我们提出了高度可行的帕累托优化（HIPPO） - 批处理采集功能，可实现多目标贝叶斯优化方法有效利用并行处理资源。多目标贝叶斯优化（MOBO）是解决昂贵的黑盒问题的非常有效的工具。但是，大多数主板算法被设计为纯粹的顺序策略，而现有的批次方法对于除最小的批量尺寸以外的所有人都非常昂贵。我们表明，通过通过以相似的预测目标值进行惩罚评估来鼓励批处理多样性，Hippo可以便宜地建立大量的信息观点。我们广泛的实验验证表明，河马至少与现有替代方案一样有效，同时产生的计算开销较低，并易于扩展到比文献中目前支持的批次大小要高得多。此外，我们证明了河马在充满挑战的热交换器设计问题上的应用，这强调了我们高度可行的MOBO方法的现实效用。

稀疏的高斯工艺是高通量贝叶斯优化（BO）循环的关键组成部分 - 越来越普遍的设置，评估预算大且高度平行。通过使用可用数据的代表性子集来构建近似后代，稀疏模型通过依靠一组较小的伪观察（即所谓的诱导点）代替完整的数据集来大大降低替代建模的计算成本。但是，当前设计诱导点的方法在BO循环中不合适，因为它们试图减少目标函数的全球不确定性。因此，牺牲了精确优化所需的有前途和数据密集区域的高保真模型，而是牺牲了计算资源，而是浪费在已经已知的亚地区的空间的建模区域上。受基于熵的BO方法的启发，我们提出了一种新颖的诱导点设计，该设计使用原则的信息理论标准选择诱导点。通过选择诱导点以最大程度地降低目标函数最大值的全局不确定性和不确定性，我们构建了能够支持高精度高通量BO的替代模型。

可拍照的分子显示了可以使用光访问的两个或多个异构体形式。将这些异构体的电子吸收带分开是选择性解决特定异构体并达到高光稳态状态的关键，同时总体红色转移带来的吸收带可以限制因紫外线暴露而限制材料损害，并增加了光疗法应用中的渗透深度。但是，通过合成设计将这些属性工程为系统仍然是一个挑战。在这里，我们提出了一条数据驱动的发现管道，用于由数据集策划和使用高斯过程的多任务学习支撑的分子照片开关。在对电子过渡波长的预测中，我们证明了使用来自四个Photoswitch转变波长的标签训练的多输出高斯过程（MOGP）产生相对于单任务模型的最强预测性能，并且在操作上超过了时间依赖时间依赖性的密度理论（TD） -dft）就预测的墙壁锁定时间而言。我们通过筛选可商购的可拍摄分子库来实验验证我们提出的方法。通过此屏幕，我们确定了几个图案，这些基序显示了它们的异构体的分离电子吸收带，表现出红移的吸收，并且适用于信息传输和光电学应用。我们的策划数据集，代码以及所有型号均可在https://github.com/ryan-rhys/the-photoswitch-dataset上提供

来自高斯过程（GP）模型的汤普森采样（TS）是一个强大的工具，用于优化黑盒功能。虽然TS享有强烈的理论担保和令人信服的实证性能，但它会引发大量的计算开销，可通过优化预算进行多项式。最近，已经提出了基于稀疏GP模型的可扩展TS方法来增加TS的范围，使其应用​​于足够多模态，嘈杂或组合需要的问题，以便要求解决超过几百个评估。但是，稀疏GPS引入的近似误差使所有现有的后悔界限无效。在这项工作中，我们对可扩展Ts进行了理论和实证分析。我们提供理论担保，并表明可以在标准TS上遗憾地享受可扩展TS的计算复杂性的急剧下降。这些概念索赔是针对合成基准测试的可扩展TS的实际实施，作为现实世界的高通量分子设计任务的一部分。

贝叶斯优化（BO）被广泛用于优化随机黑匣子功能。尽管大多数BO方法都集中在优化条件期望上，但许多应用程序都需要规避风险的策略，并且需要考虑分配尾巴的替代标准。在本文中，我们提出了针对贝叶斯分位数和预期回归的新变异模型，这些模型非常适合异形的噪声设置。我们的模型分别由有条件分位数（或期望）的两个潜在高斯过程和不对称可能性函数的比例参数组成。此外，我们提出了基于最大值熵搜索和汤普森采样的两种BO策略，这些策略是针对此类型号量身定制的，可以容纳大量点。与现有的BO进行规避风险优化的方法相反，我们的策略可以直接针对分位数和预期进行优化，而无需复制观测值或假设噪声的参数形式。如实验部分所示，所提出的方法清楚地表现出异质的非高斯案例中的最新状态。

We introduce a novel framework to track multiple objects in overhead camera videos for airport checkpoint security scenarios where targets correspond to passengers and their baggage items. We propose a Self-Supervised Learning (SSL) technique to provide the model information about instance segmentation uncertainty from overhead images. Our SSL approach improves object detection by employing a test-time data augmentation and a regression-based, rotation-invariant pseudo-label refinement technique. Our pseudo-label generation method provides multiple geometrically-transformed images as inputs to a Convolutional Neural Network (CNN), regresses the augmented detections generated by the network to reduce localization errors, and then clusters them using the mean-shift algorithm. The self-supervised detector model is used in a single-camera tracking algorithm to generate temporal identifiers for the targets. Our method also incorporates a multi-view trajectory association mechanism to maintain consistent temporal identifiers as passengers travel across camera views. An evaluation of detection, tracking, and association performances on videos obtained from multiple overhead cameras in a realistic airport checkpoint environment demonstrates the effectiveness of the proposed approach. Our results show that self-supervision improves object detection accuracy by up to $42\%$ without increasing the inference time of the model. Our multi-camera association method achieves up to $89\%$ multi-object tracking accuracy with an average computation time of less than $15$ ms.

The availability of frequent and cost-free satellite images is in growing demand in the research world. Such satellite constellations as Landsat 8 and Sentinel-2 provide a massive amount of valuable data daily. However, the discrepancy in the sensors' characteristics of these satellites makes it senseless to use a segmentation model trained on either dataset and applied to another, which is why domain adaptation techniques have recently become an active research area in remote sensing. In this paper, an experiment of domain adaptation through style-transferring is conducted using the HRSemI2I model to narrow the sensor discrepancy between Landsat 8 and Sentinel-2. This paper's main contribution is analyzing the expediency of that approach by comparing the results of segmentation using domain-adapted images with those without adaptation. The HRSemI2I model, adjusted to work with 6-band imagery, shows significant intersection-over-union performance improvement for both mean and per class metrics. A second contribution is providing different schemes of generalization between two label schemes - NALCMS 2015 and CORINE. The first scheme is standardization through higher-level land cover classes, and the second is through harmonization validation in the field.

When a human communicates with a machine using natural language on the web and online, how can it understand the human's intention and semantic context of their talk? This is an important AI task as it enables the machine to construct a sensible answer or perform a useful action for the human. Meaning is represented at the sentence level, identification of which is known as intent detection, and at the word level, a labelling task called slot filling. This dual-level joint task requires innovative thinking about natural language and deep learning network design, and as a result, many approaches and models have been proposed and applied. This tutorial will discuss how the joint task is set up and introduce Spoken Language Understanding/Natural Language Understanding (SLU/NLU) with Deep Learning techniques. We will cover the datasets, experiments and metrics used in the field. We will describe how the machine uses the latest NLP and Deep Learning techniques to address the joint task, including recurrent and attention-based Transformer networks and pre-trained models (e.g. BERT). We will then look in detail at a network that allows the two levels of the task, intent classification and slot filling, to interact to boost performance explicitly. We will do a code demonstration of a Python notebook for this model and attendees will have an opportunity to watch coding demo tasks on this joint NLU to further their understanding.

Recently, there has been increasing interest in synthesizing data to improve downstream text-to-SQL tasks. In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data. We observed two shortcomings: illogical synthetic SQL queries from independent column sampling and arbitrary table joins. To address these issues, we propose a novel synthesis framework that incorporates key relationships from schema, imposes strong typing, and conducts schema-distance-weighted column sampling. We also adopt an intermediate representation (IR) for the SQL-to-text task to further improve the quality of the generated natural language questions. When existing powerful semantic parsers are pre-finetuned on our high-quality synthesized data, our experiments show that these models have significant accuracy boosts on popular benchmarks, including new state-of-the-art performance on Spider.

Datacenter operators ensure fair and regular server maintenance by using automated processes to schedule maintenance jobs to complete within a strict time budget. Automating this scheduling problem is challenging because maintenance job duration varies based on both job type and hardware. While it is tempting to use prior machine learning techniques for predicting job duration, we find that the structure of the maintenance job scheduling problem creates a unique challenge. In particular, we show that prior machine learning methods that produce the lowest error predictions do not produce the best scheduling outcomes due to asymmetric costs. Specifically, underpredicting maintenance job duration has results in more servers being taken offline and longer server downtime than overpredicting maintenance job duration. The system cost of underprediction is much larger than that of overprediction. We present Acela, a machine learning system for predicting maintenance job duration, which uses quantile regression to bias duration predictions toward overprediction. We integrate Acela into a maintenance job scheduler and evaluate it on datasets from large-scale, production datacenters. Compared to machine learning based predictors from prior work, Acela reduces the number of servers that are taken offline by 1.87-4.28X, and reduces the server offline time by 1.40-2.80X.