Previous work has shown that a neural network with the rectified linear unit (ReLU) activation function leads to a convex polyhedral decomposition of the input space. These decompositions can be represented by a dual graph with vertices corresponding to polyhedra and edges corresponding to polyhedra sharing a facet, which is a subgraph of a Hamming graph. This paper illustrates how one can utilize the dual graph to detect and analyze adversarial attacks in the context of digital images. When an image passes through a network containing ReLU nodes, the firing or non-firing at a node can be encoded as a bit ($1$ for ReLU activation, $0$ for ReLU non-activation). The sequence of all bit activations identifies the image with a bit vector, which identifies it with a polyhedron in the decomposition and, in turn, identifies it with a vertex in the dual graph. We identify ReLU bits that are discriminators between non-adversarial and adversarial images and examine how well collections of these discriminators can ensemble vote to build an adversarial image detector. Specifically, we examine the similarities and differences of ReLU bit vectors for adversarial images, and their non-adversarial counterparts, using a pre-trained ResNet-50 architecture. While this paper focuses on adversarial digital images, ResNet-50 architecture, and the ReLU activation function, our methods extend to other network architectures, activation functions, and types of datasets.

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.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

在本文中，我们提出了针对中央，局部和洗牌模型中随机线性匪徒问题的差异私有算法。在中心模型中，我们获得了与最佳非私有算法的遗憾，这意味着我们可以免费获得隐私。特别是，我们感到遗憾的是$ \ tilde {o}（\ sqrt {t}+\ frac {1} {\ epsilon}）$匹配已知的私有线性匪徒的较低限制，而最佳以前已知的算法实现了$ \ tilde {o}（\ frac {1} {\ epsilon} \ sqrt {t}）$。在当地情况下，我们感到遗憾的是$ \ tilde {o}（\ frac {1} {\ epsilon} {\ sqrt {t}} $，与常数$ \ epsilon $相匹配的非私人遗憾，但是当$ \ epsilon $很小时，会受到遗憾的处罚。在洗牌模型中，我们还遗憾地对$ \ tilde {o}（\ sqrt {t}+\ frac {1} {\ epsilon} {\ epsilon}）$％$ \ epsilon $，如中心案例，而最佳情况是以前已知的算法对$ \ tilde {o}（\ frac {1} {\ epsilon} {t^{3/5}}）$感到遗憾。我们的数值评估验证了我们的理论结果。

变异因素之间的相关性在现实数据中普遍存在。机器学习算法可能会受益于利用这种相关性，因为它们可以提高噪声数据的预测性能。然而，通常这种相关性不稳定（例如，它们可能在域，数据集或应用程序之间发生变化），我们希望避免利用它们。解剖学方法旨在学习捕获潜伏子空间变化不同因素的表示。常用方法涉及最小化潜伏子空间之间的相互信息，使得每个潜在的底层属性。但是，当属性相关时，这会失败。我们通过强制执行可用属性上的子空间之间的独立性来解决此问题，这允许我们仅删除不导致的依赖性，这些依赖性是由于训练数据中存在的相关结构。我们通过普发的方法实现这一目标，以最小化关于分类变量的子空间之间的条件互信息（CMI）。我们首先在理论上展示了CMI最小化是对高斯数据线性问题的稳健性解剖的良好目标。然后，我们基于MNIST和Celeba在现实世界数据集上应用我们的方法，并表明它会在相关偏移下产生脱屑和强大的模型，包括弱监督设置。

目的：慢性主动脉疾病的监测成像，如解剖，依赖于在预定义主动脉地标随时间获得和比较预定义主动脉标志的横截面直径测量。由于缺乏鲁棒工具，横截面平面的方向由高训练的操作员手动定义。我们展示了如何有效地使用诊所中常规收集的手动注释来缓解该任务，尽管在测量中存在不可忽略的互操作器可变性。影响：通过利用不完美，回顾性的临床注释，可以缓解或自动化且重复的成像任务的弊端。方法论：在这项工作中，我们结合了卷积神经网络和不确定量化方法来预测这种横截面的取向。我们使用11个操作员随机处理的临床数据进行培训，并在3个独立运营商处理的较小集合上进行测试，以评估互通器变异性。结果：我们的分析表明，手动选择的横截面平面的特点是10.6 ^ \ CirC $ 10.6 ^ \ riC $和每角度为21.4美元的协议限额为95％我们的方法显示，静态误差减少3.57秒^ \ rIC $（$ 40.2 $％）和$ 4.11 ^ \ rIC $（$ 32.8 $％），而不是5.4 ^ \ rIC $（$ 49.0 $％）和16.0美元^ \ CIRC $（$ 74.6 $％）对手动处理。结论：这表明预先存在的注释可以是诊所的廉价资源，以便于易于提出和重复的任务，如横截面提取，以便监测主动脉夹层。

我们采用变化性AutoEncoders从单粒子Anderson杂质模型谱函数的数据集中提取物理洞察。培训AutoEncoders以查找低维，潜在的空间表示，其忠实地表征培训集的每个元素，通过重建误差测量。变形式自动化器，标准自动化器的概率概括，进一步条件促进了高度可解释的特征。在我们的研究中，我们发现学习的潜在变量与众所周知的众所周知，但非活动的参数强烈关联，这些参数表征了安德森杂质模型中的紧急行为。特别地，一种潜在的可变变量与粒子孔不对称相关，而另一个潜在的变量与杂质模型中动态产生的低能量尺度接近一对一的对应关系。使用符号回归，我们将此变量模拟了该变量作为已知的裸物理输入参数和“重新发现”的kondo温度的非扰动公式。我们开发的机器学习管道表明了一种通用方法，它开启了发现其他物理系统中的新领域知识的机会。

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 recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

We present the interpretable meta neural ordinary differential equation (iMODE) method to rapidly learn generalizable (i.e., not parameter-specific) dynamics from trajectories of multiple dynamical systems that vary in their physical parameters. The iMODE method learns meta-knowledge, the functional variations of the force field of dynamical system instances without knowing the physical parameters, by adopting a bi-level optimization framework: an outer level capturing the common force field form among studied dynamical system instances and an inner level adapting to individual system instances. A priori physical knowledge can be conveniently embedded in the neural network architecture as inductive bias, such as conservative force field and Euclidean symmetry. With the learned meta-knowledge, iMODE can model an unseen system within seconds, and inversely reveal knowledge on the physical parameters of a system, or as a Neural Gauge to "measure" the physical parameters of an unseen system with observed trajectories. We test the validity of the iMODE method on bistable, double pendulum, Van der Pol, Slinky, and reaction-diffusion systems.