil Instituto Nacional de Telecomunições (Inatel) Santa Rita do Sapucaí MG Brazil

The digital universe is growing at significant rates in recent years. One of the main responsible for this sentence is the Internet of Things (IoT), which requires middlewares capable of handling this increase of data volume in real-time. Solutions modeled at software, hardware and/or architecture level present limitations to handle such load, facing a scalability problem in the IoT scope. In this context, this article presents a model named Eliot (Elasticity-driven Internet of Things) which combines both cloud and high performance computing to address the IoT scalability problem in EPCglobal-compliant architectures. Based on the Eliot model, we developed a prototype that can run as a plugin solution together with any current EPCglobal-compliant middleware. The results are encouraging, presenting significant performance gains when comparing both elastic and non-elastic executions.

关键词： Elasticity Internet of things Monitoring Cloud computing Radiofrequency identification Computer architecture Load modeling

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Effect of Periodic Poling on Self-phase Modulation in Thin-film Lithium Niobate Waveguides

Effect of Periodic Poling on Self-phase Modulation in Thin-f...

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Frontiers in Optics, FiO 2022

作者： Gül, Gamze Abdelsalam, Kamal Fathpour, Sasan Lee, Kim F. Kanter, Gregory S. Kumar, Prem Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States CREOL The College of Optics and Photonics University of Central Florida OrlandoFL32816 United States Department of Electrical and Computer Engineering University of Central Florida OrlandoFL32816 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We study self-phase modulation of sub-picosecond telecom wavelength pulses in partially periodically poled thin-film lithium niobate waveguides. We experimentally and computationally investigate the effect of phase mi... 详细信息

ISBN: (纸本)9781557528209

关键词： Thin films

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A Fitness-Based Migration Policy for Biased Random-Key Genetic Algorithms 26th

A Fitness-Based Migration Policy for Biased Random-Key Gene...

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26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Boiani, Mateus Parpinelli, Rafael Stubs Dorn, Márcio Institute of Informatics Federal University of Rio Grande do Sul Rio Grande do Sul Porto Alegre Brazil Graduate Program in Applied Computing Santa Catarina State University Santa Catarina Joinville Brazil Center for Biotechnology Federal University of Rio Grande do Sul Rio Grande do Sul Porto Alegre Brazil National Institute of Science and Technology - Forensic Science Rio Grande do Sul Porto Alegre Brazil

ISBN: (纸本)9783031302282

Population diversity management is crucial for the quality of solutions in Evolutionary Algorithms. Many techniques require assistance to handle diverse problem characteristics and may prematurely converge in local optima. Maintaining diversity enables the algorithm to search the space and produce better results effectively. Parallel models are a common approach to preserving diversity;however, design decisions affect optimization process characteristics. For example, the Island model’s migration policy affects convergence speed. This study proposes and evaluates a fitness-based migration policy for the Biased Random-Key Genetic Algorithm (BRKGA) and compares it to two traditional strategies. The results in continuous search spaces demonstrate that the proposed policy can enhance BRKGA optimization with appropriate parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Comparative Methods for Addressing Imbalanced Datasets in Predicting Medical Appointment No-Shows

Comparative Methods for Addressing Imbalanced Datasets in Pr...

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Conferencia Latinoamericana En Informatica (CLEI)

作者： Marcelo Ardizzon Lovatte Leandro Colombi Resendo Karin Satie Komati Instituto Federal do Espírito Santo PPComp IFES Campus Serra Serra-ES Brazil Graduate Program in Applied Computing Instituto Federal do Espírito Santo PPComp IFES Campus Serra Serra-ES Brazil

ISBN: (数字)9798331540975

ISBN: (纸本)9798331540982

The efficiency and accessibility of healthcare delivery systems can be enhanced through solutions that minimize the impact of patient No-Shows for medical exams and appointments. The significant disparity between the “Show” and “No-Show” categories within the dataset can impair the efficacy of predictive models, necessitating the employment of dataset balancing techniques before classifier training. This study evaluated various methods to address imbalances in datasets for predicting patient No-Shows. Techniques such as undersampling (Random Removal - RR, Remove Similar - RS, Remove Farthest - RF) and oversampling (Adaptive Synthetic Sampling - ADASYN) were applied to adjust the balance between Show and No-Show categories to ratios of 80–20%, 70–30%, 60–40%, and 50–50%, alongside a cost-sensitive learning approach. Four classifiers were deployed: Support Vector Machine (SVM), Naive Bayes, k-Nearest Neighbors (k-NN), and Random Forests. Additionally, a decision tree produced by the C4.5 algorithm was utilized for the cost-sensitive learning approach. The classifiers were evaluated using metrics such as Precision, Recall, F-measure, and AUC/ROC. Among the various methods tested, the RR combined with the k-NN classifier achieved the highest AUC/ROC value. However, due to the longer computational time of k-NN, the Random Forest classifier emerged as a more pragmatic choice when processing time is a critical consideration.

关键词： Measurement Training Support vector machines Accuracy Databases Training data Predictive models Classification algorithms Random forests Classification tree analysis

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Enhancing Synthetic Well Logs with PCA-Based GAN Models

Enhancing Synthetic Well Logs with PCA-Based GAN Models

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International Conference on Machine Learning and Applications (ICMLA)

作者： Luciano Garim Garcia Gabriel De Oliveira Ramos José Manuel Marques Teixeira de Oliveira Ariane Santos Da Silveira Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos São Leopoldo Brazil Center of Excellence in Geological and Numerical Modeling Universidade do Vale do Rio dos Sinos São Leopoldo Brazil

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

The generation of synthetic well log data is crucial for enhancing the understanding and exploration of subsurface reservoirs. This paper introduces a novel Generative Adversarial Network (GAN) model that incorporates a Principal Component Analysis (PCA)-based loss function to improve the quality of synthetic well log data. The proposed method is distinguished by its ability to generate complete well log datasets, rather than just individual logs or completing partial logs. Traditional GANs utilize cross-entropy loss but often fail to capture the complex structural patterns inherent in well logs. By integrating PCA into the loss function, our model not only distinguishes real from synthetic data but also ensures that the synthetic data retains the intrinsic variability and relationships observed in real logs. We validate our approach using histograms, correlation heatmaps, dimensionality reduction techniques (PCA and t-SNE), and a discriminative task. Results show that synthetic data generated with PCA-based loss aligns closely with real data, demonstrating superior preservation of statistical and structural characteristics. This advancement in synthetic data generation holds promise for enriching subsurface data analysis and exploration.

关键词： Training Analytical models Data analysis Correlation Scalability Generative adversarial networks Transformers Data models Synthetic data Principal component analysis

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Learning from failure - Development of a Culture of Failure Scale for Higher Education

Learning from failure - Development of a Culture of Failure ...

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World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC)

作者： Michael M. Malschützky Ph.D. Jamie R. Gurganus Centre for Teaching Development and Innovation (ZIEL) & Department of Management Sciences Hochschule Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Engineering & Computing Education Program Mechanical Engineering & Graduate School University of Maryland Baltimore County Baltimore USA

ISBN: (数字)9781665475280

ISBN: (纸本)9781665475297

Benefits of a failure friendly culture, e.g., learning from failure, are widely known in occupational settings. Validated scales have been developed to measure organizational failure culture and individuals' mind-sets on failure. While research on learning from failure exists in secondary education, scales for higher education are lacking. To support both higher education engineering instructors and students, this contribution aims to develop a Culture of Failure Scale to assess students' and lecturers' handling of failures in engineering higher education settings. This data-driven approach helps inform instructors about aspects of educational settings that are safe and conducive to learning and supports their students to develop a mind-set where failures are seen as a source for improvements and learning. Exemplary application of the developed scale is shown by assessing the failure culture in different groupings, e.g., gender, between two participating German and US institutions.

关键词： Correlation Education Psychology Reliability History Engineering students Principal component analysis

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Evaluation of Resampling Techniques to Provide Better Synthesized Input Data to Super-Resolution Deep Learning Model Training

Evaluation of Resampling Techniques to Provide Better Synthe...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Vinicius Sales Ademir Marques Graciela Racolte Anderson Nunes Tainá Guimaraes Daniel Zanotta André Spigolon Luiz Gonzaga Mauricio Roberto Veronez Vizlab - X-Reality and GeoInformatics Lab UNISINOS-São Leopoldo RS Brazil Graduate Program in Applied Computing at Vale do Rio dos Sinos University São Leopoldo RS Brazil CENPES - PETROBRAS Rio de Janeiro Brazil

Hyperspectral images often have low spatial resolution due to the sensor sizes required to capture the required spectral response. Super-resolution (SR) techniques try to mitigate this by injecting more detail in the upscaled image, either with numerical methods or deep learning and Convolution Neural Networks. In the deep learning methods, the models learn image details by inferring a high-resolution (HR) image from a synthetic low-resolution (LR) image that simulates the natural degradation of sensors by applying resampling (to reduce the image detail) and noising (to add small errors and interference). Often disregarded in the literature, the resampling method applied to generate the synthetic image can impact greatly the deep learning model training. This work, evaluate several resampling techniques to measure this impact using the Harvard hyperspectral dataset. Results showed that the Lanczos filter was the best among eight other resampling methods. The Nemenyi and Friedman ranking statistical tests also indicated that the Cubic-Spline, Bicubic, and RMS achieved good results.

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