225 research outputs found
Sampling Online Social Networks via Heterogeneous Statistics
Most sampling techniques for online social networks (OSNs) are based on a
particular sampling method on a single graph, which is referred to as a
statistics. However, various realizing methods on different graphs could
possibly be used in the same OSN, and they may lead to different sampling
efficiencies, i.e., asymptotic variances. To utilize multiple statistics for
accurate measurements, we formulate a mixture sampling problem, through which
we construct a mixture unbiased estimator which minimizes asymptotic variance.
Given fixed sampling budgets for different statistics, we derive the optimal
weights to combine the individual estimators; given fixed total budget, we show
that a greedy allocation towards the most efficient statistics is optimal. In
practice, the sampling efficiencies of statistics can be quite different for
various targets and are unknown before sampling. To solve this problem, we
design a two-stage framework which adaptively spends a partial budget to test
different statistics and allocates the remaining budget to the inferred best
statistics. We show that our two-stage framework is a generalization of 1)
randomly choosing a statistics and 2) evenly allocating the total budget among
all available statistics, and our adaptive algorithm achieves higher efficiency
than these benchmark strategies in theory and experiment
Stochastic Modeling of Hybrid Cache Systems
In recent years, there is an increasing demand of big memory systems so to
perform large scale data analytics. Since DRAM memories are expensive, some
researchers are suggesting to use other memory systems such as non-volatile
memory (NVM) technology to build large-memory computing systems. However,
whether the NVM technology can be a viable alternative (either economically and
technically) to DRAM remains an open question. To answer this question, it is
important to consider how to design a memory system from a "system
perspective", that is, incorporating different performance characteristics and
price ratios from hybrid memory devices.
This paper presents an analytical model of a "hybrid page cache system" so to
understand the diverse design space and performance impact of a hybrid cache
system. We consider (1) various architectural choices, (2) design strategies,
and (3) configuration of different memory devices. Using this model, we provide
guidelines on how to design hybrid page cache to reach a good trade-off between
high system throughput (in I/O per sec or IOPS) and fast cache reactivity which
is defined by the time to fill the cache. We also show how one can configure
the DRAM capacity and NVM capacity under a fixed budget. We pick PCM as an
example for NVM and conduct numerical analysis. Our analysis indicates that
incorporating PCM in a page cache system significantly improves the system
performance, and it also shows larger benefit to allocate more PCM in page
cache in some cases. Besides, for the common setting of performance-price ratio
of PCM, "flat architecture" offers as a better choice, but "layered
architecture" outperforms if PCM write performance can be significantly
improved in the future.Comment: 14 pages; mascots 201
A Novel Method for the Absolute Pose Problem with Pairwise Constraints
Absolute pose estimation is a fundamental problem in computer vision, and it
is a typical parameter estimation problem, meaning that efforts to solve it
will always suffer from outlier-contaminated data. Conventionally, for a fixed
dimensionality d and the number of measurements N, a robust estimation problem
cannot be solved faster than O(N^d). Furthermore, it is almost impossible to
remove d from the exponent of the runtime of a globally optimal algorithm.
However, absolute pose estimation is a geometric parameter estimation problem,
and thus has special constraints. In this paper, we consider pairwise
constraints and propose a globally optimal algorithm for solving the absolute
pose estimation problem. The proposed algorithm has a linear complexity in the
number of correspondences at a given outlier ratio. Concretely, we first
decouple the rotation and the translation subproblems by utilizing the pairwise
constraints, and then we solve the rotation subproblem using the
branch-and-bound algorithm. Lastly, we estimate the translation based on the
known rotation by using another branch-and-bound algorithm. The advantages of
our method are demonstrated via thorough testing on both synthetic and
real-world dataComment: 10 pages, 7figure
Learning task-oriented dexterous grasping from human knowledge
Industrial automation requires robot dexterity to automate many processes such as product assembling, packaging, and material handling. The existing robotic systems lack the capability to determining proper grasp strategies in the context of object affordances and task designations. In this paper, a framework of task-oriented dexterous grasping is proposed to learn grasp knowledge from human experience and to deploy the grasp strategies while adapting to grasp context. Grasp topology is defined and grasp strategies are learned from an established dataset for task-oriented dexterous manipulation. To adapt to various grasp context, a reinforcement-learning based grasping policy was implement to deploy different task-oriented strategies. The performances of the system was evaluated in a simulated grasping environment by using an AR10 anthropomorphic hand installed in a Sawyer robotic arm. The proposed framework achieved a hit rate of 100% for grasp strategies and an overall top-3 match rate of 95.6%. The success rate of grasping was 85.6% during 2700 grasping experiments for manipulation tasks given in natural-language instructions
Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior
Point cloud registration is challenging in the presence of heavy outlier
correspondences. This paper focuses on addressing the robust
correspondence-based registration problem with gravity prior that often arises
in practice. The gravity directions are typically obtained by inertial
measurement units (IMUs) and can reduce the degree of freedom (DOF) of rotation
from 3 to 1. We propose a novel transformation decoupling strategy by
leveraging screw theory. This strategy decomposes the original 4-DOF problem
into three sub-problems with 1-DOF, 2-DOF, and 1-DOF, respectively, thereby
enhancing the computation efficiency. Specifically, the first 1-DOF represents
the translation along the rotation axis and we propose an interval
stabbing-based method to solve it. The second 2-DOF represents the pole which
is an auxiliary variable in screw theory and we utilize a branch-and-bound
method to solve it. The last 1-DOF represents the rotation angle and we propose
a global voting method for its estimation. The proposed method sequentially
solves three consensus maximization sub-problems, leading to efficient and
deterministic registration. In particular, it can even handle the
correspondence-free registration problem due to its significant robustness.
Extensive experiments on both synthetic and real-world datasets demonstrate
that our method is more efficient and robust than state-of-the-art methods,
even when dealing with outlier rates exceeding 99%
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