124 research outputs found
Constructing IGA-suitable planar parameterization from complex CAD boundary by domain partition and global/local optimization
In this paper, we propose a general framework for constructing IGA-suitable
planar B-spline parameterizations from given complex CAD boundaries consisting
of a set of B-spline curves. Instead of forming the computational domain by a
simple boundary, planar domains with high genus and more complex boundary
curves are considered. Firstly, some pre-processing operations including
B\'ezier extraction and subdivision are performed on each boundary curve in
order to generate a high-quality planar parameterization; then a robust planar
domain partition framework is proposed to construct high-quality patch-meshing
results with few singularities from the discrete boundary formed by connecting
the end points of the resulting boundary segments. After the topology
information generation of quadrilateral decomposition, the optimal placement of
interior B\'ezier curves corresponding to the interior edges of the
quadrangulation is constructed by a global optimization method to achieve a
patch-partition with high quality. Finally, after the imposition of
C1=G1-continuity constraints on the interface of neighboring B\'ezier patches
with respect to each quad in the quadrangulation, the high-quality B\'ezier
patch parameterization is obtained by a C1-constrained local optimization
method to achieve uniform and orthogonal iso-parametric structures while
keeping the continuity conditions between patches. The efficiency and
robustness of the proposed method are demonstrated by several examples which
are compared to results obtained by the skeleton-based parameterization
approach
Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case
Adam is a commonly used stochastic optimization algorithm in machine
learning. However, its convergence is still not fully understood, especially in
the non-convex setting. This paper focuses on exploring hyperparameter settings
for the convergence of vanilla Adam and tackling the challenges of non-ergodic
convergence related to practical application. The primary contributions are
summarized as follows: firstly, we introduce precise definitions of ergodic and
non-ergodic convergence, which cover nearly all forms of convergence for
stochastic optimization algorithms. Meanwhile, we emphasize the superiority of
non-ergodic convergence over ergodic convergence. Secondly, we establish a
weaker sufficient condition for the ergodic convergence guarantee of Adam,
allowing a more relaxed choice of hyperparameters. On this basis, we achieve
the almost sure ergodic convergence rate of Adam, which is arbitrarily close to
. More importantly, we prove, for the first time, that the last
iterate of Adam converges to a stationary point for non-convex objectives.
Finally, we obtain the non-ergodic convergence rate of for function
values under the Polyak-Lojasiewicz (PL) condition. These findings build a
solid theoretical foundation for Adam to solve non-convex stochastic
optimization problems
A Parallel Feature-preserving Mesh Variable Offsetting Method with Dynamic Programming
Mesh offsetting plays an important role in discrete geometric processing. In
this paper, we propose a parallel feature-preserving mesh offsetting framework
with variable distance. Different from the traditional method based on distance
and normal vector, a new calculation of offset position is proposed by using
dynamic programming and quadratic programming, and the sharp feature can be
preserved after offsetting. Instead of distance implicit field, a spatial
coverage region represented by polyhedral for computing offsets is proposed.
Our method can generate an offsetting model with smaller mesh size, and also
can achieve high quality without gaps, holes, and self-intersections. Moreover,
several acceleration techniques are proposed for the efficient mesh offsetting,
such as the parallel computing with grid, AABB tree and rays computing. In
order to show the efficiency and robustness of the proposed framework, we have
tested our method on the quadmesh dataset, which is available at
[https://www.quadmesh.cloud]. The source code of the proposed algorithm is
available on GitHub at [https://github.com/iGame-Lab/PFPOffset]
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals
Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant
malignancy that predominantly impacts the head and neck area. Precise
delineation of the Gross Tumor Volume (GTV) plays a pivotal role in ensuring
effective radiotherapy for NPC. Despite recent methods that have achieved
promising results on GTV segmentation, they are still limited by lacking
carefully-annotated data and hard-to-access data from multiple hospitals in
clinical practice. Although some unsupervised domain adaptation (UDA) has been
proposed to alleviate this problem, unconditionally mapping the distribution
distorts the underlying structural information, leading to inferior
performance. To address this challenge, we devise a novel Sourece-Free Active
Domain Adaptation (SFADA) framework to facilitate domain adaptation for the GTV
segmentation task. Specifically, we design a dual reference strategy to select
domain-invariant and domain-specific representative samples from a specific
target domain for annotation and model fine-tuning without relying on
source-domain data. Our approach not only ensures data privacy but also reduces
the workload for oncologists as it just requires annotating a few
representative samples from the target domain and does not need to access the
source data. We collect a large-scale clinical dataset comprising 1057 NPC
patients from five hospitals to validate our approach. Experimental results
show that our method outperforms the UDA methods and achieves comparable
results to the fully supervised upper bound, even with few annotations,
highlighting the significant medical utility of our approach. In addition,
there is no public dataset about multi-center NPC segmentation, we will release
code and dataset for future research
STUDY ON ANTI-TUMOR EFFECT OF TOTAL GLYCOSIDES FROM RADIX PAEONIAE RUBRA IN S180 TUMOR-BEARING MICE
The objective of the paper was to study the anti-tumor effect of total glycosides from Radix paeoniae rubra in S180 tumor-bearing mice, and to preliminarily explore its mechanism of action. Mice were made into S180 solid tumor model, grouped and administered with the extracts; tumor inhibition rate was measured by harvesting the tumors, and serum IL-2 and IL-4 levels were measured by taking blood samples. Total glycosides of Radix paeoniae rubra significantly inhibited the growth of tumor cells in tumor-bearing organisms, enhanced the cytotoxic activity of NK cells, and increased the serum IL-2 and IL-4 levels. Total glycosides of Radix paeoniae rubra have some anti-tumor effect in vivo, which might have been accomplished through the regulation of the immune system
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