7,896 research outputs found
Reduced Scaling Hilbert Space Variational Monte Carlo
We show that for both single-Slater-Jastrow and Jastrow geminal power wave
functions, the formal cost scaling of Hilbert space variational Monte Carlo can
be reduced from fifth to fourth order in the system size, thus bringing it in
line with the long-standing scaling of its real space counterpart. While
traditional quantum chemistry methods can reduce costs related to the
two-electron integral tensor through resolution of the identity and Cholesky
decomposition approaches, we show that such approaches are ineffective in the
presence of Hilbert space Jastrow factors. Instead, we develop a simple
semi-stochastic approach that can take similar advantage of the near-sparsity
of this four-index tensor. Through demonstrations on alkanes of increasing
length, we show that accuracy and overall statistical uncertainty are not
meaningfully affected and that a total cost crossover is reached as early as 50
electrons.Comment: 8 pages, 7 figure
Structural Embedding of Syntactic Trees for Machine Comprehension
Deep neural networks for machine comprehension typically utilizes only word
or character embeddings without explicitly taking advantage of structured
linguistic information such as constituency trees and dependency trees. In this
paper, we propose structural embedding of syntactic trees (SEST), an algorithm
framework to utilize structured information and encode them into vector
representations that can boost the performance of algorithms for the machine
comprehension. We evaluate our approach using a state-of-the-art neural
attention model on the SQuAD dataset. Experimental results demonstrate that our
model can accurately identify the syntactic boundaries of the sentences and
extract answers that are syntactically coherent over the baseline methods
Optimal Entanglement Transformations Among N-qubit W-Class States
We investigate the physically allowed probabilities for transforming one
N-partite W-class state to another by means of local operations assisted with
classical communication (LOCC). Recently, Kintas and Turgut have obtained an
upper bound for the maximum probability of transforming two such states
[arXiv:1003.2118v1]. Here, we provide a simple sufficient and necessary
condition for when this upper bound can be satisfied and thus when optimality
of state transformation can be achieved. Our discussion involves obtaining
lower bounds for the transformation of arbitrary W-class states and showing
precisely when this bound saturates the bound of [arXiv:1003.2118v1]. Finally,
we consider the question of transforming symmetric W-class states and find that
in general, the optimal one-shot procedure for converting two symmetric states
requires a non-symmetric filter by all the parties
Mass Dependence of Higgs Production at Large Transverse Momentum
The transverse momentum distribution of the Higgs at large is
complicated by its dependence on three important energy scales: , the top
quark mass , and the Higgs mass . A strategy for simplifying the
calculation of the cross section at large is to calculate only the
leading terms in its expansion in and/or . The
expansion of the cross section in inverse powers of is complicated by
logarithms of and by mass singularities. In this paper, we consider the
top-quark loop contribution to the subprocess at leading
order in . We show that the leading power of can be
expressed in the form of a factorization formula that separates the large scale
from the scale of the masses. All the dependence on and can
be factorized into a distribution amplitude for in the Higgs, a
distribution amplitude for in a real gluon, and an endpoint
contribution. The factorization formula can be used to simplify calculations of
the distribution at large to next-to-leading order in .Comment: 49 pages, 8 figure
Cracking the Network Code: Four Principles for Grantmakers
As grantmakers and nonprofits are looking for ways to collaborate more effectively, many are experimenting working with and through networks to achieve greater impact. Because networks are by definition loosely controlled and emergent, understanding how to effectively support them feels like a mystery to many grantmakers.GEO's newest publication sets out to crack the code behind the network mystique. In fact, there is a method to working more efficiently and effectively through networks, and a critical first step for grantmakers is adopting a network mindset, which may require dramatic shifts in attitude and behavior for some. "Cracking the Network Code" outlines four principles that comprise the network mindset, illustrates the principles with a range of examples of networks that have achieved real results, and offers practical questions and recommendations to help grantmakers achieve the benefits and avoid common pitfalls of working through networks
High-Performance Distributed ML at Scale through Parameter Server Consistency Models
As Machine Learning (ML) applications increase in data size and model
complexity, practitioners turn to distributed clusters to satisfy the increased
computational and memory demands. Unfortunately, effective use of clusters for
ML requires considerable expertise in writing distributed code, while
highly-abstracted frameworks like Hadoop have not, in practice, approached the
performance seen in specialized ML implementations. The recent Parameter Server
(PS) paradigm is a middle ground between these extremes, allowing easy
conversion of single-machine parallel ML applications into distributed ones,
while maintaining high throughput through relaxed "consistency models" that
allow inconsistent parameter reads. However, due to insufficient theoretical
study, it is not clear which of these consistency models can really ensure
correct ML algorithm output; at the same time, there remain many
theoretically-motivated but undiscovered opportunities to maximize
computational throughput. Motivated by this challenge, we study both the
theoretical guarantees and empirical behavior of iterative-convergent ML
algorithms in existing PS consistency models. We then use the gleaned insights
to improve a consistency model using an "eager" PS communication mechanism, and
implement it as a new PS system that enables ML algorithms to reach their
solution more quickly.Comment: 19 pages, 2 figure
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