在本文中，使用聚类和阈值算法实现了DIBA数据集细菌属和物种的半自动注释。深度学习模型经过训练，以实现细菌物种的语义分割和分类。分类精度达到95％。深度学习模型在生物医学图像处理中发现了巨大的应用。从革兰氏阴性微观图像中自动分割细菌对于诊断呼吸道和尿路感染，检测癌症等至关重要。深度学习将有助于生物学家在更少的时间内获得可靠的结果。此外，可以减少许多人类干预措施。这项工作可能有助于检测尿液涂片图像，痰液涂片图像等的细菌，以诊断尿路感染，结核病，肺炎等。

Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learning (ML) models, and numerous other papers propose defenses that can withstand most attacks. However, abundant real-world evidence suggests that actual attackers use simple tactics to subvert ML-driven systems, and as a result security practitioners have not prioritized adversarial ML defenses. Motivated by the apparent gap between researchers and practitioners, this position paper aims to bridge the two domains. We first present three real-world case studies from which we can glean practical insights unknown or neglected in research. Next we analyze all adversarial ML papers recently published in top security conferences, highlighting positive trends and blind spots. Finally, we state positions on precise and cost-driven threat modeling, collaboration between industry and academia, and reproducible research. We believe that our positions, if adopted, will increase the real-world impact of future endeavours in adversarial ML, bringing both researchers and practitioners closer to their shared goal of improving the security of ML systems.

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers decision-making. A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans. The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid. A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.

Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: a) it allows for online real-time monitoring of a dynamical system; b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; c) it is computationally fast and less intensive than the popular Extended Dynamic Mode Decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.

Pairwise compatibility measure (CM) is a key component in solving the jigsaw puzzle problem (JPP) and many of its recently proposed variants. With the rapid rise of deep neural networks (DNNs), a trade-off between performance (i.e., accuracy) and computational efficiency has become a very significant issue. Whereas an end-to-end DNN-based CM model exhibits high performance, it becomes virtually infeasible on very large puzzles, due to its highly intensive computation. On the other hand, exploiting the concept of embeddings to alleviate significantly the computational efficiency, has resulted in degraded performance, according to recent studies. This paper derives an advanced CM model (based on modified embeddings and a new loss function, called hard batch triplet loss) for closing the above gap between speed and accuracy; namely a CM model that achieves SOTA results in terms of performance and efficiency combined. We evaluated our newly derived CM on three commonly used datasets, and obtained a reconstruction improvement of 5.8% and 19.5% for so-called Type-1 and Type-2 problem variants, respectively, compared to best known results due to previous CMs.

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.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

了解强化学习（RL）代理的新兴行为可能很困难，因为这种代理通常使用高度复杂的决策程序在复杂的环境中进行训练。这引起了RL中解释性的多种方法，旨在调和可能在主体行为与观察者预期的行为之间产生的差异。最近的方法取决于域知识，这可能并非总是可用的，分析代理商的策略，或者是对基础环境的特定要素的分析，通常被建模为马尔可夫决策过程（MDP）。我们的主要主张是，即使基本的MDP尚不完全了解（例如，尚未准确地了解过渡概率），也没有由代理商维护（即，在使用无模型方法时），但仍可以利用它为自动生成解释。为此，我们建议使用以前在文献中使用的正式MDP抽象和转换来加快寻找最佳策略的搜索，以自动产生解释。由于这种转换通常基于环境的符号表示，因此它们可能代表了预期和实际代理行为之间差距的有意义的解释。我们正式定义了这个问题，建议一类可用于解释新兴行为的转换，并提出了有效搜索解释的方法。我们演示了一组标准基准测试的方法。

三维荧光显微镜通常遭受各向异性的影响，沿轴向方向的分辨率低于侧面成像平面内的分辨率。我们通过提出双周期来解决此问题，这是双环荧光图像的关节反卷积和融合的新框架。受到最近的神经清性方法的启发，双周期被设计为一种循环一致的生成网络，通过结合双视发电机和先前引导的退化模型，以自我监督的方式训练。我们在合成数据和真实数据上验证双周期，显示其最先进的性能，而无需任何外部培训数据。

光学计算是一种新兴技术，用于下一代高效人工智能（AI），其速度和效率超高。电磁场模拟对于光子设备和电路的设计，优化和验证至关重要。但是，昂贵的数值模拟显着阻碍了光子电路设计循环中的可扩展性和转环。最近，已经提出了物理信息的神经网络来预测具有预定义参数的部分微分方程（PDE）的单个实例的光场解。它们复杂的PDE公式和缺乏有效的参数化机制限制了其在实际模拟方案中的灵活性和概括。在这项工作中，首次提出了一个被称为Neurolight的物理敏捷神经操作员框架，以学习一个频率域的麦克斯韦PDE家族，以进行超快速的参数光子设备模拟。我们通过几种新技术来平衡神经照明的效率和概括。具体而言，我们将不同的设备离散到统一域中，代表具有紧凑型波的参数PDE，并通过掩盖的源建模编码入射光。我们使用参数效率高的跨形神经块设计模型，并采用基于叠加的增强来进行数据效率学习。通过这些协同方法，神经亮像可以概括为大量的看不见的模拟设置，比数值求解器显示了2个磁性的模拟速度，并且比先前的神经网络模型优于降低54％的预测误差，而降低了约44％的参数。 。我们的代码可在https://github.com/jeremiemelo/neurolight上找到。