对于头颈癌（HNC）患者管理，自动总肿瘤量（GTV）细分和准确的治疗前癌症复发预测对于协助医师设计个性化管理计划非常重要，这有可能改善治疗结果和治疗结果和HNC患者的生活质量。在本文中，我们基于HNC患者的组合预处理正电子发射断层扫描/计算机发射断层扫描（PET/CT）扫描，开发了一种自动原发性肿瘤（GTVP）和淋巴结（GTVN）分割方法。我们从分段的肿瘤体积中提取了放射素学特征，并构建了多模式肿瘤复发生存率（RFS）预测模型，该模型融合了预测由单独的CT放射线学，PET放射线学和临床模型融合在一起。我们进行了5倍的交叉验证，以训练和评估MICCAI 2022头和颈部肿瘤分割和结果预测挑战（Hecktor）数据集的方法。 GTVP和GTVN分割的测试队列的集合预测分别达到0.77和0.73，RFS预测的C-指数值为0.67。该代码公开可用（https://github.com/wangkaiwan/hecktor-2022-airt）。我们团队的名字叫艾特。

基于CBCT的在线自适应放射疗法（ART）要求准确的自动分割模型，以缩短医生编辑轮廓的时间成本，因为患者固定在治疗台上等待治疗开始。但是，CBCT图像的自动分割是一项艰巨的任务，这主要是由于图像质量较低和缺乏训练深度学习（DL）模型的真正标签。同时，与其他细分问题相比，ART中的CBCT自动分割是一项独特的任务，在该问题中，可以使用计划CT（PCT）的手动轮廓。为了利用此先验知识，我们建议将头颈患者的CBCT上的可变形图像登记（DIR）和直接分割（DS）结合在一起。首先，我们使用从PCT和CBCT之间的多种DIR方法得出的变形PCT轮廓作为用于训练的伪标签。其次，我们使用变形的PCT轮廓作为边界框来限制DS的感兴趣区域。同时，变形的PCT轮廓被用作训练的伪标签，但是由边界盒中不同的DIR算法生成的。第三，我们在真标签上使用边界框微调模型。我们发现，与仅dir-fir-fir-ly-ly-sementation相比，在没有伪标签的CBCT上进行的DS，并且没有利用任何先验知识的分割性能较差。但是，将变形的PCT轮廓添加为DS网络中的边界框可以显着改善分割性能，与仅DIR-DIR-FIR-FIRE分割相当。通过使用一些真实标签对其进行微调，可以进一步改进带有边界框的DS模型。实验表明，与仅DIR-DIR-DIRE分割相比，19个结构中有7个至少具有0.2个骰子相似性系数的增加。在DS模型中，利用变形的PCT轮廓作为训练的伪标签，作为用于形状和位置特征提取的边界框，是将DIR和DS组合的好方法。

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.

Automated text analysis has become a widely used tool in political science. In this research, we use a BERT model trained on German party manifestos to identify the individual parties' contribution to the coalition agreement of 2021.