我们在本文中提出了一个新的面部视频压缩范式。我们利用诸如stylegan之类的gan的生成能力来表示和压缩视频，包括内部和间压缩。每个帧都在StyleGAN的潜在空间中倒置，从中学习了最佳压缩。为此，使用归一化流量模型学习了差异潜在表示，可以在其中优化熵模型以用于图像编码。此外，我们提出了一种新的感知损失，比其他同行更有效。最后，在先前构造的潜在表示中还学习了用于视频间编码的熵模型。我们的方法（SGANC）很简单，训练的速度更快，并且与最新的编解码器（例如VTM，AV1和最近的深度学习技术）相比，为图像和视频编码提供了更好的结果。特别是，它在低比特速率下极大地最大程度地减少了感知失真。

事实证明，通过倒转和操纵与输入真实图像相对应的潜在代码，生成的对抗网络（GAN）对于图像编辑非常有效。这种编辑属性来自潜在空间的分离性质。在本文中，我们确定面部属性分离不是最佳的，因此依靠线性属性分离的面部编辑是有缺陷的。因此，我们建议通过监督改善语义分解。我们的方法包括使用归一化流量学习代理潜在表示，我们证明这会为面部图像编辑提供更有效的空间。

端到端的深层训练模型将超过视频和图像上传统手工制作的压缩技术的性能。核心思想是学习一个非线性转换，以深度神经网络建模，将输入图像映射到潜在空间中，并与潜在分布的熵模型共同映射到潜在的空间中。解码器也被学习为可训练的深层网络，重建图像可以测量失真。这些方法强迫潜在遵循一些先前的分布。由于这些先验是通过在整个训练组中优化学习的，因此性能平均是最佳的。但是，它不能完全适合每个新实例，因此可以通过扩大位流损坏压缩性能。在本文中，我们提出了一种简单但有效的基于实例的参数化方法，以较小的成本减少此摊销差距。所提出的方法适用于任何端到端的压缩方法，将压缩比特率提高了1％，而不会对重建质量产生任何影响。

我们在一个或多个镜头中介绍FacialFilmroll，一种用于空间和时间一致地编辑面的解决方案。我们建立在未包装马赛克[Rav-Acha等。2008年]通过专门谈谈。我们利用最近的技术适应单眼视频的3D面部模型（i）提高了Edition的Mosaic的质量，并允许从一个拍摄的射击自动转移到同一演员的其他镜头。我们解释了FacialFilmroll如何集成在生产后设施中。最后，我们在高分辨率视频上使用FacialFilmroll提供视频编辑结果。

Recently, deep networks have shown impressive performance for the segmentation of cardiac Magnetic Resonance Imaging (MRI) images. However, their achievement is proving slow to transition to widespread use in medical clinics because of robustness issues leading to low trust of clinicians to their results. Predicting run-time quality of segmentation masks can be useful to warn clinicians against poor results. Despite its importance, there are few studies on this problem. To address this gap, we propose a quality control method based on the agreement across decoders of a multi-view network, TMS-Net, measured by the cosine similarity. The network takes three view inputs resliced from the same 3D image along different axes. Different from previous multi-view networks, TMS-Net has a single encoder and three decoders, leading to better noise robustness, segmentation performance and run-time quality estimation in our experiments on the segmentation of the left atrium on STACOM 2013 and STACOM 2018 challenge datasets. We also present a way to generate poor segmentation masks by using noisy images generated with engineered noise and Rician noise to simulate undertraining, high anisotropy and poor imaging settings problems. Our run-time quality estimation method show a good classification of poor and good quality segmentation masks with an AUC reaching to 0.97 on STACOM 2018. We believe that TMS-Net and our run-time quality estimation method has a high potential to increase the thrust of clinicians to automatic image analysis tools.

Over the last decade, an approach that has gained a lot of popularity to tackle non-parametric testing problems on general (i.e., non-Euclidean) domains is based on the notion of reproducing kernel Hilbert space (RKHS) embedding of probability distributions. The main goal of our work is to understand the optimality of two-sample tests constructed based on this approach. First, we show that the popular MMD (maximum mean discrepancy) two-sample test is not optimal in terms of the separation boundary measured in Hellinger distance. Second, we propose a modification to the MMD test based on spectral regularization by taking into account the covariance information (which is not captured by the MMD test) and prove the proposed test to be minimax optimal with a smaller separation boundary than that achieved by the MMD test. Third, we propose an adaptive version of the above test which involves a data-driven strategy to choose the regularization parameter and show the adaptive test to be almost minimax optimal up to a logarithmic factor. Moreover, our results hold for the permutation variant of the test where the test threshold is chosen elegantly through the permutation of the samples. Through numerical experiments on synthetic and real-world data, we demonstrate the superior performance of the proposed test in comparison to the MMD test.

Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP). From BERT to BLOOM, LLMs have surpassed state-of-the-art results in various natural language tasks such as question answering, summarization, and text generation. Many ongoing efforts focus on understanding LLMs' capabilities, including their knowledge of the world, syntax, and semantics. However, extending the textual prowess of LLMs to symbolic reasoning has been slow and predominantly focused on tackling problems related to the mathematical field. In this paper, we explore the use of LLMs for automated planning - a branch of AI concerned with the realization of action sequences (plans) to achieve a goal, typically executed by intelligent agents, autonomous robots, and unmanned vehicles. We introduce Plansformer; an LLM fine-tuned on planning problems and capable of generating plans with favorable behavior in terms of correctness and length with reduced knowledge-engineering efforts. We also demonstrate the adaptability of Plansformer in solving different planning domains with varying complexities, owing to the transfer learning abilities of LLMs. For one configuration of Plansformer, we achieve ~97% valid plans, out of which ~95% are optimal for Towers of Hanoi - a puzzle-solving domain.

Chatbots, or bots for short, are multi-modal collaborative assistants that can help people complete useful tasks. Usually, when chatbots are referenced in connection with elections, they often draw negative reactions due to the fear of mis-information and hacking. Instead, in this paper, we explore how chatbots may be used to promote voter participation in vulnerable segments of society like senior citizens and first-time voters. In particular, we build a system that amplifies official information while personalizing it to users' unique needs transparently. We discuss its design, build prototypes with frequently asked questions (FAQ) election information for two US states that are low on an ease-of-voting scale, and report on its initial evaluation in a focus group. Our approach can be a win-win for voters, election agencies trying to fulfill their mandate and democracy at large.

Atrial Fibrillation (AF) is characterized by disorganised electrical activity in the atria and is known to be sustained by the presence of regions of fibrosis (scars) or functional cellular remodeling, both of which may lead to areas of slow conduction. Estimating the effective conductivity of the myocardium and identifying regions of abnormal propagation is therefore crucial for the effective treatment of AF. We hypothesise that the spatial distribution of tissue conductivity can be directly inferred from an array of concurrently acquired contact electrograms (EGMs). We generate a dataset of simulated cardiac AP propagation using randomised scar distributions and a phenomenological cardiac model and calculate contact electrograms at various positions on the field. A deep neural network, based on a modified U-net architecture, is trained to estimate the location of the scar and quantify conductivity of the tissue with a Jaccard index of $91$%. We adapt a wavelet-based surrogate testing analysis to confirm that the inferred conductivity distribution is an accurate representation of the ground truth input to the model. We find that the root mean square error (RMSE) between the ground truth and our predictions is significantly smaller ($p_{val}=0.007$) than the RMSE between the ground truth and surrogate samples.

自动驾驶汽车必须能够可靠地处理不利的天气条件（例如，雪地）安全运行。在本文中，我们研究了以不利条件捕获的转动传感器输入（即图像）的想法，将其下游任务（例如，语义分割）可以达到高精度。先前的工作主要将其作为未配对的图像到图像翻译问题，因为缺乏在完全相同的相机姿势和语义布局下捕获的配对图像。虽然没有完美对准的图像，但可以轻松获得粗配上的图像。例如，许多人每天在好天气和不利的天气中驾驶相同的路线；因此，在近距离GPS位置捕获的图像可以形成一对。尽管来自重复遍历的数据不太可能捕获相同的前景对象，但我们认为它们提供了丰富的上下文信息来监督图像翻译模型。为此，我们提出了一个新颖的训练目标，利用了粗糙的图像对。我们表明，我们与一致的训练方案可提高更好的图像翻译质量和改进的下游任务，例如语义分割，单眼深度估计和视觉定位。