263 research outputs found
Cross-Sender Bit-Mixing Coding
Scheduling to avoid packet collisions is a long-standing challenge in
networking, and has become even trickier in wireless networks with multiple
senders and multiple receivers. In fact, researchers have proved that even {\em
perfect} scheduling can only achieve . Here
is the number of nodes in the network, and is the {\em medium
utilization rate}. Ideally, one would hope to achieve ,
while avoiding all the complexities in scheduling. To this end, this paper
proposes {\em cross-sender bit-mixing coding} ({\em BMC}), which does not rely
on scheduling. Instead, users transmit simultaneously on suitably-chosen slots,
and the amount of overlap in different user's slots is controlled via coding.
We prove that in all possible network topologies, using BMC enables us to
achieve . We also prove that the space and time
complexities of BMC encoding/decoding are all low-order polynomials.Comment: Published in the International Conference on Information Processing
in Sensor Networks (IPSN), 201
Approximate Near Neighbors for General Symmetric Norms
We show that every symmetric normed space admits an efficient nearest
neighbor search data structure with doubly-logarithmic approximation.
Specifically, for every , , and every -dimensional
symmetric norm , there exists a data structure for
-approximate nearest neighbor search over
for -point datasets achieving query time and
space. The main technical ingredient of the algorithm is a
low-distortion embedding of a symmetric norm into a low-dimensional iterated
product of top- norms.
We also show that our techniques cannot be extended to general norms.Comment: 27 pages, 1 figur
Interval Selection in the Streaming Model
A set of intervals is independent when the intervals are pairwise disjoint.
In the interval selection problem we are given a set of intervals
and we want to find an independent subset of intervals of largest cardinality.
Let denote the cardinality of an optimal solution. We
discuss the estimation of in the streaming model, where we
only have one-time, sequential access to the input intervals, the endpoints of
the intervals lie in , and the amount of the memory is
constrained.
For intervals of different sizes, we provide an algorithm in the data stream
model that computes an estimate of that, with
probability at least , satisfies . For same-length
intervals, we provide another algorithm in the data stream model that computes
an estimate of that, with probability at
least , satisfies . The space used by our algorithms is bounded
by a polynomial in and . We also show that no better
estimations can be achieved using bits of storage.
We also develop new, approximate solutions to the interval selection problem,
where we want to report a feasible solution, that use
space. Our algorithms for the interval selection problem match the optimal
results by Emek, Halld{\'o}rsson and Ros{\'e}n [Space-Constrained Interval
Selection, ICALP 2012], but are much simpler.Comment: Minor correction
Deterministic Sampling and Range Counting in Geometric Data Streams
We present memory-efficient deterministic algorithms for constructing
epsilon-nets and epsilon-approximations of streams of geometric data. Unlike
probabilistic approaches, these deterministic samples provide guaranteed bounds
on their approximation factors. We show how our deterministic samples can be
used to answer approximate online iceberg geometric queries on data streams. We
use these techniques to approximate several robust statistics of geometric data
streams, including Tukey depth, simplicial depth, regression depth, the
Thiel-Sen estimator, and the least median of squares. Our algorithms use only a
polylogarithmic amount of memory, provided the desired approximation factors
are inverse-polylogarithmic. We also include a lower bound for non-iceberg
geometric queries.Comment: 12 pages, 1 figur
Pseudorandomness for Regular Branching Programs via Fourier Analysis
We present an explicit pseudorandom generator for oblivious, read-once,
permutation branching programs of constant width that can read their input bits
in any order. The seed length is , where is the length of the
branching program. The previous best seed length known for this model was
, which follows as a special case of a generator due to
Impagliazzo, Meka, and Zuckerman (FOCS 2012) (which gives a seed length of
for arbitrary branching programs of size ). Our techniques
also give seed length for general oblivious, read-once branching
programs of width , which is incomparable to the results of
Impagliazzo et al.Our pseudorandom generator is similar to the one used by
Gopalan et al. (FOCS 2012) for read-once CNFs, but the analysis is quite
different; ours is based on Fourier analysis of branching programs. In
particular, we show that an oblivious, read-once, regular branching program of
width has Fourier mass at most at level , independent of the
length of the program.Comment: RANDOM 201
Taylor Polynomial Estimator for Estimating Frequency Moments
We present a randomized algorithm for estimating the th moment of
the frequency vector of a data stream in the general update (turnstile) model
to within a multiplicative factor of , for , with high
constant confidence. For , the algorithm uses space words. This
improves over the current bound of
words by Andoni et. al. in \cite{ako:arxiv10}. Our space upper bound matches
the lower bound of Li and Woodruff \cite{liwood:random13} for and the lower bound of Andoni et. al. \cite{anpw:icalp13}
for .Comment: Supercedes arXiv:1104.4552. Extended Abstract of this paper to appear
in Proceedings of ICALP 201
Distance-Sensitive Hashing
Locality-sensitive hashing (LSH) is an important tool for managing
high-dimensional noisy or uncertain data, for example in connection with data
cleaning (similarity join) and noise-robust search (similarity search).
However, for a number of problems the LSH framework is not known to yield good
solutions, and instead ad hoc solutions have been designed for particular
similarity and distance measures. For example, this is true for
output-sensitive similarity search/join, and for indexes supporting annulus
queries that aim to report a point close to a certain given distance from the
query point.
In this paper we initiate the study of distance-sensitive hashing (DSH), a
generalization of LSH that seeks a family of hash functions such that the
probability of two points having the same hash value is a given function of the
distance between them. More precisely, given a distance space and a "collision probability function" (CPF) we seek a distribution over pairs of functions
such that for every pair of points the collision
probability is . Locality-sensitive
hashing is the study of how fast a CPF can decrease as the distance grows. For
many spaces, can be made exponentially decreasing even if we restrict
attention to the symmetric case where . We show that the asymmetry
achieved by having a pair of functions makes it possible to achieve CPFs that
are, for example, increasing or unimodal, and show how this leads to principled
solutions to problems not addressed by the LSH framework. This includes a novel
application to privacy-preserving distance estimation. We believe that the DSH
framework will find further applications in high-dimensional data management.Comment: Accepted at PODS'18. Abstract shortened due to character limi
Micronutrients in HIV: A Bayesian MetaAnalysis
Background: Approximately 28.5 million people living with HIV are eligible for treatment (CD4&500), but currently have no access to antiretroviral therapy. Reduced serum level of micronutrients is common in HIV disease. Micronutrient supplementation (MNS) may mitigate disease progression and mortality. Objectives: We synthesized evidence on the effect of micronutrient supplementation on mortality and rate of disease progression in HIV disease.
Methods: We searched MEDLINE, EMBASE, the Cochrane Central, AMED and CINAHL databases through December 2014, without language restriction, for studies of greater than 3 micronutrients versus any or no comparator. We built a hierarchical Bayesian random effects model to synthesize results. Inferences are based on the posterior distribution of the population effects; posterior distributions were approximated by Markov chain Monte Carlo in OpenBugs.
Principal Findings: From 2166 initial references, we selected 49 studies for full review and identified eight reporting on disease progression and/or mortality. Bayesian synthesis of data from 2,249 adults in three studies estimated the relative risk of disease progression in subjects on MNS vs. control as 0.62 (95% credible interval, 0.37, 0.96). Median number needed to treat is 8.4 (4.8, 29.9) and the Bayes Factor 53.4. Based on data reporting on 4,095 adults reporting mortality in 7 randomized controlled studies, the RR was 0.84 (0.38, 1.85), NNT is 25 (4.3, ∞).
Conclusions: MNS significantly and substantially slows disease progression in HIV+ adults not on ARV, and possibly reduces mortality. Micronutrient supplements are effective in reducing progression with a posterior probability of 97.9%. Considering MNS low cost and lack of adverse effects, MNS should be standard of care for HIV+ adults not yet on ARV
Revisiting the Direct Sum Theorem and Space Lower Bounds in Random Order Streams
Estimating frequency moments and distances are well studied problems in the adversarial data stream model and tight space bounds are known for these two problems. There has been growing interest in revisiting these problems in the framework of random-order streams. The best space lower bound known for computing the frequency moment in random-order streams is by Andoni et al., and it is conjectured that the real lower bound shall be . In this paper, we resolve this conjecture. In our approach, we revisit the direct sum theorem developed by Bar-Yossef et al. in a random-partition private messages model and provide a tight space lower bound for any -pass algorithm that approximates the frequency moment in random-order stream model to a constant factor. Finally, we also introduce the notion of space-entropy tradeoffs in random order streams, as a means of studying intermediate models between adversarial and fully random order streams. We show an almost tight space-entropy tradeoff for distance and a non-trivial tradeoff for distances
The US Commitments to NATO in the Post-Cold War Period - A Case Study on Libya
The recent history of the US commitment to NATO has been dominated by economic pressures, squabbles over NATO’s military performance in Afghanistan, and the apparent American preference for ‘leading from behind’ in Libya. The case study on Libya will be especially important in exploring the Obama administration’s understanding of the purpose of NATO in the context of current economic pressures, domestic US debates about post-War on Terror interventions, and of increasing American preoccupation with Pacific (rather than European) security. In the case of Libya, the US apparently hesitated to unfold military operations against Libyan military targets. It seems to be the first time that the US followed rather than led its European allies to a campaign. The reason why the US was reluctant to intervene in Libya at the very beginning; why it changed its mind to join the operation later; and why it transferred the Libyan mission to NATO and adopted the strategy of ‘leading from behind’, reflected on not only the redefinition of ‘American way of war’, but also the future of NATO
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