Seluk University
AI Insights
- A mobile application was developed based on this success, with future studies aiming to increase the number of classes by expanding the dataset. (ML: 0.97)
- Transfer learning: A machine learning approach where a pre-trained model is fine-tuned for a new task. (ML: 0.97)
- Future research should focus on expanding the dataset to increase the number of classes and improve the robustness of the models. (ML: 0.96)
- Convolutional Neural Networks (CNNs): A type of neural network designed for image processing and feature extraction. (ML: 0.93)
- The authors trained three different sizes of CNN models: MobileNet, DenseNet-121, and Xception, using transfer learning strategies. (ML: 0.92)
- Global Average Pooling (GAP): A technique used in CNNs to reduce spatial dimensions and improve performance. (ML: 0.92)
- The study demonstrates the effectiveness of using transfer learning and GAP in improving the accuracy of flower classification models. (ML: 0.90)
- The results highlight the potential of CNNs in mobile applications, particularly in agriculture and environmental monitoring. (ML: 0.89)
- The paper discusses the use of deep convolutional neural networks (CNNs) for flower classification, with a focus on mobile applications. (ML: 0.82)
- The DenseNet-121 architecture achieved an accuracy rate of 95.84% when using the SGD optimization algorithm and Global Average Pooling (GAP). (ML: 0.81)
Abstract
A convolutional neural network (CNN) is a deep learning algorithm that has been specifically designed for computer vision applications. The CNNs proved successful in handling the increasing amount of data in many computer vision problems, where classical machine learning algorithms were insufficient. Flowers have many uses in our daily lives, from decorating to making medicines to detoxifying the environment. Identifying flower types requires expert knowledge. However, accessing experts at any time and in any location may not always be feasible. In this study a mobile application based on CNNs was developed to recognize different types of flowers to provide non-specialists with quick and easy access to information about flower types. The study employed three distinct CNN models, namely MobileNet, DenseNet121, and Xception, to determine the most suitable model for the mobile application. The classification performances of the models were evaluated by training them with seven different optimization algorithms. The DenseNet-121 architecture, which uses the stochastic gradient descent (SGD) optimization algorithm, was the most successful, achieving 95.84 % accuracy, 96.00% precision, recall, and F1-score. This result shows that CNNs can be used for flower classification in mobile applications.
A convolutional neural network (CNN) is a deep learning algorithm that has been specifically designed for computer vision applications. The CNNs proved successful in handling the increasing amount of data in many computer vision problems, where classical machine learning algorithms were insufficient. Flowers have many uses in our daily lives, from decorating to making medicines to detoxifying the environment. Identifying flower types requires expert knowledge. However, accessing experts at any time and in any location may not always be feasible. In this study a mobile application based on CNNs was developed to recognize different types of flowers to provide non-specialists with quick and easy access to information about flower types. The study employed three distinct CNN models, namely MobileNet, DenseNet121, and Xception, to determine the most suitable model for the mobile application. The classification performances of the models were evaluated by training them with seven different optimization algorithms. The DenseNet-121 architecture, which uses the stochastic gradient descent (SGD) optimization algorithm, was the most successful, achieving 95.84 % accuracy, 96.00% precision, recall, and F1-score. This result shows that CNNs can be used for flower classification in mobile applications.
Why we are recommending this paper?
Due to your Interest in Image Recognition
This paper directly addresses image recognition using convolutional neural networks, a core interest. The focus on a mobile application suggests practical applications within your area of interest.
Due to your Interest in Image Recognition
This paper directly addresses image recognition using convolutional neural networks, a core interest. The focus on a mobile application suggests practical applications within your area of interest.
University of North Carolina at Chapel Hill
AI Insights
- The study focuses on generalized linear models but notes that the entropy-based metric can possibly be extended to other models, such as the Cox proportional hazard model. (ML: 0.98)
- The ERE metric is justified through mathematical properties and related to conventional goodness-of-fit metrics for unimodal models, as well as recent developments in modality evaluation under more restrictive models. (ML: 0.97)
- The proposed ERE metric provides a comprehensive tool set for assessing the significance of individual modalities across various multimodal models. (ML: 0.96)
- Expected Relative Entropy (ERE): A metric to quantify the information gain of individual modalities in high-dimensional multimodal generalized linear models. (ML: 0.96)
- The constructed confidence intervals maintain good coverage probabilities without relying on variable selection consistency, a desirable property given the difficulty of achieving perfect variable selection in high-dimensional models. (ML: 0.96)
- The paper proposes an Expected Relative Entropy (ERE) metric to quantify the information gain of individual modalities in high-dimensional multimodal generalized linear models. (ML: 0.96)
- Further research into such extensions would be valuable, providing a comprehensive tool set for assessing the significance of individual modalities across various multimodal models. (ML: 0.95)
- Deviance-based estimator: An estimator developed using a two-step procedure to estimate the ERE metric, with error bound and asymptotic distribution derived for statistical inference. (ML: 0.95)
- Variable selection consistency: The property of achieving perfect variable selection in high-dimensional models, which is challenging and often not achieved. (ML: 0.93)
- A deviance-based estimator of the ERE metric is developed using a two-step procedure, with error bound and asymptotic distribution derived to enable statistical inference on the ERE. (ML: 0.86)
Abstract
Despite the popular of multimodal statistical models, there lacks rigorous statistical inference tools for inferring the significance of a single modality within a multimodal model, especially in high-dimensional models. For high-dimensional multimodal generalized linear models, we propose a novel entropy-based metric, called the expected relative entropy, to quantify the information gain of one modality in addition to all other modalities in the model. We propose a deviance-based statistic to estimate the expected relative entropy, prove that it is consistent and its asymptotic distribution can be approximated by a non-central chi-squared distribution. That enables the calculation of confidence intervals and p-values to assess the significance of the expected relative entropy for a given modality. We numerically evaluate the empirical performance of our proposed inference tool by simulations and apply it to a multimodal neuroimaging dataset to demonstrate its good performance on various high-dimensional multimodal generalized linear models.
Despite the popular of multimodal statistical models, there lacks rigorous statistical inference tools for inferring the significance of a single modality within a multimodal model, especially in high-dimensional models. For high-dimensional multimodal generalized linear models, we propose a novel entropy-based metric, called the expected relative entropy, to quantify the information gain of one modality in addition to all other modalities in the model. We propose a deviance-based statistic to estimate the expected relative entropy, prove that it is consistent and its asymptotic distribution can be approximated by a non-central chi-squared distribution. That enables the calculation of confidence intervals and p-values to assess the significance of the expected relative entropy for a given modality. We numerically evaluate the empirical performance of our proposed inference tool by simulations and apply it to a multimodal neuroimaging dataset to demonstrate its good performance on various high-dimensional multimodal generalized linear models.
Why we are recommending this paper?
Due to your Interest in multimodal models
This work investigates multimodal models, specifically focusing on inferring modality significance, aligning with your interest in fusion models. The paper's approach to high-dimensional models is particularly relevant.
Due to your Interest in multimodal models
This work investigates multimodal models, specifically focusing on inferring modality significance, aligning with your interest in fusion models. The paper's approach to high-dimensional models is particularly relevant.
Uppsala University
AI Insights
- SoftMoE: A variant of MoE models that uses a soft-gating mechanism to select the most relevant experts for each input. (ML: 0.95)
- MoE models can generalize robustly in moderate-scale vision tasks when appropriately regularized. (ML: 0.93)
- Mixture-of-Experts (MoE) models: A type of neural network architecture that combines multiple experts to make predictions. (ML: 0.92)
- Mixture-of-Experts (MoE) models can generalize robustly in moderate-scale vision tasks when appropriately regularized. (ML: 0.92)
- SparseMoE: A variant of MoE models that uses a sparse-gating mechanism to select only a subset of experts for each input. (ML: 0.92)
- SoftMoE and SparseMoE architectures outperform the dense baseline on validation accuracy when expert utilization is properly regularized. (ML: 0.92)
- SoftMoE and SparseMoE architectures outperform the dense baseline on validation accuracy when expert utilization is properly regularized. (ML: 0.92)
- Hessian-based curvature analysis: A method used to analyze the geometry of the loss surface in neural networks. (ML: 0.85)
- The gap between theoretical and realized efficiency in sparse MoE models arises from the overhead of routing, selection, and aggregation operations in naive implementations. (ML: 0.74)
- Hessian-based curvature analysis reveals that SoftMoE converges to solutions with higher local curvature, while Dense and SparseMoE occupy a similar sharpness regime. (ML: 0.74)
Abstract
Mixture-of-Experts (MoE) architectures enable conditional computation by routing inputs to multiple expert subnetworks and are often motivated as a mechanism for scaling large language models. In this project, we instead study MoE behavior in an image classification setting, focusing on predictive performance, expert utilization, and generalization. We compare dense, SoftMoE, and SparseMoE classifier heads on the CIFAR10 dataset under comparable model capacity. Both MoE variants achieve slightly higher validation accuracy than the dense baseline while maintaining balanced expert utilization through regularization, avoiding expert collapse. To analyze generalization, we compute Hessian-based sharpness metrics at convergence, including the largest eigenvalue and trace of the loss Hessian, evaluated on both training and test data. We find that SoftMoE exhibits higher sharpness by these metrics, while Dense and SparseMoE lie in a similar curvature regime, despite all models achieving comparable generalization performance. Complementary loss surface perturbation analyses reveal qualitative differences in non-local behavior under finite parameter perturbations between dense and MoE models, which help contextualize curvature-based measurements without directly explaining validation accuracy. We further evaluate empirical inference efficiency and show that naively implemented conditional routing does not yield inference speedups on modern hardware at this scale, highlighting the gap between theoretical and realized efficiency in sparse MoE models.
Mixture-of-Experts (MoE) architectures enable conditional computation by routing inputs to multiple expert subnetworks and are often motivated as a mechanism for scaling large language models. In this project, we instead study MoE behavior in an image classification setting, focusing on predictive performance, expert utilization, and generalization. We compare dense, SoftMoE, and SparseMoE classifier heads on the CIFAR10 dataset under comparable model capacity. Both MoE variants achieve slightly higher validation accuracy than the dense baseline while maintaining balanced expert utilization through regularization, avoiding expert collapse. To analyze generalization, we compute Hessian-based sharpness metrics at convergence, including the largest eigenvalue and trace of the loss Hessian, evaluated on both training and test data. We find that SoftMoE exhibits higher sharpness by these metrics, while Dense and SparseMoE lie in a similar curvature regime, despite all models achieving comparable generalization performance. Complementary loss surface perturbation analyses reveal qualitative differences in non-local behavior under finite parameter perturbations between dense and MoE models, which help contextualize curvature-based measurements without directly explaining validation accuracy. We further evaluate empirical inference efficiency and show that naively implemented conditional routing does not yield inference speedups on modern hardware at this scale, highlighting the gap between theoretical and realized efficiency in sparse MoE models.
Why we are recommending this paper?
Due to your Interest in Image Processing
This paper explores Mixture-of-Experts models, a rapidly growing area within computer vision and deep learning, which is directly relevant to your interest in convolutional neural networks. The focus on scaling and generalization is a key concern in modern image processing.
Due to your Interest in Image Processing
This paper explores Mixture-of-Experts models, a rapidly growing area within computer vision and deep learning, which is directly relevant to your interest in convolutional neural networks. The focus on scaling and generalization is a key concern in modern image processing.
CASIA
AI Insights
- The EDIR evaluation protocol assesses model performance on various aspects, including recall, precision, and F1-score, as well as error analysis to identify specific weaknesses in current models. (ML: 0.97)
- Error analysis reveals that current models struggle with negation-related queries, compositional reasoning, multiple constraints, and fine-grained details, indicating a need for further research and development. (ML: 0.97)
- The dataset consists of 1000 seed images, 10 edit-instruction templates, and 10000 generated query-revision pairs, making it a comprehensive benchmark for CIR research. (ML: 0.95)
- The EDIR dataset provides a comprehensive evaluation of CIR models' ability to handle complex queries and fine-grained visual details, highlighting specific weaknesses in current models. (ML: 0.94)
- RzenEmbed-7B: A state-of-the-art model that achieved a low Recall@1 score on the EDIR benchmark. (ML: 0.90)
- Failure in handling negation (ML: 0.89)
- Qwen25-VL-32B-Instruct: A dataset used for seed image selection and edit-instruction generation in EDIR. (ML: 0.88)
- EDIR: Compositional Image Retrieval (CIR) dataset with a focus on evaluating the ability of models to handle complex queries and fine-grained visual details. (ML: 0.86)
- Qwen-Image-Edit: A dataset used for generating target images in EDIR. (ML: 0.86)
- The EDIR dataset is a challenging benchmark for Compositional Image Retrieval (CIR) tasks, with a focus on evaluating the ability of models to handle complex queries and fine-grained visual details. (ML: 0.82)
Abstract
Composed Image Retrieval (CIR) is a pivotal and complex task in multimodal understanding. Current CIR benchmarks typically feature limited query categories and fail to capture the diverse requirements of real-world scenarios. To bridge this evaluation gap, we leverage image editing to achieve precise control over modification types and content, enabling a pipeline for synthesizing queries across a broad spectrum of categories. Using this pipeline, we construct EDIR, a novel fine-grained CIR benchmark. EDIR encompasses 5,000 high-quality queries structured across five main categories and fifteen subcategories. Our comprehensive evaluation of 13 multimodal embedding models reveals a significant capability gap; even state-of-the-art models (e.g., RzenEmbed and GME) struggle to perform consistently across all subcategories, highlighting the rigorous nature of our benchmark. Through comparative analysis, we further uncover inherent limitations in existing benchmarks, such as modality biases and insufficient categorical coverage. Furthermore, an in-domain training experiment demonstrates the feasibility of our benchmark. This experiment clarifies the task challenges by distinguishing between categories that are solvable with targeted data and those that expose intrinsic limitations of current model architectures.
Composed Image Retrieval (CIR) is a pivotal and complex task in multimodal understanding. Current CIR benchmarks typically feature limited query categories and fail to capture the diverse requirements of real-world scenarios. To bridge this evaluation gap, we leverage image editing to achieve precise control over modification types and content, enabling a pipeline for synthesizing queries across a broad spectrum of categories. Using this pipeline, we construct EDIR, a novel fine-grained CIR benchmark. EDIR encompasses 5,000 high-quality queries structured across five main categories and fifteen subcategories. Our comprehensive evaluation of 13 multimodal embedding models reveals a significant capability gap; even state-of-the-art models (e.g., RzenEmbed and GME) struggle to perform consistently across all subcategories, highlighting the rigorous nature of our benchmark. Through comparative analysis, we further uncover inherent limitations in existing benchmarks, such as modality biases and insufficient categorical coverage. Furthermore, an in-domain training experiment demonstrates the feasibility of our benchmark. This experiment clarifies the task challenges by distinguishing between categories that are solvable with targeted data and those that expose intrinsic limitations of current model architectures.
Why we are recommending this paper?
Due to your Interest in Image Recognition
This paper tackles the evaluation of composed image retrieval, a critical aspect of multimodal understanding. The use of image editing provides a novel approach to assessing retrieval performance, aligning with your interest in multimodal models.
Due to your Interest in Image Recognition
This paper tackles the evaluation of composed image retrieval, a critical aspect of multimodal understanding. The use of image editing provides a novel approach to assessing retrieval performance, aligning with your interest in multimodal models.
Idiap Research Institute
AI Insights
- The results suggest that MLLMs can be a useful tool for certain applications, but they should not be relied upon as the sole means of identification. (ML: 0.98)
- MLLMs have limitations in understanding human faces and recognizing individuals, particularly when it comes to fine-grained facial features and nuanced expressions. (ML: 0.97)
- The study highlights the need for further research and development of MLLMs to improve their performance in face recognition tasks. (ML: 0.96)
- Multimodal large language models (MLLMs): Models that can process and understand multiple types of input data, such as text, images, and audio. (ML: 0.96)
- The results show that while MLLMs have made significant progress in various NLP tasks, they still struggle with fine-grained facial features and nuanced expressions. (ML: 0.95)
- The authors investigate the capabilities of MLLMs in understanding human faces and their ability to recognize individuals. (ML: 0.95)
- The study focuses on the performance of multimodal large language models (MLLMs) in face recognition tasks. (ML: 0.94)
- Face recognition: The ability to identify an individual based on their facial features. (ML: 0.93)
- Nuanced expressions: Subtle changes in a person's facial expression that convey emotions or attitudes. (ML: 0.89)
- Fine-grained facial features: Detailed characteristics of a person's face, such as the shape of their eyes or nose. (ML: 0.86)
Abstract
Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance on a wide range of vision-language tasks, raising interest in their potential use for biometric applications. In this paper, we conduct a systematic evaluation of state-of-the-art MLLMs for heterogeneous face recognition (HFR), where enrollment and probe images are from different sensing modalities, including visual (VIS), near infrared (NIR), short-wave infrared (SWIR), and thermal camera. We benchmark multiple open-source MLLMs across several cross-modality scenarios, including VIS-NIR, VIS-SWIR, and VIS-THERMAL face recognition. The recognition performance of MLLMs is evaluated using biometric protocols and based on different metrics, including Acquire Rate, Equal Error Rate (EER), and True Accept Rate (TAR). Our results reveal substantial performance gaps between MLLMs and classical face recognition systems, particularly under challenging cross-spectral conditions, in spite of recent advances in MLLMs. Our findings highlight the limitations of current MLLMs for HFR and also the importance of rigorous biometric evaluation when considering their deployment in face recognition systems.
Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance on a wide range of vision-language tasks, raising interest in their potential use for biometric applications. In this paper, we conduct a systematic evaluation of state-of-the-art MLLMs for heterogeneous face recognition (HFR), where enrollment and probe images are from different sensing modalities, including visual (VIS), near infrared (NIR), short-wave infrared (SWIR), and thermal camera. We benchmark multiple open-source MLLMs across several cross-modality scenarios, including VIS-NIR, VIS-SWIR, and VIS-THERMAL face recognition. The recognition performance of MLLMs is evaluated using biometric protocols and based on different metrics, including Acquire Rate, Equal Error Rate (EER), and True Accept Rate (TAR). Our results reveal substantial performance gaps between MLLMs and classical face recognition systems, particularly under challenging cross-spectral conditions, in spite of recent advances in MLLMs. Our findings highlight the limitations of current MLLMs for HFR and also the importance of rigorous biometric evaluation when considering their deployment in face recognition systems.
Why we are recommending this paper?
Due to your Interest in multimodal models
This paper investigates the application of multimodal large language models to face recognition, a significant area within image recognition. The systematic evaluation of MLLMs is a valuable contribution to the field.
Due to your Interest in multimodal models
This paper investigates the application of multimodal large language models to face recognition, a significant area within image recognition. The systematic evaluation of MLLMs is a valuable contribution to the field.
Universit de ParisSud now known as Universit ParisSaclay
AI Insights
- The authors highlight the importance of this work in advancing our knowledge of these areas. (ML: 0.92)
- Signalizer functor: A functor that assigns to each subgroup P of G a subgroup θ(P) of C_G(P) such that θ(P) is a complement to Z(P) in C_G(P). (ML: 0.82)
- They show that all such systems can be represented as amalgams of certain groups and provide a detailed description of their structure. (ML: 0.78)
- The paper concludes by discussing the implications of the classification of exotic fusion systems for our understanding of finite simple groups and their representation theory. (ML: 0.77)
- The authors use the theory of signalizer functors to classify these systems and show that they have similar properties to the Benson-Solomon systems. (ML: 0.72)
- The authors use the theory of signalizer functors to classify these systems and provide a detailed analysis of their properties. (ML: 0.68)
- Exotic fusion system: A fusion system over a prime p that is not equivalent to any of the known families of fusion systems, such as the Aschbacher-Kessar-Oliver fusion systems. (ML: 0.54)
- The authors apply the theory of signalizer functors to classify the Benson-Solomon fusion systems, which are the only known family of exotic fusion systems over the prime 2. (ML: 0.51)
- Aschbacher-Kessar-Oliver fusion system: A known family of fusion systems that are equivalent to the Aschbacher-Kessar-Oliver groups. (ML: 0.50)
- The authors provide a detailed analysis of the properties of the Benson-Solomon and Parker-Stroth fusion systems, including their structure, subgroups, and normalizers. (ML: 0.49)
- They also discuss other known families of exotic fusion systems, such as the Aschbacher-Kessar-Oliver fusion systems. (ML: 0.49)
- The paper discusses the classification of exotic fusion systems over a prime p, specifically focusing on the Benson-Solomon fusion systems, Parker-Stroth fusion systems, and other known families. (ML: 0.48)
- Benson-Solomon fusion system: The only known family of exotic fusion systems over the prime 2, which can be represented as amalgams of certain groups. (ML: 0.47)
- The paper also discusses the Parker-Stroth fusion systems, which are another family of exotic fusion systems over the prime 2. (ML: 0.42)
- Parker-Stroth fusion system: Another family of exotic fusion systems over the prime 2, which have similar properties to the Benson-Solomon systems. (ML: 0.41)
Abstract
We study higher limits over the centric orbit category of a fusion system realized by an amalgamated product. In so doing we provide a novel technique for studying the Diaz-Park sharpness conjecture and prove it (in the case of the cohomology Mackey functors) for all the Clelland-Parker and Parker-Stroth fusion systems. This complements previous work from Henke, Libmand and Lynd. We further use the developed technique to study the Benson-Solomon fusion systems thus relating higher limits over the centric fusion orbit category of these systems with the signalizer functors described by Aschbacher and Chermak. We believe that the proposed technique can, in future work, be used as a first step in an induction argument that can bring us closer to providing an answer to this conjecture.
We study higher limits over the centric orbit category of a fusion system realized by an amalgamated product. In so doing we provide a novel technique for studying the Diaz-Park sharpness conjecture and prove it (in the case of the cohomology Mackey functors) for all the Clelland-Parker and Parker-Stroth fusion systems. This complements previous work from Henke, Libmand and Lynd. We further use the developed technique to study the Benson-Solomon fusion systems thus relating higher limits over the centric fusion orbit category of these systems with the signalizer functors described by Aschbacher and Chermak. We believe that the proposed technique can, in future work, be used as a first step in an induction argument that can bring us closer to providing an answer to this conjecture.
Why we are recommending this paper?
Due to your Interest in fusion models
Due to your Interest in fusion models
Princeton University
AI Insights
- Mercier criterion: A stability criterion that ensures the plasma remains stable against certain types of instabilities. (ML: 0.77)
- The authors employed various targets in the optimization process, including force balance residual, toroidal current profile, major radius, and others, to achieve a desired set of physical properties for the equilibrium. (ML: 0.72)
- A proxy called the magnetic gradient scale length L ∇B was used to estimate coil feasibility, providing an indication of the minimum distance at which coils should be placed to reproduce the magnetic configuration correctly. (ML: 0.71)
- Rotational transform: A measure of how much the magnetic field lines twist and rotate as they move through the plasma. (ML: 0.67)
- Future work includes designing coils and performing neutronics analyses to assess the feasibility of the optimized configuration. (ML: 0.64)
- MHD equilibrium: A state where the plasma is in a stable, force-balanced configuration. (ML: 0.61)
- The predicted plasma-to-coil distance is approximately 1.3 m in some regions, but may be tight for a reactor design and requires further analysis. (ML: 0.59)
- DESC package: A software tool used for optimizing stellarator configurations. (ML: 0.53)
- Stellarator: A type of magnetic confinement device used in nuclear fusion research. (ML: 0.52)
- The article discusses the optimization of a stellarator magnetic configuration using the DESC package, with the goal of achieving a more efficient and stable plasma confinement. (ML: 0.51)
Abstract
In piecewise omnigenous magnetic fields, charged particles remain perfectly confined in the abscence of collisions and turbulence. This concept extends the traditional notion of omnigenity, the theoretical principle upon which most of existing magnetic fusion reactor designs, including tokamaks, are based. While piecewise omnigenity broadens the range of potentially viable stellarator reactor candidates, it is achieved by relaxing the requirement of continuity in the magnetic field strength, which could appear to pose significant challenges for the design of magnetohydrodynamic equilibria. In this work, a stellarator magnetic configuration is presented that satisfies the ideal magnetohydrodynamic equilibrium equation and that achieves unprecedented levels of piecewise omnigenity. As a result, it exhibits favorable transport characteristics, including reduced bulk radial (neoclassical and turbulent transport), bootstrap current and fast ion losses. In addition, the configuration displays robust MHD stability across a range of \b{eta} values and possesses a rotational transform profile compatible with an island divertor. Collectively, these features satisfy the standard set of physics criteria required for a viable reactor candidate which, until now, were believed to be attainable only by certain types of omnigenous stellarators.
In piecewise omnigenous magnetic fields, charged particles remain perfectly confined in the abscence of collisions and turbulence. This concept extends the traditional notion of omnigenity, the theoretical principle upon which most of existing magnetic fusion reactor designs, including tokamaks, are based. While piecewise omnigenity broadens the range of potentially viable stellarator reactor candidates, it is achieved by relaxing the requirement of continuity in the magnetic field strength, which could appear to pose significant challenges for the design of magnetohydrodynamic equilibria. In this work, a stellarator magnetic configuration is presented that satisfies the ideal magnetohydrodynamic equilibrium equation and that achieves unprecedented levels of piecewise omnigenity. As a result, it exhibits favorable transport characteristics, including reduced bulk radial (neoclassical and turbulent transport), bootstrap current and fast ion losses. In addition, the configuration displays robust MHD stability across a range of \b{eta} values and possesses a rotational transform profile compatible with an island divertor. Collectively, these features satisfy the standard set of physics criteria required for a viable reactor candidate which, until now, were believed to be attainable only by certain types of omnigenous stellarators.
Why we are recommending this paper?
Due to your Interest in fusion models
Due to your Interest in fusion models
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