323,906 research outputs found
Escaping RGBland: Selecting Colors for Statistical Graphics
Statistical graphics are often augmented by the use of color coding information contained in some variable. When this involves the shading of areas (and not only points or lines) - e.g., as in bar plots, pie charts, mosaic displays or heatmaps - it is important that the colors are perceptually based and do not introduce optical illusions or systematic bias. Here, we discuss how the perceptually-based Hue-Chroma-Luminance (HCL) color space can be used for deriving suitable color palettes for coding categorical data (qualitative palettes) and numerical variables (sequential and diverging palettes).Series: Research Report Series / Department of Statistics and Mathematic
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Is the scope of phonological planning constrained by the syntactical role of the utterance constituents?
Five experiments looked the effect of repeated phonemes in the production of color adjective+noun phrases in English ("green gun"), or noun+color adjective phrases in Spanish and French. Whereas phoneme repetition sped up naming latencies in the case of prenominal color adjectives, it induced inhibition in the postnominal case. We argue that these dissociation is not compatible with a genuine crosslinguistic difference in the scope of phonological encoding. Rather we explain it in terms of the interplay between an activation gradient, coding word order, and an activation bias, coding the syntactical role of the utterance constituents
Semiannual status report
The work performed in the previous six months can be divided into three main cases: (1) transmission of images over local area networks (LAN's); (2) coding of color mapped (pseudo-color) images; and (3) low rate video coding. A brief overview of the work done in the first two areas is presented. The third item is reported in somewhat more detail
Fast Parallel Fixed-Parameter Algorithms via Color Coding
Fixed-parameter algorithms have been successfully applied to solve numerous
difficult problems within acceptable time bounds on large inputs. However, most
fixed-parameter algorithms are inherently \emph{sequential} and, thus, make no
use of the parallel hardware present in modern computers. We show that parallel
fixed-parameter algorithms do not only exist for numerous parameterized
problems from the literature -- including vertex cover, packing problems,
cluster editing, cutting vertices, finding embeddings, or finding matchings --
but that there are parallel algorithms working in \emph{constant} time or at
least in time \emph{depending only on the parameter} (and not on the size of
the input) for these problems. Phrased in terms of complexity classes, we place
numerous natural parameterized problems in parameterized versions of AC. On
a more technical level, we show how the \emph{color coding} method can be
implemented in constant time and apply it to embedding problems for graphs of
bounded tree-width or tree-depth and to model checking first-order formulas in
graphs of bounded degree
From Instantly Decodable to Random Linear Network Coding
Our primary goal in this paper is to traverse the performance gap between two
linear network coding schemes: random linear network coding (RLNC) and
instantly decodable network coding (IDNC) in terms of throughput and decoding
delay. We first redefine the concept of packet generation and use it to
partition a block of partially-received data packets in a novel way, based on
the coding sets in an IDNC solution. By varying the generation size, we obtain
a general coding framework which consists of a series of coding schemes, with
RLNC and IDNC identified as two extreme cases. We then prove that the
throughput and decoding delay performance of all coding schemes in this coding
framework are bounded between the performance of RLNC and IDNC and hence
throughput-delay tradeoff becomes possible. We also propose implementations of
this coding framework to further improve its throughput and decoding delay
performance, to manage feedback frequency and coding complexity, or to achieve
in-block performance adaption. Extensive simulations are then provided to
verify the performance of the proposed coding schemes and their
implementations.Comment: 30 pages with double space, 14 color figure
Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?
Spiking neural networks (SNNs) equipped with latency coding and spike-timing
dependent plasticity rules offer an alternative to solve the data and energy
bottlenecks of standard computer vision approaches: they can learn visual
features without supervision and can be implemented by ultra-low power hardware
architectures. However, their performance in image classification has never
been evaluated on recent image datasets. In this paper, we compare SNNs to
auto-encoders on three visual recognition datasets, and extend the use of SNNs
to color images. The analysis of the results helps us identify some bottlenecks
of SNNs: the limits of on-center/off-center coding, especially for color
images, and the ineffectiveness of current inhibition mechanisms. These issues
should be addressed to build effective SNNs for image recognition
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