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

Polaronic Signatures in Mid-Infrared Spectra: Prediction for LaMnO3 and CaMnO3

Hole-doped LaMnO3 and electron-doped CaMnO3 form self-trapped electronic states. The spectra of these states have been calculated using a two orbital (Mn eg Jahn-Teller) model, from which the non-adiabatic optical conductivity spectra are obtained. In both cases the optical spectrum contains weight in the gap region, whose observation will indicate the self-trapped nature of the carrier states. The predicted spectra are proportional to the concentration of the doped carriers in the dilute regime, with coefficients calculated with no further model parameters.Comment: 6 pages with 3 figures imbedde

Can large language model agents simulate human trust behaviors?

Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in applications such as social science. However, one fundamental question remains: can LLM agents really simulate human behaviors? In this paper, we focus on one of the most critical behaviors in human interactions, trust, and aim to investigate whether or not LLM agents can simulate human trust behaviors. We first find that LLM agents generally exhibit trust behaviors, referred to as agent trust, under the framework of Trust Games, which are widely recognized in behavioral economics. Then, we discover that LLM agents can have high behavioral alignment with humans regarding trust behaviors, particularly for GPT-4, indicating the feasibility to simulate human trust behaviors with LLM agents. In addition, we probe into the biases in agent trust and the differences in agent trust towards agents and humans. We also explore the intrinsic properties of agent trust under conditions including advanced reasoning strategies and external manipulations. We further offer important implications of our discoveries for various scenarios where trust is paramount. Our study provides new insights into the behaviors of LLM agents and the fundamental analogy between LLMs and humans