319,385 research outputs found
An Improved Traffic Matrix Decomposition Method with Frequency-Domain Regularization
We propose a novel network traffic matrix decomposition method named Stable
Principal Component Pursuit with Frequency-Domain Regularization (SPCP-FDR),
which improves the Stable Principal Component Pursuit (SPCP) method by using a
frequency-domain noise regularization function. An experiment demonstrates the
feasibility of this new decomposition method.Comment: Accepted to IEICE Transactions on Information and System
Computation in Multicast Networks: Function Alignment and Converse Theorems
The classical problem in network coding theory considers communication over
multicast networks. Multiple transmitters send independent messages to multiple
receivers which decode the same set of messages. In this work, computation over
multicast networks is considered: each receiver decodes an identical function
of the original messages. For a countably infinite class of two-transmitter
two-receiver single-hop linear deterministic networks, the computing capacity
is characterized for a linear function (modulo-2 sum) of Bernoulli sources.
Inspired by the geometric concept of interference alignment in networks, a new
achievable coding scheme called function alignment is introduced. A new
converse theorem is established that is tighter than cut-set based and
genie-aided bounds. Computation (vs. communication) over multicast networks
requires additional analysis to account for multiple receivers sharing a
network's computational resources. We also develop a network decomposition
theorem which identifies elementary parallel subnetworks that can constitute an
original network without loss of optimality. The decomposition theorem provides
a conceptually-simpler algebraic proof of achievability that generalizes to
-transmitter -receiver networks.Comment: to appear in the IEEE Transactions on Information Theor
Self-Supervised Intrinsic Image Decomposition
Intrinsic decomposition from a single image is a highly challenging task, due
to its inherent ambiguity and the scarcity of training data. In contrast to
traditional fully supervised learning approaches, in this paper we propose
learning intrinsic image decomposition by explaining the input image. Our
model, the Rendered Intrinsics Network (RIN), joins together an image
decomposition pipeline, which predicts reflectance, shape, and lighting
conditions given a single image, with a recombination function, a learned
shading model used to recompose the original input based off of intrinsic image
predictions. Our network can then use unsupervised reconstruction error as an
additional signal to improve its intermediate representations. This allows
large-scale unlabeled data to be useful during training, and also enables
transferring learned knowledge to images of unseen object categories, lighting
conditions, and shapes. Extensive experiments demonstrate that our method
performs well on both intrinsic image decomposition and knowledge transfer.Comment: NIPS 2017 camera-ready version, project page:
http://rin.csail.mit.edu
Accurate determination of tensor network state of quantum lattice models in two dimensions
We have proposed a novel numerical method to calculate accurately the
physical quantities of the ground state with the tensor-network wave function
in two dimensions. We determine the tensor network wavefunction by a projection
approach which applies iteratively the Trotter-Suzuki decomposition of the
projection operator and the singular value decomposition of matrix. The norm of
the wavefunction and the expectation value of a physical observable are
evaluated by a coarse grain renormalization group approach. Our method allows a
tensor-network wavefunction with a high bond degree of freedom (such as D=8) to
be handled accurately and efficiently in the thermodynamic limit. For the
Heisenberg model on a honeycomb lattice, our results for the ground state
energy and the staggered magnetization agree well with those obtained by the
quantum Monte Carlo and other approaches.Comment: 4 pages 5 figures 2 table
Network slicing via function decomposition and flexible network design
Proceeding of: IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PMRC 2017)We argue for flexible network design as an architecture prototype for next generation networks. Such flexible design is developed by capitalizing on the concept of network function decomposition in conjunction with with its relation to network slicing. A detailed view of the proposed functional architecture is put forward, where the role of network function blocks for forming network slices with given requirements is underlined. We further highlight the impact of common architecture over multiple tenants and elaborate on the emerging multi-tenancy business models along with the resulting implications on security.This work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA
A closer look at arrested spinodal decomposition in protein solutions
Concentrated aqueous solutions of the protein lysozyme undergo a liquid solid
transition upon a temperature quench into the unstable spinodal region below a
characteristic arrest temperature of Tf=15C. We use video microscopy and
ultra-small angle light scattering in order to investigate the arrested
structures as a function of initial concentration, quench temperature and rate
of the temperature quench. We find that the solid-like samples show all the
features of a bicontinuous network that is formed through an arrested spinodal
decomposition process. We determine the correlation length Xi and demonstrate
that Xi exhibits a temperature dependence that closely follows the critical
scaling expected for density fluctuations during the early stages of spinodal
decomposition. These findings are in agreement with an arrest scenario based on
a state diagram where the arrest or gel line extends far into the unstable
region below the spinodal line. Arrest then occurs when during the early stage
of spinodal decomposition the volume fraction phi2 of the dense phase
intersects the dynamical arrest threshold phi2Glass, upon which phase
separation gets pinned into a space-spanning gel network with a characteristic
length Xi
Primal Recovery from Consensus-Based Dual Decomposition for Distributed Convex Optimization
Dual decomposition has been successfully employed in a variety of distributed
convex optimization problems solved by a network of computing and communicating
nodes. Often, when the cost function is separable but the constraints are
coupled, the dual decomposition scheme involves local parallel subgradient
calculations and a global subgradient update performed by a master node. In
this paper, we propose a consensus-based dual decomposition to remove the need
for such a master node and still enable the computing nodes to generate an
approximate dual solution for the underlying convex optimization problem. In
addition, we provide a primal recovery mechanism to allow the nodes to have
access to approximate near-optimal primal solutions. Our scheme is based on a
constant stepsize choice and the dual and primal objective convergence are
achieved up to a bounded error floor dependent on the stepsize and on the
number of consensus steps among the nodes
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