82 research outputs found
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
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