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Controlled catalyst transfer polymerization in graphene nanoribbon synthesis

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arXiv 2023年

作者： Pun, Sai Ho Delgado, Aidan Dadich, Christina Cronin, Adam Fischer, Felix R. Department of Chemistry University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Kavli Energy NanoScience Institute The University of California Berkeley The Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Bakar Institute of Digital Materials for the Planet Division of Computing Data Science and Society University of California BerkeleyCA94720 United States

Exercising direct control over the unusual electronic structures arising from quantum confinement effects in graphene nanoribbons (GNRs) - atomically defined quasi one-dimensional (1D) strips of graphene - is intimately linked to geometric boundary conditions imposed by the bonding within the ribbon. Besides composition and position of substitutional dopant atoms, the symmetry of the unit cell, the width, length, and termination of a GNR are integral factors that collectively can give rise to highly tuneable semiconductors1-3, innate metallicity arising from topological zero-mode engineering4,5, or magnetic ordering in spin-polarized lattices6. Here we present a rational design that integrates each of these interdependent variables within a modular bottom-up synthesis. Our hybrid chemical approach relies on a catalyst transfer polymerization (CTP) that establishes uniform control over length, width, and end-groups. Complemented by a surface-assisted cyclodehydrogenation step, uniquely enabled by matrix-assisted direct (MAD) transfer protocols7, geometry and functional handles encoded in a polymer template are faithfully mapped onto the structure of the corresponding GNR. Bond-resolved scanning tunnelling microscopy (BRSTM) and spectroscopy (STS) validate the robust correlation between polymer template design and GNR electronic structure and provide a universal and modular platform for the systematic exploration and seamless integration of functional GNRs with integrated circuit architectures. Copyright © 2023, The Authors. All rights reserved.

关键词： Electronic structure

The Fundamental Limit of Jet Tagging

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arXiv 2024年

作者： Geuskens, Joep Gite, Nishank Krämer, Michael Mikuni, Vinicius Mück, Alexander Nachman, Benjamin Reyes-González, Humberto Institute for Theoretical Particle Physics and Cosmology RWTH Aachen University Germany Department of Physics University of California BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States

Identifying the origin of high-energy hadronic jets (‘jet tagging’) has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collections of particles to be categorized. Over the last decade, machine learning-based classifiers have replaced classical observables as the state of the art in jet tagging. Increasingly complex machine learning models are leading to increasingly more effective tagger performance. Our goal is to address the question of convergence - are we getting close to the fundamental limit on jet tagging or is there still potential for computational, statistical, and physical insights for further improvements? We address this question using state-of-the-art generative models to create a realistic, synthetic dataset with a known jet tagging optimum. Various state-of-the-art taggers are deployed on this dataset, showing that there is a significant gap between their performance and the optimum. Our dataset and software are made public to provide a benchmark task for future developments in jet tagging and other areas of particle physics. © 2024, CC BY.

关键词： Hadrons

Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism

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arXiv 2023年

作者： Yang, Han Wang, Qiuli Zhang, Yue An, Zhulin Chen, Liu Zhang, Xiaohong Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China The Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Radiology The First Affiliated Hospital of Army Medical University Chongqing400032 China School of Big Data & Software Engineering Chongqing University Chongqing401331 China Institute of Computing Technology Beijing100190 China

Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations. However, these approaches fail to leverage the valuable information inherent in the consensus and disagreements among the multiple annotations. In this paper, we propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation. To this end, we introduce the Multi-Confidence Mask (MCM), which combines a Low-Confidence (LC) Mask and a High-Confidence (HC) Mask. The LC mask indicates regions with low segmentation confidence, where radiologists may have different segmentation choices. Following UAAM, we further design an Uncertainty-Guide Multi-Confidence Segmentation Network (UGMCS-Net), which contains three modules: a Feature Extracting Module that captures a general feature of a lung nodule, an Uncertainty-Aware Module that produces three features for the annotations’ union, intersection, and annotation set, and an Intersection-Union Constraining Module that uses distances between the three features to balance the predictions of final segmentation and MCM. To comprehensively demonstrate the performance of our method, we propose a Complex Nodule Validation on LIDC-IDRI, which tests UGMCS-Net’s segmentation performance on lung nodules that are difficult to segment using common methods. Experimental results demonstrate that our method can significantly improve the segmentation performance on nodules that are difficult to segment using conventional methods. © 2023, CC BY-NC-ND.

关键词： Computerized tomography

EHands: Quantum Protocol for Polynomial Computation on Real-Valued Encoded States

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arXiv 2025年

作者： Balewski, Jan Amankwah, Mercy G. Bethel, E. Wes Perciano, Talita Van Beeumen, Roel National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University ClevelandOH44106 United States Computer Science Department San Francisco State University San FranciscoCA94132 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

The novel constructive EHands protocol defines a universal set of quantum operations for multivariable polynomial transformations on quantum processors by introducing four basic subcircuits—multiplication, addition, negation, and parity flip—and by using expectation-value encoding (EVEN) to represent real numbers in quantum states. These elementary arithmetic operations can be systematically composed to compute degree-d polynomials, Pd(x), on a QPU. The resulting quantum circuit structure closely mirrors the stepwise evaluation of polynomials on a classical calculator, providing an intuitive and efficient approach to polynomial computation on quantum hardware. By enabling direct, predictable polynomial and nonlinear data transformations on a QPU, our method reduces dependence on classical post-processing in hybrid quantum-classical algorithms, enabling advancements in many quantum algorithms. The EHands quantum circuits are compact enough to deliver meaningful and accurate results on today’s noisy quantum processors. We present a detailed implementation of P4(x) and report experimental results for polynomial approximations of common functions, obtained using IBM’s Heron-class quantum processors and an ideal Qiskit simulator. Copyright © 2025, The Authors. All rights reserved.

关键词： Polynomial approximation

Score-based Generative Models for Calorimeter Shower Simulation

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arXiv 2022年

作者： Mikuni, Vinicius Nachman, Benjamin National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States

Score-based generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other state-of-the-art models for different benchmark categories and applications. In this work we introduce CaloScore, a score-based generative model for collider physics applied to calorimeter shower generation. Three different diffusion models are investigated using the Fast Calorimeter Simulation Challenge 2022 dataset. CaloScore is the first application of a score-based generative model in collider physics and is able to produce high-fidelity calorimeter images for all datasets, providing an alternative paradigm for calorimeter shower simulation. © 2022, CC BY.

关键词： Calorimeters

Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks

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arXiv 2022年

作者： Hosseini, Ryien Simini, Filippo Clyde, Austin Ramanathan, Arvind Argonne Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Department of Computer Science University of Chicago ChicagoIL60657 United States

The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design. During the process of drug discovery specifically, protein-ligand docking, or chemical docking, is a standard in-silico scoring technique that estimates the binding affinity of molecules with a specific protein target. Recently, however, as the number of virtual molecules available to test has rapidly grown, these classical docking algorithms have created a significant computational bottleneck. We address this problem by introducing Deep Surrogate Docking (DSD), a framework that applies deep learning-based surrogate modeling to accelerate the docking process substantially. DSD can be interpreted as a formalism of several earlier surrogate prefiltering techniques, adding novel metrics and practical training practices. Specifically, we show that graph neural networks (GNNs) can serve as fast and accurate estimators of classical docking algorithms. Additionally, we introduce FiLMv2, a novel GNN architecture which we show outperforms existing state-of-the-art GNN architectures, attaining more accurate and stable performance by allowing the model to filter out irrelevant information from data more efficiently. Through extensive experimentation and analysis, we show that the DSD workflow combined with the FiLMv2 architecture provides a 9.496x speedup in molecule screening with a © 2022, CC BY.

关键词： Molecules

Online-compatible unsupervised nonresonant anomaly detection

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Physical Review D 2022年第5期105卷 055006-055006页

作者： Vinicius Mikuni Benjamin Nachman David Shih National Energy Research Scientific Computing Center Berkeley Lab Berkeley California 94720 USA Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Berkeley Institute for Data Science University of California Berkeley California 94720 USA NHETC Department of Physics & Astronomy Rutgers University Piscataway New Jersey 08854 USA

There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on signal sensitivity. However, it is not enough to select anomalous events—there must also be a strategy to provide context to the selected events. We propose the first complete strategy for unsupervised detection of nonresonant anomalies that includes both signal sensitivity and a data-driven method for background estimation. Our technique is built out of two simultaneously trained autoencoders that are forced to be decorrelated from each other. This method can be deployed off-line for nonresonant anomaly detection and is also the first complete on-line-compatible anomaly detection strategy. We show that our method achieves excellent performance on a variety of signals prepared for the ADC2021 data challenge.

关键词： Particle production Hypothetical particles Artificial neural networks Hadron colliders Machine learning

Generative Unfolding with Distribution Mapping

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arXiv 2024年

作者： Butter, Anja Diefenbacher, Sascha Huetsch, Nathan Mikuni, Vinicius Nachman, Benjamin Schweitzer, Sofia Palacios Plehn, Tilman Institut für Theoretische Physik Universität Heidelberg Germany LPNHE Sorbonne Université Université Paris Cité CNRS IN2P3 Paris France Physics Division Lawrence Berkeley National Laboratory Berkeley United States National Energy Research Scientific Computing Center Berkeley Lab Berkeley94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Universität Heidelberg Germany

Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schrödinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. This brings distribution mapping to a similar level of accuracy as the state-of-the-art conditional generative unfolding methods. Numerical results are presented with a standard benchmark dataset of single jet substructure as well as for a new dataset describing a 22-dimensional phase space of Z + 2-jets. © 2024, CC BY.

关键词： Phase space methods

The development of a blockchain-based business model simulator with an application to civic medical data management

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Digital Business

Digital Business 2025年第1期5卷

作者： Cunningham, James Davies, Nigel Devaney, Sarah Harding, Mike Holm, Søren Neumann, Victoria Ainsworth, John Division of Informatics Imaging & Data Sciences The University of Manchester Manchester United Kingdom NIHR Manchester Biomedical Research Centre Manchester University Hospitals NHS Foundation Trust Manchester Academic Health Science Centre Manchester United Kingdom School of Computing and Communications Lancaster University Lancaster United Kingdom Centre for Social Ethics and Policy The University of Manchester Manchester United Kingdom Centre for Medical Ethics University of Oslo Oslo Norway

Blockchain technology is reshaping business models by altering how transactions and data integrity are managed. This shift necessitates evaluating long-term profitability and operational efficiency across various blockchain frameworks. Our research describes the development and application of a business model simulator, beginning with use case analysis, followed by the simulator's design and application to the Civic data Identity Partnership project. We developed a transaction model, ran simulations, and analyzed outcomes to identify key cost drivers and assess the viability of different blockchain architectures. The results highlight the influence of execution costs, legal frameworks, and blockchain selection on financial outcomes, providing insights into the scalability and sustainability of blockchain applications in real-world scenarios. © 2024

关键词： Blockchain Business models Civic data Simulation