近年来，生成设计技术已在许多应用领域，尤其是在工程领域中牢固地建立。这些方法证明了由于前景有希望的增长。但是，现有方法受到考虑的问题的特异性受到限制。此外，它们不提供所需的灵活性。在本文中，我们为任意生成设计问题制定了一般方法，并提出了名为Gefest（编码结构的生成进化）的新颖框架。开发的方法基于三个一般原则：采样，估计和优化。这样可以确保方法调整特定生成设计问题的方法的自由度，因此可以构建最合适的方法。进行了一系列实验研究，以确认Gefest框架的有效性。它涉及合成和现实情况（沿海工程，微流体，热力学和油田计划）。 Gefest的柔性结构使得获得超过基线溶液的结果。

Decades of progress in simulation-based surrogate-assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previously intractable complex engineering problems. This paper investigates the possible benefit of a concurrent utilization of multiple simulation-based surrogate models to solve complex discrete optimization problems. To fulfill this, the so-called Self-Adaptive Multi-surrogate Assisted Efficient Global Optimization algorithm (SAMA-DiEGO), which features a two-stage online model management strategy, is proposed and further benchmarked on fifteen binary-encoded combinatorial and fifteen ordinal problems against several state-of-the-art non-surrogate or single surrogate assisted optimization algorithms. Our findings indicate that SAMA-DiEGO can rapidly converge to better solutions on a majority of the test problems, which shows the feasibility and advantage of using multiple surrogate models in optimizing discrete problems.

Benchmarking is a key aspect of research into optimization algorithms, and as such the way in which the most popular benchmark suites are designed implicitly guides some parts of algorithm design. One of these suites is the black-box optimization benchmarking (BBOB) suite of 24 single-objective noiseless functions, which has been a standard for over a decade. Within this problem suite, different instances of a single problem can be created, which is beneficial for testing the stability and invariance of algorithms under transformations. In this paper, we investigate the BBOB instance creation protocol by considering a set of 500 instances for each BBOB problem. Using exploratory landscape analysis, we show that the distribution of landscape features across BBOB instances is highly diverse for a large set of problems. In addition, we run a set of eight algorithms across these 500 instances, and investigate for which cases statistically significant differences in performance occur. We argue that, while the transformations applied in BBOB instances do indeed seem to preserve the high-level properties of the functions, their difference in practice should not be overlooked, particularly when treating the problems as box-constrained instead of unconstrained.

本文介绍了一种基于特征空间群集产生的块来学习大型贝叶斯网络的新方法。使用归一化的共同信息获得此聚类。并且随后的块聚合使用经典学习方法完成，除了它们是输入的，其中包含有关每个块特征值组合的压缩信息。该方法的验证是针对爬山的两种分数函数的图表枚举算法进行的：BIC和MI。这样，即使对于那些被认为不适合并行学习的分数函数，也可以实现潜在的可行块学习。该方法的优势是根据工作速度以及发现结构的准确性评估的。

尽管深度学习的卓越性能（DL）在许多分割任务上，但基于DL的方法令人惊奇地过于对高偏振标签概率的预测。对于许多具有固有标签歧义的许多应用通常是不可取的，即使在人类注释中也是如此。通过利用每张图片的多个注释和分割不确定性来解决这一挑战。但是，多次图像的批次通常不可用，在真实的应用程序中，不确定性在分段结果对用户的情况下不提供完全控制。在本文中，我们提出了新的方法来改善分割概率估计，而不会在真实情景中牺牲性能，我们只有每张图片只有一个暧昧的注释。我们将估计的网络分割概率图边缘化，这是鼓励/过度的网络上/过度段，而没有惩罚平衡分割。此外，我们提出了一个统一的HyperNetwork合奏方法，以减轻培训多个网络的计算负担。我们的方法成功地估计了反映了底层结构的分割概率图，并为具有挑战性的3D医学图像分割进行了直观控制。虽然我们所提出的方法的主要重点不是提高二元分割性能，但我们的方法略微超越了最先进的。该代码可用于\ url {https://github.com/sh4174/hypernetensemble}。

在这项工作中，我们在现代射频天文学中应用于形态分类时，研究了最先进的半监督学习（SSL）算法的鲁棒性。我们测试SSL是否可以在使用较少的标记数据点时实现与本领域的当前监督状态相当的性能，以及这些结果概括使用真正未标记的数据。我们发现，尽管SSL提供了额外的正常化，但在使用很少的标签时，其性能迅速降低，并且使用真正未标记的数据导致性能显着下降。

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

Novel topological spin textures, such as magnetic skyrmions, benefit from their inherent stability, acting as the ground state in several magnetic systems. In the current study of atomic monolayer magnetic materials, reasonable initial guesses are still needed to search for those magnetic patterns. This situation underlines the need to develop a more effective way to identify the ground states. To solve this problem, in this work, we propose a genetic-tunneling-driven variance-controlled optimization approach, which combines a local energy minimizer back-end and a metaheuristic global searching front-end. This algorithm is an effective optimization solution for searching for magnetic ground states at extremely low temperatures and is also robust for finding low-energy degenerated states at finite temperatures. We demonstrate here the success of this method in searching for magnetic ground states of 2D monolayer systems with both artificial and calculated interactions from density functional theory. It is also worth noting that the inherent concurrent property of this algorithm can significantly decrease the execution time. In conclusion, our proposed method builds a useful tool for low-dimensional magnetic system energy optimization.

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.