vqSGD: Vector Quantized Stochastic Gradient Descent. (arXiv:1911.07971v1 [cs.LG])

In this work, we present a family of vector quantization schemes vqSGD (Vector-Quantized Stochastic Gradient Descent) that provide asymptotic reduction in the communication cost with convergence guarantees in distributed computation and learning settings. In particular, we consider a randomized scheme, based on convex hull of a point set, that returns an unbiased estimator of a…

Learning Permutation Invariant Representations using Memory Networks. (arXiv:1911.07984v1 [cs.LG])

Many real world tasks such as 3D object detection and high-resolution image classification involve learning from a set of instances. In these cases, only a group of instances, a set, collectively contains meaningful information and therefore only the sets have labels, and not individual data instances. In this work, we present a permutation invariant neural…

Improved clustering algorithms for the Bipartite Stochastic Block Model. (arXiv:1911.07987v1 [math.ST])

We consider a Bipartite Stochastic Block Model (BSBM) on vertex sets $V_1$ and $V_2$, and investigate asymptotic sufficient conditions of exact and almost full recovery for polynomial-time algorithms of clustering over $V_1$, in the regime where the cardinalities satisfy $|V_1|\ll|V_2|$. We improve upon the known conditions of almost full recovery for spectral clustering algorithms in…

WITCHcraft: Efficient PGD attacks with random step size. (arXiv:1911.07989v1 [cs.LG])

State-of-the-art adversarial attacks on neural networks use expensive iterative methods and numerous random restarts from different initial points. Iterative FGSM-based methods without restarts trade off performance for computational efficiency because they do not adequately explore the image space and are highly sensitive to the choice of step size. We propose a variant of Projected Gradient…

Adversarial Attacks on Grid Events Classification: An Adversarial Machine Learning Approach. (arXiv:1911.08011v1 [cs.LG])

With the ever-increasing reliance on data for data-driven applications in power grids, such as event cause analysis, the authenticity of data streams has become crucially important. The data can be prone to adversarial stealthy attacks aiming to manipulate the data such that residual-based bad data detectors cannot detect them, and the perception of system operators…

Energy Dissipation and Entropy in Collisionless Plasma. (arXiv:1911.08086v1 [physics.plasm-ph])

It is well known that collisionless systems are dissipation free from the perspective of particle collision and thus conserve entropy. On the other hand, processes such as magnetic reconnection and turbulence appear to convert large-scale magnetic energy into heat. In this paper, we investigate the energization and heating of collisionless plasma. The dissipation process is…

Controlled dynamics and number fluctuations with two strategies for quorum sensing. (arXiv:1911.08115v1 [cond-mat.stat-mech])

Understanding the hierarchical self-organization of living systems is one of the biggest conceptual challenges of the present century. A generically observed mechanism that drives such organization is interaction among the individual elements—which may represent cells, bacteria, or even enzymes—via chemical signals. We use dynamical renormalization group approach to study a stochastic model for chemotactic particles…

Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks. (arXiv:1911.08118v1 [eess.IV])

Purpose: The purpose of this study is to demonstrate a method for Specific Absorption Rate (SAR) reduction for T2-FLAIR MRI sequences at 7T by predicting the required adiabatic pulse power and scaling the amplitude in a slice-wise fashion. Methods: We used a TR-FOCI adiabatic pulse for spin inversion in a T2-FLAIR sequence to improve B1+…

Evaluation Of The PETsys TOFPET2 ASIC In Multi-Channel Coincidence Experiments. (arXiv:1911.08156v1 [physics.med-ph])

Aiming to measure the difference in arrival times of two coincident gamma-photons with an accuracy in the order of 200 ps, time-of-flight positron emission tomography (ToF-PET) systems commonly employ silicon-photomultipliers and high-resolution digitization electronics, application specific integrated circuits (ASICs). This work evaluates the performance of the TOFPET2 ASIC released by PETsys Electronics S.A. in 2017…

Tunable Cherenkov radiation-based detectors via plasmon index enhancement. (arXiv:1911.08159v1 [physics.optics])

A recent study [PRB 100, 075427 (2019)], finally, demonstrated plasmon-analog of refractive index enhancement in metal nanostructures, which has already been studied in atomic clouds for several decades. Here, we simply utilize this phenomenon for achieving tunable enhanced Cherenkov radiation in metal nanostructures. Besides enabling Cherenkov radiation from slow-moving particles, or increasing its intensity, the…