442 research outputs found
Multi-scale Spatial-temporal Interaction Network for Video Anomaly Detection
Video anomaly detection (VAD) is an essential yet challenge task in signal
processing. Since certain anomalies cannot be detected by analyzing temporal or
spatial information alone, the interaction between two types of information is
considered crucial for VAD. However, current dual-stream architectures either
limit interaction between the two types of information to the bottleneck of
autoencoder or incorporate background pixels irrelevant to anomalies into the
interaction. To this end, we propose a multi-scale spatial-temporal interaction
network (MSTI-Net) for VAD. First, to pay particular attention to objects and
reconcile the significant semantic differences between the two information, we
propose an attention-based spatial-temporal fusion module (ASTM) as a
substitute for the conventional direct fusion. Furthermore, we inject multi
ASTM-based connections between the appearance and motion pathways of a dual
stream network to facilitate spatial-temporal interaction at all possible
scales. Finally, the regular information learned from multiple scales is
recorded in memory to enhance the differentiation between anomalies and normal
events during the testing phase. Solid experimental results on three standard
datasets validate the effectiveness of our approach, which achieve AUCs of
96.8% for UCSD Ped2, 87.6% for CUHK Avenue, and 73.9% for the ShanghaiTech
dataset
Combined probabilistic linguistic term set and ELECTRE II method for solving a venture capital project evaluation problem
Multiple criteria decision making (MCDM) frameworks assist people in assessing alternatives and making reasonable decisions,
with the ELECTRE II MCDM method in particular being widely
applied to many diverse fields. As it is not always possible to
assess qualitative attributes or accurately evaluate alternatives
using precise values, this paper proposes a new approach that combines the ELECTRE II method with probabilistic linguistic term sets
(PLTS) to allow decision makers to state their qualitative preferences
using corresponding probabilities. To demonstrate the viability of
the PTLS-ELECTRE II method and assess its practicability, the proposed method was applied to a typical MCDM venture capital project evaluation problem, for which a comprehensive venture capital
project evaluation index system was constructed that included multiple qualitative and quantitative indicators, such as industry background, marketing, product technology, team management and
financial data. The reasonable evaluation sequence of alternatives
was then determined using the PTLS-ELECTRE II method which can
provide more accurate MCDM decisions
An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as An Example
In this work, an ontology-based model for AI-assisted medicine side-effect
(SE) prediction is developed, where three main components, including the drug
model, the treatment model, and the AI-assisted prediction model, of proposed
model are presented. To validate the proposed model, an ANN structure is
established and trained by two hundred and forty-two TCM prescriptions. These
data are gathered and classified from the most famous ancient TCM book and more
than one thousand SE reports, in which two ontology-based attributions, hot and
cold, are introduced to evaluate whether the prescription will cause SE or not.
The results preliminarily reveal that it is a relationship between the
ontology-based attributions and the corresponding predicted indicator that can
be learnt by AI for predicting the SE, which suggests the proposed model has a
potential in AI-assisted SE prediction. However, it should be noted that, the
proposed model highly depends on the sufficient clinic data, and hereby, much
deeper exploration is important for enhancing the accuracy of the prediction
Microstructure Characterization and Battery Performance Comparison of MOF-235 and TiO 2 -P25 Materials
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The growing interest in energy storage has led to the urgent need for the development of high-performance cathode electrodes. The commercialized materials MOF-235 and TiO2-P25 exhibit characteristics that may be suitable as electrodes but there are inherent challenges that have yet to be addressed in the literature. In this study, a high-pressure hydrothermal synthesized MOF-235 and sol-gel-made TiO2-P25 were tested for battery performance. The results indicate that MOF-235 does not possess the desired performance due to uncontrollable agglomeration. On the other hand, TiO2-P25 showed good cycling life, and the performance can be further optimized by doping and minimizing the particle size. Additionally, SEM and TEM were applied for surface characterization, providing evidence that mesoporous TiO2-25 inhibits photo-generated carrier recombination. The mesoporous energy storage mechanism of those two materials is also discussed. This research will provide technical support for the industrialization of those two mesoporous materials.Peer reviewedFinal Published versio
FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset
We present FaceVerse, a fine-grained 3D Neural Face Model, which is built
from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K
high-fidelity 3D head scan models. A novel coarse-to-fine structure is proposed
to take better advantage of our hybrid dataset. In the coarse module, we
generate a base parametric model from large-scale RGB-D images, which is able
to predict accurate rough 3D face models in different genders, ages, etc. Then
in the fine module, a conditional StyleGAN architecture trained with
high-fidelity scan models is introduced to enrich elaborate facial geometric
and texture details. Note that different from previous methods, our base and
detailed modules are both changeable, which enables an innovative application
of adjusting both the basic attributes and the facial details of 3D face
models. Furthermore, we propose a single-image fitting framework based on
differentiable rendering. Rich experiments show that our method outperforms the
state-of-the-art methods.Comment: https://github.com/LizhenWangT/FaceVers
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