4,254 research outputs found
NeRF-VPT: learning novel view representations with Neural Radiance Fields via view prompt tuning
Neural Radiance Fields (NeRF) have garnered remarkable success in novel view synthesis. Nonetheless, the task of generating high-quality images for novel views persists as a critical challenge. While the existing efforts have exhibited commendable progress, capturing intricate details, enhancing textures, and achieving superior Peak Signal-to-Noise Ratio (PSNR) metrics warrant further focused attention and advancement. In this work, we propose NeRF-VPT, an innovative method for novel view synthesis to address these challenges. Our proposed NeRF-VPT employs a cascading view prompt tuning paradigm, wherein RGB information gained from preceding rendering outcomes serves as instructive visual prompts for subsequent rendering stages, with the aspiration that the prior knowledge embedded in the prompts can facilitate the gradual enhancement of rendered image quality. NeRF-VPT only requires sampling RGB data from previous stage renderings as priors at each training stage, without relying on extra guidance or complex techniques. Thus, our NeRF-VPT is plug-and-play and can be readily integrated into existing methods. By conducting comparative analyses of our NeRF-VPT against several NeRF-based approaches on demanding real-scene benchmarks, such as Realistic Synthetic 360, Real Forward-Facing, Replica dataset, and a user-captured dataset, we substantiate that our NeRF-VPT significantly elevates baseline performance and proficiently generates more high-quality novel view images than all the compared state-of-the-art methods. Furthermore, the cascading learning of NeRF-VPT introduces adaptability to scenarios with sparse inputs, resulting in a significant enhancement of accuracy for sparse-view novel view synthesis. The source code and dataset are available at https://github.com/Freedomcls/NeRF-VPT
Conditions for proton temperature anisotropy to drive instabilities in the solar wind
Using high-resolution data from Solar Orbiter, we investigate the plasma
conditions necessary for the proton temperature anisotropy driven mirror-mode
and oblique firehose instabilities to occur in the solar wind. We find that the
unstable plasma exhibits dependencies on the angle between the direction of the
magnetic field and the bulk solar wind velocity which cannot be explained by
the double-adiabatic expansion of the solar wind alone. The angle dependencies
suggest that perpendicular heating in Alfv\'enic wind may be responsible. We
quantify the occurrence rate of the two instabilities as a function of the
length of unstable intervals as they are convected over the spacecraft. This
analysis indicates that mirror-mode and oblique firehose instabilities require
a spatial interval of length greater than 2 to 3 unstable wavelengths in order
to relax the plasma into a marginally stable state and thus closer to
thermodynamic equilibrium in the solar wind. Our analysis suggests that the
conditions for these instabilities to act effectively vary locally on scales
much shorter than the correlation length of solar wind turbulence.Comment: 16 pages, 8 figures. Accepted for publication in Ap
An evolving network model with community structure
Many social and biological networks consist of communities—groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties
Investigating star formation in the young open cluster NGC 6383
By studying young open clusters, the mechanisms important for star formation
over several Myr can be examined. For example, accretion rate as a function of
rotational velocity can be investigated. Similarly, sequential star formation
triggered by massive stars with high mass-loss rates can be studied in detail.
We identified and characterized probable members of NGC 6383, as well as
determined cluster parameters. New Stromgren uvby CCD photometry, obtained by
us, is presented. This new data, together with Johnson UBV and 2MASS data in
the NIR, was used to investigate characteristics of pre- as well as zero age
main sequence cluster members. We present Stromgren uvby CCD photometry for 272
stars in the field of NGC 6383 and derive its reddening, E(b-y)=0.21(4)mag, as
well as distance, d=1.7(3)kpc from the Sun. Several stars with NIR excess and
objects in the domain of the classical Herbig Ae/Be and T Tauri stars were
detected. Two previously known variables were identified as rapidly-rotating
PMS stars. The field population is clearly separated from the probable members
in the color-magnitude diagram. NGC 6383 is a young open cluster, with an age
of less than 4 Myr, undergoing continuous star formation. True pre-main
sequence members might be found down to absolute magnitudes of +6mag, with a
variety of rotational velocities and stellar activities.Comment: 7 pages, 6 figures, accepted by A&
Graph Inductive Biases in Transformers without Message Passing
Transformers for graph data are increasingly widely studied and successful in
numerous learning tasks. Graph inductive biases are crucial for Graph
Transformers, and previous works incorporate them using message-passing modules
and/or positional encodings. However, Graph Transformers that use
message-passing inherit known issues of message-passing, and differ
significantly from Transformers used in other domains, thus making transfer of
research advances more difficult. On the other hand, Graph Transformers without
message-passing often perform poorly on smaller datasets, where inductive
biases are more crucial. To bridge this gap, we propose the Graph Inductive
bias Transformer (GRIT) -- a new Graph Transformer that incorporates graph
inductive biases without using message passing. GRIT is based on several
architectural changes that are each theoretically and empirically justified,
including: learned relative positional encodings initialized with random walk
probabilities, a flexible attention mechanism that updates node and node-pair
representations, and injection of degree information in each layer. We prove
that GRIT is expressive -- it can express shortest path distances and various
graph propagation matrices. GRIT achieves state-of-the-art empirical
performance across a variety of graph datasets, thus showing the power that
Graph Transformers without message-passing can deliver.Comment: Published as a conference paper at ICML 2023; 17 page
Graph inductive biases in transformers without message passing
Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more crucial. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) -- a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive -- it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver
Graph inductive biases in transformers without message passing
Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more important. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) — a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive — it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver
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