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Filters
Naïve Bayesian filters for log file analysis: Despam your logs
2012
2012 IEEE Network Operations and Management Symposium
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Naïve Bayesian Spam Filters for Log File Analysis Russel W. ...
For stages 2 and 3, log entries were tested with digits normalized to zeros, with words chained together to various lengths and one or all levels of word chains used together. ...
After all, a word processor, a network driver, an operating system and a web server will have very different logging needs. ...
doi:10.1109/noms.2012.6211972
dblp:conf/noms/HavensLT12
fatcat:6ve6ei7ubvbghfwtma3crcbnbq
Learning robot grasping from 3-D images with Markov Random Fields
2011
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
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The empirical results show a significant improvement over methods that do not utilize the smoothness assumption and classify each point separately from the others. ...
Our approach is based on Markov Random Fields (MRF), and motivated by the fact that points that are geometrically close to each other tend to have similar grasp success probabilities. ...
The AMN model uses the log-linear function for representing a potential as a function of the features, i.e. log φ i (y) = w T n,y x i and log φ ij (y) = w T e,y x ij , where w n,y ∈ R dn are the weights ...
doi:10.1109/iros.2011.6048528
fatcat:3ouqyvgfqngldoyfg3q4og2wda
Inhaled nitric oxide for the adjunctive therapy of severe malaria: Protocol for a randomized controlled trial
2011
Trials
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Endothelial activation plays a central role in the pathogenesis of severe malaria, of which angiopoietin-2 (Ang-2) has recently been shown to function as a key regulator. ...
Nitric oxide (NO) is a major inhibitor of Ang-2 release from endothelium and has been shown to decrease endothelial inflammation and reduce the adhesion of parasitized erythrocytes. ...
Time to recovery Among survivors (a subgroup of randomized participants), recovery times will be analysed by survival analysis (log-rank test). ...
doi:10.1186/1745-6215-12-176
pmid:21752262
pmcid:PMC3151218
fatcat:eu5aztfjyfekdf73hfpz3cqpuq
Smoothed Analysis of Dynamic Networks
[article]
2015
arXiv
pre-print
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We generalize the technique of smoothed analysis to distributed algorithms in dynamic network models. ...
We prove that these bounds provide a spectrum of robustness when subjected to smoothing---some are extremely fragile (random walks), some are moderately fragile / robust (flooding), and some are extremely ...
An O(n 2/3 log n/k 1/3 ) Upper Bound for General Networks We now show that flooding in every k-smoothed network will complete in O(n 2/3 log n/k 1/3 ) time, with high probability. ...
arXiv:1508.03579v1
fatcat:57fdzhu2srbxlbk6phab3ue7pq
Smoothed Analysis of Dynamic Networks
[chapter]
2015
Lecture Notes in Computer Science
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Whereas in the traditional setting smoothing typically perturbs numerical input values, in our setting we define smoothing to perturb the network graph through the random addition and deletion of edges ...
Whereas standard smoothed analysis studies the impact of small random perturbations of input values on algorithm performance metrics, dynamic graph smoothed analysis studies the impact of random perturbations ...
An O(n 2/3 log n/k 1/3 ) Upper Bound for General Networks Next, we show that flooding in every k-smoothed network will complete in O(n 2/3 log n/k 1/3 ) time, with high probability. ...
doi:10.1007/978-3-662-48653-5_34
fatcat:ap72iwnbtfcelpjxweevdgc2ky
Smoothed analysis of dynamic networks
2017
Distributed computing
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Whereas in the traditional setting smoothing typically perturbs numerical input values, in our setting we define smoothing to perturb the network graph through the random addition and deletion of edges ...
Whereas standard smoothed analysis studies the impact of small random perturbations of input values on algorithm performance metrics, dynamic graph smoothed analysis studies the impact of random perturbations ...
An O(n 2/3 log n/k 1/3 ) Upper Bound for General Networks Next, we show that flooding in every k-smoothed network will complete in O(n 2/3 log n/k 1/3 ) time, with high probability. ...
doi:10.1007/s00446-017-0300-8
fatcat:5wlkxg7tubcv5nimwvqn6m3qnu
Bounding Communication Cost in Dynamic Load Balancing of Distributed Hash Tables
[chapter]
2006
Lecture Notes in Computer Science
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Our procedure requires O(log n) times more messages than any procedure maintaining the connectivity, even if the an oblivious adversary decides about the dynamics of the system. ...
As a byproduct, we show how to compute a constant approximation of the current number of nodes n in the system, provided that we know an upper bound on log n. ...
D is O(log n) in Chord [2] and O(log n/ log log n) in de Bruijn graph [5, 6] . In Subsection 2.1 we introduce a notion of weight of an interval. ...
doi:10.1007/11795490_29
fatcat:46zil2buqfeypobifm4u2bjpoq
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks
[article]
2020
arXiv
pre-print
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We study dual-based algorithms for distributed convex optimization problems over networks, where the objective is to minimize a sum ∑_i=1^mf_i(z) of functions over in a network. ...
convex nor smooth. ...
Doan, who made a lot very useful comments on the initial version of this text. This comments allows to repairs significant misprints.
Funding The work of A. Nedić and C.A. ...
arXiv:1809.00710v3
fatcat:kpjafs6pjrc67afhqkqw6icsvy
Distributed MST: A Smoothed Analysis
[article]
2019
arXiv
pre-print
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We present a distributed algorithm that, with high probability,[%s] computes an MST and runs in Õ(min{1/√(ϵ(n)) 2^O(√(log n)), D + √(n)}) rounds[%s] where ϵ is the smoothing parameter, D is the network ...
To complement our upper bound, we also show a lower bound of Ω̃(min{1/√(ϵ(n)), D+√(n)}). We note that the upper and lower bounds essentially match except for a multiplicative 2^O(√(log n))(n) factor. ...
There is a multicommodity routing algorithm on a random graph G(n, (log n)) that achieves congestion and dilation 2 O( √ log n) , and runs in time 2 O( √ log n) . ...
arXiv:1911.02628v1
fatcat:2m2nyawsjvfbrhshzwyqusvccu
Geographic Gossip: Efficient Aggregation for Sensor Networks
[article]
2006
arXiv
pre-print
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In particular, for random geometric graphs, our algorithm computes the true average to accuracy 1/n^a using O(n^1.5√( n)) radio transmissions, which reduces the energy consumption by a √(n/ n) factor over ...
For realistic sensor network model topologies like grids and random geometric graphs, the inefficiency of gossip schemes is caused by slow mixing times of random walks on those graphs. ...
form E (n, 1/n a ) = O(n 3/2 √ log n) and D(n, ǫ) = O(n 3/2 log 3/2 n). ...
arXiv:cs/0602071v1
fatcat:bx26uj4havcbnidvqell6nvvzm
Training (Overparametrized) Neural Networks in Near-Linear Time
[article]
2020
arXiv
pre-print
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Very recently, this computational overhead was mitigated by the works of [ZMG19,CGH+19, yielding an O(mn^2)-time second-order algorithm for training two-layer overparametrized neural networks of polynomial ...
Our result provides a proof-of-concept that advanced machinery from randomized linear algebra – which led to recent breakthroughs in 𝑐𝑜𝑛𝑣𝑒𝑥 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑎𝑡𝑖𝑜𝑛 (ERM, LPs, Regression) – can be ...
Suppose the width of the neural network satisfies m = Ω(max{λ −4 n 4 , λ −2 n 2 d log(16n/δ)}), then with probability 1 − δ over the random initialization of neural network and the randomness of the algorithm ...
arXiv:2006.11648v2
fatcat:b7dmwuurivetnafmbznqtghnum
On estimating the average degree
2014
Proceedings of the 23rd international conference on World wide web - WWW '14
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In this work we consider the problem of estimating the average degree of a large network using efficient random sampling, where the number of nodes is not known to the algorithm. ...
While there has been a spate of recent work on estimating the number of nodes in a network, the edge-estimation question appears to be largely unaddressed. ...
used by Smooth, Guess&Smooth executes at most log 2 (U/L) iterations, each with Θ(log(1/δ) + log log(U/L)) samples. ...
doi:10.1145/2566486.2568019
dblp:conf/www/DasguptaKS14
fatcat:drf6fumpvrejvjg4c2flru7bnu
Particle Smoothing Variational Objectives
[article]
2019
arXiv
pre-print
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at the rate O(1/√(K)). ...
Inspired by this work, we introduce Particle Smoothing Variational Objectives (SVO), a novel backward simulation technique and smoothed approximate posterior defined through a subsampling process. ...
∇ log Z + T t=2 T t ≥t+1 E ∇ w 1 t−1 Zt−1 · (w 1 t −Z t ) 2 2Z 2 t a 1 t−1 = 1 + O( 1 /K) 1 /K T t=1 E (∇ w 1 t Zt ) 2 + T t =t,t =1 T t=1 Var ∇ w 1 t Zt Var ∇ w 1 t Z t + O( T 2 /K 2 ) (16) where Z = ...
arXiv:1909.09734v1
fatcat:mjmmvwcp5jd47dvmxbmemgv6ia
Geographic gossip: efficient aggregation for sensor networks
2006
2006 5th International Conference on Information Processing in Sensor Networks
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In particular, for random geometric graphs, our algorithm computes the true average to accuracy 1/n a using O(n 1.5 √ log n) radio transmissions, which reduces the energy consumption by a q n log n factor ...
For realistic sensor network model topologies like grids and random geometric graphs, the inefficiency of gossip schemes is caused by slow mixing times of random walks on those graphs. ...
n, ) = O(n 3/2 log 3/2 n). ...
doi:10.1109/ipsn.2006.244081
fatcat:7z4oyj4s7zazxejchv6a3x747y
Geographic gossip
2006
Proceedings of the fifth international conference on Information processing in sensor networks - IPSN '06
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In particular, for random geometric graphs, our algorithm computes the true average to accuracy 1/n a using O(n 1.5 √ log n) radio transmissions, which reduces the energy consumption by a q n log n factor ...
For realistic sensor network model topologies like grids and random geometric graphs, the inefficiency of gossip schemes is caused by slow mixing times of random walks on those graphs. ...
n, ) = O(n 3/2 log 3/2 n). ...
doi:10.1145/1127777.1127791
dblp:conf/ipsn/DimakisSW06
fatcat:pbkcu3uicnf3pancc4sz5q5rqu
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