31,194 research outputs found
A generalized characterization of algorithmic probability
An a priori semimeasure (also known as "algorithmic probability" or "the
Solomonoff prior" in the context of inductive inference) is defined as the
transformation, by a given universal monotone Turing machine, of the uniform
measure on the infinite strings. It is shown in this paper that the class of a
priori semimeasures can equivalently be defined as the class of
transformations, by all compatible universal monotone Turing machines, of any
continuous computable measure in place of the uniform measure. Some
consideration is given to possible implications for the prevalent association
of algorithmic probability with certain foundational statistical principles
On Approaching the Ultimate Limits of Photon-Efficient and Bandwidth-Efficient Optical Communication
It is well known that ideal free-space optical communication at the quantum
limit can have unbounded photon information efficiency (PIE), measured in bits
per photon. High PIE comes at a price of low dimensional information efficiency
(DIE), measured in bits per spatio-temporal-polarization mode. If only temporal
modes are used, then DIE translates directly to bandwidth efficiency. In this
paper, the DIE vs. PIE tradeoffs for known modulations and receiver structures
are compared to the ultimate quantum limit, and analytic approximations are
found in the limit of high PIE. This analysis shows that known structures fall
short of the maximum attainable DIE by a factor that increases linearly with
PIE for high PIE.
The capacity of the Dolinar receiver is derived for binary coherent-state
modulations and computed for the case of on-off keying (OOK). The DIE vs. PIE
tradeoff for this case is improved only slightly compared to OOK with photon
counting. An adaptive rule is derived for an additive local oscillator that
maximizes the mutual information between a receiver and a transmitter that
selects from a set of coherent states. For binary phase-shift keying (BPSK),
this is shown to be equivalent to the operation of the Dolinar receiver.
The Dolinar receiver is extended to make adaptive measurements on a coded
sequence of coherent state symbols. Information from previous measurements is
used to adjust the a priori probabilities of the next symbols. The adaptive
Dolinar receiver does not improve the DIE vs. PIE tradeoff compared to
independent transmission and Dolinar reception of each symbol.Comment: 10 pages, 8 figures; corrected a typo in equation 3
Rate-Distortion Analysis of Multiview Coding in a DIBR Framework
Depth image based rendering techniques for multiview applications have been
recently introduced for efficient view generation at arbitrary camera
positions. Encoding rate control has thus to consider both texture and depth
data. Due to different structures of depth and texture images and their
different roles on the rendered views, distributing the available bit budget
between them however requires a careful analysis. Information loss due to
texture coding affects the value of pixels in synthesized views while errors in
depth information lead to shift in objects or unexpected patterns at their
boundaries. In this paper, we address the problem of efficient bit allocation
between textures and depth data of multiview video sequences. We adopt a
rate-distortion framework based on a simplified model of depth and texture
images. Our model preserves the main features of depth and texture images.
Unlike most recent solutions, our method permits to avoid rendering at encoding
time for distortion estimation so that the encoding complexity is not
augmented. In addition to this, our model is independent of the underlying
inpainting method that is used at decoder. Experiments confirm our theoretical
results and the efficiency of our rate allocation strategy
Auto-encoders: reconstruction versus compression
We discuss the similarities and differences between training an auto-encoder
to minimize the reconstruction error, and training the same auto-encoder to
compress the data via a generative model. Minimizing a codelength for the data
using an auto-encoder is equivalent to minimizing the reconstruction error plus
some correcting terms which have an interpretation as either a denoising or
contractive property of the decoding function. These terms are related but not
identical to those used in denoising or contractive auto-encoders [Vincent et
al. 2010, Rifai et al. 2011]. In particular, the codelength viewpoint fully
determines an optimal noise level for the denoising criterion
Online Reinforcement Learning for Dynamic Multimedia Systems
In our previous work, we proposed a systematic cross-layer framework for
dynamic multimedia systems, which allows each layer to make autonomous and
foresighted decisions that maximize the system's long-term performance, while
meeting the application's real-time delay constraints. The proposed solution
solved the cross-layer optimization offline, under the assumption that the
multimedia system's probabilistic dynamics were known a priori. In practice,
however, these dynamics are unknown a priori and therefore must be learned
online. In this paper, we address this problem by allowing the multimedia
system layers to learn, through repeated interactions with each other, to
autonomously optimize the system's long-term performance at run-time. We
propose two reinforcement learning algorithms for optimizing the system under
different design constraints: the first algorithm solves the cross-layer
optimization in a centralized manner, and the second solves it in a
decentralized manner. We analyze both algorithms in terms of their required
computation, memory, and inter-layer communication overheads. After noting that
the proposed reinforcement learning algorithms learn too slowly, we introduce a
complementary accelerated learning algorithm that exploits partial knowledge
about the system's dynamics in order to dramatically improve the system's
performance. In our experiments, we demonstrate that decentralized learning can
perform as well as centralized learning, while enabling the layers to act
autonomously. Additionally, we show that existing application-independent
reinforcement learning algorithms, and existing myopic learning algorithms
deployed in multimedia systems, perform significantly worse than our proposed
application-aware and foresighted learning methods.Comment: 35 pages, 11 figures, 10 table
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