IA Scholar Query: An Outer-Inner Approximation for Separable Mixed-Integer Nonlinear Programs.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgSun, 27 Nov 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Simulation Intelligence: Towards a New Generation of Scientific Methods
https://scholar.archive.org/work/rfujm43y4ngcnml5emnvjksbjy
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.Alexander Lavin, David Krakauer, Hector Zenil, Justin Gottschlich, Tim Mattson, Johann Brehmer, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfefferwork_rfujm43y4ngcnml5emnvjksbjySun, 27 Nov 2022 00:00:00 GMTTwo convergent NPA-like hierarchies for the quantum bilocal scenario
https://scholar.archive.org/work/ab4cmzeunzffrkmfru2wwqj5za
Characterising the correlations that arises when locally measuring a single joint quantum system is one of quantum information theory main problem. The seminal work [M. Navascu\'es et al, NJP 10,7,073013 (2008)], known as the NPA hierarchy, reformulates it as a polynomial optimisation problem over noncommutative variables and proposed a convergent hierarchy of necessary conditions, each testable using semidefinite programming. More recently, the problem of characterising the quantum network correlations that arise when locally measuring several independent quantum systems distributed in a network received considerable interest. Several generalisation of the NPA hierarchy such as the Scalar Extension [Pozas-Kerstjens et al, Phys. Rev. Lett. 123, 140503 (2019)] were introduced but remain uncharacterised. In this work, we introduce a new hierarchy, prove its equivalence to the Scalar Extension and characterise its convergence in the case of the simplest network, the bilocal scenario.Marc-Olivier Renou, Xiangling Xuwork_ab4cmzeunzffrkmfru2wwqj5zaFri, 25 Nov 2022 00:00:00 GMTA Data-Driven Simulation-Based Method for Transportation Network Design
https://scholar.archive.org/work/3fh62t5g7vbuhewnblirj6g6ty
This research develops a data-driven simulation-based method, which integrates high-resolution simulators and machine learning techniques, to model and solve the NDP. Three major tasks are tackled: a) developing a computable traffic flow model for representing traffic dynamics in interrupted multimodal environments, b) developing a generic data-driven simulation-based approach for NDP with a specific focus on network topology, by establishing a simulation-based bi-level model and a solution algorithm based on Bayesian optimization, c) incorporating practical and political constraints into the optimization of the network capacity analysis. The proposed framework can serve as a decision-making tool for transport planners and traffic engineers.RUYANG YINwork_3fh62t5g7vbuhewnblirj6g6tyWed, 23 Nov 2022 00:00:00 GMTTheory and Phenomenology of Stressed ΨDM Soliton
https://scholar.archive.org/work/lviue7gqv5cvheyuepokuxkcnu
Soliton in the hostile turbulent ΨDM halo of a galaxy agitates with various kinds of excitation, and the soliton even breathes heavily under great stress. A theory of collective excitation for a ΨDM soliton is presented. The collective excitation has different degrees of coupling to negative energy modes, where lower-order excitation generally necessitates more negative energy coupling. A constrained variational principle is developed to assess the frequencies and mode structures of small-amplitude perturbations. The predicted frequencies are in good agreement with those found in simulations. Soliton breathing at amplitudes on the verge of breakup is also a highlight of this work. Even in this extreme nonlinear regime, the wave function perturbation amplitudes are moderate. The simulation data shows a stable oscillation with frequency weakly dependent on the oscillation amplitude, and hints a self-consistent quasi-linear model for the wave function that accounts for modifications in the ground state wave function and the equilibrium density. The mock solution, constructed from the simulation data, can shed lights on the dynamics of the large-amplitude breathing soliton and supports the quasi-linear model, as evidenced by its ability to well predict the nonlinear eigenfrequency shifts and large-amplitude breathing frequency observed in simulations.Tzihong Chiueh, Yi-Hsiung Hsuwork_lviue7gqv5cvheyuepokuxkcnuWed, 23 Nov 2022 00:00:00 GMTTight Bound for Sum of Heterogeneous Random Variables: Application to Chance Constrained Programming
https://scholar.archive.org/work/qdu2mf4x2ng7xgs64oamgkfffi
We study a tight Bennett-type concentration inequality for sums of heterogeneous and independent variables, defined as a one-dimensional minimization. We show that this refinement, which outperforms the standard known bounds, remains computationally tractable: we develop a polynomial-time algorithm to compute confidence bounds, proved to terminate with an epsilon-solution. From the proposed inequality, we deduce tight distributionally robust bounds to Chance-Constrained Programming problems. To illustrate the efficiency of our approach, we consider two use cases. First, we study the chance-constrained binary knapsack problem and highlight the efficiency of our cutting-plane approach by obtaining stronger solution than classical inequalities (such as Chebyshev-Cantelli or Hoeffding). Second, we deal with the Support Vector Machine problem, where the convex conservative approximation we obtain improves the robustness of the separation hyperplane, while staying computationally tractable.Quentin Jacquetwork_qdu2mf4x2ng7xgs64oamgkfffiTue, 22 Nov 2022 00:00:00 GMTSelf-Supervised Primal-Dual Learning for Constrained Optimization
https://scholar.archive.org/work/2n5ycjps2ncfjjbn4qtnspeoze
This paper studies how to train machine-learning models that directly approximate the optimal solutions of constrained optimization problems. This is an empirical risk minimization under constraints, which is challenging as training must balance optimality and feasibility conditions. Supervised learning methods often approach this challenge by training the model on a large collection of pre-solved instances. This paper takes a different route and proposes the idea of Primal-Dual Learning (PDL), a self-supervised training method that does not require a set of pre-solved instances or an optimization solver for training and inference. Instead, PDL mimics the trajectory of an Augmented Lagrangian Method (ALM) and jointly trains primal and dual neural networks. Being a primal-dual method, PDL uses instance-specific penalties of the constraint terms in the loss function used to train the primal network. Experiments show that, on a set of nonlinear optimization benchmarks, PDL typically exhibits negligible constraint violations and minor optimality gaps, and is remarkably close to the ALM optimization. PDL also demonstrated improved or similar performance in terms of the optimality gaps, constraint violations, and training times compared to existing approaches.Seonho Park, Pascal Van Hentenryckwork_2n5ycjps2ncfjjbn4qtnspeozeTue, 22 Nov 2022 00:00:00 GMTUnivariate and multivariate statistical approaches for the analyses of omics data: sample classification and two-block integration
https://scholar.archive.org/work/mc3pdonidnh2fc64ocq3jawkhu
The wealth of information generated by high-throughput omics technologies in the context of large-scale epidemiological studies has made a significant contribution to the identification of factors influencing the onset and progression of common diseases. Advanced computational and statistical modelling techniques are required to manipulate and extract meaningful biological information from these omics data as several layers of complexity are associated with them. Recent research efforts have concentrated in the development of novel statistical and bioinformatic tools; however, studies thoroughly investigating the applicability and suitability of these novel methods in real data have often fallen behind. This thesis focuses in the analyses of proteomics and transcriptomics data from the EnviroGenoMarker project with the purpose of addressing two main research objectives: i) to critically appraise established and recently developed statistical approaches in their ability to appropriately accommodate the inherently complex nature of real-world omics data and ii) to improve the current understanding of a prevalent condition by identifying biological markers predictive of disease as well as possible biological mechanisms leading to its onset. The specific disease endpoint of interest corresponds to B-cell Lymphoma, a common haematological malignancy for which many challenges related to its aetiology remain unanswered. The seven chapters comprising this thesis are structured in the following manner: the first two correspond to introductory chapters where I describe the main omics technologies and statistical methods employed for their analyses. The third chapter provides a description of the epidemiological project giving rise to the study population and the disease outcome of interest. These are followed by three results chapters that address the research aims described above by applying univariate and multivariate statistical approaches for sample classification and data integration purposes. A summary of findings, c [...]Javiera Garrido Manriquez, Marc Chadeau, Paolo Vineis, Paul Elliott, Agencia Nacional De Investigación Y Desarrollo, Chile (ANID)work_mc3pdonidnh2fc64ocq3jawkhuTue, 22 Nov 2022 00:00:00 GMTEfficient and Accurate Physically-Based Differentiable Rendering
https://scholar.archive.org/work/glhu6sxmnza77co2lzq6gw2bmq
Physically-based rendering algorithms generate photorealistic images of virtual scenes. By simulating light paths in a scene, complex physical e ects such as shadows, re ections and volumetric scattering can be reproduced. Over the last decade, physicallybased rendering methods have become e cient enough for widespread use. They are used to synthesize realistic imagery for visual e ects, animated movies and games, as well as architectural, product and scienti c visualization. We investigate the use of physically-based rendering for inverse problems. For example, given a set of images (e.g., photographs of a real scene), we would like to reconstruct scene geometry, material properties and lighting conditions that when rendered reproduce the provided reference images. Such a task can be formalized as minimizing McGuire's computer graphics archive (Figure 6 .9). • The Lego scene was created by Håvard Dalen (Figures 6.15, 7.14). • Several scenes were created by Matthew Chapman at Meta Reality Labs (Building, City, Trees in Figure 7 .12). • The Armchair mesh was created by Blendswap user Kilt2007 (Figures 7.3, 7.6). • The Fractal object was created by Blendswap user 3dphotosystems (Figure 7 .12). • The Tree Stump model was created by Blendswap user rubberduck (Figure 7 .5). • The Drums scene was created by Blendswap user bryanajones (Figure 7 .14). • We further used several HDR images by Poly Haven.Delio Aleardo Viciniwork_glhu6sxmnza77co2lzq6gw2bmqMon, 21 Nov 2022 00:00:00 GMTProbing Cosmic Inflation with the LiteBIRD Cosmic Microwave Background Polarization Survey
https://scholar.archive.org/work/3cwyjfasvfd2zbr44k54m6wchu
LiteBIRD, the Lite (Light) satellite for the study of B-mode polarization and Inflation from cosmic background Radiation Detection, is a space mission for primordial cosmology and fundamental physics. The Japan Aerospace Exploration Agency (JAXA) selected LiteBIRD in May 2019 as a strategic large-class (L-class) mission, with an expected launch in the late 2020s using JAXA's H3 rocket. LiteBIRD is planned to orbit the Sun-Earth Lagrangian point L2, where it will map the cosmic microwave background (CMB) polarization over the entire sky for three years, with three telescopes in 15 frequency bands between 34 and 448 GHz, to achieve an unprecedented total sensitivity of 2.2μK-arcmin, with a typical angular resolution of 0.5^∘ at 100 GHz. The primary scientific objective of LiteBIRD is to search for the signal from cosmic inflation, either making a discovery or ruling out well-motivated inflationary models. The measurements of LiteBIRD will also provide us with insight into the quantum nature of gravity and other new physics beyond the standard models of particle physics and cosmology. We provide an overview of the LiteBIRD project, including scientific objectives, mission and system requirements, operation concept, spacecraft and payload module design, expected scientific outcomes, potential design extensions and synergies with other projects.LiteBIRD Collaboration: E. Allys, K. Arnold, J. Aumont, R. Aurlien, S. Azzoni, C. Baccigalupi, A. J. Banday, R. Banerji, R. B. Barreiro, N. Bartolo, L. Bautista, D. Beck, S. Beckman, M. Bersanelli, F. Boulanger, M. Brilenkov, M. Bucher, E. Calabrese, P. Campeti, A. Carones, F. J. Casas, A. Catalano, V. Chan, K. Cheung, Y. Chinone, S. E. Clark, F. Columbro, G. D'Alessandro, P. de Bernardis, T. de Haan, E. de la Hoz, M. De Petris, S. Della Torre, P. Diego-Palazuelos, M. Dobbs, T. Dotani, J. M. Duval, T. Elleflot, H. K. Eriksen, J. Errard, T. Essinger-Hileman, F. Finelli, R. Flauger, C. Franceschet, U. Fuskeland, M. Galloway, K. Ganga, M. Gerbino, M. Gervasi, R. T. Génova-Santos, T. Ghigna, S. Giardiello, E. Gjerløw, J. Grain, F. Grupp, A. Gruppuso, J. E. Gudmundsson, N. W. Halverson, P. Hargrave, T. Hasebe, M. Hasegawa, M. Hazumi, S. Henrot-Versillé, B. Hensley, L. T. Hergt, D. Herman, E. Hivon, R. A. Hlozek, A. L. Hornsby, Y. Hoshino, J. Hubmayr, K. Ichiki, T. Iida, H. Imada, H. Ishino, G. Jaehnig, N. Katayama, A. Kato, R. Keskitalo, T. Kisner, Y. Kobayashi, A. Kogut, K. Kohri, E. Komatsu, K. Komatsu, K. Konishi, N. Krachmalnicoff, C. L. Kuo, L. Lamagna, M. Lattanzi, A. T. Lee, C. Leloup, F. Levrier, E. Linder, G. Luzzi, J. Macias-Perez, T. Maciaszek, B. Maffei, D. Maino, S. Mandelli, E. Martínez-González, S. Masi, M. Massa, S. Matarrese, F. T. Matsuda, T. Matsumura, L. Mele, M. Migliaccio, Y. Minami, A. Moggi, J. Montgomery, L. Montier, G. Morgante, B. Mot, Y. Nagano, T. Nagasaki, R. Nagata, R. Nakano, T. Namikawa, F. Nati, P. Natoli, S. Nerval, F. Noviello, K. Odagiri, S. Oguri, H. Ohsaki, L. Pagano, A. Paiella, D. Paoletti, A. Passerini, G. Patanchon, F. Piacentini, M. Piat, G. Polenta, D. Poletti, T. Prouvé, G. Puglisi, D. Rambaud, C. Raum, S. Realini, M. Reinecke, M. Remazeilles, A. Ritacco, G. Roudil, J. A. Rubino-Martin, M. Russell, H. Sakurai, Y. Sakurai, M. Sasaki, D. Scott, Y. Sekimoto, K. Shinozaki, M. Shiraishi, P. Shirron, G. Signorelli, F. Spinella, S. Stever, R. Stompor, S. Sugiyama, R. M. Sullivan, A. Suzuki, T. L. Svalheim, E. Switzer, R. Takaku, H. Takakura, Y. Takase, A. Tartari, Y. Terao, J. Thermeau, H. Thommesen, K. L. Thompson, M. Tomasi, M. Tominaga, M. Tristram, M. Tsuji, M. Tsujimoto, L. Vacher, P. Vielva, N. Vittorio, W. Wang, K. Watanuki, I. K. Wehus, J. Weller, B. Westbrook, J. Wilms, E. J. Wollack, J. Yumoto, M. Zannoniwork_3cwyjfasvfd2zbr44k54m6wchuSun, 20 Nov 2022 00:00:00 GMTLectures on modular forms and strings
https://scholar.archive.org/work/rra4lqiauzezpnt4kq5ag3axnu
The goal of these lectures is to present an informal but precise introduction to a body of concepts and methods of interest in number theory and string theory revolving around modular forms and their generalizations. Modular invariance lies at the heart of conformal field theory, string perturbation theory, Montonen-Olive duality, Seiberg-Witten theory, and S-duality in Type IIB superstring theory. Automorphic forms with respect to higher arithmetic groups as well as mock modular forms enter in toroidal string compactifications and the counting of black hole microstates. After introducing the basic mathematical concepts including elliptic functions, modular forms, Maass forms, modular forms for congruence subgroups, vector-valued modular forms, and modular graph forms, we describe a small subset of the countless applications to problems in Mathematics and Physics, including those mentioned above.Eric D'Hoker, Justin Kaidiwork_rra4lqiauzezpnt4kq5ag3axnuFri, 18 Nov 2022 00:00:00 GMTAdaptive Reduced Basis Methods for Multiscale Problems and Large-scale PDE-constrained Optimization
https://scholar.archive.org/work/43ykt52yc5b4tduxemv42ohgxy
This thesis presents recent advances in model order reduction methods with the primary aim to construct online-efficient reduced surrogate models for parameterized multiscale phenomena and accelerate large-scale PDE-constrained parameter optimization methods. In particular, we present several different adaptive RB approaches that can be used in an error-aware trust-region framework for progressive construction of a surrogate model used during a certified outer optimization loop. In addition, we elaborate on several different enhancements for the trust-region reduced basis (TR-RB) algorithm and generalize it for parameter constraints. Thanks to the a posteriori error estimation of the reduced model, the resulting algorithm can be considered certified with respect to the high-fidelity model. Moreover, we use the first-optimize-then-discretize approach in order to take maximum advantage of the underlying optimality system of the problem. In the first part of this thesis, the theory is based on global RB techniques that use an accurate FEM discretization as the high-fidelity model. In the second part, we focus on localized model order reduction methods and develop a novel online efficient reduced model for the localized orthogonal decomposition (LOD) multiscale method. The reduced model is internally based on a two-scale formulation of the LOD and, in particular, is independent of the coarse and fine discretization of the LOD. The last part of this thesis is devoted to combining both results on TR-RB methods and localized RB approaches for the LOD. To this end, we present an algorithm that uses adaptive localized reduced basis methods in the framework of a trust-region localized reduced basis (TR-LRB) algorithm. The basic ideas from the TR-RB are followed, but FEM evaluations of the involved systems are entirely avoided.Tim Keilwork_43ykt52yc5b4tduxemv42ohgxyThu, 17 Nov 2022 00:00:00 GMTEfficient Algorithms for Solving Nonlinear Inverse Problems in Image Reconstruction
https://scholar.archive.org/work/frapmnrnnzaoxi6exw32e5eo5a
Throughout many fields of science, images are used to display information in a relatable matter about objects which may not be directly visible. Instead, sensors are used–beyond standard cameras–to capture measurements of the object. These measurements are then processed to reconstruct an image of the object which created them. This process of reconstructing the cause of the observed effects is known as an inverse problem. In this thesis, algorithms are proposed for solving various inverse problems in image reconstruction. These algorithms are then analyzed to demonstrate their statistical and computational efficiency. The main through-line tying these problems together is that the proposed solutions leverage inherent structural information. The thesis begins by demonstrating how to design effective spectral methods for estimating an image from phaseless measurements given approximate knowledge of the structure of the noise effecting the system. Next, an amplitude-based loss function is proposed for solving a generalized matrix phaseless sensing problem and algorithms are derived which reach a critical point of such a loss function. Continuing, stochastic variance-reduction gradient techniques are applied to an algorithmic framework for reconstructing an image known as Plug-and-Play (PnP) to achieve faster computation times while maintaining high accuracy. The thesis closes by analyzing how to reconstruct a large set of images when there exists a global structure relating the images to each other. The methods presented in this thesis are applied to phase retrieval, compressive sensing magnetic resonance imaging, and electron back-scattered diffraction microscopy.Vincent Monardowork_frapmnrnnzaoxi6exw32e5eo5aThu, 17 Nov 2022 00:00:00 GMTNear-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
https://scholar.archive.org/work/5cil662o5bclbky4ypzlw2akiq
Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.He-Liang Huang, Xiao-Yue Xu, Chu Guo, Guojing Tian, Shi-Jie Wei, Xiaoming Sun, Wan-Su Bao, Gui-Lu Longwork_5cil662o5bclbky4ypzlw2akiqThu, 17 Nov 2022 00:00:00 GMTGraph Filters for Signal Processing and Machine Learning on Graphs
https://scholar.archive.org/work/sk2yvoq4fzbkphzw43ibpjtp5u
Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power; that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signal processing and machine learning applications. Our aim is that this article serves the dual purpose of providing a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations between signal processing, machine learning, and application domains.Elvin Isufi, Fernando Gama, David I. Shuman, Santiago Segarrawork_sk2yvoq4fzbkphzw43ibpjtp5uWed, 16 Nov 2022 00:00:00 GMTTOI-1136 is a Young, Coplanar, Aligned Planetary System in a Pristine Resonant Chain
https://scholar.archive.org/work/vwqaagwxbndmnftfs6ealj7atq
Convergent disk migration has long been suspected to be responsible for forming planetary systems with a chain of mean-motion resonances (MMR). Dynamical evolution over time could disrupt the delicate resonant configuration. We present TOI-1136, a 700-Myr-old G star hosting at least 6 transiting planets between ∼2 and 5 R_⊕. The orbital period ratios deviate from exact commensurability by only 10^-4, smaller than the ∼ 10^-2 deviations seen in typical Kepler near-resonant systems. A transit-timing analysis measured the masses of the planets (3-8M_⊕) and demonstrated that the planets in TOI-1136 are in true resonances with librating resonant angles. Based on a Rossiter-McLaughlin measurement of planet d, the star's rotation appears to be aligned with the planetary orbital planes. The well-aligned planetary system and the lack of detected binary companion together suggest that TOI-1136's resonant chain formed in an isolated, quiescent disk with no stellar fly-by, disk warp, or significant axial asymmetry. With period ratios near 3:2, 2:1, 3:2, 7:5, and 3:2, TOI-1136 is the first known resonant chain involving a second-order MMR (7:5) between two first-order MMR. The formation of the delicate 7:5 resonance places strong constraints on the system's migration history. Short-scale (starting from ∼0.1 AU) Type-I migration with an inner disk edge is most consistent with the formation of TOI-1136. A low disk surface density (Σ_ 1AU≲10^3g cm^-2; lower than the minimum-mass solar nebula) and the resultant slower migration rate likely facilitated the formation of the 7:5 second-order MMR. TOI-1136's deep resonance suggests that it has not undergone much resonant repulsion during its 700-Myr lifetime. One can rule out rapid tidal dissipation within a rocky planet b or obliquity tides within the largest planets d and f.Fei Dai, Kento Masuda, Corey Beard, Paul Robertson, Max Goldberg, Konstantin Batygin, Luke Bouma, Jack J. Lissauer, Emil Knudstrup, Simon Albrecht, Andrew W. Howard, Heather A. Knutson, Erik A. Petigura, Lauren M. Weiss, Howard Isaacson, Martti Holst Kristiansen, Hugh Osborn, Songhu Wang, Xian-Yu Wang, Aida Behmard, Michael Greklek-McKeon, Shreyas Vissapragada, Natalie M. Batalha, Casey L. Brinkman, Ashley Chontos, Ian Crossfield, Courtney Dressing, Tara Fetherolf, Benjamin Fulton, Michelle L. Hill, Daniel Huber, Stephen R. Kane, Jack Lubin, Mason MacDougall, Andrew Mayo, Teo Močnik, Joseph M. Akana Murphy, Ryan A. Rubenzahl, Nicholas Scarsdale, Dakotah Tyler, Judah Van Zandt, Alex S. Polanski, Hans Martin Schwengeler, Ivan A. Terentev, Paul Benni, Allyson Bieryla, David Ciardi, Ben Falk, E. Furlan, Eric Girardin, Pere Guerra, Katharine M. Hesse, Steve B. Howell, J. Lillo-Box, Elisabeth C. Matthews, Joseph D. Twicken, Joel Villaseñor, David W. Latham, Jon M. Jenkins, George R. Ricker, Sara Seager, Roland Vanderspek, Joshua N. Winnwork_vwqaagwxbndmnftfs6ealj7atqTue, 15 Nov 2022 00:00:00 GMTThe Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae
https://scholar.archive.org/work/f2hjvawqjbhipfhvhjr5hn3faa
We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multi-color Pan-STARRS1 (PS1) griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host-galaxy associations, redshifts, spectroscopic/photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z~0.5. We present relative SN rates from YSE's magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multi-survey SN simulations to train the ParSNIP photometric classification algorithm; when validating our classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (~71%) SNe Ia, 339 (~23%) SNe II, and 96 (~6%) SNe Ib/Ic. Our approach demonstrates that simulations can be used to improve the performance of photometric classifiers applied to real data. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time.P. D. Aleo, K. Malanchev, S. Sharief, D. O. Jones, G. Narayan, R. J. Foley, V. A. Villar, C. R. Angus, V. F. Baldassare, M. J. Bustamante-Rosell, D. Chatterjee, C. Cold, D. A. Coulter, K. W. Davis, S. Dhawan, M. R. Drout, A. Engel, K. D. French, A. Gagliano, C. Gall, J. Hjorth, M. E. Huber, W. V. Jacobson-Galán, C. D. Kilpatrick, D. Langeroodi, K. S. Mandel, R. Margutti, F. Matasić, P. McGill, J. D. R. Pierel, E. Ramirez-Ruiz, C. L. Ransome, C. Rojas-Bravo, M. R. Siebert, K. W. Smith, K. M. de Soto, M. C. Stroh, S. Tinyanont, K. Taggart, S. M. Ward, R. Wojtak, K. Auchettl, P. K. Blanchard, T. J. L. de Boer, B. M. Boyd, C. M. Carroll, K. C. Chambers, L. DeMarchi, G. Dimitriadis, S. A. Dodd, N. Earl, D. Farias, H. Gao, S. Gomez, M. Grayling, C. Grillo, E. E. Hayes, T. Hung, L. Izzo, N. Khetan, J. A. P. Law-Smith, N. LeBaron, C.-C. Lin, Y. Luo, E. A. Magnier, D. Matthews, A. J. G. O'Grady, Y.-C. Pan, C. A. Politsch, S. I. Raimundo, A. Rest, R. Ridden-Harper, A. Sarangi, S. J. Smartt, G. Terreran, S. Thorp, J. Vazquez, R. J. Wainscoat, Q. Wang, A. R. Wasserman, S. K. Yadavalli, R. Yarza, Y. Zenatiwork_f2hjvawqjbhipfhvhjr5hn3faaMon, 14 Nov 2022 00:00:00 GMTMultipole higher-order topology in a multimode lattice
https://scholar.archive.org/work/ch7uazsbxna4thbr6k6y6zyqlu
The concepts of topology have a profound impact on physics research spanning the fields of condensed matter, photonics and acoustics and predicting topological states that provide unprecedented versatility in routing and control of waves of various nature. Higher-order topological insulators further expand this plethora of possibilities towards extended range of structure dimensionalities. Here, we put forward a novel class of two-dimensional multipolar higher-order topological insulators that arise due to the interference of the degenerate modes of the individual meta-atoms generalizing the mechanism of spin-orbit coupling in condensed matter systems. We prove that this model features disorder-robust corner modes and cannot be reduced to the known crystalline topological phases or conventional quadrupole insulators, providing the first example of multipolar topology in a C_3-symmetric lattice featuring quantized octupole moment. The multimode nature of the lattice gives rise to flat bands and corner states with extreme localization enabling coherent control of the topological modes. We support our predictions by assembling the designed structure, observing multipolar topological corner states and experimentally demonstrating their coherent control.Maxim Mazanov, Anton S. Kupriianov, Roman S. Savelev, Zuxian He, Maxim A. Gorlachwork_ch7uazsbxna4thbr6k6y6zyqluFri, 11 Nov 2022 00:00:00 GMTApplication-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting
https://scholar.archive.org/work/qq6l42nvevchlonbg6oaraq6wu
Forecasting and decision-making are generally modeled as two sequential steps with no feedback, following an open-loop approach. In this paper, we present application-driven learning, a new closed-loop framework in which the processes of forecasting and decision-making are merged and co-optimized through a bilevel optimization problem. We present our methodology in a general format and prove that the solution converges to the best estimator in terms of the expected cost of the selected application. Then, we propose two solution methods: an exact method based on the KKT conditions of the second-level problem and a scalable heuristic approach suitable for decomposition methods. The proposed methodology is applied to the relevant problem of defining dynamic reserve requirements and conditional load forecasts, offering an alternative approach to current ad hoc procedures implemented in industry practices. We benchmark our methodology with the standard sequential least-squares forecast and dispatch planning process. We apply the proposed methodology to an illustrative system and to a wide range of instances, from dozens of buses to large-scale realistic systems with thousands of buses. Our results show that the proposed methodology is scalable and yields consistently better performance than the standard open-loop approach.Joaquim Dias Garcia, Alexandre Street, Tito Homem-de-Mello, Francisco D. Muñozwork_qq6l42nvevchlonbg6oaraq6wuThu, 10 Nov 2022 00:00:00 GMTFibre-reinforced additive manufacturing: from design guidelines to advanced lattice structures
https://scholar.archive.org/work/yiy7lwmscvh4hkgcou5lc54uly
In pursuit of achieving ultimate lightweight designs with additive manufacturing (AM), engineers across industries are increasingly gravitating towards composites and architected cellular solids; more precisely, fibre-reinforced polymers and functionally graded lattices (FGLs). Control over material anisotropy and the cell topology in design for AM (DfAM) offer immense scope for customising a part's properties and for the efficient use of material. This research expands the knowledge on the design with fibre-reinforced AM (FRAM) and the elastic-plastic performance of FGLs. Novel toolpath strategies, design guidelines and assessment criteria for FRAM were developed. For this purpose, an open-source solution was proposed, successfully overcoming the limitations of commercial printers. The effect of infill patterns on structural performance, economy, and manufacturability was examined. It was demonstrated how print paths informed by stress trajectories and key geometric features can outperform conventional patterns, laying the groundwork for more sophisticated process planning. A compilation of the first comprehensive database on fibre-reinforced FGLs provided insights into the effect of grading on the elastic performance and energy absorption capability, subject to strut-and surface-based lattices, build direction and fibre volume fraction. It was elucidated how grading the unit cell density within a lattice offers the possibility of tailoring the stiffness and achieving higher energy absorption than ungraded lattices. Vice versa, grading the unit cell size of lattices yielded no effect on the performance and is thus exclusively governed by the density. These findings help exploit the lightweight potential of FGLs through better informed DfAM. A new and efficient methodology for predicting the elastic-plastic characteristics of FGLs under large strain deformation, assuming homogenised material properties, was presented. A phenomenological constitutive model that was calibrated based upon interpolated material data [...]János Plocher, Ajit Panesar, Vito Tagarielli, Engineering And Physical Sciences Research Councilwork_yiy7lwmscvh4hkgcou5lc54ulyThu, 10 Nov 2022 00:00:00 GMTHidden Symmetries in Gravity : Black holes and other minisuperspaces
https://scholar.archive.org/work/zxr24noinzcjtouttlzs4ebcny
This thesis is dedicated to the study of symmetries in reduced models of gravity, with some frozen degrees of freedom. We focus on the minisuperspace reduction whith a finite number of degrees of freedom. Minisuperspaces are treated as mechanical models, evolving in one spacetime direction. This evolution parameter represents the orthogonal coordinate to the homogeneous foliation of the spacetime. I investigate their classical symmetries and the algebra of the corresponding Noether charges. After presenting the formalism allowing us to describe the reduced models in terms of an action principle, we discuss the condition for having an (extended) conformal symmetry. In particular, the black hole model enlightens the subtle role of the spacelike boundary of the homogeneous slice. The latter interplays with the conformal symmetry, being associated with a conserved quantity from the mechanical point of view. The absence of the infinite tower of charges, characteristic of the full theory, is due here to a symmetry-breaking mechanism. This is made explicit by looking at the infinite-dimensional extension of the symmetry group. This allows to look at the equation of motion of the mechanical system in terms of the infinite-dimensional group, who in turn has the effect of rescaling the coupling constants of the theory. Finally, the presence of the finite symmetry group allows defining a quantum model in terms of the corresponding representation theory. At the level of the effective theory, accounting for the quantum effects, the request that the symmetry is protected provides a powerful tool to discriminate between different modifications. In the end, the conformal invariance of the black hole background opens the door to its holographic properties and might have important consequences in the corresponding perturbation theory.Francesco Sartiniwork_zxr24noinzcjtouttlzs4ebcnyWed, 09 Nov 2022 00:00:00 GMT