Understanding causal relations is vital in scientific discovery. The process
of causal structure learning involves identifying causal graphs from
observational data to understand such relations. Usually, a central server
performs this task, but sharing data with the server poses privacy risks.
Federated learning can solve this problem, but existing solutions for federated
causal structure learning make unrealistic assumptions about data and lack
convergence guarantees. FedC2SL is a federated constraint-based causal
structure learning scheme that learns causal graphs using a federated
conditional independence test, which examines conditional independence between
two variables under a condition set without collecting raw data from clients.
FedC2SL requires weaker and more realistic assumptions about data and offers
stronger resistance to data variability among clients. FedPC and FedFCI are the
two variants of FedC2SL for causal structure learning in causal sufficiency and
causal insufficiency, respectively. The study evaluates FedC2SL using both
synthetic datasets and real-world data against existing solutions and finds it
demonstrates encouraging performance and strong resilience to data
heterogeneity among clients.

本研究提出了一个名为 FedC2SL 的联邦约束因果结构学习方案，它使用联邦有条件独立性检验学习因果图，该方案需要更弱和更现实的数据假设，并针对客户端数据变异提供更强的抗性，从而解决了在保护隐私方面存在的一些问题。实验结果表明，FedC2SL 具有令人鼓舞的性能和对客户端数据异质性的强大韧性。

实现可行的联邦因果结构学习

Towards Practical Federated Causal Structure Learning

Plant species identification in the wild is a difficult problem in part due
to the high variability of the input data, but also because of complications
induced by the long-tail effects of the datasets distribution. Inspired by the
most recent fine-grained visual classification approaches which are based on
attention to mitigate the effects of data variability, we explore the idea of
using object detection as a form of attention. We introduce a bottom-up
approach based on detecting plant organs and fusing the predictions of a
variable number of organ-based species classifiers. We also curate a new
dataset with a long-tail distribution for evaluating plant organ detection and
organ-based species identification, which is publicly available.

本研究提出了一种基于物体检测的自下而上植物识别方法，该方法可以通过检测植物器官并融合器官级别物种分类器的预测来识别植物物种，同时我们还创建了一个公开的长尾分布数据集来评估此方法。

野外植物品种的细粒度视觉分类：以物体检测作为加强注意力的手段

Fine-Grained Visual Classification of Plant Species In The Wild: Object  Detection as A Reinforced Means of Attention

In this paper we outline a PhD research plan. This research contributes to
the field of one-class incremental learning and classification in case of
non-stationary environments. The goal of this PhD is to define a new
classification framework able to deal with very small learning dataset at the
beginning of the process and with abilities to adjust itself according to the
variability of the incoming data which create large scale datasets. As a
preliminary work, incremental Covariance-guided One-Class Support Vector
Machine is proposed to deal with sequentially obtained data. It is inspired
from COSVM which put more emphasis on the low variance directions while keeping
the basic formulation of incremental One-Class Support Vector Machine
untouched. The incremental procedure is introduced by controlling the possible
changes of support vectors after the addition of new data points, thanks to the
Karush-Kuhn-Tucker conditions, that have to be maintained on all previously
acquired data. Comparative experimental results with contemporary incremental
and non-incremental one-class classifiers on numerous artificial and real data
sets show that our method results in significantly better classification
performance.

本研究旨在提出一种能够处理起始数据集非常小、能够根据不断变化的数据自适应调整的分类框架，以此为基础，提出了增量协方差导向一类支持向量机，通过控制新数据点引起的支持向量变化，取得了在人工和真实数据集上的显著分类表现。