将联合学习（FL）模型概括为未IID数据的解读客户是一个至关重要的主题，但到目前为止未解决。在这项工作中，我们建议从新的因果角度来解决这个问题。具体而言，我们形成培训结构因果模型（SCM），以解释模型泛化在分布式学习范式中的挑战。基于此，我们介绍了一种使用测试特定和势头批量标准化（TSMobn）的简单且有效的方法来推广到测试客户端。通过制定另一个测试SCM来说，给出了因果分析，并证明了TSMobn的关键因素是特定的特定于特定的统计数据（即，均值和方差）。这种统计数据可以被视为因果干预的代理变量。此外，通过考虑FL中的泛化界，我们表明我们的TSMobn方法可以减少训练和测试特征分布之间的分歧，这实现了比标准模型测试更低的泛化差距。我们广泛的实验评估表明，在具有各种类型的特征分布和客户端数量的三个数据集上对看不见的客户端概括的显着改进。值得注意的是，我们的建议方法可以灵活地应用于不同的最先进的联邦学习算法，并且与现有的域泛化方法正交。

联合学习允许多方协作，在不共享本地数据的情况下协作培训联合模型。这使得机器学习在固有的分布式的，诸如医疗领域中的固有分布式的未差异数据的设置中的应用。在实践中，通常通过聚合当地模型来实现联合培训，其中当地培训目标必须与联合（全球）目标相似。然而，通常，当地数据集是如此之小，即当地目标从全球目标差异很大，导致联合学习失败。我们提出了一种新的方法，它与本地模型的排列交织在一起。排列将每个本地模型暴露给当地数据集的菊花链，导致数据稀疏域中的更有效培训。这使得能够培训极小的本地数据集，例如跨医院的患者数据，同时保留联合学习的培训效率和隐私效益。

猜测损耗曲线的平坦度被猜测以连接到机器学习模型的泛化能力，特别是神经网络。虽然已经经验观察到，平坦度措施与泛化持续强烈地相关，但仍然是一个开放的理论问题，为什么和在这种情况下，在这种情况下，平坦度与泛化相连，特别是根据改变某些平坦度措施但仍然不变的regarameteration。我们通过将其与来自代表性数据的插值相关联的平整度和泛化之间的联系，从而导出代表性的概念，并具有鲁棒性。概念允许我们严格地连接平坦度和泛化，并识别连接保持的条件。此外，它们产生了一种新颖，但自然的相对平坦度量，泛化强烈地相关，简化了普通最小二乘的脊回归，并解决了重新支柱化问题。

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.