IA Scholar Query: Finite-Sum Compositional Stochastic Optimization: Theory and Applications.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgSat, 31 Dec 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440A Survey on Concept Drift in Process Mining
https://scholar.archive.org/work/hvmkupdorzf5df4tts42gzykjm
Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and online PM for evolving environments. Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, datasets, and metrics.Denise Maria Vecino Sato, Sheila Cristiana De Freitas, Jean Paul Barddal, Edson Emilio Scalabrinwork_hvmkupdorzf5df4tts42gzykjmSat, 31 Dec 2022 00:00:00 GMTAn Inversion Method for Coupled Typical Error Sources based on Remote Sensing Image
https://scholar.archive.org/work/l44agwljvjfo5jwpq2lmehqd2q
According to the error sources and their error amounts obtained by the remote sensing imaging coupled typical error sources inversion method, we can improve the imaging quality of optical systems and make high-quality remote sensing images more useful in military and civilian fields. Based on the distorted remote sensing images, this paper proposes a remote sensing imaging coupled typical error sources inversion method, which can accurately invert the typical error sources of remote sensing imaging and their error amounts. Firstly, a set of coupled typical error decoupled equations are constructed according to the modulation transfer function model of typical error sources and the decoupled principle of the coupled error sources. The initial values of coupled typical error sources are subsequently determined based on the Deep Residual Shrinkage Network (DRSN). Finally, the Levenberg Marquardt-Particle Swarm Optimization (LM-PSO) hybrid optimization algorithm is used to solve the system of coupled typical error decoupled equations to invert the typical error sources and their error amounts of the remote sensing imaging system. The experimental results show that the relative error between the inverse value and the real value of the coupled typical error sources of the distorted remote sensing images by the method in this paper does not exceed 20% at most, and most of them are below 10%, which has excellent inversion performance.Junhua Yan, Mengwei Shi, Xiangyang Lv, Yin Zhang, Yue Mawork_l44agwljvjfo5jwpq2lmehqd2qTue, 01 Nov 2022 00:00:00 GMTThe Sufficiency of Off-policyness and Soft Clipping: PPO is insufficient according to an Off-policy Measure
https://scholar.archive.org/work/5sktjl2ajzedbgbzbc73mhgsci
Many policy gradient methods optimize the objective, max_πE_π[A_π_old(s,a)], where A_π_old is the advantage function of the old policy. The objective is not feasible to be directly optimized because we don't have samples for the new policy yet. Thus the importance sampling (IS) ratio arises, giving an IS corrected objective or the CPI objective, max_πE_π_old[π(s,a)/π_old(s,a)A_π_old(s,a)]. However, optimizing this objective is still problematic due to extremely large IS ratios that can cause algorithms to fail catastrophically. Thus PPO uses a surrogate objective, and seeks an approximation to the solution in a clipped policy space, Π_ϵ={π; |π(s,a)/π_old(s,a)-1|<ϵ}, where ϵ is a small positive number. One question that drives this paper is, How grounded is this hypothesis that Π_ϵ contains good enough policies? Does there exist better policies outside of Π_ϵ? Using a novel surrogate objective that employs the sigmoid function resulting in an interesting way of exploration, we found that there indeed exists much better policies out of Π_ϵ; In addition, these policies are located very far from it. We compare with several best-performing algorithms on both discrete and continuous tasks and the results showed that PPO is insufficient in off-policyness, and our new method P3O is more off-policy than PPO according to the "off-policyness" measured by the DEON off-policy metric, and P3O explores in a much larger policy space than PPO.Xing Chen, Dongcui Diao, Hechang Chen, Hengshuai Yao, Jielong Yang, Haiyin Piao, Zhixiao Sun, Bei Jiang, Yi Changwork_5sktjl2ajzedbgbzbc73mhgsciMon, 08 Aug 2022 00:00:00 GMTMachine learning the real discriminant locus
https://scholar.archive.org/work/lozxhkxtzjebberzqltfe4j2ui
Parameterized systems of polynomial equations arise in many applications in science and engineering with the real solutions describing, for example, equilibria of a dynamical system, linkages satisfying design constraints, and scene reconstruction in computer vision. Since different parameter values can have a different number of real solutions, the parameter space is decomposed into regions whose boundary forms the real discriminant locus. This article views locating the real discriminant locus as a supervised classification problem in machine learning where the goal is to determine classification boundaries over the parameter space, with the classes being the number of real solutions. For multidimensional parameter spaces, this article presents a novel sampling method which carefully samples the parameter space. At each sample point, homotopy continuation is used to obtain the number of real solutions to the corresponding polynomial system. Machine learning techniques including nearest neighbor and deep learning are used to efficiently approximate the real discriminant locus. One application of having learned the real discriminant locus is to develop a real homotopy method that only tracks the real solution paths unlike traditional methods which track all~complex~solution~paths. Examples show that the proposed approach can efficiently approximate complicated solution boundaries such as those arising from the equilibria of the Kuramoto model.Edgar A. Bernal, Jonathan D. Hauenstein, Dhagash Mehta, Margaret H. Regan, Tingting Tangwork_lozxhkxtzjebberzqltfe4j2uiMon, 08 Aug 2022 00:00:00 GMTD-Flat: A Differentiable Flat-Optics Framework for End-to-End Metasurface Visual Sensor Design
https://scholar.archive.org/work/qsukifsaj5b4regqqg3l5r7laa
Optical metasurfaces are planar substrates with custom-designed, nanoscale features that selectively modulate incident light with respect to direction, wavelength, and polarization. When coupled with photodetectors and appropriate post-capture processing, they provide a means to create computational imagers and sensors that are exceptionally small and have distinctive capabilities. We introduce D-Flat, a framework in TensorFlow that renders physically-accurate images induced by metasurface optical systems. This framework is fully differentiable with respect to metasurface shape and post-capture computational parameters and allows simultaneous optimization with respect to almost any measure of sensor performance. D-Flat enables simulation of millimeter to centimeter diameter metasurfaces on commodity computers, and it is modular in the sense of accommodating a variety of wave optics models for scattering at the metasurface and for propagation to photosensors. We validate D-Flat against symbolic calculations and previous experimental measurements, and we provide simulations that demonstrate its ability to discover novel computational sensor designs for two applications: single-shot depth sensing and single-shot spatial frequency filtering.Dean S. Hazineh, Soon Wei Daniel Lim, Zhujun Shi, Federico Capasso, Todd Zickler, Qi Guowork_qsukifsaj5b4regqqg3l5r7laaMon, 08 Aug 2022 00:00:00 GMTPOSYDON: A General-Purpose Population Synthesis Code with Detailed Binary-Evolution Simulations
https://scholar.archive.org/work/xdd6bux7wrca7f64gzngo2xu4y
Most massive stars are members of a binary or a higher-order stellar systems, where the presence of a binary companion can decisively alter their evolution via binary interactions. Interacting binaries are also important astrophysical laboratories for the study of compact objects. Binary population synthesis studies have been used extensively over the last two decades to interpret observations of compact-object binaries and to decipher the physical processes that lead to their formation. Here, we present POSYDON, a novel, binary population synthesis code that incorporates full stellar-structure and binary-evolution modeling, using the MESA code, throughout the whole evolution of the binaries. The use of POSYDON enables the self-consistent treatment of physical processes in stellar and binary evolution, including: realistic mass-transfer calculations and assessment of stability, internal angular-momentum transport and tides, stellar core sizes, mass-transfer rates and orbital periods. This paper describes the detailed methodology and implementation of POSYDON, including the assumed physics of stellar- and binary-evolution, the extensive grids of detailed single- and binary-star models, the post-processing, classification and interpolation methods we developed for use with the grids, and the treatment of evolutionary phases that are not based on pre-calculated grids. The first version of POSYDON targets binaries with massive primary stars (potential progenitors of neutron stars or black holes) at solar metallicity.Tassos Fragos, Jeff J. Andrews, Simone S. Bavera, Christopher P. L. Berry, Scott Coughlin, Aaron Dotter, Prabin Giri, Vicky Kalogera, Aggelos Katsaggelos, Konstantinos Kovlakas, Shamal Lalvani, Devina Misra, Philipp M. Srivastava, Ying Qin, Kyle A. Rocha, Jaime Roman-Garza, Juan Gabriel Serra, Petter Stahle, Meng Sun, Xu Teng, Goce Trajcevski, Nam Hai Tran, Zepei Xing, Emmanouil Zapartas, Michael Zevinwork_xdd6bux7wrca7f64gzngo2xu4ySun, 07 Aug 2022 00:00:00 GMT4th International Conference "Nanotechnologies"
https://scholar.archive.org/work/6geddo3xj5fobdgnpdjgfnrnwe
The development of a global preferred orientation in the thermotropic phase of the propanoate ester of hydroxypropylcellulose subjected to shear flow is evaluated using time-resolving X-ray scattering procedures. At low shear rates the global orientation parameters, , , are close to zero, but at a critical shear rate which shows some dependence on the temperature, there is a marked increase in orientation with shear rate. However, a flow aligning regime is not attained for the shear rate range considered here (from 0.1 to 190 s -1 ). Upon cessation of shear flow, the system relaxes to a globally isotropic state with a rate which is independent of the prior shear rate but is strongly dependent on the temperature.Alex Gerasomov, Levan Chkhartishviliwork_6geddo3xj5fobdgnpdjgfnrnweSat, 06 Aug 2022 00:00:00 GMTDynamic modeling and optimization of a continuous fluidized bed process for the separation of enantiomers by preferential crystallization
https://scholar.archive.org/work/gg2bm6hnkrbu3gvwcov43wrv6a
This thesis is concerned with investigating a purification process of chemical compounds called enantiomers using kinetically controlled preferential crystallization in fluidized beds. Attention is paid to three main aspects - the analysis of the crystallization process, its modeling, and subsequent application of the developed model to improve the process performance. Typically, particles are not uniform, and different properties, such as size, shape, and internal composition, are distributed over the particle population. The distribution of these properties may significantly impact the process performance. Modeling crystallization processes in a simple way, such as calculating just yield using thermodynamics, does not provide important information about system evolution. In combination with the process kinetics, the Population Balance concept is applied in this study to construct a model and predict the crystal size distribution. The developed model describes the interaction between crystal growth, particle transport, and fluid dynamics in the non-isothermal case. In contrast to previous studies, the model equations distinguish between the target and the counter-enantiomer. Thus, the productivity of the process and the purity of the product can be evaluated. According to experimental conditions, periodic crystals removal is implemented based on the height of the fluidized bed. The results of the laboratory experiments generated in a parallel doctoral research project allowed the model validation using a racemic mixture of asparagine monohydrate and water as the solvent. Based on the good quantitative agreement between the experimental and simulation results, general conclusions are drawn to highlight the significant potential of the model. Moreover, the research identified the relevant operational parameters to ensure that the process is highly productive. The relevance of the model parameters is studied by performing a local sensitivity analysis. To assess the parameter influences, a normalized sensitivity fun [...]Nadiia Huskova, Universitäts- Und Landesbibliothek Sachsen-Anhalt, Martin-Luther Universität, Andreas Seidel-Morgensternwork_gg2bm6hnkrbu3gvwcov43wrv6aFri, 05 Aug 2022 00:00:00 GMTClimate sensitivity, commitment and abrupt change: toward an ontology for climate tipping point research
https://scholar.archive.org/work/ljbtoiu4s5h35grjokhraw3rvq
This report is part of TiPES deliverable D6.2. We review a number of notions in tipping point research that have been studied within work package 6 of the TiPES project and sketch how to organize them in a prototype ontology. For climate sensitivity and climate change commitment, we propose an operational semantics by giving their generic computational structure.Nicola Botta, Nuria Brede, Michel Crucifix, Marina Martínez Monterowork_ljbtoiu4s5h35grjokhraw3rvqFri, 05 Aug 2022 00:00:00 GMTQuantum Annealing: An Overview
https://scholar.archive.org/work/ftglkdpbuvabpfk5tl47qk6ovq
In this review, after providing the basic physical concept behind quantum annealing (or adiabatic quantum computation), we present an overview of some recent theoretical as well as experimental developments pointing to the issues which are still debated. With a brief discussion on the fundamental ideas of continuous and discontinuous quantum phase transitions, we discuss the Kibble-Zurek scaling of defect generation following a ramping of a quantum many body system across a quantum critical point. In the process, we discuss associated models, both pure and disordered, and shed light on implementations and some recent applications of the quantum annealing protocols. Furthermore, we discuss the effect of environmental coupling on quantum annealing. Some possible ways to speed up the annealing protocol in closed systems are elaborated upon: We especially focus on the recipes to avoid discontinuous quantum phase transitions occurring in some models where energy gaps vanish exponentially with the system size.Atanu Rajak, Sei Suzuki, Amit Dutta, Bikas K. Chakrabartiwork_ftglkdpbuvabpfk5tl47qk6ovqFri, 05 Aug 2022 00:00:00 GMTPower of Quantum Generative Learning
https://scholar.archive.org/work/o7rfpm5hu5fd3ipxhulx7nwsrm
The intrinsic probabilistic nature of quantum mechanics invokes endeavors of designing quantum generative learning models (QGLMs). Despite the empirical achievements, the foundations and the potential advantages of QGLMs remain largely obscure. To narrow this knowledge gap, here we explore the generalization property of QGLMs, the capability to extend the model from learned to unknown data. We consider two prototypical QGLMs, quantum circuit Born machines and quantum generative adversarial networks, and explicitly give their generalization bounds. The result identifies superiorities of QGLMs over classical methods when quantum devices can directly access the target distribution and quantum kernels are employed. We further employ these generalization bounds to exhibit potential advantages in quantum state preparation and Hamiltonian learning. Numerical results of QGLMs in loading Gaussian distribution and estimating ground states of parameterized Hamiltonians accord with the theoretical analysis. Our work opens the avenue for quantitatively understanding the power of quantum generative learning models.Yuxuan Du, Zhuozhuo Tu, Bujiao Wu, Xiao Yuan, Dacheng Taowork_o7rfpm5hu5fd3ipxhulx7nwsrmFri, 05 Aug 2022 00:00:00 GMTIs there evidence for exponential quantum advantage in quantum chemistry?
https://scholar.archive.org/work/5u55u4v7ovgf3c2c5zyt6sbnde
The idea to use quantum mechanical devices to simulate other quantum systems is commonly ascribed to Feynman. Since the original suggestion, concrete proposals have appeared for simulating molecular and materials chemistry through quantum computation, as a potential "killer application". Indications of potential exponential quantum advantage in artificial tasks have increased interest in this application, thus, it is critical to understand the basis for potential exponential quantum advantage in quantum chemistry. Here we gather the evidence for this case in the most common task in quantum chemistry, namely, ground-state energy estimation. We conclude that evidence for such an advantage across chemical space has yet to be found. While quantum computers may still prove useful for quantum chemistry, it may be prudent to assume exponential speedups are not generically available for this problem.Seunghoon Lee, Joonho Lee, Huanchen Zhai, Yu Tong, Alexander M. Dalzell, Ashutosh Kumar, Phillip Helms, Johnnie Gray, Zhi-Hao Cui, Wenyuan Liu, Michael Kastoryano, Ryan Babbush, John Preskill, David R. Reichman, Earl T. Campbell, Edward F. Valeev, Lin Lin, Garnet Kin-Lic Chanwork_5u55u4v7ovgf3c2c5zyt6sbndeFri, 05 Aug 2022 00:00:00 GMTCatoni-style Confidence Sequences under Infinite Variance
https://scholar.archive.org/work/cp77wg72q5ecvdacexp2iax7f4
In this paper, we provide an extension of confidence sequences for settings where the variance of the data-generating distribution does not exist or is infinite. Confidence sequences furnish confidence intervals that are valid at arbitrary data-dependent stopping times, naturally having a wide range of applications. We first establish a lower bound for the width of the Catoni-style confidence sequences for the finite variance case to highlight the looseness of the existing results. Next, we derive tight Catoni-style confidence sequences for data distributions having a relaxed bounded p^th-moment, where p ∈ (1,2], and strengthen the results for the finite variance case of p =2. The derived results are shown to better than confidence sequences obtained using Dubins-Savage inequality.Sujay Bhatt and Guanhua Fang and Ping Li and Gennady Samorodnitskywork_cp77wg72q5ecvdacexp2iax7f4Fri, 05 Aug 2022 00:00:00 GMTA DSL for Monadic Decision Problems, Responsibility under Uncertainty and Tipping Point Notions
https://scholar.archive.org/work/apyjmwfikvgbrgunqgxwsh7jqq
This report is part of TiPES deliverable D6.2. We develop a domain-specific language (DSL) for the specification of decision problems in the context of tipping point research, on top of a lightweight version the generic Botta et al. 2017 framework for specifying and solving monadic sequential decision problems. The aim is to improve accountability in the context of climate policy advice by narrowing the gap between mathematical problem specification and implementation. This is achieved by using a programming language based on Dependent Type Theory, in which it is possible to express specification, implementation and proof that the implementation fulfills certain properties within the same language. We extend the Botta et al. theory with generic measures of responsibility and a syntax to transparently express goals of decision making. The usage of the framework is illustrated by the specification of a conceptual stochastic greenhouse gas emission problem. In a further extension of the basic theory, we show the correctness of the generic backward induction algorithm implemented in the framework in a more general setting than commonly considered in control theory.Nicola Botta, Nuria Brede, Michel Crucifix, Marina Martínez Monterowork_apyjmwfikvgbrgunqgxwsh7jqqFri, 05 Aug 2022 00:00:00 GMTCFARnet: deep learning for target detection with constant false alarm rate
https://scholar.archive.org/work/kgklpabdbnfatmdaiilrfbxkzq
We consider the problem of learning detectors with a Constant False Alarm Rate (CFAR). Classical model-based solutions to composite hypothesis testing are sensitive to imperfect models and are often computationally expensive. In contrast, data-driven machine learning is often more robust and yields classifiers with fixed computational complexity. Learned detectors usually do not have a CFAR as required in many applications. To close this gap, we introduce CFARnet where the loss function is penalized to promote similar distributions of the detector under any null hypothesis scenario. Asymptotic analysis in the case of linear models with general Gaussian noise reveals that the classical generalized likelihood ratio test (GLRT) is actually a minimizer of the CFAR constrained Bayes risk. Experiments in both synthetic data and real hyper-spectral images show that CFARnet leads to near CFAR detectors with similar accuracy as their competitors.Tzvi Diskin, Yiftach Beer, Uri Okun, Ami Wieselwork_kgklpabdbnfatmdaiilrfbxkzqThu, 04 Aug 2022 00:00:00 GMTComputing Real Numbers with Large-Population Protocols Having a Continuum of Equilibria
https://scholar.archive.org/work/fdjsn7ibbff3ziqp2fqu65g54i
Bournez, Fraigniaud, and Koegler [Bournez et al., 2012] defined a number in [0,1] as computable by their Large-Population Protocol (LPP) model, if the proportion of agents in a set of marked states converges to said number over time as the population grows to infinity. The notion, however, restricts the ordinary differential equations (ODEs) associated with an LPP to have only finitely many equilibria. This restriction places an intrinsic limitation on the model. As a result, a number is computable by an LPP if and only if it is algebraic, namely, not a single transcendental number can be computed under this notion. In this paper, we lift the finitary requirement on equilibria. That is, we consider systems with a continuum of equilibria. We show that essentially all numbers in [0,1] that are computable by bounded general-purpose analog computers (GPACs) or chemical reaction networks (CRNs) can also be computed by LPPs under this new definition. This implies a rich series of numbers (e.g., the reciprocal of Euler's constant, π/4, Euler's γ, Catalan's constant, and Dottie number) are all computable by LPPs. Our proof is constructive: We develop an algorithm that transfers bounded GPACs/CRNs into LPPs. Our algorithm also fixes a gap in Bournez et al.'s construction of LPPs designed to compute any arbitrary algebraic number in [0,1].Xiang Huang, Rachel N. Huls, Thomas E. Ouldridge, Shelley F. J. Wickhamwork_fdjsn7ibbff3ziqp2fqu65g54iThu, 04 Aug 2022 00:00:00 GMTLIPIcs, Volume 238, DNA 28, Complete Volume
https://scholar.archive.org/work/627o3xn4vbbgpdox5dwolgcuny
LIPIcs, Volume 238, DNA 28, Complete VolumeThomas E. Ouldridge, Shelley F. J. Wickhamwork_627o3xn4vbbgpdox5dwolgcunyThu, 04 Aug 2022 00:00:00 GMTGeneralization Analysis of Message Passing Neural Networks on Large Random Graphs
https://scholar.archive.org/work/oh2vcb3bkfhjvglwrh2tqh24rq
Message passing neural networks (MPNN) have seen a steep rise in popularity since their introduction as generalizations of convolutional neural networks to graph-structured data, and are now considered state-of-the-art tools for solving a large variety of graph-focused problems. We study the generalization error of MPNNs in graph classification and regression. We assume that graphs of different classes are sampled from different random graph models. We show that, when training a MPNN on a dataset sampled from such a distribution, the generalization gap increases in the complexity of the MPNN, and decreases, not only with respect to the number of training samples, but also with the average number of nodes in the graphs. This shows how a MPNN with high complexity can generalize from a small dataset of graphs, as long as the graphs are large. The generalization bound is derived from a uniform convergence result, that shows that any MPNN, applied on a graph, approximates the MPNN applied on the geometric model that the graph discretizes.Sohir Maskey, Ron Levie, Yunseok Lee, Gitta Kutyniokwork_oh2vcb3bkfhjvglwrh2tqh24rqThu, 04 Aug 2022 00:00:00 GMTGradient-based Bi-level Optimization for Deep Learning: A Survey
https://scholar.archive.org/work/dvthpccd75gf5cu6jkrg7vc7ze
Bi-level optimization, especially the gradient-based category, has been widely used in the deep learning community including hyperparameter optimization and meta knowledge extraction. Bi-level optimization embeds one problem within another and the gradient-based category solves the outer level task by computing the hypergradient, which is much more efficient than classical methods such as the evolutionary algorithm. In this survey, we first give a formal definition of the gradient-based bi-level optimization. Secondly, we illustrate how to formulate a research problem as a bi-level optimization problem, which is of great practical use for beginners. More specifically, there are two formulations: the single-task formulation to optimize hyperparameters such as regularization parameters and the distilled data, and the multi-task formulation to extract meta knowledge such as the model initialization. With a bi-level formulation, we then discuss four bi-level optimization solvers to update the outer variable including explicit gradient update, proxy update, implicit function update, and closed-form update. Last but not least, we conclude the survey by pointing out the great potential of gradient-based bi-level optimization on science problems (AI4Science).Can Chen, Xi Chen, Chen Ma, Zixuan Liu, Xue Liuwork_dvthpccd75gf5cu6jkrg7vc7zeThu, 04 Aug 2022 00:00:00 GMTDeviation bounds and concentration inequalities for quantum noises
https://scholar.archive.org/work/qadmxypmcnex3ont2iuyplag4e
We provide a stochastic interpretation of non-commutative Dirichlet forms in the context of quantum filtering. For stochastic processes motivated by quantum optics experiments, we derive an optimal finite time deviation bound expressed in terms of the non-commutative Dirichlet form. Introducing and developing new non-commutative functional inequalities, we deduce concentration inequalities for these processes. Examples satisfying our bounds include tensor products of quantum Markov semigroups as well as Gibbs samplers above a threshold temperature.Tristan Benoist, Lisa Hänggli, Cambyse Rouzéwork_qadmxypmcnex3ont2iuyplag4eThu, 04 Aug 2022 00:00:00 GMT