655 research outputs found
Transporting long-lived quantum spin coherence in a photonic crystal fiber
Confining particles in hollow-core photonic crystal fibers has opened up new
prospects to scale up the distance and time over which particles can be made to
interact with light. However, maintaining long-lived quantum spin coherence
and/or transporting it over macroscopic distances in a waveguide remain
challenging. Here, we demonstrate coherent guiding of ground-state
superpositions of 85Rb atoms over a centimeter range and hundreds of
milliseconds inside a hollow-core photonic crystal fiber. The decoherence is
mainly due to dephasing from residual differential light shift (DLS) from the
optical trap and the inhomogeneity of ambient magnetic field. Our experiment
establishes an important step towards a versatile platform that can lead to
applications in quantum information networks and matter wave circuit for
quantum sensing.Comment: Accepted by Physical Review Letter
Text-to-3D using Gaussian Splatting
In this paper, we present Gaussian Splatting based text-to-3D generation
(GSGEN), a novel approach for generating high-quality 3D objects. Previous
methods suffer from inaccurate geometry and limited fidelity due to the absence
of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a
recent state-of-the-art representation, to address existing shortcomings by
exploiting the explicit nature that enables the incorporation of 3D prior.
Specifically, our method adopts a progressive optimization strategy, which
includes a geometry optimization stage and an appearance refinement stage. In
geometry optimization, a coarse representation is established under a 3D
geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and
3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an
iterative refinement to enrich details. In this stage, we increase the number
of Gaussians by compactness-based densification to enhance continuity and
improve fidelity. With these designs, our approach can generate 3D content with
delicate details and more accurate geometry. Extensive evaluations demonstrate
the effectiveness of our method, especially for capturing high-frequency
components. Video results are provided at https://gsgen3d.github.io. Our code
is available at https://github.com/gsgen3d/gsgenComment: Project page: https://gsgen3d.github.io. Code:
https://github.com/gsgen3d/gsge
TabuLa: Harnessing Language Models for Tabular Data Synthesis
Given the ubiquitous use of tabular data in industries and the growing
concerns in data privacy and security, tabular data synthesis emerges as a
critical research area. The recent state-of-the-art methods show that large
language models (LLMs) can be adopted to generate realistic tabular data. As
LLMs pre-process tabular data as full text, they have the advantage of avoiding
the curse of dimensionality associated with one-hot encoding high-dimensional
data. However, their long training time and limited re-usability on new tasks
prevent them from replacing exiting tabular generative models. In this paper,
we propose Tabula, a tabular data synthesizer based on the language model
structure. Through Tabula, we demonstrate the inherent limitation of employing
pre-trained language models designed for natural language processing (NLP) in
the context of tabular data synthesis. Our investigation delves into the
development of a dedicated foundational model tailored specifically for tabular
data synthesis. Additionally, we propose a token sequence compression strategy
to significantly reduce training time while preserving the quality of synthetic
data. Extensive experiments on six datasets demonstrate that using a language
model structure without loading the well-trained model weights yields a better
starting model for tabular data synthesis. Moreover, the Tabula model,
previously trained on other tabular data, serves as an excellent foundation
model for new tabular data synthesis tasks. Additionally, the token sequence
compression method substantially reduces the model's training time. Results
show that Tabula averagely reduces 46.2% training time per epoch comparing to
current LLMs-based state-of-the-art algorithm and consistently achieves even
higher synthetic data utility
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