State-of-the-art image and text classification models, such as Convectional Neural Networks and Transformers, have long been able to classify their respective unimodal reasoning satisfactorily with accuracy close to or exceeding human accuracy. However, images embedded with text, such as hateful memes, are hard to classify using unimodal reasoning when difficult examples, such as benign confounders, are incorporated into the data set. We attempt to generate more labeled memes in addition to the Hateful Memes data set from Facebook AI, based on the framework of a winning team from the Hateful Meme Challenge. To increase the number of labeled memes, we explore semi-supervised learning using pseudo-labels for newly introduced, unlabeled memes gathered from the Memotion Dataset 7K. We find that the semi-supervised learning task on unlabeled data required human intervention and filtering and that adding a limited amount of new data yields no extra classification performance.

Learning enabled autonomous systems provide increased capabilities compared to traditional systems. However, the complexity of and probabilistic nature in the underlying methods enabling such capabilities present challenges for current systems engineering processes for assurance, and test, evaluation, verification, and validation (TEVV). This paper provides a preliminary attempt to map recently developed technical approaches in the assurance and TEVV of learning enabled autonomous systems (LEAS) literature to a traditional systems engineering v-model. This mapping categorizes such techniques into three main approaches: development, acquisition, and sustainment. We review the latest techniques to develop safe, reliable, and resilient learning enabled autonomous systems, without recommending radical and impractical changes to existing systems engineering processes. By performing this mapping, we seek to assist acquisition professionals by (i) informing comprehensive test and evaluation planning, and (ii) objectively communicating risk to leaders.

State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning or Riemannian-Geometry-based decoders. Recently, there is growing interest in Deep Riemannian Networks (DRNs) possibly combining the advantages of both previous classes of methods. However, there are still a range of topics where additional insight is needed to pave the way for a more widespread application of DRNs in EEG. These include architecture design questions such as network size and end-to-end ability as well as model training questions. How these factors affect model performance has not been explored. Additionally, it is not clear how the data within these networks is transformed, and whether this would correlate with traditional EEG decoding. Our study aims to lay the groundwork in the area of these topics through the analysis of DRNs for EEG with a wide range of hyperparameters. Networks were tested on two public EEG datasets and compared with state-of-the-art ConvNets. Here we propose end-to-end EEG SPDNet (EE(G)-SPDNet), and we show that this wide, end-to-end DRN can outperform the ConvNets, and in doing so use physiologically plausible frequency regions. We also show that the end-to-end approach learns more complex filters than traditional band-pass filters targeting the classical alpha, beta, and gamma frequency bands of the EEG, and that performance can benefit from channel specific filtering approaches. Additionally, architectural analysis revealed areas for further improvement due to the possible loss of Riemannian specific information throughout the network. Our study thus shows how to design and train DRNs to infer task-related information from the raw EEG without the need of handcrafted filterbanks and highlights the potential of end-to-end DRNs such as EE(G)-SPDNet for high-performance EEG decoding.

Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL) approach. However, we observe that the homogeneous exploration process in PPO could cause an unexpected stability issue in the training phase. To address this issue, we propose PPO-UE, a PPO variant equipped with self-adaptive uncertainty-aware explorations (UEs) based on a ratio uncertainty level. The proposed PPO-UE is designed to improve convergence speed and performance with an optimized ratio uncertainty level. Through extensive sensitivity analysis by varying the ratio uncertainty level, our proposed PPO-UE considerably outperforms the baseline PPO in Roboschool continuous control tasks.

Powerful hardware services and software libraries are vital tools for quickly and affordably designing, testing, and executing quantum algorithms. A robust large-scale study of how the performance of these platforms scales with the number of qubits is key to providing quantum solutions to challenging industry problems. Such an evaluation is difficult owing to the availability and price of physical quantum processing units. This work benchmarks the runtime and accuracy for a representative sample of specialized high-performance simulated and physical quantum processing units. Results show the QMware cloud computing service can reduce the runtime for executing a quantum circuit by up to 78% compared to the next fastest option for algorithms with fewer than 27 qubits. The AWS SV1 simulator offers a runtime advantage for larger circuits, up to the maximum 34 qubits available with SV1. Beyond this limit, QMware provides the ability to execute circuits as large as 40 qubits. Physical quantum devices, such as Rigetti's Aspen-M2, can provide an exponential runtime advantage for circuits with more than 30. However, the high financial cost of physical quantum processing units presents a serious barrier to practical use. Moreover, of the four quantum devices tested, only IonQ's Harmony achieves high fidelity with more than four qubits. This study paves the way to understanding the optimal combination of available software and hardware for executing practical quantum algorithms.

尽管在最近的研究中，冷水珊瑚的分布模式（例如paragorgia achorea）受到了越来越多的关注，但对它们的原位活性模式知之甚少。在本文中，我们使用机器学习技术检查了灰木杆菌中的息肉活动，以分析从挪威Stjernsund部署的自主登录机群集获得的高分辨率时间序列数据和照片。本文得出的模型的互动说明是作为补充材料提供的。我们发现，珊瑚息肉扩展程度的最佳预测指标是当前方向，滞后为三个小时。与水流无直接相关的其他变量（例如温度和盐度）提供了更少的有关息肉活动的信息。有趣的是，可以通过对测量位点上方的水柱中的层流进行采样，而不是通过对珊瑚的直接流中的更湍流流进行采样。我们的结果表明，灰木息肉的活性模式受Stjernsund的强潮流状态的控制。看来，木托氏菌对环境当前状态的较短变化没有反应，而是根据潮汐周期本身的大规模模式来调整其行为，以优化营养的吸收。

对国际气候变化小组（IPCC）的第六次评估指出，“过去十年（2010-2019）的累积净二氧化碳排放量与剩下的11个碳预算可能会限制为1.5C（中等信心）大约相同）。”这样的报告直接培养了公众的话语，但是诸如信念和信心程度之类的细微差别常常失去。在本文中，我们提出了一个正式的帐户，以允许在抽象论证设置中使用这种信念和相关的信心来标记论证。与概率论证中的其他建议不同，我们关注对Sato分布语义的选择构建的概率推断的任务，Sato的分布语义已被证明涵盖了包括贝叶斯网络的语义在内的各种情况。从有关此类语义的大量文献中借用，我们研究了如何在考虑不确定概率的情况下在实践中处理此类任务，并与现有的概率论点的现有建议讨论联系。

在二阶不确定的贝叶斯网络中，条件概率仅在分布中已知，即概率上的概率。Delta方法已应用于扩展精确的一阶推理方法，以通过从贝叶斯网络得出的总和产物网络传播均值和方差，从而表征了认知不确定性或模型本身的不确定性。另外，已经证明了Polytrees的二阶信仰传播，但没有针对一般的定向无环形结构。在这项工作中，我们将循环信念传播扩展到二阶贝叶斯网络的设置，从而产生二阶循环信念传播（SOLBP）。对于二阶贝叶斯网络，SOLBP生成了与Sum-Propoduct网络生成的网络一致的推论，同时更加有效且可扩展。

当历史数据受到限制时，与贝叶斯网络节点相关的条件概率不确定，并且可以在经验上进行估计。二阶估计方法为估计概率和量化这些估计的不确定性提供了一个框架。我们将这些案例称为Uncer Tain或二阶贝叶斯网络。当完成此类数据时，即每个实例化都观察到所有可变值，已知有条件的概率是dirichlet分布的。本文通过使他们能够学习参数（即条件概率），通过不完整的数据来学习不确定的贝叶斯网络的当前最新方法。我们广泛评估各种方法，通过各种查询的置信界的所需和经验得出的强度来学习参数的后验。

这项工作介绍了Seleseet，这是一种新的大型多标签土地覆盖物和土地使用场景的理解数据集。 It includes $1\,759\,830$ images from Sentinel-2 tiles, with 12 spectral bands and patch sizes of up to $ 120 \ \mathrm{px} \times 120 \ \mathrm{px}$.每张图像都带有来自德国土地覆盖型LBM-DE2018的大型像素级标签，其土地覆盖类别基于Corine Land Cover数据库（CLC）2018，而最小映射单元（MMU）的五倍比原始CLC映射小五倍。 。我们提供了所有四个季节的像素同步示例，以及额外的雪套装。这些属性使Seasonet成为当前最广泛，最大的遥感场景理解数据集，其应用程序从土地覆盖地图上的场景分类到基于内容的跨季节图像检索和自我审议的功能学习。我们通过评估场景分类和语义分割方案中新数据集中的最新深层网络来提供基线结果。