Class imbalance (CI) in classification problems arises when the number of observations belonging to one class is lower than the other. Ensemble learning combines multiple models to obtain a robust model and has been prominently used with data augmentation methods to address class imbalance problems. In the last decade, a number of strategies have been added to enhance ensemble learning and data augmentation methods, along with new methods such as generative adversarial networks (GANs). A combination of these has been applied in many studies, and the evaluation of different combinations would enable a better understanding and guidance for different application domains. In this paper, we present a computational study to evaluate data augmentation and ensemble learning methods used to address prominent benchmark CI problems. We present a general framework that evaluates 9 data augmentation and 9 ensemble learning methods for CI problems. Our objective is to identify the most effective combination for improving classification performance on imbalanced datasets. The results indicate that combinations of data augmentation methods with ensemble learning can significantly improve classification performance on imbalanced datasets. We find that traditional data augmentation methods such as the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) are not only better in performance for selected CI problems, but also computationally less expensive than GANs. Our study is vital for the development of novel models for handling imbalanced datasets.

Abstract--We introduce a transformation of a Neural Radiance Field (NeRF) to an equivalent Poisson Point Process (PPP). This PPP transformation allows for rigorous quantification of uncertainty in NeRFs, in particular, for computing collision probabilities for a robot navigating through a NeRF environment. The PPP is a generalization of a probabilistic occupancy grid to the continuous volume and is fundamental to the volumetric ray-tracing model underlying radiance fields. Building upon this PPP representation, we present a chance-constrained trajectory optimization method for safe robot navigation in NeRFs. Our method relies on a voxel representation called the Probabilistic Unsafe Robot Region (PURR) that spatially fuses the chance constraint with the NeRF model to facilitate fast trajectory optimization. We then combine a graph-based search with a spline-based trajectory optimization to yield robot trajectories through the NeRF that are guaranteed to satisfy a user-specific collision probability. We validate our chance constrained planning method through simulations and hardware experiments, showing superior performance compared to prior works on trajectory planning in NeRF environments. Figure 1: (a) Ground-truth of the Stonehenge scene, (b) Poisson Constructing an environment model from onboard sensors, Point Process (PPP) of the scene represented as a point cloud, such as RGB(-D) cameras, lidar, or touch sensors, is a fundamental (c) Probabilistically Unsafe Robot Region (PURR) of scene, challenge for any autonomous system. Radiance Fields (NeRFs) [1] have emerged as a promising 3D scene representation with potential applications in a variety of robotics domains including SLAM [2], pose estimation [3], such as (watertight) triangle meshes [9], occupancy grids [10], [4], reinforcement learning [5], and grasping [6]. NeRFs offer or Signed Distance Fields (SDFs) [11], occupancy is welldefined several potential benefits over traditional scene representations: and simple to query. NeRFs, however, do not admit they can be trained using only monocular RGB images, they simple point-wise occupancy queries, since they represent the provide a continuous representation of obstacle geometry, and scene geometry implicitly through a continuous volumetric they are memory-efficient, especially considering the photorealistic density field.

Commented [ZS1]: Add ORCIDs to all authors that have them. Abstract Blood Pressure (BP) estimation plays a pivotal role in diagnosing various health conditions, highlighting the need for innovative approaches to overcome conventional measurement challenges. Leveraging machine learning and speech signals, this study investigates accurate BP estimation with a focus on preprocessing, feature extraction, and real-time applications. An advanced clusteringbased strategy, incorporating the k-means algorithm and the proposed Fact-Finding Instructor optimization algorithm, is introduced to enhance accuracy. The combined outcome of these clustering techniques enables robust BP estimation. Moreover, extending beyond these insights, this study delves into the dynamic realm of contemporary digital content consumption. Platforms like YouTube have emerged as influential spaces, presenting an array of videos that evoke diverse emotions. Within this context, this research investigates the interplay between YouTube videos and physiological responses, particularly Blood Pressure (BP) levels. By integrating advanced BP estimation techniques with the emotional dimensions of YouTube videos, this study enriches our understanding of how modern media environments intersect with health implications. Performance evaluation through metrics including Davies Bouldin score, Homogeneity, completeness, Jacquard similarity, Silhouette score, and Dunn's index demonstrates substantial enhancements, particularly with a 90% training percentage. This method offers promising potential for accurate BP estimation, contributing to the evolution of assessment methodologies and ultimately enhancing healthcare outcomes. Introduction: According to Kaur et al. (2019) a human disease is a particular aberrant state that has a detrimental effect on an organism's overall structure or function but is not instantly caused by an external injury. According to Gautam et al.(2019) there are four main groups of diseases that affect humans: infectious diseases, deficiency disorders, hereditary diseases, and physiological diseases.

Energy consumption analysis plays a pivotal role in addressing the challenges of sustainability and resource management. This paper introduces a novel approach to effectively cluster monthly energy consumption patterns by integrating two powerful techniques: Self-organizing maps and K-means clustering. The proposed method aims to exploit the benefits of both of these algorithms to enhance the accuracy and interpretability of clustering results for a dataset in which finding patterns is difficult. The main focus of this study is on a selection of time series energy consumption data from the Smart meters in London dataset. The data was preprocessed and reduced in dimensionality to capture essential temporal patterns while retaining their underlying structures. The SOM algorithm was utilized to extract the central representatives of the consumption patterns for each one of the houses over the course of each month, effectively reducing the dimensionality of the dataset and making it easier for analysis. Subsequently, the obtained SOM centroids were clustered using K-means, a popular centroid-based clustering technique. The experimental results demonstrated a significant silhouette score of 66%, indicating strong intra-cluster cohesion and inter-cluster separation which confirms the effectiveness of the proposed approach in the clustering task.

The JEM-EUSO (Joint Exploratory Missions for Extreme Universe Space Observatory) collaboration is developing a program of studying ultra-high energy cosmic rays (UHECRs) with a wide angle telescope from a low Earth orbit [1, 2, 3]. The idea is based on the possibility to register fluorescence and Cherenkov radiation in the ultraviolet (UV) range that is emitted during development of extensive air showers generated by primary particles hitting the atmosphere [4]. There are several benefits of this technique in comparison with ground-based experiments: (i) it can provide a huge exposure necessary for collecting sufficient statistics of these extremely rare events; (ii) the celestial sphere can be observed almost uniformly, which is important for anisotropy studies; and (iii) the whole sky can be observed with one instrument. It became clear at early stages of the development of the JEM-EUSO program that an orbital telescope aimed at studying UHECRs can serve as a tool for exploring other phenomena that manifest themselves in the UV range in the nocturnal atmosphere of Earth [5]. It was demonstrated by TUS, the world's first orbital fluorescence telescope aimed for testing the technique of studying UHECRs from space, that such an instrument can provide data on transient luminous events, thunderstorm activity, meteors, anthropogenic illumination of different kinds, and other types of signals [6, 7].

Multi-dimensional classification (MDC) can be employed in a range of applications where one needs to predict multiple class variables for each given instance. Many existing MDC methods suffer from at least one of inaccuracy, scalability, limited use to certain types of data, hardness of interpretation or lack of probabilistic (uncertainty) estimations. This paper is an attempt to address all these disadvantages simultaneously. We propose a formal framework for probabilistic MDC in which learning an optimal multi-dimensional classifier can be decomposed, without loss of generality, into learning a set of (smaller) single-variable multi-class probabilistic classifiers and a directed acyclic graph. Current and future developments of both probabilistic classification and graphical model learning can directly enhance our framework, which is flexible and provably optimal. A collection of experiments is conducted to highlight the usefulness of this MDC framework.

Linear principal component analysis (PCA), nonlinear PCA, and linear independent component analysis (ICA) -- those are three methods with single-layer autoencoder formulations for learning linear transformations from data. Linear PCA learns orthogonal transformations (rotations) that orient axes to maximise variance, but it suffers from a subspace rotational indeterminacy: it fails to find a unique rotation for axes that share the same variance. Both nonlinear PCA and linear ICA reduce the subspace indeterminacy from rotational to permutational by maximising statistical independence under the assumption of unit variance. The relationship between all three can be understood by the singular value decomposition of the linear ICA transformation into a sequence of rotation, scale, rotation. Linear PCA learns the first rotation; nonlinear PCA learns the second. The scale is simply the inverse of the standard deviations. The problem is that, in contrast to linear PCA, conventional nonlinear PCA cannot be used directly on the data to learn the first rotation, the first being special as it reduces dimensionality and orders by variances. In this paper, we have identified the cause, and as a solution we propose $\sigma$-PCA: a unified neural model for linear and nonlinear PCA as single-layer autoencoders. One of its key ingredients: modelling not just the rotation but also the scale -- the variances. This model bridges the disparity between linear and nonlinear PCA. And so, like linear PCA, it can learn a semi-orthogonal transformation that reduces dimensionality and orders by variances, but, unlike linear PCA, it does not suffer from rotational indeterminacy.

In this paper, we discuss the nlpBDpatriots entry to the shared task on Sentiment Analysis of Bangla Social Media Posts organized at the first workshop on Bangla Language Processing (BLP) co-located with EMNLP. The main objective of this task is to identify the polarity of social media content using a Bangla dataset annotated with positive, neutral, and negative labels provided by the shared task organizers. Our best system for this task is a transfer learning approach with data augmentation which achieved a micro F1 score of 0.71. Our best system ranked 12th among 30 teams that participated in the competition.

Given the increasing prevalence of wildland fires in the Western US, there is a critical need to develop tools to understand and accurately predict burn severity. We develop a machine learning model to predict post-fire burn severity using pre-fire remotely sensed data. Hydrological, ecological, and topographical variables collected from four regions of California - the sites of the Kincade fire (2019), the CZU Lightning Complex fire (2020), the Windy fire (2021), and the KNP Fire (2021) - are used as predictors of the difference normalized burn ratio. We hypothesize that a Super Learner (SL) algorithm that accounts for spatial autocorrelation using Vecchia's Gaussian approximation will accurately model burn severity. In all combinations of test and training sets explored, the results of our model showed the SL algorithm outperformed standard Linear Regression methods. After fitting and verifying the performance of the SL model, we use interpretable machine learning tools to determine the main drivers of severe burn damage, including greenness, elevation and fire weather variables. These findings provide actionable insights that enable communities to strategize interventions, such as early fire detection systems, pre-fire season vegetation clearing activities, and resource allocation during emergency responses. When implemented, this model has the potential to minimize the loss of human life, property, resources, and ecosystems in California.

When dealing with text data containing subjective labels like speaker emotions, inaccuracies or discrepancies among labelers are not uncommon. Such discrepancies can significantly affect the performance of machine learning algorithms. This study investigates the potential of identifying and addressing outliers in text data with subjective labels, aiming to enhance classification outcomes. We utilized the Deep SVDD algorithm, a one-class classification method, to detect outliers in nine text-based emotion and sentiment analysis datasets. By employing both a small-sized language model (DistilBERT base model with 66 million parameters) and non-deep learning machine learning algorithms (decision tree, KNN, Logistic Regression, and LDA) as the classifier, our findings suggest that the removal of outliers can lead to enhanced results in most cases. Additionally, as outliers in such datasets are not necessarily unlearnable, we experienced utilizing a large language model -- DeBERTa v3 large with 131 million parameters, which can capture very complex patterns in data. We continued to observe performance enhancements across multiple datasets.