Deep sparse networks are widely investigated as a neural network architecture
for prediction tasks with high-dimensional sparse features, with which feature
interaction selection is a critical component. While previous methods primarily
focus on how to search feature interaction in a coarse-grained space, less
attention has been given to a finer granularity. In this work, we introduce a
hybrid-grained feature interaction selection approach that targets both feature
field and feature value for deep sparse networks. To explore such expansive
space, we propose a decomposed space which is calculated on the fly. We then
develop a selection algorithm called OptFeature, which efficiently selects the
feature interaction from both the feature field and the feature value
simultaneously. Results from experiments on three large real-world benchmark
datasets demonstrate that OptFeature performs well in terms of accuracy and
efficiency. Additional studies support the feasibility of our method.

研究了稀疏深度网络作为高维稀疏特征的预测任务的神经网络架构，并引入了一种混合粒度的特征交互选择方法，该方法同时针对特征域和特征值进行选择，通过实验结果证明该方法在准确性和效率方面表现出色。

深度稀疏网络的混合级特征交互选择

Towards Hybrid-grained Feature Interaction Selection for Deep Sparse  Network

Ultrasound (US) image segmentation is an active research area that requires
real-time and highly accurate analysis in many scenarios. The detect-to-segment
(DTS) frameworks have been recently proposed to balance accuracy and
efficiency. However, existing approaches may suffer from inadequate contour
encoding or fail to effectively leverage the encoded results. In this paper, we
introduce a novel Fourier-anchor-based DTS framework called Fourier Feature
Pyramid Network (FFPN) to address the aforementioned issues. The contributions
of this paper are two fold. First, the FFPN utilizes Fourier Descriptors to
adequately encode contours. Specifically, it maps Fourier series with similar
amplitudes and frequencies into the same layer of the feature map, thereby
effectively utilizing the encoded Fourier information. Second, we propose a
Contour Sampling Refinement (CSR) module based on the contour proposals and
refined features produced by the FFPN. This module extracts rich features
around the predicted contours to further capture detailed information and
refine the contours. Extensive experimental results on three large and
challenging datasets demonstrate that our method outperforms other DTS methods
in terms of accuracy and efficiency. Furthermore, our framework can generalize
well to other detection or segmentation tasks.

在超声波图像分割中，通过使用傅里叶锚点的特征金字塔网络 (FFPN) 和轮廓采样细化 (CSR) 模块，可以提高准确性和效率。

FFPN：傅里叶特征金字塔网络用于超声图像分割

FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation

A recurrent task in coordinated systems is managing (estimating, predicting,
or controlling) signals that vary in space, such as distributed sensed data or
computation outcomes. Especially in large-scale settings, the problem can be
addressed through decentralised and situated computing systems: nodes can
locally sense, process, and act upon signals, and coordinate with neighbours to
implement collective strategies. Accordingly, in this work we devise
distributed coordination strategies for the estimation of a spatial phenomenon
through collaborative adaptive sampling. Our design is based on the idea of
dynamically partitioning space into regions that compete and grow/shrink to
provide accurate aggregate sampling. Such regions hence define a sort of
virtualised space that is "fluid", since its structure adapts in response to
pressure forces exerted by the underlying phenomenon. We provide an adaptive
sampling algorithm in the field-based coordination framework, and prove it is
self-stabilising and locally optimal. Finally, we verify by simulation that the
proposed algorithm effectively carries out a spatially adaptive sampling while
maintaining a tuneable trade-off between accuracy and efficiency.

讨论在分布式计算系统中，如何通过自适应采样算法实现对空间现象的分布式协调估计，以达到对精度和效率进行可调平衡的采样过程。