A Dynamic Shift-Share Analysis on the China’s R&D: A Structure Analysis

To evaluate the R&D development in China, we can inspect both the R&D expenditure and the research talent pool. In this paper, we analyze the structure of the researcher groups by using dynamic shift-share analysis (DSSA). The DSSA results show that there is still much room to improve in the structure of research group. The provinces/municipalities from eastern China did not perform well in engineering and education researcher groups while the provinces/municipalities from central and western China perform well in engineering, agriculture and education researcher groups. We suggest that the government planners should implement more effective measures to improve the structure of the researcher groups in order to spend the R&D fund wisely and attract more extra fund in R&D

Efficient Neural Radiance Fields for Interactive Free-viewpoint Video

This paper aims to tackle the challenge of efficiently producing interactive free-viewpoint videos. Some recent works equip neural radiance fields with image encoders, enabling them to generalize across scenes. When processing dynamic scenes, they can simply treat each video frame as an individual scene and perform novel view synthesis to generate free-viewpoint videos. However, their rendering process is slow and cannot support interactive applications. A major factor is that they sample lots of points in empty space when inferring radiance fields. We propose a novel scene representation, called ENeRF, for the fast creation of interactive free-viewpoint videos. Specifically, given multi-view images at one frame, we first build the cascade cost volume to predict the coarse geometry of the scene. The coarse geometry allows us to sample few points near the scene surface, thereby significantly improving the rendering speed. This process is fully differentiable, enabling us to jointly learn the depth prediction and radiance field networks from RGB images. Experiments on multiple benchmarks show that our approach exhibits competitive performance while being at least 60 times faster than previous generalizable radiance field methods.Comment: SIGGRAPH Asia 2022; Project page: https://zju3dv.github.io/enerf

An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue Functions

We present an approximate nonsmooth algorithm to solve a minimization problem, in which the objective function is the sum of a maximum eigenvalue function of matrices and a convex function. The essential idea to solve the optimization problem in this paper is similar to the thought of proximal bundle method, but the difference is that we choose approximate subgradient and function value to construct approximate cutting-plane model to solve the above mentioned problem. An important advantage of the approximate cutting-plane model for objective function is that it is more stable than cutting-plane model. In addition, the approximate proximal bundle method algorithm can be given. Furthermore, the sequences generated by the algorithm converge to the optimal solution of the original problem

4K4D: Real-Time 4D View Synthesis at 4K Resolution

This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K resolution. Recently, some methods on dynamic view synthesis have shown impressive rendering quality. However, their speed is still limited when rendering high-resolution images. To overcome this problem, we propose 4K4D, a 4D point cloud representation that supports hardware rasterization and enables unprecedented rendering speed. Our representation is built on a 4D feature grid so that the points are naturally regularized and can be robustly optimized. In addition, we design a novel hybrid appearance model that significantly boosts the rendering quality while preserving efficiency. Moreover, we develop a differentiable depth peeling algorithm to effectively learn the proposed model from RGB videos. Experiments show that our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an RTX 4090 GPU, which is 30x faster than previous methods and achieves the state-of-the-art rendering quality. Our project page is available at https://zju3dv.github.io/4k4d/.Comment: Project Page: https://zju3dv.github.io/4k4

Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask

We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single images from the Internet. Prior works on 3D-aware image synthesis either require multi-view supervision or learning category-level prior for specific classes of objects, which can hardly work for natural scenes. Our key idea to solve this challenging problem is to use a semantic field as the intermediate representation, which is easier to reconstruct from an input semantic mask and then translate to a radiance field with the assistance of off-the-shelf semantic image synthesis models. Experiments show that our method outperforms baseline methods and produces photorealistic, multi-view consistent videos of a variety of natural scenes.Comment: Project website: https://zju3dv.github.io/paintingnature

LEVER: Learning to Verify Language-to-Code Generation with Execution

The advent of pre-trained code language models (CodeLMs) has lead to significant progress in language-to-code generation. State-of-the-art approaches in this area combine CodeLM decoding with sample pruning and reranking using test cases or heuristics based on the execution results. However, it is challenging to obtain test cases for many real-world language-to-code applications, and heuristics cannot well capture the semantic features of the execution results, such as data type and value range, which often indicates the correctness of the program. In this work, we propose LEVER, a simple approach to improve language-to-code generation by learning to verify the generated programs with their execution results. Specifically, we train verifiers to determine whether a program sampled from the CodeLM is correct or not based on the natural language input, the program itself and its execution results. The sampled programs are reranked by combining the verification score with the CodeLM generation probability, and marginalizing over programs with the same execution results. On four datasets across the domains of table QA, math QA and basic Python programming, LEVER consistently improves over the base CodeLMs (4.6% to 10.9% with code-davinci-002) and achieves new state-of-the-art results on all of them.Comment: 23 page

Preventive Effects of a Chinese Herbal Formula, Shengjiang Xiexin Decoction, on Irinotecan-Induced Delayed-Onset Diarrhea in Rats

Irinotecan is a well-known chemotherapy drug for the treatment of various cancers. However, delayed-onset diarrhea is a common adverse reaction, limiting the application of the drug. The study presented was designed to evaluate the preventive effects of Shengjiang Xiexin decoction (SXD) on irinotecan-induced diarrhea and to explore the possible mechanisms of this action. We established a diarrhea rat model. The condition of the rats was observed. The proliferation and apoptosis of intestinal cells were measured using immunohistochemical assays and a caspase-3 activity assay, respectively. The expression of Lgr5 and CD44 staining were used to observe intestinal stem cells (ISCs). In addition, the activity of β-glucuronidase in the rats’ feces was measured. Our results showed that the number of proliferating intestinal cells in the SXD groups was obviously higher, while the activity of caspase-3 was lower. The expression of Lgr5 and the integrated option density (IOD) of CD44 stain were increased significantly by SXD. Additionally, SXD decreased the activity of β-glucuronidase after irinotecan administration. In conclusion, SXD exhibited preventive effects on irinotecan-induced diarrhea, and this action was associated with an inhibitory effect on intestinal apoptosis and β-glucuronidase and a promotive effect on intestinal cell proliferation due to increased maintenance of ISCs

InCoder: A Generative Model for Code Infilling and Synthesis

Code is seldom written in a single left-to-right pass and is instead repeatedly edited and refined. We introduce InCoder, a unified generative model that can perform program synthesis (via left-to-right generation) as well as editing (via infilling). InCoder is trained to generate code files from a large corpus of permissively licensed code, where regions of code have been randomly masked and moved to the end of each file, allowing code infilling with bidirectional context. Our model is the first generative model that is able to directly perform zero-shot code infilling, which we evaluate on challenging tasks such as type inference, comment generation, and variable re-naming. We find that the ability to condition on bidirectional context substantially improves performance on these tasks, while still performing comparably on standard program synthesis benchmarks in comparison to left-to-right only models pretrained at similar scale. The InCoder models and code are publicly released. https://sites.google.com/view/incoder-code-modelsComment: 25 pages, 13 figures. v2: added NeoX-20B results & StackOverflow corpus inf

Mineralogical characterization of manganese oxide minerals of the Devonian Xialei manganese deposit

The Guangxi Zhuang Autonomous Region is an important manganese ore district in Southwest China, with manganese ore resource reserves accounting for 23% of the total manganese ore resource reserves in China. The Xialei manganese deposit (Daxin County, Guangxi) is the first super-large manganese deposit discovered in China. The Mn oxide in the supergene oxidation zone of the Xialei deposit was characterized using scanning electron microscopy (SEM), energy spectrometer (EDS), transmission electron microscopy (TEM, HRTEM), and X-ray diffraction analysis (XRD). The Mn oxides have a gray-black/steel-gray color, a semi-metallic-earthy luster, and appear as oolitic, pisolitic, banded, massive, and cellular textures. Scanning electron microscopy images show that the manganese oxide minerals are present as fine-spherical particles with an earthy surface. TEM and HRTEM indicate the presence of oriented bundled and staggered nanorods, and nanopores between the crystals. The Mn oxide ore can be classified into two textural types: (1) oolitic and pisolitic (often with annuli) Mn oxide, and (2) massive Mn oxide. Pyrolusite, cryptomelane, and hollandite are the main Mn oxide minerals. The potassium contents of cryptomelane and pyrolusite are discussed. The unit cell parameters of pyrolusite are refined