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Naïve Bayesian filters for log file analysis: Despam your logs

2012
*
2012 IEEE Network Operations and Management Symposium
*

Naïve Bayesian Spam Filters for

doi:10.1109/noms.2012.6211972
dblp:conf/noms/HavensLT12
fatcat:6ve6ei7ubvbghfwtma3crcbnbq
*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. ...##
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Learning robot grasping from 3-D images with Markov Random Fields

2011
*
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
*

The empirical results show

doi:10.1109/iros.2011.6048528
fatcat:3ouqyvgfqngldoyfg3q4og2wda
*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 ...##
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Inhaled nitric oxide for the adjunctive therapy of severe malaria: Protocol for a randomized controlled trial

2011
*
Trials
*

Endothelial activation plays

doi:10.1186/1745-6215-12-176
pmid:21752262
pmcid:PMC3151218
fatcat:eu5aztfjyfekdf73hfpz3cqpuq
*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). ...##
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Smoothed Analysis of Dynamic Networks
[article]

2015
*
arXiv
*
pre-print

We generalize the technique of

arXiv:1508.03579v1
fatcat:57fdzhu2srbxlbk6phab3ue7pq
*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. ...##
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Smoothed Analysis of Dynamic Networks
[chapter]

2015
*
Lecture Notes in Computer Science
*

Whereas in the traditional setting

doi:10.1007/978-3-662-48653-5_34
fatcat:ap72iwnbtfcelpjxweevdgc2ky
*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. ...##
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Smoothed analysis of dynamic networks

2017
*
Distributed computing
*

Whereas in the traditional setting

doi:10.1007/s00446-017-0300-8
fatcat:5wlkxg7tubcv5nimwvqn6m3qnu
*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. ...##
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Bounding Communication Cost in Dynamic Load Balancing of Distributed Hash Tables
[chapter]

2006
*
Lecture Notes in Computer Science
*

Our procedure requires

doi:10.1007/11795490_29
fatcat:46zil2buqfeypobifm4u2bjpoq
*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. ...##
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A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks
[article]

2020
*
arXiv
*
pre-print

We study dual-based algorithms for distributed convex optimization problems over

arXiv:1809.00710v3
fatcat:kpjafs6pjrc67afhqkqw6icsvy
*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. ...##
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Distributed MST: A Smoothed Analysis
[article]

2019
*
arXiv
*
pre-print

We present

arXiv:1911.02628v1
fatcat:2m2nyawsjvfbrhshzwyqusvccu
*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) . ...##
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Geographic Gossip: Efficient Aggregation for Sensor Networks
[article]

2006
*
arXiv
*
pre-print

In particular, for

arXiv:cs/0602071v1
fatcat:bx26uj4havcbnidvqell6nvvzm
*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). ...##
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Training (Overparametrized) Neural Networks in Near-Linear Time
[article]

2020
*
arXiv
*
pre-print

Very recently, this computational overhead was mitigated by the works of [ZMG19,CGH+19, yielding an

arXiv:2006.11648v2
fatcat:b7dmwuurivetnafmbznqtghnum
*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 ...##
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On estimating the average degree

2014
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Proceedings of the 23rd international conference on World wide web - WWW '14
*

In this work we consider the problem of estimating the average degree of

doi:10.1145/2566486.2568019
dblp:conf/www/DasguptaKS14
fatcat:drf6fumpvrejvjg4c2flru7bnu
*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. ...##
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Particle Smoothing Variational Objectives
[article]

2019
*
arXiv
*
pre-print

at the rate

arXiv:1909.09734v1
fatcat:mjmmvwcp5jd47dvmxbmemgv6ia
*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 = ...##
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Geographic gossip: efficient aggregation for sensor networks

2006
*
2006 5th International Conference on Information Processing in Sensor Networks
*

In particular, for

doi:10.1109/ipsn.2006.244081
fatcat:7z4oyj4s7zazxejchv6a3x747y
*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). ...##
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Geographic gossip

2006
*
Proceedings of the fifth international conference on Information processing in sensor networks - IPSN '06
*

In particular, for

doi:10.1145/1127777.1127791
dblp:conf/ipsn/DimakisSW06
fatcat:pbkcu3uicnf3pancc4sz5q5rqu
*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). ...
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