Strategic noise maps are essential for environmental exposure assessment under the European Environmental Noise Directive, yet producing them with physics-based propagation models can be data- and compute-intensive at continental scale. This paper presents a transferable surrogate noise-mapping pipeline that predicts the average level L_den on a dense 10m grid using multi-class semantic segmentation. We train on a collection of 5-band L_den rasters derived from CNOSSOS-EU simulations for 11 labelled European cities, and deploy the same model without fine-tuning to six additional cities. Inputs are a four-channel stack of open or satellite-derived predictors: (i) ground absorption coefficients from Copernicus Urban Atlas 2018 land-use and infrastructure products, (ii) Euclidean distance-to-road, (iii) smoothed OpenStreetMap maximum speed, and (iv) Local Climate Zones (LCZ). Our proposed architecture, BigResSegNet-ASPP, is a deep residual encoder–decoder augmented with convolutional block attention (CBAM) and atrous spatial pyramid pooling (ASPP) to capture multi-scale road-to-neighbourhood context. To address severe class imbalance, we optimize a composite objective combining class-weighted cross-entropy with label smoothing, weighted Dice overlap, and focal modulation. We provide a fully tiled training/inference implementation, a reproducible preprocessing protocol, and an evaluation framework including mIOU and band-wise noise error.

Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domains. Despite this, the convergence proof for the TMs, particularly for the AND operator (conjunction of literals), in the generalized case (inputs greater than two bits) remains an open problem. This paper aims to fill this gap by presenting a comprehensive convergence analysis of Tsetlin automaton-based Machine Learning algorithms. We introduce a novel framework, referred to as Probabilistic Concept Learning (PCL), which simplifies the TM structure while incorporating dedicated feedback mechanisms and dedicated inclusion/exclusion probabilities for literals. Given n features, PCL aims to learn a set of conjunction clauses Ci each associated with a distinct inclusion probability pi. Most importantly, we establish a theoretical proof confirming that, for any clause k, PCL converges to a conjunction of literals when pk is between 0.5 and 1. This result serves as a stepping stone for future research on the convergence properties of Tsetlin automaton-based learning algorithms. Our findings not only contribute to the theoretical understanding of Tsetlin automaton-based learning algorithms but also have implications for their practical application, potentially leading to more robust and interpretable machine learning models.

Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations to improve generalization by co-locating similar words in vector space. For instance, Word2Vec is a self-supervised predictive model that captures the context of words using a neural network. Similarly, GLoVe is a popular unsupervised model incorporating corpus-wide word co-occurrence statistics. Such word embedding has significantly boosted important NLP tasks, including sentiment analysis, document classification, and machine translation. However, the embeddings are dense floating-point vectors, making them expensive to compute and difficult to interpret. In this paper, we instead propose to represent the semantics of words with a few defining words that are related using propositional logic. To produce such logical embeddings, we introduce a Tsetlin Machine-based autoencoder that learns logical clauses self-supervised. The clauses consist of contextual words like "black," "cup," and "hot" to define other words like "coffee," thus being human-understandable. We evaluate our embedding approach on several intrinsic and extrinsic benchmarks, outperforming GLoVe on six classification tasks. Furthermore, we investigate the interpretability of our embedding using the logical representations acquired during training. We also visualize word clusters in vector space, demonstrating how our logical embedding co-locate similar words.

This paper presents a novel deep learning-based methodology for estimating environmental noise across diverse European cities, significantly enhancing the coverage and precision of noise assessment and management. Utilizing an extensive range of environmental, traffic, and satellite-based land monitoring datasets, our models achieve a comprehensive and dynamic understanding of noise pollution patterns. The proposed method recontextualizes noise estimation as an image classification problem, employing advanced data preprocessing techniques and deep convolutional neural networks (CNNs) to predict noise levels with high accuracy. Our approach demonstrates scalability and cost-effectiveness in continuous noise monitoring, delivering critical insights into the health and policy implications of environmental noise. By filling data gaps in compliance with the European Environmental Noise Directive, our work facilitates the development of robust noise reduction strategies and informed urban planning. The results underscore the transformative potential of deep learning in environmental monitoring and decision-making, contributing significantly to the discourse on sustainable and resilient urban development.1

In response to the escalating ecological challenges that threaten global sustainability, there's a need to investigate alternative methods of commerce, such as rental economies. Like most online commerce, rental or otherwise, a functioning recommender system is crucial for their success. Yet the domain has, until this point, been largely neglected by the recommender system research community. Our dataset, derived from our collaboration with the leading Norwegian fashion rental company Vibrent, encompasses 77.1k transactions, rental histories from 7.4k anonymized users, and 15.6k unique outfits in which each physical item's attributes and rental history is meticulously tracked. All outfits are listed as individual items or their corresponding item groups, referring to shared designs between the individual items. This notation underlines the novel challenges of rental as compared to more traditional recommender system problems where items are generally interchangeable. For example, an RS for rental items requires tracking each physical item to ensure it isn't rented for the same time period to several different customers, as compared to retail, in which tracking or recommending individual items is largely unnecessary. Each outfit is accompanied by a set of tags describing some of their attributes. We also provide a total of 50.1k images displaying across all items, along with a set of precomputed zero-shot embeddings. We apply a myriad of common recommender system methods to the dataset to provide a performance baseline. This baseline is calculated for both the traditional fashion recommender system problem of recommending outfit groups and the novel problem of predicting individual item rental. To our knowledge, this is the first published article to directly discuss fashion rental recommender systems, as well as the first published dataset intended for this purpose. We hope that the publication of this dataset will serve as a catalyst for a new branch of research for specialized fashion rental recommender systems. The dataset has been made freely available at https://www.kaggle. com/datasets/kaborg15/vibrent-clothes-rental-dataset All code associated with the project have been made available at:https://github.com/cair/Vibrent_Clothes_Rental_Dataset_Collection

This study introduces deep learning (DL) methods for imputing missing allele-frequency information in large-scale genome-wide pooled re-sequencing (Pool-Seq) data, using the comprehensive DEST dataset based on over 270 global samples of the vinegar fly Drosophila melanogaster as a use case. The primary challenge addressed here is gap filling in DNA sequences, a critical issue in large-scale genomic studies. An empirical baseline for missing allele frequencies was established using an inverse-distance-weighting (IDW) method, leveraging geographical and temporal proximity among densely sampled populations. Additionally, a machine learning (ML) approach with k-means clustering grouped populations based on allele frequencies, independent of their spatiotemporal context. The core contribution of this research is the application of advanced DL models, specifically Masked Autoencoders (MAE), Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN). These models excel in learning the data distribution and generating plausible imputations for missing sequences, outperforming the IDW and k-means based methods. Their effectiveness is due to their ability to handle high-dimensional genetic sequences and capture complex data correlations while maintaining sequential integrity. The study demonstrates the efficacy of DL in genomic data analysis, particularly for large-scale, complex datasets. VAE and GAN models offer a significant advancement over traditional ML methods, providing more accurate and efficient solutions for gap filling in genetic sequences. This research highlights the potential of DL in genomics, setting a precedent for future AI applications in biological data analysis and demonstrating a novel application area for deep learning techniques in handling complex long-sequence biological datasets.

Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domains. Despite this, the convergence proof for the TMs, particularly for the AND operator (\emph{conjunction} of literals), in the generalized case (inputs greater than two bits) remains an open problem. This paper aims to fill this gap by presenting a comprehensive convergence analysis of Tsetlin automaton-based Machine Learning algorithms. We introduce a novel framework, referred to as Probabilistic Concept Learning (PCL), which simplifies the TM structure while incorporating dedicated feedback mechanisms and dedicated inclusion/exclusion probabilities for literals. Given $n$ features, PCL aims to learn a set of conjunction clauses $C_i$ each associated with a distinct inclusion probability $p_i$. Most importantly, we establish a theoretical proof confirming that, for any clause $C_k$, PCL converges to a conjunction of literals when $0.5<p_k<1$. This result serves as a stepping stone for future research on the convergence properties of Tsetlin automaton-based learning algorithms. Our findings not only contribute to the theoretical understanding of Tsetlin Machines but also have implications for their practical application, potentially leading to more robust and interpretable machine learning models.

BACKGROUND:Natural language processing (NLP) based clinical decision support systems (CDSSs) have demonstrated the ability to extract vital information from patient electronic health records (EHRs) to facilitate important decision support tasks. While obtaining accurate, medical domain interpretable results is crucial, it is demanding because real-world EHRs contain many inconsistencies and inaccuracies. Further, testing of such machine learning-based systems in clinical practice has received limited attention and are yet to be accepted by clinicians for regular use.METHODS:We present our results from the evaluation of an NLP-driven CDSS developed and implemented in a Norwegian Hospital. The system incorporates unsupervised and supervised machine learning combined with rule-based algorithms for clinical concept-based searching to identify and classify allergies of concern for anesthesia and intensive care. The system also implements a semi-supervised machine learning approach to automatically annotate medical concepts in the narrative.RESULTS:Evaluation of system adoption was performed by a mixed methods approach applying The Unified Theory of Acceptance and Use of Technology (UTAUT) as a theoretical lens. Most of the respondents demonstrated a high degree of system acceptance and expressed a positive attitude towards the system in general and intention to use the system in the future. Increased detection of patient allergies, and thus improved quality of practice and patient safety during surgery or ICU stays, was perceived as the most important advantage of the system.CONCLUSIONS:Our combined machine learning and rule-based approach benefits system performance, efficiency, and interpretability. The results demonstrate that the proposed CDSS increases detection of patient allergies, and that the system received high-level acceptance by the clinicians using it. Useful recommendations for further system improvements and implementation initiatives are reducing the quantity of alarms, expansion of the system to include more clinical concepts, closer EHR system integration, and more workstations available at point of care.

BackgroundData mining of electronic health records (EHRs) has a huge potential for improving clinical decision support and to help healthcare deliver precision medicine. Unfortunately, the rule-based and machine learning-based approaches used for natural language processing (NLP) in healthcare today all struggle with various shortcomings related to performance, efficiency, or transparency.MethodsIn this paper, we address these issues by presenting a novel method for NLP that implements unsupervised learning of word embeddings, semi-supervised learning for simplified and accelerated clinical vocabulary and concept building, and deterministic rules for fine-grained control of information extraction. The clinical language is automatically learnt, and vocabulary, concepts, and rules supporting a variety of NLP downstream tasks can further be built with only minimal manual feature engineering and tagging required from clinical experts. Together, these steps create an open processing pipeline that gradually refines the data in a transparent way, which greatly improves the interpretable nature of our method. Data transformations are thus made transparent and predictions interpretable, which is imperative for healthcare. The combined method also has other advantages, like potentially being language independent, demanding few domain resources for maintenance, and able to cover misspellings, abbreviations, and acronyms. To test and evaluate the combined method, we have developed a clinical decision support system (CDSS) named Information System for Clinical Concept Searching (ICCS) that implements the method for clinical concept tagging, extraction, and classification.ResultsIn empirical studies the method shows high performance (recall 92.6%, precision 88.8%, F-measure 90.7%), and has demonstrated its value to clinical practice. Here we employ a real-life EHR-derived dataset to evaluate the method's performance on the task of classification (i.e., detecting patient allergies) against a range of common supervised learning algorithms. The combined method achieves state-of-the-art performance compared to the alternative methods we evaluate. We also perform a qualitative analysis of common word embedding methods on the task of word similarity to examine their potential for supporting automatic feature engineering for clinical NLP tasks.ConclusionsBased on the promising results, we suggest more research should be aimed at exploiting the inherent synergies between unsupervised, supervised, and rule-based paradigms for clinical NLP.

Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classification, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches a single literal. We finally analyze CSC-TM power consumption and derive new convergence properties.

Tsetlin Machines (TsMs) are a promising and interpretable machine learning method which can be applied for various classification tasks. We present an exact encoding of TsMs into propositional logic and formally verify properties of TsMs using a SAT solver. In particular, we introduce in this work a notion of similarity of machine learning models and apply our notion to check for similarity of TsMs. We also consider notions of robustness and equivalence from the literature and adapt them for TsMs. Then, we show the correctness of our encoding and provide results for the properties: adversarial robustness, equivalence, and similarity of TsMs. In our experiments, we employ the MNIST and IMDB datasets for (respectively) image and sentiment classification. We discuss the results for verifying robustness obtained with TsMs with those in the literature obtained with Binarized Neural Networks on MNIST.

Logic-based machine learning has the crucial advantage of transparency. However, despite significant recent progress, further research is needed to close the accuracy gap between logic-based architectures and deep neural network ones. This paper introduces a novel variant of the Tsetlin machine (TM) that randomly drops clauses, the logical learning element of TMs. In effect, TM with Drop Clause ignores a random selection of the clauses in each epoch, selected according to a predefined probability. In this way, the TM learning phase becomes more diverse. To explore the effects that Drop Clause has on accuracy, training time and robustness, we conduct extensive experiments on nine benchmark datasets in natural language processing (IMDb, R8, R52, MR, and TREC) and image classification (MNIST, Fashion MNIST, CIFAR-10, and CIFAR-100). Our proposed model outperforms baseline machine learning algorithms by a wide margin and achieves competitive performance compared with recent deep learning models, such as BERT-Large and AlexNet-DFA. In brief, we observe up to +10% increase in accuracy and 2x to 4x faster learning than for the standard TM. We visualize the patterns learnt by Drop Clause TM in the form of heatmaps and show evidence of the ability of drop clause to learn more unique and discriminative patterns. We finally evaluate how Drop Clause affects learning robustness by introducing corruptions and alterations in the image/language test data, which exposes increased learning robustness.

Detecting visually similar images is a particularly useful attribute to look to when calculating product recommendations. Embedding similarity, which utilizes pre-trained computer vision models to extract high-level image features, has demonstrated remarkable efficacy in identifying images with similar compositions. However, there is a lack of methods for evaluating the embeddings generated by these models, as conventional loss and performance metrics do not adequately capture their performance in image similarity search tasks. In this paper, we evaluate the viability of the image embeddings from numerous pre-trained computer vision models using a novel approach named CorrEmbed. Our approach computes the correlation between distances in image embeddings and distances in human-generated tag vectors. We extensively evaluate numerous pre-trained Torchvision models using this metric, revealing an intuitive relationship of linear scaling between ImageNet1k accuracy scores and tag-correlation scores. Importantly, our method also identifies deviations from this pattern, providing insights into how different models capture high-level image features. By offering a robust performance evaluation of these pre-trained models, CorrEmbed serves as a valuable tool for researchers and practitioners seeking to develop effective, data-driven approaches to similar item recommendations in fashion retail. All code and experiments are openly available at https://github.com/cair/CorrEmbed_Evaluating_Pre-train ed_Model_Efficacy/tree/main .

Recommendation Systems (RSs) are ubiquitous in modern society and are one of the largest points of interaction between humans and AI. Modern RSs are often implemented using deep learning models, which are infamously difficult to interpret. This problem is particularly exasperated in the context of recommendation scenarios, as it erodes the user's trust in the RS. In contrast, the newly introduced Tsetlin Machines (TM) possess some valuable properties due to their inherent interpretability. TMs are still fairly young as a technology. As no RS has been developed for TMs before, it has become necessary to perform some preliminary research regarding the practicality of such a system. In this paper, we develop the first RS based on TMs to evaluate its practicality in this application domain. This paper compares the viability of TMs with other machine learning models prevalent in the field of RS. We train and investigate the performance of the TM compared with a vanilla feed-forward deep learning model. These comparisons are based on model performance, interpretability/explainability, and scalability. Further, we provide some benchmark performance comparisons to similar machine learning solutions relevant to RSs.

This paper introduces an interpretable contextual bandit algorithm using Tsetlin Machines, which solves complex pattern recognition tasks using propositional logic. The proposed bandit learning algorithm relies on straightforward bit manipulation, thus simplifying computation and interpretation. We then present a mechanism for performing Thompson sampling with Tsetlin Machine, given its non-parametric nature. Our empirical analysis shows that Tsetlin Machine as a base contextual bandit learner outperforms other popular base learners on eight out of nine datasets. We further analyze the interpretability of our learner, investigating how arms are selected based on propositional expressions that model the context.

Brain tumour segmentation on 3D MRI imaging is one of the most critical deep learning applications. In this paper, for the segmentation of tumour sub-regions in brain MRI images, we study some popular architecture for medical imaging segmentation. We further, inspired by them, proposed an architecture that is an end-to-end trainable, fully convolutional neural network that uses attention block to learn localization of different features of the multiple sub-regions of a tumour. We also experiment with a combination of the weighted cross-entropy loss function and dice loss function on the model’s performance and the quality of the output segmented labels. The results of the evaluation of our model are received through BraTS’19 dataset challenge. The model can achieve a dice score of 0.80 for the whole tumour segmentation and dice scores of 0.639 and 0.536 for the other two sub-regions within the tumour on the validation dataset.

Tsetlin Machine (TM) has been gaining popularity as an inherently interpretable machine leaning method that is able to achieve promising performance with low computational complexity on a variety of applications. The interpretability and the low computational complexity of the TM are inherited from the Boolean expressions for representing various sub-patterns. Although possessing favorable properties, TM has not been the go-to method for AI applications, mainly due to its conceptual and theoretical differences compared with perceptrons and neural networks, which are more widely known and well understood. In this paper, we provide detailed insights for the operational concept of the TM, and try to bridge the gap in the theoretical understanding between the perceptron and the TM. More specifically, we study the operational concept of the TM following the analytical structure of perceptrons, showing the resemblance between the perceptrons and the TM. Through the analysis, we indicated that the TM's weight update can be considered as a special case of the gradient weight update. We also perform an empirical analysis of TM by showing the flexibility in determining the clause length, visualization of decision boundaries and obtaining interpretable boolean expressions from TM. In addition, we also discuss the advantages of TM in terms of its structure and its ability to solve more complex problems.

Tsetlin Machines learn from input data by creating patterns in propositional logical, using the literals available in the data. These patterns vote for the classes in a classification task. Despite their simplistic premise, Tsetlin machine (TM)s have been performing at with other popular machine learning methods across various benchmarks. Not only accuracy, TMs also perform well in terms of energy efficiency and learning speed. The general TM scheme works best when there is sufficient discriminatory information available between two classes. In this paper, we explore the use of focused negative sampling (FNS) to discriminate between classes which are not easily distinguishable from each other. We carry out experiments across diverse classification tasks ranging over natural language processing, image processing, reinforcement learning to show that this approach forces the TM to arrive at patterns that can successfully tell apart two classes that are correlated. Further, we show that the proposed method achieves accuracy comparable to a vanilla Tsetlin Machine approach but in approximately 42% less number of epochs on average.

In this paper, we introduce a novel application of using scene semantic image segmentation for fire emergency situation analysis. To analyse a fire emergency scene, we propose to use deep convolutional image segmentation networks to identify and classify objects in a scene based on their build material and their vulnerability to catch fire. We introduce our own fire emergency scene segmentation dataset for this purpose. It consists of real world images with objects annotated on the basis of their build material. We use state-of-the-art segmentation models: DeepLabv3, DeepLabv3+, PSPNet, FCN, SegNet and UNet to compare and evaluate their performance on the fire emergency scene parsing task. During inference time, we only run the encoder (backbone) network to determine whether there is a fire or not in the image. If there is a fire, only then the decoder is activated to segment the emergency scene. This results in dispensing with unnecessary computation, i.e. the decoder. We achieve this by using multitask learning. We show the importance of transfer learning and the difference in performance between models pretrained on different benchmark datasets. The results show that segmentation models can accurately analyse an emergency situation, if properly trained to do so. Our fire emergency scene parsing dataset is available here: https://github.com/cair .