61 research outputs found
Universal coding for correlated sources with complementary delivery
This paper deals with a universal coding problem for a certain kind of
multiterminal source coding system that we call the complementary delivery
coding system. In this system, messages from two correlated sources are jointly
encoded, and each decoder has access to one of the two messages to enable it to
reproduce the other message. Both fixed-to-fixed length and fixed-to-variable
length lossless coding schemes are considered. Explicit constructions of
universal codes and bounds of the error probabilities are clarified via
type-theoretical and graph-theoretical analyses. [[Keywords]] multiterminal
source coding, complementary delivery, universal coding, types of sequences,
bipartite graphsComment: 18 pages, some of the material in this manuscript has been already
published in IEICE Transactions on Fundamentals, September 2007. Several
additional results are also include
A quick search method for audio signals based on a piecewise linear representation of feature trajectories
This paper presents a new method for a quick similarity-based search through
long unlabeled audio streams to detect and locate audio clips provided by
users. The method involves feature-dimension reduction based on a piecewise
linear representation of a sequential feature trajectory extracted from a long
audio stream. Two techniques enable us to obtain a piecewise linear
representation: the dynamic segmentation of feature trajectories and the
segment-based Karhunen-L\'{o}eve (KL) transform. The proposed search method
guarantees the same search results as the search method without the proposed
feature-dimension reduction method in principle. Experiment results indicate
significant improvements in search speed. For example the proposed method
reduced the total search time to approximately 1/12 that of previous methods
and detected queries in approximately 0.3 seconds from a 200-hour audio
database.Comment: 20 pages, to appear in IEEE Transactions on Audio, Speech and
Language Processin
Toward Defensive Letter Design
A major approach for defending against adversarial attacks aims at
controlling only image classifiers to be more resilient, and it does not care
about visual objects, such as pandas and cars, in images. This means that
visual objects themselves cannot take any defensive actions, and they are still
vulnerable to adversarial attacks. In contrast, letters are artificial symbols,
and we can freely control their appearance unless losing their readability. In
other words, we can make the letters more defensive to the attacks. This paper
poses three research questions related to the adversarial vulnerability of
letter images: (1) How defensive are the letters against adversarial attacks?
(2) Can we estimate how defensive a given letter image is before attacks? (3)
Can we control the letter images to be more defensive against adversarial
attacks? For answering the first and second questions, we measure the
defensibility of letters by employing Iterative Fast Gradient Sign Method
(I-FGSM) and then build a deep regression model for estimating the
defensibility of each letter image. We also propose a two-step method based on
a generative adversarial network (GAN) for generating character images with
higher defensibility, which solves the third research question.Comment: 14 pages, 8 figures, accepted at ACPR 202
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