28,286 research outputs found
Optimal universal quantum circuits for unitary complex conjugation
Let be a unitary operator representing an arbitrary -dimensional
unitary quantum operation. This work presents optimal quantum circuits for
transforming a number of calls of into its complex conjugate
. Our circuits admit a parallel implementation and are proven to be
optimal for any and with an average fidelity of
. Optimality is shown for
average fidelity, robustness to noise, and other standard figures of merit.
This extends previous works which considered the scenario of a single call
() of the operation , and the special case of calls. We then
show that our results encompass optimal transformations from calls of
to for any arbitrary homomorphism from the group of
-dimensional unitary operators to itself, since complex conjugation is the
only non-trivial automorphisms on the group of unitary operators. Finally, we
apply our optimal complex conjugation implementation to design a probabilistic
circuit for reversing arbitrary quantum evolutions.Comment: 19 pages, 5 figures. Improved presentation, typos corrected, and some
proofs are now clearer. Closer to the published versio
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V-LoTSS: The circularly polarised LOFAR Two-metre Sky Survey
We present the detection of 68 sources from the most sensitive radio survey in circular polarisation conducted to date. We used the second data release of the 144 MHz LOFAR Two-metre Sky Survey to produce circularly polarised maps with a median noise of 140 µJy beam−1 and resolution of 20″ for ≈27% of the northern sky (5634 deg2). The leakage of total intensity into circular polarisation is measured to be ≈0.06%, and our survey is complete at flux densities ≥1 mJy. A detection is considered reliable when the circularly polarised fraction exceeds 1%. We find the population of circularly polarised sources is composed of four distinct classes: stellar systems, pulsars, active galactic nuclei, and sources unidentified in the literature. The stellar systems can be further separated into chromospherically active stars, M dwarfs, and brown dwarfs. Based on the circularly polarised fraction and lack of an optical counterpart, we show it is possible to infer whether the unidentified sources are likely unknown pulsars or brown dwarfs. By the completion of this survey of the northern sky, we expect to detect 300±100 circularly polarised sources
A family of total Lagrangian Petrov-Galerkin Cosserat rod finite element formulations
The standard in rod finite element formulations is the Bubnov-Galerkin
projection method, where the test functions arise from a consistent variation
of the ansatz functions. This approach becomes increasingly complex when highly
nonlinear ansatz functions are chosen to approximate the rod's centerline and
cross-section orientations. Using a Petrov-Galerkin projection method, we
propose a whole family of rod finite element formulations where the nodal
generalized virtual displacements and generalized velocities are interpolated
instead of using the consistent variations and time derivatives of the ansatz
functions. This approach leads to a significant simplification of the
expressions in the discrete virtual work functionals. In addition, independent
strategies can be chosen for interpolating the nodal centerline points and
cross-section orientations. We discuss three objective interpolation strategies
and give an in-depth analysis concerning locking and convergence behavior for
the whole family of rod finite element formulations.Comment: arXiv admin note: text overlap with arXiv:2301.0559
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
Sleep abnormalities can have severe health consequences. Automated sleep
staging, i.e. labelling the sequence of sleep stages from the patient's
physiological recordings, could simplify the diagnostic process. Previous work
on automated sleep staging has achieved great results, mainly relying on the
EEG signal. However, often multiple sources of information are available beyond
EEG. This can be particularly beneficial when the EEG recordings are noisy or
even missing completely. In this paper, we propose CoRe-Sleep, a Coordinated
Representation multimodal fusion network that is particularly focused on
improving the robustness of signal analysis on imperfect data. We demonstrate
how appropriately handling multimodal information can be the key to achieving
such robustness. CoRe-Sleep tolerates noisy or missing modalities segments,
allowing training on incomplete data. Additionally, it shows state-of-the-art
performance when testing on both multimodal and unimodal data using a single
model on SHHS-1, the largest publicly available study that includes sleep stage
labels. The results indicate that training the model on multimodal data does
positively influence performance when tested on unimodal data. This work aims
at bridging the gap between automated analysis tools and their clinical
utility.Comment: 10 pages, 4 figures, 2 tables, journa
TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation
Multi-modal fusion of sensors is a commonly used approach to enhance the
performance of odometry estimation, which is also a fundamental module for
mobile robots. However, the question of \textit{how to perform fusion among
different modalities in a supervised sensor fusion odometry estimation task?}
is still one of challenging issues remains. Some simple operations, such as
element-wise summation and concatenation, are not capable of assigning adaptive
attentional weights to incorporate different modalities efficiently, which make
it difficult to achieve competitive odometry results. Recently, the Transformer
architecture has shown potential for multi-modal fusion tasks, particularly in
the domains of vision with language. In this work, we propose an end-to-end
supervised Transformer-based LiDAR-Inertial fusion framework (namely
TransFusionOdom) for odometry estimation. The multi-attention fusion module
demonstrates different fusion approaches for homogeneous and heterogeneous
modalities to address the overfitting problem that can arise from blindly
increasing the complexity of the model. Additionally, to interpret the learning
process of the Transformer-based multi-modal interactions, a general
visualization approach is introduced to illustrate the interactions between
modalities. Moreover, exhaustive ablation studies evaluate different
multi-modal fusion strategies to verify the performance of the proposed fusion
strategy. A synthetic multi-modal dataset is made public to validate the
generalization ability of the proposed fusion strategy, which also works for
other combinations of different modalities. The quantitative and qualitative
odometry evaluations on the KITTI dataset verify the proposed TransFusionOdom
could achieve superior performance compared with other related works.Comment: Submitted to IEEE Sensors Journal with some modifications. This work
has been submitted to the IEEE for possible publication. Copyright may be
transferred without notice, after which this version may no longer be
accessibl
Mechanical behaviour of rubber blocks
This study investigates the behaviour of rubber blocks bonded between two plates under combined compression and shear loading, using experimental and numerical analyses, and also approximate analytical theories. First, experimental data from a series of compression and shear tests of rubber blocks with different aspect ratios are presented. Next, numerical simulations are carried out with three-dimensional finite element (FE) models, allowing insight to be gained into the stress and strain fields within the blocks. Existing analytical theories for blocks under compression and combined compressive and shear loading are then reviewed, and their accuracy is evaluated against test and numerical results. The study shows that those theories accounting for the effect of the axial shortening of the blocks provide a better description of the combined compression and shear behaviour, compared to theories, developed for laminated structural bearings with many thin rubber layers, that ignore this effect. An improved theory is also proposed, which better describes the effects of the bulging of the compressed blocks on their shear and flexural parameters and provides a better fit to experimental and numerical results
ENABLING EFFICIENT FLEET COMPOSITION SELECTION THROUGH THE DEVELOPMENT OF A RANK HEURISTIC FOR A BRANCH AND BOUND METHOD
In the foreseeable future, autonomous mobile robots (AMRs) will become a key enabler
for increasing productivity and flexibility in material handling in warehousing facilities,
distribution centers and manufacturing systems.
The objective of this research is to develop and validate parametric models of AMRs,
develop ranking heuristic using a physics-based algorithm within the framework of the
Branch and Bound method, integrate the ranking algorithm into a Fleet Composition
Optimization (FCO) tool, and finally conduct simulations under various scenarios to
verify the suitability and robustness of the developed tool in a factory equipped with
AMRs. Kinematic-based equations are used for computing both energy and time
consumption. Multivariate linear regression, a data-driven method, is used for designing
the ranking heuristic. The results indicate that the unique physical structures and
parameters of each robot are the main factors contributing to differences in energy and
time consumption. improvement on reducing computation time was achieved by
comparing heuristic-based search and non-heuristic-based search. This research is
expected to significantly improve the current nested fleet composition optimization tool
by reducing computation time without sacrificing optimality. From a practical
perspective, greater efficiency in reducing energy and time costs can be achieved.Ford Motor CompanyNo embargoAcademic Major: Aerospace Engineerin
Advancing Model Pruning via Bi-level Optimization
The deployment constraints in practical applications necessitate the pruning
of large-scale deep learning models, i.e., promoting their weight sparsity. As
illustrated by the Lottery Ticket Hypothesis (LTH), pruning also has the
potential of improving their generalization ability. At the core of LTH,
iterative magnitude pruning (IMP) is the predominant pruning method to
successfully find 'winning tickets'. Yet, the computation cost of IMP grows
prohibitively as the targeted pruning ratio increases. To reduce the
computation overhead, various efficient 'one-shot' pruning methods have been
developed, but these schemes are usually unable to find winning tickets as good
as IMP. This raises the question of how to close the gap between pruning
accuracy and pruning efficiency? To tackle it, we pursue the algorithmic
advancement of model pruning. Specifically, we formulate the pruning problem
from a fresh and novel viewpoint, bi-level optimization (BLO). We show that the
BLO interpretation provides a technically-grounded optimization base for an
efficient implementation of the pruning-retraining learning paradigm used in
IMP. We also show that the proposed bi-level optimization-oriented pruning
method (termed BiP) is a special class of BLO problems with a bi-linear problem
structure. By leveraging such bi-linearity, we theoretically show that BiP can
be solved as easily as first-order optimization, thus inheriting the
computation efficiency. Through extensive experiments on both structured and
unstructured pruning with 5 model architectures and 4 data sets, we demonstrate
that BiP can find better winning tickets than IMP in most cases, and is
computationally as efficient as the one-shot pruning schemes, demonstrating 2-7
times speedup over IMP for the same level of model accuracy and sparsity.Comment: Thirty-sixth Conference on Neural Information Processing Systems
(NeurIPS 2022
Qluster: An easy-to-implement generic workflow for robust clustering of health data
The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors
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