与LTE网络相比，5G的愿景在于提供较高的数据速率，低延迟（为了实现近实时应用程序），大大增加了基站容量以及用户的接近完美服务质量（QoS）。为了提供此类服务，5G系统将支持LTE，NR，NR-U和Wi-Fi等访问技术的各种组合。每种无线电访问技术（RAT）都提供不同类型的访问，这些访问应在用户中对其进行最佳分配和管理。除了资源管理外，5G系统还将支持双重连接服务。因此，网络的编排对于系统经理在旧式访问技术方面来说是一个更困难的问题。在本文中，我们提出了一种基于联合元学习（FML）的大鼠分配算法，该算法使RAN Intelligent Controller（RIC）能够更快地适应动态变化的环境。我们设计了一个包含LTE和5G NR服务技术的模拟环境。在模拟中，我们的目标是在传输的截止日期内满足UE需求，以提供更高的QoS值。我们将提出的算法与单个RL试剂，爬行动物算法和基于规则的启发式方法进行了比较。仿真结果表明，提出的FML方法分别在第一部部署回合21％和12％时达到了较高的缓存率。此外，在比较方法中，提出的方法最快地适应了新任务和环境。

下一代网络将积极采用人工智能（AI）和机器学习（ML）技术，用于自动化网络和最佳网络操作策略。以Open Ran（O-Ran）为代表的新兴网络结构符合这一趋势，其规范中心的无线电智能控制器（RIC）用作ML应用程序主机。各种ML模型，尤其是强化学习（RL）模型，被认为是解决与RAN相关的多目标优化问题的关键。但是，应该认识到，当前大多数RL成功都局限于抽象和简化的仿真环境，这可能不会直接转化为复杂的真实环境中的高性能。主要原因之一是模拟与真实环境之间的建模差距，这可能会使RL代理通过模拟训练不适合真实环境。此问题称为SIM2REAL差距。本文在O-Ran的背景下引起了SIM2REAL挑战。具体而言，它强调了数字双胞胎（DT）可以作为模型开发和验证的地方的特征和好处。提出了几种用例，以举例说明并证明在真实环境中训练有训练的RL模型的故障模式。讨论了DT在协助RL算法开发方面的有效性。然后提出了通常用于克服SIM2REAL挑战的基于学习的基于艺术学习的方法。最后，从数据交互，环境瓶颈和算法设计等潜在问题的角度讨论了O-RAN中RL应用程序实现的开发和部署问题。

无线电接入网络（RAN）技术继续见证巨大的增长，开放式运行越来越最近的势头。在O-RAN规范中，RAN智能控制器（RIC）用作自动化主机。本文介绍了对O-RAN堆栈相关的机器学习（ML）的原则，特别是加强学习（RL）。此外，我们审查无线网络的最先进的研究，并将其投入到RAN框架和O-RAN架构的层次结构上。我们在整个开发生命周期中提供ML / RL模型面临的挑战的分类：从系统规范到生产部署（数据采集，模型设计，测试和管理等）。为了解决挑战，我们将一组现有的MLOPS原理整合，当考虑RL代理时，具有独特的特性。本文讨论了系统的生命周期模型开发，测试和验证管道，称为：RLOPS。我们讨论了RLOP的所有基本部分，包括：模型规范，开发和蒸馏，生产环境服务，运营监控，安全/安全和数据工程平台。根据这些原则，我们提出了最佳实践，以实现自动化和可重复的模型开发过程。

We aim to bridge the gap between our common-sense few-sample human learning and large-data machine learning. We derive a theory of human-like few-shot learning from von-Neuman-Landauer's principle. modelling human learning is difficult as how people learn varies from one to another. Under commonly accepted definitions, we prove that all human or animal few-shot learning, and major models including Free Energy Principle and Bayesian Program Learning that model such learning, approximate our theory, under Church-Turing thesis. We find that deep generative model like variational autoencoder (VAE) can be used to approximate our theory and perform significantly better than baseline models including deep neural networks, for image recognition, low resource language processing, and character recognition.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is of high possibility to be degraded due to noises and distortions. In this paper, we propose two novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (i.e., signal and object)-domain curvature regularization model. Fast numerical optimization algorithms are developed relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, which are further accelerated by GPU implementation. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.

In this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset. The proposed benchmark provides drastic appearance variations caused by seasonal changes and diverse weather and illumination conditions. While significant progress has been made in advancing visual SLAM on small-scale datasets with similar conditions, there is still a lack of unified benchmarks representative of real-world scenarios for autonomous driving. We introduce a new unified benchmark for jointly evaluating visual odometry, global place recognition, and map-based visual localization performance which is crucial to successfully enable autonomous driving in any condition. The data has been collected for more than one year, resulting in more than 300 km of recordings in nine different environments ranging from a multi-level parking garage to urban (including tunnels) to countryside and highway. We provide globally consistent reference poses with up to centimeter-level accuracy obtained from the fusion of direct stereo-inertial odometry with RTK GNSS. We evaluate the performance of several state-of-the-art visual odometry and visual localization baseline approaches on the benchmark and analyze their properties. The experimental results provide new insights into current approaches and show promising potential for future research. Our benchmark and evaluation protocols will be available at https://www.4seasons-dataset.com/.