773 research outputs found

    Impulse Response Interpolation via Optimal Transport

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    Interpolation between multiple room impulse responses is often necessary for dynamic auralization of virtual acoustic environments, in which a listener can move with six degrees-of-freedom. The spatial room impulse response (SRIR) represents the combined effects of the surround room as sound propagates from a source to the listener and varies as the source or listener positions change. The early portion of the SRIR contains sparse reflections, considered to be distinct sound events, that tend to be impaired with interpolation methods based on simple linear combinations. With parametric processing of SRIRs, corresponding sound events are able to be mapped to one another and produce a more physically accurate spatiotemporal interpolation of the early portion of the SRIR. In this thesis, a novel method for parametric SRIR interpolation is proposed based on the principle of optimal transportation. First, SRIRs are represented as point clouds of sound pressure in a 3D virtual source space. Mappings between two point clouds are obtained by defining a partial optimal transport problem problem, solvable with familiar linear programming techniques. The partial relaxation is implemented by permitting both point-to-point mappings and dummy mappings. The obtained optimal transport plan is used to compute the interpolated point cloud which is converted back to an SRIR. Testing of the proposed method against three baseline comparison methods was done with SRIRs generated by geometrical acoustical modeling. An error metric based on the difference in energy between low-passed rendering of the omnidirectional room impulse response was used. Statistical results indicate that the proposed method consistently outperforms the baseline methods of interpolation. Qualitative examination of the mapping methods confirms that partial transport produces more physically accurate spatiotemporal mappings. For future work, it is suggested to consider different cost functions, interpolate between measured SRIRs, and to render the responses to allow perceptual tests

    The Geometry and Calculus of Losses

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    Statistical decision problems lie at the heart of statistical machine learning. The simplest problems are binary and multiclass classification and class probability estimation. Central to their definition is the choice of loss function, which is the means by which the quality of a solution is evaluated. In this paper we systematically develop the theory of loss functions for such problems from a novel perspective whose basic ingredients are convex sets with a particular structure. The loss function is defined as the subgradient of the support function of the convex set. It is consequently automatically proper (calibrated for probability estimation). This perspective provides three novel opportunities. It enables the development of a fundamental relationship between losses and (anti)-norms that appears to have not been noticed before. Second, it enables the development of a calculus of losses induced by the calculus of convex sets which allows the interpolation between different losses, and thus is a potential useful design tool for tailoring losses to particular problems. In doing this we build upon, and considerably extend existing results on MM-sums of convex sets. Third, the perspective leads to a natural theory of ``polar'' loss functions, which are derived from the polar dual of the convex set defining the loss, and which form a natural universal substitution function for Vovk's aggregating algorithm.Comment: 65 pages, 17 figure

    Metric Flows with Neural Networks

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    We develop a theory of flows in the space of Riemannian metrics induced by neural network gradient descent. This is motivated in part by recent advances in approximating Calabi-Yau metrics with neural networks and is enabled by recent advances in understanding flows in the space of neural networks. We derive the corresponding metric flow equations, which are governed by a metric neural tangent kernel, a complicated, non-local object that evolves in time. However, many architectures admit an infinite-width limit in which the kernel becomes fixed and the dynamics simplify. Additional assumptions can induce locality in the flow, which allows for the realization of Perelman's formulation of Ricci flow that was used to resolve the 3d Poincar\'e conjecture. We apply these ideas to numerical Calabi-Yau metrics, including a discussion on the importance of feature learning.Comment: 29 pages + reference

    Neutrinos from horizon to sub-galactic scales

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    A first determination of the mass scale set by the lightest neutrino remains a crucial outstanding challenge for cosmology and particle physics, with profound implications for the history of the Universe and physics beyond the Standard Model. In this thesis, we present the results from three methodological papers and two applications that contribute to our understanding of the cosmic neutrino background. First, we introduce a new method for the noise-suppressed evaluation of neutrino phase-space statistics. Its primary application is in cosmological N-body simulations, where it reduces the computational cost of simulating neutrinos by orders of magnitude without neglecting their nonlinear evolution. Second, using a recursive formulation of Lagrangian perturbation theory, we derive higher-order neutrino corrections and show that these can be used for the accurate and consistent initialisation of cosmological neutrino simulations. Third, we present a new code for the initialisation of neutrino particles, accounting both for relativistic effects and the full Boltzmann hierarchy. Taken together, these papers demonstrate that with the combination of the methods described therein, we can accurately simulate the evolution of the neutrino background over 13.8 Gyr from the linear and ultra-relativistic regime at z=109z=10^9 down to the non-relativistic yet nonlinear regime at z=0z=0. Moreover, they show that the accuracy of large-scale structure predictions can be controlled at the sub-percent level needed for a neutrino mass determination. In a first application of these methods, we present a forecast for direct detection of the neutrino background, taking into account the gravitational enhancement (or indeed suppression) of the local density due to the Milky Way and the observed large-scale structure within 200 Mpc/h. We determine that the large-scale structure is more important than the Milky Way for neutrino masses below 0.1 eV, predict the orientation of the neutrino dipole, and study small-scale anisotropies. We predict that the angular distribution of neutrinos is anti-correlated with the projected matter density, due to the capture or deflection of neutrinos by massive objects along the line of sight. Finally, we present the first results from a new suite of hydrodynamical simulations, which includes the largest ever simulation with neutrinos and galaxies. We study the extent to which variations in neutrino mass can be treated independently of astrophysical processes, such as feedback from supernovae and black holes. Our findings show that baryonic feedback is weakly dependent on neutrino mass, with feedback being stronger for models with larger neutrino masses. By studying individual dark matter halos, we attribute this effect to the increased baryon density relative to cold dark matter and a reduction in the binding energies of halos. We show that percent-level accurate modelling of the matter power spectrum in a cosmologically interesting parameter range is only possible if the cosmology-dependence of feedback is taken into account

    Geometric Learning on Graph Structured Data

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    Graphs provide a ubiquitous and universal data structure that can be applied in many domains such as social networks, biology, chemistry, physics, and computer science. In this thesis we focus on two fundamental paradigms in graph learning: representation learning and similarity learning over graph-structured data. Graph representation learning aims to learn embeddings for nodes by integrating topological and feature information of a graph. Graph similarity learning brings into play with similarity functions that allow to compute similarity between pairs of graphs in a vector space. We address several challenging issues in these two paradigms, designing powerful, yet efficient and theoretical guaranteed machine learning models that can leverage rich topological structural properties of real-world graphs. This thesis is structured into two parts. In the first part of the thesis, we will present how to develop powerful Graph Neural Networks (GNNs) for graph representation learning from three different perspectives: (1) spatial GNNs, (2) spectral GNNs, and (3) diffusion GNNs. We will discuss the model architecture, representational power, and convergence properties of these GNN models. Specifically, we first study how to develop expressive, yet efficient and simple message-passing aggregation schemes that can go beyond the Weisfeiler-Leman test (1-WL). We propose a generalized message-passing framework by incorporating graph structural properties into an aggregation scheme. Then, we introduce a new local isomorphism hierarchy on neighborhood subgraphs. We further develop a novel neural model, namely GraphSNN, and theoretically prove that this model is more expressive than the 1-WL test. After that, we study how to build an effective and efficient graph convolution model with spectral graph filters. In this study, we propose a spectral GNN model, called DFNets, which incorporates a novel spectral graph filter, namely feedback-looped filters. As a result, this model can provide better localization on neighborhood while achieving fast convergence and linear memory requirements. Finally, we study how to capture the rich topological information of a graph using graph diffusion. We propose a novel GNN architecture with dynamic PageRank, based on a learnable transition matrix. We explore two variants of this GNN architecture: forward-euler solution and invariable feature solution, and theoretically prove that our forward-euler GNN architecture is guaranteed with the convergence to a stationary distribution. In the second part of this thesis, we will introduce a new optimal transport distance metric on graphs in a regularized learning framework for graph kernels. This optimal transport distance metric can preserve both local and global structures between graphs during the transport, in addition to preserving features and their local variations. Furthermore, we propose two strongly convex regularization terms to theoretically guarantee the convergence and numerical stability in finding an optimal assignment between graphs. One regularization term is used to regularize a Wasserstein distance between graphs in the same ground space. This helps to preserve the local clustering structure on graphs by relaxing the optimal transport problem to be a cluster-to-cluster assignment between locally connected vertices. The other regularization term is used to regularize a Gromov-Wasserstein distance between graphs across different ground spaces based on degree-entropy KL divergence. This helps to improve the matching robustness of an optimal alignment to preserve the global connectivity structure of graphs. We have evaluated our optimal transport-based graph kernel using different benchmark tasks. The experimental results show that our models considerably outperform all the state-of-the-art methods in all benchmark tasks

    The Cinematic Daydream as a Tool of Political Emancipation: Plus-de-Jouir, Aufhebung and the Parallax

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    In this research, we will start by expo- sing the paradox of ‘surplus enjoyment’ (the Lacanian plus-de-jouir), showing that its parallax structure of lack and excess is also applicable to the pheno- menon of (surplus) repression. Linking his concept with the Hegelian Aufhebung, understood as a ‘failed negation of negation’ or a ‘negation of negation’ as failure, we will focus in detail on the central example illustrating our theoretical positions, which is Iciar Bollain’s film Tambien la Lluvia (Even the Rain). In analyzing its narrative structures that address the neocolonial reality, we will tend to approach indirectly, by reading the medium of cinematic narration, the ‘neocolonial question.

    Entropic Gromov-Wasserstein Distances: Stability, Algorithms, and Distributional Limits

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    The Gromov-Wasserstein (GW) distance quantifies discrepancy between metric measure spaces, but suffers from computational hardness. The entropic Gromov-Wasserstein (EGW) distance serves as a computationally efficient proxy for the GW distance. Recently, it was shown that the quadratic GW and EGW distances admit variational forms that tie them to the well-understood optimal transport (OT) and entropic OT (EOT) problems. By leveraging this connection, we derive two notions of stability for the EGW problem with the quadratic or inner product cost. The first stability notion enables us to establish convexity and smoothness of the objective in this variational problem. This results in the first efficient algorithms for solving the EGW problem that are subject to formal guarantees in both the convex and non-convex regimes. The second stability notion is used to derive a comprehensive limit distribution theory for the empirical EGW distance and, under additional conditions, asymptotic normality, bootstrap consistency, and semiparametric efficiency thereof.Comment: 66 pages, 3 figure

    Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

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    This article intends to systematically identify and comparatively analyze state-of-the-art supply chain (SC) forecasting strategies and technologies. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research

    Exponential integrators: tensor structured problems and applications

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    The solution of stiff systems of Ordinary Differential Equations (ODEs), that typically arise after spatial discretization of many important evolutionary Partial Differential Equations (PDEs), constitutes a topic of wide interest in numerical analysis. A prominent way to numerically integrate such systems involves using exponential integrators. In general, these kinds of schemes do not require the solution of (non)linear systems but rather the action of the matrix exponential and of some specific exponential-like functions (known in the literature as phi-functions). In this PhD thesis we aim at presenting efficient tensor-based tools to approximate such actions, both from a theoretical and from a practical point of view, when the problem has an underlying Kronecker sum structure. Moreover, we investigate the application of exponential integrators to compute numerical solutions of important equations in various fields, such as plasma physics, mean-field optimal control and computational chemistry. In any case, we provide several numerical examples and we perform extensive simulations, eventually exploiting modern hardware architectures such as multi-core Central Processing Units (CPUs) and Graphic Processing Units (GPUs). The results globally show the effectiveness and the superiority of the different approaches proposed
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