TSDF-Sampling: Efficient Sampling for Neural Surface Field using Truncated Signed Distance Field

Multi-view neural surface reconstruction has exhibited impressive results. However, a notable limitation is the prohibitively slow inference time when compared to traditional techniques, primarily attributed to the dense sampling, required to maintain the rendering quality. This paper introduces a novel approach that substantially reduces the number of samplings by incorporating the Truncated Signed Distance Field (TSDF) of the scene. While prior works have proposed importance sampling, their dependence on initial uniform samples over the entire space makes them unable to avoid performance degradation when trying to use less number of samples. In contrast, our method leverages the TSDF volume generated only by the trained views, and it proves to provide a reasonable bound on the sampling from upcoming novel views. As a result, we achieve high rendering quality by fully exploiting the continuous neural SDF estimation within the bounds given by the TSDF volume. Notably, our method is the first approach that can be robustly plug-and-play into a diverse array of neural surface field models, as long as they use the volume rendering technique. Our empirical results show an 11-fold increase in inference speed without compromising performance. The result videos are available at our project page: https://tsdf-sampling.github.io

Electronic Structure and Insulating Gap in Epitaxial VO\u3csub\u3e2\u3c/sub\u3e Polymorphs

Determining the origin of the insulating gap in the monoclinic VO2(M1) is a long-standing issue. The difficulty of this study arises from the simultaneous occurrence of structural and electronic transitions upon thermal cycling. Here, we compare the electronic structure of the M1 phase with that of single crystalline insulating VO2(A) and VO2(B) thin films to better understand the insulating phase of VO2. As these A and B phases do not undergo a structural transition upon thermal cycling, we comparatively study the origin of the gap opening in the insulating VO2 phases. By x-ray absorption and optical spectroscopy, we find that the shift of unoccupied t2g orbitals away from the Fermi level is a common feature, which plays an important role for the insulating behavior in VO2 polymorphs. The distinct splitting of the half-filled t2g orbital is observed only in the M1 phase, widening the bandgap up to ∼0.6 eV. Our approach of comparing all three insulating VO2 phases provides insight into a better understanding of the electronic structure and the origin of the insulating gap in VO2

Orbital-selective Mott and Peierls transition in HxVO2

Materials displaying metal-insulator transitions (MITs) as a function of external parameters such as temperature, pressure, or composition are most intriguing from the fundamental point of view and also hold high promise for applications. Vanadium dioxide (VO2) is one of the most prominent examples of MIT having prospective applications ranging from intelligent coatings, infrared sensing, or imaging, to Mott memory and neuromorphic devices. The key aspects conditioning possible applications are the controllability and reversibility of the transition. Here we present an intriguing MIT in hydrogenated vanadium dioxide, HxVO2. The transition relies on an increase of the electron occupancy through hydrogenation on the transition metal vanadium, driving the system insulating by a hybrid of two distinct MIT mechanisms. The insulating phase observed in HVO2 with a nominal d2 electronic configuration contrasts with other rutile d2 systems, most of which are metallic. Using spectroscopic tools and state-of-the-art many-body electronic structure calculations, our investigation reveals a correlation-enhanced Peierls and a Mott transition taking place in an orbital-selective manner cooperate to stabilize an insulating phase. The identification of the hybrid mechanism for MIT controlled by hydrogenation opens the way to radically design strategies for future correlated oxide devices by controlling phase reversibly while maintaining high crystallinity

Tuning orbital-selective phase transitions in a two-dimensional Hund's correlated system

Hund's rule coupling ($\textit{J}$) has attracted much attention recently for its role in the description of the novel quantum phases of multi orbital materials. Depending on the orbital occupancy, $\textit{J}$ can lead to various intriguing phases. However, experimental confirmation of the orbital occupancy dependency has been difficult as controlling the orbital degrees of freedom normally accompanies chemical inhomogeneities. Here, we demonstrate a method to investigate the role of orbital occupancy in $\textit{J}$ related phenomena without inducing inhomogeneities. By growing SrRuO$_3$ monolayers on various substrates with symmetry-preserving interlayers, we gradually tune the crystal field splitting and thus the orbital degeneracy of the Ru \textit{t_2_g$}$ orbitals. It effectively varies the orbital occupancies of two-dimensional (2D) ruthenates. Via in-situ angle-resolved photoemission spectroscopy, we observe a progressive metal-insulator transition (MIT). It is found that the MIT occurs with orbital differentiation: concurrent opening of a band insulating gap in the $\textit{d$_x_y} band and a Mott gap in the \textit{d_x$$_z$$_/$$_y$$_z} bands. Our study provides an effective experimental method for investigation of orbital-selective phenomena in multi-orbital materials

Does Information from the Higher Education and R&D Institutes Improve the Innovation Efficiency of Logistic Firms?

The efficiency of logistics innovation is a challenging task for logistics companies competing in rapid environmental changes. It is also essentially crucial for logistics companies to understand the importance of various sources of information to achieve innovation efficiently. Thus, this study measured the innovation efficiency of 72 logistics firms in South Korea by using ‘the sales’ as an output factor, and ‘the number of employees’ and ‘the innovative activity cost’ as an input factor. In particular, this study tested whether the distribution of measured innovation efficiency value differs in terms of the degree of consideration of higher education institutes and research and development (R & D) institutes as sources of information. To investigate the innovation efficiency of Korean logistics firms, we used a combined method of the input-oriented data envelopment analysis (DEA) with constant return to scale and Kruskal Wallis one-way ANOVA (analysis of variance) techniques. The findings in the study suggest that the logistics firms in Korea tend not to seriously consider higher education institutes and R & D institutes as crucial sources of information for achieving their logistics innovation. However, the results show that the innovation efficiency of logistics firms may be low if they completely exclude or do not consider the information sources for their innovation activities. Keywords: Source of Information, Logistic firms, Innovation efficiency nonparametric statistics, Higher education institutes, R&D institute