贝叶斯优化（BO）是一种基于替代物的全球优化策略，依靠高斯流程回归（GPR）模型来近似目标函数和采集功能，以建议候选点。众所周知，对于高维问题，BO不能很好地扩展，因为GPR模型需要更多的数据点才能实现足够的准确性，并且在高维度中，获取优化在计算上变得昂贵。最近的几项旨在解决这些问题的旨在，例如，实现在线变量选择的方法或对原始搜索空间的较低维度次级manifold进行搜索。本文提出了我们以前的PCA-BO的工作，该作品学习了线性子字节，因此提出了一种新颖的内核PCA辅助BO（KPCA-BO）算法，该算法将非线性子词嵌入搜索空间中并在搜索空间中执行BO这个子manifold。直观地，在较低维度的子序列上构建GPR模型有助于提高建模准确性，而无需从目标函数中获得更多数据。此外，我们的方法定义了较低维度的子元素的采集函数，从而使采集优化更易于管理。我们将KPCA-BO与香草bo的性能以及有关可可/BBOB基准套件的多模式问题的PCA-BO进行了比较。经验结果表明，在大多数测试问题上，KPCA-BO在收敛速度方面都优于BO，并且当维度增加时，这种好处变得更加显着。对于60D功能，KPCA-BO在许多测试用例中取得比PCA-BO更好的结果。与Vanilla BO相比，它有效地减少了训练GPR模型所需的CPU时间并优化与香草BO相比的采集功能。

在连续学习期间，人工神经网络（ANNS）经历灾难性的遗忘（CF）。相比之下，大脑可以在没有任何灾难性遗忘的迹象的情况下连续学习。尖峰神经网络（SNNS）是下一代ANN，具有从生物神经网络借入的许多功能。因此，SNNS可能希望更好地适应CF。在本文中，我们研究SNNS对CF的易感性，并测试几种用于减轻灾难性遗忘的生物启发方法。 SNNS受到基于Spike-Timing依赖的塑性（STDP）的生物合理的本地培训规则。本地培训禁止基于全局损失函数的梯度直接使用CF防御方法。我们开发并测试了该方法，以确定基于随机Langevin动态的突触（重量）的重要性，而无需梯度。还测试了一种从模拟神经网络改编的灾难性遗忘预防的其他几种方法。实验是在Spyketorch环境中自由的数据集进行的。

We investigate a model for image/video quality assessment based on building a set of codevectors representing in a sense some basic properties of images, similar to well-known CORNIA model. We analyze the codebook building method and propose some modifications for it. Also the algorithm is investigated from the point of inference time reduction. Both natural and synthetic images are used for building codebooks and some analysis of synthetic images used for codebooks is provided. It is demonstrated the results on quality assessment may be improves with the use if synthetic images for codebook construction. We also demonstrate regimes of the algorithm in which real time execution on CPU is possible for sufficiently high correlations with mean opinion score (MOS). Various pooling strategies are considered as well as the problem of metric sensitivity to bitrate.

Simultaneous localization and mapping (SLAM) is one of the key components of a control system that aims to ensure autonomous navigation of a mobile robot in unknown environments. In a variety of practical cases a robot might need to travel long distances in order to accomplish its mission. This requires long-term work of SLAM methods and building large maps. Consequently the computational burden (including high memory consumption for map storage) becomes a bottleneck. Indeed, state-of-the-art SLAM algorithms include specific techniques and optimizations to tackle this challenge, still their performance in long-term scenarios needs proper assessment. To this end, we perform an empirical evaluation of two widespread state-of-the-art RGB-D SLAM methods, suitable for long-term navigation, i.e. RTAB-Map and Voxgraph. We evaluate them in a large simulated indoor environment, consisting of corridors and halls, while varying the odometer noise for a more realistic setup. We provide both qualitative and quantitative analysis of both methods uncovering their strengths and weaknesses. We find that both methods build a high-quality map with low odometry noise but tend to fail with high odometry noise. Voxgraph has lower relative trajectory estimation error and memory consumption than RTAB-Map, while its absolute error is higher.

DNA-Encoded Library (DEL) technology has enabled significant advances in hit identification by enabling efficient testing of combinatorially-generated molecular libraries. DEL screens measure protein binding affinity though sequencing reads of molecules tagged with unique DNA-barcodes that survive a series of selection experiments. Computational models have been deployed to learn the latent binding affinities that are correlated to the sequenced count data; however, this correlation is often obfuscated by various sources of noise introduced in its complicated data-generation process. In order to denoise DEL count data and screen for molecules with good binding affinity, computational models require the correct assumptions in their modeling structure to capture the correct signals underlying the data. Recent advances in DEL models have focused on probabilistic formulations of count data, but existing approaches have thus far been limited to only utilizing 2-D molecule-level representations. We introduce a new paradigm, DEL-Dock, that combines ligand-based descriptors with 3-D spatial information from docked protein-ligand complexes. 3-D spatial information allows our model to learn over the actual binding modality rather than using only structured-based information of the ligand. We show that our model is capable of effectively denoising DEL count data to predict molecule enrichment scores that are better correlated with experimental binding affinity measurements compared to prior works. Moreover, by learning over a collection of docked poses we demonstrate that our model, trained only on DEL data, implicitly learns to perform good docking pose selection without requiring external supervision from expensive-to-source protein crystal structures.

This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results. It presented the findings of the follow-up project of the research "Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting". First, the project included an application of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to provide a novel way of predicting stock option trends. Additionally, it examined the dependence of the ML models by evaluating the experimental method of combining multiple ML models to improve prediction results and decision-making. Lastly, two improved trading strategies and simulated investing results were presented. The Binomial Asset Pricing Model with discrete time stochastic process analysis and portfolio hedging was applied and suggested an optimized investment expectation. These results can be utilized in real-life trading strategies to optimize stock option investment results based on historical data.

在机器人研究中，在不平坦的地形中安全导航是一个重要的问题。在本文中，我们提出了一个2.5D导航系统，该系统包括高程图构建，路径规划和本地路径，随后避免了障碍。对于本地路径，我们使用模型预测路径积分（MPPI）控制方法。我们为MPPI提出了新的成本功能，以使其适应高程图和通过不平衡运动。我们在多个合成测试和具有不同类型的障碍物和粗糙表面的模拟环境中评估系统。

多标签图像分类允许从给定图像预测一组标签。与多类分类不同，每个图像只有一个标签，此类设置适用于更广泛的应用程序。在这项工作中，我们重新审视了多标签分类的两种流行方法：基于变压器的头和标签关系信息信息图处理分支。尽管基于变压器的头被认为比基于图基的分支更好地取得了更好的结果，但我们认为，使用适当的训练策略，基于图形的方法可以证明精确度的较小，同时将计算资源减少到推理上。在我们的训练策略中，我们在角度空间中引入了其修饰作用，而不是非对称损失（ASL）（ASL），而不是非对称损失（ASL）。与二进制跨熵损失相比，它隐含地学习了每个班级单位超球的代理特征向量，从而提供更好的歧视能力。根据提出的损失和训练策略，我们在单个模态方法中获得SOTA结果，以广泛的多标签分类基准，例如MS-Coco，Pascal-Voc，Nus wide和Visual Genome 500。 OpenVino培训扩展https://github.com/openvinotoolkit/deep-object-reid/tree/tree/multilabel

现代基于深度学习的系统的性能极大地取决于输入对象的质量。例如，对于模糊或损坏的输入，面部识别质量将较低。但是，在更复杂的情况下，很难预测输入质量对所得准确性的影响。我们提出了一种深度度量学习的方法，该方法允许直接估算不确定性，几乎没有额外的计算成本。开发的\ textit {scaleface}算法使用可训练的比例值，以修改嵌入式空间中的相似性。这些依赖于输入的量表值代表了对识别结果的信心的度量，从而允许估计不确定性。我们提供了有关面部识别任务的全面实验，这些实验表明与其他不确定性感知的面部识别方法相比，比例表面的表现出色。我们还将结果扩展到了文本到图像检索的任务，表明所提出的方法以显着的利润击败了竞争对手。

This paper presents a novel way to apply mathematical finance and machine learning (ML) to forecast stock options prices. Following results from the paper Quasi-Reversibility Method and Neural Network Machine Learning to Solution of Black-Scholes Equations (appeared on the AMS Contemporary Mathematics journal), we create and evaluate new empirical mathematical models for the Black-Scholes equation to analyze data for 92,846 companies. We solve the Black-Scholes (BS) equation forwards in time as an ill-posed inverse problem, using the Quasi-Reversibility Method (QRM), to predict option price for the future one day. For each company, we have 13 elements including stock and option daily prices, volatility, minimizer, etc. Because the market is so complicated that there exists no perfect model, we apply ML to train algorithms to make the best prediction. The current stage of research combines QRM with Convolutional Neural Networks (CNN), which learn information across a large number of data points simultaneously. We implement CNN to generate new results by validating and testing on sample market data. We test different ways of applying CNN and compare our CNN models with previous models to see if achieving a higher profit rate is possible.