3,293 research outputs found
Primordial Black Holes from Sound Speed Resonance during Inflation
We report on a novel phenomenon of the resonance effect of primordial density
perturbations arisen from a sound speed parameter with an oscillatory behavior,
which can generically lead to the formation of primordial black holes in the
early Universe. For a general inflaton field, it can seed primordial density
fluctuations and their propagation is governed by a parameter of sound speed
square. Once if this parameter achieves an oscillatory feature for a while
during inflation, a significant non-perturbative resonance effect on the
inflaton field fluctuations takes place around a critical length scale, which
results in significant peaks in the primordial power spectrum. By virtue of
this robust mechanism, primordial black holes with specific mass function can
be produced with a sufficient abundance for dark matter in sizable parameter
ranges.Comment: 6 pages, 4 figures; v2: figures replotted with corrections, analysis
extended, version accepted by Phys.Rev.Let
Is the partner of in the nonet?
Based on a mass matrix, the mixing angle of the axial vector
states and is determined to be , and the
theoretical results about the decay and production of the two states are
presented. The theoretical results are in good agreement with the present
experimental results, which suggests that can be assigned as the
partner of in the nonet. We also suggest that
the existence of needs further experimental confirmation.Comment: Latex, 6 pages, to be published in Chin. Phys. let
Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading
Brain tumor represents one of the most fatal cancers around the world, and is
very common in children and the elderly. Accurate identification of the type
and grade of tumor in the early stages plays an important role in choosing a
precise treatment plan. The Magnetic Resonance Imaging (MRI) protocols of
different sequences provide clinicians with important contradictory information
to identify tumor regions. However, manual assessment is time-consuming and
error-prone due to big amount of data and the diversity of brain tumor types.
Hence, there is an unmet need for MRI automated brain tumor diagnosis. We
observe that the predictive capability of uni-modality models is limited and
their performance varies widely across modalities, and the commonly used
modality fusion methods would introduce potential noise, which results in
significant performance degradation. To overcome these challenges, we propose a
novel cross-modality guidance-aided multi-modal learning with dual attention
for addressing the task of MRI brain tumor grading. To balance the tradeoff
between model efficiency and efficacy, we employ ResNet Mix Convolution as the
backbone network for feature extraction. Besides, dual attention is applied to
capture the semantic interdependencies in spatial and slice dimensions
respectively. To facilitate information interaction among modalities, we design
a cross-modality guidance-aided module where the primary modality guides the
other secondary modalities during the process of training, which can
effectively leverage the complementary information of different MRI modalities
and meanwhile alleviate the impact of the possible noise
DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling
To reduce uploading bandwidth and address privacy concerns, deep learning at
the network edge has been an emerging topic. Typically, edge devices
collaboratively train a shared model using real-time generated data through the
Parameter Server framework. Although all the edge devices can share the
computing workloads, the distributed training processes over edge networks are
still time-consuming due to the parameters and gradients transmission
procedures between parameter servers and edge devices. Focusing on accelerating
distributed Convolutional Neural Networks (CNNs) training at the network edge,
we present DynaComm, a novel scheduler that dynamically decomposes each
transmission procedure into several segments to achieve optimal layer-wise
communications and computations overlapping during run-time. Through
experiments, we verify that DynaComm manages to achieve optimal layer-wise
scheduling for all cases compared to competing strategies while the model
accuracy remains untouched.Comment: 16 pages, 12 figures. IEEE Journal on Selected Areas in
Communication
Quasinormal modes in the background of charged Kaluza-Klein black hole with squashed horizons
We study the scalar perturbation in the background of the charged
Kaluza-Klein black holes with squashed horizons. We find that the position of
infinite discontinuities of the heat capacities can be reflected in quasinormal
spectrum. This shows the possible non-trivial relation between the
thermodynamical and dynamical properties of black holes.Comment: revised version, accepted for publication in Phys.Lett.
OPT: One-shot Pose-Controllable Talking Head Generation
One-shot talking head generation produces lip-sync talking heads based on
arbitrary audio and one source face. To guarantee the naturalness and realness,
recent methods propose to achieve free pose control instead of simply editing
mouth areas. However, existing methods do not preserve accurate identity of
source face when generating head motions. To solve the identity mismatch
problem and achieve high-quality free pose control, we present One-shot
Pose-controllable Talking head generation network (OPT). Specifically, the
Audio Feature Disentanglement Module separates content features from audios,
eliminating the influence of speaker-specific information contained in
arbitrary driving audios. Later, the mouth expression feature is extracted from
the content feature and source face, during which the landmark loss is designed
to enhance the accuracy of facial structure and identity preserving quality.
Finally, to achieve free pose control, controllable head pose features from
reference videos are fed into the Video Generator along with the expression
feature and source face to generate new talking heads. Extensive quantitative
and qualitative experimental results verify that OPT generates high-quality
pose-controllable talking heads with no identity mismatch problem,
outperforming previous SOTA methods.Comment: Accepted by ICASSP202
OSM-Net: One-to-Many One-shot Talking Head Generation with Spontaneous Head Motions
One-shot talking head generation has no explicit head movement reference,
thus it is difficult to generate talking heads with head motions. Some existing
works only edit the mouth area and generate still talking heads, leading to
unreal talking head performance. Other works construct one-to-one mapping
between audio signal and head motion sequences, introducing ambiguity
correspondences into the mapping since people can behave differently in head
motions when speaking the same content. This unreasonable mapping form fails to
model the diversity and produces either nearly static or even exaggerated head
motions, which are unnatural and strange. Therefore, the one-shot talking head
generation task is actually a one-to-many ill-posed problem and people present
diverse head motions when speaking. Based on the above observation, we propose
OSM-Net, a \textit{one-to-many} one-shot talking head generation network with
natural head motions. OSM-Net constructs a motion space that contains rich and
various clip-level head motion features. Each basis of the space represents a
feature of meaningful head motion in a clip rather than just a frame, thus
providing more coherent and natural motion changes in talking heads. The
driving audio is mapped into the motion space, around which various motion
features can be sampled within a reasonable range to achieve the one-to-many
mapping. Besides, the landmark constraint and time window feature input improve
the accurate expression feature extraction and video generation. Extensive
experiments show that OSM-Net generates more natural realistic head motions
under reasonable one-to-many mapping paradigm compared with other methods.Comment: Paper Under Revie
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