评估成像中的乳腺癌风险仍然是一个主观过程，在该过程中，放射科医生采用计算机辅助检测（CAD）系统或定性视觉评估来估计乳房密度（PD）。更先进的机器学习（ML）模型已成为量化早期，准确和公平诊断的乳腺癌风险的最有希望的方法，但是医学研究中的这种模型通常仅限于小型单一机构数据。由于患者人口统计和成像特征可能在成像站点之间有很大差异，因此在单机构数据中训练的模型往往不会很好地概括。为了应对这个问题，提出了Mammodl，这是一种开源软件工具，利用UNET体系结构来准确估计乳腺PD和数字乳房X线摄影（DM）的复杂性。通过开放的联合学习（OpenFL）库，该解决方案可以在多个机构的数据集上进行安全培训。 Mammodl是一个比其前任更精简，更灵活的模型，由于对更大，更具代表性的数据集的支持培训，因此具有改进的概括。

Quantum computing (QC) promises significant advantages on certain hard computational tasks over classical computers. However, current quantum hardware, also known as noisy intermediate-scale quantum computers (NISQ), are still unable to carry out computations faithfully mainly because of the lack of quantum error correction (QEC) capability. A significant amount of theoretical studies have provided various types of QEC codes; one of the notable topological codes is the surface code, and its features, such as the requirement of only nearest-neighboring two-qubit control gates and a large error threshold, make it a leading candidate for scalable quantum computation. Recent developments of machine learning (ML)-based techniques especially the reinforcement learning (RL) methods have been applied to the decoding problem and have already made certain progress. Nevertheless, the device noise pattern may change over time, making trained decoder models ineffective. In this paper, we propose a continual reinforcement learning method to address these decoding challenges. Specifically, we implement double deep Q-learning with probabilistic policy reuse (DDQN-PPR) model to learn surface code decoding strategies for quantum environments with varying noise patterns. Through numerical simulations, we show that the proposed DDQN-PPR model can significantly reduce the computational complexity. Moreover, increasing the number of trained policies can further improve the agent's performance. Our results open a way to build more capable RL agents which can leverage previously gained knowledge to tackle QEC challenges.

Arbitrary Style Transfer is a technique used to produce a new image from two images: a content image, and a style image. The newly produced image is unseen and is generated from the algorithm itself. Balancing the structure and style components has been the major challenge that other state-of-the-art algorithms have tried to solve. Despite all the efforts, it's still a major challenge to apply the artistic style that was originally created on top of the structure of the content image while maintaining consistency. In this work, we solved these problems by using a Deep Learning approach using Convolutional Neural Networks. Our implementation will first extract foreground from the background using the pre-trained Detectron 2 model from the content image, and then apply the Arbitrary Style Transfer technique that is used in SANet. Once we have the two styled images, we will stitch the two chunks of images after the process of style transfer for the complete end piece.

Deep neural networks (DNN) are prone to miscalibrated predictions, often exhibiting a mismatch between the predicted output and the associated confidence scores. Contemporary model calibration techniques mitigate the problem of overconfident predictions by pushing down the confidence of the winning class while increasing the confidence of the remaining classes across all test samples. However, from a deployment perspective, an ideal model is desired to (i) generate well-calibrated predictions for high-confidence samples with predicted probability say >0.95, and (ii) generate a higher proportion of legitimate high-confidence samples. To this end, we propose a novel regularization technique that can be used with classification losses, leading to state-of-the-art calibrated predictions at test time; From a deployment standpoint in safety-critical applications, only high-confidence samples from a well-calibrated model are of interest, as the remaining samples have to undergo manual inspection. Predictive confidence reduction of these potentially ``high-confidence samples'' is a downside of existing calibration approaches. We mitigate this by proposing a dynamic train-time data pruning strategy that prunes low-confidence samples every few epochs, providing an increase in "confident yet calibrated samples". We demonstrate state-of-the-art calibration performance across image classification benchmarks, reducing training time without much compromise in accuracy. We provide insights into why our dynamic pruning strategy that prunes low-confidence training samples leads to an increase in high-confidence samples at test time.

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.

已知现代深度神经网络模型将错误地将分布式（OOD）测试数据分类为具有很高信心的分数（ID）培训课程之一。这可能会对关键安全应用产生灾难性的后果。一种流行的缓解策略是训练单独的分类器，该分类器可以在测试时间检测此类OOD样本。在大多数实际设置中，在火车时间尚不清楚OOD的示例，因此，一个关键问题是：如何使用合成OOD样品来增加ID数据以训练这样的OOD检测器？在本文中，我们为称为CNC的OOD数据增强提出了一种新颖的复合腐败技术。 CNC的主要优点之一是，除了培训集外，它不需要任何固定数据。此外，与当前的最新技术（SOTA）技术不同，CNC不需要在测试时间进行反向传播或结合，从而使我们的方法在推断时更快。我们与过去4年中主要会议的20种方法进行了广泛的比较，表明，在OOD检测准确性和推理时间方面，使用基于CNC的数据增强训练的模型都胜过SOTA。我们包括详细的事后分析，以研究我们方法成功的原因，并确定CNC样本的较高相对熵和多样性是可能的原因。我们还通过对二维数据集进行零件分解分析提供理论见解，以揭示（视觉和定量），我们的方法导致ID类别周围的边界更紧密，从而更好地检测了OOD样品。源代码链接：https：//github.com/cnc-ood

社交媒体通常被用作自然灾害期间交流的生命线。传统上，自然灾害推文使用自然灾害的名称从Twitter流进行过滤，并将过滤的推文发送以进行人体注释。人类注释创建用于机器学习模型的标签集的过程是费力的，耗时的，有时不准确的，更重要的是，在大小和实时使用方面不可扩展。在这项工作中，我们使用薄弱的监督来策划一个银标准数据集。为了验证其效用，我们在弱监督的数据上训练机器学习模型，以识别三种不同类型的自然灾害，即地震，飓风和洪水。我们的结果表明，在对手动策划的金标准数据集进行分类时，经过银标准数据集训练的模型大于90％。为了启用可重现的研究和其他下游公用事业，我们为科学界发布了银标准数据集。

本文向许多受访者调查了同时的偏好和度量学习。一组由$ d $二维功能向量和表格的配对比较``项目$ i $都比item $ j $更可取'的项目。我们的模型共同学习了一个距离指标，该指标表征了人群对项目相似性的一般度量，以及每个用户反映其个人喜好的潜在理想点。该模型具有捕获个人喜好的灵活性，同时享受在人群中摊销的度量学习样本成本。我们首先以无声的，连续的响应设置（即等于项目距离的差异）来研究这个问题，以了解学习的基本限制。接下来，我们建立了嘈杂的预测错误保证，可以从人类受访者那里收集诸如二进制测量值，并显示样品复杂性在基础度量较低时如何提高。最后，我们根据响应分布的假设建立恢复保证。我们在模拟数据和大量用户的颜色偏好判断数据集上演示了模型的性能。

在医学图像处理的领域中，医疗设备制造商在许多情况下通过仅运输编译软件来保护他们的知识产权，即可以执行的二进制代码，但难以通过潜在的攻击者理解。在本文中，我们研究了该过程能够保护图像处理算法的程度如何。特别是，我们研究了从双能量CT数据的单能量图像和碘映射的计算是否可以通过机器学习方法反向设计。我们的结果表明，两者只能在所有研究中以非常高的精度使用一个单片图像作为训练数据，以非常高的精度，在所有调查的情况下，结构相似度大于0.98。

在当前的数字化时代，在线支付系统吸引了相当大的兴趣。提高支付系统的效率很重要，因为它对企业的收入有很大影响。网关是每次交易都被路由的付款系统的一个组成部分。在在线支付系统中，付款处理器通过各种配置与这些网关集成，例如定价，方法，风险检查等。这些配置称为终端。每个网关都可以有多个与之相关的终端。通过最佳终端路由付款交易至关重要，以提高付款交易的概率成功。机器学习（ML）和人工智能（AI）技术可用于基于先前的性能和各种支付相关属性准确地预测最佳终端。我们设计了一种由静态和动态模块组成的管道。静态模块使用静态规则和预测网关下降时间的逻辑回归模型进行终端初始过滤。随后，动态模块基于成功率，支付属性，时间滞后等来计算大量的新颖功能以准确地模拟终端行为。使用反馈循环实时使用自适应时间衰减速率算法更新这些功能，并传递给随机林分类器以预测每个终端的成功概率。该管道目前正在razorpay在Razorpay提供数百万次交易中实时生产，并在所有支付方法（信用卡，借记卡，UPI，净银行）的成功率上有4-6 \％。这使得我们的支付系统更加适应表现下降，这已经提高了用户体验，灌输了更多信任商家，并提升了业务的收入。