426 research outputs found
The exponential cost optimality for finite horizon semi-Markov decision processes
summary:This paper considers an exponential cost optimality problem for finite horizon semi-Markov decision processes (SMDPs). The objective is to calculate an optimal policy with minimal exponential costs over the full set of policies in a finite horizon. First, under the standard regular and compact-continuity conditions, we establish the optimality equation, prove that the value function is the unique solution of the optimality equation and the existence of an optimal policy by using the minimum nonnegative solution approach. Second, we establish a new value iteration algorithm to calculate both the value function and the -optimal policy. Finally, we give a computable machine maintenance system to illustrate the convergence of the algorithm
AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory
Addressing the communication bottleneck inherent in federated learning (FL),
over-the-air FL (AirFL) has emerged as a promising solution, which is, however,
hampered by deep fading conditions. In this paper, we propose AirFL-Mem, a
novel scheme designed to mitigate the impact of deep fading by implementing a
\emph{long-term} memory mechanism. Convergence bounds are provided that account
for long-term memory, as well as for existing AirFL variants with short-term
memory, for general non-convex objectives. The theory demonstrates that
AirFL-Mem exhibits the same convergence rate of federated averaging (FedAvg)
with ideal communication, while the performance of existing schemes is
generally limited by error floors. The theoretical results are also leveraged
to propose a novel convex optimization strategy for the truncation threshold
used for power control in the presence of Rayleigh fading channels.
Experimental results validate the analysis, confirming the advantages of a
long-term memory mechanism for the mitigation of deep fading.Comment: 8 pages, 3 figures, submitted for possible publicatio
Tensor Completion via Tensor Train Based Low-Rank Quotient Geometry under a Preconditioned Metric
This paper investigates the low-rank tensor completion problem, which is
about recovering a tensor from partially observed entries. We consider this
problem in the tensor train format and extend the preconditioned metric from
the matrix case to the tensor case. The first-order and second-order quotient
geometry of the manifold of fixed tensor train rank tensors under this metric
is studied in detail. Algorithms, including Riemannian gradient descent,
Riemannian conjugate gradient, and Riemannian Gauss-Newton, have been proposed
for the tensor completion problem based on the quotient geometry. It has also
been shown that the Riemannian Gauss-Newton method on the quotient geometry is
equivalent to the Riemannian Gauss-Newton method on the embedded geometry with
a specific retraction. Empirical evaluations on random instances as well as on
function-related tensors show that the proposed algorithms are competitive with
other existing algorithms in terms of recovery ability, convergence
performance, and reconstruction quality.Comment: The manuscript has been adjusted in several place
Understanding tourists’ dining behaviors at traditional Chinese nutraceutical restaurants
Nutraceutical restaurants providing medical and/or health benefits have become an emerging market; however, the underlying factors and the mechanism explaining dining behaviours in nutraceutical restaurants remain unknown. This study utilized a mixed-methods approach to bridge this research gap. An exploratory qualitative interview was conducted to identify the determinants of patronage behaviour at nutraceutical restaurants. We further conducted a quantitative study utilizing an extended value-attitude-behaviour model to provide quantitative evidence. The results showed that health, cultural values, and social norms significantly influenced customers’ attitudes, thus leading to their revisit intentions. Furthermore, we found a significant role of social norms in determining nutraceutical consumption. Additionally, age was found to moderate the effects of health values and social norms on revisit intention
Designing Enhanced Multi-dimensional Constellations for Code-Domain NOMA
This paper presents an enhanced design of multi-dimensional (MD)
constellations which play a pivotal role in many communication systems such as
code-domain non-orthogonal multiple access (CD-NOMA). MD constellations are
attractive as their structural properties, if properly designed, lead to signal
space diversity and hence improved error rate performance. Unlike the existing
works which mostly focus on MD constellations with large minimum Euclidean
distance (MED), we look for new MD constellations with additional feature that
the minimum product distance (MPD) is also large. To this end, a non-convex
optimization problem is formulated and then solved by the convex-concave
procedure (CCCP). Compared with the state-of-the-art literature, our proposed
MD constellations lead to significant error performance enhancement over
Rayleigh fading channels whilst maintaining almost the same performance over
the Gaussian channels. To demonstrate their application, we also show that
these MD constellations give rise to good codebooks in sparse code multiple
access systems. All the obtained MD constellations can be found in
https://github.com/Aureliano1/Multi-dimensional-constellation
A Search for Spectral Galaxy Pairs of Overlapping Galaxies based on Fuzzy Recognition
The Spectral Galaxy Pairs (SGPs) are defined as the composite galaxy spectra
which contain two independent redshift systems. These spectra are useful for
studying dust properties of the foreground galaxies. In this paper, a total of
165 spectra of SGPs are mined out from Sloan Digital Sky Survey (SDSS) Data
Release 9 (DR9) using the concept of membership degree from the fuzzy set
theory particularly defined to be suitable for fuzzily identifying emission
lines. The spectra and images of this sample are classified according to the
membership degree and their image features, respectively. Many of these 2nd
redshift systems are too small or too dim to select from the SDSS images alone,
making the sample a potentially unique source of information on dust effects in
low-luminosity or low-surface-brightness galaxies that are underrepresented in
morphological pair samples. The dust extinction of the objects with high
membership degree is also estimated by Balmer decrement. Additionally, analyses
for a series of spectroscopic observations of one SGP from 165 systems indicate
that a newly star-forming region of our Milky Way might occur.Comment: 16pages, 6figure
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