Machine Learning Science and Technology最新文献_第7页

End-to-end simulation of particle physics events with flow matching and generator oversampling 利用流量匹配和发生器超采样对粒子物理事件进行端到端模拟

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-09 DOI: 10.1088/2632-2153/ad563c

F Vaselli, F Cattafesta, P Asenov, A Rizzi

{"title":"End-to-end simulation of particle physics events with flow matching and generator oversampling","authors":"F Vaselli, F Cattafesta, P Asenov, A Rizzi","doi":"10.1088/2632-2153/ad563c","DOIUrl":"https://doi.org/10.1088/2632-2153/ad563c","url":null,"abstract":"The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomena not previously observed. We show that novel machine learning algorithms, specifically Normalizing Flows and Flow Matching, can be used to replicate accurate simulations from traditional approaches with several orders of magnitude of speed-up. The classical simulation chain starts from a physics process of interest, computes energy deposits of particles and electronics response, and finally employs the same reconstruction algorithms used for data. Eventually, the data are reduced to some high-level analysis format. Instead, we propose an end-to-end approach, simulating the final data format directly from physical generator inputs, skipping any intermediate steps. We use particle jets simulation as a benchmark for comparing both <italic toggle=\"yes\">discrete</italic> and <italic toggle=\"yes\">continuous</italic> Normalizing Flows models. The models are validated across a variety of metrics to identify the most accurate. We discuss the scaling of performance with the increase in training data, as well as the generalization power of these models on physical processes different from the training one. We investigate sampling multiple times from the same physical generator inputs, a procedure we name <italic toggle=\"yes\">oversampling</italic>, and we show that it can effectively reduce the statistical uncertainties of a dataset. This class of ML algorithms is found to be capable of learning the expected detector response independently of the physical input process. The speed and accuracy of the models, coupled with the stability of the training procedure, make them a compelling tool for the needs of current and future experiments.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"38 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty quantification by direct propagation of shallow ensembles 通过直接传播浅层集合量化不确定性

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-04 DOI: 10.1088/2632-2153/ad594a

Matthias Kellner and Michele Ceriotti

{"title":"Uncertainty quantification by direct propagation of shallow ensembles","authors":"Matthias Kellner and Michele Ceriotti","doi":"10.1088/2632-2153/ad594a","DOIUrl":"https://doi.org/10.1088/2632-2153/ad594a","url":null,"abstract":"Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further source of error on top of the intrinsic limitations of the experimental or theoretical setup. Uncertainty estimation is essential to quantify this error, and to make application of data-centric approaches more trustworthy. To ensure that uncertainty quantification is used widely, one should aim for algorithms that are accurate, but also easy to implement and apply. In particular, including uncertainty quantification on top of an existing architecture should be straightforward, and add minimal computational overhead. Furthermore, it should be easy to manipulate or combine multiple machine-learning predictions, propagating uncertainty over further modeling steps. We compare several well-established uncertainty quantification frameworks against these requirements, and propose a practical approach, which we dub direct propagation of shallow ensembles, that provides a good compromise between ease of use and accuracy. We present benchmarks for generic datasets, and an in-depth study of applications to the field of atomistic machine learning for chemistry and materials. These examples underscore the importance of using a formulation that allows propagating errors without making strong assumptions on the correlations between different predictions of the model.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"13 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AMCG: a graph dual atomic-molecular conditional molecular generator AMCG：图双原子-分子条件分子发生器

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-04 DOI: 10.1088/2632-2153/ad5bbf

Carlo Abate, Sergio Decherchi and Andrea Cavalli

引用次数: 0

The R-mAtrIx Net R-mAtrIx 网络

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-03 DOI: 10.1088/2632-2153/ad56f9

Shailesh Lal, Suvajit Majumder and Evgeny Sobko

引用次数: 0

TomOpt: differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography TomOpt：以μ介子断层成像为背景，对粒子探测器的任务和约束感知设计进行差分优化

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-02 DOI: 10.1088/2632-2153/ad52e7

Giles C Strong, Maxime Lagrange, Aitor Orio, Anna Bordignon, Florian Bury, Tommaso Dorigo, Andrea Giammanco, Mariam Heikal, Jan Kieseler, Max Lamparth, Pablo Martínez Ruíz del Árbol, Federico Nardi, Pietro Vischia and Haitham Zaraket

引用次数: 0

Mixed noise and posterior estimation with conditional deepGEM 利用条件 deepGEM 进行混合噪声和后验估计

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-01 DOI: 10.1088/2632-2153/ad5926

Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich and Gabriele Steidl

引用次数: 0

Deciphering peptide-protein interactions via composition-based prediction: a case study with survivin/BIRC5 通过基于成分的预测解密肽与蛋白质之间的相互作用：Survivin/BIRC5 案例研究

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-27 DOI: 10.1088/2632-2153/ad5784

Atsarina Larasati Anindya, Torbjörn Nur Olsson, Maja Jensen, Maria-Jose Garcia-Bonete, Sally P Wheatley, Maria I Bokarewa, Stefano A Mezzasalma and Gergely Katona

{"title":"Deciphering peptide-protein interactions via composition-based prediction: a case study with survivin/BIRC5","authors":"Atsarina Larasati Anindya, Torbjörn Nur Olsson, Maja Jensen, Maria-Jose Garcia-Bonete, Sally P Wheatley, Maria I Bokarewa, Stefano A Mezzasalma and Gergely Katona","doi":"10.1088/2632-2153/ad5784","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5784","url":null,"abstract":"In the realm of atomic physics and chemistry, composition emerges as the most powerful means of describing matter. Mendeleev’s periodic table and chemical formulas, while not entirely free from ambiguities, provide robust approximations for comprehending the properties of atoms, chemicals, and their collective behaviours, which stem from the dynamic interplay of their constituents. Our study illustrates that protein-protein interactions follow a similar paradigm, wherein the composition of peptides plays a pivotal role in predicting their interactions with the protein survivin, using an elegantly simple model. An analysis of these predictions within the context of the human proteome not only confirms the known cellular locations of survivin and its interaction partners, but also introduces novel insights into biological functionality. It becomes evident that electrostatic- and primary structure-based descriptions fall short in predictive power, leading us to speculate that protein interactions are orchestrated by the collective dynamics of functional groups.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"236 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unlearning regularization for Boltzmann machines 为波尔兹曼机解除学习正则化

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-26 DOI: 10.1088/2632-2153/ad5a5f

Enrico Ventura, Simona Cocco, Rémi Monasson and Francesco Zamponi

{"title":"Unlearning regularization for Boltzmann machines","authors":"Enrico Ventura, Simona Cocco, Rémi Monasson and Francesco Zamponi","doi":"10.1088/2632-2153/ad5a5f","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5a5f","url":null,"abstract":"Boltzmann machines (BMs) are graphical models with interconnected binary units, employed for the unsupervised modeling of data distributions. When trained on real data, BMs show the tendency to behave like critical systems, displaying a high susceptibility of the model under a small rescaling of the inferred parameters. This behavior is not convenient for the purpose of generating data, because it slows down the sampling process, and induces the model to overfit the training-data. In this study, we introduce a regularization method for BMs to improve the robustness of the model under rescaling of the parameters. The new technique shares formal similarities with the unlearning algorithm, an iterative procedure used to improve memory associativity in Hopfield-like neural networks. We test our unlearning regularization on synthetic data generated by two simple models, the Curie–Weiss ferromagnetic model and the Sherrington–Kirkpatrick spin glass model. We show that it outperforms Lp-norm schemes and discuss the role of parameter initialization. Eventually, the method is applied to learn the activity of real neuronal cells, confirming its efficacy at shifting the inferred model away from criticality and coming out as a powerful candidate for actual scientific implementations.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"9 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141532643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unification of symmetries inside neural networks: transformer, feedforward and neural ODE 神经网络内部对称性的统一：变压器、前馈和神经 ODE

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-26 DOI: 10.1088/2632-2153/ad5927

Koji Hashimoto, Yuji Hirono and Akiyoshi Sannai

{"title":"Unification of symmetries inside neural networks: transformer, feedforward and neural ODE","authors":"Koji Hashimoto, Yuji Hirono and Akiyoshi Sannai","doi":"10.1088/2632-2153/ad5927","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5927","url":null,"abstract":"Understanding the inner workings of neural networks, including transformers, remains one of the most challenging puzzles in machine learning. This study introduces a novel approach by applying the principles of gauge symmetries, a key concept in physics, to neural network architectures. By regarding model functions as physical observables, we find that parametric redundancies of various machine learning models can be interpreted as gauge symmetries. We mathematically formulate the parametric redundancies in neural ODEs, and find that their gauge symmetries are given by spacetime diffeomorphisms, which play a fundamental role in Einstein’s theory of gravity. Viewing neural ODEs as a continuum version of feedforward neural networks, we show that the parametric redundancies in feedforward neural networks are indeed lifted to diffeomorphisms in neural ODEs. We further extend our analysis to transformer models, finding natural correspondences with neural ODEs and their gauge symmetries. The concept of gauge symmetries sheds light on the complex behavior of deep learning models through physics and provides us with a unifying perspective for analyzing various machine learning architectures.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"2016 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance deterioration of deep learning models after clinical deployment: a case study with auto-segmentation for definitive prostate cancer radiotherapy 深度学习模型在临床部署后性能下降：前列腺癌放射治疗自动分割案例研究

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-24 DOI: 10.1088/2632-2153/ad580f

Biling Wang, Michael Dohopolski, Ti Bai, Junjie Wu, Raquibul Hannan, Neil Desai, Aurelie Garant, Daniel Yang, Dan Nguyen, Mu-Han Lin, Robert Timmerman, Xinlei Wang and Steve B Jiang

{"title":"Performance deterioration of deep learning models after clinical deployment: a case study with auto-segmentation for definitive prostate cancer radiotherapy","authors":"Biling Wang, Michael Dohopolski, Ti Bai, Junjie Wu, Raquibul Hannan, Neil Desai, Aurelie Garant, Daniel Yang, Dan Nguyen, Mu-Han Lin, Robert Timmerman, Xinlei Wang and Steve B Jiang","doi":"10.1088/2632-2153/ad580f","DOIUrl":"https://doi.org/10.1088/2632-2153/ad580f","url":null,"abstract":"Our study aims to explore the long-term performance patterns for deep learning (DL) models deployed in clinic and to investigate their efficacy in relation to evolving clinical practices. We conducted a retrospective study simulating the clinical implementation of our DL model involving 1328 prostate cancer patients treated between January 2006 and August 2022. We trained and validated a U-Net-based auto-segmentation model on data obtained from 2006 to 2011 and tested on data from 2012 to 2022, simulating the model’s clinical deployment starting in 2012. We visualized the trends of the model performance using exponentially weighted moving average (EMA) curves. Additionally, we performed Wilcoxon Rank Sum Test and multiple linear regression to investigate Dice similarity coefficient (DSC) variations across distinct periods and the impact of clinical factors, respectively. Initially, from 2012 to 2014, the model showed high performance in segmenting the prostate, rectum, and bladder. Post-2015, a notable decline in EMA DSC was observed for the prostate and rectum, while bladder contours remained stable. Key factors impacting the prostate contour quality included physician contouring styles, using various hydrogel spacers, CT scan slice thickness, MRI-guided contouring, and intravenous (IV) contrast (p < 0.0001, p < 0.0001, p = 0.0085, p = 0.0012, p < 0.0001, respectively). Rectum contour quality was notably influenced by factors such as slice thickness, physician contouring styles, and the use of various hydrogel spacers. The quality of the bladder contour was primarily affected by IV contrast. The deployed DL model exhibited a substantial decline in performance over time, aligning with the evolving clinical settings.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"159 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0