DeepIrisNet2 learns iris codes without iris segmentation or normalization.
problem Iris recognition under non-ideal conditions.
method Deep learning framework with spatial transformer layers and dual CNN segmentation.
result Significantly improves iris recognition performance.
Researchers create adversarial examples to deceive iris recognition systems.
problem Tackling the vulnerability of iris recognition systems to adversarial attacks.
method Developed a deep auto-encoder surrogate network to generate adversarial examples for iris recognition systems.
result Demonstrated that adversarial examples can fool iris recognition systems in both white-box and black-box settings.
Deep learning improves death cause coding accuracy.
problem Geospatial discrepancies in human-coded death certificates.
method Deep artificial neural network model trained on CépiDc database.
result 97.8% accuracy in automated coding compared to 75% for Iris software.
Paper uses machine learning to estimate IRI from pavement distress types, densities, and severities.
problem Costly IRI measurements exclude many road classes; estimating IRI from distress data is needed.
method Data from in-service pavements; machine learning methods used to predict IRI.
result Machine learning can reliably estimate IRI based on distress types, densities, and severities.
Study uses Android smartphones to measure road roughness, finds ML better than RMS.
problem Measuring road roughness using smartphones.
method Three smartphones, Android OS, RMS and ML methods, comparison with IRI.
result ML method outperforms RMS in detecting road roughness.
For almost all Riemannian metrics (in the C∞ Baire sense) on a closed manifold Mn+1, 3≤(n+1)≤7, we prove that there is a sequence of closed, smooth, embedded, connected minimal hypersurfaces that is equidistributed in M. This gives a quantitative version of the main result of \cite{irie-marques…
Study finds inventory inaccuracies are linked to store activity and product perishability.
problem Inventory record inaccuracy in grocery retailing environments.
method Analysis of 24,000 SKUs across 11 stores, field quasi-experiment on audits.
result Inventory audits can boost sales by 11%, especially for perishable items.
Paper defends iris recognition from adversarial examples using wavelet decomposition.
problem Adversarial examples threaten deep neural networks in biometric applications.
method Wavelet domain denoising of input examples to detect and mitigate adversarial attacks.
result Proposed defense strategies improve recognition accuracy against adversarial attacks.
InfoQGAN uses mutual information to improve QGANs, overcoming mode collapse and feature disentanglement issues.
problem Mode collapse and lack of feature control in QGANs.
method Integrates InfoGAN principles with variational quantum circuit, classical discriminator, and MINE for mutual information optimization.
result InfoQGAN effectively mitigates mode collapse and achieves robust feature disentanglement.
This paper evaluates and validates cluster results using external and internal evaluation methods.
problem Evaluating and validating the quality of clustering results.
method External evaluation using Homogeneity, Correctness, and V-measure scores; internal evaluation using Silhouette Index and Sum of Square Errors.
result Validation of the number of clusters using dendrogram and statistical frequency distribution.
Develops new shape metrics for high-dimensional objects.
problem Lack of single metrics to describe shape in high dimensions.
method Introduces hyper-Sphericity and hyper-Shape Proportion metrics.
result Discriminates between different shapes in high dimensions.
OpenEDS dataset captures eye images for VR applications.
problem Lack of high-quality eye image datasets for VR applications.
method Captured eye images using VR head-mounted display with synchronized cameras.
result Baseline experiment achieved 98.3% mIoU for pupil, iris, sclera segmentation.
Paper proves geodesics are evenly distributed on surfaces.
problem Existence of equidistributed closed geodesics on surfaces.
method Volume property of embedded contact homology, local variational constructions, and transversality arguments.
result Equidistribution of nondegenerate closed geodesics for generic metrics on closed surfaces.
Producing overlapping schemes is a major issue in clustering. Recent proposed overlapping methods relies on the search of an optimal covering and are based on different metrics, such as Euclidean distance and I-Divergence, used to measure closeness between observations. In this paper, we propose the use of another meas…
Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for c…
Hollow-tree Super resolves feature importance in large datasets.
problem Lack of effective scaling for large feature numbers in boosted tree models.
method Hollow-tree Super (HOTS) methodology for feature importance visualization.
result HOTS effectively resolves feature importance and directionality in high-dimensional neuroscientific data.
This paper proves an C∞ closing lemma for Hamiltonian flows on symplectic 4-manifolds.
problem Proving the C∞ closing lemma for Hamiltonian flows on symplectic 4-manifolds. method Combining results from geodesic flows on Finsler surfaces with the dual lens map technique, extending to Hamiltonian flows with certain restrictions.
result Established the C∞ closing lemma for a large family of Hamiltonian flows on 4-dimensional symplectic manifolds. Proposes a method to generate counterfactual and contrastive explanations using SHAP.
problem Need for explainable AI and legal requirement for model interpretability.
method Model agnostic method using SHAP to generate contrastive and counterfactual explanations.
result Demonstrates effectiveness of the method on various datasets.
We prove a Weyl Law for the phase transition spectrum based on the techniques of Liokumovich-Marques-Neves. As an application we give phase transition adaptations of the proofs of the density and equidistribution of minimal hypersufaces for generic metrics by Irie-Marques-Neves and Marques-Neves-Song, respectively. We …
Polynomially inscribe 6 concyclic points into any smooth curve.
problem Inscribing points into smooth curves using polynomials.
method Combines Fukaya-Irie theorem and Floer homology computation.
result Supports conjecture about polynomial inscriptions.
We study the problem of nonnegative rank-one approximation of a nonnegative tensor, and show that the globally optimal solution that minimizes the generalized Kullback-Leibler divergence can be efficiently obtained, i.e., it is not NP-hard. This result works for arbitrary nonnegative tensors with an arbitrary number of…
A-kNN improves kNN's ability to classify unknown instances.
problem kNN's inability to predict unknown instances.
method Developed Advanced kNN (A-kNN) algorithm.
result A-kNN significantly improves accuracy in classifying unknown instances.
Quantum machine learning model for binary classification.
problem Efficiency in high-dimensional binary classification tasks.
method Quantum-classical hybrid algorithm and quantum computer for inference.
result Quantum discriminator achieves 99% accuracy on Iris dataset.
Upper bounds for Lagrangian capacities of Liouville domains
problem Lagrangian capacity of Liouville domains
method Using S1-equivariant techniques result Extremal Lagrangian torus on the boundary of ellipsoid
In this paper we develop a statistical theory and an implementation of deep learning models. We show that an elegant variable splitting scheme for the alternating direction method of multipliers optimises a deep learning objective. We allow for non-smooth non-convex regularisation penalties to induce sparsity in parame…
The study proves a dichotomy for minimal hypersurfaces in certain thick manifolds.
problem Characterizing minimal hypersurfaces in thick manifolds.
method Generalizes Yau's conjecture using bounded geometry or thick at infinity conditions.
result Either infinitely many saddle points or no minimal hypersurfaces exist.
Paper develops PGMM framework for debiased inference on nonparametric IV estimators.
problem Automatic debiased inference on nonparametric IV functionals.
method Penalized GMM (PGMM) framework for functionals of IV estimators.
result PGMM-based debiased estimator performs well, achieving near-nominal coverage.
ECGID research focuses on rest but not exercise, this study evaluates both.
problem ECGID performance under exercise is insufficiently studied.
method Various learning methods applied to ECG dataset.
result Current ECGID methods fail under exercise conditions.
Enhances k-NN accuracy through randomized hyperstructure.
problem Improves k-NN accuracy by optimizing neighbor selection.
method Constructs a random n-dimensional hyperstructure around test instances to refine neighbor selection.
result 85.71% accuracy on Haberman's Cancer Survival dataset, compared to 80.95% for conventional k-NN.
Despite all the success that deep neural networks have seen in classifying certain datasets, the challenge of finding optimal solutions that generalize still remains. In this paper, we propose the Boundary Optimizing Network (BON), a new approach to generalization for deep neural networks when used for supervised learn…
Quantum machine learning: Adiabatic quantum SVM outperforms classical methods.
problem Training support vector machines efficiently on large datasets.
method Adiabatic quantum computing for SVM training.
result Quantum approach outperforms classical methods in accuracy and scalability.
Recommender systems have been widely adopted by electronic commerce and entertainment industries for individualized prediction and recommendation, which benefit consumers and improve business intelligence. In this article, we propose an innovative method, namely the recommendation engine of multilayers (REM), for tenso…
Develops deep learning for logical code segmentation.
problem Lack of logically segmented source code.
method Novel deep learning approach to generate logical code segments.
result Improves software analysis tasks like commenting, bug detection, and code synthesis.
New method accelerates large margin metric learning for nearest neighbor classification.
problem Efficiently learning metrics for nearest neighbor classification.
method Triplet mining and stratified sampling for large margin metric learning.
result Improved efficiency and scalability of optimization.
This study compares feature importance and explainability in quantum vs classical ML models.
problem Lack of transparency in ML models, especially in sensitive fields.
method Comparison of classical ML (SVM, Random Forest) and hybrid quantum ML (VQC, QSVC) models using feature importance and explainability methods.
result Quantum ML models provide insights similar to classical models but with unique quantum features.
Pattern recognition and machine learning are becoming integral parts of algorithms in a wide range of applications. Different algorithms and approaches for machine learning include different tradeoffs between performance and computation, so during algorithm development it is often necessary to explore a variety of diff…
TreeCaps improves code comprehension for software developers.
problem Processing code efficiently for software developers.
method Tree-based capsule networks for capturing code syntactical structures and dependencies.
result TreeCaps outperforms other approaches in classifying program functionalities.
Study re-evaluates MIMIC-III codes, finding many are under-coded.
problem Validity of MIMIC-III clinical codes is questionable.
method Open-source, reproducible methodology for assessing codes.
result Most frequently assigned codes are under-coded up to 35%
Paper proposes new gradient codes for robust distributed machine learning.
problem Robustness against slow machines (stragglers) in distributed machine learning.
method Constructs gradient codes based on probabilistic and Kronecker product methods.
result Gradient codes exist for a wide range of system parameters with comparable error performance.
New model creates code semantics vectors for better understanding.
problem Improving code understanding and embedding quality.
method Siamese recurrent neural network on Python source code.
result Model significantly outperforms bag-of-tokens embeddings.
New quantum codes improve error correction with local tests.
problem Improving quantum error correction efficiency.
method Introducing hemicubic codes and exploiting their local testability.
result Quantum codes with improved local testability and error correction.
CoNCRA uses CNN to find code snippets matching developer intent.
problem Finding relevant code snippets using general search engines.
method Convolutional Neural Network approach to code retrieval.
result Improved code retrieval by 5% on average, top 3 positions 80% of the time.
Paper proposes graph-based separable transforms for video coding.
problem Improving video coding efficiency by better capturing residual block statistics.
method Derives graph-based separable transforms (GBSTs) from line graphs with weights determined by parameters.
result GBSTs achieve about 0.4% average coding gain over existing transforms in VVC.
Adversarial attacks found to be effective on code models.
problem Adversarial robustness of code models.
method Instantiated adversarial attacks for code, demonstrated vulnerability, and improved robustness.
result Neural models for code are vulnerable to adversarial attacks.
Contrastive Code Representation Learning improves code summarization and type inference.
problem Code representations are sensitive to edits, hindering downstream semantic understanding tasks.
method ContraCode: a contrastive pre-training task that learns code functionality.
result Contrastive pre-training improves code summarization and type inference accuracy.
In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face, iris, and fingerprint) for person identification. The proposed deep multimodal fusion algorithm consists of multiple streams of modality-specific Convolutional Neural Networks (CNNs), which are jointly optimized at multiple fe…
Deep neural network predicts semantic labels for source code.
problem Difficulty in labeling and understanding new programming languages and functionalities.
method Language-agnostic deep convolutional neural network trained on Stack Overflow code snippets.
result Mean area under ROC of 0.957 and top-1 accuracy of 86.6% on Github code documents.
This work tackles runtime complexity prediction for code, using machine learning and a new dataset.
problem Predicting runtime complexity of code is hard and mathematically impossible.
method Modelled as a machine learning task, using feature engineering and code embeddings, with a new dataset.
result Achieved state-of-the-art results in runtime complexity prediction.