100 research outputs found
VeRi3D: Generative Vertex-based Radiance Fields for 3D Controllable Human Image Synthesis
Unsupervised learning of 3D-aware generative adversarial networks has lately
made much progress. Some recent work demonstrates promising results of learning
human generative models using neural articulated radiance fields, yet their
generalization ability and controllability lag behind parametric human models,
i.e., they do not perform well when generalizing to novel pose/shape and are
not part controllable. To solve these problems, we propose VeRi3D, a generative
human vertex-based radiance field parameterized by vertices of the parametric
human template, SMPL. We map each 3D point to the local coordinate system
defined on its neighboring vertices, and use the corresponding vertex feature
and local coordinates for mapping it to color and density values. We
demonstrate that our simple approach allows for generating photorealistic human
images with free control over camera pose, human pose, shape, as well as
enabling part-level editing
From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery
Molecule discovery serves as a cornerstone in numerous scientific domains,
fueling the development of new materials and innovative drug designs. Recent
developments of in-silico molecule discovery have highlighted the promising
results of cross-modal techniques, which bridge molecular structures with their
descriptive annotations. However, these cross-modal methods frequently
encounter the issue of data scarcity, hampering their performance and
application. In this paper, we address the low-resource challenge by utilizing
artificially-real data generated by Large Language Models (LLMs). We first
introduce a retrieval-based prompting strategy to construct high-quality pseudo
data, then explore the optimal method to effectively leverage this pseudo data.
Experiments show that using pseudo data for domain adaptation outperforms all
existing methods, while also requiring a smaller model scale, reduced data size
and lower training cost, highlighting its efficiency. Furthermore, our method
shows a sustained improvement as the volume of pseudo data increases, revealing
the great potential of pseudo data in advancing low-resource cross-modal
molecule discovery
GLS-CSC: A Simple but Effective Strategy to Mitigate Chinese STM Models' Over-Reliance on Superficial Clue
Pre-trained models have achieved success in Chinese Short Text Matching (STM)
tasks, but they often rely on superficial clues, leading to a lack of robust
predictions. To address this issue, it is crucial to analyze and mitigate the
influence of superficial clues on STM models. Our study aims to investigate
their over-reliance on the edit distance feature, commonly used to measure the
semantic similarity of Chinese text pairs, which can be considered a
superficial clue. To mitigate STM models' over-reliance on superficial clues,
we propose a novel resampling training strategy called Gradually Learn Samples
Containing Superficial Clue (GLS-CSC). Through comprehensive evaluations of
In-Domain (I.D.), Robustness (Rob.), and Out-Of-Domain (O.O.D.) test sets, we
demonstrate that GLS-CSC outperforms existing methods in terms of enhancing the
robustness and generalization of Chinese STM models. Moreover, we conduct a
detailed analysis of existing methods and reveal their commonality
whu-nercms at trecvid2021:instance search task
We will make a brief introduction of the experimental methods and results of
the WHU-NERCMS in the TRECVID2021 in the paper. This year we participate in the
automatic and interactive tasks of Instance Search (INS). For the automatic
task, the retrieval target is divided into two parts, person retrieval, and
action retrieval. We adopt a two-stage method including face detection and face
recognition for person retrieval and two kinds of action detection methods
consisting of three frame-based human-object interaction detection methods and
two video-based general action detection methods for action retrieval. After
that, the person retrieval results and action retrieval results are fused to
initialize the result ranking lists. In addition, we make attempts to use
complementary methods to further improve search performance. For interactive
tasks, we test two different interaction strategies on the fusion results. We
submit 4 runs for automatic and interactive tasks respectively. The
introduction of each run is shown in Table 1. The official evaluations show
that the proposed strategies rank 1st in both automatic and interactive tracks.Comment: 9 pages, 4 figure
Intranasally inoculated bacterium-like particles displaying porcine epidemic diarrhea virus S1 protein induced intestinal mucosal immune response in mice
Porcine epidemic diarrhea virus (PEDV) causes acute watery diarrhea and high mortality in newborn piglets. Activation of intestinal mucosal immunity is crucial to anti-PEDV infection. To develop a vaccine capable of stimulating intestinal mucosal immunity, we prepared a bacterium (Lactococcus lactis)-like particle (BLP) vaccine (S1-BLPs) displaying the S1 protein, a domain of PEDV spike protein (S), based on gram-positive enhancer matrix (GEM) particle display technology. We further compared the effects of different vaccination routes on mucosal immune responses in mice induced by S1-BLPs. The specific IgG titer in serum of intramuscularly immunized mice with S1-BLPs was significantly higher than that of the intranasally administered. The specific IgA antibody was found in the serum and intestinal lavage fluid of mice vaccinated intranasally, but not intramuscularly. Moreover, the intranasally inoculated S1-BLPs induced higher levels of IFN-γ and IL-4 in serum than the intramuscularly inoculated. In addition, the ratio of serum IgG2a/IgG1 of mice inoculated intramuscularly was significantly higher with S1-BLPs compared to that of with S1 protein, suggesting that the immune responses induced by S1-BLPs was characterized by helper T (Th) cell type 1 immunity. The results indicated that S1-BLPs induced systemic and local immunity, and the immunization routes significantly affected the specific antibody classes and Th immune response types. The intranasally administered S1-BLPs could effectively stimulate intestinal mucosal specific secretory IgA response. S1-BLPs have the potential to be developed as PEDV mucosal vaccine
Two-element interferometer for millimeter-wave solar flare observations
In this paper, we present the design and implementation of a two-element
interferometer working in the millimeter wave band (39.5 GHz - 40 GHz) for
observing solar radio emissions through nulling interference. The system is
composed of two 50 cm aperture Cassegrain antennas mounted on a common
equatorial mount, with a separation of 230 wavelengths. The cross-correlation
of the received signals effectively cancels the quiet solar component of the
large flux density (~3000 sfu) that reduces the detection limit due to
atmospheric fluctuations. The system performance is obtained as follows: the
noise factor of the AFE in the observation band is less than 2.1 dB, system
sensitivity is approximately 12.4 K (~34 sfu) with an integration time constant
of 0.1 ms (default), the frequency resolution is 153 kHz, and the dynamic range
is larger than 30 dB. Through actual testing, the nulling interferometer
observes a quiet sun with a low level of output fluctuations (of up to 50 sfu)
and has a significantly lower radiation flux variability (of up to 190 sfu)
than an equivalent single-antenna system, even under thick cloud cover. As a
result, this new design can effectively improve observation sensitivity by
reducing the impact of atmospheric and system fluctuations during observation
Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese
Large Language Models (LLMs) have demonstrated remarkable success in diverse
natural language processing (NLP) tasks in general domains. However, LLMs
sometimes generate responses with the hallucination about medical facts due to
limited domain knowledge. Such shortcomings pose potential risks in the
utilization of LLMs within medical contexts. To address this challenge, we
propose knowledge-tuning, which leverages structured medical knowledge bases
for the LLMs to grasp domain knowledge efficiently and facilitate reliable
response generation. We also release cMedKnowQA, a Chinese medical knowledge
question-answering dataset constructed from medical knowledge bases to assess
the medical knowledge proficiency of LLMs. Experimental results show that the
LLMs which are knowledge-tuned with cMedKnowQA, can exhibit higher levels of
accuracy in response generation compared with vanilla instruction-tuning and
offer a new reliable way for the domain adaptation of LLMs.Comment: 11 pages, 5 figure
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