6,495 research outputs found
Exploring low-degree nodes first accelerates network exploration
We consider information diffusion on Web-like networks and how random walks can simulate it.
A well-studied problem in this domain is Partial Cover Time, i.e., the calculation of the expected number of steps a random walker needs to visit a given fraction of the nodes of the network.
We notice that some of the fastest solutions in fact require that nodes have perfect knowledge of the degree distribution of their neighbors, which in many practical cases is not obtainable, e.g., for privacy reasons.
We thus introduce a version of the Cover problem that considers such limitations: Partial Cover Time with Budget.
The budget is a limit on the number of neighbors that can be inspected for their degree; we have adapted optimal random walks strategies from the literature to operate under such budget.
Our solution is called Min-degree (MD) and, essentially, it biases random walkers towards visiting peripheral areas of the network first.
Extensive benchmarking on six real datasets proves that the---perhaps counter-intuitive strategy---MD strategy is in fact highly competitive wrt. state-of-the-art algorithms for cover
From modular to centralized organization of synchronization in functional areas of the cat cerebral cortex
Recent studies have pointed out the importance of transient synchronization
between widely distributed neural assemblies to understand conscious
perception. These neural assemblies form intricate networks of neurons and
synapses whose detailed map for mammals is still unknown and far from our
experimental capabilities. Only in a few cases, for example the C. elegans, we
know the complete mapping of the neuronal tissue or its mesoscopic level of
description provided by cortical areas. Here we study the process of transient
and global synchronization using a simple model of phase-coupled oscillators
assigned to cortical areas in the cerebral cat cortex. Our results highlight
the impact of the topological connectivity in the developing of
synchronization, revealing a transition in the synchronization organization
that goes from a modular decentralized coherence to a centralized synchronized
regime controlled by a few cortical areas forming a Rich-Club connectivity
pattern.Comment: 24 pages, 8 figures. Final version published in PLoS On
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of
accurate correspondences between points in 3D space. The correspondence problem
is generally addressed by the design of discriminative 3D local descriptors on
the one hand, and the development of robust matching strategies on the other
hand. In this work, we first propose a multi-view local descriptor, which is
learned from the images of multiple views, for the description of 3D keypoints.
Then, we develop a robust matching approach, aiming at rejecting outlier
matches based on the efficient inference via belief propagation on the defined
graphical model. We have demonstrated the boost of our approaches to
registration on the public scanning and multi-view stereo datasets. The
superior performance has been verified by the intensive comparisons against a
variety of descriptors and matching methods
A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation
Origin-Destination Matrix (ODM) estimation is a classical problem in
transport engineering aiming to recover flows from every Origin to every
Destination from measured traffic counts and a priori model information. In
addition to traffic counts, the present contribution takes advantage of probe
trajectories, whose capture is made possible by new measurement technologies.
It extends the concept of ODM to that of Link dependent ODM (LODM), keeping the
information about the flow distribution on links and containing inherently the
ODM assignment. Further, an original formulation of LODM estimation, from
traffic counts and probe trajectories is presented as an optimisation problem,
where the functional to be minimized consists of five convex functions, each
modelling a constraint or property of the transport problem: consistency with
traffic counts, consistency with sampled probe trajectories, consistency with
traffic conservation (Kirchhoff's law), similarity of flows having close
origins and destinations, positivity of traffic flows. A primal-dual algorithm
is devised to minimize the designed functional, as the corresponding objective
functions are not necessarily differentiable. A case study, on a simulated
network and traffic, validates the feasibility of the procedure and details its
benefits for the estimation of an LODM matching real-network constraints and
observations
Recommended from our members
Optimizing Data-Intensive Computing with Efficient Configuration Tuning
As the complexity of distributed analytics systems evolves over time, more configuration parameters get exposed for tuning. While these numerous parameters allow users more control over how their workloads are executed, this flexibility comes at a cost, since finding the right configurations for such systems in a cost-effective way becomes challenging. In practice, several factors contribute to the complexity of tuning the configuration of those systems: the large configuration space, the diversity of the served workloads (each workload possibly requiring a different resource allocation strategy to run optimally), and the dynamic
characteristics of these systems’ environment (e.g., increase in input data size, changes in the allocation of resources). Paradoxically, existing solutions for workload tuning either assume static tuning environment or workloads that are inexpensive to run (i.e. requiring hundreds of execution samples). Recently, Bayesian Optimisation (BO) strategies have been applied as a solution to enable efficient autotuning. They build a probabilistic model incrementally to predict the impact of the parameters on performance using a small number of execution samples. The incrementally constructed BO model is used to guide the tuning process and accelerate convergence to a near-optimal configuration. Unfortunately, for distributed analytics systems, the configuration space is too large to construct a good model using traditional BO, which fails to provide quick convergence in high dimensional configuration space.
I argue that cost-effective tuning strategies can only be developed when taking into account: the frequent changes that can happen in the analytics workload/environment, the amortization of tuning costs and how this influences tuning profitability, the high dimensionality of configuration
space and the need to cater for diverse workloads. To tackle these challenges, I propose Tuneful, an efficient configuration tuning framework
for such expensive to tune systems. It works efficiently both initially (when little data is available) as well as later (as more tuning knowledge is acquired). It starts with learning workload-specific influential parameters incrementally and tunes those only, then when more tuning knowledge becomes available, it detects similarity across workloads and utilizes multitask BO to share the tuning knowledge across similar workloads. I show how augmenting the BO approach with parameters’ significance and workload similarity characteristics enables an
efficient configuration tuning in high dimensional configuration space. Over diverse analytics workloads, this significantly accelerates both configuration tuning and cost amortization, saving search time by 2.7-3.7X at median compared to the-state-of-the-art approaches
- …