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Talita Perciano

Lawrence Berkeley National Laboratory, Computational Research Division, Post-Doc

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graduated at Computer Science from Universidade Federal de Alagoas (2005), acquired the master's degree at Computer Science from Universidade Federal de São Carlos (2007) and the PhD degree at Computer Science from Universidade de São Paulo - Instituto de Matemática e Estatística (2012). She has also done a PhD stage of one year and three months in Paris, France, at Télécom ParisTech. She had a postdoctoral position at Universidade de São Paulo until 2013. She is currently a postdoctoral researcher in the Computational Research Division at Lawrence Berkeley National Laboratory. Has experience in Computer Science, focusing on Image Processing and Analysis, acting on the following subjects: image processing and analysis, high performance, remote sensing image processing, image segmentation, vibro-acoustography, image restoration, Markov Random Fields, R system, descriptive analysis, data mining, scientific visualization, materials science and eScience.
Supervisors: Daniela Ushizima
Phone: +1 415-767-9416

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Papers by Talita Perciano

Quantum Computing and Visualization: A Disruptive Technological Change Ahead

IEEE Computer Graphics and Applications

How to combine TerraSAR-X and CosmoSkyMed Images for a better scene understanding ?

HAL (Le Centre pour la Communication Scientifique Directe), Jul 1, 2012

Performance Tradeoffs in Shared-memory Platform Portable Implementations of a Stencil Kernel

Performance Analysis of Traditional and Data-Parallel Primitive Implementations of Visualization and Analysis Kernels

arXiv (Cornell University), Oct 5, 2020

Fast detection of material deformation through structural dissimilarity

Designing materials that are resistant to extreme temperatures and brittleness relies on assessin... more Designing materials that are resistant to extreme temperatures and brittleness relies on assessing structural dynamics of samples. Algorithms are critically important to characterize material deformation under stress conditions. Here, we report on our design of coarse-grain parallel algorithms for image quality assessment based on structural information and on crack detection of gigabyte-scale experimental datasets. We show how key steps can be decomposed into distinct processing flows, one based on structural similarity (SSIM) quality measure, and another on spectral content. These algorithms act upon image blocks that fit into memory, and can execute independently. We discuss the scientific relevance of the problem, key developments, and decomposition of complementary tasks into separate executions. We show how to apply SSIM to detect material degradation, and illustrate how this metric can be allied to spectral analysis for structure probing, while using tiled multi-resolution pyramids stored in HDF5 chunked multi-dimensional arrays. Results show that the proposed experimental data representation supports an average compression rate of 10X, and data compression scales linearly with the data size. We also illustrate how to correlate SSIM to crack formation, and how to use our numerical schemes to enable fast detection of deformation from 3D datasets evolving in time.

Sparse Matrix-Based HPC Tomography

Lecture Notes in Computer Science, 2020

Quantum Image Pixel Library (QPIXL++) v0.1.0

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Sep 17, 2021

QPIXL++ is a software package to compile quantum circuits for compressed representation of images... more

Quantum pixel representations and compression for N-dimensional images

Scientific Reports, May 11, 2022

Author Correction: Quantum pixel representations and compression for N-dimensional images

Scientific Reports, Dec 19, 2022

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

Springer eBooks, 2020

Rotational dynamics and transition mechanisms of surface-adsorbed proteins

Proceedings of the National Academy of Sciences of the United States of America, Apr 11, 2022

Significance The exquisite organization exhibited by hybrid biomolecular–inorganic materials in n... more Significance The exquisite organization exhibited by hybrid biomolecular–inorganic materials in nature has inspired the development of synthetic analogues for numerous applications. Nevertheless, a mechanistic picture of the energetic controls and response dynamics leading to organization is lacking. Here, we pair high-speed atomic force microscopy with machine learning and Monte Carlo simulations to analyze the rotational dynamics of rod-like proteins on a crystal lattice, simultaneously quantifying the orientational energy landscape and transition probabilities between energetically favorable orientations. Although rotations largely follow Brownian diffusion, proteins often make large jumps in orientation, thus rapidly overcoming barriers that usually inhibit rotation. Moreover, the rotational dynamics can be tuned via protein size and solution chemistry, providing tools for controlling biomolecular assembly at inorganic interfaces.

Three-dimensional localization of nanoscale battery reactions using soft X-ray tomography

Nature Communications, Mar 2, 2018

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

arXiv (Cornell University), Jan 18, 2023

DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives

arXiv (Cornell University), Sep 13, 2018

Subcellular structure segmentation from cryo-electron tomograms via machine learning

bioRxiv (Cold Spring Harbor Laboratory), Apr 9, 2020

Machine learning for micro-tomography

Machine learning has revolutionized a number of fields, but many micro-tomography users have neve... more Machine learning has revolutionized a number of fields, but many micro-tomography users have never used it for their work. The micro-tomography beamline at the Advanced Light Source (ALS), in collaboration with the Center for Applied Mathematics for Energy Research Applications (CAMERA) at Lawrence Berkeley National Laboratory, has now deployed a series of tools to automate data processing for ALS users using machine learning. This includes new reconstruction algorithms, feature extraction tools, and image classification and recommen- dation systems for scientific image. Some of these tools are either in automated pipelines that operate on data as it is collected or as stand-alone software. Others are deployed on computing resources at Berkeley Lab–from workstations to supercomputers–and made accessible to users through either scripting or easy-to-use graphical interfaces. This paper presents a progress report on this work.

Quantum pixel representations and compression for N-dimensional images

Scientific Reports

We introduce a novel and uniform framework for quantum pixel representations that overarches many... more We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI. The proposed QPIXL framework results in more efficient circuit implementations and significantly reduces the gate complexity for all considered quantum pixel representations. Our method scales linearly in the number of pixels and does not use ancilla qubits. Furthermore, the circuits only consist of $$R_y$$ R y gates and $$\text {CNOT}$$ CNOT gates making them practical in the NISQ era. Additionally, we propose a circuit and image compression algorithm that is shown to be highly effective, being able to reduce the necessary gates to prepare an FRQI state for example scientific images by up to 90% without sacrificing image quality. Our algorithms are made publicly available as part of , a Quantum Image Pixel Library.

Building Mathematics, Algorithms, and Software for Experimental Facilities

A two-level Markov random field for road network extraction and its application with optical, SAR, and multitemporal data

International Journal of Remote Sensing, 2016

Real-time data-intensive computing

AIP Conference Proceedings, 2016

Quantum Computing and Visualization: A Disruptive Technological Change Ahead

IEEE Computer Graphics and Applications

How to combine TerraSAR-X and CosmoSkyMed Images for a better scene understanding ?

HAL (Le Centre pour la Communication Scientifique Directe), Jul 1, 2012

Performance Tradeoffs in Shared-memory Platform Portable Implementations of a Stencil Kernel

Performance Analysis of Traditional and Data-Parallel Primitive Implementations of Visualization and Analysis Kernels

arXiv (Cornell University), Oct 5, 2020

Fast detection of material deformation through structural dissimilarity

Designing materials that are resistant to extreme temperatures and brittleness relies on assessin... more Designing materials that are resistant to extreme temperatures and brittleness relies on assessing structural dynamics of samples. Algorithms are critically important to characterize material deformation under stress conditions. Here, we report on our design of coarse-grain parallel algorithms for image quality assessment based on structural information and on crack detection of gigabyte-scale experimental datasets. We show how key steps can be decomposed into distinct processing flows, one based on structural similarity (SSIM) quality measure, and another on spectral content. These algorithms act upon image blocks that fit into memory, and can execute independently. We discuss the scientific relevance of the problem, key developments, and decomposition of complementary tasks into separate executions. We show how to apply SSIM to detect material degradation, and illustrate how this metric can be allied to spectral analysis for structure probing, while using tiled multi-resolution pyramids stored in HDF5 chunked multi-dimensional arrays. Results show that the proposed experimental data representation supports an average compression rate of 10X, and data compression scales linearly with the data size. We also illustrate how to correlate SSIM to crack formation, and how to use our numerical schemes to enable fast detection of deformation from 3D datasets evolving in time.

Sparse Matrix-Based HPC Tomography

Lecture Notes in Computer Science, 2020

Quantum Image Pixel Library (QPIXL++) v0.1.0

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Sep 17, 2021

QPIXL++ is a software package to compile quantum circuits for compressed representation of images... more

Quantum pixel representations and compression for N-dimensional images

Scientific Reports, May 11, 2022

Author Correction: Quantum pixel representations and compression for N-dimensional images

Scientific Reports, Dec 19, 2022

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

Springer eBooks, 2020

Rotational dynamics and transition mechanisms of surface-adsorbed proteins

Proceedings of the National Academy of Sciences of the United States of America, Apr 11, 2022

Significance The exquisite organization exhibited by hybrid biomolecular–inorganic materials in n... more Significance The exquisite organization exhibited by hybrid biomolecular–inorganic materials in nature has inspired the development of synthetic analogues for numerous applications. Nevertheless, a mechanistic picture of the energetic controls and response dynamics leading to organization is lacking. Here, we pair high-speed atomic force microscopy with machine learning and Monte Carlo simulations to analyze the rotational dynamics of rod-like proteins on a crystal lattice, simultaneously quantifying the orientational energy landscape and transition probabilities between energetically favorable orientations. Although rotations largely follow Brownian diffusion, proteins often make large jumps in orientation, thus rapidly overcoming barriers that usually inhibit rotation. Moreover, the rotational dynamics can be tuned via protein size and solution chemistry, providing tools for controlling biomolecular assembly at inorganic interfaces.

Three-dimensional localization of nanoscale battery reactions using soft X-ray tomography

Nature Communications, Mar 2, 2018

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

arXiv (Cornell University), Jan 18, 2023

DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives

arXiv (Cornell University), Sep 13, 2018

Subcellular structure segmentation from cryo-electron tomograms via machine learning

bioRxiv (Cold Spring Harbor Laboratory), Apr 9, 2020

Machine learning for micro-tomography

Machine learning has revolutionized a number of fields, but many micro-tomography users have neve... more Machine learning has revolutionized a number of fields, but many micro-tomography users have never used it for their work. The micro-tomography beamline at the Advanced Light Source (ALS), in collaboration with the Center for Applied Mathematics for Energy Research Applications (CAMERA) at Lawrence Berkeley National Laboratory, has now deployed a series of tools to automate data processing for ALS users using machine learning. This includes new reconstruction algorithms, feature extraction tools, and image classification and recommen- dation systems for scientific image. Some of these tools are either in automated pipelines that operate on data as it is collected or as stand-alone software. Others are deployed on computing resources at Berkeley Lab–from workstations to supercomputers–and made accessible to users through either scripting or easy-to-use graphical interfaces. This paper presents a progress report on this work.

Quantum pixel representations and compression for N-dimensional images

Scientific Reports

We introduce a novel and uniform framework for quantum pixel representations that overarches many... more We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI. The proposed QPIXL framework results in more efficient circuit implementations and significantly reduces the gate complexity for all considered quantum pixel representations. Our method scales linearly in the number of pixels and does not use ancilla qubits. Furthermore, the circuits only consist of $$R_y$$ R y gates and $$\text {CNOT}$$ CNOT gates making them practical in the NISQ era. Additionally, we propose a circuit and image compression algorithm that is shown to be highly effective, being able to reduce the necessary gates to prepare an FRQI state for example scientific images by up to 90% without sacrificing image quality. Our algorithms are made publicly available as part of , a Quantum Image Pixel Library.

Building Mathematics, Algorithms, and Software for Experimental Facilities

A two-level Markov random field for road network extraction and its application with optical, SAR, and multitemporal data

International Journal of Remote Sensing, 2016

Real-time data-intensive computing

AIP Conference Proceedings, 2016