360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360∘^\circ Radiance Fields

Virtual tour among sparse 360$^\circ$ images is widely used while hindering smooth and immersive roaming experiences. The emergence of Neural Radiance Field (NeRF) has showcased significant progress in synthesizing novel views, unlocking the potential for immersive scene exploration. Nevertheless, previous NeRF works primarily focused on object-centric scenarios, resulting in noticeable performance degradation when applied to outward-facing and large-scale scenes due to limitations in scene parameterization. To achieve seamless and real-time indoor roaming, we propose a novel approach using geometry-aware radiance fields with adaptively assigned local radiance fields. Initially, we employ multiple 360$^\circ$ images of an indoor scene to progressively reconstruct explicit geometry in the form of a probabilistic occupancy map, derived from a global omnidirectional radiance field. Subsequently, we assign local radiance fields through an adaptive divide-and-conquer strategy based on the recovered geometry. By incorporating geometry-aware sampling and decomposition of the global radiance field, our system effectively utilizes positional encoding and compact neural networks to enhance rendering quality and speed. Additionally, the extracted floorplan of the scene aids in providing visual guidance, contributing to a realistic roaming experience. To demonstrate the effectiveness of our system, we curated a diverse dataset of 360$^\circ$ images encompassing various real-life scenes, on which we conducted extensive experiments. Quantitative and qualitative comparisons against baseline approaches illustrated the superior performance of our system in large-scale indoor scene roaming

A Review on Mechanics and Mechanical Properties of 2D Materials - Graphene and Beyond

Since the first successful synthesis of graphene just over a decade ago, a variety of two-dimensional (2D) materials (e.g., transition metal-dichalcogenides, hexagonal boron-nitride, etc.) have been discovered. Among the many unique and attractive properties of 2D materials, mechanical properties play important roles in manufacturing, integration and performance for their potential applications. Mechanics is indispensable in the study of mechanical properties, both experimentally and theoretically. The coupling between the mechanical and other physical properties (thermal, electronic, optical) is also of great interest in exploring novel applications, where mechanics has to be combined with condensed matter physics to establish a scalable theoretical framework. Moreover, mechanical interactions between 2D materials and various substrate materials are essential for integrated device applications of 2D materials, for which the mechanics of interfaces (adhesion and friction) has to be developed for the 2D materials. Here we review recent theoretical and experimental works related to mechanics and mechanical properties of 2D materials. While graphene is the most studied 2D material to date, we expect continual growth of interest in the mechanics of other 2D materials beyond graphene

LEGO-Prover: Neural Theorem Proving with Growing Libraries

Despite the success of large language models (LLMs), the task of theorem proving still remains one of the hardest reasoning tasks that is far from being fully solved. Prior methods using language models have demonstrated promising results, but they still struggle to prove even middle school level theorems. One common limitation of these methods is that they assume a fixed theorem library during the whole theorem proving process. However, as we all know, creating new useful theorems or even new theories is not only helpful but crucial and necessary for advancing mathematics and proving harder and deeper results. In this work, we present LEGO-Prover, which employs a growing skill library containing verified lemmas as skills to augment the capability of LLMs used in theorem proving. By constructing the proof modularly, LEGO-Prover enables LLMs to utilize existing skills retrieved from the library and to create new skills during the proving process. These skills are further evolved (by prompting an LLM) to enrich the library on another scale. Modular and reusable skills are constantly added to the library to enable tackling increasingly intricate mathematical problems. Moreover, the learned library further bridges the gap between human proofs and formal proofs by making it easier to impute missing steps. LEGO-Prover advances the state-of-the-art pass rate on miniF2F-valid (48.0% to 57.0%) and miniF2F-test (45.5% to 47.1%). During the proving process, LEGO-Prover also manages to generate over 20,000 skills (theorems/lemmas) and adds them to the growing library. Our ablation study indicates that these newly added skills are indeed helpful for proving theorems, resulting in an improvement from a success rate of 47.1% to 50.4%. We also release our code and all the generated skills

Discerning Cultural Shifts in China? Commentary on Hamamura et al. (2021)

By examining the changes in the conceptual associations between individualism-collectivism and 10 other concepts based on the Google Ngram Chinese Corpus from the 1950s to the 1990s, Hamamura et al. (2021) inferred (a) no rise in individualism; (b) continuing collectivism; and (c) no effect of modernization on individualism in contemporary China. We question the validity of these conclusions given the following issues in their research: (a) misinterpretation of statistical results; (b) improper calculation of cultural associations; and (c) inappropriate generalization of specific findings. Contrary to their original findings, our reanalysis of their data suggests that individualism has been increasingly accepted and associated with some positive (vs. negative) aspects of life (e.g., income vs. loss, richness vs. poverty) over recent decades in China. Future research should use more rigorous methods and diverse corpora to clarify and explain changes in individualism and collectivism in China

Thermal Conductivity of Fractal-Textured Foamed Concrete

To provide scientific guidance for the use of foamed concrete (FC) in construction engineering, a thermal conductivity calculation method, based on the fractal model of FC, has been developed. The thermal conductivity (TC) of FC has been tested by the transient planar heat source method in order to verify the reliability of the proposed calculation model. The FC was made of cement, fly ash, and ore powder, and cured under natural conditions for 7 d, 14 d, 28 d, and 42 d, respectively. The TC of FC gradually decreases with the increase in age. The fractal dimension of FC can be determined by both the box-counting method and compressive strength test, and the dimensions determined by both methods are similar. The TC of FC at different porosities and curing ages can be calculated by the fractal dimension, and the estimated values are basically consistent with the test data

Comparison of Multiple Machine Learning Models for Estimating the Forest Growing Stock in Large-Scale Forests Using Multi-Source Data

The forest growing stock is one of the key indicators in monitoring forest resources, and its quantitative estimation is of great significance. Based on multi-source data, including Sentinel-1 radar remote sensing data, Sentinel-2 optical remote sensing data, digital elevation model (DEM), and inventory data for forest management planning and design, the Lasso feature selection method was used to remove the non-significant indicators, and three machine learning algorithms, GBDT, XGBoost, and CatBoost, were used to estimate forest growing stock. In addition, four category features, forest population, dominant tree species, humus thickness, and slope direction, were involved in estimating forest growing stock. The results showed that the addition of category features significantly improved the performance of the models. To a certain extent, radar remote sensing data also could improve estimating accuracy. Among the three models, the CatBoost model (R2 = 0.78, MSE = 0.62, MAE = 0.59, MAPE = 16.20%) had the highest estimating accuracy, followed by XGBoost (R2 = 0.75, MSE = 0.71, MAE = 0.62, MAPE = 18.28%) and GBDT (R2 = 0.72, MSE = 0.78, MAE = 0.68, MAPE = 20.28%)

Radial anisotropy in the crust of SE Tibet and SW China from ambient noise interferometry

We use Rayleigh and Love wave Green's functions estimated from ambient seismic noise to study crustal structure and radial anisotropy in the tectonically complex and seismically active region west of the Sichuan Basin and around the Eastern Himalaya Syntaxis. In agreement with previous studies, low velocity zones are ubiquitous in the mid-lower crust, with substantial variations both laterally and vertically. Discrepancies between 3-D shear velocity from either Rayleigh (V[subscript SV]) or Love (V[subscript SH]) waves are examined both in view of non-uniqueness of tomographic solutions and radial anisotropy. Low shear wave speed and radial anisotropy with V[subscript SH] > V[subscript SV] are most prominent in mid-lower crust in area northwest to the Lijiang-Muli fault and around the Red River and Xiaojiang faults. This anisotropy could be caused by sub-horizontal mica fabric and its association with low velocity zones suggests mica alignment due to flow in deep crustal zones of relatively low mechanical strength.National Science Foundation (U.S.) (Grant EAR‐0910618