Data assimilation, in its most comprehensive form, addresses the Bayesian inverse problem of identifying plausible state trajectories that explain noisy or incomplete observations of stochastic dynamical systems. Various approaches have been proposed to solve this problem, including particle-based and variational methods. However, most algorithms depend on the transition dynamics for inference, which becomes intractable for long time horizons or for high-dimensional systems with complex dynamics, such as oceans or atmospheres. In this work, we introduce score-based data assimilation for trajectory inference. We learn a score-based generative model of state trajectories based on the key insight that the score of an arbitrarily long trajectory can be decomposed into a series of scores over short segments. After training, inference is carried out using the score model, in a non-autoregressive manner by generating all states simultaneously. Quite distinctively, we decouple the observation model from the training procedure and use it only at inference to guide the generative process, which enables a wide range of zero-shot observation scenarios. We present theoretical and empirical evidence supporting the effectiveness of our method.

This book aims to provide an introduction to the topic of deep learning algorithms. We review essential components of deep learning algorithms in full mathematical detail including different artificial neural network (ANN) architectures (such as fully-connected feedforward ANNs, convolutional ANNs, recurrent ANNs, residual ANNs, and ANNs with batch normalization) and different optimization algorithms (such as the basic stochastic gradient descent (SGD) method, accelerated methods, and adaptive methods). We also cover several theoretical aspects of deep learning algorithms such as approximation capacities of ANNs (including a calculus for ANNs), optimization theory (including Kurdyka-{\L}ojasiewicz inequalities), and generalization errors. In the last part of the book some deep learning approximation methods for PDEs are reviewed including physics-informed neural networks (PINNs) and deep Galerkin methods. We hope that this book will be useful for students and scientists who do not yet have any background in deep learning at all and would like to gain a solid foundation as well as for practitioners who would like to obtain a firmer mathematical understanding of the objects and methods considered in deep learning.

The accurate identification of walnuts within orchards brings forth a plethora of advantages, profoundly amplifying the efficiency and productivity of walnut orchard management. Nevertheless, the unique characteristics of walnut trees, characterized by their closely resembling shapes, colors, and textures between the walnuts and leaves, present a formidable challenge in precisely distinguishing between them during the annotation process. In this study, we present a novel approach to improve walnut detection efficiency, utilizing YOLOv5 trained on an enriched image set that incorporates both real and synthetic RGB and NIR images. Our analysis comparing results from our original and augmented datasets shows clear improvements in detection when using the synthetic images.

This paper provides an overview of the intricate relationship between social dynamics, technological advancements, and pioneering figures in the fields of cybernetics and artificial intelligence. It explores the impact of collaboration and interpersonal relationships among key scientists, such as McCulloch, Wiener, Pitts, and Rosenblatt, on the development of cybernetics and neural networks. It also discusses the contested attribution of credit for important innovations like the backpropagation algorithm and the potential consequences of unresolved debates within emerging scientific domains. It emphasizes how interpretive flexibility, public perception, and the influence of prominent figures can shape the trajectory of a new field. It highlights the role of funding, media attention, and alliances in determining the success and recognition of various research approaches. Additionally, it points out the missed opportunities for collaboration and integration between symbolic AI and neural network researchers, suggesting that a more unified approach may be possible in today's era without the historical baggage of past debates.

Advancement in the field of machine learning is unavoidable, but something of major concern is preserving the privacy of the users whose data is being used for training these machine learning algorithms. Federated learning(FL) has emerged as a promising paradigm for training machine learning models in a distributed and privacy-preserving manner which enables one to collaborate and train a global model without sharing local data. But starting this learning process on each device in the right way, called ``model initialization" is critical. The choice of initialization methods used for models plays a crucial role in the performance, convergence speed, communication efficiency, privacy guarantees of federated learning systems, etc. In this survey, we dive deeper into a comprehensive study of various ways of model initialization techniques in FL.Unlike other studies, our research meticulously compares, categorizes, and delineates the merits and demerits of each technique, examining their applicability across diverse FL scenarios. We highlight how factors like client variability, data non-IIDness, model caliber, security considerations, and network restrictions influence FL model outcomes and propose how strategic initialization can address and potentially rectify many such challenges. The motivation behind this survey is to highlight that the right start can help overcome challenges like varying data quality, security issues, and network problems. Our insights provide a foundational base for experts looking to fully utilize FL, also while understanding the complexities of model initialization.

In recent decades, the field of robotic mapping has witnessed widespread research and development in LiDAR (Light Detection And Ranging)-based simultaneous localization and mapping (SLAM) techniques. In this paper, we review the state-of-the-art in LiDAR-based SLAM and explore the remaining challenges that still require attention to satisfy the needs of contemporary applications. A distinctive aspect of this study lies in its literature survey, which specifically investigates the application of various types and configurations of LiDAR, setting it apart from prior reviews. Furthermore, several representative comparisons of LiDAR-based SLAM algorithms are presented, which can serve as a point of reference. Finally, the paper concludes with an insightful discussion on the emergence of new frontiers in the domain of LiDAR-based SLAM.

Understanding emergent abilities, such as in-context learning (ICL) and chain-of-thought (CoT) prompting in large language models (LLMs), is of utmost importance. This importance stems not only from the better utilization of these capabilities across various tasks, but also from the proactive identification and mitigation of potential risks, including concerns of truthfulness, bias, and toxicity, that may arise alongside these capabilities. In this paper, we present a thorough survey on the interpretation and analysis of emergent abilities of LLMs. First, we provide a concise introduction to the background and definition of emergent abilities. Then, we give an overview of advancements from two perspectives: 1) a macro perspective, emphasizing studies on the mechanistic interpretability and delving into the mathematical foundations behind emergent abilities; and 2) a micro-perspective, concerning studies that focus on empirical interpretability by examining factors associated with these abilities. We conclude by highlighting the challenges encountered and suggesting potential avenues for future research. We believe that our work establishes the basis for further exploration into the interpretation of emergent abilities.

The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history. This has resulted in concerns that the field will become homogenized and resource-intensive. The new status quo has put many academic researchers, especially PhD students, at a disadvantage. This paper aims to define a new NLP playground by proposing 20+ PhD-dissertation-worthy research directions, covering theoretical analysis, new and challenging problems, learning paradigms, and interdisciplinary applications.

Abstract--Mobile robots often have limited battery life and need to recharge periodically. This paper presents an RRTbased path-planning algorithm that addresses battery power management. A path is generated continuously from the robot's current position to its recharging station. The robot decides if a recharge is needed based on the energy required to travel on that path and the robot's current power. RRT* is used to generate the first path, and then subsequent paths are made using information from previous trees. Finally, the presented algorithm was compared with Extended Rate Random Tree (ERRT) algorithm [4].

Multilingualism is widespread around the world and code-switching (CSW) is a common practice among different language pairs/tuples across locations and regions. However, there is still not much progress in building successful CSW systems, despite the recent advances in Massive Multilingual Language Models (MMLMs). We investigate the reasons behind this setback through a critical study about the existing CSW data sets (68) across language pairs in terms of the collection and preparation (e.g. transcription and annotation) stages. This in-depth analysis reveals that \textbf{a)} most CSW data involves English ignoring other language pairs/tuples \textbf{b)} there are flaws in terms of representativeness in data collection and preparation stages due to ignoring the location based, socio-demographic and register variation in CSW. In addition, lack of clarity on the data selection and filtering stages shadow the representativeness of CSW data sets. We conclude by providing a short check-list to improve the representativeness for forthcoming studies involving CSW data collection and preparation.