118 research outputs found

    Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory

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    Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems (McClelland et al., 1995) suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (semantic memory). In this work we develop a new principled Bayesian memory allocation scheme that bridges the gap between episodic and semantic memory via a hierarchical latent variable model. We take inspiration from traditional heap allocation and extend the idea of locally contiguous memory to the Kanerva Machine, enabling a novel differentiable block allocated latent memory. In contrast to the Kanerva Machine, we simplify the process of memory writing by treating it as a fully feed forward deterministic process, relying on the stochasticity of the read key distribution to disperse information within the memory. We demonstrate that this allocation scheme improves performance in memory conditional image generation, resulting in new state-of-the-art conditional likelihood values on binarized MNIST (<=41.58 nats/image) , binarized Omniglot (<=66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32x32

    Robust and stable region-of-interest tomographic reconstruction using a robust width prior

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    Region-of-interest computed tomography (ROI CT) aims at reconstructing a region within the field of view by using only ROI-focused projections. The solution of this inverse problem is challenging and methods of tomographic reconstruction that are designed to work with full projection data may perform poorly or fail when applied to this setting. In this work, we study the ROI CT problem in the presence of measurement noise and formulate the reconstruction problem by relaxing data fidelity and consistency requirements. Under the assumption of a robust width prior that provides a form of stability for data satisfying appropriate sparsity-inducing norms, we derive reconstruction performance guarantees and controllable error bounds. Based on this theoretical setting, we introduce a novel iterative reconstruction algorithm from ROI-focused projection data that is guaranteed to converge with controllable error while satisfying predetermined fidelity and consistency tolerances. Numerical tests on experimental data show that our algorithm for ROI CT is competitive with state-of-the-art methods especially when the ROI radius is small

    Augmented breast tumor classification by perfusion analysis

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    Magnetic resonance and computed tomography imaging aid in the diagnosis and analysis of pathologic conditions. Blood flow, or perfusion, through a region of tissue can be computed from a time series of contrast-enhanced images. Perfusion is an important set of physiological parameters that reflect angiogenesis. In cancer, heightened angiogenesis is a key process in the growth and spread of tumorous masses. An automatic classification technique using recovered perfusion may prove to be a highly accurate diagnostic tool. Such a classification system would supplement existing histopathological tests, and help physicians to choose the most optimal treatment protocol. Perfusion is obtained through deconvolution of signal intensity series and a pharmacokinetic model. However, many computational problems complicate the accurate-consistent recovery of perfusion. The high time-resolution acquisition of images decreases signal-to-noise, producing poor deconvolution solutions. The delivery of contrast agent as a function of time must also be determined or sampled before deconvolution can proceed. Some regions of the body, such as the brain, provide a nearby artery to serve as this arterial input function. Poor estimates can lead to an over or under estimation of perfusion. Breast tissue is an example of one tissue region where a clearly defined artery is not present. This proposes a new method of using recovered perfusion and spatial information in an automated classifier. This classifier grades suspected lesions as benign or malignant. This method can be integrated into a computer-aided diagnostic system to enhance the value of medical imagery

    Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

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    Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest

    Recursive Estimation of Discrete Time System Parameters and Time Delay

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    Mechanical Engineerin

    Position based constraint enforcement in game physics

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    La simulación de sistemas mecánicos para videojuegos y otras aplicaciones interactivas impone restricciones importantes en cuanto a estabilidad, flexibilidad en las escenas y complejidad computacional. En los últimos años han aparecido varias estrategias para la resolución de sistemas mecánicos con restricciones. Algunas de las más populares en el desarrollo de videojuegos usan solamente la posición de las partículas y un algoritmo de proyección sobre la variedad definida por las restricciones, evitando la manipulación de la primera derivada del sistema (las velocidades). De esta forma se consigue una gran estabilidad numérica. El principal defecto de estos métodos es su dependencia en parámetros sin significado físico, por lo que es difícil simular materiales concretos. En este trabajo explicamos los susodichos métodos y nos centramos en la simulación de materiales elásticos, tomando como referencia otro modelo ampliamente estudiado que depende de parámetros físicos reales. Proponemos un algoritmo para ajustar los parámetros no físicos del algoritmo basado en posiciones y probamos este procedimiento en un cubo elástico. La extrapolación a otros objetos más complejos no debería resultar muy difícil. Como última contribución relacionamos estos algoritmos con algunos métodos numéricos clásicos y resaltamos las principales hipótesis que se asumen en el proceso. Esta parte, aunque no es muy robusta porque no llegamos a alcanzar un resultado cerrado, puede ser útil como un primer paso para trabajos futuros que involucren el tema de la convergencia de este tipo de métodos.Mechanical systems simulation for video games and other interactive applications imposes important restrictions as regards to stability, flexibility in the scenes and computational complexity. In the last few years several resolution strategies for mechanical systems with constraints have appeared. Some of the most popular ones in the development of video games use only the positions of the particles and a projection algorithm over the manifold defi ned by the constraints, avoiding manipulation of the system's fi rst derivative (velocities). In this way, a great numerical stability is obtained. The main drawback of these methods is its dependence in non-physical parameters, so is hard to simulate a speci c material. In this work we explain all the aforementioned methods and focus in the simulation of elastic materials taking as reference another model deeply studied that depends on real, physical parameters. We propose an algorithm to fit the non-physical parameters of the position based algorithm and test this procedure in a elastic cube. An extrapolation to other, more complex objects should not be dificult. As a last contribution we relate these algorithms with some classical numerical methods and point out which are the main hypothesis assumed in the process. This part, although is not very robust since we have not been able to reach a closed result, can be useful as a fi rst step for future works dealing with the convergence topic of this kind of methods

    Non-intrusive reduced order modelling for aerodynamic applications

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    During the design and optimisation of aerodynamic components, the simulations to be performed involve a large number of parameters related to the geometry and flow conditions. In this scenario, the simulation of all possible configurations is not af-fordable. To overcome this problem, the present work proposes a novel multi-output neural network (NN) for the prediction of aerodynamic coefficients of aerofoils and wings using compressible flow data. Contrary to existing NNs that are designed to predict aerodynamic quantities of interest, the proposed network considers as output the pressure or stresses at a number of selected points on the aerodynamic surface. The proposed approach is compared against the more traditional networks where the aero-dynamic coefficients are directly the outputs of the network. Furthermore, a detailed comparison of the proposed NN against the popular proper orthogonal decomposi-tion (POD) method is presented. The numerical results, involving high dimensional problems with flow and geometric parameters, show the benefits of the proposed ap-proach.The proposed NN is used to accelerate the evaluation of the objective function in an inverse aerodynamic shape design problem. The optimisation algorithm uses the gradient-free modified cuckoo search method. Applications in two and three dimen-sions are shown, demonstrating the potential of the proposed framework in the con-text of both optimisation and inverse design problems. The performance of the pro-posed optimisation framework is also compared against existing frameworks where the more traditional NNs are employed

    Massively Parallel Approach to Modeling 3D Objects in Machine Vision

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    Electrical Engineerin

    Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

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    More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy
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