Time-resolved image sensors that capture light at pico-to-nanosecond timescales were once limited to niche applications but are now rapidly becoming mainstream in consumer devices. We propose low-cost and low-power imaging modalities that capture scene information from minimal time-resolved image sensors with as few as one pixel. The key idea is to flood illuminate large scene patches (or the entire scene) with a pulsed light source and measure the time-resolved reflected light by integrating over the entire illuminated area. The one-dimensional measured temporal waveform, called \emph{transient}, encodes both distances and albedoes at all visible scene points and as such is an aggregate proxy for the scene's 3D geometry. We explore the viability and limitations of the transient waveforms by themselves for recovering scene information, and also when combined with traditional RGB cameras. We show that plane estimation can be performed from a single transient and that using only a few more it is possible to recover a depth map of the whole scene. We also show two proof-of-concept hardware prototypes that demonstrate the feasibility of our approach for compact, mobile, and budget-limited applications.
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This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement them in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed in detail.
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为了避免维度的诅咒，聚集高维数据的一种常见方法是首先将数据投射到缩小尺寸的空间中，然后将投影数据聚集。尽管有效，但这种两阶段的方法阻止了降低维度降低和聚类模型的关节优化，并掩盖了完整模型描述数据的很好。在这里，我们展示了如何将这样的两阶段模型的家族组合成一个单一的分层模型，我们称之为高斯（HMOG）的分层混合物。 HMOG同时捕获了降低性降低和聚类，并且其性能通过似然函数以封闭形式量化。通过用指数式的家庭理论制定和扩展现有模型，我们展示了如何最大程度地提高HMOGS具有期望最大化的可能性。我们将HMOGS应用于合成数据和RNA测序数据，并演示它们如何超过两阶段模型的局限性。最终，HMOG是对共同统计框架的严格概括，并为研究人员提供了一种在聚集高维数据时改善模型性能的方法。
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范式变革的动态模型可以阐明最简单的过程可能导致意外结果，从而可以揭示观察到的语言现象的新潜在解释。 Ackerman＆Malouf（2015）展示了一种模型，其中拐点通过吸引力的动态的作用减少了紊乱，其中lexemes只会随着时间的推移而彼此相似。在这里，我们强调：（1）仅吸引力的模型不能发展结构化的分集，其特征是真正的拐点系统，因为它们不可避免地去除所有变化; （2）吸引力和排斥的模型使得能够出现令人惊叹的方式让人想起形态学结构，如拐点。因此，仅基于不相似性的一个小型成分 - 改变 - 将倾向于均匀性的模型分离，因此对于折射形态来说，从那些演变稳定的形态的结构的情况下可能是难以置信的。这些模型有可能改变我们如何试图考虑形态复杂性。
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密集的语义预测通过推断未观察到的未来图像的像素级语义来预测视频中的未来事件。我们提出了一种适用于各种单帧架构和任务的新方法。我们的方法包括两个模块。功能 - 动作（F2M）模块预测了密集的变形领域，将过去的功能扭曲到其未来的位置。功能到特征（F2F）模块直接回归未来功能，因此能够考虑紧急风景。化合物F2MF模型以任务 - 不可行的方式与新奇效果的运动效果脱钩。我们的目标是将F2MF预测应用于所需单帧模型的最自述和最抽象的最摘要表示。我们的设计利用了相邻时间瞬间可变形卷曲和空间相关系数。我们在三个密集预测任务中执行实验：语义分割，实例级分割和Panoptic分割。结果介绍了三个密集预测任务的最先进的预测精度。
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