Generative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more complex, they require large amounts of data to perform accurately. In medical image analysis, such generative models play a crucial role as the available data is limited due to challenges related to data privacy, lack of data diversity, or uneven data distributions. In this paper, we present a method to generate brain tumor MRI images using generative adversarial networks. We have utilized StyleGAN2 with ADA methodology to generate high-quality brain MRI with tumors while using a significantly smaller amount of training data when compared to the existing approaches. We use three pre-trained models for transfer learning. Results demonstrate that the proposed method can learn the distributions of brain tumors. Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues. The approach can addresses the limited data availability by generating realistic-looking brain MRI with tumors. The code is available at: ~\url{https://github.com/rizwanqureshi123/Brain-Tumor-Synthetic-Data}.

In recent years, social media has been widely explored as a potential source of communication and information in disasters and emergency situations. Several interesting works and case studies of disaster analytics exploring different aspects of natural disasters have been already conducted. Along with the great potential, disaster analytics comes with several challenges mainly due to the nature of social media content. In this paper, we explore one such challenge and propose a text classification framework to deal with Twitter noisy data. More specifically, we employed several transformers both individually and in combination, so as to differentiate between relevant and non-relevant Twitter posts, achieving the highest F1-score of 0.87.

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.

Medical professionals frequently work in a data constrained setting to provide insights across a unique demographic. A few medical observations, for instance, informs the diagnosis and treatment of a patient. This suggests a unique setting for meta-learning, a method to learn models quickly on new tasks, to provide insights unattainable by other methods. We investigate the use of meta-learning and robustness techniques on a broad corpus of benchmark text and medical data. To do this, we developed new data pipelines, combined language models with meta-learning approaches, and extended existing meta-learning algorithms to minimize worst case loss. We find that meta-learning on text is a suitable framework for text-based data, providing better data efficiency and comparable performance to few-shot language models and can be successfully applied to medical note data. Furthermore, meta-learning models coupled with DRO can improve worst case loss across disease codes.

Learning long-horizon tasks such as navigation has presented difficult challenges for successfully applying reinforcement learning. However, from another perspective, under a known environment model, methods such as sampling-based planning can robustly find collision-free paths in environments without learning. In this work, we propose Control Transformer which models return-conditioned sequences from low-level policies guided by a sampling-based Probabilistic Roadmap (PRM) planner. Once trained, we demonstrate that our framework can solve long-horizon navigation tasks using only local information. We evaluate our approach on partially-observed maze navigation with MuJoCo robots, including Ant, Point, and Humanoid, and show that Control Transformer can successfully navigate large mazes and generalize to new, unknown environments. Additionally, we apply our method to a differential drive robot (Turtlebot3) and show zero-shot sim2real transfer under noisy observations.

联合学习（FL）旨在对多个数据所有者持有的分布式数据执行隐私的机器学习。为此，FL要求数据所有者在本地执行培训，并与中央服务器共享梯度更新（而不是私人输入），然后将其安全地汇总在多个数据所有者上。尽管汇总本身并不能证明提供隐私保护，但先前的工作表明，如果批处理大小足够大，则足够了。在本文中，我们提出了鸡尾酒会攻击（CPA），与先前的信念相反，能够从汇总的渐变中恢复私人输入，这是批量较大的大小。 CPA利用了至关重要的见解，即来自完全连接的层的总梯度是其输入的线性组合，这使我们将梯度反演作为盲源分离（BSS）问题（非正式地称为鸡尾酒会问题）。我们适应独立的组件分析（ICA） - BSS问题的经典解决方案 - 恢复针对完全连接和卷积网络的私人输入，并表明CPA明显优于先前的梯度反转攻击，对成像网的输入量表，并表现出Imagenet大小的输入的范围最高可达1024的大批量。

我们研究了使用社交媒体数据预测加密货币未来表现的问题。我们提出了一个新模型，以根据与社交媒体帖子的互动来衡量用户与社交媒体讨论的主题的参与。该模型克服了以前的卷和基于情感的方法的局限性。我们使用此模型来估计2019年至2021年之间使用来自加密货币存在的第一个月的数据在2019年至2021年之间创建的48个加密货币的参与系数。我们发现加密货币的未来回报取决于参与系数。参与系数太低或太高的加密货币的回报较低。低参与系数表明缺乏兴趣，而高参与系数信号是人工活动，这可能来自自动化的bot。我们测量了加密货币的机器人柱数量，并发现通常，具有更多机器人柱的加密货币的未来回报较低。尽管未来的回报取决于机器人活动和参与系数，但依赖性对于参与系数最强，尤其是对于短期收益。我们显示，以超过固定阈值的参与系数选择加密货币的简单投资策略在几个月的固定时间内表现良好。

对于旨在提供家庭服务，搜索和救援，狭窄的检查和医疗援助的机器人来说，在未知，混乱的环境中进行积极的感测和计划是一个公开挑战。尽管存在许多主动感应方法，但它们通常考虑开放空间，假设已知设置，或者大多不概括为现实世界的场景。我们介绍了活跃的神经传感方法，该方法通过手持摄像头生成机器人操纵器的运动学可行视点序列，以收集重建基础环境所需的最小观测值。我们的框架积极收集视觉RGBD观测值，将它们汇总到场景表示中，并执行对象形状推断，以避免与环境的不必要的机器人相互作用。我们使用域随机化训练我们的合成数据方法，并通过SIM到实现的传递成功地执行了其成功执行，以重建狭窄，覆盖的，现实的机柜环境，这些环境杂乱无章。由于周围的障碍物和环境较低的照明条件，自然机柜场景对机器人运动和场景重建构成了重大挑战。然而，尽管设置不利，但就各种环境重建指标（包括计划速度，观点数量和整体场景覆盖）而言，我们的方法与基线相比表现出高性能。

由于Facebook重命名为Meta，因此对Metaverse是什么，其工作原理以及可能利用它的可能方法进行了很多关注，辩论和探索。可以预料，Metaverse将成为迅速新兴技术，用户酶，能力和经验的连续性，这些技术将弥补这一目标的下一个互联网发展。一些研究人员已经调查了有关人工智能（AI）和无线通信的文献，以实现元评估。但是，由于技术的迅速出现，需要对AI，6G和两者在实现元元体验中的AI，6G和Nexus的作用进行全面和深入的评论。因此，在这项调查中，我们首先介绍了增强现实（AR），虚拟现实（VR），混合现实（MR）和空间计算的背景和持续进展，其次是AI和6G的技术方面。然后，我们通过回顾深度学习，计算机视觉和边缘AI中最新的AI来调查AI在元评估中的作用。接下来，我们研究了B5G/6G对Metaverse的有前途的服务，然后确定AI在6G网络和6G网络中的作用在AI中为支持元应用程序。最后，我们征集了现有的和潜在的应用程序，用户赛和项目，以强调元元中进步的重要性。此外，为了向研究人员提供潜在的研究指示，我们从上述技术的文献综述中提出了挑战，研究差距和经验教训。

我们旨在教机器人通过观看单个视频演示来执行简单的对象操纵任务。为了实现这一目标，我们提出了一种优化方法，该方法输出了一个粗糙且在时间上不断发展的3D场景，以模仿输入视频中所示的动作。与以前的工作相似，可区分的渲染器可确保3D场景和2D视频之间的感知忠诚度。我们的关键新颖性在于包含一种可区分方法来求解一组普通微分方程（ODE），该方程使我们能够近似建模物理定律，例如重力，摩擦，手动对象或对象对象相互作用。这不仅使我们能够显着提高估计的手和物体状态的质量，而且还可以产生可接受的轨迹，这些轨迹可以直接转化为机器人，而无需进行昂贵的强化学习。我们在3D重建任务上评估了我们的方法，该任务由54个视频演示组成，这些视频演示来自9个动作，例如将某物从右到左拉或将某物放在某物前。我们的方法将以前的最先进的方法提高了近30％，在涉及两个物体（例如将某物）的物理互动的特别挑战性的动作上表现出了卓越的质量。最后，我们在Franka Emika Panda机器人上展示了博学的技能。