26,323 research outputs found
The placement of the head that maximizes predictability. An information theoretic approach
The minimization of the length of syntactic dependencies is a
well-established principle of word order and the basis of a mathematical theory
of word order. Here we complete that theory from the perspective of information
theory, adding a competing word order principle: the maximization of
predictability of a target element. These two principles are in conflict: to
maximize the predictability of the head, the head should appear last, which
maximizes the costs with respect to dependency length minimization. The
implications of such a broad theoretical framework to understand the
optimality, diversity and evolution of the six possible orderings of subject,
object and verb are reviewed.Comment: in press in Glottometric
Quantum Information and Entropy
Thermodynamic entropy is not an entirely satisfactory measure of information
of a quantum state. This entropy for an unknown pure state is zero, although
repeated measurements on copies of such a pure state do communicate
information. In view of this, we propose a new measure for the informational
entropy of a quantum state that includes information in the pure states and the
thermodynamic entropy. The origin of information is explained in terms of an
interplay between unitary and non-unitary evolution. Such complementarity is
also at the basis of the so-called interaction-free measurement.Comment: 21 pages, 3 figure
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis
The purposes of this study were to investigate: 1) the effect of placement of
region-of-interest (ROI) for texture analysis of subchondral bone in knee
radiographs, and 2) the ability of several texture descriptors to distinguish
between the knees with and without radiographic osteoarthritis (OA). Bilateral
posterior-anterior knee radiographs were analyzed from the baseline of OAI and
MOST datasets. A fully automatic method to locate the most informative region
from subchondral bone using adaptive segmentation was developed. We used an
oversegmentation strategy for partitioning knee images into the compact regions
that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick
features, Shannon entropy, and HOG methods were computed within the standard
ROI and within the proposed adaptive ROIs. Subsequently, we built logistic
regression models to identify and compare the performances of each texture
descriptor and each ROI placement method using 5-fold cross validation setting.
Importantly, we also investigated the generalizability of our approach by
training the models on OAI and testing them on MOST dataset.We used area under
the receiver operating characteristic (ROC) curve (AUC) and average precision
(AP) obtained from the precision-recall (PR) curve to compare the results. We
found that the adaptive ROI improves the classification performance (OA vs.
non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC).
We also observed that, from all texture parameters, LBP yielded the best
performance in all settings with the best AUC of 0.840 [0.825, 0.852] and
associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art
approaches, our results suggest that the proposed adaptive ROI approach in
texture analysis of subchondral bone can increase the diagnostic performance
for detecting the presence of radiographic OA
A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
Optimal coding and the origins of Zipfian laws
The problem of compression in standard information theory consists of
assigning codes as short as possible to numbers. Here we consider the problem
of optimal coding -- under an arbitrary coding scheme -- and show that it
predicts Zipf's law of abbreviation, namely a tendency in natural languages for
more frequent words to be shorter. We apply this result to investigate optimal
coding also under so-called non-singular coding, a scheme where unique
segmentation is not warranted but codes stand for a distinct number. Optimal
non-singular coding predicts that the length of a word should grow
approximately as the logarithm of its frequency rank, which is again consistent
with Zipf's law of abbreviation. Optimal non-singular coding in combination
with the maximum entropy principle also predicts Zipf's rank-frequency
distribution. Furthermore, our findings on optimal non-singular coding
challenge common beliefs about random typing. It turns out that random typing
is in fact an optimal coding process, in stark contrast with the common
assumption that it is detached from cost cutting considerations. Finally, we
discuss the implications of optimal coding for the construction of a compact
theory of Zipfian laws and other linguistic laws.Comment: in press in the Journal of Quantitative Linguistics; definition of
concordant pair corrected, proofs polished, references update
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