69 research outputs found
The Dual Frequency Anisotropic Magneto-Optical Trap
The cloud of cold atoms produced by a Magneto-Optical Trap is known to
exhibit instabilities. We examine in this paper in which limits it could be
possible to realize an experimental trap similar to the configurations studied
theoretically, i.e. mainly traps where one direction is privileged. We study
the static behavior of an anisotropic trap, where anisotropy results
essentially from the use of two different laser frequencies for the arms of the
trap. Such a trap has very surprising behaviors, in particular the cloud
disappears for some laser frequencies, while it exists for smaller and larger
frequencies. A model is build to explain these behaviors. We show in particular
that, to reproduce the experimental observations, the model has to take into
account the cross saturation effects. Moreover, the couplings between the
different directions cannot be neglected
Phase-space description of the magneto-optical trap
An exhaustive kinetic model for the atoms in a 1D Magneto-Optical Trap is
derived, without any approximations. It is shown that the atomic density is
described by a Vlasov-Fokker-Planck equation, coupled with two simple
differential equations describing the trap beam propagation. The analogy of
such a system with plasmas is discussed. This set of equations is then
simplified through some approximations, and it is shown that corrective terms
have to be added to the models usually used in this context
A Degeneracy Framework for Scalable Graph Autoencoders
In this paper, we present a general framework to scale graph autoencoders
(AE) and graph variational autoencoders (VAE). This framework leverages graph
degeneracy concepts to train models only from a dense subset of nodes instead
of using the entire graph. Together with a simple yet effective propagation
mechanism, our approach significantly improves scalability and training speed
while preserving performance. We evaluate and discuss our method on several
variants of existing graph AE and VAE, providing the first application of these
models to large graphs with up to millions of nodes and edges. We achieve
empirically competitive results w.r.t. several popular scalable node embedding
methods, which emphasizes the relevance of pursuing further research towards
more scalable graph AE and VAE.Comment: International Joint Conference on Artificial Intelligence (IJCAI
2019
Gravity-Inspired Graph Autoencoders for Directed Link Prediction
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged
as powerful node embedding methods. In particular, graph AE and VAE were
successfully leveraged to tackle the challenging link prediction problem,
aiming at figuring out whether some pairs of nodes from a graph are connected
by unobserved edges. However, these models focus on undirected graphs and
therefore ignore the potential direction of the link, which is limiting for
numerous real-life applications. In this paper, we extend the graph AE and VAE
frameworks to address link prediction in directed graphs. We present a new
gravity-inspired decoder scheme that can effectively reconstruct directed
graphs from a node embedding. We empirically evaluate our method on three
different directed link prediction tasks, for which standard graph AE and VAE
perform poorly. We achieve competitive results on three real-world graphs,
outperforming several popular baselines.Comment: ACM International Conference on Information and Knowledge Management
(CIKM 2019
Of Spiky SVDs and Music Recommendation
The truncated singular value decomposition is a widely used methodology in
music recommendation for direct similar-item retrieval or embedding musical
items for downstream tasks. This paper investigates a curious effect that we
show naturally occurring on many recommendation datasets: spiking formations in
the embedding space. We first propose a metric to quantify this spiking
organization's strength, then mathematically prove its origin tied to
underlying communities of items of varying internal popularity. With this
new-found theoretical understanding, we finally open the topic with an
industrial use case of estimating how music embeddings' top-k similar items
will change over time under the addition of data.Comment: Accepted for RecSys 2023 (Singapour, 18-22 September
Learning Unsupervised Hierarchies of Audio Concepts
Music signals are difficult to interpret from their low-level features,
perhaps even more than images: e.g. highlighting part of a spectrogram or an
image is often insufficient to convey high-level ideas that are genuinely
relevant to humans. In computer vision, concept learning was therein proposed
to adjust explanations to the right abstraction level (e.g. detect clinical
concepts from radiographs). These methods have yet to be used for MIR.
In this paper, we adapt concept learning to the realm of music, with its
particularities. For instance, music concepts are typically non-independent and
of mixed nature (e.g. genre, instruments, mood), unlike previous work that
assumed disentangled concepts. We propose a method to learn numerous music
concepts from audio and then automatically hierarchise them to expose their
mutual relationships. We conduct experiments on datasets of playlists from a
music streaming service, serving as a few annotated examples for diverse
concepts. Evaluations show that the mined hierarchies are aligned with both
ground-truth hierarchies of concepts -- when available -- and with proxy
sources of concept similarity in the general case.Comment: ISMIR 202
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