Continuous-time Markov chains are used to model stochastic systems where transitions can occur at irregular times, e.g., birth-death processes, chemical reaction networks, population dynamics, and gene regulatory networks. We develop a method to learn a continuous-time Markov chain's transition rate functions from fully observed time series. In contrast with existing methods, our method allows for transition rates to depend nonlinearly on both state variables and external covariates. The Gillespie algorithm is used to generate trajectories of stochastic systems where propensity functions (reaction rates) are known. Our method can be viewed as the inverse: given trajectories of a stochastic reaction network, we generate estimates of the propensity functions. While previous methods used linear or log-linear methods to link transition rates to covariates, we use neural networks, increasing the capacity and potential accuracy of learned models. In the chemical context, this enables the method to learn propensity functions from non-mass-action kinetics. We test our method with synthetic data generated from a variety of systems with known transition rates. We show that our method learns these transition rates with considerably more accuracy than log-linear methods, in terms of mean absolute error between ground truth and predicted transition rates. We also demonstrate an application of our methods to open-loop control of a continuous-time Markov chain.

This paper focuses on a stochastic system identification problem: given time series observations of a stochastic differential equation (SDE) driven by L\'{e}vy $\alpha$-stable noise, estimate the SDE's drift field. For $\alpha$ in the interval $[1,2)$, the noise is heavy-tailed, leading to computational difficulties for methods that compute transition densities and/or likelihoods in physical space. We propose a Fourier space approach that centers on computing time-dependent characteristic functions, i.e., Fourier transforms of time-dependent densities. Parameterizing the unknown drift field using Fourier series, we formulate a loss consisting of the squared error between predicted and empirical characteristic functions. We minimize this loss with gradients computed via the adjoint method. For a variety of one- and two-dimensional problems, we demonstrate that this method is capable of learning drift fields in qualitative and/or quantitative agreement with ground truth fields.

我们开发方法以在一个空间尺寸中学习时间依赖的Kohn-Sham（TDK）系统的相关电位。我们从一条低维两电子系统开始，我们可以在数值上解决时间依赖的SCHR \“odinger方程;这产生适用于培训相关潜力的培训模型的电子密度。我们将学习问题框架作为优化的一个对动态遵守TDKS方程的限制的限制最小二乘目标。应用伴侣，我们开发有效的方法来计算梯度，从而学习相关势的模型。我们的结果表明可以学习相关潜力的值所得到的电子密度匹配地面真理密度。我们还展示了如何使用内存学习相关潜在功能，演示一个这样的模型，为训练集外的轨迹产生合理的结果。

SKA脉冲星搜索管道将用于实时检测脉冲星。SKA等现代射电望远镜将在其全面运行中生成数据。因此，基于经验和数据驱动的算法对于诸如候选检测等应用是必不可少的。在这里，我们描述了我们的发现，从测试一种称为Mask R-CNN的最先进的对象检测算法来检测SKA PULSAR搜索管道中的候选标志。我们已经训练了蒙版R-CNN模型来检测候选图像。开发了一种自定义注释工具，以有效地标记大型数据集中感兴趣的区域。我们通过检测模拟数据集中的候选签名成功证明了该算法。本文介绍了这项工作的详细信息，并重点介绍了未来的前景。

数值天气预报（NWP）和机器学习（ML）方法对于太阳能预测是流行的。但是，NWP模型具有多种可能的物理参数化，其需要特定于站点的NWP优化。当区域NWP模型与具有不同可能的参数化的全球气候模型一起使用时，这进一步复杂化。在该研究中，提出了一种替代方法，并评估了四种辐射模型。天气研究和预测（WRF）模型在全球和区域模式中运行，以提供太阳能辐照度的估计。然后使用ML后处理该估计以提供最终预测。该ML误差校正模型，来自WRF的归一化根均方误差高达40-50％。使用CAM，GFDL，新戈达德和RRTMG辐射模型获得的结果在此校正后可比，否定了WRF参数化调整的需求。还评估了包含附近地点和传感器数据的其他模型，后者是特别有前途的。

假新闻是制作作为真实的信息，有意欺骗读者。最近，依靠社交媒体的人民币为新闻消费的人数显着增加。由于这种快速增加，错误信息的不利影响会影响更广泛的受众。由于人们对这种欺骗性的假新闻的脆弱性增加，在早期阶段检测错误信息的可靠技术是必要的。因此，作者提出了一种基于图形的基于图形的框架社会图，其具有多头关注和发布者信息和新闻统计网络（SOMPS-Net），包括两个组件 - 社交交互图（SIG）和发布者和新闻统计信息（PNS）。假设模型在HealthStory DataSet上进行了实验，并在包括癌症，阿尔茨海默，妇产科和营养等各种医疗主题上推广。 Somps-Net明显优于其他基于现实的图表的模型，在HealthStory上实验17.1％。此外，早期检测的实验表明，Somps-Net预测的假新闻文章在其广播仅需8小时内为79％确定。因此，这项工作的贡献奠定了在早期阶段捕获多种医疗主题的假健康新闻的基础。

一个多世纪以前，伊万·P·帕夫洛夫（Ivan P. Pavlov）在经典实验中展示了狗如何学会将铃铛与食物联系起来，从而导致戒指导致唾液。如今，很少发现使用Pavlovian类型的关联学习用于人工智能（AI）应用程序，即使其他学习概念，尤其是对人工神经网络（ANN）的反向传播也蓬勃发展。但是，使用反向传播方法的训练在“常规” ANN上，尤其是现代深神经网络（DNNS）的形式，是计算和能量密集型的。在这里，我们在实验上展示了使用单个（或单一）关联硬件元素的无反向传播学习形式。我们使用相位变换材料与芯片级联方向耦合器相结合的集成光子平台上意识到这一点。然后，我们使用我们的Monadic Pavlovian光子硬件开发扩展的电路网络，该硬件可以基于单元素关联提供独特的机器学习框架，并且重要的是，重要的是，使用无反向传播的架构来解决一般学习任务。我们的方法通过在传统的神经网络方法中学习来减轻施加的计算负担，从而提高了速度，同时还提供了我们光子实现固有的更高带宽。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detecting and understanding human emotions, generating its gait, and responding via sounds and expression on a screen. To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback. This paper aims to establish the framework of simulating a quadruped robot that is emotionally intelligent and can primarily respond to audio-visual stimuli using motor or audio response. The emotion detection from the speech was not as performant as ERANNs or Zeta Policy learning, still managing an accuracy of 63.5%. The video emotion detection system produced results that are almost at par with the state of the art, with an accuracy of 99.66%. Due to its "on-policy" learning process, the PPO algorithm was extremely rapid to learn, allowing the simulated dog to demonstrate a remarkably seamless gait across the different cadences and variations. This enabled the quadruped robot to respond to generated stimuli, allowing us to conclude that it functions as predicted and satisfies the aim of this work.