Majority Voting Research Articles

2023년 민사소송법 중요 판례 평석

This paper examines key civil procedure and execution law rulings issued by the Supreme Court in 2023. We analyze seven significant civil procedure precedents, focusing on: 1. Exclusive international jurisdiction agreements, 2. Ratification of objections and compulsory mediation, 3. Lease contract requirements and enforcement, 4. Protection by provisional seizure: Non-existence and cessation of prescription, 5. Causal relationship and burden of proof in medical malpractice, 6. Reconciliation replacement and res judicata in claim objection lawsuits, 7. Preservation of public property and cancellation registration lawsuits. Each precedent is categorized, summarized by facts, rulings of lower courts and the Supreme Court, with analysis of relevant law and the author's perspective. While some rulings involve international private law, civil law, or commercial law aspects, these are addressed only if relevant to civil procedure. The paper also briefly reviews four major rulings on civil execution law: 1. Indirect coercion, incidental obligations, and claim objection, 2. Execution claim seizure after full order service and its effect, 3. Public notice procedure violation and auction effect, 4. Distribution objection and lawsuit succession. Additionally, a section on “Other Civil Procedure Act Precedents” highlights notable rulings due to space constraints. While not exhaustive, this analysis aims to be a valuable resource for future civil procedure research.

ASEAN at the Crossroads of US-China Rivalry: The Role of Majority Voting and the Introduction of a Permanent Secretary-General

This paper examines ASEAN's need for structural reform amidst the US-China rivalry, highlighting the limitations of its consensus approach during crises. It proposes majority voting as an emergency measure and advocates for a Permanent Secretary-General position, ensuring consistent leadership. The analysis balances the benefits and challenges of these reforms, emphasizing the necessity for ASEAN to evolve while maintaining its core values, ensuring relevance and unity in addressing major geopolitical challenges.

AI-assisted automatic MRI-based tongue volume evaluation in motor neuron disease (MND)

PurposeMotor neuron disease (MND) causes damage to the upper and lower motor neurons including the motor cranial nerves, the latter resulting in bulbar involvement with atrophy of the tongue muscle. To measure tongue atrophy, an operator independent automatic segmentation of the tongue is crucial. The aim of this study was to apply convolutional neural network (CNN) to MRI data in order to determine the volume of the tongue.MethodsA single triplanar CNN of U-Net architecture trained on axial, coronal, and sagittal planes was used for the segmentation of the tongue in MRI scans of the head. The 3D volumes were processed slice-wise across the three orientations and the predictions were merged using different voting strategies. This approach was developed using MRI datasets from 20 patients with ‘classical’ spinal amyotrophic lateral sclerosis (ALS) and 20 healthy controls and, in a pilot study, applied to the tongue volume quantification to 19 controls and 19 ALS patients with the variant progressive bulbar palsy (PBP).ResultsConsensus models with softmax averaging and majority voting achieved highest segmentation accuracy and outperformed predictions on single orientations and consensus models with union and unanimous voting. At the group level, reduction in tongue volume was not observed in classical spinal ALS, but was significant in the PBP group, as compared to controls.ConclusionUtilizing single U-Net trained on three orthogonal orientations with consequent merging of respective orientations in an optimized consensus model reduces the number of erroneous detections and improves the segmentation of the tongue. The CNN-based automatic segmentation allows for accurate quantification of the tongue volumes in all subjects. The application to the ALS variant PBP showed significant reduction of the tongue volume in these patients and opens the way for unbiased future longitudinal studies in diseases affecting tongue volume.

An accurate hypertension detection model based on a new odd-even pattern using ballistocardiograph signals

BackgroundBallistocardiography (BCG) signals play an important role in identifying hypertension. These signals coupled with machine learning methods can be used for detecting hypertension. In this study, we presented a novel feature engineering architecture to detect hypertension using BCG signals. Methods and architectureIn this work, we used a publicly available BCG signal dataset to develop the novel model. Our model consists of a unique feature extraction process, named the Odd-Even Pattern (OEP). This technique generates three specific feature vectors based on alternating odd and even indices when fed with BCG signals. We employed singular pooling to address the shortcomings of OEP in extracting advanced-level features. This technique combines singular value decomposition with statistical feature extraction to capture the intricate details from the BCG signals. We obtained 14 features by merging OEP with statistical features. We then employed two advanced feature selectors yielding 28 selected feature vectors, hence the model is named as OddEven28. The classification is done using k-nearest neighbors (kNN) algorithm, along with iterative majority voting. ResultsOur proposed OddEven28 model achieved a classification accuracy of 97.78% using six different feature vectors on the dataset. ConclusionsOur developed OddEven28 architecture performed better than the deep learning-based models developed for the automated detection of hypertension using BCG signals. The robustness of the model can be improved by training with huge diverse data obtained from various centers.

Justifying political choice: from probability and institutions to communication

The article is devoted to the study of the dynamics of development of concepts that justify political choice. There is a general turn from the moral justification of political choice to ontological concepts based on the ideas of pragmatic and impartial behavior in the search for criteria for the “correct” decision, which includes the truth and the institutions that ensure it. The epistemic concept of democracy, based on Condorcet's jury theorem, defines the possibility of achieving the correct choice through majority rule voting, implies a competent, independent and sincere voter, which ensures an increase in the probability of the correct choice. The expansion of the composition of participants in political choice increased the role of diversity of knowledge and opinions in the justification, which led to the concept of the “smart plurality”, which ensures the correct choice is made by reducing its overall error through random mass participation. The development of social networks and their argumentative function in disputes have contributed to the transition to the concepts of argumentative networks that ensure the correct option is selected through communication. The identified dynamics allow us to conclude that the concept of politics is reviving its importance as a reasoning.

Diagnosis of Cotton Nitrogen Nutrient Levels Using Ensemble MobileNetV2FC, ResNet101FC, and DenseNet121FC

Nitrogen plays a crucial role in cotton growth, making the precise diagnosis of its nutrition levels vital for the scientific and rational application of fertilizers. Addressing this need, our study introduced an EMRDFC-based diagnosis model specifically for cotton nitrogen nutrition levels. In our field experiments, cotton was subjected to five different nitrogen application rates. To enhance the diagnostic capabilities of our model, we employed ResNet101, MobileNetV2, and DenseNet121 as base models and integrated the CBAM (Convolutional Block Attention Module) into each to improve their ability to differentiate among various nitrogen levels. Additionally, the Focal loss function was introduced to address issues of data imbalance. The model’s effectiveness was further augmented by employing integration strategies such as relative majority voting, simple averaging, and weighted averaging. Our experimental results indicated significant accuracy improvements in the enhanced ResNet101, MobileNetV2, and DenseNet121 models by 2.3%, 2.91%, and 2.93%, respectively. Notably, the integration of these models consistently improved accuracy, with gains of 0.87% and 1.73% compared to the highest-performing single model, DenseNet121FC. The optimal ensemble model, which utilized the weighted average method, demonstrated superior learning and generalization capabilities. The proposed EMRDFC model shows great promise in precisely identifying cotton nitrogen status, offering critical insights into the diagnosis of crop nutrient status. This research contributes significantly to the field of agricultural technology by providing a reliable tool for nitrogen-level assessment in cotton cultivation.

Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial

ObjectivesBackground parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI. MethodsThis study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating. ResultsThe accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE). ConclusionIt is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories.

PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine

As an effective tool for eliciting the power of Large Language Models (LLMs), prompting has recently demonstrated unprecedented abilities across a variety of complex tasks. To further improve the performance, prompt ensemble has attracted substantial interest for tackling the hallucination and instability of LLMs. However, existing methods usually adopt a two-stage paradigm, which requires a pre-prepared set of prompts with substantial manual effort, and is unable to perform directed optimization for different weak learners. In this paper, we propose a simple, universal, and automatic method named PREFER (Prompt Ensemble learning via Feedback-Reflect-Refine) to address the stated limitations. Specifically, given the fact that weak learners are supposed to focus on hard examples during boosting, PREFER builds a feedback mechanism for reflecting on the inadequacies of existing weak learners. Based on this, the LLM is required to automatically synthesize new prompts for iterative refinement. Moreover, to enhance stability of the prompt effect evaluation, we propose a novel prompt bagging method involving forward and backward thinking, which is superior to majority voting and is beneficial for both feedback and weight calculation in boosting. Extensive experiments demonstrate that our PREFER achieves state-of-the-art performance in multiple types of tasks by a significant margin. We have made our code publicly available.

Abstract 7492: Automated identification and enumeration of CELLSEARCH Circulating Tumor Cells (CTC) with a deep learning algorithm

Abstract Introduction: CTC enumeration in blood samples with CELLSEARCH® is a prognostic biomarker in metastatic breast, prostate and colorectal cancer (full intended use documents.cellsearchctc.com). Currently, CTC identification is performed by visual assessment, which is time-consuming and potentially affected by subjective interpretations. Recently, we developed a Deep Learning (DL) algorithm for automated CTC identification in CELLSEARCH images (CellFind®, Menarini Silicon Biosystems, research use only). This study aims to assess the CTC identification performance of CellFind on 2 different datasets: the first for comparison with human reviewers, and the second for validation with much higher number of images. Methods: CellFind is composed of an image segmentation followed by a classification network, which was trained using 13067 CTC and 52890 non-CTC images from 215 breast, 123 prostate and 180 colorectal cancer samples. The performance of CellFind was tested on 2 separate datasets. 1) The first dataset was made of CELLSEARCH gallery images from 26 breast cancer, 68 prostate cancer, and 40 benign samples. 8 human reviewers, qualified for CTC image analysis, performed blind labeling. The Ground Truth (GT) was generated by the classification of the 3 most experienced reviewers by majority voting (1621 CTCs out of 17080 images). Accuracy and F1 metrics were used to rank the performance of DL and 5 reviewers against the GT. 2) The second dataset was made of CELLSEARCH gallery images from 63 breast, 66 prostate and 32 colorectal cancer samples. 3 experienced reviewers created the GT classification by majority voting, identifying 20052 CTCs out of 117447 images.Finally, each patient was assigned to the favorable or unfavorable group by applying the validated cutoff on CTC enumeration per sample (CTC≥5 for breast and prostate cancer, CTC≥3 for colorectal cancer). Results: 1) On 17080 gallery images from 134 samples, CellFind reached top human-level performance, as ranked both by accuracy (97.8% for DL vs. 96.8-98.0%) and F1 (88.6% for DL vs. 83.3-89.9% for reviewers). On the 94 cancer samples, DL accuracy on favorable/unfavorable prognosis was 98.9% vs. 95.8-97.9% for reviewers. 2) On the bigger dataset with 117447 gallery images from 161 cancer samples, CellFind obtained accuracy = 96.0% and F1 = 87.8% for CTC identification (TP = 17039, FP = 1735, FN = 3013, TN = 95660). For favorable/unfavorable prognosis the DL accuracy was 95.4%. Conclusion: Automated identification and enumeration of CTCs in CELLSEARCH images with CellFind can remove human subjectivity from the review process and maximize standardization among different research centers. CellFind performed better than most operators and reduced the data processing time required for each blood sample by the operator. Citation Format: Luca Biasiolli, Pietro Ansaloni, Nicolò Gentili, Daniele Giardiello, Francesco Montanari, Ramona Miserendino, Giulio Signorini, Gianni Medoro. Automated identification and enumeration of CELLSEARCH Circulating Tumor Cells (CTC) with a deep learning algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7492.

Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge

Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories. This observation motivated the inception of the 2022 challenge on MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains. Ground truth for mitotic figure detection was established in two ways: a three-expert majority vote and an independent, immunohistochemistry-assisted set of labels. This work represents an overview of the challenge tasks, the algorithmic strategies employed by the participants, and potential factors contributing to their success. With an F1 score of 0.764 for the top-performing team, we summarize that domain generalization across various tumor domains is possible with today’s deep learning-based recognition pipelines. However, we also found that domain characteristics not present in the training set (feline as new species, spindle cell shape as new morphology and a new scanner) led to small but significant decreases in performance. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, with only minor changes in the order of participants in the ranking.

Contribution to historical manuscript dating: A hybrid approach employing hand-crafted features with vision transformers

Precisely dating historical manuscripts represents a paramount endeavor in the comprehension and the interpretation of their historical significance as well as in the preservation of our cultural heritage; however, despite the strides made in computer-based dating methodologies, the quest for heightened robustness persists. Recent advancements in vision transformers, renowned for their success across diverse image processing domains, have stimulated our inquiry into their potential applicability in the domain of historical manuscript dating. In our pursuit of achieving efficient refinement of the manuscript and ensuring pristine datasets for subsequent analysis, we initiate a meticulous dataset preprocessing step. This involves employing accurate methodologies for denoising through the Non-Local-Means algorithm, and binarization using the Canny-edge detector. Following these preprocessing steps, we delve into the intricate realm of feature detection using the Harris-corner detector. This detector is employed to extract keypoints from the manuscript, and we subsequently apply clustering to these keypoints using the k-means algorithm. Our dual objective here is to extract significant patches of specific dimensions and engage in a systematic augmentation of our dataset. Through this process, we aim to amplify the depth and diversity of our dataset, thereby empowering our models with an enriched corpus of historical knowledge. The final phase of our proposed system unfolds as we leverage the latent power of the sophisticated deep learning architecture known as vision transformers. The model is finetuned to our specific task and re-learned with the automatically extracted handcrafted features, emerges as a formidable classification framework. As an added layer of refinement, we deploy a majority vote mechanism on image patches, meticulously engineered to heighten system accuracy. Our rigorous testing regimen, carried out on the well-known MPS historical document dataset, has yielded results of remarkable caliber. Our system's prowess is vividly reflected in its performance metrics, boasting an impressive Mean Absolute Error (MAE) of 3.97 for document-level evaluations and a resounding 7.42 MAE for patch-level assessments. The significance of this work lies in its potential to revolutionize historical manuscript dating, not only by enhancing precision but also by providing a versatile framework adaptable to diverse historical contexts and document types. Beyond its immediate applications, this research paves the way for a deeper understanding of historical narratives, cultures, and the invaluable insights that lie within the annals of our past.

Recommender-based bone tumour classification with radiographs—a link to the past

ObjectivesTo develop an algorithm to link undiagnosed patients to previous patient histories based on radiographs, and simultaneous classification of multiple bone tumours to enable early and specific diagnosis.Materials and methodsFor this retrospective study, data from 2000 to 2021 were curated from our database by two orthopaedic surgeons, a radiologist and a data scientist. Patients with complete clinical and pre-therapy radiographic data were eligible. To ensure feasibility, the ten most frequent primary tumour entities, confirmed histologically or by tumour board decision, were included. We implemented a ResNet and transformer model to establish baseline results. Our method extracts image features using deep learning and then clusters the k most similar images to the target image using a hash-based nearest-neighbour recommender approach that performs simultaneous classification by majority voting. The results were evaluated with precision-at-k, accuracy, precision and recall. Discrete parameters were described by incidence and percentage ratios. For continuous parameters, based on a normality test, respective statistical measures were calculated.ResultsIncluded were data from 809 patients (1792 radiographs; mean age 33.73 ± 18.65, range 3–89 years; 443 men), with Osteochondroma (28.31%) and Ewing sarcoma (1.11%) as the most and least common entities, respectively. The dataset was split into training (80%) and test subsets (20%). For k = 3, our model achieved the highest mean accuracy, precision and recall (92.86%, 92.86% and 34.08%), significantly outperforming state-of-the-art models (54.10%, 55.57%, 19.85% and 62.80%, 61.33%, 23.05%).ConclusionOur novel approach surpasses current models in tumour classification and links to past patient data, leveraging expert insights.Clinical relevance statementThe proposed algorithm could serve as a vital support tool for clinicians and general practitioners with limited experience in bone tumour classification by identifying similar cases and classifying bone tumour entities.Key Points• Addressed accurate bone tumour classification using radiographic features.• Model achieved 92.86%, 92.86% and 34.08% mean accuracy, precision and recall, respectively, significantly surpassing state-of-the-art models.• Enhanced diagnosis by integrating prior expert patient assessments.Graphical abstract

New approach for satellite DEM accuracy enhancement by combing machine learning, fuzzy majority voting, and weighted interpolation techniques

The digital elevation model (DEM) is crucial in many global and regional scientific studies in civilian and military applications. The aim of this research is to develop and test a new DEM approach for correcting the various errors in the Shuttle Radar Topography Mission (SRTM) digital elevation model. Firstly, the DEMs with the feature attributes from Sentinel-2 multispectral imagery are generated. Secondly, SRTM DEM with one band and attributes of a sentinel-2 image with eight bands are used as input data in supervised max-like hood, an artificial neural network (ANN), and support vector machine (SVM) classification models. Thirdly, ANN, supervised max-like hood, and SVM classification models, which have various properties, are fused by fuzzy majority voting (probability fusion). Finally, the fused probability is assigned for each pixel of the image, which has 12 fixed ground control points (GCPs), which is considered new input data for the inverse probability weighted interpolation (IPWI) approach to create the corrected SRTM elevations. The results were contrasted with a reference DEM (RD) created by image matching with Worldview-1 stereo satellite images, which had a 1-m vertical accuracy. The results of this study demonstrated that the RMSE of the original SRTM DEM was 5.95. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 1.98 compared with that of the MLR method (3.01). The study shows a series of significant improvements in the SRTM when assessed with the reference DEM, with an RMSE reduction of (66.72%) when compared to the widely utilized multiple linear regression (MLR) method. It can be concluded that the elevation error of the original SRTM DEM is clearly reduced by the suggested approach.

Balancing democracy: majoritarianism versus expression of preference intensity

This paper evaluates three prominent voting systems: the Majority Rule (MR), Borda Rule (BR), and Plurality Rule (PR). Our analysis centers on the susceptibilities of each system to potential transgressions of two foundational principles: the respect for majority preference (majoritarianism) and the acknowledgment of the intensity of individual preferences. We operationalize the concept of 'cost' as the expected deviation from the aforementioned principles. A comparative assessment of MR, BR, and PR is undertaken in terms of their costs. Our findings underscore the superiority of PR over MR, whilst also highlighting the comparative advantage of MR against BR.

DEEP LEARNING FOR AUTOMATIC PREDICTION OF EARLY ACTIVATION OF TREATMENT-NAIVE NONEXUDATIVE MACULAR NEOVASCULARIZATIONS IN AGE-RELATED MACULAR DEGENERATION.

Around 30% of nonexudative macular neovascularizations exudate within 2 years from diagnosis in patients with age-related macular degeneration. The aim of this study is to develop a deep learning classifier based on optical coherence tomography (OCT) and OCT angiography (OCTA) to identify nonexudative macular neovascularizations at risk of exudation. Patients with age-related macular degeneration showing OCTA and fluorescein angiography-documented nonexudative macular neovascularization with a 2-year minimum imaging follow-up were retrospectively selected. Patients showing OCT B-scan-documented macular neovascularization exudation within the first 2 years formed the EX GROUP while the others formed the QU GROUP. ResNet-101, Inception-ResNet-v2, and DenseNet-201 were independently trained on OCTA and OCT B-scan images. Combinations of the six models were evaluated with major and soft voting techniques. Eighty-nine eyes of 89 patients with a follow-up of 5.7 ± 1.5 years were recruited (35 EX GROUP and 54 QU GROUP). Inception-ResNet-v2 was the best performing among the three single convolutional neural networks. The major voting model resulting from the association of the three different convolutional neural networks resulted in an improvement of performance both for OCTA and OCT B-scan (both significantly higher than human graders' performance). The soft voting model resulting from the combination of OCTA and OCT B-scan-based major voting models showed a testing accuracy of 94.4%. Peripheral arcades and large vessels on OCTA en face imaging were more prevalent in the QU GROUP. Artificial intelligence shows high performances in identifications of nonexudative macular neovascularizations at risk for exudation within the first 2 years of follow-up, allowing better customization of follow-up timing and avoiding treatment delay. Better results are obtained with the combination of OCTA and OCT B-scan image analysis.

Fusion-based approach for hydrometeorological drought modeling: a regional investigation for Iran.

The objective of this study was to model a new drought index called the Fusion-based Hydrological Meteorological Drought Index (FHMDI) to simultaneously monitor hydrological and meteorological drought. Aiming to estimate drought more accurately, local measurements were classified into various clusters using the AGNES clustering algorithm. Four single artificial intelligence (SAI) models-namely, Gaussian Process Regression (GPR), Ensemble, Feedforward Neural Networks (FNN), and Support Vector Regression (SVR)-were developed for each cluster. To promote the results of single of products and models, four fusion-based approaches, namely, Wavelet-Based (WB), Weighted Majority Voting (WMV), Extended Kalman Filter (EKF), and Entropy Weight (EW) methods, were used to estimate FHMDI in different time scales, precipitation, and runoff. The performance of single and combined products and models was assessed through statistical error metrics, such as Kling-Gupta efficiency (KGE), Mean Bias Error (MBE), and Normalized Root Mean Square Error (NRMSE). The performance of the proposed methodology was tested over 24 main river basins in Iran. The validation results of the FHMDI (the compliance of the index with the pre-existing drought index) revealed that it accurately identified drought conditions. The results indicated that individual products performed well in some river basins, while fusion-based models improved dataset accuracy more compared to local measurements. The WMV with the highest accuracy (lowest NRMSE) had a good performance in 60% of the cases compared to all other products and fusion-based models. WMV also showed higher efficiency in 100% of the cases than all other fusion-based and SAI models for simultaneous hydrological and meteorological drought estimation. In light of these findings, we recommend the use of fusion-based approach to improve drought modeling.

Identification and interpretation of gait analysis features and foot conditions by explainable AI

Clinical gait analysis is a crucial step for identifying foot disorders and planning surgery. Automating this process is essential for efficiently assessing the substantial amount of gait data. In this study, we explored the potential of state-of-the-art machine learning (ML) and explainable artificial intelligence (XAI) algorithms to automate all various steps involved in gait analysis for six specific foot conditions. To address the complexity of gait data, we manually created new features, followed by recursive feature elimination using Support Vector Machines (SVM) and Random Forests (RF) to eliminate low-variance features. SVM, RF, K-nearest Neighbor (KNN), and Logistic Regression (LREGR) were compared for classification, with a Majority Voting (MV) model combining trained models. KNN and MV achieved mean balanced accuracy, recall, precision, and F1 score of 0.87. All models were interpreted using Local Interpretable Model-agnostic Explanation (LIME) method and the five most relevant features were identified for each foot condition. High success scores indicate a strong relationship between selected features and foot conditions, potentially indicating clinical relevance. The proposed ML pipeline, adaptable for other foot conditions, showcases its potential in aiding experts in foot condition identification and planning surgeries.

Populist Jurisprudence? Examining Selected Case Law of the Polish Constitutional Court After 2016

Since the parliamentary elections in 2015 and the subsequent change in the personal composition of the Polish Constitutional Court, this institution is in crisis. The Court, once one of the main guardians of the rule of law and a model for the constitutional judiciary in the region of Central and Eastern Europe, is criticized. Judges are accused of lack of proper appointment and party subordination. Court activities are perceived as part of illiberal democracy and populist constitutionalism, that is, introducing majority rule by “switching off” the checks and balances mechanisms by democratically elected parties and groups. However, what is often overlooked in this type of analysis is the more internal perspective of jurisprudence and legal reasoning. What kind of decisions does the “populist” constitutional court issue? How does it justify its decisions? The paper will discuss three cases of the Polish Constitutional Court. The first case is from 2017 and concerns the right of assembly in connection with the introduction of a special category of “cyclical assemblies”. The second, of 2019, is the so-called “printer case”, which concerned the possibility of refusing to provide a service for reasons of conscience (a refusal to print a poster because of opposition to “LGBT promotion”). The third case is the controversial ruling narrowing access to abortion from 2020. The aim of the analysis is to answer the question of whether the current jurisprudence of the Court is the breaking or continuation of the previously dominant liberal constitutionalism. I will be particularly interested in whether these decisions introduce any changes at the level of possible rights holders (legal subjects), the introduction of a new or changed scope of existing rights, and new ways of resolving conflicts between rights.

Artificial intelligence in tongue diagnosis: classification of tongue lesions and normal tongue images using deep convolutional neural network

ObjectiveThis study aims to classify tongue lesion types using tongue images utilizing Deep Convolutional Neural Networks (DCNNs).MethodsA dataset consisting of five classes, four tongue lesion classes (coated, geographical, fissured tongue, and median rhomboid glossitis), and one healthy/normal tongue class, was constructed using tongue images of 623 patients who were admitted to our clinic. Classification performance was evaluated on VGG19, ResNet50, ResNet101, and GoogLeNet networks using fusion based majority voting (FBMV) approach for the first time in the literature.ResultsIn the binary classification problem (normal vs. tongue lesion), the highest classification accuracy performance of 93,53% was achieved utilizing ResNet101, and this rate was increased to 95,15% with the application of the FBMV approach. In the five-class classification problem of tongue lesion types, the VGG19 network yielded the best accuracy rate of 83.93%, and the fusion approach improved this rate to 88.76%.ConclusionThe obtained test results showed that tongue lesions could be identified with a high accuracy by applying DCNNs. Further improvement of these results has the potential for the use of the proposed method in clinic applications.

Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method

One of the most important goals of cooperative driving is to control connected automated vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals. As a typical application scenario, allocating right-of-way reasonably at unsignalized intersections can effectively avoid collisions and reduce traffic delays. Proposed here is a new cooperative driving strategy for CAVs at unsignalized intersections based on distributed Monte Carlo tree search (MCTS). A task-area partition framework is also proposed to decompose the mission of cooperative driving into three main tasks: vehicle information sharing, passing order optimization, and trajectory control. Based on the schedule tree of the vehicle passing order, the root parallelization of MCTS combined with the majority voting rule is used to explore as many feasible passing orders (leaf nodes) as possible in a distributed way and find a nearly global-optimal passing order within the limited planning time. The aim is for CAVs to perform proper trajectory adjustments based on the obtained passing order to minimize traffic delays while making the slightest acceleration adjustments. A coupled simulation platform integrating SUMO and Python is developed to construct the unsignalized intersection scenarios and generate the proposed distributed cooperative driving strategy. Comparative analysis with conventional driving strategies demonstrates that the proposed strategy significantly enhances efficiency, safety, comfort, and emission, aligning well with innovative and environmentally friendly urban mobility aspirations.