人们的个人卫生习惯在每日生活方式中照顾身体和健康的状况。保持良好的卫生习惯不仅减少了患疾病的机会，而且还可以降低社区中传播疾病的风险。鉴于目前的大流行，每天的习惯，例如洗手或定期淋浴，在人们中至关重要，尤其是对于单独生活在家里或辅助生活设施中的老年人。本文提出了一个新颖的非侵入性框架，用于使用我们采用机器学习技术的振动传感器监测人卫生。该方法基于地球通传感器，数字化器和实用外壳中具有成本效益的计算机板的组合。监测日常卫生常规可能有助于医疗保健专业人员积极主动，而不是反应性，以识别和控制社区内潜在暴发的传播。实验结果表明，将支持向量机（SVM）用于二元分类，在不同卫生习惯的分类中表现出约95％的有希望的准确性。此外，基于树的分类器（随机福雷斯特和决策树）通过实现最高精度（100％）优于其他模型，这意味着可以使用振动和非侵入性传感器对卫生事件进行分类，以监测卫生活动。

结构性因果模型（SCM）提供了一种原则方法，可以从经济学到医学的学科中的观察和实验数据中识别因果关系。但是，通常以图形模型表示的SCM不仅可以依靠数据，而要支持域知识的支持。在这种情况下，一个关键的挑战是缺乏以系统的方式将先验（背景知识）编码为因果模型的方法学框架。我们提出了一个称为因果知识层次结构（CKH）的抽象，用于将先验编码为因果模型。我们的方法基于医学中“证据水平”的基础，重点是对因果信息的信心。使用CKH，我们提出了一个方法学框架，用于编码来自各种信息源的因果研究，并将它们组合起来以得出SCM。我们在模拟数据集上评估了我们的方法，并与敏感性分析的地面真实因果模型相比，证明了整体性能。

Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations. For real-world applications of RL, recovering underlying latent states is crucial, particularly when sensory inputs contain irrelevant and exogenous information. In this work, we study how information bottlenecks can be used to construct latent states efficiently in the presence of task-irrelevant information. We propose architectures that utilize variational and discrete information bottlenecks, coined as RepDIB, to learn structured factorized representations. Exploiting the expressiveness bought by factorized representations, we introduce a simple, yet effective, bottleneck that can be integrated with any existing self-supervised objective for RL. We demonstrate this across several online and offline RL benchmarks, along with a real robot arm task, where we find that compressed representations with RepDIB can lead to strong performance improvements, as the learned bottlenecks help predict only the relevant state while ignoring irrelevant information.

Automatic medical image classification is a very important field where the use of AI has the potential to have a real social impact. However, there are still many challenges that act as obstacles to making practically effective solutions. One of those is the fact that most of the medical imaging datasets have a class imbalance problem. This leads to the fact that existing AI techniques, particularly neural network-based deep-learning methodologies, often perform poorly in such scenarios. Thus this makes this area an interesting and active research focus for researchers. In this study, we propose a novel loss function to train neural network models to mitigate this critical issue in this important field. Through rigorous experiments on three independently collected datasets of three different medical imaging domains, we empirically show that our proposed loss function consistently performs well with an improvement between 2%-10% macro f1 when compared to the baseline models. We hope that our work will precipitate new research toward a more generalized approach to medical image classification.

Quantitative cephalometric analysis is the most widely used clinical and research tool in modern orthodontics. Accurate localization of cephalometric landmarks enables the quantification and classification of anatomical abnormalities, however, the traditional manual way of marking these landmarks is a very tedious job. Endeavours have constantly been made to develop automated cephalometric landmark detection systems but they are inadequate for orthodontic applications. The fundamental reason for this is that the amount of publicly available datasets as well as the images provided for training in these datasets are insufficient for an AI model to perform well. To facilitate the development of robust AI solutions for morphometric analysis, we organise the CEPHA29 Automatic Cephalometric Landmark Detection Challenge in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI 2023). In this context, we provide the largest known publicly available dataset, consisting of 1000 cephalometric X-ray images. We hope that our challenge will not only derive forward research and innovation in automatic cephalometric landmark identification but will also signal the beginning of a new era in the discipline.

We propose RANA, a relightable and articulated neural avatar for the photorealistic synthesis of humans under arbitrary viewpoints, body poses, and lighting. We only require a short video clip of the person to create the avatar and assume no knowledge about the lighting environment. We present a novel framework to model humans while disentangling their geometry, texture, and also lighting environment from monocular RGB videos. To simplify this otherwise ill-posed task we first estimate the coarse geometry and texture of the person via SMPL+D model fitting and then learn an articulated neural representation for photorealistic image generation. RANA first generates the normal and albedo maps of the person in any given target body pose and then uses spherical harmonics lighting to generate the shaded image in the target lighting environment. We also propose to pretrain RANA using synthetic images and demonstrate that it leads to better disentanglement between geometry and texture while also improving robustness to novel body poses. Finally, we also present a new photorealistic synthetic dataset, Relighting Humans, to quantitatively evaluate the performance of the proposed approach.

Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.

Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and limits their real-world application. To address this issue, we present a novel physics-guided motion diffusion model (PhysDiff), which incorporates physical constraints into the diffusion process. Specifically, we propose a physics-based motion projection module that uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically-plausible motion. The projected motion is further used in the next diffusion step to guide the denoising diffusion process. Intuitively, the use of physics in our model iteratively pulls the motion toward a physically-plausible space. Experiments on large-scale human motion datasets show that our approach achieves state-of-the-art motion quality and improves physical plausibility drastically (>78% for all datasets).

Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques could help in combating this problem. In this paper, we demonstrate that a model trained on the time direction of functional neuro-imaging data could help in any downstream task, for example, classifying diseases from healthy controls in fMRI data. We train a Deep Neural Network on Independent components derived from fMRI data using the Independent component analysis (ICA) technique. It learns time direction in the ICA-based data. This pre-trained model is further trained to classify brain disorders in different datasets. Through various experiments, we have shown that learning time direction helps a model learn some causal relation in fMRI data that helps in faster convergence, and consequently, the model generalizes well in downstream classification tasks even with fewer data records.

在这个时代，作为医疗的主要重点，这一时刻已经到来了。尽管令人印象深刻，但已经开发出来检测疾病的多种技术。此时，有一些类型的疾病COVID-19，正常烟，偏头痛，肺病，心脏病，肾脏疾病，糖尿病，胃病，胃病，胃病，骨骼疾病，自闭症是非常常见的疾病。在此分析中，我们根据疾病的症状进行了分析疾病症状的预测。我们研究了一系列症状，并接受了人们的调查以完成任务。已经采用了几种分类算法来训练模型。此外，使用性能评估矩阵来衡量模型的性能。最后，我们发现零件分类器超过了其他分类器。