115,762 research outputs found
3D-BEVIS: Bird's-Eye-View Instance Segmentation
Recent deep learning models achieve impressive results on 3D scene analysis
tasks by operating directly on unstructured point clouds. A lot of progress was
made in the field of object classification and semantic segmentation. However,
the task of instance segmentation is less explored. In this work, we present
3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on
point clouds. Following the idea of previous proposal-free instance
segmentation approaches, our model learns a feature embedding and groups the
obtained feature space into semantic instances. Current point-based methods
scale linearly with the number of points by processing local sub-parts of a
scene individually. However, to perform instance segmentation by clustering,
globally consistent features are required. Therefore, we propose to combine
local point geometry with global context information from an intermediate
bird's-eye view representation.Comment: camera-ready version for GCPR '1
Towards a warped inflationary brane scanning
We present a detailed systematics for comparing warped brane inflation with
the observations, incorporating the effects of both moduli stabilization and
ultraviolet bulk physics. We explicitly construct an example of the inflaton
potential governing the motion of a mobile D3 brane in the entire warped
deformed conifold. This allows us to precisely identify the corresponding
scales of the cosmic microwave background. The effects due to bulk fluxes or
localized sources are parametrized using gauge/string duality. We next perform
some sample scannings to explore the parameter space of the complete potential,
and first demonstrate that without the bulk effects there can be large
degenerate sets of parameters with observationally consistent predictions. When
the bulk perturbations are included, however, the observational predictions are
generally spoiled. For them to remain consistent, the magnitudes of the bulk
effects need to be highly suppressed via fine tuning.Comment: (v1) 11 pages, 2 figures, 2 tables; (v2) more clarifications and
references added; (v3) 12 pages, more discussions, to appear in Physical
Review
Shot noise spectrum of superradiant entangled excitons
The shot noise produced by tunneling of electrons and holes into a double dot
system incorporated inside a p-i-n junction is investigated theoretically. The
enhancement of the shot noise is shown to originate from the entangled
electron-hole pair created by superradiance. The analogy to the superconducting
cooper pair box is pointed out. A series of Zeno-like measurements is shown to
destroy the entanglement, except for the case of maximum entanglement.Comment: 5 pages, 3 figures, to appear in Phys. Rev. B (2004
Sleep Analytics and Online Selective Anomaly Detection
We introduce a new problem, the Online Selective Anomaly Detection (OSAD), to
model a specific scenario emerging from research in sleep science. Scientists
have segmented sleep into several stages and stage two is characterized by two
patterns (or anomalies) in the EEG time series recorded on sleep subjects.
These two patterns are sleep spindle (SS) and K-complex. The OSAD problem was
introduced to design a residual system, where all anomalies (known and unknown)
are detected but the system only triggers an alarm when non-SS anomalies
appear. The solution of the OSAD problem required us to combine techniques from
both machine learning and control theory. Experiments on data from real
subjects attest to the effectiveness of our approach.Comment: Submitted to 20th ACM SIGKDD Conference on Knowledge Discovery and
Data Mining 201
Topo-Geometric Filtration Scheme for Geometric Active Contours and Level Sets: Application to Cerebrovascular Segmentation
One of the main problems of the existing methods for the
segmentation of cerebral vasculature is the appearance in the segmentation
result of wrong topological artefacts such as the kissing vessels.
In this paper, a new approach for the detection and correction of such
errors is presented. The proposed technique combines robust topological
information given by Persistent Homology with complementary geometrical
information of the vascular tree. The method was evaluated on 20
images depicting cerebral arteries. Detection and correction success rates
were 81.80% and 68.77%, respectively
Qubit-portraits of qudit states and quantum correlations
The machinery of qubit-portraits of qudit states, recently presented, is
consider here in more details in order to characterize the presence of quantum
correlations in bipartite qudit states. In the tomographic representation of
quantum mechanics, Bell-like inequalities are interpreted as peculiar
properties of a family of classical joint probability distributions which
describe the quantum state of two qudits. By means of the qubit-portraits
machinery a semigroup of stochastic matrices can be associated to a given
quantum state. The violation of the CHSH inequalities is discussed in this
framework with some examples, we found that quantum correlations in qutrit
isotropic states can be detected by the suggested method while it cannot in the
case of qutrit Werner states.Comment: 12 pages, 4 figure
Reconstructing the geometric structure of a Riemannian symmetric space from its Satake diagram
The local geometry of a Riemannian symmetric space is described completely by
the Riemannian metric and the Riemannian curvature tensor of the space. In the
present article I describe how to compute these tensors for any Riemannian
symmetric space from the Satake diagram, in a way that is suited for the use
with computer algebra systems. As an example application, the totally geodesic
submanifolds of the Riemannian symmetric space SU(3)/SO(3) are classified.
The submission also contains an example implementation of the algorithms and
formulas of the paper as a package for Maple 10, the technical documentation
for this implementation, and a worksheet carrying out the computations for the
space SU(3)/SO(3) used in the proof of Proposition 6.1 of the paper.Comment: 23 pages, also contains two Maple worksheets and technical
documentatio
On the Clark-alpha model of turbulence: global regularity and long--time dynamics
In this paper we study a well-known three--dimensional turbulence model, the
filtered Clark model, or Clark-alpha model. This is Large Eddy Simulation (LES)
tensor-diffusivity model of turbulent flows with an additional spatial filter
of width alpha (). We show the global well-posedness of this model with
constant Navier-Stokes (eddy) viscosity. Moreover, we establish the existence
of a finite dimensional global attractor for this dissipative evolution system,
and we provide an anaytical estimate for its fractal and Hausdorff dimensions.
Our estimate is proportional to , where is the integral spatial
scale and is the viscous dissipation length scale. This explicit bound is
consistent with the physical estimate for the number of degrees of freedom
based on heuristic arguments. Using semi-rigorous physical arguments we show
that the inertial range of the energy spectrum for the Clark- model has
the usual Kolmogorov power law for wave numbers and
decay power law for This is evidence that the
Clark model parameterizes efficiently the large wave numbers within
the inertial range, , so that they contain much less translational
kinetic energy than their counterparts in the Navier-Stokes equations.Comment: 11 pages, no figures, submitted to J of Turbulenc
Superconductivity in the Two-Dimensional - Model at Low Hole Doping
By combining a generalized Lanczos scheme with the variational Monte Carlo
method we can optimize the short- and long-range properties of the groundstate
separately. This allows us to measure the long-range order of the groundstate
of the - model as a function of the coupling constant , and identify
a region of finite d-wave superconducting long-range order. With a lattice size
of 50 sites we can reliably examine hole densities down to 0.16.Comment: 12 pages and 4 PostScript figures, ReVTeX 3.0, ETH-TH/94-1
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