1,459 research outputs found
A Two-stage Classification Method for High-dimensional Data and Point Clouds
High-dimensional data classification is a fundamental task in machine
learning and imaging science. In this paper, we propose a two-stage multiphase
semi-supervised classification method for classifying high-dimensional data and
unstructured point clouds. To begin with, a fuzzy classification method such as
the standard support vector machine is used to generate a warm initialization.
We then apply a two-stage approach named SaT (smoothing and thresholding) to
improve the classification. In the first stage, an unconstraint convex
variational model is implemented to purify and smooth the initialization,
followed by the second stage which is to project the smoothed partition
obtained at stage one to a binary partition. These two stages can be repeated,
with the latest result as a new initialization, to keep improving the
classification quality. We show that the convex model of the smoothing stage
has a unique solution and can be solved by a specifically designed primal-dual
algorithm whose convergence is guaranteed. We test our method and compare it
with the state-of-the-art methods on several benchmark data sets. The
experimental results demonstrate clearly that our method is superior in both
the classification accuracy and computation speed for high-dimensional data and
point clouds.Comment: 21 pages, 4 figure
Quenched large deviation principles for random projections of balls
Let be a sequence of positive integers growing to
infinity at a sublinear rate, and as . Given a sequence of -dimensional random
vectors belonging to a certain class, which
includes uniform distributions on suitably scaled -balls or
-spheres, , and product distributions with sub-Gaussian
marginals, we study the large deviations behavior of the corresponding sequence
of -dimensional orthogonal projections , where is an -dimensional
projection matrix lying in the Stiefel manifold of orthonormal -frames in
. For almost every sequence of projection matrices, we establish
a large deviation principle (LDP) for the corresponding sequence of
projections, with a fairly explicit rate function that does not depend on the
sequence of projection matrices. As corollaries, we also obtain quenched LDPs
for sequences of -norms and -norms of the coordinates of
the projections. Past work on LDPs for projections with growing dimension has
mainly focused on the annealed setting, where one also averages over the random
projection matrix, chosen from the Haar measure, in which case the coordinates
of the projection are exchangeable. The quenched setting lacks such symmetry
properties, and gives rise to significant new challenges in the setting of
growing projection dimension. Along the way, we establish new Gaussian
approximation results on the Stiefel manifold that may be of independent
interest. Such LDPs are of relevance in asymptotic convex geometry, statistical
physics and high-dimensional statistics.Comment: 53 page
Central limit theorem for the complex eigenvalues of Gaussian random matrices
We establish a central limit theorem for the counting function of the
eigenvalues of a matrix of real Gaussian random variables.Comment: 11 page
Data-driven discovery of dimensionless numbers and scaling laws from experimental measurements
Dimensionless numbers and scaling laws provide elegant insights into the
characteristic properties of physical systems. Classical dimensional analysis
and similitude theory fail to identify a set of unique dimensionless numbers
for a highly-multivariable system with incomplete governing equations. In this
study, we embed the principle of dimensional invariance into a two-level
machine learning scheme to automatically discover dominant and unique
dimensionless numbers and scaling laws from data. The proposed methodology,
called dimensionless learning, can reduce high-dimensional parametric spaces
into descriptions involving just a few physically-interpretable dimensionless
parameters, which significantly simplifies the process design and optimization
of the system. We demonstrate the algorithm by solving several challenging
engineering problems with noisy experimental measurements (not synthetic data)
collected from the literature. The examples include turbulent Rayleigh-Benard
convection, vapor depression dynamics in laser melting of metals, and porosity
formation in 3D printing. We also show that the proposed approach can identify
dimensionally-homogeneous differential equations with minimal parameters by
leveraging sparsity-promoting techniques
An Investigation of Cyberinfrastructure Adoption in University Libraries
This study aims to understand factors that affect university libraries’ adoption of cyberinfrastructure for big data sharing and reuse. A cyberinfrastructure adoption model which contains 10 factors has been developed based on the technology-organization-environment (TOE) framework and the literature regarding tradeoffs of applying cyberinfrastructure. This paper describes the proposed cyberinfrastructure adoption model and explains the survey in-struments. The next steps of the study are also presented
Quantum Circuit Implementation and Resource Analysis of LBlock and LiCi
Due to Grover's algorithm, any exhaustive search attack of block ciphers can
achieve a quadratic speed-up. To implement Grover,s exhaustive search and
accurately estimate the required resources, one needs to implement the target
ciphers as quantum circuits. Recently, there has been increasing interest in
quantum circuits implementing lightweight ciphers. In this paper we present the
quantum implementations and resource estimates of the lightweight ciphers
LBlock and LiCi. We optimize the quantum circuit implementations in the number
of gates, required qubits and the circuit depth, and simulate the quantum
circuits on ProjectQ. Furthermore, based on the quantum implementations, we
analyze the resources required for exhaustive key search attacks of LBlock and
LiCi with Grover's algorithm. Finally, we compare the resources for
implementing LBlock and LiCi with those of other lightweight ciphers.Comment: 29 pages,21 figure
What Makes Good Open-Vocabulary Detector: A Disassembling Perspective
Open-vocabulary detection (OVD) is a new object detection paradigm, aiming to
localize and recognize unseen objects defined by an unbounded vocabulary. This
is challenging since traditional detectors can only learn from pre-defined
categories and thus fail to detect and localize objects out of pre-defined
vocabulary. To handle the challenge, OVD leverages pre-trained cross-modal VLM,
such as CLIP, ALIGN, etc. Previous works mainly focus on the open vocabulary
classification part, with less attention on the localization part. We argue
that for a good OVD detector, both classification and localization should be
parallelly studied for the novel object categories. We show in this work that
improving localization as well as cross-modal classification complement each
other, and compose a good OVD detector jointly. We analyze three families of
OVD methods with different design emphases. We first propose a vanilla
method,i.e., cropping a bounding box obtained by a localizer and resizing it
into the CLIP. We next introduce another approach, which combines a standard
two-stage object detector with CLIP. A two-stage object detector includes a
visual backbone, a region proposal network (RPN), and a region of interest
(RoI) head. We decouple RPN and ROI head (DRR) and use RoIAlign to extract
meaningful features. In this case, it avoids resizing objects. To further
accelerate the training time and reduce the model parameters, we couple RPN and
ROI head (CRR) as the third approach. We conduct extensive experiments on these
three types of approaches in different settings. On the OVD-COCO benchmark, DRR
obtains the best performance and achieves 35.8 Novel AP, an absolute 2.8
gain over the previous state-of-the-art (SOTA). For OVD-LVIS, DRR surpasses the
previous SOTA by 1.9 AP in rare categories. We also provide an object
detection dataset called PID and provide a baseline on PID
Practical Computation of the Charge Mobility in Molecular Semiconductors Using Transient Localization Theory
We describe a practical and flexible
procedure to compute the charge
carrier mobility in the transient localization regime. The method
is straightforward to implement and computationally very inexpensive.
We highlight the practical steps and provide sample computer codes.
To demonstrate the flexibility of the method and generalize the theory,
the correlation between the fluctuations of the transfer integrals
is assessed. The method can be transparently linked with the results
of electronic structure calculations and can therefore be used to
extract the charge mobility at no additional cost
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