Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image registration for abdominal imaging. Methods: Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting. Results: Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance on the latter application, removing the need for a large dataset to yield satisfactory performance. Dynamic loss weighting also marginally improved performance, all without impacting inference runtime. Conclusion: Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph. In future work, our framework, DDMR, should be validated on different datasets to further assess its value.
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A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.
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Domain adaptation has been vastly investigated in computer vision but still requires access to target images at train time, which might be intractable in some conditions, especially for long-tail samples. In this paper, we propose the task of `Prompt-driven Zero-shot Domain Adaptation', where we adapt a model trained on a source domain using only a general textual description of the target domain, i.e., a prompt. First, we leverage a pretrained contrastive vision-language model (CLIP) to optimize affine transformations of source features, bringing them closer to target text embeddings, while preserving their content and semantics. Second, we show that augmented features can be used to perform zero-shot domain adaptation for semantic segmentation. Experiments demonstrate that our method significantly outperforms CLIP-based style transfer baselines on several datasets for the downstream task at hand. Our prompt-driven approach even outperforms one-shot unsupervised domain adaptation on some datasets, and gives comparable results on others. The code is available at https://github.com/astra-vision/PODA.
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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.
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ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。
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人工智能使在各个领域的问题上实施了更准确，更有效的解决方案。在农业部门，主要需求之一是在始终了解农作物所占据或不占领的土地，以提高生产和盈利能力。传统的计算方法需要手动收集数据，并在现场亲自收集，从而导致较高的人工成本，执行时间和结果不准确。目前的工作提出了一种基于深度学习技术的新方法，该技术与常规编程相辅相成，以确定人口稠密和人口不足的作物区域的面积。我们认为作为案例研究是厄瓜多尔种植和收获甘蔗中最知名的公司之一。该策略结合了生成的对抗神经网络（GAN），该网络在天然和城市景观的航空照片数据集上进行了训练，以改善图像分辨率；卷积神经网络（CNN）在甘蔗地块的航空照片数据集上训练，以区分人口稠密的农作物区域；以及以百分比方式计算区域的标准图像处理模块。进行的实验表明，航空照片的质量有显着改善，以及人口稠密的农作物区域和未吞噬的作物区域之间的显着差异，因此，耕种和未经耕种的地区更准确。所提出的方法可以扩展到可能的害虫，杂草植被，动态作物发展以及定性和定量质量控制的检测。
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神经胶质瘤是由不同高度异质组织学子区域组成的脑肿瘤。鉴定相关肿瘤子结构的图像分析技术具有改善患者诊断，治疗和预后的高潜力。但是，由于神经胶质瘤的异质性高，分割任务目前是医学图像分析领域的主要挑战。在目前的工作中，研究了由神经胶质瘤的多模式MRI扫描组成的2018年脑肿瘤分割（BRAT）挑战的数据库。提出了基于卷积神经网络（CNN）的设计和应用的分割方法，并结合了原始的后处理技术，其计算需求较低。后处理技术是分割中获得的结果的主要负责。分段区域是整个肿瘤，肿瘤核和增强的肿瘤核，分别获得等于0.8934、0.8376和0.8113的平均骰子系数。这些结果达到了由挑战的获胜者确定的神经胶质瘤分割的最新现状。
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在计算和数据方面，大型语言模型的预培训通常需要大量资源。经常使用的Web源（例如Common Crawl）可能包含足够的噪声，以使这种预训练的亚地区。在这项工作中，我们尝试了西班牙语版本的MC4的不同采样方法，并提出了一种新颖的以数据为中心的技术，我们将其命名为$ \ textit {Perplexity sampling} $，该技术可实现大约一半的语言模型的预培训步骤并使用五分之一的数据。最终的模型与当前的最新机构相当，甚至可以为某些任务获得更好的结果。我们的工作证明了变形金刚的多功能性，并为小型团队以有限的预算培训模型铺平了道路。我们的型号可在此$ \ href {https://huggingface.co/bertin-project} {url} $中获得。
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联邦学习（FL）已成为一种前瞻性解决方案，可促进对高性能的集中模型的培训，而不会损害用户的隐私。尽管成功，但目前的研究受到了在实验初期建立现实的大规模FL系统的可能性的限制。仿真可以帮助加速这一过程。为了促进异构客户的有效可扩展的FL模拟，我们设计和实施ProteA，这是使用FL框架花朵在联合系统中灵活且轻巧的客户型分析组件。它允许自动收集系统级统计信息并估算每个客户所需的资源，从而以资源感知方式运行模拟。结果表明，我们的设计成功地增加了1.66 $ \ times $ $更快的壁挂时间和2.6 $ \ times $更好的GPU利用率的平行性，这可以对异构客户进行大规模实验。
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尽管结果令人印象深刻，但深度学习的技术还引起了经常在数据中心进行的培训程序引起的严重隐私和环境问题。作为回应，已经出现了集中培训的替代方案，例如联邦学习（FL）。也许出乎意料的是，FL开始在全球范围内部署，这些公司必须遵守源自倡导隐私保护的政府和社会团体的新法律要求和政策。 \ textit {但是，与FL有关的潜在环境影响仍然不清楚和未开发。本文提供了有关佛罗里达碳足迹的首次系统研究。然后，我们将FL的碳足迹与传统的集中学习进行了比较。我们的发现表明，根据配置，FL可以比集中的机器学习高达两个数量级。但是，在某些情况下，由于嵌入式设备的能源消耗减少，它可以与集中学习相提并论。我们使用FL进行了不同类型的数据集，设置和各种深度学习模型的广泛实验。最后，我们强调并将报告的结果与FL的未来挑战和趋势联系起来，以减少其环境影响，包括算法效率，硬件能力和更强的行业透明度。
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