为了治疗别人，希望被治疗是金色规则（GR）的常见配方。然而，尽管它在整个历史中作为公理的普遍存在，但不存在这种道德哲学进入计算系统的转移。在本文中，我们考虑如何进行算法方式运作这一规则，以便它可以用于测量诸如男孩伤害女孩的句子，并将其分类为公平或不公平。出于本文的目的，我们将公平的行为定义为一个人是接受的，如果它已经对自己做了。提出了对GR批评的审查和回复。我们共享GR的数字化的代码，并用句子列表测试它。在两种语言模型，使用和Albert中实现它，我们发现分别为78.0,85.0的F1分数。建议如何实施技术以避免单词嵌入中的不公平偏见 - 鉴于个人通常不希望处于不公平行为的接收结束，例如种族主义，而不管是否使用了这些语料库歧视为值得称道。

公平是世界各地的文明可以观察到的主要社会价值。这表明这是社会协议，通常用文本描述，例如合同。然而，尽管存在普遍性，但描述了描述社会法案的文本的公平度量仍然存在。为了解决这个问题，我们会返回基于第一个校长的问题来考虑问题。我们利用社会心理学文献而不是使用规则或模板来确定人类在进行公平评估时使用的主要因素。然后，我们尝试将这些单词嵌入式数字化为一个多维句子级公平感知向量，以用作这些公平感知的近似。该方法利用Word Embeddings内的Pro-社会偏见，我们获得F1 = 81.0。基于所述公平逼近向量的PCA和ML使用PCA和M1产生的第二种方法产生86.2的F1得分。我们详细介绍了方法中可以制作的改进，以将句子嵌入到公平性的子空间表示的投影。

在这项工作中，我们提出了一种基于从Marmoset猴的大脑收集的局部场潜在数据，提出了与帕金森病相关的新生物物理计算模型。帕金森病是一种神经退行性疾病，与大量NIGRA PARSCACTCA的多巴胺能神经元的死亡有关，这影响了大脑基底神经节 - 丘脑 - 皮质神经元电路的正常动态。尽管存在多种疾病的机制，但仍然缺少这些机制和分子发病机制的完整描述，仍然没有治愈。为了解决这种差距，已经提出了类似于动物模型中发现的神经生物学方面的计算模型。在我们的模型中，我们执行了一种数据驱动方法，其中使用差分演变优化了一组生物学限制参数。进化模型成功地类似于来自健康和Parkinsonian Marmoset脑数据的单神经元均值射击和局部场势的光谱签名。据我们所知，这是帕金森病的第一个基于来自Marmoset Monkeys的七个脑区域的同时电生理学记录的第一个计算模型。结果表明，该拟议的模型可以促进PD机制的调查，并支持可以表明新疗法的技术的发展。它还可以应用于其他计算神经科学问题，其中可以使用生物数据来适应大规模模型的脑电路。

Accurate determination of a small molecule candidate (ligand) binding pose in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate are closer to experimental co-crystal structures than those generated by docking software, and more importantly achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

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.

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.

Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at $\eta=-5.69$

The Government of Kerala had increased the frequency of supply of free food kits owing to the pandemic, however, these items were static and not indicative of the personal preferences of the consumers. This paper conducts a comparative analysis of various clustering techniques on a scaled-down version of a real-world dataset obtained through a conjoint analysis-based survey. Clustering carried out by centroid-based methods such as k means is analyzed and the results are plotted along with SVD, and finally, a conclusion is reached as to which among the two is better. Once the clusters have been formulated, commodities are also decided upon for each cluster. Also, clustering is further enhanced by reassignment, based on a specific cluster loss threshold. Thus, the most efficacious clustering technique for designing a food kit tailored to the needs of individuals is finally obtained.

Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes. While neural networks have achieved competitive performance, the resulting policies are often over-parameterized black boxes that are difficult to interpret and deploy efficiently. More recent symbolic RL frameworks have shown that high-level domain-specific programming logic can be designed to handle both policy learning and symbolic planning. However, these approaches rely on coded primitives with little feature learning, and when applied to high-dimensional visual scenes, they can suffer from scalability issues and perform poorly when images have complex object interactions. To address these challenges, we propose \textit{Differentiable Symbolic Expression Search} (DiffSES), a novel symbolic learning approach that discovers discrete symbolic policies using partially differentiable optimization. By using object-level abstractions instead of raw pixel-level inputs, DiffSES is able to leverage the simplicity and scalability advantages of symbolic expressions, while also incorporating the strengths of neural networks for feature learning and optimization. Our experiments demonstrate that DiffSES is able to generate symbolic policies that are simpler and more and scalable than state-of-the-art symbolic RL methods, with a reduced amount of symbolic prior knowledge.

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifier, TSR values were predicted and the results were compared to a visual TSR estimation made by a pathologist. The results suggest that classification accuracy does not improve when domain-specific data are used in the pre-training of the convolutional neural network models in the task at hand. Classification accuracy for stroma, tumor and other reached 96.1$\%$ on an independent test set. Among the three classes the best model gained the highest accuracy (99.3$\%$) for class tumor. When TSR was predicted with the best model, the correlation between the predicted values and values estimated by an experienced pathologist was 0.57. Further research is needed to study associations between computationally predicted TSR values and other clinicopathological factors of colorectal cancer and the overall survival of the patients.