20,537 research outputs found

    Topology-constrained Synthesis of Vector Patterns

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    International audienceDecorative patterns are observed in many forms of art, typically enriching the visual aspect of otherwise simple shapes. Such patterns are especially difficult to create, as they often exhibit intricate structural details and at the same time have to precisely match the size and shape of the underlying geometry. In the field of Computer Graphics, several approaches have been proposed to automatically synthesize a decorative pattern along a curve, from an example. This empowers non expert users with a simple brush metaphor, allowing them to easily paint complex structured decorations.We extend this idea to the space of design and fabrication. The major challenge is to properly account for the topology of the produced patterns. In particular, our technique ensures that synthesized patterns will be made of exactly one connected component, so that once printed they form a single object. To achieve this goal we propose a two steps synthesis process, first synthesizing the topology of the pattern and later synthesizing its exact geometry. We introduce topology descriptors that efficiently capture the topology of the pattern synthesized so far.We propose several applications of our method, from designing objects using synthesized patterns along curves and within rectangles, to the decoration of surfaces with a dedicated smooth frame interpolation. Using our technique, designers paint structured patterns that can be fabricated into solid, tangible objects, creating unusual and surprising designs of lamps, chairs and laces from examples

    A Sub-optimal Algorithm to Synthesize Control Laws for a Network of Dynamic Agents

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    We study the synthesis problem of an LQR controller when the matrix describing the control law is constrained to lie in a particular vector space. Our motivation is the use of such control laws to stabilize networks of autonomous agents in a decentralized fashion; with the information flow being dictated by the constraints of a pre-specified topology. In this paper, we consider the finite-horizon version of the problem and provide both a computationally intensive optimal solution and a sub-optimal solution that is computationally more tractable. Then we apply the technique to the decentralized vehicle formation control problem and show that the loss in performance due to the use of the sub-optimal solution is not huge; however the topology can have a large effect on performance

    A Method to Identify and Analyze Biological Programs through Automated Reasoning.

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    Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function

    FINN: A Framework for Fast, Scalable Binarized Neural Network Inference

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    Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values. In this paper, we present FINN, a framework for building fast and flexible FPGA accelerators using a flexible heterogeneous streaming architecture. By utilizing a novel set of optimizations that enable efficient mapping of binarized neural networks to hardware, we implement fully connected, convolutional and pooling layers, with per-layer compute resources being tailored to user-provided throughput requirements. On a ZC706 embedded FPGA platform drawing less than 25 W total system power, we demonstrate up to 12.3 million image classifications per second with 0.31 {\mu}s latency on the MNIST dataset with 95.8% accuracy, and 21906 image classifications per second with 283 {\mu}s latency on the CIFAR-10 and SVHN datasets with respectively 80.1% and 94.9% accuracy. To the best of our knowledge, ours are the fastest classification rates reported to date on these benchmarks.Comment: To appear in the 25th International Symposium on Field-Programmable Gate Arrays, February 201

    Sparse Array DFT Beamformers for Wideband Sources

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    Sparse arrays are popular for performance optimization while keeping the hardware and computational costs down. In this paper, we consider sparse arrays design method for wideband source operating in a wideband jamming environment. Maximizing the signal-to-interference plus noise ratio (MaxSINR) is adopted as an optimization objective for wideband beamforming. Sparse array design problem is formulated in the DFT domain to process the source as parallel narrowband sources. The problem is formulated as quadratically constraint quadratic program (QCQP) alongside the weighted mixed l1l_{1-\infty}-norm squared penalization of the beamformer weight vector. The semidefinite relaxation (SDR) of QCQP promotes sparse solutions by iteratively re-weighting beamformer based on previous iteration. It is shown that the DFT approach reduces the computational cost considerably as compared to the delay line approach, while efficiently utilizing the degrees of freedom to harness the maximum output SINR offered by the given array aperture

    Expansive evolution of the TREHALOSE-6-PHOSPHATE PHOSPHATASE gene family in Arabidopsis

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    Trehalose is a nonreducing sugar used as a reserve carbohydrate and stress protectant in a variety of organisms. While higher plants typically do not accumulate high levels of trehalose, they encode large families of putative trehalose biosynthesis genes. Trehalose biosynthesis in plants involves a two-step reaction in which trehalose-6-phosphate (T6P) is synthesized from UDPglucose and glucose-6-phosphate (catalyzed by T6P synthase [TPS]), and subsequently dephosphorylated to produce the disaccharide trehalose (catalyzed by T6P phosphatase [TPP]). In Arabidopsis (Arabidopsis thaliana), 11 genes encode proteins with both TPS- and TPP-like domains but only one of these (AtTPS1) appears to be an active (TPS) enzyme. In addition, plants contain a large family of smaller proteins with a conserved TPP domain. Here, we present an in-depth analysis of the 10 TPP genes and gene products in Arabidopsis (TPPA-TPPJ). Collinearity analysis revealed that all of these genes originate from whole-genome duplication events. Heterologous expression in yeast (Saccharomyces cerevisiae) showed that all encode active TPP enzymes with an essential role for some conserved residues in the catalytic domain. These results suggest that the TPP genes function in the regulation of T6P levels, with T6P emerging as a novel key regulator of growth and development in higher plants. Extensive gene expression analyses using a complete set of promoter-beta-glucuronidase/green fluorescent protein reporter lines further uncovered cell- and tissue-specific expression patterns, conferring spatiotemporal control of trehalose metabolism. Consistently, phenotypic characterization of knockdown and overexpression lines of a single TPP, AtTPPG, points to unique properties of individual TPPs in Arabidopsis, and underlines the intimate connection between trehalose metabolism and abscisic acid signaling
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