Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the temperature attained by servers under variable cooling setups. Due to the complex thermal dynamics of data rooms, accurate runtime data center temperature prediction has remained as an important challenge. By using Gramatical Evolution techniques, this paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of CPU and inlet temperature under variable cooling setups. As opposed to time costly Computational Fluid Dynamics techniques, our models do not need specific knowledge about the problem, can be used in arbitrary data centers, re-trained if conditions change and have negligible overhead during runtime prediction. Our models have been trained and tested by using traces from real Data Center scenarios. Our results show how we can fully predict the temperature of the servers in a data rooms, with prediction errors below 2 C and 0.5 C in CPU and server inlet temperature respectively.

我们研究了使用K代理商最佳检查地下（水下）画廊的问题。我们考虑了一个带有单个开口的画廊，并带有树拓扑结构。由于管道的直径很小（洞穴），代理是小型机器人，自主权有限，在画廊的开口处有一个供应站。因此，它们最初被放置在根部，并且需要定期返回供应站。我们的目标是设计离线策略，以有效地使用$ k $小型机器人覆盖树。我们考虑两个目标功能：覆盖时间（最大集体时间）和覆盖距离（总行驶距离）。最大的集体时间是机器人花费的最大时间需要完成其分配的任务（假设所有机器人同时启动）；总行进距离是所有覆盖步行的长度的总和。由于问题对于大树很棘手，因此我们提出了近似算法。通过密集的数值实验，均匀溶液的效率和准确性均可显示随机树的经验表明。

科学机器学习的进步改善了现代计算科学和工程应用。数据驱动的方法（例如动态模式分解（DMD））可以从动态系统生成的时空数据中提取相干结构，并推断上述系统的不同方案。时空数据作为快照，每次瞬间包含空间信息。在现代工程应用中，高维快照的产生可能是时间和/或资源要求。在本研究中，我们考虑了在大型数值模拟中增强DMD工作流程的两种策略：（i）快照压缩以减轻磁盘压力； （ii）使用原位可视化图像在运行时重建动力学（或部分）。我们通过两个3D流体动力学模拟评估我们的方法，并考虑DMD重建解决方案。结果表明，快照压缩大大减少了所需的磁盘空间。我们已经观察到，损耗的压缩将存储降低了几乎$ 50 \％$，而信号重建和其他关注数量的相对错误则较低。我们还使用原位可视化工具将分析扩展到了直接生成的数据，在运行时生成状态向量的图像文件。在大型模拟中，快照的产生可能足够慢，可以使用批处理算法进行推理。流DMD利用增量SVD算法，并随着每个新快照的到来更新模式。我们使用流式DMD来重建原位生成的图像的动力学。我们证明此过程是有效的，并且重建的动力学是准确的。

可激发的光电设备代表了在神经形态（脑启发）光子系统中实施人工尖峰神经元的关键构件之一。这项工作介绍并实验研究了用谐振隧穿二极管（RTD）构建的光电 - 光学（O/E/O）人工神经元，该神经元（RTD）耦合到光电探测器作为接收器和垂直腔表面发射激光器作为发射机。我们证明了一个明确定义的兴奋性阈值，在此上面，该神经元在该神经元中产生100 ns的光学尖峰反应，具有特征性的神经样耐受性。我们利用其粉丝功能来执行设备中的重合检测（逻辑和）以及独家逻辑或（XOR）任务。这些结果提供了基于RTD的Spiking光电神经元的确定性触发和任务的首次实验验证，并具有输入和输出光学（I/O）终端。此外，我们还从理论上研究了拟议系统的纳米光子实施的前景，并结合了纳米级RTD元素和纳米剂的整体设计。因此，在未来的神经形态光子硬件中，证明了基于RTD的综合兴奋节点对低足迹，高速光电尖峰神经元的潜力。

背景：以自我为中心的视频已成为监测社区中四肢瘫痪者的手部功能的潜在解决方案，尤其是因为它在家庭环境中检测功能使用的能力。目的：开发和验证一个基于可穿戴视力的系统，以测量四肢植物患者的家庭使用。方法：开发并比较了几种用于检测功能手动相互作用的深度学习算法。最精确的算法用于从20名参与者在家庭中记录的65小时的无脚本视频中提取手部功能的度量。这些措施是：总记录时间（PERC）的交互时间百分比；单个相互作用的平均持续时间（DUR）；每小时互动数（NUM）。为了证明技术的临床有效性，以验证的措施与经过验证的手部功能和独立性的临床评估相关（逐渐定义了强度，敏感性和预性的评估 - GRASSP，上肢运动评分 - UEM和脊髓独立措施 - SICIM- SICIM- SICIM） 。结果：手动相互作用以0.80（0.67-0.87）的中位数得分自动检测到手动相互作用。我们的结果表明，较高的UEM和更好的预性与花费更长的时间相互作用有关，而较高的cim和更好的手动感觉会导致在以eg中心的视频记录期间进行的更多相互作用。结论：第一次，在四肢瘫痪者中，在不受约束的环境中自动估计的手部功能的度量已得到了国际接受的手部功能量度的验证。未来的工作将需要对基于以自我为中心的手工使用的绩效指标的可靠性和响应能力进行正式评估。

这项工作提出了一种用于参与感测的无线传感器网络的提议，其中IOT传感装置特别用于监测和预测空气质量，作为高成本气象站的替代方案。该系统称为PMSening，旨在测量颗粒材料。通过将原型收集的数据与来自车站的数据进行比较来完成验证。比较表明，结果是关闭的，这可以为问题提供低成本解决方案。该系统仍然呈现了使用反复性神经网络的预测分析，在这种情况下，在这种情况下，预测呈现与实际数据相关的高精度。

在过去的几年中，在深度学习中，在深度学习中广泛研究了域的概括问题，但对对比增强成像的关注受到了有限的关注。但是，临床中心之间的对比度成像方案存在明显差异，尤其是在对比度注入和图像采集之间，而与可用的非对抗成像的可用数据集相比，访问多中心对比度增强图像数据受到限制。这需要新的工具来概括单个中心的深度学习模型，跨越新的看不见的域和临床中心，以对比增强成像。在本文中，我们介绍了深度学习技术的详尽评估，以实现对对比度增强图像分割的看不见的临床中心的普遍性。为此，研究，优化和系统评估了几种技术，包括数据增强，域混合，转移学习和域的适应性。为了证明域泛化对对比增强成像的潜力，评估了对对比增强心脏磁共振成像（MRI）中的心室分割的方法。结果是根据位于三个国家（法国，西班牙和中国）的四家医院中获得的多中心心脏对比增强的MRI数据集获得的。他们表明，数据增强和转移学习的组合可以导致单中心模型，这些模型可以很好地推广到训练过程中未包括的新临床中心。在对比增强成像中，具有合适的概括程序的单域神经网络可以达到甚至超过多中心多供应商模型的性能，从而消除了对综合多中心数据集的需求，以训练可概括的模型。

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

In this work a novel recommender system (RS) for Tourism is presented. The RS is context aware as is now the rule in the state-of-the-art for recommender systems and works on top of a tourism ontology which is used to group the different items being offered. The presented RS mixes different types of recommenders creating an ensemble which changes on the basis of the RS's maturity. Starting from simple content-based recommendations and iteratively adding popularity, demographic and collaborative filtering methods as rating density and user cardinality increases. The result is a RS that mutates during its lifetime and uses a tourism ontology and natural language processing (NLP) to correctly bin the items to specific item categories and meta categories in the ontology. This item classification facilitates the association between user preferences and items, as well as allowing to better classify and group the items being offered, which in turn is particularly useful for context-aware filtering.