Temporal data like time series are often observed at irregular intervals which is a challenging setting for existing machine learning methods. To tackle this problem, we view such data as samples from some underlying continuous function. We then define a diffusion-based generative model that adds noise from a predefined stochastic process while preserving the continuity of the resulting underlying function. A neural network is trained to reverse this process which allows us to sample new realizations from the learned distribution. We define suitable stochastic processes as noise sources and introduce novel denoising and score-matching models on processes. Further, we show how to apply this approach to the multivariate probabilistic forecasting and imputation tasks. Through our extensive experiments, we demonstrate that our method outperforms previous models on synthetic and real-world datasets.

我们介绍了多变量时间序列中异常检测问题的新型，实际相关的变化：内在的异常检测。它出现在从DevOps到IoT的各种实践场景中，我们想认识到在周围环境影响下运行的系统的故障。固有的异常是时间序列之间的功能依赖性结构的变化，该时间序列代表代表所述环境中系统内部状态的环境和时间序列。我们将此问题形式化，为其提供了不足的公共和新的专用数据集，并提供了处理内在异常检测的方法。这些解决了无法区分系统状态的预期变化和意外情况的现有异常检测方法的缩写，即，偏离环境影响的系统的变化。我们最有前途的方法是完全无监督的，并结合了对抗性学习和时间序列表示学习，从而解决了标签稀疏性和主观性等问题，同时允许导航并改善臭名昭著的有问题的异常检测数据集。

This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectively. For LETT, we used three models namely BERT, RoBERTa, and Distil BERTA obtaining an F1-score of 0.6256, 0.6744, and 0.6723, respectively.

In recent years, social media has been widely explored as a potential source of communication and information in disasters and emergency situations. Several interesting works and case studies of disaster analytics exploring different aspects of natural disasters have been already conducted. Along with the great potential, disaster analytics comes with several challenges mainly due to the nature of social media content. In this paper, we explore one such challenge and propose a text classification framework to deal with Twitter noisy data. More specifically, we employed several transformers both individually and in combination, so as to differentiate between relevant and non-relevant Twitter posts, achieving the highest F1-score of 0.87.

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

多媒体分析，计算机视觉（CV）和人工智能（AI）算法的最新进步导致了几种有趣的工具，允许自动分析和检索用户利益的多媒体内容。但是，检索感兴趣的内容通常涉及语义特征的分析和提取，例如情感和兴趣级别。这种有意义的信息的提取是一项复杂的任务，通常，单个算法的性能非常低。增强单个算法性能的一种方法是使用融合方案结合多种算法的预测能力。这使各个算法可以相互补充，从而提高了性能。本文提出了有关媒体趣味性得分预测任务的几种融合方法。CLEFFusion 2022中引入了。所提出的方法既包括一个天真的融合方案，其中所有诱导剂均得到同等处理和基于功绩的融合方案，其中采用了多重重量优化方法为单个诱导者分配权重。我们总共使用了六种优化方法，包括粒子群优化（PSO），遗传算法（GA），Nelder Mead，信任区域约束（TRC）和有限的MEMORY BROYDEN FLECHER GOLDFARB SHANNO SHANNO算法（LBFGSA）以及截断的牛顿牛顿算法（TNA）。总体而言，通过PSO和TNA达到0.109的平均平均精度为10。任务是复杂的，通常得分很低。我们认为，提出的分析将为未来在领域的研究提供基准。

由于视频处理方法的稀缺性，图像处理操作通过独立处理每个框架来天真地扩展到视频域。这种无视视频处理中的时间连接通常会导致严重的时间不一致。解决这些不一致之处的最先进的技术取决于未经加工的视频的可用性来虹吸一致的视频动态，以恢复框架处理的视频的时间一致性。我们为这项任务提出了一个新颖的通用框架，该框架学会从不一致的视频中推断出一致的运动动力学，以减轻时间闪烁，同时保留时间相邻和相对较远的框架的感知质量。提出的框架在两个大规模数据集（戴维斯和videvo.net）上产生最新的结果，这些数据集以馈送方式处理众多图像处理任务进行处理。接受代码和训练有素的模型将在接受后发布。

病理诊所中癌症的诊断，预后和治疗性决策现在可以基于对多吉吉像素组织图像的分析，也称为全斜图像（WSIS）。最近，已经提出了深层卷积神经网络（CNN）来得出无监督的WSI表示。这些很有吸引力，因为它们不太依赖于繁琐的专家注释。但是，一个主要的权衡是，较高的预测能力通常以解释性为代价，这对他们的临床使用构成了挑战，通常通常期望决策中的透明度。为了应对这一挑战，我们提出了一个基于Deep CNN的手工制作的框架，用于构建整体WSI级表示。基于有关变压器在自然语言处理领域的内部工作的最新发现，我们将其过程分解为一个更透明的框架，我们称其为手工制作的组织学变压器或H2T。基于我们涉及各种数据集的实验，包括总共5,306个WSI，结果表明，与最近的最新方法相比，基于H2T的整体WSI级表示具有竞争性能，并且可以轻松用于各种下游分析任务。最后，我们的结果表明，H2T框架的最大14倍，比变压器模型快14倍。

本文着重于重要的环境挑战。也就是说，通过分析社交媒体作为直接反馈来源的潜力，水质。这项工作的主要目的是自动分析和检索与水质相关的社交媒体帖子，并特别注意描述水质不同方面的文章，例如水彩，气味，味觉和相关疾病。为此，我们提出了一个新颖的框架，其中包含不同的预处理，数据增强和分类技术。总共有三个不同的神经网络（NNS）架构，即来自变形金刚（BERT）的双向编码器表示，（ii）可靠优化的BERT预训练方法（XLM-ROBERTA）和（iii）自定义长期短期内存（LSTM）模型用于基于优异的融合方案。对于基于绩效的重量分配到模型，比较了几种优化和搜索技术，包括粒子群优化（PSO），遗传算法（GA），蛮力（BF），Nelder-Mead和Powell的优化方法。我们还提供了单个模型的评估，其中使用BERT模型获得了最高的F1评分为0.81。在基于绩效的融合中，BF以F1得分得分为0.852，可以获得总体更好的结果。我们还提供了与现有方法的比较，在该方法中，我们提出的解决方案得到了重大改进。我们认为对这个相对新主题的严格分析将为未来的研究提供基准。

计算病理（CPATH）是一种具有关于组织病理研究的新兴领域，通过计算和分析组织载玻片的数字化高分辨率图像的处理算法。CPATH最近的深度学习的发展已经成功地利用了组织学图像中的原始像素数据的纯粹体积，以预测诊断域，预测，治疗敏感性和患者分层中的目标参数 - 覆盖新数据驱动的AI时代的承诺既组织病理学和肿瘤。使用作为燃料和作为发动机的燃料和AI的数据，CPATH算法准备好用于起飞和最终发射到临床和药物轨道中。在本文中，我们讨论了CPATH限制和相关挑战，使读者能够区分HIPE的希望，并为未来的研究提供指示，以克服这个崭露头角领域的一些主要挑战，以使其发射到两个轨道上。