2,064 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
The Global Media and Information Literacy Week: Moving Towards MIL Cities
The Global Media and Information Literacy Week commemorates the progress in achieving “MIL for all” by aggregating various MIL-related local and international events and actions across different disciplines around the world.The MIL Global Week 2018, 24 to 31 October, was marked by the United Nations Educational, Scientific and Cultural Organization in collaboration with various organizations including the UN Alliance of Civilizations, the Global Alliance for Partnership on MIL, the International Federation of Library Associations, the International Association of School Libraries, and the UNESCO-UNAOC University Cooperation Programme on Media and Information Literacy and Intercultural Dialogue
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
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.
Permanent and live load model for probabilistic structural fire analysis : a review
Probabilistic analysis is receiving increased attention from fire engineers, assessment bodies and researchers. It is however often unclear which probabilistic models are appropriate for the analysis. For example, in probabilistic structural fire engineering, the models used to describe the permanent and live load differ widely between studies. Through a literature review, it is observed that these diverging load models largely relate to the same underlying datasets and basic methodologies, while differences can be attributed (largely) to specific assumptions in different background papers which have become consolidated through repeated use in application studies by different researchers. Taking into account the uncovered background information, consolidated probabilistic load models are proposed
Computing a Nonnegative Matrix Factorization -- Provably
In the Nonnegative Matrix Factorization (NMF) problem we are given an nonnegative matrix and an integer . Our goal is to express
as where and are nonnegative matrices of size
and respectively. In some applications, it makes sense to ask
instead for the product to approximate -- i.e. (approximately)
minimize \norm{M - AW}_F where \norm{}_F denotes the Frobenius norm; we
refer to this as Approximate NMF. This problem has a rich history spanning
quantum mechanics, probability theory, data analysis, polyhedral combinatorics,
communication complexity, demography, chemometrics, etc. In the past decade NMF
has become enormously popular in machine learning, where and are
computed using a variety of local search heuristics. Vavasis proved that this
problem is NP-complete. We initiate a study of when this problem is solvable in
polynomial time:
1. We give a polynomial-time algorithm for exact and approximate NMF for
every constant . Indeed NMF is most interesting in applications precisely
when is small.
2. We complement this with a hardness result, that if exact NMF can be solved
in time , 3-SAT has a sub-exponential time algorithm. This rules
out substantial improvements to the above algorithm.
3. We give an algorithm that runs in time polynomial in , and
under the separablity condition identified by Donoho and Stodden in 2003. The
algorithm may be practical since it is simple and noise tolerant (under benign
assumptions). Separability is believed to hold in many practical settings.
To the best of our knowledge, this last result is the first example of a
polynomial-time algorithm that provably works under a non-trivial condition on
the input and we believe that this will be an interesting and important
direction for future work.Comment: 29 pages, 3 figure
Analog and Digital
In this article, I have tried to provide a comprehensive understanding of fundamental differences, historical evolution, and societal implications of analog and digital technologies. Analog technology, characterized by continuous signal representations of physical quantities, is contrasted with digital technology's binary nature. While digital technologies have surged in popularity, reshaping entire industries and daily life, analog technologies persist in niche applications. The historical narrative traces the digital revolution's inception from the introduction of the ENIAC computer in the 1940s to the miniaturization enabled by transistors in the 1950s. Mainframe computers, microprocessors, and the advent of personal computers in the 1970s and 1980s are pivotal milestones. The internet's emergence in the late 20th century and the proliferation of smartphones in the 21st century further demonstrate digital technology's transformative impact. I have also presented a case to show how digital and analog watches might have social and cultural implications, far beyond their technological nature
Probabilistic Modeling of Structural Forces
Since forces acting on structures fluctuate widely with time and space during the lifetime of a structure, variations of the forces should be considered by probability distributions. Probabilistic definition of forces is expressed by random field variables including stochastic parameters. Structural forces are simulated by adopting Normal and Gamma probability distribution functions. The basic model given by JCSS (Joint Committee on Structural Safety) code principles is used as model to take into account the variations. In the simulation of the live loads comprised of sustained and intermittent loads, time intervals are assumed to follow a Poisson process and their distributions are defined by exponential distributions. The simulated loads are evaluated in terms of percentiles, correlation effects, reduction factors and extreme values. Results are compared with those of deterministic model as well. It has been observed that probabilistic model is more realistic and the results can be used in the calculation of specific fractiles like load and resistance factor design
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