150 research outputs found

    Neural and computational principles of social vs non-social decision-making under risk

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    Decisions feel daunting, as the weight of selecting one path over another engenders a sense of unease and hesitation. This is a natural consequence of the fact that most decisions in the real world involve uncertainty. A choice involves two or more alternatives and usually resolves in the selection of a subjectively preferable option. Very often, however, decision-making requires consideration of multiple options and their numerous possible outcomes, as well as determining what ’preferable’ stands for. This makes choices risky.’ With this thesis, I studied the neural principles associated with risk, and I tested fluctuations of risk-taking as predicted by a novel computational model and social identity theory. In the first experiment, I used social stimuli as cues and recorded trial-by-trial fluctuations in EEG to try and capture brain responses to estimates of risk and risk prediction errors. The results reveal distinct spatio-temporal EEG component sassociated with the computation of risk and violations of expected risk. In the second experiment, I tested mediators of trial-by-trial risk-seeking. More specifically, independent of participants’ risk propensities in real life, I implemented a task to drive risk-taking choice in some trials and risk avoidance in others. I showed that innon-social contexts positive reward prediction errors can predict risk-taking, and I found brain responses associated with this process. In the final chapter, I discuss the same task in which I tried to induce risk-seeking with the addition of a social factor aiming to test predictions from social identity theory. I showed that the mere online presence of an in-group and an out-group member was enough to alter behaviour during the task, although possible explanations can span from exploratory behaviour in some groups of participants, to overall arousal or increased stress in the participants while being observed. Together these experiments show the importance of incorporating social factors into studies of decision-making, the benefit of computational methods for a better understanding of risky decision-making and model-based neural responses, and the importance of accounting for individual differences when studying value based choice

    Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement

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    Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction. However, their implicit volumetric representations differ significantly from the widely-adopted polygonal meshes and lack support from common 3D software and hardware, making their rendering and manipulation inefficient. To overcome this limitation, we present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency decomposed appearance with a NeRF. Subsequently, a coarse mesh is extracted, and an iterative surface refining algorithm is developed to adaptively adjust both vertex positions and face density based on re-projected rendering errors. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering. Extensive experiments demonstrate that our method achieves superior mesh quality and competitive rendering quality.Comment: ICCV 2023 camera-ready, Project Page: https://me.kiui.moe/nerf2mes

    Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs

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    Recently, theoretical analyses of deep neural networks have broadly focused on two directions: 1) Providing insight into neural network training by SGD in the limit of infinite hidden-layer width and infinitesimally small learning rate (also known as gradient flow) via the Neural Tangent Kernel (NTK), and 2) Globally optimizing the regularized training objective via cone-constrained convex reformulations of ReLU networks. The latter research direction also yielded an alternative formulation of the ReLU network, called a gated ReLU network, that is globally optimizable via efficient unconstrained convex programs. In this work, we interpret the convex program for this gated ReLU network as a Multiple Kernel Learning (MKL) model with a weighted data masking feature map and establish a connection to the NTK. Specifically, we show that for a particular choice of mask weights that do not depend on the learning targets, this kernel is equivalent to the NTK of the gated ReLU network on the training data. A consequence of this lack of dependence on the targets is that the NTK cannot perform better than the optimal MKL kernel on the training set. By using iterative reweighting, we improve the weights induced by the NTK to obtain the optimal MKL kernel which is equivalent to the solution of the exact convex reformulation of the gated ReLU network. We also provide several numerical simulations corroborating our theory. Additionally, we provide an analysis of the prediction error of the resulting optimal kernel via consistency results for the group lasso.Comment: Accepted to Neurips 202

    Graph learning and its applications : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Albany, Auckland, New Zealand

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    Since graph features consider the correlations between two data points to provide high-order information, i.e., more complex correlations than the low-order information which considers the correlations in the individual data, they have attracted much attention in real applications. The key of graph feature extraction is the graph construction. Previous study has demonstrated that the quality of the graph usually determines the effectiveness of the graph feature. However, the graph is usually constructed from the original data which often contain noise and redundancy. To address the above issue, graph learning is designed to iteratively adjust the graph and model parameters so that improving the quality of the graph and outputting optimal model parameters. As a result, graph learning has become a very popular research topic in traditional machine learning and deep learning. Although previous graph learning methods have been applied in many fields by adding a graph regularization to the objective function, they still have some issues to be addressed. This thesis focuses on the study of graph learning aiming to overcome the drawbacks in previous methods for different applications. We list the proposed methods as follows. • We propose a traditional graph learning method under supervised learning to consider the robustness and the interpretability of graph learning. Specifically, we propose utilizing self-paced learning to assign important samples with large weights, conducting feature selection to remove redundant features, and learning a graph matrix from the low dimensional data of the original data to preserve the local structure of the data. As a consequence, both important samples and useful features are used to select support vectors in the SVM framework. • We propose a traditional graph learning method under semi-supervised learning to explore parameter-free fusion of graph learning. Specifically, we first employ the discrete wavelet transform and Pearson correlation coefficient to obtain multiple fully connected Functional Connectivity brain Networks (FCNs) for every subject, and then learn a sparsely connected FCN for every subject. Finally, the ℓ1-SVM is employed to learn the important features and conduct disease diagnosis. • We propose a deep graph learning method to consider graph fusion of graph learning. Specifically, we first employ the Simple Linear Iterative Clustering (SLIC) method to obtain multi-scale features for every image, and then design a new graph fusion method to fine-tune features of every scale. As a result, the multi-scale feature fine-tuning, graph learning, and feature learning are embedded into a unified framework. All proposed methods are evaluated on real-world data sets, by comparing to state-of-the-art methods. Experimental results demonstrate that our methods outperformed all comparison methods

    Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion

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    In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions. This comes up in remote sensing applications such as pansharpening~(MS+PAN), hyperspectral sharpening~(HS+PAN), and HS-MS fusion~(HS+MS). Remote sensing images are textured and have repetitive structures. Motivated by nonlocal patch-based methods for image restoration, we propose a convex regularizer that (i) takes into account long-distance correlations, (ii) penalizes patch variation, which is more effective than pixel variation for capturing texture information, and (iii) uses the higher spatial resolution image as a guide image for weight computation. We come up with an efficient ADMM algorithm for optimizing the regularizer along with a standard least-squares loss function derived from the imaging model. The novelty of our algorithm is that by expressing patch variation as filtering operations and by judiciously splitting the original variables and introducing latent variables, we are able to solve the ADMM subproblems efficiently using FFT-based convolution and soft-thresholding. As far as the reconstruction quality is concerned, our method is shown to outperform state-of-the-art variational and deep learning techniques.Comment: Accepted in IEEE Transactions on Computational Imagin

    Reconstruction de l'activité corticale à partir de données MEG à l'aide de réseaux cérébraux et de délais de transmission estimés à partir d'IRMd

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    White matter fibers transfer information between brain regions with delays that are observable with magnetoencephalography and electroencephalography (M/EEG) due to their millisecond temporal resolution. We can represent the brain as a graph where nodes are the cortical sources or areas and edges are the physical connections between them: either local (between adjacent vertices on the cortical mesh) or non-local (long-range white matter fibers). Long-range anatomical connections can be obtained with diffusion MRI (dMRI) tractography which yields a set of streamlines representing white matter fiber bundles. Given the streamlines’ lengths and the information conduction speed, transmission delays can be estimated for each connection. dMRI can thus give an insight into interaction delays of the macroscopicbrain network.Localizing and recovering electrical activity of the brain from M/EEG measurements is known as the M/EEG inverse problem. Generally, there are more unknowns (brain sources) than the number of sensors, so the solution is non-unique and the problem ill-posed. To obtain a unique solution, prior constraints on the characteristics of source distributions are needed. Traditional linear inverse methods deploy different constraints which can favour solutions with minimum norm, impose smoothness constraints in space and/or time along the cortical surface, etc. Yet, structural connectivity is rarely considered and transmission delays almost always neglected.The first contribution of this thesis consists of a multimodal preprocessing pipeline used to integrate structural MRI, dMRI and MEG data into a same framework, and of a simulation procedure of source-level brain activity that was used as a synthetic dataset to validate the proposed reconstruction approaches.In the second contribution, we proposed a new framework to solve the M/EEG inverse problem called Connectivity-Informed M/EEG Inverse Problem (CIMIP), where prior transmission delays supported by dMRI were included to enforce temporal smoothness between time courses of connected sources. This was done by incorporating a Laplacian operator into the regularization, that operates on a time-dependent connectivity graph. Nonetheless, some limitations of the CIMIP approach arised, mainly due to the nature of the Laplacian, which acts on the whole graph, favours smooth solutions across all connections, for all delays, and it is agnostic to directionality.In this thesis, we aimed to investigate patterns of brain activity during visuomotor tasks, during which only a few regions typically get significantly activated, as shown by previous studies. This led us to our third contribution, an extension of the CIMIP approach that addresses the aforementioned limitations, named CIMIP_OML (“Optimal Masked Laplacian”). We restricted the full source space network (the whole cortical mesh) to a network of regions of interest and tried to find how the information is transferred between its nodes. To describe the interactions between nodes in a directed graph, we used the concept of network motifs. We proposed an algorithm that (1) searches for an optimal network motif – an optimal pattern of interaction between different regions and (2) reconstructs source activity given the found motif. Promising results are shown for both simulated and real MEG data for a visuomotor task and compared with 3 different state-of-the-art reconstruction methods.To conclude, we tackled a difficult problem of exploiting delays supported by dMRI for the reconstruction of brain activity, while also considering the directionality in the information transfer, and provided new insights into the complex patterns of brain activity.Les fibres de la matière blanche permettent le transfert d’information dans le cerveau avec des délais observables en Magnétoencéphalographie et Électroencéphalographie (M/EEG) grâce à leur haute résolution temporelle. Le cerveau peut être représenté comme un graphe où les nœuds sont les régions corticales et les liens sont les connexions physiques entre celles-ci: soit locales (entre sommets adjacents sur le maillage cortical), soit non locales (fibres de la matière blanche). Les connexions non-locales peuvent être reconstruites avec la tractographie de l’IRM de diffusion (IRMd) qui génère un ensemble de courbes («streamlines») représentant des fibres de la matière blanche. Sachant les longueurs des fibres et la vitesse de conduction de l’information, les délais de transmission peuvent être estimés. L’IRMd peut donc donner un aperçu des délais d’interaction du réseau cérébral macroscopique.La localisation et la reconstruction de l’activité électrique cérébrale à partir des mesures M/EEG est un problème inverse. En général, il y a plus d’inconnues (sources cérébrales) que de capteurs. La solution n’est donc pas unique et le problème est dit mal posé. Pour obtenir une solution unique, des hypothèses sur les caractéristiques des distributions de sources sont requises. Les méthodes inverses linéaires traditionnelles utilisent différentes hypothèses qui peuvent favoriser des solutions de norme minimale, imposer des contraintes de lissage dans l’espace et/ou dans le temps, etc. Pourtant, la connectivité structurelle est rarement prise en compte et les délais de transmission sont presque toujours négligés.La première contribution de cette thèse est un pipeline de prétraitement multimodal utilisé pour l’intégration des données d’IRM, IRMd et MEG dans un même cadre, et d’une méthode de simulation de l’activité corticale qui a été utilisée comme jeu de données synthétiques pour valider les approches de reconstruction proposées. Nous proposons également une nouvelle approche pour résoudre le problème inverse M/EEG appelée «Problème Inverse M/EEG Informé par la Connectivité» (CIMIP pour Connectivity-Informed M/EEG Inverse Problem), où des délais de transmission provenant de l’IRMd sont inclus pour renforcer le lissage temporel entre les décours des sources connectées. Pour cela, un opérateur Laplacien, basé sur un graphe de connectivité en fonction du temps, a été intégré dans la régularisation. Cependant, certaines limites de l’approche CIMIP sont apparues en raison de la nature du Laplacien qui agit sur le graphe entier et favorise les solutions lisses sur toutes les connexions, pour tous les délais, et indépendamment de la directionnalité.Lors de tâches visuo-motrices, seules quelques régions sont généralement activées significativement. Notre troisième contribution est une extension de CIMIP pour ce type de tâches qui répond aux limitations susmentionnées, nommée CIMIP_OML («Optimal Masked Laplacian») ou Laplacien Masqué Optimal. Nous essayons de trouver comment l’information est transférée entre les nœuds d’un sous-réseau de régions d’intérêt du réseau complet de l’espace des sources. Pour décrire les interactions entre nœuds dans un graphe orienté, nous utilisons le concept de motifs de réseau. Nous proposons un algorithme qui 1) cherche un motif de réseau optimal- un modèle optimal d’interaction entre régions et 2) reconstruit l’activité corticale avec le motif trouvé. Des résultats prometteurs sont présentés pour des données MEG simulées et réelles (tâche visuo-motrice) et comparés avec 3 méthodes de l’état de l’art. Pour conclure, nous avons abordé un problème difficile d’exploitation des délais de l’IRMd lors l’estimation de l’activité corticale en tenant compte de la directionalité du transfert d’information, fournissant ainsi de nouvelles perspectives sur les patterns complexes de l’activité cérébrale

    An optimization method for the design of beads in long fiber reinforced polymer structures including the manufacturing process as an approach to realize methodically identified lightweight potentials = Eine Optimierungsmethode zur Gestaltung von Sicken in langfaserverstärkten Kunststoffstrukturen unter Berücksichtigung des Herstellungsprozesses als Ansatz zur Realisierung methodisch identifizierter Leichtbaupotentiale

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    Mobilitätssysteme befinden sich in einer Zeit des Umbruchs, da sich die Randbedingungen aus Politik und Gesellschaft gerade stark verändern. Im Fokus steht dabei, den Energie- und Ressourcenverbrauch sowie die CO2-Emissionen zu senken. Eine Möglichkeit diesen Herausforderungen zu begegnen, stellt der Leichtbau dar. Um die größtmögliche Gewichtsreduzierung erreichen zu können, ist eine konsequente Integration der Leichtbauaktivitäten in den gesamten Produktentstehungsprozess notwendig. Die vorliegende Arbeit liefert einen Beitrag zur Unterstützung des Produktentwicklers in verschiedenen Aktivitäten des Produktentstehungsprozesses, indem sie sich mit den Fragen „wie können Bauteile aus langfaserverstärkten Kunststoffen fasergerecht gestaltet werden“ und „wo sind diese in einem intelligenten Multi-Material Design (MMD) zielführend einzusetzen“ beschäftigt. Zur Frage „wie“ wird für die initiale Designfindung von versickten, langfaserverstärkten Bauteilen eine rechnergestützte, automatisierte Optimierungsmethode entwickelt. Bei diesem anisotropen Werkstoff ist es entscheidend, dass die aus dem Herstellungsprozess resultierenden Faserorientierungen im Bauteildesign berücksichtigt werden. Deshalb liegt der Optimierungsmethode eine iterative Kopplung von validierten Prozess- und Struktursimulationen zugrunde. Die Ergebnisse zeigen, dass die Berücksichtigung der lokal anisotropen Materialeigenschaften im Vergleich zu einer isotropen Materialmodellierung zu deutlich unterschiedlichen Designs führt. Um diese last- und fasergerecht designten Bauteile in einem MMD zielführend einsetzen zu können, ist jedoch zusätzlich die Frage nach dem „wo“ zu beantworten. Deshalb beschäftigt sich die Arbeit weiterhin mit der Entwicklung des funktionsbasierten Erweiterten Target Weighing Ansatzes (ETWA) zur komponentenübergreifenden Identifikation und Evaluation von Leichtbaupotentialen. Der ETWA unterstützt den Produktentwickler bei der Konzeptgenerierung in frühen Phasen des Produktentstehungsprozesses, in denen bereits ein Großteil des späteren Produktgewichts festgelegt wird. Dabei werden sowohl in der Analyse als auch in der Synthese die mit dem Gewicht und dessen Reduktion einhergehenden Kosten und CO2-Emissionen gemeinschaftlich betrachtet. Die Ergebnisse der mithilfe des ETWA im Rahmen dieser Arbeit entwickelten MMD eines Automobil-Federbeindoms und -Längsträgers zeigen das Potential der Methode auf. Die Kombination des ETWA und der fasergerechten Gestaltung versickter langfaserverstärkter Bauteile bietet eine methodische Unterstützung bei der Entwicklung von MMD insbesondere in frühen Phasen der Produktentstehung
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