274 research outputs found
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
We present a large-scale collection of diverse natural language inference
(NLI) datasets that help provide insight into how well a sentence
representation captures distinct types of reasoning. The collection results
from recasting 13 existing datasets from 7 semantic phenomena into a common NLI
structure, resulting in over half a million labeled context-hypothesis pairs in
total. We refer to our collection as the DNC: Diverse Natural Language
Inference Collection. The DNC is available online at https://www.decomp.net,
and will grow over time as additional resources are recast and added from novel
sources.Comment: To be presented at EMNLP 2018. 15 page
Idiosyncratic behaviour of tRNA-like structures in translation of plant viral RNA genomes
Supramolecular & Biomaterials Chemistr
Colloidal particles at a nematic-isotropic interface: effects of confinement
When captured by a flat nematic-isotropic interface, colloidal particles can
be dragged by it. As a result spatially periodic structures may appear, with
the period depending on a particle mass, size, and interface
velocity~\cite{west.jl:2002}. If liquid crystal is sandwiched between two
substrates, the interface takes a wedge-like shape, accommodating the
interface-substrate contact angle and minimizing the director distortions on
its nematic side. Correspondingly, particles move along complex trajectories:
they are first captured by the interface and then `glide' towards its vertex
point. Our experiments quantify this scenario, and numerical minimization of
the Landau-de Gennes free energy allow for a qualitative description of the
interfacial structure and the drag force.Comment: 7 pages, 9 figure
Two-Qubit Gate Set Tomography with Fewer Circuits
Gate set tomography (GST) is a self-consistent and highly accurate method for
the tomographic reconstruction of a quantum information processor's quantum
logic operations, including gates, state preparations, and measurements.
However, GST's experimental cost grows exponentially with qubit number. For
characterizing even just two qubits, a standard GST experiment may have tens of
thousands of circuits, making it prohibitively expensive for platforms. We show
that, because GST experiments are massively overcomplete, many circuits can be
discarded. This dramatically reduces GST's experimental cost while still
maintaining GST's Heisenberg-like scaling in accuracy. We show how to exploit
the structure of GST circuits to determine which ones are superfluous. We
confirm the efficacy of the resulting experiment designs both through numerical
simulations and via the Fisher information for said designs. We also explore
the impact of these techniques on the prospects of three-qubit GST.Comment: 46 pages, 13 figures. V2: Minor edits to acknowledgment
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