机器学习，特别是深度学习方法在许多模式识别和数据处理问题，游戏玩法中都优于人类的能力，现在在科学发现中也起着越来越重要的作用。机器学习在分子科学中的关键应用是通过使用密度函数理论，耦合群或其他量子化学方法获得的电子schr \“ odinger方程的Ab-Initio溶液中的势能表面或力场。我们回顾了一种最新和互补的方法：使用机器学习来辅助从第一原理中直接解决量子化学问题。具体来说，我们专注于使用神经网络ANSATZ功能的量子蒙特卡洛（QMC）方法，以解决电子SCHR \ “ Odinger方程在第一和第二量化中，计算场和激发态，并概括多个核构型。与现有的量子化学方法相比，这些新的深QMC方法具有以相对适度的计算成本生成高度准确的Schr \“ Odinger方程的溶液。

我们介绍了革兰氏 - 哈达马德密度运算符（GHDO），这是一种新的深神经网络结构，可以用多项式资源编码指数级的正差半准密度运算符。然后，我们展示如何在GHDO中嵌入自回归结构，以直接对概率分布进行采样。当表示与环境相互作用的系统的混合量子状态时，这些属性尤为重要。最后，我们通过模拟耗散横向场模型的稳态来对此结构进行基准测试。估计局部可观察物和r \'enyi熵，我们对先前最新的变异方法显示出显着改善。

我们证明，任何矩阵产品状态（MP）可以通过线性内存更新的复发神经网络（RNN）来精确表示。我们使用多线性内存更新将此RNN体系结构推广到2D晶格。它支持在多项式时间内的完美采样和波功能评估，并且可以代表纠缠熵的区域定律。数值证据表明，与MPS相比，它可以使用键尺寸较低的键尺寸编码波函数，其精度可以通过增加键尺寸来系统地改善。

我们介绍了Netket的版本3，机器学习工具箱适用于许多身体量子物理学。Netket围绕神经网络量子状态构建，并为其评估和优化提供有效的算法。这个新版本是基于JAX的顶部，一个用于Python编程语言的可差分编程和加速的线性代数框架。最重要的新功能是使用机器学习框架的简明符号来定义纯Python代码中的任意神经网络ANS \“凝固的可能性，这允许立即编译以及渐变的隐式生成自动化。Netket 3还带来了GPU和TPU加速器的支持，对离散对称组的高级支持，块以缩放多程度的自由度，Quantum动态应用程序的驱动程序，以及改进的模块化，允许用户仅使用部分工具箱是他们自己代码的基础。

复杂的高尺寸概率分布的高效采样是计算科学中的核心任务。机器学习方法，如自动增加神经网络，与马尔可夫链蒙特卡罗采样一起使用，为这种分布提供良好的近似，但遭受内在偏差或高方差。在这封信中，我们提出了一种方法来使这种近似不偏不倚，方差低。我们的方法使用物理对称和可变大小的群集更新，它利用自回归分解的结构。我们测试我们的古典自旋系统的第一阶和二阶相变的方法，显示其对关键系统和亚稳态存在的可行性。

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps, and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing Indirect ImmunoFluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is far from the conventional neural network approach, but it is equivalent to their quantitative and qualitative performance, and it is also solid to adversative noise. The method is robust, based on formally correct functions, and does not suffer from tuning on specific data sets. Results: This work demonstrates the robustness of the method against the variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on two datasets (Neuroblastoma and NucleusSegData) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional to a structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) to segment cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.

The broad usage of mobile devices nowadays, the sensitiveness of the information contained in them, and the shortcomings of current mobile user authentication methods are calling for novel, secure, and unobtrusive solutions to verify the users' identity. In this article, we propose TypeFormer, a novel Transformer architecture to model free-text keystroke dynamics performed on mobile devices for the purpose of user authentication. The proposed model consists in Temporal and Channel Modules enclosing two Long Short-Term Memory (LSTM) recurrent layers, Gaussian Range Encoding (GRE), a multi-head Self-Attention mechanism, and a Block-Recurrent structure. Experimenting on one of the largest public databases to date, the Aalto mobile keystroke database, TypeFormer outperforms current state-of-the-art systems achieving Equal Error Rate (EER) values of 3.25% using only 5 enrolment sessions of 50 keystrokes each. In such way, we contribute to reducing the traditional performance gap of the challenging mobile free-text scenario with respect to its desktop and fixed-text counterparts. Additionally, we analyse the behaviour of the model with different experimental configurations such as the length of the keystroke sequences and the amount of enrolment sessions, showing margin for improvement with more enrolment data. Finally, a cross-database evaluation is carried out, demonstrating the robustness of the features extracted by TypeFormer in comparison with existing approaches.

Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital archives are still unbalanced: writers from non-Western countries are less represented, and such a condition leads to the perpetration of old forms of discrimination. In this paper, we present the Under-Represented Writers Knowledge Graph (URW-KG), a resource designed to explore and possibly amend this lack of representation by gathering and mapping information about works and authors from Wikidata and three other sources: Open Library, Goodreads, and Google Books. The experiments based on KG embeddings showed that the integrated information encoded in the graph allows scholars and users to be more easily exposed to non-Western literary works and authors with respect to Wikidata alone. This opens to the development of fairer and effective tools for author discovery and exploration.

Content-Controllable Summarization generates summaries focused on the given controlling signals. Due to the lack of large-scale training corpora for the task, we propose a plug-and-play module RelAttn to adapt any general summarizers to the content-controllable summarization task. RelAttn first identifies the relevant content in the source documents, and then makes the model attend to the right context by directly steering the attention weight. We further apply an unsupervised online adaptive parameter searching algorithm to determine the degree of control in the zero-shot setting, while such parameters are learned in the few-shot setting. By applying the module to three backbone summarization models, experiments show that our method effectively improves all the summarizers, and outperforms the prefix-based method and a widely used plug-and-play model in both zero- and few-shot settings. Tellingly, more benefit is observed in the scenarios when more control is needed.

Anticipating future actions based on video observations is an important task in video understanding, which would be useful for some precautionary systems that require response time to react before an event occurs. Since the input in action anticipation is only pre-action frames, models do not have enough information about the target action; moreover, similar pre-action frames may lead to different futures. Consequently, any solution using existing action recognition models can only be suboptimal. Recently, researchers have proposed using a longer video context to remedy the insufficient information in pre-action intervals, as well as the self-attention to query past relevant moments to address the anticipation problem. However, the indirect use of video input features as the query might be inefficient, as it only serves as the proxy to the anticipation goal. To this end, we propose an inductive attention model, which transparently uses prior prediction as the query to derive the anticipation result by induction from past experience. Our method naturally considers the uncertainty of multiple futures via the many-to-many association. On the large-scale egocentric video datasets, our model not only shows consistently better performance than state of the art using the same backbone, and is competitive to the methods that employ a stronger backbone, but also superior efficiency in less model parameters.