在现代统计学和机器学习中的许多问题中，人们通常是为了确定非凸风险功能上的一阶方法最终进入了风险是本地凸的参数空间区域。我们得出了渐近比较不平等，我们称之为Sudakov-Fernique后AMP不平等现象，在涉及GOE矩阵的某些类别的问题中，它能够探测优化景观的特性，该特性在近似消息传递的迭代范围内当地（AMP）算法。作为其使用的一个例子，我们提供了Selentano等人的某些结果的新的，可以说是简单的证明。 （2021），它确定$ \ mathbb {z} _2 $ -Synchronization问题中所谓的Tap自由能在其收敛到的区域中是本地凸。我们进一步证明了El Alaoui等人的猜想。 （2022）涉及相关但独特的自由能的局部凸度，结果证实了它们的算法有效地从Sherrington-Kirkpatrick Gibbs中进行了整个“ Easy”状态的测量。

尽管有许多有吸引力的财产，但内核方法受到维度的诅咒受到严重影响。例如，在$ \ mathbb {r} ^ d $的内部产品内核的情况下，再现内核希尔伯特空间（RKHS）规范对于依赖于小方向子集（RIDGE函数）的功能往往非常大。相应地，使用内核方法难以学习这样的功能。这种观察结果有动力研究内核方法的概括，由此rkhs规范 - 它等同于加权$ \ ell_2 $ norm - 被加权函数$ \ ell_p $ norm替换，我们将其称为$ \ mathcal {f} _p $ norm。不幸的是，这些方法的陶油是不清楚的。内核技巧不可用，最大限度地减少这些规范要求解决无限维凸面问题。我们将随机特征近似于这些规范，表明，对于$ p> 1 $，近似于原始学习问题所需的随机功能的数量是由样本大小的多项式的上限。因此，使用$ \ mathcal {f} _p $ norms在这些情况下是易行的。我们介绍了一种基于双重均匀浓度的证明技术，这可以对超分子化模型的研究更广泛。对于$ p = 1 $，我们对随机功能的保证近似分解。我们证明了使用$ \ mathcal {f} _1 $ norm的学习是在随机减少的$ \ mathsf {np} $ - 基于噪音的半个空间问题的问题。

套索是一种高维回归的方法，当时，当协变量$ p $的订单数量或大于观测值$ n $时，通常使用它。由于两个基本原因，经典的渐近态性理论不适用于该模型：$（1）$正规风险是非平滑的； $（2）$估算器$ \ wideHat {\ boldsymbol {\ theta}} $与true参数vector $ \ boldsymbol {\ theta}^*$无法忽略。结果，标准的扰动论点是渐近正态性的传统基础。另一方面，套索估计器可以精确地以$ n $和$ p $大，$ n/p $的订单为一。这种表征首先是在使用I.I.D的高斯设计的情况下获得的。协变量：在这里，我们将其推广到具有非偏差协方差结构的高斯相关设计。这是根据更简单的``固定设计''模型表示的。我们在两个模型中各种数量的分布之间的距离上建立了非反应界限，它们在合适的稀疏类别中均匀地固定在信号上$ \ boldsymbol {\ theta}^*$。作为应用程序，我们研究了借助拉索的分布，并表明需要校正程度对于计算有效的置信区间是必要的。

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.