arXiv research

Researchers create adversarial examples to deceive iris recognition systems.

We first, introduce a deep learning based framework named as DeepIrisNet2 for visible spectrum and NIR Iris representation. The framework can work without classical iris normalization step or very accurate iris segmentation; allowing to work under non-ideal situation. The framework contains spatial transformer layers t…

Deep learning improves death cause coding accuracy.

Paper uses machine learning to estimate IRI from pavement distress types, densities, and severities.

Study uses Android smartphones to measure road roughness, finds ML better than RMS.

For almost all Riemannian metrics (in the $C^\infty$ Baire sense) on a closed manifold $M^{n+1}$, $3\leq (n+1)\leq 7$, 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.

Paper defends iris recognition from adversarial examples using wavelet decomposition.

InfoQGAN uses mutual information to improve QGANs, overcoming mode collapse and feature disentanglement issues.

This paper evaluates and validates cluster results using external and internal evaluation methods.

Develops new shape metrics for high-dimensional objects.

We present a large scale data set, OpenEDS: Open Eye Dataset, of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152…

The main result in this paper is the $C^{\infty}$ closing lemma for a large family of Hamiltonian flows on $4$-dimensional symplectic manifolds, which includes classical Hamiltonian systems. First we prove the $C^{\infty}$ closing lemma and the $C^r$ general density theorem for geodesic flows on closed Finsler surfaces…

Paper proves geodesics are evenly distributed on 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.

Proposes a method to generate counterfactual and contrastive explanations using SHAP.

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 …

Let $(M,g)$ be a complete $(n+1)$-dimensional Riemannian manifold with $2\leq n\leq 6$. Our main theorem generalizes the solution of S.-T. Yau's conjecture on the abundance of minimal surfaces and builds on a result of M. Gromov. Suppose that $(M,g)$ has bounded geometry, or more generally is thick at infinity. Then th…

Polynomially inscribe 6 concyclic points into any smooth curve.

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.

Quantum machine learning model for binary classification.

Upper bounds for Lagrangian capacities of Liouville domains

As the advancement of information security, human recognition as its core technology, has absorbed an increasing amount of attention in the past few years. A myriad of biometric features including fingerprint, face, iris, have been applied to security systems, which are occasionally considered vulnerable to forgery and…

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…

Paper develops PGMM framework for debiased inference on nonparametric IV estimators.

Enhances k-NN accuracy through randomized hyperstructure.

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.

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.

New method accelerates large margin metric learning for nearest neighbor classification.

This study compares feature importance and explainability in quantum vs classical ML models.

TreeCaps improves code comprehension for software developers.

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…

Study re-evaluates MIMIC-III codes, finding many are under-coded.

The practical success of widely used machine learning (ML) and deep learning (DL) algorithms in Artificial Intelligence (AI) community owes to availability of large datasets for training and huge computational resources. Despite the enormous practical success of AI, these algorithms are only loosely inspired from the b…

In this paper we presented a novel constructive approach for training deep neural networks using geometric approaches. We show that a topological covering can be used to define a class of distributed linear matrix inequalities, which in turn directly specify the shape and depth of a neural network architecture. The key…

Paper proposes new gradient codes for robust distributed machine learning.

New quantum codes improve error correction with local tests.

CoNCRA uses CNN to find code snippets matching developer intent.

Paper proposes graph-based separable transforms for video coding.

Adversarial attacks found to be effective on code models.

Contrastive Code Representation Learning improves code summarization and type inference.

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.