Reviewerly/exHarmony huggingface dataset:
A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models

Machine learning models may not be able to effectively learn and predict from imbalanced data in the fields of machine learning and data mining. This study proposed a method for analyzing the performance impact of imbalanced binary data on machine learning models. It systematically analyzes 1. the relationship between varying performance in machine learning models and imbalance rate (IR); 2. the performance stability of machine learning models on imbalanced binary data. In the proposed method, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced dataset with gradually varying IR. Then, in order to obtain more objective classification results, the evaluation metric AFG, arithmetic mean of area under the receiver operating characteristic curve (AUC), F-measure and G-mean are used to evaluate the classification performance of machine learning models. Finally, based on AFG and coefficient of variation (CV), the performance stability evaluation method of machine learning models is proposed. Experiments of eight widely used machine learning models on 48 different imbalanced datasets demonstrate that the classification performance of machine learning models decreases with the increase of IR on the same imbalanced data. Meanwhile, the classification performances of LR, DT and SVC are unstable, while GNB, BNB, KNN, RF and GBDT are relatively stable and not susceptible to imbalanced data. In particular, the BNB has the most stable classification performance. The Friedman and Nemenyi post hoc statistical tests also confirmed this result. The SMOTE method is used in oversampling-based imbalanced data augmentation, and determining whether other oversampling methods can obtain consistent results needs further research. In the future, an imbalanced data augmentation algorithm based on undersampling and hybrid sampling should be used to analyze the performance impact of imbalanced binary data on machine learning models.
Deep Learning-Based Human Body Posture Recognition and Tracking for Unmanned Aerial Vehicles

For many applications (e.g., surveillance and disaster response), situational awareness is essential. In these applications, human body posture recognition in real time plays a crucial role for corresponding response. Traditional posture recognition suffers from accuracy, due to the low robustness against uncertainty. Those uncertainties include variation from the environment (e.g., viewpoint, illumination and occlusion) and the postures (e.g., ambiguous posture and the overlap of multiple people). This paper proposed a drone surveillance system to distinguish human behaviors among violent, normal and help needed based on deep learning approach under the influence of those uncertainties. First, the real-time pose estimation is performed by the OpenPose network, and then the DeepSort algorithm is applied for tracking multi-person. The deep neural network model (YOLO) is trained to recognize each person’s postures based on a single frame of joints obtained from OpenPose. Finally, the fuzzy logic is applied to interpret those postures. The trained deep learning model is evaluated via the metrics (accuracy, precision, recall, P-R curve and F1 score). The empirical results show the proposed drone surveillance system can effectively recognize the targeted human behaviors with strong robustness in the presence of uncertainty and operated efficiently with high real-time performance.
The MeshCODE to scale—visualising synaptic binary information

The Mercator projection map of the world provides a useful, but distorted, view of the relative scale of countries. Current cellular models suffer from a similar distortion. Here, we undertook an in-depth structural analysis of the molecular dimensions in the cell's computational machinery, the MeshCODE, that is assembled from a meshwork of binary switches in the scaffolding proteins talin and vinculin. Talin contains a series of force-dependent binary switches and each domain switching state introduces quantised step-changes in talin length on a micrometre scale. The average dendritic spine is 1 μm in diameter so this analysis identifies a plausible Gearbox-like mechanism for dynamic regulation of synaptic function, whereby the positioning of enzymes and substrates relative to each other, mechanically-encoded by the MeshCODE switch patterns, might control synaptic transmission. Based on biophysical rules and experimentally derived distances, this analysis yields a novel perspective on biological digital information.
BoxPaste: An Effective Data Augmentation Method for SAR Ship Detection

Data augmentation is a crucial technique for convolutional neural network (CNN)-based object detection. Thus, this work proposes BoxPaste, a simple but powerful data augmentation method appropriate for ship detection in Synthetic Aperture Radar (SAR) imagery. BoxPaste crops ship objects from one SAR image using bounding box annotations and pastes them on another SAR image to artificially increase the object density in each training image. Furthermore, we dive deep into the characteristics of the SAR ship detection task and draw a principle for designing a SAR ship detector—light models may perform better. Our proposed data augmentation method and modified ship detector attain a 95.5% Average Precision (AP) and 96.6% recall on the SAR Ship Detection Dataset (SSDD), 4.7% and 5.5% higher than the fully convolutional one-stage (FCOS) object detection baseline method. Furthermore, we also combine our data augmentation scheme with two current detectors, RetinaNet and adaptive training sample selection (ATSS), to validate its effectiveness. The experimental results demonstrate that our newly proposed SAR-ATSS architecture achieves 96.3% AP, employing ResNet-50 as the backbone. The experimental results show that the method can significantly improve detection performance.
Ensembles of Random SHAPs

The ensemble-based modifications of the well-known SHapley Additive exPlanations (SHAP) method for the local explanation of a black-box model are proposed. The modifications aim to simplify the SHAP which is computationally expensive when there is a large number of features. The main idea behind the proposed modifications is to approximate the SHAP by an ensemble of SHAPs with a smaller number of features. According to the first modification, called the ER-SHAP, several features are randomly selected many times from the feature set, and the Shapley values for the features are computed by means of “small” SHAPs. The explanation results are averaged to obtain the final Shapley values. According to the second modification, called the ERW-SHAP, several points are generated around the explained instance for diversity purposes, and the results of their explanation are combined with weights depending on the distances between the points and the explained instance. The third modification, called the ER-SHAP-RF, uses the random forest for a preliminary explanation of the instances and determines a feature probability distribution which is applied to the selection of the features in the ensemble-based procedure of the ER-SHAP. Many numerical experiments illustrating the proposed modifications demonstrate their efficiency and properties for a local explanation.
Earth system science applications of next-generation SEM-EDS automated mineral mapping

Sedimentary rocks contain a unique record of the evolution of the Earth system. Deciphering this record requires a robust understanding of the identity, origin, composition, and post-depositional history of individual constituents. Petrographic analysis informed by Scanning Electron Microscope - Energy Dispersive Spectroscopy (SEM-EDS) mineral mapping can reveal the mineral identity, morphology and petrological context of each imaged grain, making it a valuable tool in the Earth Scientist’s analytical arsenal. Recent technological developments, including quantitative deconvolution of mixed-phase spectra (producing “mixels”), now allow rapid quantitative SEM-EDS-based analysis of a broad range of sedimentary rocks, including the previously troublesome fine-grained lithologies that comprise most of the sedimentary record. Here, we test the reliability and preferred mineral mapping work flow of a modern Field-Emission scanning electron microscope equipped with the Thermofisher Scientific Maps Mineralogy mineral mapping system, focusing on mud/siltstones and calcareous shales. We demonstrate that SEM-EDS mineral mapping that implements 1) a strict error minimization spectral matching approach and 2) spectral deconvolution to produce ‘mixels’ for mixed-phase X-ray volumes can robustly identify individual grains and produce quantitative mineralogical data sets comparable to conventional X-ray diffraction (XRD) analysis ( R 2 &amp;gt; 0.95). The correlation between SEM-EDS and XRD-derived mineralogy is influenced by mineral abundance, processing modes and mapped area characteristics. Minerals with higher abundance (&amp;gt;10 wt%) show better correlation, likely the result of increased uncertainty for XRD quantification of low-abundance phases. Automated spectral deconvolution to produce ‘mixels’ greatly reduces the proportion of unclassified pixels, especially in the fine-grained fraction, ultimately improving mineral identification and quantification. Mapping of larger areas benefits bulk mineralogy analysis, while customized area size and shape allows high-resolution in situ mineralogical analysis. Finally, we review SEM-EDS-based mineral mapping applications in the Earth Sciences, via case studies illustrating 1) approaches for the quantitative differentiation of various mineral components including detrital (allogenic), syndepositional (authigenic) and burial diagenetic phases, 2) the origin and significance of lamination, 3) the effectiveness and appropriateness of sequential leaching in geochemical studies, and 4) the utility of mineral maps to identify target grains within specific petrological contexts for in situ geochemical or geochronological analysis.
Effective hospital readmission prediction models using machine-learned features

Abstract Background: Hospital readmissions are one of the costliest challenges facing healthcare systems, but conventional models fail to predict readmissions well. Many existing models use exclusively manually-engineered features, which are labor intensive and dataset-specific. Our objective was to develop and evaluate models to predict hospital readmissions using derived features that are automatically generated from longitudinal data using machine learning techniques. Methods: We studied patients discharged from acute care facilities in 2015 and 2016 in Alberta, Canada, excluding those who were hospitalized to give birth or for a psychiatric condition. We used population-level linked administrative hospital data from 2011 to 2017 to train prediction models using both manually derived features and features generated automatically from observational data. The target value of interest was 30-day all-cause hospital readmissions, with the success of prediction measured using the area under the curve (AUC) statistic. Results: Data from 428,669 patients (62% female, 38% male, 27% 65 years or older) were used for training and evaluating models: 24,974 (5.83%) were readmitted within 30 days of discharge for any reason. Patients were more likely to be readmitted if they utilized hospital care more, had more physician office visits, had more prescriptions, had a chronic condition, or were 65 years old or older. The LACE readmission prediction model had an AUC of 0.66 ± 0.0064 while the machine learning model’s test set AUC was 0.83 ± 0.0045, based on learning a gradient boosting machine on a combination of machine-learned and manually-derived features. Conclusion: Applying a machine learning model to the computer-generated and manual features improved prediction accuracy over the LACE model and a model that used only manually-derived features. Our model can be used to identify high-risk patients, for whom targeted interventions may potentially prevent readmissions.
Factorizer: A scalable interpretable approach to context modeling for medical image segmentation

Convolutional Neural Networks (CNNs) with U-shaped architectures have dominated medical image segmentation, which is crucial for various clinical purposes. However, the inherent locality of convolution makes CNNs fail to fully exploit global context, essential for better recognition of some structures, e.g., brain lesions. Transformers have recently proven promising performance on vision tasks, including semantic segmentation, mainly due to their capability of modeling long-range dependencies. Nevertheless, the quadratic complexity of attention makes existing Transformer-based models use self-attention layers only after somehow reducing the image resolution, which limits the ability to capture global contexts present at higher resolutions. Therefore, this work introduces a family of models, dubbed Factorizer, which leverages the power of low-rank matrix factorization for constructing an end-to-end segmentation model. Specifically, we propose a linearly scalable approach to context modeling, formulating Nonnegative Matrix Factorization (NMF) as a differentiable layer integrated into a U-shaped architecture. The shifted window technique is also utilized in combination with NMF to effectively aggregate local information. Factorizers compete favorably with CNNs and Transformers in terms of accuracy, scalability, and interpretability, achieving state-of-the-art results on the BraTS dataset for brain tumor segmentation and ISLES'22 dataset for stroke lesion segmentation. Highly meaningful NMF components give an additional interpretability advantage to Factorizers over CNNs and Transformers. Moreover, our ablation studies reveal a distinctive feature of Factorizers that enables a significant speed-up in inference for a trained Factorizer without any extra steps and without sacrificing much accuracy. The code and models are publicly available at https://github.com/pashtari/factorizer.
Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory

We present a motion planning algorithm for a class of uncertain control-affine nonlinear systems which guarantees runtime safety and goal reachability when using high-dimensional sensor measurements (e.g., RGB-D images) and a learned perception module in the feedback control loop. First, given a dataset of states and observations, we train a perception system that seeks to invert a subset of the state from an observation, and estimate an upper bound on the perception error which is valid with high probability in a trusted domain near the data. Next, we use contraction theory to design a stabilizing state feedback controller and a convergent dynamic state observer which uses the learned perception system to update its state estimate. We derive a bound on the trajectory tracking error when this controller is subjected to errors in the dynamics and incorrect state estimates. Finally, we integrate this bound into a sampling-based motion planner, guiding it to return trajectories that can be safely tracked at runtime using sensor data. We demonstrate our approach in simulation on a 4D car, a 6D planar quadrotor, and a 17D manipulation task with RGB(-D) sensor measurements, demonstrating that our method safely and reliably steers the system to the goal, while baselines that fail to consider the trusted domain or state estimation errors can be unsafe.
Parallelized integrated nested Laplace approximations for fast Bayesian inference

There is a growing demand for performing larger-scale Bayesian inference tasks, arising from greater data availability and higher-dimensional model parameter spaces. In this work we present parallelization strategies for the methodology of integrated nested Laplace approximations (INLA), a popular framework for performing approximate Bayesian inference on the class of Latent Gaussian models. Our approach makes use of nested thread-level parallelism, a parallel line search procedure using robust regression in INLA's optimization phase and the state-of-the-art sparse linear solver PARDISO. We leverage mutually independent function evaluations in the algorithm as well as advanced sparse linear algebra techniques. This way we can flexibly utilize the power of today's multi-core architectures. We demonstrate the performance of our new parallelization scheme on a number of different real-world applications. The introduction of parallelism leads to speedups of a factor 10 and more for all larger models. Our work is already integrated in the current version of the open-source R-INLA package, making its improved performance conveniently available to all users.
DyRep: Bootstrapping Training with Dynamic Re-parameterization

Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks. Despite the prevalence, current Rep methods simply re-parameterize all operations into an augmented network, including those that rarely contribute to the model's performance. As such, the price to pay is an expensive computational overhead to manipulate these unnecessary behaviors. To eliminate the above caveats, we aim to boot-strap the training with minimal cost by devising a dynamic re-parameterization (DyRep) method, which encodes Rep technique into the training process that dynamically evolves the network structures. Concretely, our proposal adaptively finds the operations which contribute most to the loss in the network, and applies Rep to enhance their representational capacity. Besides, to suppress the noisy and redundant operations introduced by Rep, we devise a de-parameterization technique for a more compact re-parameterization. With this regard, DyRep is more efficient than Rep since it smoothly evolves the given network instead of constructing an over-parameterized network. Experimental results demonstrate our effectiveness, e.g., DyRep improves the accuracy of ResNet-18 by 2.04% on ImageNet and reduces 22% runtime over the baseline. Code is avail-able at: https://github.com/hunto/DyRep.
Learning Ego 3D Representation as Ray Tracing

A self-driving perception model aims to extract 3D semantic representations from multiple cameras collectively into the bird’s-eye-view (BEV) coordinate frame of the ego car in order to ground downstream planner. Existing perception methods often rely on error-prone depth estimation of the whole scene or learning sparse virtual 3D representations without the target geometry structure, both of which remain limited in performance and/or capability. In this paper, we present a novel end-to-end architecture for ego 3D representation learning from an arbitrary number of unconstrained camera views. Inspired by the ray tracing principle, we design a polarized grid of “imaginary eyes” as the learnable ego 3D representation and formulate the learning process with the adaptive attention mechanism in conjunction with the 3D-to-2D projection. Critically, this formulation allows extracting rich 3D representation from 2D images without any depth supervision, and with the built-in geometry structure consistent w.r.t BEV. Despite its simplicity and versatility, extensive experiments on standard BEV visual tasks (e.g., camera-based 3D object detection and BEV segmentation) show that our model outperforms all state-of-the-art alternatives significantly, with an extra advantage in computational efficiency from multi-task learning.
Exploratory analysis and implementation of machine learning techniques for predictive assessment of fraud in banking systems

Regarding combating financial fraud in banking systems, this study analyzes machine learning algorithms used in predictive assessments for fraud detection. For a given methodological approach, a database with more than six million records referring to the financial transactions of a given banking organization is used. First, an exploratory data analysis clarifies the main variables influencing the evaluation process, with binary and financial percentages related to fraud loss. Concerning the unbalance between the expected records and those classified as fraud, Random Under Sampling, SMOTE, and ADASYN techniques are used to balance and train these bases by Logistic Regression, Naive Bayes, KNN, and Perceptron techniques. With the implementation of machine learning algorithms, we present the main feasibilities of each model in each scenario. At the end of the study, we expose the final considerations and proposals for future work.
Detection-Friendly Dehazing: Object Detection in Real-World Hazy Scenes

Adverse weather conditions in real-world scenarios lead to performance degradation of deep learning-based detection models. A well-known method is to use image restoration methods to enhance degraded images before object detection. However, how to build a positive correlation between these two tasks is still technically challenging. The restoration labels are also unavailable in practice. To this end, taking the hazy scene as an example, we propose a union architecture BAD-Net that connects the dehazing module and detection module in an end-to-end manner. Specifically, we design a two-branch structure with an attention fusion module for fully combining hazy and dehazing features. This reduces bad impacts on the detection module when the dehazing module performs poorly. Besides, we introduce a self-supervised haze robust loss that enables the detection module to deal with different degrees of haze. Most importantly, an interval iterative data refinement training strategy is proposed to guide the dehazing module learning with weak supervision. BAD-Net improves further detection performance through detection-friendly dehazing. Extensive experiments on RTTS and VOChaze datasets show that BAD-Net achieves higher accuracy compared to the recent state-of-the-art methods. It is a robust detection framework for bridging the gap between low-level dehazing and high-level detection.