1,210 research outputs found
On the Free Boundary Problems for the Ideal Incompressible MHD Equations
We investigate the general plasma-vacuum interface problems for the ideal
incompressible MHD equations with or without surface tension and prove their
nonlinear local well-posedness in standard Sobolev spaces under either non-zero
surface tension or the stability condition that the magnetic fields are
everywhere non-collinear on the interface. In particular, the results show that
both capillary forces and tangential magnetic fields can stabilize the motion
of the plasma-vacuum interfaces. Moreover, the vanishing surface tension limit
results are established under the Rayleigh-Taylor sign condition or the
non-collinearity condition. All these results hold with no graph assumption on
the free interface.Comment: arXiv admin note: text overlap with arXiv:2309.0353
Local Well-posedness of the Incompressible Current-Vortex Sheet Problems
We prove the local well-posedness of the incompressible current-vortex sheet
problems in standard Sobolev spaces under the surface tension or the
Syrovatskij condition, which shows that both capillary forces and large
tangential magnetic fields can stabilize the motion of current-vortex sheets.
Furthermore, under the Syrovatskij condition, the vanishing surface tension
limit is established for the motion of current-vortex sheets. These results
hold without assuming the interface separating the two plasmas being a graph
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Plug-and-play (PnP) is a non-convex framework that integrates modern
denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or
other proximal algorithms. An advantage of PnP is that one can use pre-trained
denoisers when there is not sufficient data for end-to-end training. Although
PnP has been recently studied extensively with great empirical success,
theoretical analysis addressing even the most basic question of convergence has
been insufficient. In this paper, we theoretically establish convergence of
PnP-FBS and PnP-ADMM, without using diminishing stepsizes, under a certain
Lipschitz condition on the denoisers. We then propose real spectral
normalization, a technique for training deep learning-based denoisers to
satisfy the proposed Lipschitz condition. Finally, we present experimental
results validating the theory.Comment: Published in the International Conference on Machine Learning, 201
Measurement Axis Searching Model for Terrestrial Laser Scans Registration
Nowadays, terrestrial Lidar scans can cover rather a large area; the point densities are strongly varied because of the line-of-sight measurement principle in potential overlaps with scans taken from different viewpoints. Most of the traditional methods focus on registration algorithm and ignore searching model. Sometimes the traditional methods are directly used to align two point clouds; a large critically unsolved problem of the large biases will be created in areas distant from the overlaps while the local overlaps are often aligned well. So a novel measurement axis searching model (MASM) has been proposed in this paper. The method includes four steps: (1) the principal axis fitting, (2) the measurement axis generation, (3) low-high-precision search, and (4) result generation. The principal axis gives an orientation to the point cloud; the search scope is limited by the measurement axis. The point cloud orientation can be adjusted gradually until the achievement of the global optimum using low- and high-precision search. We perform some experiments with simulated point clouds and real terrestrial laser scans. The results of simulated point clouds have shown the processing steps of our method, and the results of real terrestrial laser scans have shown the sensitivity of the approach with respect to the indoor and outdoor scenes
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection
Emotion detection is a critical technology extensively employed in diverse
fields. While the incorporation of commonsense knowledge has proven beneficial
for existing emotion detection methods, dialogue-based emotion detection
encounters numerous difficulties and challenges due to human agency and the
variability of dialogue content.In dialogues, human emotions tend to accumulate
in bursts. However, they are often implicitly expressed. This implies that many
genuine emotions remain concealed within a plethora of unrelated words and
dialogues.In this paper, we propose a Dynamic Causal Disentanglement Model
based on hidden variable separation, which is founded on the separation of
hidden variables. This model effectively decomposes the content of dialogues
and investigates the temporal accumulation of emotions, thereby enabling more
precise emotion recognition. First, we introduce a novel Causal Directed
Acyclic Graph (DAG) to establish the correlation between hidden emotional
information and other observed elements. Subsequently, our approach utilizes
pre-extracted personal attributes and utterance topics as guiding factors for
the distribution of hidden variables, aiming to separate irrelevant ones.
Specifically, we propose a dynamic temporal disentanglement model to infer the
propagation of utterances and hidden variables, enabling the accumulation of
emotion-related information throughout the conversation. To guide this
disentanglement process, we leverage the ChatGPT-4.0 and LSTM networks to
extract utterance topics and personal attributes as observed
information.Finally, we test our approach on two popular datasets in dialogue
emotion detection and relevant experimental results verified the model's
superiority
Implicit Diffusion Models for Continuous Super-Resolution
Image super-resolution (SR) has attracted increasing attention due to its
wide applications. However, current SR methods generally suffer from
over-smoothing and artifacts, and most work only with fixed magnifications.
This paper introduces an Implicit Diffusion Model (IDM) for high-fidelity
continuous image super-resolution. IDM integrates an implicit neural
representation and a denoising diffusion model in a unified end-to-end
framework, where the implicit neural representation is adopted in the decoding
process to learn continuous-resolution representation. Furthermore, we design a
scale-controllable conditioning mechanism that consists of a low-resolution
(LR) conditioning network and a scaling factor. The scaling factor regulates
the resolution and accordingly modulates the proportion of the LR information
and generated features in the final output, which enables the model to
accommodate the continuous-resolution requirement. Extensive experiments
validate the effectiveness of our IDM and demonstrate its superior performance
over prior arts.Comment: 8 pages, 9 figures, published to CVPR202
IPDreamer: Appearance-Controllable 3D Object Generation with Image Prompts
Recent advances in text-to-3D generation have been remarkable, with methods
such as DreamFusion leveraging large-scale text-to-image diffusion-based models
to supervise 3D generation. These methods, including the variational score
distillation proposed by ProlificDreamer, enable the synthesis of detailed and
photorealistic textured meshes. However, the appearance of 3D objects generated
by these methods is often random and uncontrollable, posing a challenge in
achieving appearance-controllable 3D objects. To address this challenge, we
introduce IPDreamer, a novel approach that incorporates image prompts to
provide specific and comprehensive appearance information for 3D object
generation. Our results demonstrate that IPDreamer effectively generates
high-quality 3D objects that are consistent with both the provided text and
image prompts, demonstrating its promising capability in
appearance-controllable 3D object generation.Comment: 11 pages, 7 figure
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