10,278 research outputs found

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    Swarm Reinforcement Learning For Adaptive Mesh Refinement

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    The Finite Element Method, an important technique in engineering, is aided by Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a favorable trade-off between computational speed and simulation accuracy. Classical methods for AMR depend on task-specific heuristics or expensive error estimators, hindering their use for complex simulations. Recent learned AMR methods tackle these problems, but so far scale only to simple toy examples. We formulate AMR as a novel Adaptive Swarm Markov Decision Process in which a mesh is modeled as a system of simple collaborating agents that may split into multiple new agents. This framework allows for a spatial reward formulation that simplifies the credit assignment problem, which we combine with Message Passing Networks to propagate information between neighboring mesh elements. We experimentally validate the effectiveness of our approach, Adaptive Swarm Mesh Refinement (ASMR), showing that it learns reliable, scalable, and efficient refinement strategies on a set of challenging problems. Our approach significantly speeds up computation, achieving up to 30-fold improvement compared to uniform refinements in complex simulations. Additionally, we outperform learned baselines and achieve a refinement quality that is on par with a traditional error-based AMR strategy without expensive oracle information about the error signal.Comment: Version 1 of this paper is a preliminary workshop version that was accepted as a workshop paper in the ICLR 2023 Workshop on Physics for Machine Learnin

    Measurement of the associated production of a top quark pair and a Higgs boson (t¯tH) with boosted topologies

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    This thesis presents three studies focusing on boosted topologies that utilise machine learning techniques for boosted H → b¯b reconstruction using the ATLAS detector. The measurement of the t¯tH cross-section is a direct way of accessing the Higgs top Yukawa coupling (yt). Firstly, an all-hadronic feasibility study is shown, aimed at assessing boosted topologies in the all-hadronic t¯tH decay channel. It was found to have low statistical significance, with considerable efforts and data driven techniques required to reduce the QCD-multijet background. Secondly, the boosted contribution to the recent t¯tH, H → b¯b measurement using the full Run-2 ATLAS data set, 139f b−1 at √s = 13 TeV, is analysed. There is a considerable contribution from the boosted region to this result, particularly to the differential cross-section measurement of the Simplified Template Cross-Section (STXS) bins [300, 450) and [450, ∞) GeV. The result of the inclusive profile-likelihood fit is μ = 0.35+0.36−0.34, which corresponds to σ = 1.0(2.7) observed(expected) significance compared to the background-only hypothesis. Thirdly work on retraining the boosted H → b¯b reconstruction deep neural network (DNN) is shown for the Run-2 Legacy re-analysis. The bespoke DNN trained for the analysis showed some improvements over the previous round due to the updated analysis algorithms. It also outperformed the general purpose H → b¯b Xbb tagger. The final motivation for use of the bespoke DNN is that it allows the choice of boosted jet collection (RC-jets vs LR-jets). RC-jets re cluster “small” (∆R = 0.4) jets with ∆R = 1.0 while LR-jets directly cluster the calorimeter clusters with ∆R = 1.0, both using the anti-kt algorithm. The RC-jets jets are found to be advantageous. This is due to the ease of propagating systematics for combining with resolved regions and the good modelling observed using samples made with the Atlfast-2 detector simulation

    Machine Learning Empowered Reconfigurable Intelligent Surfaces

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    Reconfigurable intelligent surfaces (RISs) or known as intelligent reflecting surfaces (IRSs) have emerged as potential auxiliary equipment for future wireless networks, which attracts extensive research interest in their characteristics, applications, and potential. RIS is a panel surface equipped with a number of reflective elements, which can artificially modify the propagation environment of the electrogenic signals. Specifically, RISs have the ability to precisely adjust the propagation direction, amplitude, and phase-shift of the signals, providing users with a set of cascaded channels in addition to direct channels, and thereby improving the communication performances for users. Compared with other candidate technologies such as active relays, RIS has advantages in terms of flexible deployment, economical cost, and high energy efficiency. Thus, RISs have been considered a potential candidate technique for future wireless networks. In this thesis, a wireless network paradigm for the sixth generation (6G) wireless networks is proposed, where RISs are invoked to construct smart radio environments (SRE) to enhance communication performances for mobile users. In addition, beyond the conventional reselecting-only RIS, a novel model of RIS is originally proposed, namely, simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS splits the incident signal into transmitted and reflected signals, making full utilization of them to generate 360360^{\circ} coverage around the STAR-RIS panel, improving the coverage of the RIS. In order to fully exert the channel domination and beamforming ability of the RISs and STAR-RSIs to construct SREs, several machine learning algorithms, including deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) approaches are developed to optimize the communication performance in respect of sum data rate or energy efficiency for the RIS-assisted networks. Specifically, several problems are investigated including 1) the passive beamforming problem of the RIS with consideration of configuration overhead is resolved by a DL and a DRL algorithm, where the time overhead of configuration of RIS is successfully reduced by the machine learning algorithms. Consequently, the throughput during a time frame improved 95.2%95.2\% by invoking the proposed algorithms; 2) a novel framework of mobile RISs-enhanced indoor wireless networks is proposed, and a FL enhanced DRL algorithm is proposed for the deployment and beamforming optimization of the RIS. The average throughput of the indoor users severed by the mobile RIS is improved 15.1%15.1\% compared to the case of conventional fixed RIS; 3) A STAR-RIS assisted multi-user downlink multiple-input single-output (MISO) communication system is investigated, and a pair of hybrid reinforcement learning algorithms are proposed for the hybrid control of the transmitting and reflecting beamforming of the STAR-RIS, which ameliorate 7%7\% of the energy efficiency of the STAR-RIS assisted networks; 4) A tile-based low complexity beamforming approach is proposed for STAR-RISs, and the proposed tile-based beamforming approach is capable of achieving homogeneous data rate performance with element-based beamforming with appreciable lower complexity. By designing and operating the computer simulation, this thesis demonstrated 1) the performance gain in terms of sum data rate or energy efficiency by invoking the proposed RIS in the wireless communication networks; 2) the data rate or energy efficient performance gain of the proposed STAR-RIS compared to the existing reflecting-only RIS; 3) the effect of the proposed machine learning algorithms in terms of convergence rate, optimality, and complexity compared to the benchmarks of existing algorithms

    An extension to VORO++ for multithreaded computation of Voronoi cells

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    VORO++ is a software library written in C++ for computing the Voronoi tessellation, a technique in computational geometry that is widely used for analyzing systems of particles. VORO++ was released in 2009 and is based on computing the Voronoi cell for each particle individually. Here, we take advantage of modern computer hardware, and extend the original serial version to allow for multithreaded computation of Voronoi cells via the OpenMP application programming interface. We test the performance of the code, and demonstrate that we can achieve parallel efficiencies greater than 95% in many cases. The multithreaded extension follows standard OpenMP programming paradigms, allowing it to be incorporated into other programs. We provide an example of this using the VoroTop software library, performing a multithreaded Voronoi cell topology analysis of up to 102.4 million particles.Comment: Fix typo and section number

    Quantum coherent manipulation of spin information in molecular nanomagnets

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    Los sistemas cuánticos de dos niveles basados en estados de espín, conocidos como ``qubits de espín'', son bloques prometedores para el desarrollo de tecnologías cuánticas. Entre las distintas plataformas físicas, los qubits de espín definidos en imanes de molécula única (SMM) son candidatos prometedores porque su estructura electrónica puede ajustarse fácilmente mediante ingeniería química (es decir, el Hamiltoniano de espín molecular puede modificarse con facilidad). Sin embargo, los qubits moleculares de espín generados en SMM se enfrentan a varios retos: coherencia cuántica frágil, control coherente insuficiente de los estados de espín y generación de entrelazamiento entre los qubits de espín para aplicaciones de procesamiento de información cuántica. Para abordar estos retos y lograr la manipulación coherente de la información de espín, necesitamos comprender la relación entre los estados de espín, los movimientos moleculares (vibraciones o fonones) y la polarización de la carga (por ejemplo, la generada por un campo E externo). La presente Tesis explora la relación entre los estados de espín, las vibraciones y la polarización desde una perspectiva teórica. Inicialmente, estudiamos la interacción entre los estados de espín y las vibraciones (acoplamiento vibrónico) como una fuente importante de disipación de información de espín. En particular, se emplea un modelado detallado de los acoplamientos vibrónicos, apoyado por pruebas experimentales, para descifrar las vías de decoherencia en diferentes SMM. Nuestros resultados revelan que sólo algunas distorsiones moleculares asociadas a determinados modos vibracionales son capaces de acoplarse fuertemente a grados de libertad de espín y, por tanto, promover la decoherencia. Además, también identificamos que los espectros dispersos entre los estados de espín y fonón son cruciales para preservar las superposiciones cuánticas durante más tiempo. En segundo lugar, presentamos un estudio exhaustivo del control coherente de los estados de espín mediante campos eléctricos en un sistema qubit molecular que presenta transiciones de reloj (HoW10). Este control coherente se modela evaluando el acoplamiento espín-eléctrico (SEC); es decir, encontrando una relación entre los estados de espín, la polarización de la carga y las distorsiones moleculares. El fuerte SEC observado en HoW10 es suficiente para permitir el direccionamiento selectivo de los espines mediante un campo E local a nivel práctico. Por último, exploramos la posibilidad de construir una puerta de entrelazamiento de dos qubits en un par de dos reloj-qubit acoplados dipolarmente (HoW10--HoW10), donde el campo eléctrico se utiliza para controlar localmente los estados de los qubits. El trabajo presentado en esta Tesis avanza en la comprensión de los qubits de espín moleculares para su potencial aplicación en el procesamiento cuántico de la información.Quantum two-level systems based on spin states known as ``spin-qubits’’ are promising building blocks for the development of quantum technologies. Among different physical platforms, spin-qubits defined in single-molecule-magnets (SMMs) are promising candidates because their electronic structure can be easily tuned by chemical engineering (i.e., the molecular spin Hamiltonian can be easily modified). However, molecular spin qubits generated in SMMs faces several challenges: fragile quantum coherence, insufficient coherent control over spin states and generation of entanglement between the spin-qubits for quantum information processing applications. To address these challenges and achieve the coherent manipulation of spin information, we need to understand the relationship between spin states, molecular motions (vibrations or phonons) and charge polarization (e.g., that generated by an external E-field). The current Thesis explores the relationship between spin states, vibrations and polarization from a theoretical perspective. Initially, we study the interaction between spin states and vibrations (vibronic coupling) as an important source of spin information dissipation. In particular, a detailed modelling of vibronic couplings, supported by experimental evidence, is employed to decipher the decoherence pathways in different SMMs. Our outcomes reveal that only some molecular distortions associated to certain vibrational modes are able to strongly couple to spin degrees of freedom and, thus, promoting decoherence. Additionally, we also identified that sparse spectra between spin and phonon states are crucial to preserve quantum superpositions longer times. Secondly, we present a comprehensive study of coherent control over spin states using electrical fields in a molecular qubit system that exhibits clock transitions (HoW10). This coherent control is modelled by evaluating the spin-electric coupling (SEC); that is, finding a relation between spin states, charge polarization, and molecular distortions. The strong SEC observed in HoW10 is sufficient to allow selective addressing of the spins using a local E-field at practical level. Finally, we explore the possibility of constructing two-qubit entanglement gate in a pair of two dipolar-coupled clock-qubit (HoW10--HoW10), where electrical field is used to locally control the qubit states. The work presented in this Thesis advances the understanding of molecular spin qubits for their potential application in quantum information processing

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Data Tiling for Sparse Computation

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    Many real-world data contain internal relationships. Efficient analysis of these relationship data is crucial for important problems including genome alignment, network vulnerability analysis, ranking web pages, among others. Such relationship data is frequently sparse and analysis on it is called sparse computation. We demonstrate that the important technique of data tiling is more powerful than previously known by broadening its application space. We focus on three important sparse computation areas: graph analysis, linear algebra, and bioinformatics. We demonstrate data tiling's power by addressing key issues and providing significant improvements---to both runtime and solution quality---in each area. For graph analysis, we focus on fast data tiling techniques that can produce well-structured tiles and demonstrate theoretical hardness results. These tiles are suitable for graph problems as they reduce data movement and ultimately improve end-to-end runtime performance. For linear algebra, we introduce a new cache-aware tiling technique and apply it to the key kernel of sparse matrix by sparse matrix multiplication. This technique tiles the second input matrix and then uses a small, summary matrix to guide access to the tiles during computation. Our approach results in the fastest known implementation across three distinct CPU architectures. In bioinformatics, we develop a tiling based de novo genome assembly pipeline. We start with reads and develop either a graph or hypergraph that captures internal relationships between reads. This is then tiled to minimize connections while maintaining balance. We then treat each resulting tile independently as the input to an existing, shared-memory assembler. Our pipeline improves existing state-of-the-art de novo genome assemblers and brings both runtime and quality improvements to them on both real-world and simulated datasets.Ph.D

    MLP-AIR: An Efficient MLP-Based Method for Actor Interaction Relation Learning in Group Activity Recognition

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    The task of Group Activity Recognition (GAR) aims to predict the activity category of the group by learning the actor spatial-temporal interaction relation in the group. Therefore, an effective actor relation learning method is crucial for the GAR task. The previous works mainly learn the interaction relation by the well-designed GCNs or Transformers. For example, to infer the actor interaction relation, GCNs need a learnable adjacency, and Transformers need to calculate the self-attention. Although the above methods can model the interaction relation effectively, they also increase the complexity of the model (the number of parameters and computations). In this paper, we design a novel MLP-based method for Actor Interaction Relation learning (MLP-AIR) in GAR. Compared with GCNs and Transformers, our method has a competitive but conceptually and technically simple alternative, significantly reducing the complexity. Specifically, MLP-AIR includes three sub-modules: MLP-based Spatial relation modeling module (MLP-S), MLP-based Temporal relation modeling module (MLP-T), and MLP-based Relation refining module (MLP-R). MLP-S is used to model the spatial relation between different actors in each frame. MLP-T is used to model the temporal relation between different frames for each actor. MLP-R is used further to refine the relation between different dimensions of relation features to improve the feature's expression ability. To evaluate the MLP-AIR, we conduct extensive experiments on two widely used benchmarks, including the Volleyball and Collective Activity datasets. Experimental results demonstrate that MLP-AIR can get competitive results but with low complexity.Comment: Submit to Neurocomputin

    A Deep Learning Approach to Analyzing Continuous-Time Systems

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    Scientists often use observational time series data to study complex natural processes, but regression analyses often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to the performance of models of complex processes, but deep learning is generally not used for scientific analysis. Here we show that deep learning can be used to analyze complex processes, providing flexible function approximation while preserving interpretability. Our approach relaxes standard simplifying assumptions (e.g., linearity, stationarity, and homoscedasticity) that are implausible for many natural systems and may critically affect the interpretation of data. We evaluate our model on incremental human language processing, a domain with complex continuous dynamics. We demonstrate substantial improvements on behavioral and neuroimaging data, and we show that our model enables discovery of novel patterns in exploratory analyses, controls for diverse confounds in confirmatory analyses, and opens up research questions that are otherwise hard to study.Comment: Main article: 12 pages, 1 table, 3 figures; Supplementary Information: 54 pages, 6 tables, 30 figure
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