这项研究提出了依赖电压突触可塑性（VDSP），这是一种新型的脑启发的无监督的本地学习规则，用于在线实施HEBB对神经形态硬件的可塑性机制。拟议的VDSP学习规则仅更新了突触后神经元的尖峰的突触电导，这使得相对于标准峰值依赖性可塑性（STDP）的更新数量减少了两倍。此更新取决于突触前神经元的膜电位，该神经元很容易作为神经元实现的一部分，因此不需要额外的存储器来存储。此外，该更新还对突触重量进行了正规化，并防止重复刺激时的重量爆炸或消失。进行严格的数学分析以在VDSP和STDP之间达到等效性。为了验证VDSP的系统级性能，我们训练一个单层尖峰神经网络（SNN），以识别手写数字。我们报告85.01 $ \ pm $ 0.76％（平均$ \ pm $ s.d。）对于MNIST数据集中的100个输出神经元网络的精度。在缩放网络大小时，性能会提高（400个输出神经元的89.93 $ \ pm $ 0.41％，500个神经元为90.56 $ \ pm $ 0.27），这验证了大规模计算机视觉任务的拟议学习规则的适用性。有趣的是，学习规则比STDP更好地适应输入信号的频率，并且不需要对超参数进行手动调整。

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

Domain adaptation is one of the prominent strategies for handling both domain shift, that is widely encountered in large-scale land use/land cover map calculation, and the scarcity of pixel-level ground truth that is crucial for supervised semantic segmentation. Studies focusing on adversarial domain adaptation via re-styling source domain samples, commonly through generative adversarial networks, have reported varying levels of success, yet they suffer from semantic inconsistencies, visual corruptions, and often require a large number of target domain samples. In this letter, we propose a new unsupervised domain adaptation method for the semantic segmentation of very high resolution images, that i) leads to semantically consistent and noise-free images, ii) operates with a single target domain sample (i.e. one-shot) and iii) at a fraction of the number of parameters required from state-of-the-art methods. More specifically an image-to-image translation paradigm is proposed, based on an encoder-decoder principle where latent content representations are mixed across domains, and a perceptual network module and loss function is further introduced to enforce semantic consistency. Cross-city comparative experiments have shown that the proposed method outperforms state-of-the-art domain adaptation methods. Our source code will be available at \url{https://github.com/Sarmadfismael/LRM_I2I}.

我们提出了一种新的概率方法，用于检测称为贝叶斯光源分离器（BLISS）的天文来源，进行分类和分类。Bliss基于深层生成模型，该模型将神经网络嵌入贝叶斯模型中。对于后推断，Bliss使用一种新形式的变分推断，称为正向摊销变异推断。幸福推理例程很快，一旦训练了编码器网络，就需要GPU上的编码网络的单个正向通行证。Bliss可以在几秒钟内对百万像素图像执行完全贝叶斯的推断，并产生高度准确的目录。Bliss是高度可扩展的，除了产生概率目录外，还可以直接回答下游科学问题。

由于它们所需的大量集中，最深度增强学习算法的状态是对渐近性能的大量集中的效率低。由哺乳动物海马的启发的episodic加强学习（ERL）算法通常使用扩展的内存系统从过去的事件开始学习，以克服这个样本效率问题。然而，这种内存增强通常用作仅仅是缓冲区，从中绘制了孤立的过去经验，以便以离线方式学习（例如，重播）。这里，我们证明包括从集扩展抽样顺序导出的所获取的内存内容中的偏差来提高弹性控制算法的样本和存储器效率。我们在觅食任务中测试了我们的顺序焦点控制（SEC）模型，以显示存储和使用集成剧集作为事件序列导致更快的学习，与较少的内存要求相反，与隔离的缓冲区相比只有事件。我们还研究了内存约束的影响，忘记了SEC算法的顺序和非顺序版本。此外，我们讨论了类似海马的快速记忆系统如何在哺乳动物大脑中引导慢速皮质和皮质学习习惯的习惯。

近年来，美国西部野蛮火灾的大小和频率显着增加。在高火灾日，小火点火可以迅速增长并失控。早期检测初始烟雾的火点火可以帮助响应在难以管理之前对这种火灾进行响应。过去的野火烟雾检测的深入学习方法遭受了小型或不可靠的数据集，使得难以将性能推断为现实世界的情景。在这项工作中，我们展示了火点火图书馆（Figlib），这是一个近25,000个标记的野火烟雾图像的公共数据集，从南加州部署的固定视图相机看。我们还介绍了Smokeynet，一种新的深度学习架构，使用相机图像的时空信息，用于实时野火烟雾检测。在迪拉布数据集上培训时，SmokeyNet优于相当的基线和竞争对手的人类性能。我们希望Figlib数据集和Smokynet架构的可用性将激励进一步研究野火烟雾检测的深度学习方法，导致自动化通知系统，减少野火响应的时间。