514 research outputs found
MVMR-FS : Non-parametric feature selection algorithm based on Maximum inter-class Variation and Minimum Redundancy
How to accurately measure the relevance and redundancy of features is an
age-old challenge in the field of feature selection. However, existing
filter-based feature selection methods cannot directly measure redundancy for
continuous data. In addition, most methods rely on manually specifying the
number of features, which may introduce errors in the absence of expert
knowledge. In this paper, we propose a non-parametric feature selection
algorithm based on maximum inter-class variation and minimum redundancy,
abbreviated as MVMR-FS. We first introduce supervised and unsupervised kernel
density estimation on the features to capture their similarities and
differences in inter-class and overall distributions. Subsequently, we present
the criteria for maximum inter-class variation and minimum redundancy (MVMR),
wherein the inter-class probability distributions are employed to reflect
feature relevance and the distances between overall probability distributions
are used to quantify redundancy. Finally, we employ an AGA to search for the
feature subset that minimizes the MVMR. Compared with ten state-of-the-art
methods, MVMR-FS achieves the highest average accuracy and improves the
accuracy by 5% to 11%
Carbon nanofibers with inter-bonded fibrous structure for a supercapacitance application
Investigating Graph Structure Information for Entity Alignment with Dangling Cases
Entity alignment (EA) aims to discover the equivalent entities in different
knowledge graphs (KGs), which play an important role in knowledge engineering.
Recently, EA with dangling entities has been proposed as a more realistic
setting, which assumes that not all entities have corresponding equivalent
entities. In this paper, we focus on this setting. Some work has explored this
problem by leveraging translation API, pre-trained word embeddings, and other
off-the-shelf tools. However, these approaches over-rely on the side
information (e.g., entity names), and fail to work when the side information is
absent. On the contrary, they still insufficiently exploit the most fundamental
graph structure information in KG. To improve the exploitation of the
structural information, we propose a novel entity alignment framework called
Weakly-Optimal Graph Contrastive Learning (WOGCL), which is refined on three
dimensions : (i) Model. We propose a novel Gated Graph Attention Network to
capture local and global graph structure similarity. (ii) Training. Two
learning objectives: contrastive learning and optimal transport learning are
designed to obtain distinguishable entity representations via the optimal
transport plan. (iii) Inference. In the inference phase, a PageRank-based
method is proposed to calculate higher-order structural similarity. Extensive
experiments on two dangling benchmarks demonstrate that our WOGCL outperforms
the current state-of-the-art methods with pure structural information in both
traditional (relaxed) and dangling (consolidated) settings. The code will be
public soon
Quantitative Implementation of Artificial Intelligence Based on Task Completion Analysis
With the further development of the new generation of artificial intelligence science and technology, the new generation of artificial intelligence science and technology has been applied in many fields. AlphaGo program uses high technology of quantitative analysis to realize qualitative research and development of artificial intelligence, which has important reference significance for the research and development of a new generation of artificial intelligence in the future. From the perspective of task accessibility, this paper analyzes the defects of the disturbance, so as to achieve the quantitative implementation of the new generation of artificial intelligence task accessibility analysis method
Hg 0 capture over MoS2 nanosheets containing adsorbent: effects of temperature, space velocity, and other gas species
Fossil fuel burning is the largest anthropogenic source of mercury emission, which is expected to be the first industrial sector to be addressed under Minamata Convention. In this research, the preliminary investigation has been carried out to understand the effects of temperature, space velocity, and SO2 and O2 on Hg0 capture over MoS2 nanosheets containing elemental mercury adsorbent. The adsorbent exhibited excellent performance in the removal of Hg0 at a low temperature below 125°C (particularly at 50°C) with a space velocity below 9.0×104 ml/(h·g). It was found that the presence of O2 had positive effect on Hg0 removal whilst SO2 had slightly negative effect on mercury capture at low temperature, such as 50°C. However, such negative effect became negligible when O2 co-existed with SO2 in the simulated flue gas. The research provided fundamental information for further development of the 2D graphene-like MoS2 nanosheets containing adsorbent for mercury capture
A virus-like particle of the hepatitis B virus preS antigen elicits robust neutralizing antibodies and T cell responses in mice
The preS antigen of hepatitis B virus (HBV) corresponds to the N-terminal polypeptide in the large (L) antigen in addition to the small (S) antigen. The virus-like particle (VLP) of the S antigen is widely used as a vaccine to protect the population from HBV infection. The presence of the S antigen and its antibodies in patient blood has been used as markers to monitor hepatitis B. However, there is very limited knowledge about the preS antigen. We generated a preS VLP that is formed by a chimeric protein between preS and hemagglutinin (HA), and the matrix protein M1 of influenza virus. The HBV preS antigen is displayed on the surface of preS VLP. Asn112 and Ser98 of preS in VLP were found to be glycosylated and O-glycosylation of Ser98 has not been reported previously. The preS VLP shows a significantly higher immunogenicity than recombinant preS, eliciting robust anti-preS neutralizing antibodies. In addition, preS VLP is also capable of stimulating preS-specific CD8+ and CD4+ T cell responses in Balb/c mice and HBV transgenic mice. Furthermore, preS VLP immunization provided protection against hydrodynamic transfection of HBV DNA in mice. The data clearly suggest that this novel preS VLP could elicit robust immune responses to the HBV antigen, and can be potentially developed into prophylactic and therapeutic vaccines
- …