我们提出了一个具有物理信息的神经网络，作为生物样品层析成像重建的正向模型。我们证明，通过用Helmholtz方程训练该网络作为物理损失，我们可以准确预测散射场。可以证明，可以对不同的样本进行微调的验证网络，并用于与其他数值解决方案更快地解决散射问题。我们通过数值和实验结果评估我们的方法。我们的物理知识神经网络可以推广到任何前进和反向散射问题。

光学衍射断层扫描（ODT）是一种新兴的3D成像技术，用于半透明样品的折射率（RI）的3D重建。已经提出了各种逆模型，以基于对不同样品（例如BORN和RYTOV近似）的全息检测来重建3D RI。但是，这种近似通常会遭受所谓的缺失键问题，从而导致沿光轴的最终重建伸长。已经提出了不同的迭代方案，以解决依靠物理前向模型和旨在填充K空间的错误函数的丢失锥问题，从而消除缺失的问题问题并达到更好的重建精度。在本文中，我们提出了一种使用3D神经网络（NN）的不同方法。 NN经过基于光波传播物理的物理模型得出的成本函数训练。 3D NN以3D RI重建（即出生或Rytov）的初始猜测开始，并旨在根据错误函数重建更好的3D重建。通过这种技术，可以对NN进行训练，而无需任何示例，即不适当的重建（出生或Rytov）与地面真相（真实形状）之间的关系。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.

由于对人工智能的大量解释，我们日常生活的各个领域都使用了机器学习技术。在世界上，在许多情况下，可以预防简单的犯罪，甚至可能发生或找到对此负责的人。面孔是我们拥有的一个独特特征，并且可以轻松区分许多其他物种。但是，不仅不同的物种，它在确定与我们同一物种的人的人类中也起着重要作用。关于这个关键功能，如今最常发生一个问题。当相机指向时，它无法检测到一个人的脸，并且变成了糟糕的图像。另一方面，在安装了抢劫和安全摄像头的地方，由于较低的摄像头，强盗的身份几乎无法区分。但是，仅制作出出色的算法来工作和检测面部就会降低硬件的成本，而专注于该领域的成本并不多。面部识别，小部件控制等可以通过正确检测到面部来完成。这项研究旨在创建和增强正确识别面孔的机器学习模型。总共有627个数据是从孟加拉国不同的四个天使的面孔中收集的。在这项工作中，CNN，Harr Cascade，Cascaded CNN，Deep CNN和MTCNN是实施的五种机器学习方法，以获得我们数据集的最佳准确性。创建和运行模型后，多任务卷积神经网络（MTCNN）通过培训数据而不是其他机器学习模型实现了96.2％的最佳模型精度。

手写数字识别（HDR）是光学特征识别（OCR）领域中最具挑战性的任务之一。不管语言如何，HDR都存在一些固有的挑战，这主要是由于个人跨个人的写作风格的变化，编写媒介和环境的变化，无法在反复编写任何数字等时保持相同的笔触。除此之外，特定语言数字的结构复杂性可能会导致HDR的模棱两可。多年来，研究人员开发了许多离线和在线HDR管道，其中不同的图像处理技术与传统的机器学习（ML）基于基于的和/或基于深度学习（DL）的体系结构相结合。尽管文献中存在有关HDR的广泛审查研究的证据，例如：英语，阿拉伯语，印度，法尔西，中文等，但几乎没有对孟加拉人HDR（BHDR）的调查，这缺乏对孟加拉语HDR（BHDR）的研究，而这些调查缺乏对孟加拉语HDR（BHDR）的研究。挑战，基础识别过程以及可能的未来方向。在本文中，已经分析了孟加拉语手写数字的特征和固有的歧义，以及二十年来最先进的数据集的全面见解和离线BHDR的方法。此外，还详细讨论了一些涉及BHDR的现实应用特定研究。本文还将作为对离线BHDR背后科学感兴趣的研究人员的汇编，煽动了对相关研究的新途径的探索，这可能会进一步导致在不同应用领域对孟加拉语手写数字进行更好的离线认识。

In this paper, we present a method for unconstrained end-to-end head pose estimation. We address the problem of ambiguous rotation labels by introducing the rotation matrix formalism for our ground truth data and propose a continuous 6D rotation matrix representation for efficient and robust direct regression. This way, our method can learn the full rotation appearance which is contrary to previous approaches that restrict the pose prediction to a narrow-angle for satisfactory results. In addition, we propose a geodesic distance-based loss to penalize our network with respect to the SO(3) manifold geometry. Experiments on the public AFLW2000 and BIWI datasets demonstrate that our proposed method significantly outperforms other state-of-the-art methods by up to 20\%. We open-source our training and testing code along with our pre-trained models: https://github.com/thohemp/6DRepNet.

学习以上对象的场景表示对于实现复杂场景的结构理解和抽象至关重要。然而，由于目前为无监督的对象表示学习的方法建立在静止观察者假设或静态场景假设之上，它们通常是：i）遭受单视图空间歧义，或ii）从动态场景中不正确或不准确的对象表示。为了解决此问题，我们提出了动态感知的多目标网络（DYMON），这是一种扩展多视图以对象的表示学习学习到动态场景的方法的方法。我们在多视图 - 动态场景数据上训练Dymon，并显示Dymon学习 - 没有监督 - 从一系列观察序列来构建观察者动作和场景对象动态的纠缠效果，并构建适合渲染的场景对象空间表示在任意次（跨时间查询）和任意视点（查询空间）。我们还显示分解场景表示（W.R.T.对象）支持通过独立和时间通过空间和时间查询单个对象。

Pennylane是用于量子计算机可区分编程的Python 3软件框架。该库为近期量子计算设备提供了统一的体系结构，支持量子和连续变化的范例。 Pennylane的核心特征是能够以与经典技术（例如反向传播）兼容的方式来计算变异量子电路的梯度。因此，Pennylane扩展了在优化和机器学习中常见的自动分化算法，以包括量子和混合计算。插件系统使该框架与任何基于门的量子模拟器或硬件兼容。我们为硬件提供商提供插件，包括Xanadu Cloud，Amazon Braket和IBM Quantum，允许Pennylane优化在公开访问的量子设备上运行。在古典方面，Pennylane与加速的机器学习库（例如Tensorflow，Pytorch，Jax和Autograd）接口。 Pennylane可用于优化变分的量子本素体，量子近似优化，量子机学习模型和许多其他应用。

The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.