延迟的诊断联合性不稳定会导致踝关节的显着发病和关节炎的加速变化。使用3D体积测量值，重量计算机断层扫描（WBCT）已显示出有希望的早期和可靠检测分离出的集团不稳定性的潜力。尽管据报道这些测量值高度准确，但它们也依赖于经验，耗时，并且需要一种特定的3D测量软件工具，该工具导致临床医生仍然对传统的诊断方法表现出更大的兴趣。这项研究的目的是通过使用WBCT扫描来自动化3D体积解剖结构的3D体积评估来提高准确性，加速分析时间并减少观察者间偏置。我们使用了先前收集的单侧联合不稳定性患者的WBCT扫描进行了回顾性研究。评估了144个双侧踝WBCT扫描（48个不稳定，96个对照）。我们开发了三个深度学习（DL）模型，用于分析WBCT扫描以识别集团不稳定性。这三个模型包括两个最先进的模型（模型1-3D卷积神经网络[CNN]和具有长短期内存[LSTM]的模型2-CNN）和一个新的模型（模型3-差分差异我们在这项研究中介绍的CNN LSTM）。模型1未能分析WBCT扫描（F1得分= 0）。模型2仅错误分类两种情况（F1得分= 0.80）。模型3的表现优于模型2，并实现了几乎完美的性能，在对照组中仅误导了一个情况（F1得分= 0.91），因为不稳定，而比模型2更快。

普通射线照相被广泛用于检测总髋关节置换（THR）植入物的机械松动。目前，X光片是由医疗专业人员手动评估的，这可能是差的，并且观察者内部可靠性和准确性较低。此外，手动检测THR植入物的机械松动需要经验丰富的临床医生，这些临床医生可能总是很容易获得，可能导致诊断延迟。在这项研究中，我们提出了一种新型的，全自动和可解释的方法，用于使用深卷积神经网络（CNN）从纯X线照片中检测THR植入物的机械松动。我们使用五倍交叉验证对40名患者进行了40名患者的CNN培训，并将其性能与大量板认证的骨科医生（AFC）进行了比较。为了提高对机器结局的信心，我们还实施了显着图，以可视化CNN在哪里进行诊断。 CNN在诊断植入物的机械松动方面优于骨科医生，其敏感性明显高于敏感性（0.94），其特异性相同（0.96）（0.96）。显着图显示，CNN着眼于临床相关的特征以进行诊断。此类CNN可用于自动放射植入物的机械松动，以补充从业者的决策过程，提高其诊断准确性，并释放它们以进行以患者为中心的护理。

Self-supervised learning (SSL) aims to produce useful feature representations without access to any human-labeled data annotations. Due to the success of recent SSL methods based on contrastive learning, such as SimCLR, this problem has gained popularity. Most current contrastive learning approaches append a parametrized projection head to the end of some backbone network to optimize the InfoNCE objective and then discard the learned projection head after training. This raises a fundamental question: Why is a learnable projection head required if we are to discard it after training? In this work, we first perform a systematic study on the behavior of SSL training focusing on the role of the projection head layers. By formulating the projection head as a parametric component for the InfoNCE objective rather than a part of the network, we present an alternative optimization scheme for training contrastive learning based SSL frameworks. Our experimental study on multiple image classification datasets demonstrates the effectiveness of the proposed approach over alternatives in the SSL literature.

Foundation models are redefining how AI systems are built. Practitioners now follow a standard procedure to build their machine learning solutions: download a copy of a foundation model, and fine-tune it using some in-house data about the target task of interest. Consequently, the Internet is swarmed by a handful of foundation models fine-tuned on many diverse tasks. Yet, these individual fine-tunings often lack strong generalization and exist in isolation without benefiting from each other. In our opinion, this is a missed opportunity, as these specialized models contain diverse features. Based on this insight, we propose model recycling, a simple strategy that leverages multiple fine-tunings of the same foundation model on diverse auxiliary tasks, and repurposes them as rich and diverse initializations for the target task. Specifically, model recycling fine-tunes in parallel each specialized model on the target task, and then averages the weights of all target fine-tunings into a final model. Empirically, we show that model recycling maximizes model diversity by benefiting from diverse auxiliary tasks, and achieves a new state of the art on the reference DomainBed benchmark for out-of-distribution generalization. Looking forward, model recycling is a contribution to the emerging paradigm of updatable machine learning where, akin to open-source software development, the community collaborates to incrementally and reliably update machine learning models.

We study the task of training regression models with the guarantee of label differential privacy (DP). Based on a global prior distribution on label values, which could be obtained privately, we derive a label DP randomization mechanism that is optimal under a given regression loss function. We prove that the optimal mechanism takes the form of a ``randomized response on bins'', and propose an efficient algorithm for finding the optimal bin values. We carry out a thorough experimental evaluation on several datasets demonstrating the efficacy of our algorithm.

Social recommender systems (SocialRS) simultaneously leverage user-to-item interactions as well as user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users' tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention. In particular, with the advance of Graph Neural Networks (GNN), many GNN-based SocialRS methods have been developed recently. Therefore, we conduct a comprehensive and systematic review of the literature on GNN-based SocialRS. In this survey, we first identify 80 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). Then, we comprehensively review them in terms of their inputs and architectures to propose a novel taxonomy: (1) input taxonomy includes 5 groups of input type notations and 7 groups of input representation notations; (2) architecture taxonomy includes 8 groups of GNN encoder, 2 groups of decoder, and 12 groups of loss function notations. We classify the GNN-based SocialRS methods into several categories as per the taxonomy and describe their details. Furthermore, we summarize the benchmark datasets and metrics widely used to evaluate the GNN-based SocialRS methods. Finally, we conclude this survey by presenting some future research directions.

One of the major errors affecting GNSS signals in urban canyons is GNSS multipath error. In this work, we develop a Gazebo plugin which utilizes a ray tracing technique to account for multipath effects in a virtual urban canyon environment using virtual satellites. This software plugin balances accuracy and computational complexity to run the simulation in real-time for both software-in-the-loop (SITL) and hardware-in-the-loop (HITL) testing. We also construct a 3D virtual environment of Hong Kong and compare the results from our plugin with the GNSS data in the publicly available Urban-Nav dataset, to validate the efficacy of the proposed Gazebo Plugin. The plugin is openly available to all the researchers in the robotics community. https://github.com/kpant14/multipath_sim

Synthetic data offers the promise of cheap and bountiful training data for settings where lots of labeled real-world data for tasks is unavailable. However, models trained on synthetic data significantly underperform on real-world data. In this paper, we propose Proportional Amplitude Spectrum Training Augmentation (PASTA), a simple and effective augmentation strategy to improve out-of-the-box synthetic-to-real (syn-to-real) generalization performance. PASTA involves perturbing the amplitude spectrums of the synthetic images in the Fourier domain to generate augmented views. We design PASTA to perturb the amplitude spectrums in a structured manner such that high-frequency components are perturbed relatively more than the low-frequency ones. For the tasks of semantic segmentation (GTAV to Real), object detection (Sim10K to Real), and object recognition (VisDA-C Syn to Real), across a total of 5 syn-to-real shifts, we find that PASTA outperforms more complex state-of-the-art generalization methods while being complementary to the same.

Deep learning models, though having achieved great success in many different fields over the past years, are usually data hungry, fail to perform well on unseen samples, and lack of interpretability. Various prior knowledge often exists in the target domain and their use can alleviate the deficiencies with deep learning. To better mimic the behavior of human brains, different advanced methods have been proposed to identify domain knowledge and integrate it into deep models for data-efficient, generalizable, and interpretable deep learning, which we refer to as knowledge-augmented deep learning (KADL). In this survey, we define the concept of KADL, and introduce its three major tasks, i.e., knowledge identification, knowledge representation, and knowledge integration. Different from existing surveys that are focused on a specific type of knowledge, we provide a broad and complete taxonomy of domain knowledge and its representations. Based on our taxonomy, we provide a systematic review of existing techniques, different from existing works that survey integration approaches agnostic to taxonomy of knowledge. This survey subsumes existing works and offers a bird's-eye view of research in the general area of knowledge-augmented deep learning. The thorough and critical reviews of numerous papers help not only understand current progresses but also identify future directions for the research on knowledge-augmented deep learning.

Pictionary, the popular sketch-based guessing game, provides an opportunity to analyze shared goal cooperative game play in restricted communication settings. However, some players occasionally draw atypical sketch content. While such content is occasionally relevant in the game context, it sometimes represents a rule violation and impairs the game experience. To address such situations in a timely and scalable manner, we introduce DrawMon, a novel distributed framework for automatic detection of atypical sketch content in concurrently occurring Pictionary game sessions. We build specialized online interfaces to collect game session data and annotate atypical sketch content, resulting in AtyPict, the first ever atypical sketch content dataset. We use AtyPict to train CanvasNet, a deep neural atypical content detection network. We utilize CanvasNet as a core component of DrawMon. Our analysis of post deployment game session data indicates DrawMon's effectiveness for scalable monitoring and atypical sketch content detection. Beyond Pictionary, our contributions also serve as a design guide for customized atypical content response systems involving shared and interactive whiteboards. Code and datasets are available at https://drawm0n.github.io.