在神经形式上建立具有神经网络的微分方程的经典方法，其中可以使用具有透明域的离散方式直接构造成本函数。利用用于时间依赖性微分方程的神经形式，可以应用最近开发的域碎片方法。也就是说，域可以被分成几个子域，在该子域中解决了优化问题。在用于求解微分方程的经典自适应数值方法中，可以分别改进或分解域的网格以及域，以提高精度。还可以调整近似精度的程度。希望将这种重要和成功的基于神经网络解决方案的策略转移。在目前的工作中，我们提出了一种新的自适应神经方法，以满足这种旨在解决时间依赖性问题。为此，每个子域尺寸减小，直到优化被解析为预定义的训练准确性。另外，虽然所采用的神经网络是默认小的，但是也可以以自适应方式调整神经元的数量。我们引入条件以自动确认解决方案可靠性并在必要时进行计算参数。我们为三个仔细选择的示例初始值问题提供了结果，并说明了该方法的重要属性。

用于求解微分方程的几种神经网络方法采用前馈神经网络采用试用解决方案。存在不同的方法来纳入结​​构中的试验解决方案，例如可以将它们直接包括在成本函数中。在相应的神经网络中使用，试验解决方案定义所谓的神经形式。这种神经形式代表一般，灵活的工具，通过该工具可以解决各种微分方程。在本文中，我们考虑时间依赖的初始值问题，需要充分设置神经表单框架。现在在文献中呈现的神经形式可以被认为是第一阶多项式。在这项工作中，我们建议延长神经形式的多项式顺序。新型搭配型结构包括几个前馈神经网络，每个馈电是每个订单的。此外，我们提出计算域的碎片到子域名。神经形式求解在每个子域上，而接口网格点重叠以便在整个碎片上提供初始值。我们在实验中说明了凸起神经形式的搭配和域碎片的组合允许在具有高精度和可靠性的大型域上解决初始值问题。

前馈神经网络提供了一种用于求解微分方程的有希望的方法。然而，近似的可靠性和准确性仍然代表了当前文献中没有完全解决的细微问题。计算方法一般高度依赖于各种计算参数以及优化方法的选择，这一点必须与成本函数的结构一起看。本文的目的是迈出解决这些公开问题的一步。为此，我们在这里研究了一种简单但基本的常见常见微分方程建模阻尼系统的解决方案。我们考虑通过神经形式求解微分方程的两种计算方法。这些是定义成本函数的经典但仍然是实际的试验解决方案方法，以及最近直接建设与试验解决方案方法相关的成本函数。让我们注意到我们学习的设置可以很容易地应用，包括偏微分方程的解。通过一个非常详细的计算研究，我们表明可以识别用于参数和方法的优选选择。我们还照亮了神经网络模拟中可观察到的一些有趣的效果。总的来说，我们通过展示通过神经网络方法获得可靠和准确的结果来实现现场的当前文献。通过这样做，我们说明了仔细选择计算设置的重要性。

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

We present a dynamic path planning algorithm to navigate an amphibious rotor craft through a concave time-invariant obstacle field while attempting to minimize energy usage. We create a nonlinear quaternion state model that represents the rotor craft dynamics above and below the water. The 6 degree of freedom dynamics used within a layered architecture to generate motion paths for the vehicle to follow and the required control inputs. The rotor craft has a 3 dimensional map of its surroundings that is updated via limited range onboard sensor readings within the current medium (air or water). Path planning is done via PRM and D* Lite.

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age.

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

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.