In a world of ephemeral moments, our brain diligently sieves through a
cascade of experiences, like a skilled gold prospector searching for precious
nuggets amidst the river's relentless flow. This study delves into the elusive
"moment of memorability" -- a fleeting, yet vital instant where experiences are
prioritised for consolidation in our memory. By transforming subjects' encoding
phase electroencephalography (EEG) signals into the visual domain using
scaleograms and leveraging deep learning techniques, we investigate the neural
signatures that underpin this moment, with the aim of predicting
subject-specific recognition of video. Our findings not only support the
involvement of theta band (4-8Hz) oscillations over the right temporal lobe in
the encoding of declarative memory, but also support the existence of a
distinct moment of memorability, akin to the gold nuggets that define our
personal river of experiences.

通过将受试者的脑电图（EEG）信号转化为视觉领域，并利用深度学习技术，研究了记忆性中具有关键意义的瞬间，以预测个体对视频的识别。这些发现不仅支持了节律波（4-8Hz）在右颞叶与陈述性记忆编码中的作用，也支持了存在类似黄金般定义个人经历的有别于其他瞬间的记忆性瞬间。

创造中的记忆：通过编码阶段的 EEG 预测视频可记性

Memories in the Making: Predicting Video Memorability with Encoding  Phase EEG

To address the issue of catastrophic forgetting in neural networks, we
propose a novel, simple, and effective solution called neuron-level plasticity
control (NPC). While learning a new task, the proposed method preserves the
knowledge for the previous tasks by controlling the plasticity of the network
at the neuron level. NPC estimates the importance value of each neuron and
consolidates important \textit{neurons} by applying lower learning rates,
rather than restricting individual connection weights to stay close to certain
values. The experimental results on the incremental MNIST (iMNIST) and
incremental CIFAR100 (iCIFAR100) datasets show that neuron-level consolidation
is substantially more effective compared to the connection-level consolidation
approaches.

为解决神经网络中经常出现的灾难性遗忘问题，本文提出了一种称为神经元级可塑性控制 (NPC) 的新颖且有效的解决方案，该方法通过在神经元水平上控制网络的可塑性，在学习新任务的同时保留以前任务的知识，并且实验证明，相对于连接级别的保持方法，神经元级别的保持策略更为有效。

通过神经元层面可塑性控制克服灾难性遗忘

Overcoming Catastrophic Forgetting by Neuron-level Plasticity Control

Point clouds obtained from 3D scans are typically sparse, irregular, and
noisy, and required to be consolidated. In this paper, we present the first
deep learning based edge-aware technique to facilitate the consolidation of
point clouds. We design our network to process points grouped in local patches,
and train it to learn and help consolidate points, deliberately for edges. To
achieve this, we formulate a regression component to simultaneously recover 3D
point coordinates and point-to-edge distances from upsampled features, and an
edge-aware joint loss function to directly minimize distances from output
points to 3D meshes and to edges. Compared with previous neural network based
works, our consolidation is edge-aware. During the synthesis, our network can
attend to the detected sharp edges and enable more accurate 3D reconstructions.
Also, we trained our network on virtual scanned point clouds, demonstrated the
performance of our method on both synthetic and real point clouds, presented
various surface reconstruction results, and showed how our method outperforms
the state-of-the-arts.

介绍了第一种基于深度学习的边缘感知技术，用于辅助点云的合并。通过处理本地 patches 中的点并训练其以学习和有针对性地巩固点，并且通过回归组件来恢复 3D 点坐标和点到边缘距离，并采用边缘感知的联合损失函数直接最小化输出点到 3D 网格和边缘之间的距离。