近年来，由于需要更强大的计算方法，优化问题变得越来越普遍。随着人工智能等技术的最近出现，需要新的综合学，增强古典算法的能力。最近，研究人员一直在寻找查尔斯达尔文的自然选择和演变的理论，作为使用机器学习加强电流方法的手段。1960年，第一个遗传算法由John H. Holland和他的学生开发。我们探讨了使用高斯突变的发展系统中遗传算法的数学直觉，以及在解决优化问题方面的影响。

The simple idea that not all things are equally difficult has surprising implications when applied in a fairness context. In this work we explore how "difficulty" is model-specific, such that different models find different parts of a dataset challenging. When difficulty correlates with group information, we term this difficulty disparity. Drawing a connection with recent work exploring the inductive bias towards simplicity of SGD-trained models, we show that when such a disparity exists, it is further amplified by commonly-used models. We quantify this amplification factor across a range of settings aiming towards a fuller understanding of the role of model bias. We also present a challenge to the simplifying assumption that "fixing" a dataset is sufficient to ensure unbiased performance.

Accomplishing safe and efficient driving is one of the predominant challenges in the controller design of connected automated vehicles (CAVs). It is often more convenient to address these goals separately and integrate the resulting controllers. In this study, we propose a controller integration scheme to fuse performance-based controllers and safety-oriented controllers safely for the longitudinal motion of a CAV. The resulting structure is compatible with a large class of controllers, and offers flexibility to design each controller individually without affecting the performance of the others. We implement the proposed safe integration scheme on a connected automated truck using an optimal-in-energy controller and a safety-oriented connected cruise controller. We validate the premise of the safe integration through experiments with a full-scale truck in two scenarios: a controlled experiment on a test track and a real-world experiment on a public highway. In both scenarios, we achieve energy efficient driving without violating safety.

Climate change is threatening human health in unprecedented orders and many ways. These threats are expected to grow unless effective and evidence-based policies are developed and acted upon to minimize or eliminate them. Attaining such a task requires the highest degree of the flow of knowledge from science into policy. The multidisciplinary, location-specific, and vastness of published science makes it challenging to keep track of novel work in this area, as well as making the traditional knowledge synthesis methods inefficient in infusing science into policy. To this end, we consider developing multiple domain-specific language models (LMs) with different variations from Climate- and Health-related information, which can serve as a foundational step toward capturing available knowledge to enable solving different tasks, such as detecting similarities between climate- and health-related concepts, fact-checking, relation extraction, evidence of health effects to policy text generation, and more. To our knowledge, this is the first work that proposes developing multiple domain-specific language models for the considered domains. We will make the developed models, resources, and codebase available for the researchers.

While modern Text-to-Speech (TTS) systems can produce speech rated highly in terms of subjective evaluation, the distance between real and synthetic speech distributions remains understudied, where we use the term \textit{distribution} to mean the sample space of all possible real speech recordings from a given set of speakers; or of the synthetic samples that could be generated for the same set of speakers. We evaluate the distance of real and synthetic speech distributions along the dimensions of the acoustic environment, speaker characteristics and prosody using a range of speech processing measures and the respective Wasserstein distances of their distributions. We reduce these distribution distances along said dimensions by providing utterance-level information derived from the measures to the model and show they can be generated at inference time. The improvements to the dimensions translate to overall distribution distance reduction approximated using Automatic Speech Recognition (ASR) by evaluating the fitness of the synthetic data as training data.

Self-supervised speech models have grown fast during the past few years and have proven feasible for use in various downstream tasks. Some recent work has started to look at the characteristics of these models, yet many concerns have not been fully addressed. In this work, we conduct a study on emotional corpora to explore a popular self-supervised model -- wav2vec 2.0. Via a set of quantitative analysis, we mainly demonstrate that: 1) wav2vec 2.0 appears to discard paralinguistic information that is less useful for word recognition purposes; 2) for emotion recognition, representations from the middle layer alone perform as well as those derived from layer averaging, while the final layer results in the worst performance in some cases; 3) current self-supervised models may not be the optimal solution for downstream tasks that make use of non-lexical features. Our work provides novel findings that will aid future research in this area and theoretical basis for the use of existing models.

我们考虑在重复的未知游戏中进行规避风险的学习，在这种游戏中，代理商的目标是最大程度地减少其个人产生高成本的风险。具体而言，代理商使用处于风险的条件值（CVAR）作为风险措施，并以每集选定动作的成本值的形式依靠强盗反馈来估算其CVAR值并更新其动作。使用匪徒反馈来估计CVAR的一个主要挑战是，代理只能访问其自身的成本值，但是，这取决于所有代理的行为。为了应对这一挑战，我们提出了一种新的规避风险的学习算法，并利用有关成本价值的完整历史信息。我们表明，该算法实现了子线性的遗憾，并匹配了文献中最著名的算法。我们为欧洲大师游戏提供了数值实验，该游戏表明我们的方法表现优于现有方法。

Rendezvous是一个古老的问题，即确保两个或多个政党最初是分开的，不知道彼此的地位，不允许交流，而无需在会议点上进行预先审议。这个问题已经在古典计算机科学中进行了广泛的研究，并且对现代应用程序具有生动的重要性，例如协调敌人领土上的无人机舰队。量子非局部性（如贝尔不平等现象）表明，在许多情况下，量子纠缠可以改善与经典来源相比的两个分开方的协调。在许多情况下，不信号的相关性甚至加强了这种现象。在这项工作中，我们分析了如何通过试图在有限数量步骤的有限网络上对接的位置感知的代理如何使用贝尔非本地性。我们使用量子资源为这两个代理提供了最佳解决方案，并且仅具有``经典''计算能力的代理。我们的结果表明，对于立方图和周期，可以通过允许代理使用纠缠量子状态的帮助来获得优势。

我们介绍了一些源自摄影师的本地化数据集，他们实际上试图了解他们拍摄的图像中的视觉内容。它包括有4,500多个视觉障碍者拍摄的超过4,500张图像中的100个类别的近10,000个细分。与现有的少数弹射对象检测和实例分段数据集相比，我们的数据集是第一个在对象中找到孔（例如，在我们的分段的12.3 \％中找到），它显示的对象相对于占据相对于尺寸的范围较大。图像和文本在我们的对象中的常见五倍以上（例如，在我们的分割的22.4％中找到）。对三种现代少量定位算法的分析表明，它们概括为我们的新数据集。这些算法通常很难找到带有孔，非常小且非常大的物体以及缺乏文本的物体的对象。为了鼓励更大的社区致力于这些尚未解决的挑战，我们在https://vizwiz.org上公开分享了带注释的少数数据集。

图像组成有助于研究图像结构，并有助于发现跨艺术形式和样式描绘的基础场景的语义。近年来，随着艺术品的数字化，可能会将成千上万个特定场景或叙述的图像联系在一起。但是，将这些数据与一致的客观性联系起来可能是一项高度挑战和耗时的任务。在这项工作中，我们提出了一种称为图像组成画布（ICC ++）的新方法，以比较和检索具有相似组成元素的图像。 ICC ++是对ICC的改进，专门针对由Max Imdahl的工作激发的低水平和高级功能（组成元素）。为此，我们与传统和最先进的方法（SOTA）方法进行了严格的定量和定性比较，表明我们所提出的方法优于所有这些方法。结合深度功能，我们的方法优于最佳的基于深度学习的方法，为数字人文学科的可解释机器学习打开了研究方向。我们将发布代码和数据后的数据。