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Sample efficiency and systematic generalization are two long-standing challenges in reinforcement learning. Previous studies have shown that involving natural language along with other observation modalities can improve generalization and sample efficiency due to its compositional and open-ended nature. However, to transfer these properties of language to the decision-making process, it is necessary to establish a proper language grounding mechanism. One approach to this problem is applying inductive biases to extract fine-grained and informative representations from the observations, which makes them more connectable to the language units. We provide architecture-level inductive biases for modularity and sparsity mainly based on Neural Production Systems (NPS). Alongside NPS, we assign a central role to memory in our architecture. It can be seen as a high-level information aggregator which feeds policy/value heads with comprehensive information and simultaneously guides selective attention in NPS through attentional feedback. Our results in the BabyAI environment suggest that the proposed model's systematic generalization and sample efficiency are improved significantly compared to previous models. An extensive ablation study on variants of the proposed method is conducted, and the effectiveness of each employed technique on generalization, sample efficiency, and training stability is specified., Comment: Under review at Machine Learning (Springer Nature)

Accurate forecasting of contagious illnesses has become increasingly important to public health policymaking, and better prediction could prevent the loss of millions of lives. To better prepare for future pandemics, it is essential to improve forecasting methods and capabilities. In this work, we propose a new infectious disease forecasting model based on physics-informed neural networks (PINNs), an emerging area of scientific machine learning. The proposed PINN model incorporates dynamical systems representations of disease transmission into the loss function, thereby assimilating epidemiological theory and data using neural networks (NNs). Our approach is designed to prevent model overfitting, which often occurs when training deep learning models with observation data alone. In addition, we employ an additional sub-network to account for mobility, vaccination, and other covariates that influence the transmission rate, a key parameter in the compartment model. To demonstrate the capability of the proposed model, we examine the performance of the model using state-level COVID-19 data in California. Our simulation results show that predictions of PINN model on the number of cases, deaths, and hospitalizations are consistent with existing benchmarks. In particular, the PINN model outperforms the basic NN model and naive baseline forecast. We also show that the performance of the PINN model is comparable to a sophisticated Gaussian infection state space with time dependence (GISST) forecasting model that integrates the compartment model with a data observation model and a regression model for inferring parameters in the compartment model. Nonetheless, the PINN model offers a simpler structure and is easier to implement. Our results show that the proposed forecaster could potentially serve as a new computational tool to enhance the current capacity of infectious disease forecasting.

Condensation processes, which are responsible for the main chemical differences between gas and solids in the Galaxy, are the major mechanisms that control the cycle of dust from evolved stars to planetary systems. However, they are still poorly understood, mainly because the thermodynamics and kinetic models of nucleation or grain growth lack experimental data. To bridge this gap, we used a large-volume three-phase alternating-current plasma torch to obtain a full high-temperature condensation sequence at an elevated carbon-to-oxygen ratio from a fluxed chondritic gas composition. We show that the crystallized suites of carbides, silicides, nitrides, sulfides, oxides and silicates and the bulk composition of the condensates are properly modelled by a kinetically inhibited condensation scenario controlled by gas flow. This validates the thermodynamic predictions of the condensation sequence at a high carbon-to-oxygen ratio. On this basis and using appropriate optical properties, we also demonstrate the influence of pressure on dust chemistry as well as the low probability of forming and detecting iron silicides in asymptotic giant branch C-rich circumstellar environments as well as in our chondritic meteorites. By demonstrating the potential of predicting dust mineralogy in these environments, this approach holds high promise for quantitatively characterizing dust composition and formation in diverse astrophysical settings., Comment: 19 pages, 10 figures, paper published in Nature Astronomy

In this work we present more comprehensive evaluations on our airborne Gimbal mounted inertial measurement unit (IMU) signal simulator which also considers flight dynamic model (FDM). A flexible IMU signal simulator is an enabling tool in design, development, improvement, test and verification of aided inertial navigation systems (INS). Efforts by other researchers had been concentrated on simulation of the strapdown INS (SINS) with the IMU rigidly attached to the moving body frame. However custom airborne surveying/mapping applications that need pointing and stabilizing camera or any other surveying sensor, require mounting the IMU beside the sensor on a Gimbal onboard the airframe. Hence the proposed Gimbal mounted IMU signal simulator is of interest whilst itself requires further analysis and verifications. Extended evaluation results in terms of both unit tests and functional/integration tests (using aided inertial navigation algorithms with variable/dynamic lever arms), verifies the simulator and its applicability for the mentioned tasks. We have further packaged and published our MATLAB code for the proposed simulator as an open source GitHub repository., Comment: 10 pages, 8 figures, 4 tables, Submitted to Journal of Aerospace Science and Technology (JAST)

The prediction of student performance and the analysis of students' learning behavior play an important role in enhancing online courses. By analysing a massive amount of clickstream data that captures student behavior, educators can gain valuable insights into the factors that influence academic outcomes and identify areas of improvement in courses. In this study, we developed ClickTree, a tree-based methodology, to predict student performance in mathematical assignments based on students' clickstream data. We extracted a set of features, including problem-level, assignment-level and student-level features, from the extensive clickstream data and trained a CatBoost tree to predict whether a student successfully answers a problem in an assignment. The developed method achieved an AUC of 0.78844 in the Educational Data Mining Cup 2023 and ranked second in the competition. Furthermore, our results indicate that students encounter more difficulties in the problem types that they must select a subset of answers from a given set as well as problem subjects of Algebra II. Additionally, students who performed well in answering end-unit assignment problems engaged more with in-unit assignments and answered more problems correctly, while those who struggled had higher tutoring request rate. The proposed method can be utilized to improve students' learning experiences, and the above insights can be integrated into mathematical courses to enhance students' learning outcomes., Comment: Submitted to the Journal of Educational Data Mining