765 research outputs found
Nanoscale vector AC magnetometry with a single nitrogen-vacancy center in diamond
Detection of AC magnetic fields at the nanoscale is critical in applications
ranging from fundamental physics to materials science. Isolated quantum spin
defects, such as the nitrogen-vacancy center in diamond, can achieve the
desired spatial resolution with high sensitivity. Still, vector AC magnetometry
currently relies on using different orientations of an ensemble of sensors,
with degraded spatial resolution, and a protocol based on a single NV is
lacking. Here we propose and experimentally demonstrate a protocol that
exploits a single NV to reconstruct the vectorial components of an AC magnetic
field by tuning a continuous driving to distinct resonance conditions. We map
the spatial distribution of an AC field generated by a copper wire on the
surface of the diamond. The proposed protocol combines high sensitivity, broad
dynamic range, and sensitivity to both coherent and stochastic signals, with
broad applications in condensed matter physics, such as probing spin
fluctuations
Multi-Speaker Expressive Speech Synthesis via Semi-supervised Contrastive Learning
This paper aims to build an expressive TTS system for multi-speakers,
synthesizing a target speaker's speech with multiple styles and emotions. To
this end, we propose a novel contrastive learning-based TTS approach to
transfer style and emotion across speakers. Specifically, we construct
positive-negative sample pairs at both utterance and category (such as
emotion-happy or style-poet or speaker A) levels and leverage contrastive
learning to better extract disentangled style, emotion, and speaker
representations from speech. Furthermore, we introduce a semi-supervised
training strategy to the proposed approach to effectively leverage multi-domain
data, including style-labeled data, emotion-labeled data, and unlabeled data.
We integrate the learned representations into an improved VITS model, enabling
it to synthesize expressive speech with diverse styles and emotions for a
target speaker. Experiments on multi-domain data demonstrate the good design of
our model.Comment: 5 pages, 3 figure
Growth of Outward Propagating Fast-magnetosonic/Whistler Waves in the Inner Heliosphere Observed by Parker Solar Probe
The solar wind in the inner heliosphere has been observed by Parker Solar Probe (PSP) to exhibit abundant wave activities. The cyclotron wave modes responding to ions or electrons are among the most crucial wave components. However, their origin and evolution in the inner heliosphere close to the Sun remains a mystery. Specifically, it remains unknown whether it is an emitted signal from the solar atmosphere or an eigenmode growing locally in the heliosphere due to plasma instability. To address and resolve this controversy, we must investigate the key quantity of the energy change rate of the wave mode. We develop a new technique to measure the energy change rate of plasma waves, and apply this technique to the wave electromagnetic fields measured by PSP. We provide the wave Poynting flux in the solar wind frame, identify the wave nature to be the outward propagating fast-magnetosonic/whistler wave mode instead of the sunward propagating waves. We provide the first evidence for growth of the fast-magnetosonic/whistler wave mode in the inner heliosphere based on the derived spectra of the real and imaginary parts of the wave frequencies. The energy change rate rises and stays at a positive level in the same wavenumber range as the bumps of the electromagnetic field power spectral densities, clearly manifesting that the observed fast-magnetosonic/whistler waves are locally growing to a large amplitude
A Clustering-guided Contrastive Fusion for Multi-view Representation Learning
The past two decades have seen increasingly rapid advances in the field of
multi-view representation learning due to it extracting useful information from
diverse domains to facilitate the development of multi-view applications.
However, the community faces two challenges: i) how to learn robust
representations from a large amount of unlabeled data to against noise or
incomplete views setting, and ii) how to balance view consistency and
complementary for various downstream tasks. To this end, we utilize a deep
fusion network to fuse view-specific representations into the view-common
representation, extracting high-level semantics for obtaining robust
representation. In addition, we employ a clustering task to guide the fusion
network to prevent it from leading to trivial solutions. For balancing
consistency and complementary, then, we design an asymmetrical contrastive
strategy that aligns the view-common representation and each view-specific
representation. These modules are incorporated into a unified method known as
CLustering-guided cOntrastiVE fusioN (CLOVEN). We quantitatively and
qualitatively evaluate the proposed method on five datasets, demonstrating that
CLOVEN outperforms 11 competitive multi-view learning methods in clustering and
classification. In the incomplete view scenario, our proposed method resists
noise interference better than those of our competitors. Furthermore, the
visualization analysis shows that CLOVEN can preserve the intrinsic structure
of view-specific representation while also improving the compactness of
view-commom representation. Our source code will be available soon at
https://github.com/guanzhou-ke/cloven.Comment: 13 pages, 9 figure
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