407 research outputs found
Correlation Decay up to Uniqueness in Spin Systems
We give a complete characterization of the two-state anti-ferromagnetic spin
systems which are of strong spatial mixing on general graphs. We show that a
two-state anti-ferromagnetic spin system is of strong spatial mixing on all
graphs of maximum degree at most \Delta if and only if the system has a unique
Gibbs measure on infinite regular trees of degree up to \Delta, where \Delta
can be either bounded or unbounded. As a consequence, there exists an FPTAS for
the partition function of a two-state anti-ferromagnetic spin system on graphs
of maximum degree at most \Delta when the uniqueness condition is satisfied on
infinite regular trees of degree up to \Delta. In particular, an FPTAS exists
for arbitrary graphs if the uniqueness is satisfied on all infinite regular
trees. This covers as special cases all previous algorithmic results for
two-state anti-ferromagnetic systems on general-structure graphs.
Combining with the FPRAS for two-state ferromagnetic spin systems of
Jerrum-Sinclair and Goldberg-Jerrum-Paterson, and the very recent hardness
results of Sly-Sun and independently of Galanis-Stefankovic-Vigoda, this gives
a complete classification, except at the phase transition boundary, of the
approximability of all two-state spin systems, on either degree-bounded
families of graphs or family of all graphs.Comment: 27 pages, submitted for publicatio
Learning Robust Representations of Text
Deep neural networks have achieved remarkable results across many language
processing tasks, however these methods are highly sensitive to noise and
adversarial attacks. We present a regularization based method for limiting
network sensitivity to its inputs, inspired by ideas from computer vision, thus
learning models that are more robust. Empirical evaluation over a range of
sentiment datasets with a convolutional neural network shows that, compared to
a baseline model and the dropout method, our method achieves superior
performance over noisy inputs and out-of-domain data.Comment: 5 pages with 2 pages reference, 2 tables, 1 figur
Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination
Measurement of operator quality performance has been challenging in the
construction fabrication industry. Among various causes, the learning effect is
a significant factor, which needs to be incorporated in achieving a reliable
operator quality performance analysis. This research aims to enhance a
previously developed operator quality performance measurement approach by
incorporating the learning effect (i.e., work experience). To achieve this
goal, the Plateau learning model is selected to quantitatively represent the
relationship between quality performance and work experience through a
beta-binomial regression approach. Based on this relationship, an informative
prior determination approach, which incorporates operator work experience
information, is developed to enhance the previous Bayesian-based operator
quality performance measurement. Academically, this research provides a
systematic approach to derive Bayesian informative priors through integrating
multi-source information. Practically, the proposed approach reliably measures
operator quality performance in fabrication quality control processes.Comment: 8 pages, 5 figures, 2 tables, i3CE 201
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