297 research outputs found
BRAIN COGNITIVE CHARACTERISTICS OF EMOTIONAL RESPONSE OF COLLEGE STUDENTS IN ENGLISH LEARNING
BRAIN COGNITIVE CHARACTERISTICS OF EMOTIONAL RESPONSE OF COLLEGE STUDENTS IN ENGLISH LEARNING
The optimal connection model for blood vessels segmentation and the MEA-Net
Vascular diseases have long been regarded as a significant health concern.
Accurately detecting the location, shape, and afflicted regions of blood
vessels from a diverse range of medical images has proven to be a major
challenge. Obtaining blood vessels that retain their correct topological
structures is currently a crucial research issue. Numerous efforts have sought
to reinforce neural networks' learning of vascular geometric features,
including measures to ensure the correct topological structure of the
segmentation result's vessel centerline. Typically, these methods extract
topological features from the network's segmentation result and then apply
regular constraints to reinforce the accuracy of critical components and the
overall topological structure. However, as blood vessels are three-dimensional
structures, it is essential to achieve complete local vessel segmentation,
which necessitates enhancing the segmentation of vessel boundaries.
Furthermore, current methods are limited to handling 2D blood vessel
fragmentation cases. Our proposed boundary attention module directly extracts
boundary voxels from the network's segmentation result. Additionally, we have
established an optimal connection model based on minimal surfaces to determine
the connection order between blood vessels. Our method achieves
state-of-the-art performance in 3D multi-class vascular segmentation tasks, as
evidenced by the high values of Dice Similarity Coefficient (DSC) and
Normalized Surface Dice (NSD) metrics. Furthermore, our approach improves the
Betti error, LR error, and BR error indicators of vessel richness and
structural integrity by more than 10% compared to other methods, and
effectively addresses vessel fragmentation and yields blood vessels with a more
precise topological structure.Comment: 19 page
A novel hybrid firefly algorithm for global optimization
Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate
An Efficient Source Model Selection Framework in Model Databases
With the explosive increase of big data, training a Machine Learning (ML)
model becomes a computation-intensive workload, which would take days or even
weeks. Thus, reusing an already trained model has received attention, which is
called transfer learning. Transfer learning avoids training a new model from
scratch by transferring knowledge from a source task to a target task. Existing
transfer learning methods mostly focus on how to improve the performance of the
target task through a specific source model, and assume that the source model
is given. Although many source models are available, it is difficult for data
scientists to select the best source model for the target task manually. Hence,
how to efficiently select a suitable source model in a model database for model
reuse is an interesting but unsolved problem. In this paper, we propose SMS, an
effective, efficient, and flexible source model selection framework. SMS is
effective even when the source and target datasets have significantly different
data labels, and is flexible to support source models with any type of
structure, and is efficient to avoid any training process. For each source
model, SMS first vectorizes the samples in the target dataset into soft labels
by directly applying this model to the target dataset, then uses Gaussian
distributions to fit for clusters of soft labels, and finally measures the
distinguishing ability of the source model using Gaussian mixture-based metric.
Moreover, we present an improved SMS (I-SMS), which decreases the output number
of the source model. I-SMS can significantly reduce the selection time while
retaining the selection performance of SMS. Extensive experiments on a range of
practical model reuse workloads demonstrate the effectiveness and efficiency of
SMS
A physical study of the LLL algorithm
This paper presents a study of the LLL algorithm from the perspective of
statistical physics. Based on our experimental and theoretical results, we
suggest that interpreting LLL as a sandpile model may help understand much of
its mysterious behavior. In the language of physics, our work presents evidence
that LLL and certain 1-d sandpile models with simpler toppling rules belong to
the same universality class.
This paper consists of three parts. First, we introduce sandpile models whose
statistics imitate those of LLL with compelling accuracy, which leads to the
idea that there must exist a meaningful connection between the two. Indeed, on
those sandpile models, we are able to prove the analogues of some of the most
desired statements for LLL, such as the existence of the gap between the
theoretical and the experimental RHF bounds. Furthermore, we test the formulas
from the finite-size scaling theory (FSS) against the LLL algorithm itself, and
find that they are in excellent agreement. This in particular explains and
refines the geometric series assumption (GSA), and allows one to extrapolate
various quantities of interest to the dimension limit. In particular, we
predict the empirical average RHF converges to as dimension
goes to infinity.Comment: Augmented version of 1804.03285; expect some overlap
Experimental investigation and modeling of the spread and burning behaviors of continuous spill fires on a water surface
Systematic HydrogenâBond Manipulations To Establish Polysaccharide StructureâProperty Correlations
A dense hydrogenâbond network is responsible for the mechanical and structural properties of polysaccharides. Random derivatization alters the properties of the bulk material by disrupting the hydrogen bonds, but obstructs detailed structureâfunction correlations. We have prepared wellâdefined unnatural oligosaccharides including methylated, deoxygenated, deoxyfluorinated, as well as carboxymethylated cellulose and chitin analogues with full control over the degree and pattern of substitution. Molecular dynamics simulations and crystallographic analysis show how distinct hydrogenâbond modifications drastically affect the solubility, aggregation behavior, and crystallinity of carbohydrate materials. This systematic approach to establishing detailed structureâproperty correlations will guide the synthesis of novel, tailorâmade carbohydrate materials
- âŠ