Talita Perciano

Lawrence Berkeley National Laboratory

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i-10 index

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Co-Authors

Daniela Ushizima

Lawrence Berkeley National Laboratory

Dilworth Y. Parkinson

Lawrence Berkeley National Laboratory

Harinarayan Krishnan

Lovely Professional University

James A. Sethian

Lawrence Berkeley National Laboratory

Stefano Marchesini

SLAC National Accelerator Laboratory

Alejandro C. Frery

Universidade Estadual Paulista (Unesp)

Zhe Bai

Lawrence Berkeley National Laboratory

E. Wes Bethel

San Francisco State University

Colleen Heinemann

University of Illinois Urbana-Champaign

Daan Camps

Lawrence Berkeley National Laboratory

Cooperative Institutions

Lawrence Berkeley National Laboratory

112

University of California, Berkeley

Alive Hospice

Berkeley College

Research Applications (United States)

Argonne National Laboratory

Advanced Light Source

SLAC National Accelerator Laboratory

Massachusetts Institute of Technology

National Energy Research Scientific Computing Center

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Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics

Journal of Synchrotron Radiation (2017)

Talita Perciano Daniela Ushizima Harinarayan Krishnan Dilworth Y. Parkinson Natalie Larson

Three-dimensional (3D) micro-tomography (µ-CT) has proven to be an important imaging modality in industry and scientific domains. Understanding the properties of material structure and behavior has produced many scientific advances. An important component of the 3D µ-CT pipeline is image partitioning (or image segmentation), a step that is used to separate various phases or components in an image. Image partitioning schemes require specific rules for different scientific fields, but a common strategy consists of devising metrics to quantify performance and accuracy. The present article proposes a set of protocols to systematically analyze and compare the results of unsupervised classification methods used for segmentation of synchrotron-based data. The proposed dataflow for Materials Segmentation and Metrics (MSM) provides 3D micro-tomography image segmentation algorithms, such as statistical region merging (SRM), k -means algorithm and parallel Markov random field (PMRF), while offering different metrics to evaluate segmentation quality, confidence and conformity with standards. Both experimental and synthetic data are assessed, illustrating quantitative results through the MSM dashboard, which can return sample information such as media porosity and permeability. The main contributions of this work are: (i) to deliver tools to improve material design and quality control; (ii) to provide datasets for benchmarking and reproducibility; (iii) to yield good practices in the absence of standards or ground-truth for ceramic composite analysis.

Ground truth

Markov random field

Benchmarking

10.1107/s1600577517010955

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Citations (20)

SHARP: a distributed GPU-based ptychographic solver

Journal of Applied Crystallography (2016)

Stefano Marchesini Harinarayan Krishnan Benedikt J. Daurer David A. Shapiro Talita Perciano

Ever brighter light sources, fast parallel detectors and advances in phase retrieval methods have made ptychography a practical and popular imaging technique. Compared to previous techniques, ptychography provides superior robustness and resolution at the expense of more advanced and time-consuming data analysis. By taking advantage of massively parallel architectures, high-throughput processing can expedite this analysis and provide microscopists with immediate feedback. These advances allow real-time imaging at wavelength-limited resolution, coupled with a large field of view. This article describes a set of algorithmic and computational methodologies used at the Advanced Light Source and US Department of Energy light sources. These are packaged as a CUDA-based software environment named SHARP (http://camera.lbl.gov/sharp), aimed at providing state-of-the-art high-throughput ptychography reconstructions for the coming era of diffraction-limited light sources.

Ptychography

Robustness

10.1107/s1600576716008074

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Citations (89)

Definitions and Notation

SpringerBriefs in computer science (2013)

Alejandro C. Frery Talita Perciano

Representation

Mathematical notation

10.1007/978-1-4471-4950-7_1

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Citations (2)

Fusion RF Modeling Machine Learning (FusionML_RF) v1.0

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (2022)

Zhe Bai Robbie Sadre Talita Perciano Edward W. Bethel G. M. Wallace

10.11578/dc.20230713.12

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Citations (0)

Adaptable Deep Learning and Probabilistic Graphical Model System for Semantic Segmentation

Advances in Artificial Intelligence and Machine Learning (2022)

Matthew Avaylon Robbie Sadre Zhe Bai Talita Perciano

Semantic segmentation algorithms based on deep learning architectures have been applied to a diverse set of problems. Consequently, new methodologies have emerged to push the state-of-the-art in this field forward, and the need for powerful user-friendly software increased significantly. The combination of Conditional Random Fields (CRFs) and Convolutional Neural Networks (CNNs) boosted the results of pixel-level classification predictions. Recent work using a fully integrated CRF-RNN layer have shown strong advantages in segmentation benchmarks over the base models. Despite this success, the rigidity of these frameworks prevents mass adaptability for complex scientific datasets and presents challenges in optimally scaling these models. In this work, we introduce a new encoder-decoder system that overcomes both these issues. We adapt multiple CNNs as encoders, allowing for the definition of multiple function parameter arguments to structure the models according to the targeted datasets and scientific problem. We leverage the flexibility of the U-Net architecture to act as a scalable decoder. The CRF-RNN layer is integrated into the decoder as an optional final layer, keeping the entire system fully compatible with back-propagation. To evaluate the performance of our implementation, we performed experiments on the Oxford-IIIT Pet Dataset and to experimental scientific data acquired via micro-computed tomography (microCT), revealing the adaptability of this framework and the performance benefits from a fully end-to-end CNN-CRF system on a both experimental and benchmark datasets.

Leverage (statistics)

CRFS

Adaptability

Graphical model

Benchmark (surveying)

10.54364/aaiml.2022.1119

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Citations (2)

Towards fast, accurate predictions of RF simulations via data-driven modeling: Forward and lateral models

AIP conference proceedings (2023)

G. M. Wallace Zhe Bai N. Bertelli E. W. Bethel Talita Perciano

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations.A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications.More accurate simulations with fast electron diffusion are even slower, requiring multiple hours of run time with parallel processing.The machine learning models use a database of 16,000+ GEN-RAY/CQL3D simulations for training, validation, and testing.Latin hypercube sampling methods implemented in πScope ensure that the database covers the range of 9 input parameters (n e0 , T e0 , I p , B t , R 0 , n || , Z e f f ,V loop , P LHCD ) with sufficient density in all regions of parameter space.The surrogate models reduce the computation time from minutes-hours to ms with high accuracy across the input parameter space.Data-driven surrogate models also allow for solving inverse and "lateral" problems.A surrogate model for the inverse problem maps from a desired current drive or power deposition profile to a set of input parameters that would result in such a profile, while a surrogate model for the lateral problem maps from a measured experimental quantity such as hard x-ray emission to a current drive or power deposition profile.The πScope database creation workflow is flexible and applicable to other RF simulation codes such as TORIC.

10.1063/5.0162422

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Citations (1)

Shared-Memory Parallel Probabilistic Graphical Modeling Optimization: Comparison of Threads, OpenMP, and Data-Parallel Primitives

Lecture notes in computer science (2020)

Talita Perciano Colleen Heinemann David Camp Brenton Lessley E. Wes Bethel

Software portability

Distributed memory

Implementation

Runtime system

10.1007/978-3-030-50743-5_7

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Citations (3)

Fast Detection of Material Deformation through Structural Dissimilarity

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (2015)

Daniela Ushizima Talita Perciano Dilworth Y. Parkinson

Source

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Citations (0)

Quantum-parallel vectorized data encodings and computations on trapped-ions and transmons QPUs

arXiv (Cornell University) (2023)

J. Balewski Mercy G. Amankwah Roel Van Beeumen E. Wes Bethel Talita Perciano

Compact quantum data representations are essential to the emerging field of quantum algorithms for data analysis. We introduce two new data encoding schemes, QCrank and QBArt, which have a high degree of quantum parallelism through uniformly controlled rotation gates. QCrank encodes a sequence of real-valued data as rotations of the data qubits, allowing for high storage density. QBArt directly embeds a binary representation of the data in the computational basis, requiring fewer quantum measurements and lending itself to well-understood arithmetic operations on binary data. We present several applications of the proposed encodings for different types of data. We demonstrate quantum algorithms for DNA pattern matching, Hamming weight calculation, complex value conjugation, and retrieving an O(400) bits image, all executed on the Quantinuum QPU. Finally, we use various cloud-accessible QPUs, including IBMQ and IonQ, to perform additional benchmarking experiments.

10.48550/arxiv.2301.07841

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Citations (0)

Reduced-complexity image segmentation under parallel Markov Random Field formulation using graph partitioning

2022 IEEE International Conference on Image Processing (ICIP) (2016)

Talita Perciano Daniela Ushizima E. Wes Bethel Yariv Dror Mizrahi Dilworth Y. Parkinson

Markov Random Field (MRF) algorithms are powerful tools in image analysis to explore contextual information of data. However, the application of these methods to large data means that alternative approaches must be found to circumvent the NP-hard complexity of the MRF optimization. We introduce a MRF-based framework that overcomes this issue by using graph partitioning. The computational complexity is decreased as the optimization/parameter estimation is executed on small subgraphs. PMRF targets 3D microCT datasets, but we include evaluation on the Berkeley Segmentation Dataset (ranking 7th place) to fully compare our method with well-known segmentation algorithms. Segmentation results on the microCT datasets achieve precision higher than 95%.

Markov random field

Cut

10.1109/icip.2016.7532560

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Citations (8)