16,601 research outputs found
Recommended from our members
On Robust Stability of Limit Cycles for Hybrid Systems with Multiple Jumps
In this paper, we address stability and robustness properties of hybrid limit cycles for a class of hybrid systems with multiple jumps in one period. The main results entail equivalent characterizations of stability of hybrid limit cycles for hybrid systems. The hybrid limit cycles may have multiple jumps in one period and the jumps are allowed to occur on sets. Conditions guaranteeing robustness of hybrid limit cycles are also presented
Around Kolmogorov complexity: basic notions and results
Algorithmic information theory studies description complexity and randomness
and is now a well known field of theoretical computer science and mathematical
logic. There are several textbooks and monographs devoted to this theory where
one can find the detailed exposition of many difficult results as well as
historical references. However, it seems that a short survey of its basic
notions and main results relating these notions to each other, is missing.
This report attempts to fill this gap and covers the basic notions of
algorithmic information theory: Kolmogorov complexity (plain, conditional,
prefix), Solomonoff universal a priori probability, notions of randomness
(Martin-L\"of randomness, Mises--Church randomness), effective Hausdorff
dimension. We prove their basic properties (symmetry of information, connection
between a priori probability and prefix complexity, criterion of randomness in
terms of complexity, complexity characterization for effective dimension) and
show some applications (incompressibility method in computational complexity
theory, incompleteness theorems). It is based on the lecture notes of a course
at Uppsala University given by the author
Adaptive Measurement Network for CS Image Reconstruction
Conventional compressive sensing (CS) reconstruction is very slow for its
characteristic of solving an optimization problem. Convolu- tional neural
network can realize fast processing while achieving compa- rable results. While
CS image recovery with high quality not only de- pends on good reconstruction
algorithms, but also good measurements. In this paper, we propose an adaptive
measurement network in which measurement is obtained by learning. The new
network consists of a fully-connected layer and ReconNet. The fully-connected
layer which has low-dimension output acts as measurement. We train the
fully-connected layer and ReconNet simultaneously and obtain adaptive
measurement. Because the adaptive measurement fits dataset better, in contrast
with random Gaussian measurement matrix, under the same measuremen- t rate, it
can extract the information of scene more efficiently and get better
reconstruction results. Experiments show that the new network outperforms the
original one.Comment: 11pages,8figure
An intra-laboratory investigation of on-wafer measurement reproducibility at millimeter-wave frequencies
Understanding the relative contribution of contact repeatability and overall reproducibility for on-wafer measurements provides useful insight into the significance of measurement comparisons. We report on an intra-laboratory investigation into contact repeatability and the variation that may be anticipated when measurements are reproduced in different laboratories using different equipment. We pay particular attention to the dispersion in measurement results arising from the use of on-wafer and off-wafer calibration. Experimental results are reported for measurements in the frequency range 140 GHz to 220 GHz, together with preliminary estimates of the repeatability limits for this type of measurement
Zero-Annotation Object Detection with Web Knowledge Transfer
Object detection is one of the major problems in computer vision, and has
been extensively studied. Most of the existing detection works rely on
labor-intensive supervision, such as ground truth bounding boxes of objects or
at least image-level annotations. On the contrary, we propose an object
detection method that does not require any form of human annotation on target
tasks, by exploiting freely available web images. In order to facilitate
effective knowledge transfer from web images, we introduce a multi-instance
multi-label domain adaption learning framework with two key innovations. First
of all, we propose an instance-level adversarial domain adaptation network with
attention on foreground objects to transfer the object appearances from web
domain to target domain. Second, to preserve the class-specific semantic
structure of transferred object features, we propose a simultaneous transfer
mechanism to transfer the supervision across domains through pseudo strong
label generation. With our end-to-end framework that simultaneously learns a
weakly supervised detector and transfers knowledge across domains, we achieved
significant improvements over baseline methods on the benchmark datasets.Comment: Accepted in ECCV 201
Lovelock gravity from entropic force
In this paper, we first generalize the formulation of entropic gravity to
(n+1)-dimensional spacetime. Then, we propose an entropic origin for
Gauss-Bonnet gravity and more general Lovelock gravity in arbitrary dimensions.
As a result, we are able to derive Newton's law of gravitation as well as the
corresponding Friedmann equations in these gravity theories. This procedure
naturally leads to a derivation of the higher dimensional gravitational
coupling constant of Friedmann/Einstein equation which is in complete agreement
with the results obtained by comparing the weak field limit of Einstein
equation with Poisson equation in higher dimensions. Our study shows that the
approach presented here is powerful enough to derive the gravitational field
equations in any gravity theory. PACS: 04.20.Cv, 04.50.-h, 04.70.Dy.Comment: 10 pages, new versio
Effective Capacity in Broadcast Channels with Arbitrary Inputs
We consider a broadcast scenario where one transmitter communicates with two
receivers under quality-of-service constraints. The transmitter initially
employs superposition coding strategies with arbitrarily distributed signals
and sends data to both receivers. Regarding the channel state conditions, the
receivers perform successive interference cancellation to decode their own
data. We express the effective capacity region that provides the maximum
allowable sustainable data arrival rate region at the transmitter buffer or
buffers. Given an average transmission power limit, we provide a two-step
approach to obtain the optimal power allocation policies that maximize the
effective capacity region. Then, we characterize the optimal decoding regions
at the receivers in the space spanned by the channel fading power values. We
finally substantiate our results with numerical presentations.Comment: This paper will appear in 14th International Conference on
Wired&Wireless Internet Communications (WWIC
A note on the differences of computably enumerable reals
We show that given any non-computable left-c.e. real α there exists a left-c.e. real β such that α≠β+γ for all left-c.e. reals and all right-c.e. reals γ. The proof is non-uniform, the dichotomy being whether the given real α is Martin-Loef random or not. It follows that given any universal machine U, there is another universal machine V such that the halting probability of U is not a translation of the halting probability of V by a left-c.e. real. We do not know if there is a uniform proof of this fact
Oral yeasts and coliforms in HIV-infected patients in Hong Kong
published_or_final_versio
- …