627 research outputs found

    BLADE: Filter Learning for General Purpose Computational Photography

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    The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicing, and stylization

    Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems

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    Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.Comment: (Under review

    Energy and Route Optimization of Moving Devices

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    This thesis highlights our efforts in energy and route optimization of moving devices. We have focused on three categories of such devices; industrial robots in a multi-robot environment, generic vehicles in a vehicle routing problem (VRP) context, automatedguided vehicles (AGVs) in a large-scale flexible manufacturing system (FMS). In the first category, the aim is to develop a non-intrusive energy optimization technique, based on a given set of paths and sequences of operations, such that the original cycle time is not exceeded. We develop an optimization procedure based on a mathematical programming model that aims to minimize the energy consumption and peak power. Our technique has several advantages. It is non-intrusive, i.e. it requires limited changes in the robot program and can be implemented easily. Moreover,it is model-free, in the sense that no particular, and perhaps secret, parameter or dynamic model is required. Furthermore, the optimization can be done offline, within seconds using a generic solver. Through careful experiments, we have shown that it is possible to reduce energy and peak-power up to about 30% and 50% respectively. The second category of moving devices comprises of generic vehicles in a VRP context. We have developed a hybrid optimization approach that integrates a distributed algorithm based on a gossip protocol with a column generation (CG) algorithm, which manages to solve the tested problems faster than the CG algorithm alone. The algorithm is developed for a VRP variation including time windows (VRPTW), which is meant to model the task of scheduling and routing of caregivers in the context of home healthcare routing and scheduling problems (HHRSPs). Moreover,the developed algorithm can easily be parallelized to further increase its efficiency. The last category deals with AGVs. The choice of AGVs was not arbitrary; by design, we decided to transfer our knowledge of energy optimization and routing algorithms to a class of moving devices in which both techniques are of interest. Initially, we improve an existing method of conflict-free AGV scheduling and routing, such that the new algorithm can manage larger problems. A heuristic version of the algorithm manages to solve the problem instances in a reasonable amount of time. Later, we develop strategies to reduce the energy consumption. The study is carried out using an AGV system installed at Volvo Cars. The results are promising; (1)the algorithm reduces performance measures such as makespan up to 50%, while reducing the total travelled distance of the vehicles about 14%, leading to an energy saving of roughly 14%, compared to the results obtained from the original traffic controller. (2) It is possible to reduce the cruise velocities such that more energy is saved, up to 20%, while the new makespan remains better than the original one

    Optimal Planning Modulo Theories

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    Planning for real-world applications requires algorithms and tools with the ability to handle the complexity such scenarios entail. However, meeting the needs of such applications poses substantial challenges, both representational and algorithmic. On the one hand, expressive languages are needed to build faithful models. On the other hand, efficient solving techniques that can support these languages need to be devised. A response to this challenge is underway, and the past few years witnessed a community effort towards more expressive languages, including decidable fragments of first-order theories. In this work we focus on planning with arithmetic theories and propose Optimal Planning Modulo Theories, a framework that attempts to provide efficient means of dealing with such problems. Leveraging generic Optimization Modulo Theories (OMT) solvers, we first present domain-specific encodings for optimal planning in complex logistic domains. We then present a more general, domain- independent formulation that allows to extend OMT planning to a broader class of well-studied numeric problems in planning. To the best of our knowledge, this is the first time OMT procedures are employed in domain-independent planning

    A Transformational Approach to Parametric Accumulated-Cost Static Profiling

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    Traditional static resource analyses estimate the total resource usage of a program, without executing it. In this paper we present a novel resource analysis whose aim is instead the static profiling of accumulated cost, i.e., to discover, for selected parts of the program, an estimate or bound of the resource usage accumulated in each of those parts. Traditional resource analyses are parametric in the sense that the results can be functions on input data sizes. Our static profiling is also parametric, i.e., our accumulated cost estimates are also parameterized by input data sizes. Our proposal is based on the concept of cost centers and a program transformation that allows the static inference of functions that return bounds on these accumulated costs depending on input data sizes, for each cost center of interest. Such information is much more useful to the software developer than the traditional resource usage functions, as it allows identifying the parts of a program that should be optimized, because of their greater impact on the total cost of program executions. We also report on our implementation of the proposed technique using the CiaoPP program analysis framework, and provide some experimental results

    Applied Harmonic Analysis and Data Processing

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    Massive data sets have their own architecture. Each data source has an inherent structure, which we should attempt to detect in order to utilize it for applications, such as denoising, clustering, anomaly detection, knowledge extraction, or classification. Harmonic analysis revolves around creating new structures for decomposition, rearrangement and reconstruction of operators and functions—in other words inventing and exploring new architectures for information and inference. Two previous very successful workshops on applied harmonic analysis and sparse approximation have taken place in 2012 and in 2015. This workshop was the an evolution and continuation of these workshops and intended to bring together world leading experts in applied harmonic analysis, data analysis, optimization, statistics, and machine learning to report on recent developments, and to foster new developments and collaborations
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