Most existing Spiking Neural Network (SNN) works state that SNNs may utilize temporal information dynamics of spikes. However, an explicit analysis of temporal information dynamics is still missing. In this paper, we ask several important questions for providing a fundamental understanding of SNNs: What are temporal information dynamics inside SNNs? How can we measure the temporal information dynamics? How do the temporal information dynamics affect the overall learning performance? To answer these questions, we estimate the Fisher Information of the weights to measure the distribution of temporal information during training in an empirical manner. Surprisingly, as training goes on, Fisher information starts to concentrate in the early timesteps. After training, we observe that information becomes highly concentrated in earlier few timesteps, a phenomenon we refer to as temporal information concentration. We observe that the temporal information concentration phenomenon is a common learning feature of SNNs by conducting extensive experiments on various configurations such as architecture, dataset, optimization strategy, time constant, and timesteps. Furthermore, to reveal how temporal information concentration affects the performance of SNNs, we design a loss function to change the trend of temporal information. We find that temporal information concentration is crucial to building a robust SNN but has little effect on classification accuracy. Finally, we propose an efficient iterative pruning method based on our observation on temporal information concentration. Code is available at https://github.com/Intelligent-Computing-Lab-Yale/Exploring-Temporal-Information-Dynamics-in-Spiking-Neural-Networks.

End-to-End automatic speech recognition (ASR) models aim to learn a generalised speech representation to perform recognition. In this domain there is little research to analyse internal representation dependencies and their relationship to modelling approaches. This paper investigates cross-domain language model dependencies within transformer architectures using SVCCA and uses these insights to exploit modelling approaches. It was found that specific neural representations within the transformer layers exhibit correlated behaviour which impacts recognition performance. Altogether, this work provides analysis of the modelling approaches affecting contextual dependencies and ASR performance, and can be used to create or adapt better performing End-to-End ASR models and also for downstream tasks.

研究人员通常会采用数值方法来理解和预测海洋动力学，这是掌握环境现象的关键任务。在地形图很复杂，有关基础过程的知识不完整或应用程序至关重要的情况下，此类方法可能不适合。另一方面，如果观察到海洋动力学，则可以通过最近的机器学习方法来利用它们。在本文中，我们描述了一种数据驱动的方法，可以预测环境变量，例如巴西东南海岸的Santos-Sao Vicente-Bertioga estuarine系统的当前速度和海面高度。我们的模型通过连接最新的序列模型（LSTM和Transformers）以及关系模型（图神经网络）来利用时间和空间归纳偏见，以学习时间特征和空间特征，观察站点之间共享的关系。我们将结果与桑托斯运营预测系统（SOFS）进行比较。实验表明，我们的模型可以实现更好的结果，同时保持灵活性和很少的领域知识依赖性。

图形神经网络（GNNS）通过考虑其内在的几何形状来扩展神经网络的成功到图形结构化数据。尽管根据图表学习基准的集合，已经对开发具有卓越性能的GNN模型进行了广泛的研究，但目前尚不清楚其探测给定模型的哪些方面。例如，他们在多大程度上测试模型利用图形结构与节点特征的能力？在这里，我们开发了一种原则性的方法来根据$ \ textit {敏感性配置文件} $进行基准测试数据集，该方法基于由于图形扰动的集合而导致的GNN性能变化了多少。我们的数据驱动分析提供了对GNN利用哪些基准测试数据特性的更深入的了解。因此，我们的分类法可以帮助选择和开发适当的图基准测试，并更好地评估未来的GNN方法。最后，我们在$ \ texttt {gtaxogym} $软件包中的方法和实现可扩展到多个图形预测任务类型和未来数据集。

像汤普森采样等多武装强盗算法可用于进行自适应实验，其中最大化奖励意味着数据用于逐步为更多参与者分配更有效的武器。这些转让策略增加了统计假设试验的风险，鉴定武器之间的差异，当没有一个时，并且在真正是一个是一个时，武器的差异存在差异。我们为2臂实验仿真，探讨了两种算法，这些算法结合了统计分析的均匀随机化的益处，具有通过Thompson采样（TS）实现的奖励最大化的益处。首先，前两种汤普森采样增加了固定量的均匀随机分配（UR）随时间均匀传播。二，一种新的启发式算法，称为TS Postdiff（差异后概率）。 Ts Postdiff采用贝叶斯方法来混合TS和UR：使用UR分配分配参与者的概率是后部概率，即两个臂之间的差异是“小”（低于某个阈值），允许在存在时探索更多的探索很少或没有奖励获得。我们发现TS PostDiff方法跨多种效果大小进行良好，因此不需要根据真实效果大小的猜测进行调整。

关键点检测和描述是计算机视觉系统中常用的构建块，特别是用于机器人和自主驾驶。然而，大多数迄今为止的技术都集中在标准相机上，几乎没有考虑到Fisheye相机，这些摄像机通常用于城市驾驶和自动停车处。在本文中，我们提出了一种用于鱼眼图像的新型培训和评估管道。我们利用SuperPoint作为我们的基线，这是一个自我监督的Keypoint检测器和描述符，该探测器和描述符已经实现了最先进的同位估计。我们介绍了一种Fisheye适应管道，以便在未造成的Fisheye图像上培训。我们通过在牛津机Robotcar数据集上引入用于检测可重复性和描述符的鱼眼基于评估方法来评估HPAPTES基准测试的性能。

Novel topological spin textures, such as magnetic skyrmions, benefit from their inherent stability, acting as the ground state in several magnetic systems. In the current study of atomic monolayer magnetic materials, reasonable initial guesses are still needed to search for those magnetic patterns. This situation underlines the need to develop a more effective way to identify the ground states. To solve this problem, in this work, we propose a genetic-tunneling-driven variance-controlled optimization approach, which combines a local energy minimizer back-end and a metaheuristic global searching front-end. This algorithm is an effective optimization solution for searching for magnetic ground states at extremely low temperatures and is also robust for finding low-energy degenerated states at finite temperatures. We demonstrate here the success of this method in searching for magnetic ground states of 2D monolayer systems with both artificial and calculated interactions from density functional theory. It is also worth noting that the inherent concurrent property of this algorithm can significantly decrease the execution time. In conclusion, our proposed method builds a useful tool for low-dimensional magnetic system energy optimization.

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.

Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthetic and real data obtained from crashworthiness analyses. The results show a superiority of the here described approach over state of the art surrogate modelling techniques, Polynomial Chaos Expansions and Neural Networks.

Purpose: Tracking the 3D motion of the surgical tool and the patient anatomy is a fundamental requirement for computer-assisted skull-base surgery. The estimated motion can be used both for intra-operative guidance and for downstream skill analysis. Recovering such motion solely from surgical videos is desirable, as it is compliant with current clinical workflows and instrumentation. Methods: We present Tracker of Anatomy and Tool (TAToo). TAToo jointly tracks the rigid 3D motion of patient skull and surgical drill from stereo microscopic videos. TAToo estimates motion via an iterative optimization process in an end-to-end differentiable form. For robust tracking performance, TAToo adopts a probabilistic formulation and enforces geometric constraints on the object level. Results: We validate TAToo on both simulation data, where ground truth motion is available, as well as on anthropomorphic phantom data, where optical tracking provides a strong baseline. We report sub-millimeter and millimeter inter-frame tracking accuracy for skull and drill, respectively, with rotation errors below 1{\deg}. We further illustrate how TAToo may be used in a surgical navigation setting. Conclusion: We present TAToo, which simultaneously tracks the surgical tool and the patient anatomy in skull-base surgery. TAToo directly predicts the motion from surgical videos, without the need of any markers. Our results show that the performance of TAToo compares favorably to competing approaches. Future work will include fine-tuning of our depth network to reach a 1 mm clinical accuracy goal desired for surgical applications in the skull base.