使用复杂的数学方法建模的工程问题或者以昂贵的测试或实验为特征，占用有限预算或有限计算资源。此外，行业的实际情景，基于物流和偏好，对可以进行实验的方式施加限制。例如，材料供应可以仅在单次或计算模型的情况下仅实现少量实验，因此可以基于共享计算资源面临显着的等待时间。在这种情况下，一个人通常以允许最大化一个人的知识的方式进行实验，同时满足上述实际限制。实验顺序设计（Sdoe）是一种流行的方法套件，近年来越来越多的不同工程和实际问题。利用贝叶斯形式主义的普通战略是贝叶斯Sdoe，它通常在一步一步的一步中选择单一实验的一步或近视场景中最好的工作。在这项工作中，我们的目标是扩展SDOE策略，以批量输入查询实验或计算机代码。为此，我们利用基于深度加强学习（RL）的政策梯度方法，提出批次选择的查询，以考虑到整个预算。该算法保留了SDOE中固有的顺序性质，同时基于来自深rl域的任务的奖励元素。所提出的方法的独特能力是其应用于多个任务的能力，例如函数的优化，一旦其培训。我们展示了在合成问题上提出了算法的性能，以及挑战的高维工程问题。

Object movement identification is one of the most researched problems in the field of computer vision. In this task, we try to classify a pixel as foreground or background. Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them are the need for large amounts of ground truth data and their inferior performance on unseen videos. Since every pixel of every frame has to be labeled, acquiring large amounts of data for these techniques gets rather expensive. Recently, Zhao et al. [1] proposed one of a kind Arithmetic Distribution Neural Network (ADNN) for universal background subtraction which utilizes probability information from the histogram of temporal pixels and achieves promising results. Building onto this work, we developed an intelligent video surveillance system that uses ADNN architecture for motion detection, trims the video with parts only containing motion, and performs anomaly detection on the trimmed video.

Test-time adaptation is the problem of adapting a source pre-trained model using test inputs from a target domain without access to source domain data. Most of the existing approaches address the setting in which the target domain is stationary. Moreover, these approaches are prone to making erroneous predictions with unreliable uncertainty estimates when distribution shifts occur. Hence, test-time adaptation in the face of non-stationary target domain shift becomes a problem of significant interest. To address these issues, we propose a principled approach, PETAL (Probabilistic lifElong Test-time Adaptation with seLf-training prior), which looks into this problem from a probabilistic perspective using a partly data-dependent prior. A student-teacher framework, where the teacher model is an exponential moving average of the student model naturally emerges from this probabilistic perspective. In addition, the knowledge from the posterior distribution obtained for the source task acts as a regularizer. To handle catastrophic forgetting in the long term, we also propose a data-driven model parameter resetting mechanism based on the Fisher information matrix (FIM). Moreover, improvements in experimental results suggest that FIM based data-driven parameter restoration contributes to reducing the error accumulation and maintaining the knowledge of recent domain by restoring only the irrelevant parameters. In terms of predictive error rate as well as uncertainty based metrics such as Brier score and negative log-likelihood, our method achieves better results than the current state-of-the-art for online lifelong test time adaptation across various benchmarks, such as CIFAR-10C, CIFAR-100C, ImageNetC, and ImageNet3DCC datasets.

Only limited studies and superficial evaluations are available on agents' behaviors and roles within a Multi-Agent System (MAS). We simulate a MAS using Reinforcement Learning (RL) in a pursuit-evasion (a.k.a predator-prey pursuit) game, which shares task goals with target acquisition, and we create different adversarial scenarios by replacing RL-trained pursuers' policies with two distinct (non-RL) analytical strategies. Using heatmaps of agents' positions (state-space variable) over time, we are able to categorize an RL-trained evader's behaviors. The novelty of our approach entails the creation of an influential feature set that reveals underlying data regularities, which allow us to classify an agent's behavior. This classification may aid in catching the (enemy) targets by enabling us to identify and predict their behaviors, and when extended to pursuers, this approach towards identifying teammates' behavior may allow agents to coordinate more effectively.

The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that use space weather drivers to produce a density response, but these models are typically computationally expensive or inaccurate for certain space weather conditions. In response, this work aims to employ a probabilistic machine learning (ML) method to create an efficient surrogate for the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM), a physics-based thermosphere model. Our method leverages principal component analysis to reduce the dimensionality of TIE-GCM and recurrent neural networks to model the dynamic behavior of the thermosphere much quicker than the numerical model. The newly developed reduced order probabilistic emulator (ROPE) uses Long-Short Term Memory neural networks to perform time-series forecasting in the reduced state and provide distributions for future density. We show that across the available data, TIE-GCM ROPE has similar error to previous linear approaches while improving storm-time modeling. We also conduct a satellite propagation study for the significant November 2003 storm which shows that TIE-GCM ROPE can capture the position resulting from TIE-GCM density with < 5 km bias. Simultaneously, linear approaches provide point estimates that can result in biases of 7 - 18 km.

While speech recognition Word Error Rate (WER) has reached human parity for English, long-form dictation scenarios still suffer from segmentation and punctuation problems resulting from irregular pausing patterns or slow speakers. Transformer sequence tagging models are effective at capturing long bi-directional context, which is crucial for automatic punctuation. Automatic Speech Recognition (ASR) production systems, however, are constrained by real-time requirements, making it hard to incorporate the right context when making punctuation decisions. In this paper, we propose a streaming approach for punctuation or re-punctuation of ASR output using dynamic decoding windows and measure its impact on punctuation and segmentation accuracy across scenarios. The new system tackles over-segmentation issues, improving segmentation F0.5-score by 13.9%. Streaming punctuation achieves an average BLEU-score improvement of 0.66 for the downstream task of Machine Translation (MT).

我们提出了对基于模型的RL问题的交织勘探和开发时期的探索和剥削（DSEE）算法的确定性测序，旨在同时学习系统模型，即马尔可夫决策过程（MDP）以及相关的最佳政策。在探索过程中，DSEE探索环境并更新预期奖励和过渡概率的估计值。在开发过程中，使用系统动力学的最新估计值用于获得具有很高概率的强大策略。我们设计了探索和剥削时期的长度，以使累积遗憾成为时间的亚线性功能。我们还讨论了一种使用多跳跃MDP和大都市杂货算法的有效探索方法，以均匀地对每个州行动对采样，概率很高。

由于（1）低资源语言的数据稀缺，（2）培训和清爽100+单语言模型的昂贵计算成本，培训和部署混合语音识别的变压器LMS以低资源语言重新排行第二通道是具有挑战性的。，以及（3）考虑流量稀疏的效率低下。在这项研究中，我们提出了一种新的方法，将多个低资源的区域分组在一起，并优化ASR中多语言变压器LMS的性能。我们的本地组多语言变压器LMS的表现优于传统的多语言LM，以及降低维护成本和运营费用。此外，对于部署单语模型的低资源但人口流量的地区是可行的，我们表明，对我们的语言环境组的多语言LMS进行微调可产生比基线单语LMS更好的单语LM候选者。

自1970年代初以来，已经开发并改进了质谱仪和不连贯的散射雷达（MSIS）模型家族。 MSI的最新版本是海军研究实验室（NRL）MSIS 2.0经验大气模型。 NRLMSIS 2.0提供物种密度，质量密度和温度估计作为位置和空间天气条件的功能。长期以来，MSIS模型一直是研究和运营社区中的大气模型的流行选择，但与许多模型一样，并未提供不确定性估计。在这项工作中，我们开发了基于机器学习（ML）的外层温度模型，该模型可与NRLMSIS 2.0一起使用，以相对于高保真卫星密度估计值校准其。我们的模型（称为MSIS-UQ）没有提供点估计，而是输出一个分布，该分布将使用称为校准误差评分的度量进行评估。我们表明，MSIS-UQ的DEMIAS nRLMSIS 2.0导致模型和卫星密度之间的差异减少25％，并且比太空力量的高精度卫星阻力模型更接近卫星密度。我们还通过生成物种密度，质量密度和温度的高度曲线来显示模型的不确定性估计功能。这明确证明了外层温度概率如何影响NRLMSIS 2.0内的密度和温度曲线。另一项研究显示，相对于单独的NRLMSIS 2.0，迅速过冷的能力提高了，从而增强了它可以捕获的现象。

使用物理互动设备（如小鼠和键盘）阻碍了自然主义的人机相互作用，并增加了大流行期间表面接触的可能性。现有的手势识别系统不具备用户身份验证，使其不可靠。当前手势识别技术中的静态手势会引入较长的适应周期并降低用户兼容性。我们的技术非常重视用户识别和安全。我们使用有意义且相关的手势进行任务操作，从而获得更好的用户体验。本文旨在设计一个强大的，具有面部验证的手势识别系统，该系统利用图形用户界面，主要通过用户识别和授权专注于安全性。面部模型使用MTCNN和FACENET来验证用户，而我们的LSTM-CNN体系结构进行手势识别，并以五类的手势获得了95％的精度。通过我们的研究开发的原型已成功执行了上下文依赖性任务，例如保存，打印，控制视频播放器操作和退出以及无上下文的操作系统任务，例如睡眠，关闭和直观地解锁。我们的应用程序和数据集可作为开源。