7,462 research outputs found
Cyclic proof systems for modal fixpoint logics
This thesis is about cyclic and ill-founded proof systems for modal fixpoint logics, with and without explicit fixpoint quantifiers.Cyclic and ill-founded proof-theory allow proofs with infinite branches or paths, as long as they satisfy some correctness conditions ensuring the validity of the conclusion. In this dissertation we design a few cyclic and ill-founded systems: a cyclic one for the weak Grzegorczyk modal logic K4Grz, based on our explanation of the phenomenon of cyclic companionship; and ill-founded and cyclic ones for the full computation tree logic CTL* and the intuitionistic linear-time temporal logic iLTL. All systems are cut-free, and the cyclic ones for K4Grz and iLTL have fully finitary correctness conditions.Lastly, we use a cyclic system for the modal mu-calculus to obtain a proof of the uniform interpolation property for the logic which differs from the original, automata-based one
ELITR Non-Native Speech Translation at IWSLT 2020
This paper is an ELITR system submission for the non-native speech translation task at IWSLT 2020. We describe systems for offline ASR, real-time ASR, and our cascaded approach to offline SLT and real-time SLT. We select our primary candidates from a pool of pre-existing systems, develop a new end-toend general ASR system, and a hybrid ASR trained on non-native speech. The provided small validation set prevents us from carrying out a complex validation, but we submit all the unselected candidates for contrastive evaluation on the test set
Adaptive stiffness in lattice metastructures through tensile-buckling inspired topology morphing
This paper explores the use of simultaneous tensile buckling of unit cells to induce a transformation in lattice topology. Under tension, unit cells undergo passive transformation from a rectangle-like to a triangle-/pentagon-like topology, with an associated change in the effective stiffness properties. This behaviour is investigated through finite element analysis and experiments, with analytical results providing insights into the observed behaviour. The analysis identifies (i) that the initial unit cell topology (rectangular) is dominated by membrane effects, (ii) the transformation phase is associated with negative stiffness, and (iii) once formed, the new topology (triangular/pentagonal) exhibits increased stiffness in both compression and tension. Finite element analysis confirms that the unit cell behaviour is also preserved in lattices. Under tension, the lattice undergoes a seven-fold increase in stiffness as it transitions from its initial to the new topology, with a regime of negative stiffness during this transformation accounting for approximately 82% of its total elastic deformation. This new approach to elastically tailor the nonlinear response of (meta-)materials/structures has the potential to contribute to the development of novel tensile energy absorbers
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Assembling Single RbCs Molecules with Optical Tweezers
Optical tweezer arrays are useful tools for manipulating single atoms and molecules.
An exciting avenue for research with optical tweezers is using the interactions between polar molecules for quantum computation or quantum simulation.
Molecules can be assembled in an optical tweezer array starting from pairs of atoms.
The atoms must be initialised in the relative motional ground state of a common trap.
This work outlines the design of a Raman sideband cooling protocol which is implemented to prepare an 87-Rubidium atom in the motional ground state of an 817 nm tweezer, and a 133-Caesium atom in the motional ground state of a 938 nm tweezer.
The protocol circumvents strong heating and dephasing associated with the trap by operating at lower trap depths and cooling from outside the Lamb-Dicke regime.
By analysing several sources of heating, we design and implement a merging sequence that transfers the Rb atom and the Cs atom to a common trap with minimal motional excitation.
Subsequently, we perform a detailed characterisation of AC Stark shifts caused by the tweezer light, and identify several situations in which the confinement of the atom pair influences their interactions.
Then, we demonstrate the preparation of a molecular bound state after an adiabatic ramp across a magnetic Feshbach resonance.
Measurements of molecular loss rates provide evidence that the atoms are in fact associated during the merging sequence, before the magnetic field ramp.
By preparing a weakly-bound molecule in an optical tweezer, we carry out important steps towards assembling an array of ultracold RbCs molecules in their rovibrational ground states
GNN-Assisted Phase Space Integration with Application to Atomistics
Overcoming the time scale limitations of atomistics can be achieved by
switching from the state-space representation of Molecular Dynamics (MD) to a
statistical-mechanics-based representation in phase space, where approximations
such as maximum-entropy or Gaussian phase packets (GPP) evolve the atomistic
ensemble in a time-coarsened fashion. In practice, this requires the
computation of expensive high-dimensional integrals over all of phase space of
an atomistic ensemble. This, in turn, is commonly accomplished efficiently by
low-order numerical quadrature. We show that numerical quadrature in this
context, unfortunately, comes with a set of inherent problems, which corrupt
the accuracy of simulations -- especially when dealing with crystal lattices
with imperfections. As a remedy, we demonstrate that Graph Neural Networks,
trained on Monte-Carlo data, can serve as a replacement for commonly used
numerical quadrature rules, overcoming their deficiencies and significantly
improving the accuracy. This is showcased by three benchmarks: the thermal
expansion of copper, the martensitic phase transition of iron, and the energy
of grain boundaries. We illustrate the benefits of the proposed technique over
classically used third- and fifth-order Gaussian quadrature, we highlight the
impact on time-coarsened atomistic predictions, and we discuss the
computational efficiency. The latter is of general importance when performing
frequent evaluation of phase space or other high-dimensional integrals, which
is why the proposed framework promises applications beyond the scope of
atomistics
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Fourier Neural Operators (FNOs) have proven to be an efficient and effective
method for resolution-independent operator learning in a broad variety of
application areas across scientific machine learning. A key reason for their
success is their ability to accurately model long-range dependencies in
spatio-temporal data by learning global convolutions in a computationally
efficient manner. To this end, FNOs rely on the discrete Fourier transform
(DFT), however, DFTs cause visual and spectral artifacts as well as pronounced
dissipation when learning operators in spherical coordinates since they
incorrectly assume a flat geometry. To overcome this limitation, we generalize
FNOs on the sphere, introducing Spherical FNOs (SFNOs) for learning operators
on spherical geometries. We apply SFNOs to forecasting atmospheric dynamics,
and demonstrate stable auto\-regressive rollouts for a year of simulated time
(1,460 steps), while retaining physically plausible dynamics. The SFNO has
important implications for machine learning-based simulation of climate
dynamics that could eventually help accelerate our response to climate change
A Case of Exponential Convergence Rates for SVM
Classification is often the first problem described in introductory machine
learning classes. Generalization guarantees of classification have historically
been offered by Vapnik-Chervonenkis theory. Yet those guarantees are based on
intractable algorithms, which has led to the theory of surrogate methods in
classification. Guarantees offered by surrogate methods are based on
calibration inequalities, which have been shown to be highly sub-optimal under
some margin conditions, failing short to capture exponential convergence
phenomena. Those "super" fast rates are becoming to be well understood for
smooth surrogates, but the picture remains blurry for non-smooth losses such as
the hinge loss, associated with the renowned support vector machines. In this
paper, we present a simple mechanism to obtain fast convergence rates and we
investigate its usage for SVM. In particular, we show that SVM can exhibit
exponential convergence rates even without assuming the hard Tsybakov margin
condition.Comment: 16 pages, 6 figure
Impact of conditional modelling for universal autoregressive quantum states
We present a generalized framework to adapt universal quantum state
approximators, enabling them to satisfy rigorous normalization and
autoregressive properties. We also introduce filters as analogues to
convolutional layers in neural networks to incorporate translationally
symmetrized correlations in arbitrary quantum states. By applying this
framework to the Gaussian process state, we enforce autoregressive and/or
filter properties, analyzing the impact of the resulting inductive biases on
variational flexibility, symmetries, and conserved quantities. In doing so we
bring together different autoregressive states under a unified framework for
machine learning-inspired ans\"atze. Our results provide insights into how the
autoregressive construction influences the ability of a variational model to
describe correlations in spin and fermionic lattice models, as well as ab
initio electronic structure problems where the choice of representation affects
accuracy. We conclude that, while enabling efficient and direct sampling, thus
avoiding autocorrelation and loss of ergodicity issues in Metropolis sampling,
the autoregressive construction materially constrains the expressivity of the
model in many systems
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