113,565 research outputs found
Weakly-supervised appraisal analysis
This article is concerned with the computational treatment of Appraisal, a Systemic Functional Linguistic theory of the types of language employed to communicate opinion in English. The theory considers aspects such as Attitude (how writers communicate their point of view), Engagement (how writers align themselves with respect to the opinions of others) and Graduation (how writers amplify or diminish their attitudes and engagements). To analyse text according to the theory we employ a weakly-supervised approach to text classification, which involves comparing the similarity of words with prototypical examples of classes. We evaluate the method's performance using a collection of book reviews annotated according to the Appraisal theory
Parametrising arbitrary galaxy morphologies: potentials and pitfalls
We demonstrate that morphological observables (e.g. steepness of the radial
light profile, ellipticity, asymmetry) are intertwined and cannot be measured
independently of each other. We present strong arguments in favour of
model-based parametrisation schemes, namely reliability assessment,
disentanglement of morphological observables, and PSF modelling. Furthermore,
we demonstrate that estimates of the concentration and Sersic index obtained
from the Zurich Structure & Morphology catalogue are in excellent agreement
with theoretical predictions. We also demonstrate that the incautious use of
the concentration index for classification purposes can cause a severe loss of
the discriminative information contained in a given data sample. Moreover, we
show that, for poorly resolved galaxies, concentration index and M_20 suffer
from strong discontinuities, i.e. similar morphologies are not necessarily
mapped to neighbouring points in the parameter space. This limits the
reliability of these parameters for classification purposes. Two-dimensional
Sersic profiles accounting for centroid and ellipticity are identified as the
currently most reliable parametrisation scheme in the regime of intermediate
signal-to-noise ratios and resolutions, where asymmetries and substructures do
not play an important role. We argue that basis functions provide good
parametrisation schemes in the regimes of high signal-to-noise ratios and
resolutions. Concerning Sersic profiles, we show that scale radii cannot be
compared directly for profiles of different Sersic indices. Furthermore, we
show that parameter spaces are typically highly nonlinear. This implies that
significant caution is required when distance-based classificaton methods are
used.Comment: 18 pages, 13 figure
Visual assessment of multi-photon interference
Classical machine learning algorithms can provide insights on high-dimensional processes that are hardly accessible with conventional approaches. As a notable example, t-distributed Stochastic Neighbor Embedding (t-SNE) represents the state of the art for visualization of data sets of large dimensionality. An interesting question is then if this algorithm can provide useful information also in quantum experiments with very large Hilbert spaces. Leveraging these considerations, in this work we apply t-SNE to probe the spatial distribution of n-photon events in m-dimensional Hilbert spaces, showing that its findings can be beneficial for validating genuine quantum interference in boson sampling experiments. In particular, we find that nonlinear dimensionality reduction is capable to capture distinctive features in the spatial distribution of data related to multi-photon states with different evolutions. We envisage that this approach will inspire further theoretical investigations, for instance for a reliable assessment of quantum computational advantage
Mathematical Structure of Loop Quantum Cosmology: Homogeneous Models
The mathematical structure of homogeneous loop quantum cosmology is analyzed,
starting with and taking into account the general classification of homogeneous
connections not restricted to be Abelian. As a first consequence, it is seen
that the usual approach of quantizing Abelian models using spaces of functions
on the Bohr compactification of the real line does not capture all properties
of homogeneous connections. A new, more general quantization is introduced
which applies to non-Abelian models and, in the Abelian case, can be mapped by
an isometric, but not unitary, algebra morphism onto common representations
making use of the Bohr compactification. Physically, the Bohr compactification
of spaces of Abelian connections leads to a degeneracy of edge lengths and
representations of holonomies. Lifting this degeneracy, the new quantization
gives rise to several dynamical properties, including lattice refinement seen
as a direct consequence of state-dependent regularizations of the Hamiltonian
constraint of loop quantum gravity. The representation of basic operators -
holonomies and fluxes - can be derived from the full theory specialized to
lattices. With the new methods of this article, loop quantum cosmology comes
closer to the full theory and is in a better position to produce reliable
predictions when all quantum effects of the theory are taken into account
3DCFS : Fast and robust joint 3D semantic-instance segmentation via coupled feature selection
We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost
The QoSxLabel: a quality of service cross layer label
A quality of service cross layer label
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