Cooper minimum of high-order harmonic spectra from MgO crystal in an ultrashort laser pulse. (arXiv:1911.12092v1 [physics.atm-clus])

Cooper minimum structure of high-order harmonic spectra from atoms or molecules has been extensively studied. In this paper, we demonstrate that the crystal harmonic spectra from an ultrashort mid-infrared laser pulse also exhibit the Cooper minimum characteristic. Based on the accurate band dispersion and k-dependent transition dipole moment (TDM) from the first-principle calculations, it can…

Compressed MRI Reconstruction Exploiting a Rotation-Invariant Total Variation Discretization. (arXiv:1911.11854v1 [eess.IV])

Inspired by the first-order method of Malitsky and Pock, we propose a novel variational framework for compressed MR image reconstruction which introduces the application of a rotation-invariant discretization of total variation functional into MR imaging while exploiting BM3D frame as a sparsifying transform. The proposed model is presented as a constrained optimization problem, however, we…

Asymmetric Correntropy for Robust Adaptive Filtering. (arXiv:1911.11855v1 [eess.SP])

In recent years, correntropy has been seccessfully applied to robust adaptive filtering to eliminate adverse effects of impulsive noises or outliers. Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables. This definition is reasonable when the error between the two random variables is symmetrically distributed around zero. For the…

Approximating the Permanent by Sampling from Adaptive Partitions. (arXiv:1911.11856v1 [cs.LG])

Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics. However, this problem is also #P-complete, which leaves little hope for finding an exact solution that can be computed efficiently. While the problem admits a fully polynomial randomized approximation scheme, this method has seen…

Matrix Decompositions and Sparse Graph Regularity. (arXiv:1911.11868v1 [cs.DS])

We introduce and study a matrix decomposition that is a common generalization of the singular value decomposition (SVD), cut decomposition, CUR decomposition, and others. For any given set of pairs $P \subseteq \mathbb{R}^m \times \mathbb{R}^n$ and matrix $A \in \mathbb{R}^{m \times n}$, we write $A$ as a weighted sum of rank one matrices formed by…

Hyperproperties for Robotics: Motion Planning via HyperLTL. (arXiv:1911.11870v1 [cs.RO])

There is a growing interest on formal methods-based robotic motion planning for temporal logic objectives. In this work, we extend the scope of existing synthesis methods to hyper-temporal logics. We are motivated by the fact that important planning objectives, such as optimality, robustness, and privacy, (maybe implicitly) involve the interrelation between multiple paths; such objectives…

Artificial Intelligence for Diagnosis of Skin Cancer: Challenges and Opportunities. (arXiv:1911.11872v1 [eess.IV])

Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population, and a lack of adequate clinical expertise and services, there is an immediate need for AI systems to assist clinicians in…

LqRT: Robust Hypothesis Testing of Location Parameters using Lq-Likelihood-Ratio-Type Test in Python. (arXiv:1911.11922v1 [stat.ME])

A t-test is considered a standard procedure for inference on population means and is widely used in scientific discovery. However, as a special case of a likelihood-ratio test, t-test often shows drastic performance degradation due to the deviations from its hard-to-verify distributional assumptions. Alternatively, in this article, we propose a new two-sample Lq-likelihood-ratio-type test (LqRT)…

Improving Fictitious Play Reinforcement Learning with Expanding Models. (arXiv:1911.11928v1 [cs.LG])

Fictitious play with reinforcement learning is a general and effective framework for zero-sum games. However, using the current deep neural network models, the implementation of fictitious play faces crucial challenges. Neural network model training employs gradient descent approaches to update all connection weights, and thus is easy to forget the old opponents after training to…

Survey of Attacks and Defenses on Edge-Deployed Neural Networks. (arXiv:1911.11932v1 [cs.CR])

Deep Neural Network (DNN) workloads are quickly moving from datacenters onto edge devices, for latency, privacy, or energy reasons. While datacenter networks can be protected using conventional cybersecurity measures, edge neural networks bring a host of new security challenges. Unlike classic IoT applications, edge neural networks are typically very compute and memory intensive, their execution…