94 research outputs found
iPUNet:Iterative Cross Field Guided Point Cloud Upsampling
Point clouds acquired by 3D scanning devices are often sparse, noisy, and
non-uniform, causing a loss of geometric features. To facilitate the usability
of point clouds in downstream applications, given such input, we present a
learning-based point upsampling method, i.e., iPUNet, which generates dense and
uniform points at arbitrary ratios and better captures sharp features. To
generate feature-aware points, we introduce cross fields that are aligned to
sharp geometric features by self-supervision to guide point generation. Given
cross field defined frames, we enable arbitrary ratio upsampling by learning at
each input point a local parameterized surface. The learned surface consumes
the neighboring points and 2D tangent plane coordinates as input, and maps onto
a continuous surface in 3D where arbitrary ratios of output points can be
sampled. To solve the non-uniformity of input points, on top of the cross field
guided upsampling, we further introduce an iterative strategy that refines the
point distribution by moving sparse points onto the desired continuous 3D
surface in each iteration. Within only a few iterations, the sparse points are
evenly distributed and their corresponding dense samples are more uniform and
better capture geometric features. Through extensive evaluations on diverse
scans of objects and scenes, we demonstrate that iPUNet is robust to handle
noisy and non-uniformly distributed inputs, and outperforms state-of-the-art
point cloud upsampling methods
Exploring Gut Microbiota in Patients with Colorectal Disease Based on 16S rRNA Gene Amplicon and Shallow Metagenomic Sequencing
The gastrointestinal tract, the largest human microbial reservoir, is highly dynamic. The gut microbes play essential roles in causing colorectal diseases. In the present study, we explored potential keystone taxa during the development of colorectal diseases in central China. Fecal samples of some patients were collected and were allocated to the adenoma (Group A), colorectal cancer (Group C), and hemorrhoid (Group H) groups. The 16S rRNA amplicon and shallow metagenomic sequencing (SMS) strategies were used to recover the gut microbiota. Microbial diversities obtained from 16S rRNA amplicon and SMS data were similar. Group C had the highest diversity, although no significant difference in diversity was observed among the groups. The most dominant phyla in the gut microbiota of patients with colorectal diseases were Bacteroidetes, Firmicutes, and Proteobacteria, accounting for >95% of microbes in the samples. The most abundant genera in the samples were Bacteroides, Prevotella, and Escherichia/Shigella, and further species-level and network analyses identified certain potential keystone taxa in each group. Some of the dominant species, such as Prevotella copri, Bacteroides dorei, and Bacteroides vulgatus, could be responsible for causing colorectal diseases. The SMS data recovered diverse antibiotic resistance genes of tetracycline, macrolide, and beta-lactam, which could be a result of antibiotic overuse. This study explored the gut microbiota of patients with three different types of colorectal diseases, and the microbial diversity results obtained from 16S rRNA amplicon sequencing and SMS data were found to be similar. However, the findings of this study are based on a limited sample size, which warrants further large-scale studies. The recovery of gut microbiota profiles in patients with colorectal diseases could be beneficial for future diagnosis and treatment with modulation of the gut microbiota. Moreover, SMS data can provide accurate species- and gene-level information, and it is economical. It can therefore be widely applied in future clinical metagenomic studies
Automatic Data Visualization Generation from Chinese Natural Language Questions
Data visualization has emerged as an effective tool for getting insights from
massive datasets. Due to the hardness of manipulating the programming languages
of data visualization, automatic data visualization generation from natural
languages (Text-to-Vis) is becoming increasingly popular. Despite the plethora
of research effort on the English Text-to-Vis, studies have yet to be conducted
on data visualization generation from questions in Chinese. Motivated by this,
we propose a Chinese Text-to-Vis dataset in the paper and demonstrate our first
attempt to tackle this problem. Our model integrates multilingual BERT as the
encoder, boosts the cross-lingual ability, and infuses the -gram information
into our word representation learning. Our experimental results show that our
dataset is challenging and deserves further research
Composition and biological activity of tea polysaccharides obtained by water extraction and enzymatic extraction
The composition and biological activities of tea polysaccharides (TPS) obtained by traditional water extraction, boiling water extraction and enzymatic extraction were investigated. Boiling water extraction (100 °C for 2 h) was found to be the optimal method with higher yield of TPS (1.91%) consisting of higher contents of neutral saccharides (57.82%) and acid saccharides (26.95%) with lowest protein content (3.06%). TPS obtained by boiling water extraction exhibited a strong inhibitory effect on α-glucosidase with the inhibitory rate of 86.67%. The inhibitory effect of TPS on α-glucosidase increased with increasing neutral polysaccharides content in TPS. TPS obtained by boiling water extraction (50 μg/mL) had very strong proliferation effect on lymphocyte.Colegio de Farmacéuticos de la Provincia de Buenos Aire
Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey
The emergence of natural language processing has revolutionized the way users
interact with tabular data, enabling a shift from traditional query languages
and manual plotting to more intuitive, language-based interfaces. The rise of
large language models (LLMs) such as ChatGPT and its successors has further
advanced this field, opening new avenues for natural language processing
techniques. This survey presents a comprehensive overview of natural language
interfaces for tabular data querying and visualization, which allow users to
interact with data using natural language queries. We introduce the fundamental
concepts and techniques underlying these interfaces with a particular emphasis
on semantic parsing, the key technology facilitating the translation from
natural language to SQL queries or data visualization commands. We then delve
into the recent advancements in Text-to-SQL and Text-to-Vis problems from the
perspectives of datasets, methodologies, metrics, and system designs. This
includes a deep dive into the influence of LLMs, highlighting their strengths,
limitations, and potential for future improvements. Through this survey, we aim
to provide a roadmap for researchers and practitioners interested in developing
and applying natural language interfaces for data interaction in the era of
large language models.Comment: 20 pages, 4 figures, 5 tables. Submitted to IEEE TKD
Multifactor and multi-objective coupling design of hydrogen circulation pump
The hydrogen circulation pump (HCP) is an important power component of the hydrogen fuel system, used to recover the unconsumed hydrogen from the anode and transport it back to the inlet of the battery stack to improve the hydrogen utilization efficiency. In this paper, to determine the optimal parameter configuration of the HCP, a multifactor and multi-objective optimization design method is proposed, and the influences of various design parameters on the performance of the HCP are analyzed based on the verified overset grid simulation method. The research results show that the proposed coupling design method can effectively achieve the optimal parameter configuration of the HCP, with diameter-to-pitch ratio κ = 1.47, rotor blade number Z = 3, and helix angle φ = 60°, which is validated using another model with significant performance advantages. In the process of studying the influence of design parameters, it is found that the average flow rate of the HCP is directly proportional to the diameter-to-pitch ratio and the blade number, gradually decreases in the range of helix angle from 0° to 22.5°, and increases in the range of helix angle from 22.5° to 60°. The flow pulsation value and pressure pulsation value of the HCP are less affected by the diameter-to-pitch ratio, decrease with the increase of the blade number, and show a trend of first increasing and then decreasing with the increase of the helix angle
3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
Teeth localization, segmentation, and labeling from intra-oral 3D scans are
essential tasks in modern dentistry to enhance dental diagnostics, treatment
planning, and population-based studies on oral health. However, developing
automated algorithms for teeth analysis presents significant challenges due to
variations in dental anatomy, imaging protocols, and limited availability of
publicly accessible data. To address these challenges, the 3DTeethSeg'22
challenge was organized in conjunction with the International Conference on
Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022,
with a call for algorithms tackling teeth localization, segmentation, and
labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans
from 900 patients was prepared, and each tooth was individually annotated by a
human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this
dataset. In this study, we present the evaluation results of the 3DTeethSeg'22
challenge. The 3DTeethSeg'22 challenge code can be accessed at:
https://github.com/abenhamadou/3DTeethSeg22_challengeComment: 29 pages, MICCAI 2022 Singapore, Satellite Event, Challeng
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