Reviewerly/exHarmony huggingface dataset:
Drivers of gender differentiated self‐employment in developing countries: The instances of finance and information and communication technology

Abstract This study unveils new insights into how financial inclusion and information and communication technology (ICT) influence self‐employment (aggregate and gender‐specific) in developing countries. The dearth of information in this perspective, particularly in the context of Africa, compels this fresh evaluation. Thus, on account of the annual panel series for 52 African countries from 2005 to 2019, the system‐generalized method‐of‐moments (sysGMM) and the novel method‐of‐moments quantile regression (MM‐QR), the following empirical narratives emerged. First, the estimates of both procedures demonstrate that self‐employment in Africa is self‐exacerbating. Second, the sysGMM demonstrates that financial inclusions could not influence self‐employment substantially; however, the estimates of the MM‐QR demonstrate varying significant positive relationships mostly at the upper quantiles for all categories of self‐employment. Third, the sysGMM reveals that ICT produced marginal positive effects on all categories except women's self‐employment. Fourth, the MM‐QR rectified a significant positive influence of ICT at the lower and middle quantiles for the aggregate, only at the lower quantiles (q10–q25) for men, and at the upper quantiles (q75–q90) for women. Lastly, it was revealed that although unemployment, GDP and inflation rates are not sufficient drivers of self‐employment, energy poverty constitutes a drag for self‐employment in the continent. Overall, the study is of immense relevance to policymakers, practicing and prospective entrepreneurs particularly in developing countries. More policy options have been provided within the study.
Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model

An automated segmentation method for computed tomography (CT) images of liver tumors is an urgent clinical need. Tumor areas within liver cancer images are easily missed as they are small and have unclear borders. To address these issues, an improved liver tumor segmentation method based on U-Net is proposed. This involves incorporating attention mechanisms into the U-Net’s skip connections, giving higher weights to important regions. Through dynamically adjusting the attention recognition characteristics, the method achieves accurate localization that is focused on and discriminates target regions. Testing using the LiTS (liver tumor segmentation) public dataset resulted in a Dice similarity coefficient of 0.69. The experiments demonstrated that this method can accurately segment liver tumors.
Implementing a Rules-Based Chatbot for Reference Service at a Large University Library

A library's website is the virtual front door for many of our users.Libraries offer online assistance in several ways, including a variety of options to request help.Live chat and email services are not new, but the recent development of chatbots provides opportunities to rethink and enhance online support.While some chatbots or virtual assistants generate automatic and custom responses to user input, this paper explores the implementation of a rules-based chatbot, where all responses have been input and configured by library staff.Details are provided to describe the process before, during, and after the initial implementation with data and observations from the pilot in mid-2023.
The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Abstract Autoregressive Neural Networks (ARNNs) have shown exceptional results in generation tasks across image, language, and scientific domains. Despite their success, ARNN architectures often operate as black boxes without a clear connection to underlying physics or statistical models. This research derives an exact mapping of the Boltzmann distribution of binary pairwise interacting systems in autoregressive form. The parameters of the ARNN are directly related to the Hamiltonian’s couplings and external fields, and commonly used structures like residual connections and recurrent architecture emerge from the derivation. This explicit formulation leverages statistical physics techniques to derive ARNNs for specific systems. Using the Curie–Weiss and Sherrington–Kirkpatrick models as examples, the proposed architectures show superior performance in replicating the associated Boltzmann distributions compared to commonly used designs. The findings foster a deeper connection between physical systems and neural network design, paving the way for tailored architectures and providing a physical lens to interpret existing ones.
Adaptive and robust fractional gain based interpolatory cubature Kalman filter

In this study, we put forward the robust fractional gain based interpolatory cubature Kalman filter (FGBICKF) and the adaptive FGBICKF (AFGBICKF) for the development of the state estimators for stochastic nonlinear dynamics system. FGBICKF introduces a fractional gain to interpolatory cubature Kalman filter to increase the robustness of state estimation. AFGBICKF is developed to enhance the state estimation adaptive to stochastic nonlinear dynamics system with unknown process noise covariance through recursive estimation. The simulations on re-entry target tracking system have shown that the performance of FGBICKF is superior to that of cubature Kalman filter and interpolatory cubature Kalman filter, and standard deviation of FGBICKF is closer to posterior Cramér-Rao lower bound. Moreover, our simulations have also demonstrated that AFGBICKF remains stable even when the initial process noise covariance increase, proving its adaptiveness, robustness, and effectiveness on state estimation.
CSI-Based Location Independent Human Activity Recognition Using Deep Learning

Abstract Human Activity Recognition (HAR) is widely used in various applications, from smart homes and healthcare to the Internet of Things (IoT) and virtual reality gaming. However, existing HAR technologies suffer from limitations such as location dependency, sensitivity to noise and interference, and lack of flexibility in recognizing diverse activities and environments. In this paper, we present a novel approach to HAR that addresses these challenges and enables real-time classification and absolute location-independent sensing. The approach is based on an adaptive algorithm that leverages sequential learning activity features to simplify the recognition process and accommodate variations in human activities across different people and environments by extracting the features that match the signal with the surroundings. We employ the Raspberry Pi 4 and Channel State Information (CSI) data to extract activity recognition data, which provides reliable and high-quality signal information. We propose a signal segmentation method using the Long Short-Term Memory (LSTM) algorithm to accurately determine the start and endpoint of human activities. Our experiments show that our approach achieves a high accuracy of up to 97% in recognizing eight activities and mapping activities associated with environments that were not used in training. The approach represents a significant advancement in HAR technology and has the potential to revolutionize many domains, including healthcare, smart homes, and IoT.
Teaching Strategies Against Artificial Intelligence in the Learning of 21st Century Students

Objective: Analyze pedagogical strategies regarding artificial intelligence.&#x0D; &#x0D; Methods: A documentary exploration was used with the use of the PRISMA model, 33 documents were studied.&#x0D; &#x0D; Results: The results indicated that the variable, teaching strategies, must have characteristics such as innovation, flexibility, criticism, prospective and guiding level, which allows facing the interference of artificial intelligence that has levels of understanding and versatility with applications. and uses.&#x0D; &#x0D; Conclusions: Students can use Artificial Intelligence appropriately to develop their knowledge, considering that its trend will increase, and teachers must adapt to the new technological reality.
Non-uniform Sampling-Based Breast Cancer Classification

The emergence of deep learning models and their remarkable success in visual object recognition and detection have fueled the medical imaging community's interest in integrating these algorithms to improve medical screening and diagnosis. However, natural images, which have been the main focus of deep learning models, and medical images, such as mammograms, have fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions. To fit these images to deep learning approaches, they must be heavily downsampled. Otherwise, models that address high-resolution mammograms require many exams and complex architectures. Spatially resizing mammograms leads to losing discriminative details that are essential for accurate diagnosis. To address this limitation, we develop an approach to exploit the relative importance of pixels in mammograms by conducting non-uniform sampling based on task-salient regions generated by a convolutional network. Classification results demonstrate that non-uniformly sampled images preserve discriminant features requiring lower resolutions to outperform their uniformly sampled counterparts.
ARHNet: Adaptive Region Harmonization for Lesion-Aware Augmentation to Improve Segmentation Performance

Accurately segmenting brain lesions in MRI scans is critical for providing patients with prognoses and neurological monitoring. However, the performance of CNN-based segmentation methods is constrained by the limited training set size. Advanced data augmentation is an effective strategy to improve the model's robustness. However, they often introduce intensity disparities between foreground and background areas and boundary artifacts, which weakens the effectiveness of such strategies. In this paper, we propose a foreground harmonization framework (ARHNet) to tackle intensity disparities and make synthetic images look more realistic. In particular, we propose an Adaptive Region Harmonization (ARH) module to dynamically align foreground feature maps to the background with an attention mechanism. We demonstrate the efficacy of our method in improving the segmentation performance using real and synthetic images. Experimental results on the ATLAS 2.0 dataset show that ARHNet outperforms other methods for image harmonization tasks, and boosts the down-stream segmentation performance. Our code is publicly available at https://github.com/King-HAW/ARHNet .
Normative Aging for an Individual’s Full Brain MRI Using Style GANs to Detect Localized Neurodegeneration

In older adults, changes in brain structure can be used to identify and predict the risk of neurodegenerative disorders and dementias. Traditional 'brainAge' methods seek to identify differences between chronological age and biological brain age predicted from MRI that can indicate deviations from normative aging trajectories. These methods provide one metric for the entire brain and lack anatomical specificity and interpretability. By predicting an individual's healthy brain at a specific age, one may be able to identify regional deviations and abnormalities in a true brain scan relative to healthy aging. This study aims to address the problem of domain transfer in estimating age-related brain differences. We develop a fully unsupervised generative adversarial network (GAN) with cycle consistency reconstruction losses, trained on 4,000 cross-sectional brain MRI data from UK Biobank participants aged 60 to 80. By converting the individual anatomic information from their T1-weighted MRI as "content" and adding the "style" information related to age and sex from a reference group, we demonstrate that brain MRIs for healthy males and females at any given age can be predicted from one cross-sectional scan. Paired with a full brain T1w harmonization method, this new MRI can also be generated for any image from any scanner. Results in the ADNI cohort showed that without relying on longitudinal data from the participants, our style-encoding domain transfer model might successfully predict cognitively normal follow-up brain MRIs. We demonstrate how variations from the expected structure are a sign of a potential risk for neurodegenerative diseases.
Towards Unified Modality Understanding for Alzheimer’s Disease Diagnosis Using Incomplete Multi-modality Data

Multi-modal neuroimaging data, e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET), has greatly advanced computer-aided diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, incomplete multi-modality data often limits the diagnostic performance of deep learning-based methods, as only partial data can be used for training neural networks, and meanwhile it is challenging to synthesize missing scans (e.g., PET) with meaningful patterns associated with AD. To this end, we propose a novel unified modality understanding network to directly extract discriminative features from incomplete multi-modal data for AD diagnosis. Specifically, the incomplete multi-modal neuroimages are first branched into the corresponding encoders to extract modality-specific features and a Transformer is then applied to adaptively fuse the incomplete multi-modal features for AD diagnosis. To alleviate the potential problem of domain shift due to incomplete multi-modal input, the cross-modality contrastive learning strategy is further leveraged to align the incomplete multi-modal features into a unified embedding space. On the other hand, the proposed network also employs inter-modality and intra-modality attention weights for achieving local- to-local and local-to-global attention consistency so as to better transfer the diagnostic knowledge from one modality to another. Meanwhile, we leverage multi-instance attention rectification to rectify the localization of AD-related atrophic area. Extensive experiments on ADNI datasets with 1,950 subjects demonstrate the superior performance of the proposed methods for AD diagnosis and MCI conversion prediction.
Delving into Ipsilateral Mammogram Assessment Under Multi-view Network

In many recent years, multi-view mammogram analysis has been focused widely on AI-based cancer assessment. In this work, we aim to explore diverse fusion strategies (average and concatenate) and examine the model's learning behavior with varying individuals and fusion pathways, involving Coarse Layer and Fine Layer. The Ipsilateral Multi-View Network, comprising five fusion types (Pre, Early, Middle, Last, and Post Fusion) in ResNet-18, is employed. Notably, the Middle Fusion emerges as the most balanced and effective approach, enhancing deep-learning models' generalization performance by +5.29% (concatenate) and +5.9% (average) in VinDr-Mammo dataset and +2.03% (concatenate) and +3% (average) in CMMD dataset on macro F1-Score. The paper emphasizes the crucial role of layer assignment in multi-view network extraction with various strategies.
Post-Deployment Adaptation with Access to Source Data via Federated Learning and Source-Target Remote Gradient Alignment

Deployment of Deep Neural Networks in medical imaging is hindered by distribution shift between training data and data processed after deployment, causing performance degradation. Post-Deployment Adaptation (PDA) addresses this by tailoring a pre-trained, deployed model to the target data distribution using limited labelled or entirely unlabelled target data, while assuming no access to source training data as they cannot be deployed with the model due to privacy concerns and their large size. This makes reliable adaptation challenging due to limited learning signal. This paper challenges this assumption and introduces FedPDA, a novel adaptation framework that brings the utility of learning from remote data from Federated Learning into PDA. FedPDA enables a deployed model to obtain information from source data via remote gradient exchange, while aiming to optimize the model specifically for the target domain. Tailored for FedPDA, we introduce a novel optimization method StarAlign (Source-Target Remote Gradient Alignment) that aligns gradients between source-target domain pairs by maximizing their inner product, to facilitate learning a target-specific model. We demonstrate the method's effectiveness using multi-center databases for the tasks of cancer metastases detection and skin lesion classification, where our method compares favourably to previous work. Code is available at: https://github.com/FelixWag/StarAlign .
MEDKD: Enhancing Medical Image Classification with Multiple Expert Decoupled Knowledge Distillation for Long-Tail Data

Medical image classification is a challenging task, particularly when dealing with long-tailed datasets where rare diseases are underrepresented. The imbalanced class distribution in such datasets poses significant challenges in accurately classifying minority classes. Existing methods for alleviating the long-tail problem in medical image classification suffer from limitations such as noise introduction, loss of crucial information, and the need for manual tuning and additional computational resources. In this study, we propose a novel framework called Multiple Expert Decoupled Knowledge Distillation (MEDKD) to tackle the imbalanced class distribution in medical image classification. The knowledge distillation of multiple teacher models can significantly alleviate the class imbalance by partitioning the dataset into several subsets. However, current frameworks of this kind have not yet explored the integration of more advanced distillation methods. Our framework incorporating TCKD and NCKD concepts to improve classification performance. Through comprehensive experiments on publicly available datasets, we evaluate the performance of MEDKD and compare it with state-of-the-art methods. Our results demonstrate remarkable accuracy improvements achieved by the proposed method, highlighting its effectiveness in alleviating the challenges of medical image classification with long-tailed datasets.
Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon

With the advent of digital scanners and deep learning, diagnostic operations may move from a microscope to a desktop. Hematoxylin and Eosin (H &E) staining is one of the most frequently used stains for disease analysis, diagnosis, and grading, but pathologists do need different immunohistochemical (IHC) stains to analyze specific structures or cells. Obtaining all of these stains (H &E and different IHCs) on a single specimen is a tedious and time-consuming task. Consequently, virtual staining has emerged as an essential research direction. Here, we propose a novel generative model, Structural Cycle-GAN (SC-GAN), for synthesizing IHC stains from H &E images, and vice versa. Our method expressly incorporates structural information in the form of edges (in addition to color data) and employs attention modules exclusively in the decoder of the proposed generator model. This integration enhances feature localization and preserves contextual information during the generation process. In addition, a structural loss is incorporated to ensure accurate structure alignment between the generated and input markers. To demonstrate the efficacy of the proposed model, experiments are conducted with two IHC markers emphasizing distinct structures of glands in the colon: the nucleus of epithelial cells (CDX2) and the cytoplasm (CK818). Quantitative metrics such as FID and SSIM are frequently used for the analysis of generative models, but they do not correlate explicitly with higher-quality virtual staining results. Therefore, we propose two new quantitative metrics that correlate directly with the virtual staining specificity of IHC markers.
Safe Feedback Motion Planning in Unknown Environments: An Instantaneous Local Control Barrier Function Approach

Abstract Mobile robots are desired with resilience to safely interact with prior-unknown environments and finally accomplish given tasks. This paper utilizes instantaneous local sensory data to stimulate the safe feedback motion planning (SFMP) strategy with adaptability to diverse prior-unknown environments without building a global map. This is achieved by the numerical optimization with the constraints, referred to as instantaneous local control barrier functions (IL-CBFs) and goal-driven control Lyapunov functions (GD-CLFs), learned from perceptional signals. In particular, the IL-CBFs reflecting potential collisions and GD-CLFs encoding incrementally discovered subgoals are first online learned from local perceptual data. Then, the learned IL-CBFs are united with GD-CLFs in the context of quadratic programming (QP) to generate the safe feedback motion planning strategy. Rather importantly, an optimization over the admissible control space of IL-CBFs is conducted to enhance the solution feasibility of QP. The SFMP strategy is developed with theoretically guaranteed collision avoidance and convergence to destinations. Numerical simulations are conducted to reveal the effectiveness of the proposed SFMP strategy that drives mobile robots to safely reach the destination incrementally in diverse prior-unknown environments.
Brain MRI to PET Synthesis and Amyloid Estimation in Alzheimer’s Disease via 3D Multimodal Contrastive GAN

Positron emission tomography (PET) can detect brain amyloid-β (Aβ) deposits, a diagnostic hallmark of Alzheimer's disease and a target for disease modifying treatment. However, PET-Aβ is expensive, not widely available, and, unlike magnetic resonance imaging (MRI), exposes the patient to ionizing radiation. Here we propose a novel 3D multimodal generative adversarial network with contrastive learning to synthesize PET-Aβ images from cheaper, more accessible, and less invasive MRI scans (T1-weighted and fluid attenuated inversion recovery [FLAIR] images). In tests on independent samples of paired MRI/PET-Aβ data, our synthetic PET-Aβ images were of high quality with a structural similarity index measure of 0.94, which outperformed previously published methods. We also evaluated synthetic PET-Aβ images by extracting standardized uptake value ratio measurements. The synthetic images could identify amyloid positive patients with a balanced accuracy of 79%, holding promise for potential future use in a diagnostic clinical setting.
Fact-Checking of AI-Generated Reports

With advances in generative artificial intelligence (AI), it is now possible to produce realistic-looking automated reports for preliminary reads of radiology images. However, it is also well-known that such models often hallucinate, leading to false findings in the generated reports. In this paper, we propose a new method of fact-checking of AI-generated reports using their associated images. Specifically, the developed examiner differentiates real and fake sentences in reports by learning the association between an image and sentences describing real or potentially fake findings. To train such an examiner, we first created a new dataset of fake reports by perturbing the findings in the original ground truth radiology reports associated with images. Text encodings of real and fake sentences drawn from these reports are then paired with image encodings to learn the mapping to real/fake labels. The examiner is then demonstrated for verifying automatically generated reports.