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Metal additive manufacturing (AM) processes are very complex and the process parameters required to fabricate quality parts can be very complicated and challenging to determine. For this reason, there is a continuous demand for AM simulations which can assist users to determine optimal process parameters in a timely manner. However, current commercial simulation packages are expensive and not designed...
Data sparsity and cold-start remains to be the main limitations and weaknesses in recommendation systems that employ collaborative filtering (CF). These limitations cause lack of convergence in CF recommendation algorithms which ultimately affect the overall accuracy of the recommendation system. Efforts to alleviate these limitations typically require additional user or item information such as social...
The standard linear regression (SLR) problem is to recover a vector x0 from noisy linear observations y = Ax0 + w. The approximate message passing (AMP) algorithm recently proposed by Donoho, Maleki, and Montanari is a computationally efficient iterative approach to SLR that has a remarkable property: for large i.i.d. sub-Gaussian matrices A, its periteration behavior is rigorously characterized by...
Vector approximate message passing (VAMP) is a computationally simple approach to the recovery of a signal x from noisy linear measurements y = Ax + w. Like the AMP proposed by Donoho, Maleki, and Montanari in 2009, VAMP is characterized by a rigorous state evolution (SE) that holds under certain large random matrices and that matches the replica prediction of optimality. But while AMP's SE holds...
Learning high-dimensional systems from data is often computationally challenging in the presence of nonlinearities and dynamics. This paper proposes a novel approach for identification of high-dimensional systems based on decomposing systems into networks of low-dimensional linear dynamical subsystems with memoryless, scalar nonlinear feedback elements and memoryless, linear interactions. The proposed...
The generalized linear model (GLM), where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform output z = Ax, arises in a range of applications such as robust regression, binary classification, quantized compressed sensing, phase retrieval, photon-limited imaging, and inference from neural spike trains. When A is large and i.i.d. Gaussian, the generalized...
Fluorescent calcium imaging provides a potentially powerful tool for inferring connectivity in large neural circuits. However, a key challenge in using calcium imaging for connectivity detection is that current systems often have a temporal response and frame rates that can be orders of magnitude slower than the underlying neural spiking process. Bayesian inference methods based on expectation-maximization...
The growing number of Additive Manufacturing Web (AMW) services, offered by different providers over the Internet, makes it challenging for consumers to compare these AMW services to select a service of their choice. In addition, it is even more challenging for consumers to compare these AMW services against their personal preferences. This is because, consumers personal preferences on multiple non-functional...
Existing service recommendation methods, that employ memory-based collaborative filtering (CF) techniques, compute the similarity between users or items using nonfunctional attribute values obtained at service invocation. However, using these nonfunctional attribute values from invoked services alone in similarity computation for personalized service recommendation is not sufficient. This is because...
Generalized Linear Models (GLMs), where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform z = Ax arise in a range of applications in nonlinear filtering and regression. Approximate Message Passing (AMP) methods, based on loopy belief propagation, are a promising class of approaches for approximate inference in these models. AMP methods are computationally...
The Histogram-Probabilistic Multi-Hypothesis Tracker (H-PMHT) is an efficient multi-target tracking approach to the Track-Before-Detect (TkBD) problem. However, it cannot adequately deal with fluctuating targets and this can degrade track management performance. By assuming an alternative measurement model based on a Poisson distribution, the H-PMHT algorithm can be re-derived to incorporate a time-correlated...
Neural mass models provide an attractive framework for modeling complex behavior in cortical circuits. The models are based on describing the dynamics of large neural populations through the space and time evolution of a small number of key aggregate statistical quantities. Fitting these models to electrode array recordings can provide insight into connectivity and structure of neural circuits as...
In this paper we propose a method for aggregating ranked services. The ranked services are generated from multiple user requests for the same service domain. First, a service search for each individual request is performed and the search results are ranked based on the user's personalized non-functional attributes and trade-offs. Next, the ranked lists of services are then aggregated and top-ranked...
The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is an efficient multi-target approach to the Track-before-detect (TkBD) problem. The tracking is based on the generation of a synthetic histogram by quantising the energy in the sensor data. The resultant quantised measurement is then modelled using a multinomial distribution and target state estimation is performed via Expectation-Maximisation...
We consider the problem of estimating a rank-one matrix in Gaussian noise under a probabilistic model for the left and right factors of the matrix. The probabilistic model can impose constraints on the factors including sparsity and positivity that arise commonly in learning problems. We propose a simple iterative procedure that reduces the problem to a sequence of scalar estimation computations....
Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable attention due to their computational simplicity, analytic tractability, and wide applicability in optimization and statistical inference problems. This paper summarizes a systematic framework for incorporating such approximate message passing (AMP) methods in general graphical models. The key...
Web service selection based on quality of service (QoS) has been a research focus in an environment where many similar web services exist. Current methods of service selection usually focus on a single service request at a time and the selection of a service with the best QoS at the user's own discretion. The selection does not consider multiple requests for the same functional web services. Usually,...
The replica method is a non-rigorous but widely-accepted technique from statistical physics used in the asymptotic analysis of large, random, nonlinear problems. This paper applies the replica method to analyze non-Gaussian maximum a posteriori (MAP) estimation. The main result is a counterpart to Guo and Verdú's replica analysis of minimum mean-squared error estimation. The replica MAP analysis...
A new type of neutron spectrometer, called a Magnetic Recoil Spectrometer (MRS) has been built and activated at OMEGA, and currently being developed at the National Ignition Facility (NIF), for measurements of the absolute neutron spectrum in the range 6 to 30 MeV, from which ??R, Ti and yield can be determined. From the down-scattered neutrons in the range 6- 0 MeV, a ??R can be inferred. From the...
Sparse signal models arise commonly in audio and image processing. Recent work in the area of compressed sensing has provided estimates of the performance of certain widely-used sparse signal processing techniques such as basis pursuit and matching pursuit. However, the optimal achievable performance with sparse signal approximation remains unknown. This paper provides bounds on the ability to estimate...
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