由于生成对抗网络（GAN）的突破，3D可控制的肖像合成已大大提高。但是，用精确的3D控制操纵现有的面部图像仍然具有挑战性。虽然连接gan倒置和3D感知，但噪声到图像是一种直接的解决方案，但它效率低下，可能导致编辑质量明显下降。为了填补这一空白，我们提出了3D-FM GAN，这是一个专门为3D可控制的面部操作设计的新型有条件GAN框架，并且在端到端学习阶段后不需要任何调整。通过小心地编码输入面图像和3D编辑的基于物理的渲染，我们的图像生成器提供了高质量，具有身份的3D控制面部操纵。为了有效地学习这种新颖的框架，我们制定了两种基本的训练策略和一种新颖的乘法共同调制体系结构，可在天真的方案上显着改善。通过广泛的评估，我们表明我们的方法在各种任务上的表现优于先前的艺术，具有更好的编辑性，更强的身份保存和更高的照片真实性。此外，我们在大型姿势编辑和室外图像上展示了设计更好的概括性。

神经网络的结构设计对于深度学习的成功至关重要。尽管大多数先前在进化学习方面的工作旨在直接搜索网络的结构，但在另一个有希望的轨道频道修剪中，几乎没有尝试过，最近在设计有效的深度学习模型方面取得了重大进展。实际上，先前的修剪方法采用人造修剪功能来评估渠道对渠道修剪的重要性，这需要域知识，并且可以是最佳的。为此，我们开创了使用遗传编程（GP）自动发现强度修剪指标的。具体而言，我们制作了一个新颖的设计空间来表达高质量和可转移的修剪功能，从而确保了端到端的演化过程，在该过程中，进化功能不需要手动修改以使其在演变后的传递性。与先前的方法不同，我们的方法可以提供紧凑的修剪网络，以提供有效的推理和新颖的封闭形式的修剪指标，这些指标在数学上可以解释，因此可以推广到不同的修剪任务。尽管演变是在小型数据集上进行的，但我们的功能在应用于更具挑战性的数据集时显示出令人鼓舞的结果，与演化过程中使用的功能不同。例如，在ILSVRC-2012上，进化的函数可获得最新的修剪结果。

A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variability across labs and inherent problems of the data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by the Atrous Spatial Pyramid Pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: i) background, ii) heart muscle, iii) blood and iv) scar areas. New models were compared with state-of-art models and manual quantification. Our models showed favorable performance in global segmentation and scar tissue detection relative to state-of-the-art work, including a four-fold better performance in matching scar pixels to contours produced by clinicians.

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

Some recent pieces of work in the Machine Learning (ML) literature have demonstrated the usefulness of assessing which observations are hardest to have their label predicted accurately. By identifying such instances, one may inspect whether they have any quality issues that should be addressed. Learning strategies based on the difficulty level of the observations can also be devised. This paper presents a set of meta-features that aim at characterizing which instances of a dataset are hardest to have their label predicted accurately and why they are so, aka instance hardness measures. Both classification and regression problems are considered. Synthetic datasets with different levels of complexity are built and analyzed. A Python package containing all implementations is also provided.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Previous virtual try-on methods usually focus on aligning a clothing item with a person, limiting their ability to exploit the complex pose, shape and skin color of the person, as well as the overall structure of the clothing, which is vital to photo-realistic virtual try-on. To address this potential weakness, we propose a fill in fabrics (FIFA) model, a self-supervised conditional generative adversarial network based framework comprised of a Fabricator and a unified virtual try-on pipeline with a Segmenter, Warper and Fuser. The Fabricator aims to reconstruct the clothing image when provided with a masked clothing as input, and learns the overall structure of the clothing by filling in fabrics. A virtual try-on pipeline is then trained by transferring the learned representations from the Fabricator to Warper in an effort to warp and refine the target clothing. We also propose to use a multi-scale structural constraint to enforce global context at multiple scales while warping the target clothing to better fit the pose and shape of the person. Extensive experiments demonstrate that our FIFA model achieves state-of-the-art results on the standard VITON dataset for virtual try-on of clothing items, and is shown to be effective at handling complex poses and retaining the texture and embroidery of the clothing.

在同时定位和映射（SLAM）中，环路闭合检测（LCD）对于在识别先前访问的地方时最小化漂移至关重要。视觉袋（VBOW）一直是许多最先进的大满贯系统的LCD算法。它使用一组视觉功能来提供健壮的位置识别，但无法感知特征点之间的语义或空间关系。先前的工作主要集中在解决这些问题上，通过将VBOW与现场对象的语义和空间信息相结合。但是，他们无法利用局部视觉特征的空间信息，并且缺乏统一语义对象和视觉特征的结构，因此限制了两个组件之间的共生。本文提出了SymbiolCD2，该symbiolcd2创建了一个统一的图形结构，以在共生的方式集成语义对象和视觉特征。我们的新型基于图的LCD系统通过应用具有时间限制的Weisfeiler-Lehman图内核来利用统一的图结构，以稳健地预测循环闭合候选者。对所提出的系统的评估表明，具有结合语义对象和视觉特征的统一图结构提高了LCD预测精度，这说明了所提出的图形结构在这两个互补组件之间提供了强烈的共生。它还优于其他机器学习算法 - 例如SVM，决策树，随机森林，神经网络和基于GNN的图形匹配网络。此外，它在比最先进的SLAM系统的早期检测循环闭合候选方面表现出良好的性能，这表明统一图结构的扩展语义和空间意识会显着影响LCD的性能。