2,377 research outputs found
F1000 recommendations as a new data source for research evaluation: A comparison with citations
F1000 is a post-publication peer review service for biological and medical
research. F1000 aims to recommend important publications in the biomedical
literature, and from this perspective F1000 could be an interesting tool for
research evaluation. By linking the complete database of F1000 recommendations
to the Web of Science bibliographic database, we are able to make a
comprehensive comparison between F1000 recommendations and citations. We find
that about 2% of the publications in the biomedical literature receive at least
one F1000 recommendation. Recommended publications on average receive 1.30
recommendations, and over 90% of the recommendations are given within half a
year after a publication has appeared. There turns out to be a clear
correlation between F1000 recommendations and citations. However, the
correlation is relatively weak, at least weaker than the correlation between
journal impact and citations. More research is needed to identify the main
reasons for differences between recommendations and citations in assessing the
impact of publications
Implementing Snow Load Monitoring to Control Reliability of a Stadium Roof
This contribution shows how monitoring can be
used to control reliability of a structure not complying
with the requirements of Eurocodes. A general
methodology to obtain cost-optimal decisions using limit
state design, probabilistic reliability analysis and cost
estimates is utilised in a full-scale case study dealing with
the roof of a stadium located in Northern Italy. The
results demonstrate the potential of monitoring systems
and probabilistic reliability analysis to support decisions
regarding safety measures such as snow removal, or
temporary closure of the stadium
Flexual buckling of structural glass columns. Initial geometrical imperfection as a base for Monte Carlo simulation
In this paper Monte Carlo simulations of
structural glass columns are presented. The simulation
was performed according to the analytical second order
theory of compressed elastic rods. A previous research
on shape and size of initial geometrical imperfections is
briefly summarized. An experimental analysis of glass
columns that were performed for evaluation of equivalent
geometrical imperfections is mentioned too
Hardness Amplification of Optimization Problems
In this paper, we prove a general hardness amplification scheme for optimization problems based on the technique of direct products.
We say that an optimization problem ? is direct product feasible if it is possible to efficiently aggregate any k instances of ? and form one large instance of ? such that given an optimal feasible solution to the larger instance, we can efficiently find optimal feasible solutions to all the k smaller instances. Given a direct product feasible optimization problem ?, our hardness amplification theorem may be informally stated as follows:
If there is a distribution D over instances of ? of size n such that every randomized algorithm running in time t(n) fails to solve ? on 1/?(n) fraction of inputs sampled from D, then, assuming some relationships on ?(n) and t(n), there is a distribution D\u27 over instances of ? of size O(n??(n)) such that every randomized algorithm running in time t(n)/poly(?(n)) fails to solve ? on 99/100 fraction of inputs sampled from D\u27.
As a consequence of the above theorem, we show hardness amplification of problems in various classes such as NP-hard problems like Max-Clique, Knapsack, and Max-SAT, problems in P such as Longest Common Subsequence, Edit Distance, Matrix Multiplication, and even problems in TFNP such as Factoring and computing Nash equilibrium
Active classification with comparison queries
We study an extension of active learning in which the learning algorithm may
ask the annotator to compare the distances of two examples from the boundary of
their label-class. For example, in a recommendation system application (say for
restaurants), the annotator may be asked whether she liked or disliked a
specific restaurant (a label query); or which one of two restaurants did she
like more (a comparison query).
We focus on the class of half spaces, and show that under natural
assumptions, such as large margin or bounded bit-description of the input
examples, it is possible to reveal all the labels of a sample of size using
approximately queries. This implies an exponential improvement over
classical active learning, where only label queries are allowed. We complement
these results by showing that if any of these assumptions is removed then, in
the worst case, queries are required.
Our results follow from a new general framework of active learning with
additional queries. We identify a combinatorial dimension, called the
\emph{inference dimension}, that captures the query complexity when each
additional query is determined by examples (such as comparison queries,
each of which is determined by the two compared examples). Our results for half
spaces follow by bounding the inference dimension in the cases discussed above.Comment: 23 pages (not including references), 1 figure. The new version
contains a minor fix in the proof of Lemma 4.
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