Rain precipitation prediction is a challenging task as it depends on weather and meteorological features which vary from location to location. As a result, a prediction model that performs well at one location does not perform well at other locations due to the distribution shifts. In addition, due to global warming, the weather patterns are changing very rapidly year by year which creates the possibility of ineffectiveness of those models even at the same location as time passes. In our work, we have proposed an adaptive deep learning-based framework in order to provide a solution to the aforementioned challenges. Our method can generalize the model for the prediction of precipitation for any location where the methods without adaptation fail. Our method has shown 43.51%, 5.09%, and 38.62% improvement after adaptation using a deep neural network for predicting the precipitation of Paris, Los Angeles, and Tokyo, respectively.

Gaining a deeper understanding of weather and being able to predict its future conduct have always been considered important endeavors for the growth of our society. This research paper explores the advancements in understanding and predicting nature's behavior, particularly in the context of weather forecasting, through the application of machine learning algorithms. By leveraging the power of machine learning, data mining, and data analysis techniques, significant progress has been made in this field. This study focuses on analyzing the contributions of various machine learning algorithms in predicting precipitation and temperature patterns using a 20-year dataset from a single weather station in Dhaka city. Algorithms such as Gradient Boosting, AdaBoosting, Artificial Neural Network, Stacking Random Forest, Stacking Neural Network, and Stacking KNN are evaluated and compared based on their performance metrics, including Confusion matrix measurements. The findings highlight remarkable achievements and provide valuable insights into their performances and features correlation.

We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81 % of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.

Global climate change is a huge challenge facing society today. The rapid growth of computing overall and of machine learning (ML) in particular rightfully raises concerns about their carbon footprints. As an early and enthusiastic adopter of ML, a manufacturer of millions of smartphones annually, and a significant cloud provider, Google is in a nearly unique position to compare the impact and efficiency of ML on the two ends of the information technology (IT) computing spectrum. Keep in mind this article is not a comparison of all computation done on phones and the cloud, but solely on the impact of ML on energy use and operational CO2e. We provide the data to support these insights. While primarily focused on operational CO2e generated from computer use, we also address the relative impact of embodied CO2e. Computers in datacenters draw electricity from the grid continuously. Because smartphones operate from a battery, they only draw electricity from the grid when connected to a charger. To account for smartphone ML energy accurately, we must include the energy overhead of their chargers. Wireless charging is increasingly popular due to its convenience and the reduction in smartphone wear and tear by avoiding the repeated insertion of a cable. For wired charging, energy is lost from the AC/DC power adapter in the charger and in the power management integrated circuit (PMIC) battery charger in the phone. Wireless charging loses additional energy through the inductive coils.

Network-on-Chip (NoC) is widely used to facilitate communication between components in sophisticated System-on-Chip (SoC) designs. Security of the on-chip communication is crucial because exploiting any vulnerability in shared NoC would be a goldmine for an attacker that puts the entire computing infrastructure at risk. NoC security relies on effective countermeasures against diverse attacks, including attacks on anonymity. We investigate the security strength of existing anonymous routing protocols in NoC architectures. Specifically, this paper makes two important contributions. We show that the existing anonymous routing is vulnerable to machine learning (ML) based flow correlation attacks on NoCs. We propose lightweight anonymous routing with traffic obfuscation techniques to defend against ML-based flow correlation attacks. Experimental studies using both real and synthetic traffic reveal that our proposed attack is successful against state-of-the-art anonymous routing in NoC architectures with high accuracy (up to 99%) for diverse traffic patterns, while our lightweight countermeasure can defend against ML-based attacks with minor hardware and performance overhead.

We propose a physics-constrained convolutional neural network (PC-CNN) to solve two types of inverse problems in partial differential equations (PDEs), which are nonlinear and vary both in space and time. In the first inverse problem, we are given data that is offset by spatially varying systematic error (i.e., the bias, also known as the epistemic uncertainty). The task is to uncover from the biased data the true state, which is the solution of the PDE. In the second inverse problem, we are given sparse information on the solution of a PDE. The task is to reconstruct the solution in space with high-resolution. First, we present the PC-CNN, which constrains the PDE with a simple time-windowing scheme to handle sequential data. Second, we analyse the performance of the PC-CNN for uncovering solutions from biased data. We analyse both linear and nonlinear convection-diffusion equations, and the Navier-Stokes equations, which govern the spatiotemporally chaotic dynamics of turbulent flows. We find that the PC-CNN correctly recovers the true solution for a variety of biases, which are parameterised as non-convex functions. Third, we analyse the performance of the PC-CNN for reconstructing solutions from biased data for the turbulent flow. We reconstruct the spatiotemporal chaotic solution on a high-resolution grid from only 2\% of the information contained in it. For both tasks, we further analyse the Navier-Stokes solutions. We find that the inferred solutions have a physical spectral energy content, whereas traditional methods, such as interpolation, do not. This work opens opportunities for solving inverse problems with partial differential equations.

In particular, PCAC is implemented as a cold-start A fundamental necessity for autonomous atmospheric indirect adaptive controller, where the plant model order is flight vehicles is a reliable autopilot for controlling the specified as a hyperparameter, but otherwise no plant model attitude and flight path. For a fixed-wing vehicle, stability and is assumed to be available. The identified model updated control derivatives are typically determined through windtunnel by RLS is linear, and thus it is suitable for modeling the testing or computational modeling over a range of aircraft dynamics near trim. In practice, an autopilot designed Mach number, angle of attack, and sideslip angle. This to operate over a wide range of flight conditions depends modeling data is then used to develop an autopilot based on on gain scheduling of multiple linear controllers. The goal gain scheduling, feedback linearization, or dynamic inversion of this study is to investigate, via numerical experiments, [1], [2]. In practice, however, the aerodynamics of an aircraft the viability and potential performance of PCAC under may be too expensive to model with high accuracy or may conditions of high uncertainty, in effect, no prior modeling change due to atmospheric conditions, such as icing, as well information, without the need for gain scheduling.

In recent years, deep learning has gained increasing popularity in the fields of Partial Differential Equations (PDEs) and Reduced Order Modeling (ROM), providing domain practitioners with new powerful data-driven techniques such as Physics-Informed Neural Networks (PINNs), Neural Operators, Deep Operator Networks (DeepONets) and Deep-Learning based ROMs (DL-ROMs). In this context, deep autoencoders based on Convolutional Neural Networks (CNNs) have proven extremely effective, outperforming established techniques, such as the reduced basis method, when dealing with complex nonlinear problems. However, despite the empirical success of CNN-based autoencoders, there are only a few theoretical results supporting these architectures, usually stated in the form of universal approximation theorems. In particular, although the existing literature provides users with guidelines for designing convolutional autoencoders, the subsequent challenge of learning the latent features has been barely investigated. Furthermore, many practical questions remain unanswered, e.g., the number of snapshots needed for convergence or the neural network training strategy. In this work, using recent techniques from sparse high-dimensional function approximation, we fill some of these gaps by providing a new practical existence theorem for CNN-based autoencoders when the parameter-to-solution map is holomorphic. This regularity assumption arises in many relevant classes of parametric PDEs, such as the parametric diffusion equation, for which we discuss an explicit application of our general theory.

Recent advancements in deep reinforcement learning (DRL) techniques have sparked its multifaceted applications in the automation sector. Managing complex decision-making problems with DRL encourages its use in the nuclear industry for tasks such as optimizing radiation exposure to the personnel during normal operating conditions and potential accidental scenarios. However, the lack of efficient reward function and effective exploration strategy thwarted its implementation in the development of radiation-aware autonomous unmanned aerial vehicle (UAV) for achieving maximum radiation protection. Here, in this article, we address these intriguing issues and introduce a deep Q-learning based architecture (RadDQN) that operates on a radiation-aware reward function to provide time-efficient minimum radiation-exposure pathway in a radiation zone. We propose a set of unique exploration strategies that fine-tune the extent of exploration and exploitation based on the state-wise variation in radiation exposure during training. Further, we benchmark the predicted path with grid-based deterministic method. We demonstrate that the formulated reward function in conjugation with adequate exploration strategy is effective in handling several scenarios with drastically different radiation field distributions. When compared to vanilla DQN, our model achieves a superior convergence rate and higher training stability.

Model ensembles are simple and effective tools for estimating the prediction uncertainty of deep learning atomistic force fields. Despite this, widespread adoption of ensemble-based uncertainty quantification (UQ) techniques is limited by the high computational costs incurred by ensembles during both training and inference. In this work we leverage the cumulative distribution functions (CDFs) of per-sample errors obtained over the course of training to efficiently represent the model ensemble, and couple them with a distance-based similarity search in the model latent space. Using these tools, we develop a simple UQ metric (which we call LTAU) that leverages the strengths of ensemble-based techniques without requiring the evaluation of multiple models during either training or inference. As an initial test, we apply our method towards estimating the epistemic uncertainty in atomistic force fields (LTAU-FF) and demonstrate that it can be easily calibrated to accurately predict test errors on multiple datasets from the literature. We then illustrate the utility of LTAU-FF in two practical applications: 1) tuning the training-validation gap for an example dataset, and 2) predicting errors in relaxation trajectories on the OC20 IS2RS task. Though in this work we focus on the use of LTAU with deep learning atomistic force fields, we emphasize that it can be readily applied to any regression task, or any ensemble-generation technique, to provide a reliable and easy-to-implement UQ metric.