Recently proposed methods for implicitly representing signals such as images,
scenes, or geometries using coordinate-based neural network architectures often
do not leverage the choice of activation functions, or do so only to a limited
extent. In this paper, we introduce the Hyperbolic Oscillation function (HOSC),
a novel activation function with a controllable sharpness parameter. Unlike any
previous activations, HOSC has been specifically designed to better capture
sudden changes in the input signal, and hence sharp or acute features of the
underlying data, as well as smooth low-frequency transitions. Due to its
simplicity and modularity, HOSC offers a plug-and-play functionality that can
be easily incorporated into any existing method employing a neural network as a
way of implicitly representing a signal. We benchmark HOSC against other
popular activations in an array of general tasks, empirically showing an
improvement in the quality of obtained representations, provide the
mathematical motivation behind the efficacy of HOSC, and discuss its
limitations.

本文介绍了一种新的激活函数 —— 超半波振荡函数（HOSC），其具有可控的锐度参数，并且相比于现有的激活函数，HOSC 更好地捕捉了输入信号的突变和锐利特征以及平滑的低频过渡。我们将 HOSC 与其他流行的激活函数进行了对比，并在多个一般任务中经验性地展示了 HOSC 所得到表示的质量的提升，并提供了 HOSC 有效性背后的数学原理以及讨论了它的局限性。

HOSC: 保留隐式神经表示中锐利特征的周期性激活函数

HOSC: A Periodic Activation Function for Preserving Sharp Features in  Implicit Neural Representations

Recently, deep reinforcement learning (RL) has been proposed to learn the
tractography procedure and train agents to reconstruct the structure of the
white matter without manually curated reference streamlines. While the
performances reported were competitive, the proposed framework is complex, and
little is still known about the role and impact of its multiple parts. In this
work, we thoroughly explore the different components of the proposed framework,
such as the choice of the RL algorithm, seeding strategy, the input signal and
reward function, and shed light on their impact. Approximately 7,400 models
were trained for this work, totalling nearly 41,000 hours of GPU time. Our goal
is to guide researchers eager to explore the possibilities of deep RL for
tractography by exposing what works and what does not work with the category of
approach. As such, we ultimately propose a series of recommendations concerning
the choice of RL algorithm, the input to the agents, the reward function and
more to help future work using reinforcement learning for tractography. We also
release the open source codebase, trained models, and datasets for users and
researchers wanting to explore reinforcement learning for tractography.

本文通过训练 7,400 个模型总计接近 41,000 小时的 GPU 时间，全面探究了深度强化学习在白质束重建中的应用方式，比较了 RL 算法、种子策略、输入信号和奖励函数等部件的优缺点，提出了一系列使用建议，并开放代码库、训练模型和数据集，以促进未来深度强化学习在该领域的研究。

强化学习在神经纤维束追踪中的关键因素

What Matters in Reinforcement Learning for Tractography

Today, the optimal performance of existing noise-suppression algorithms, both
data-driven and those based on classic statistical methods, is range bound to
specific levels of instantaneous input signal-to-noise ratios. In this paper,
we present a new approach to improve the adaptivity of such algorithms enabling
them to perform robustly across a wide range of input signal and noise types.
Our methodology is based on the dynamic control of algorithmic parameters via
reinforcement learning. Specifically, we model the noise-suppression module as
a black box, requiring no knowledge of the algorithmic mechanics except a
simple feedback from the output. We utilize this feedback as the reward signal
for a reinforcement-learning agent that learns a policy to adapt the
algorithmic parameters for every incoming audio frame (16 ms of data). Our
preliminary results show that such a control mechanism can substantially
increase the overall performance of the underlying noise-suppression algorithm;
42% and 16% improvements in output SNR and MSE, respectively, when compared to
no adaptivity.

本文通过强化学习控制噪声抑制算法参数，从而提升算法的鲁棒性和整体性能，实验结果显示相较于无适应性的算法有着显著的 42% 和 16% 的输出 SNR 和 MSE 改进。