Pub Date : 2024-08-13DOI: 10.1088/2632-2153/ad69ff
Jonas Elsborg, Arghya Bhowmik
Finding low-energy atomic ordering in compositionally complex materials is one of the hardest problems in materials discovery, the solution of which can lead to breakthroughs in functional materials—from alloys to ceramics. In this work, we present the Artificial Structure Arranging Net (ArtiSAN)—a reinforcement learning agent utilizing graph representation that is trained to find low-energy atomic configurations of multicomponent systems through a series of atomic switch operations. ArtiSAN is trained on small alloy supercells ranging from binary to septenary. Strikingly, ArtiSAN generalizes to much larger systems of more than a thousand atoms, which are inaccessible with state-of-the-art methods due to the combinatorially larger search space. The performance of the current ArtiSAN agent is tested and deployed on several compositions that can be correlated with known experimental and high-fidelity computational structures. ArtiSAN demonstrates transfer across size and composition and finds physically meaningful structures using no energy evaluation calls once fully trained. While ArtiSAN will require further modifications to capture all variability in structure search, it is a remarkable step towards solving the structural part of the problem of disordered materials discovery.
{"title":"ArtiSAN: navigating the complexity of material structures with deep reinforcement learning","authors":"Jonas Elsborg, Arghya Bhowmik","doi":"10.1088/2632-2153/ad69ff","DOIUrl":"https://doi.org/10.1088/2632-2153/ad69ff","url":null,"abstract":"Finding low-energy atomic ordering in compositionally complex materials is one of the hardest problems in materials discovery, the solution of which can lead to breakthroughs in functional materials—from alloys to ceramics. In this work, we present the <bold>Arti</bold>ficial <bold>S</bold>tructure <bold>A</bold>rranging <bold>N</bold>et (<bold>ArtiSAN</bold>)—a reinforcement learning agent utilizing graph representation that is trained to find low-energy atomic configurations of multicomponent systems through a series of atomic switch operations. ArtiSAN is trained on small alloy supercells ranging from binary to septenary. Strikingly, ArtiSAN generalizes to much larger systems of more than a thousand atoms, which are inaccessible with state-of-the-art methods due to the combinatorially larger search space. The performance of the current ArtiSAN agent is tested and deployed on several compositions that can be correlated with known experimental and high-fidelity computational structures. ArtiSAN demonstrates transfer across size and composition and finds physically meaningful structures using no energy evaluation calls once fully trained. While ArtiSAN will require further modifications to capture all variability in structure search, it is a remarkable step towards solving the structural part of the problem of disordered materials discovery.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"11 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197706","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}
Pub Date : 2024-08-13DOI: 10.1088/2632-2153/ad6ad2
Sangwon Kim, Byoung Chul Ko
In fields requiring high accountability, it is necessary to understand how deep-learning models make decisions when analyzing the causes of image classification. Concept-based interpretation methods have recently been introduced to reveal the internal mechanisms of deep learning models using high-level concepts. However, such methods are constrained by a trade-off between accuracy and interpretability. For instance, in real-world environments, unlike in well-curated training data, the accurate prediction of expected concepts becomes a challenge owing to the various distortions and complexities introduced by different objects. To overcome this tradeoff, we propose concept graph embedding models (CGEM), reflecting the complex dependencies and structures among concepts through the learning of mutual directionalities. The concept graph convolutional neural network (Concept GCN), a downstream task of CGEM, differs from previous methods that solely determine the presence of concepts because it performs a final classification based on the relationships between con- cepts learned through graph embedding. This process endows the model with high resilience even in the presence of incorrect concepts. In addition, we utilize a deformable bipartite GCN for object- centric concept encoding in the earlier stages, which enhances the homogeneity of the concepts. The experimental results show that, based on deformable concept encoding, the CGEM mitigates the trade-off between task accuracy and interpretability. Moreover, it was confirmed that this approach allows the model to increase the resilience and interpretability while maintaining robustness against various real-world concept distortions and incorrect concept interventions. Our code is available at https://github.com/jumpsnack/cgem.
{"title":"Concept graph embedding models for enhanced accuracy and interpretability","authors":"Sangwon Kim, Byoung Chul Ko","doi":"10.1088/2632-2153/ad6ad2","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6ad2","url":null,"abstract":"In fields requiring high accountability, it is necessary to understand how deep-learning models make decisions when analyzing the causes of image classification. Concept-based interpretation methods have recently been introduced to reveal the internal mechanisms of deep learning models using high-level concepts. However, such methods are constrained by a trade-off between accuracy and interpretability. For instance, in real-world environments, unlike in well-curated training data, the accurate prediction of expected concepts becomes a challenge owing to the various distortions and complexities introduced by different objects. To overcome this tradeoff, we propose concept graph embedding models (CGEM), reflecting the complex dependencies and structures among concepts through the learning of mutual directionalities. The concept graph convolutional neural network (Concept GCN), a downstream task of CGEM, differs from previous methods that solely determine the presence of concepts because it performs a final classification based on the relationships between con- cepts learned through graph embedding. This process endows the model with high resilience even in the presence of incorrect concepts. In addition, we utilize a deformable bipartite GCN for object- centric concept encoding in the earlier stages, which enhances the homogeneity of the concepts. The experimental results show that, based on deformable concept encoding, the CGEM mitigates the trade-off between task accuracy and interpretability. Moreover, it was confirmed that this approach allows the model to increase the resilience and interpretability while maintaining robustness against various real-world concept distortions and incorrect concept interventions. Our code is available at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/jumpsnack/cgem\">https://github.com/jumpsnack/cgem</ext-link>.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"69 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197707","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}
Pub Date : 2024-08-13DOI: 10.1088/2632-2153/ad5fdb
Philipp Schmidt, Sören Arlt, Carlos Ruiz-Gonzalez, Xuemei Gu, Carla Rodríguez, Mario Krenn
Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive virtual reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way—as a human-in-the-loop—to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger–Horne–Zeilinger-state analyzer. Our results show the potential of VR to enhance a researcher’s ability to derive knowledge from graph-based generative AI. This type of AI is a widely used abstract data representation in various scientific fields.
{"title":"Virtual reality for understanding artificial-intelligence-driven scientific discovery with an application in quantum optics","authors":"Philipp Schmidt, Sören Arlt, Carlos Ruiz-Gonzalez, Xuemei Gu, Carla Rodríguez, Mario Krenn","doi":"10.1088/2632-2153/ad5fdb","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5fdb","url":null,"abstract":"Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive virtual reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way—as a human-in-the-loop—to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger–Horne–Zeilinger-state analyzer. Our results show the potential of VR to enhance a researcher’s ability to derive knowledge from graph-based generative AI. This type of AI is a widely used abstract data representation in various scientific fields.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"15 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197704","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}
Pub Date : 2024-08-11DOI: 10.1088/2632-2153/ad65b5
M Paiano, S Martina, C Giannelli and F Caruso
The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A notable example of such data scarcity is found in the domain of marine biology, where it is useful to develop methods to automatically detect submarine species for environmental monitoring. To address this data limitation, the state-of-the-art machine learning strategies employ two main approaches. The first involves pretraining models on existing datasets before generalizing to the specific domain of interest. The second strategy is to create synthetic datasets specifically tailored to the target domain using methods like copy-paste techniques or ad-hoc simulators. The first strategy often faces a significant domain shift, while the second demands custom solutions crafted for the specific task. In response to these challenges, here we propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an object detector in a few-real data regime. This is achieved through a diffusion-based generative model that was pretrained on large generic datasets. With respect to the state-of-the-art, we find that it is not necessary to fine tune the generative model on the specific domain of interest. We believe that this is an important advance because it mitigates the labor-intensive task of manual labeling the images in object detection tasks. We validate our approach focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting. Our method achieves detection performance comparable to models trained on thousands of images, using only a few hundreds of input data. Our results pave the way for new generative AI-based protocols for machine learning applications in various domains, for instance ranging from geophysics to biology and medicine.
{"title":"Transfer learning with generative models for object detection on limited datasets","authors":"M Paiano, S Martina, C Giannelli and F Caruso","doi":"10.1088/2632-2153/ad65b5","DOIUrl":"https://doi.org/10.1088/2632-2153/ad65b5","url":null,"abstract":"The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A notable example of such data scarcity is found in the domain of marine biology, where it is useful to develop methods to automatically detect submarine species for environmental monitoring. To address this data limitation, the state-of-the-art machine learning strategies employ two main approaches. The first involves pretraining models on existing datasets before generalizing to the specific domain of interest. The second strategy is to create synthetic datasets specifically tailored to the target domain using methods like copy-paste techniques or ad-hoc simulators. The first strategy often faces a significant domain shift, while the second demands custom solutions crafted for the specific task. In response to these challenges, here we propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an object detector in a few-real data regime. This is achieved through a diffusion-based generative model that was pretrained on large generic datasets. With respect to the state-of-the-art, we find that it is not necessary to fine tune the generative model on the specific domain of interest. We believe that this is an important advance because it mitigates the labor-intensive task of manual labeling the images in object detection tasks. We validate our approach focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting. Our method achieves detection performance comparable to models trained on thousands of images, using only a few hundreds of input data. Our results pave the way for new generative AI-based protocols for machine learning applications in various domains, for instance ranging from geophysics to biology and medicine.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"192 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931252","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}
Pub Date : 2024-08-05DOI: 10.1088/2632-2153/ad6830
André Sequeira, Luis Paulo Santos and Luis Soares Barbosa
This research explores the trainability of Parameterized Quantum Circuit-based policies in Reinforcement Learning, an area that has recently seen a surge in empirical exploration. While some studies suggest improved sample complexity using quantum gradient estimation, the efficient trainability of these policies remains an open question. Our findings reveal significant challenges, including standard Barren Plateaus with exponentially small gradients and gradient explosion. These phenomena depend on the type of basis-state partitioning and the mapping of these partitions onto actions. For a polynomial number of actions, a trainable window can be ensured with a polynomial number of measurements if a contiguous-like partitioning of basis-states is employed. These results are empirically validated in a multi-armed bandit environment.
{"title":"Trainability issues in quantum policy gradients","authors":"André Sequeira, Luis Paulo Santos and Luis Soares Barbosa","doi":"10.1088/2632-2153/ad6830","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6830","url":null,"abstract":"This research explores the trainability of Parameterized Quantum Circuit-based policies in Reinforcement Learning, an area that has recently seen a surge in empirical exploration. While some studies suggest improved sample complexity using quantum gradient estimation, the efficient trainability of these policies remains an open question. Our findings reveal significant challenges, including standard Barren Plateaus with exponentially small gradients and gradient explosion. These phenomena depend on the type of basis-state partitioning and the mapping of these partitions onto actions. For a polynomial number of actions, a trainable window can be ensured with a polynomial number of measurements if a contiguous-like partitioning of basis-states is employed. These results are empirically validated in a multi-armed bandit environment.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"76 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931176","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}
Pub Date : 2024-08-05DOI: 10.1088/2632-2153/ad652c
Khaled Kahouli, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller, Shinichi Nakajima, Stefan Gugler and Niklas Wolf Andreas Gebauer
Molecular relaxation, finding the equilibrium state of a non-equilibrium structure, is an essential component of computational chemistry to understand reactivity. Classical force field (FF) methods often rely on insufficient local energy minimization, while neural network FF models require large labeled datasets encompassing both equilibrium and non-equilibrium structures. As a remedy, we propose MoreRed, molecular relaxation by reverse diffusion, a conceptually novel and purely statistical approach where non-equilibrium structures are treated as noisy instances of their corresponding equilibrium states. To enable the denoising of arbitrarily noisy inputs via a generative diffusion model, we further introduce a novel diffusion time step predictor. Notably, MoreRed learns a simpler pseudo potential energy surface (PES) instead of the complex physical PES. It is trained on a significantly smaller, and thus computationally cheaper, dataset consisting of solely unlabeled equilibrium structures, avoiding the computation of non-equilibrium structures altogether. We compare MoreRed to classical FFs, equivariant neural network FFs trained on a large dataset of equilibrium and non-equilibrium data, as well as a semi-empirical tight-binding model. To assess this quantitatively, we evaluate the root-mean-square deviation between the found equilibrium structures and the reference equilibrium structures as well as their energies.
{"title":"Molecular relaxation by reverse diffusion with time step prediction","authors":"Khaled Kahouli, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller, Shinichi Nakajima, Stefan Gugler and Niklas Wolf Andreas Gebauer","doi":"10.1088/2632-2153/ad652c","DOIUrl":"https://doi.org/10.1088/2632-2153/ad652c","url":null,"abstract":"Molecular relaxation, finding the equilibrium state of a non-equilibrium structure, is an essential component of computational chemistry to understand reactivity. Classical force field (FF) methods often rely on insufficient local energy minimization, while neural network FF models require large labeled datasets encompassing both equilibrium and non-equilibrium structures. As a remedy, we propose MoreRed, molecular relaxation by reverse diffusion, a conceptually novel and purely statistical approach where non-equilibrium structures are treated as noisy instances of their corresponding equilibrium states. To enable the denoising of arbitrarily noisy inputs via a generative diffusion model, we further introduce a novel diffusion time step predictor. Notably, MoreRed learns a simpler pseudo potential energy surface (PES) instead of the complex physical PES. It is trained on a significantly smaller, and thus computationally cheaper, dataset consisting of solely unlabeled equilibrium structures, avoiding the computation of non-equilibrium structures altogether. We compare MoreRed to classical FFs, equivariant neural network FFs trained on a large dataset of equilibrium and non-equilibrium data, as well as a semi-empirical tight-binding model. To assess this quantitatively, we evaluate the root-mean-square deviation between the found equilibrium structures and the reference equilibrium structures as well as their energies.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"303 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931253","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}
Pub Date : 2024-08-04DOI: 10.1088/2632-2153/ad66af
Xiaobo Zhu, Yan Wu, Jin Che, Chao Wang, Liying Wang and Zhanheng Chen
Representation learning over graph networks has recently gained popularity, with many models showing promising results. However, several challenges remain: (1) most methods are designed for static or discrete-time dynamic graphs; (2) existing continuous-time dynamic graph algorithms focus on a single evolving perspective; and (3) many continuous-time dynamic graph approaches necessitate numerous temporal neighbors to capture long-term dependencies. In response, this paper introduces a Multi-Perspective Feedback-Attention Coupling (MPFA) model. MPFA incorporates information from both evolving and original perspectives to effectively learn the complex dynamics of dynamic graph evolution processes. The evolving perspective considers the current state of historical interaction events of nodes and uses a temporal attention module to aggregate current state information. This perspective also makes it possible to capture long-term dependencies of nodes using a small number of temporal neighbors. Meanwhile, the original perspective utilizes a feedback attention module with growth characteristic coefficients to aggregate the original state information of node interactions. Experimental results on one dataset organized by ourselves and seven public datasets validate the effectiveness and competitiveness of our proposed model.
{"title":"Multi-perspective feedback-attention coupling model for continuous-time dynamic graphs","authors":"Xiaobo Zhu, Yan Wu, Jin Che, Chao Wang, Liying Wang and Zhanheng Chen","doi":"10.1088/2632-2153/ad66af","DOIUrl":"https://doi.org/10.1088/2632-2153/ad66af","url":null,"abstract":"Representation learning over graph networks has recently gained popularity, with many models showing promising results. However, several challenges remain: (1) most methods are designed for static or discrete-time dynamic graphs; (2) existing continuous-time dynamic graph algorithms focus on a single evolving perspective; and (3) many continuous-time dynamic graph approaches necessitate numerous temporal neighbors to capture long-term dependencies. In response, this paper introduces a Multi-Perspective Feedback-Attention Coupling (MPFA) model. MPFA incorporates information from both evolving and original perspectives to effectively learn the complex dynamics of dynamic graph evolution processes. The evolving perspective considers the current state of historical interaction events of nodes and uses a temporal attention module to aggregate current state information. This perspective also makes it possible to capture long-term dependencies of nodes using a small number of temporal neighbors. Meanwhile, the original perspective utilizes a feedback attention module with growth characteristic coefficients to aggregate the original state information of node interactions. Experimental results on one dataset organized by ourselves and seven public datasets validate the effectiveness and competitiveness of our proposed model.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"14 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968649","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}
Pub Date : 2024-08-04DOI: 10.1088/2632-2153/ad6390
Tracey Oellerich and Maria Emelianenko
Conservation laws are an inherent feature in many systems modeling real world phenomena, in particular, those modeling biological and chemical systems. If the form of the underlying dynamical system is known, linear algebra and algebraic geometry methods can be used to identify the conservation laws. Our work focuses on using data-driven methods to identify the conservation law(s) in the absence of the knowledge of system dynamics. We develop a robust data-driven computational framework that automates the process of identifying the number and type of the conservation law(s) while keeping the amount of required data to a minimum. We demonstrate that due to relative stability of singular vectors to noise we are able to reconstruct correct conservation laws without the need for excessive parameter tuning. While we focus primarily on biological examples, the framework proposed herein is suitable for a variety of data science applications and can be coupled with other machine learning approaches.
{"title":"Towards robust data-driven automated recovery of symbolic conservation laws from limited data","authors":"Tracey Oellerich and Maria Emelianenko","doi":"10.1088/2632-2153/ad6390","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6390","url":null,"abstract":"Conservation laws are an inherent feature in many systems modeling real world phenomena, in particular, those modeling biological and chemical systems. If the form of the underlying dynamical system is known, linear algebra and algebraic geometry methods can be used to identify the conservation laws. Our work focuses on using data-driven methods to identify the conservation law(s) in the absence of the knowledge of system dynamics. We develop a robust data-driven computational framework that automates the process of identifying the number and type of the conservation law(s) while keeping the amount of required data to a minimum. We demonstrate that due to relative stability of singular vectors to noise we are able to reconstruct correct conservation laws without the need for excessive parameter tuning. While we focus primarily on biological examples, the framework proposed herein is suitable for a variety of data science applications and can be coupled with other machine learning approaches.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"28 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931254","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}
Pub Date : 2024-08-04DOI: 10.1088/2632-2153/ad64a6
Ryan Humble, Zhe Zhang, Finn O’Shea, Eric Darve and Daniel Ratner
Anomaly detection is an important task for complex scientific experiments and other complex systems (e.g. industrial facilities, manufacturing), where failures in a sub-system can lead to lost data, poor performance, or even damage to components. While scientific facilities generate a wealth of data, labeled anomalies may be rare (or even nonexistent), and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called coincident learning for anomaly detection (CoAD), which is specifically designed for multi-modal tasks and identifies anomalies based on coincident behavior across two different slices of the feature space. We define an unsupervised metric, , out of analogy to the supervised classification Fβ statistic. CoAD uses to train an anomaly detection algorithm on unlabeled data, based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and our motivating task of identifying RF station anomalies in a particle accelerator.
{"title":"Coincident learning for unsupervised anomaly detection of scientific instruments","authors":"Ryan Humble, Zhe Zhang, Finn O’Shea, Eric Darve and Daniel Ratner","doi":"10.1088/2632-2153/ad64a6","DOIUrl":"https://doi.org/10.1088/2632-2153/ad64a6","url":null,"abstract":"Anomaly detection is an important task for complex scientific experiments and other complex systems (e.g. industrial facilities, manufacturing), where failures in a sub-system can lead to lost data, poor performance, or even damage to components. While scientific facilities generate a wealth of data, labeled anomalies may be rare (or even nonexistent), and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called coincident learning for anomaly detection (CoAD), which is specifically designed for multi-modal tasks and identifies anomalies based on coincident behavior across two different slices of the feature space. We define an unsupervised metric, , out of analogy to the supervised classification Fβ statistic. CoAD uses to train an anomaly detection algorithm on unlabeled data, based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and our motivating task of identifying RF station anomalies in a particle accelerator.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"76 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931255","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}
Pub Date : 2024-08-01DOI: 10.1088/2632-2153/ad66ad
Joschka Birk, Anna Hallin and Gregor Kasieczka
Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of downstream applications. The successful development of such general-purpose models for physics data would be a major breakthrough as they could improve the achievable physics performance while at the same time drastically reduce the required amount of training time and data. We report significant progress on this challenge on several fronts. First, a comprehensive set of evaluation methods is introduced to judge the quality of an encoding from physics data into a representation suitable for the autoregressive generation of particle jets with transformer architectures (the common backbone of foundation models). These measures motivate the choice of a higher-fidelity tokenization compared to previous works. Finally, we demonstrate transfer learning between an unsupervised problem (jet generation) and a classic supervised task (jet tagging) with our new OmniJet-α model. This is the first successful transfer between two different and actively studied classes of tasks and constitutes a major step in the building of foundation models for particle physics.
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