单细胞转录组学的分析通常依赖于聚类细胞，然后进行差异基因表达（DGE）来识别这些簇之间变化的基因。这些离散分析成功地确定了细胞类型和标记。但是，可能无法检测到细胞类型内部和之间的连续变化。我们提出了三种拓扑动机的数学方法，用于无监督的特征选择，这些方法可以同时在多个尺度上同时考虑离散和连续的转录模式。 eigenscores（$ \ mathrm {eig} _i $）基于其与图形laplacian的频谱分解在数据中与低频内在图案的对应相对的对应。多尺度拉普拉斯评分（MLS）是一种无监督的方法，用于在数据中定位相关量表并选择在这些相应量表上相干表达的基因。持续的瑞利商（PRQ）采用了配备过滤的数据，允许在分叉过程中具有不同作用的基因（例如伪时间）。我们通过将它们应用于已发布的单细胞转录组数据集来证明这些技术的实用性。该方法验证了先前鉴定的基因并检测具有相干表达模式的其他基因。通过研究基因信号与基础空间的几何形状之间的相互作用，这三种方法给出了基因的多维排名和它们之间关系的可视化。

疾病并发症会改变血管网络形态并破坏组织功能。例如，糖尿病性视网膜病是1型和2型糖尿病的并发症，可能引起失明。通过视觉检查视网膜图像来评估微血管疾病，但是当疾病表现出沉默的症状或患者无法参加面对面的会议时，这可能是具有挑战性的。我们检查了在对分段视网膜血管图像的统计和拓扑摘要进行培训时，在检测微血管疾病中的机器学习算法的性能。我们将方法应用于三个公共可用数据集，并发现，在我们考虑的13个总数描述符向量中，要么是统计框计数描述符向量，要么是拓扑洪水描述符矢量可在这些数据集中达到最高准确度。然后，我们通过合并几个数据集创建了第四个数据集：盒子计数向量优于该数据集上的所有描述符，包括对组合数据集中注释样式的差异敏感的拓扑洪水向量。我们的工作是确定哪种计算方法最适合识别微血管疾病以及其当前局限性的第一步。从长远来看，这些方法可以纳入自动化疾病评估工具中。

结合PersonAs信息允许在对话响应生成中多样化和接触响应。不幸的是，事先作品主要专注于自我的人物，并忽视了合作伙伴角色的价值。此外，在实际应用中，实际伙伴角色的可用性通常不是这种情况。本文试图通过提供一种新颖的框架来解决这些问题，这些框架利用自动合作伙伴角色生成来增强成功的对话一代。我们将强化学习纳入了一个专门设计的批评网络，以获得奖励判断。自动和人类评估的实验结果表明a）我们的框架能够产生相关，信息丰富的合作伙伴角色，甚至与地面真理合作伙伴角色相比。 b）生成的合作伙伴角色增强了后续的响应生成，从而超越了当在推理阶段缺少合作伙伴角色时超越了我们的基线和比较模型。 c）我们的框架在推理期间产生的响应比我们的基线在地面真理合作伙伴角色上的基线更具信息丰富和参与。 d）我们专门设计的批评批评网络有效地加强了我们的框架。最后，我们的框架提供了更好的解释性，并降低了对伙伴角色的外部数据库的需求。

从开放式网络策略的大规模未过滤数据集培训的语言模型获取从其培训数据的系统偏差，偏见和有害视图。我们提出了一种从Web级数据集上以编程方式识别和删除有害文本的方法。预先训练的语言模型用于计算在特定文档上调节的研究员写入触发短语的日志可能性，该语言用于从数据集中识别和过滤文档。我们证明，在该过滤的数据集上培训的模型表现出较低的倾向，以产生有害文本，与未过滤的基线相比，标准语言建模基准的性能下降了下降。通过从标准语言建模基准测试的讨论语音和其他不良内容的介绍来提供对这种性能差异的部分解释。最后，我们讨论了这种方法的概括以及如何通过研究人员使用反映特定值的触发短语来构建与其值更紧密对齐的语言模型。

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.

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

We derive a set of causal deep neural networks whose architectures are a consequence of tensor (multilinear) factor analysis. Forward causal questions are addressed with a neural network architecture composed of causal capsules and a tensor transformer. The former estimate a set of latent variables that represent the causal factors, and the latter governs their interaction. Causal capsules and tensor transformers may be implemented using shallow autoencoders, but for a scalable architecture we employ block algebra and derive a deep neural network composed of a hierarchy of autoencoders. An interleaved kernel hierarchy preprocesses the data resulting in a hierarchy of kernel tensor factor models. Inverse causal questions are addressed with a neural network that implements multilinear projection and estimates the causes of effects. As an alternative to aggressive bottleneck dimension reduction or regularized regression that may camouflage an inherently underdetermined inverse problem, we prescribe modeling different aspects of the mechanism of data formation with piecewise tensor models whose multilinear projections are well-defined and produce multiple candidate solutions. Our forward and inverse neural network architectures are suitable for asynchronous parallel computation.