225 research outputs found
Any-k: Anytime Top-k Tree Pattern Retrieval in Labeled Graphs
Many problems in areas as diverse as recommendation systems, social network
analysis, semantic search, and distributed root cause analysis can be modeled
as pattern search on labeled graphs (also called "heterogeneous information
networks" or HINs). Given a large graph and a query pattern with node and edge
label constraints, a fundamental challenge is to nd the top-k matches ac-
cording to a ranking function over edge and node weights. For users, it is di
cult to select value k . We therefore propose the novel notion of an any-k
ranking algorithm: for a given time budget, re- turn as many of the top-ranked
results as possible. Then, given additional time, produce the next lower-ranked
results quickly as well. It can be stopped anytime, but may have to continues
until all results are returned. This paper focuses on acyclic patterns over
arbitrary labeled graphs. We are interested in practical algorithms that
effectively exploit (1) properties of heterogeneous networks, in particular
selective constraints on labels, and (2) that the users often explore only a
fraction of the top-ranked results. Our solution, KARPET, carefully integrates
aggressive pruning that leverages the acyclic nature of the query, and
incremental guided search. It enables us to prove strong non-trivial time and
space guarantees, which is generally considered very hard for this type of
graph search problem. Through experimental studies we show that KARPET achieves
running times in the order of milliseconds for tree patterns on large networks
with millions of nodes and edges.Comment: To appear in WWW 201
Env2Vec: accelerating VNF testing with deep learning
The adoption of fast-paced practices for developing virtual network functions (VNFs) allows for continuous software delivery and creates a market advantage for network operators. This adoption, however, is problematic for testing engineers that need to assure, in shorter development cycles, certain quality of highly-configurable product releases running on heterogeneous clouds. Machine learning (ML) can accelerate testing workflows by detecting performance issues in new software builds. However, the overhead of maintaining several models for all combinations of build types, network configurations, and other stack parameters, can quickly become prohibitive and make the application of ML infeasible.
We propose Env2Vec, a deep learning architecture that combines contextual features with historical resource usage, and characterizes the various stack parameters that influence the test execution within an embedding space, which allows it to generalize model predictions to previously unseen environments. We integrate a single ML model in the testing workflow to automatically debug errors and pinpoint performance bottlenecks. Results obtained with real testing data show an accuracy between 86.2%-100%, while reducing the false alarm rate by 20.9%-38.1% when reporting performance issues compared to state-of-the-art approaches
Geometric Heuristics for Transfer Learning in Decision Trees
Motivated by a network fault detection problem, we study how
recall can be boosted in a decision tree classifier, without sacrificing
too much precision. This problem is relevant and novel in the context of transfer learning (TL), in which few target domain training
samples are available. We define a geometric optimization problem
for boosting the recall of a decision tree classifier, and show it is
NP-hard. To solve it efficiently, we propose several near-linear time
heuristics, and experimentally validate these heuristics in the context of TL. Our evaluation includes 7 public datasets, as well as 6
network fault datasets, and we compare our heuristics with several
existing TL algorithms, as well as exact mixed integer linear programming (MILP) solutions to our optimization problem. We find
that our heuristics boost recall in a manner similar to optimal MILP
solutions, yet require several orders of magnitude less compute
time. In many cases th
Evaluation of an influenza-like illness case definition in the diagnosis of influenza among patients with acute febrile illness in cambodia
<p>Abstract</p> <p>Background</p> <p>Influenza-like illness (ILI) is often defined as fever (>38.0°C) with cough or sore throat. In this study, we tested the sensitivity, specificity, and positive and negative predictive values of this case definition in a Cambodia patient population.</p> <p>Methods</p> <p>Passive clinic-based surveillance was established at nine healthcare centers to identify the causes of acute undifferentiated fever in patients aged two years and older seeking treatment. Fever was defined as tympanic membrane temperature >38°C lasting more than 24 hours and less than 10 days. Influenza virus infections were identified by polymerase chain reaction.</p> <p>Results</p> <p>From July 2008 to December 2008, 2,639 patients were enrolled. From 884 (33%) patients positive for influenza, 652 presented with ILI and 232 acute fever patients presented without ILI. Analysis by age group identified no significant differences between influenza positive patients from the two groups. Positive predictive values (PPVs) varied during the course of the influenza season and among age groups.</p> <p>Conclusion</p> <p>The ILI case definition can be used to identify a significant percentage of patients with influenza infection during the influenza season in Cambodia, assisting healthcare providers in its diagnosis and treatment. However, testing samples based on the criteria of fever alone increased our case detection by 34%.</p
2019 international consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations : summary from the basic life support; advanced life support; pediatric life support; neonatal life support; education, implementation, and teams; and first aid task forces
The International Liaison Committee on Resuscitation has initiated a continuous review of new, peer-reviewed, published cardiopulmonary resuscitation science. This is the third annual summary of the International Liaison Committee on Resuscitation International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations. It addresses the most recent published resuscitation evidence reviewed by International Liaison Committee on Resuscitation Task Force science experts. This summary addresses the role of cardiac arrest centers and dispatcher-assisted cardiopulmonary resuscitation, the role of extracorporeal cardiopulmonary resuscitation in adults and children, vasopressors in adults, advanced airway interventions in adults and children, targeted temperature management in children after cardiac arrest, initial oxygen concentration during resuscitation of newborns, and interventions for presyncope by first aid providers. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the certainty of the evidence on the basis of the Grading of Recommendations, Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence to Decision Framework Highlights sections. The task forces also listed priority knowledge gaps for further research
Acute kidney disease and renal recovery : consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup
Consensus definitions have been reached for both acute kidney injury (AKI) and chronic kidney disease (CKD) and these definitions are now routinely used in research and clinical practice. The KDIGO guideline defines AKI as an abrupt decrease in kidney function occurring over 7 days or less, whereas CKD is defined by the persistence of kidney disease for a period of > 90 days. AKI and CKD are increasingly recognized as related entities and in some instances probably represent a continuum of the disease process. For patients in whom pathophysiologic processes are ongoing, the term acute kidney disease (AKD) has been proposed to define the course of disease after AKI; however, definitions of AKD and strategies for the management of patients with AKD are not currently available. In this consensus statement, the Acute Disease Quality Initiative (ADQI) proposes definitions, staging criteria for AKD, and strategies for the management of affected patients. We also make recommendations for areas of future research, which aim to improve understanding of the underlying processes and improve outcomes for patients with AKD
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