Low-cost open-source high-frequency portable pulse counter for Raspberry Pi and its application to Xray transmission rate measurement. (arXiv:1911.08168v1 [physics.ins-det])

Affordable electronics for instrumentation play a vital role in academia since research budgets are tight nowadays. In this paper a low-cost open-source high-frequency portable Raspberry Pi-based pulse counter is presented. Although it is designed as a $ 2 $ channel counter, it can be easily modified to a $ 4 $ channel counter. It provides…

Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems. (arXiv:1911.08009v1 [cs.IT])

End-to-end learning of a communications system using the deep learning-based autoencoder concept has drawn interest in recent research due to its simplicity, flexibility and its potential of adapting to complex channel models and practical system imperfections. In this paper, we have compared the bit error rate (BER) performance of autoencoder based systems and conventional channel…

Convolutional Neural Network and decision support in medical imaging: case study of the recognition of blood cell subtypes. (arXiv:1911.08010v1 [eess.IV])

Identifying and characterizing the patient’s blood samples is indispensable in diagnostics of malignance suspicious. A painstaking and sometimes subjective task is used in laboratories to manually classify white blood cells. Neural mathematical methods as deep learnings can be very useful in the automated recognition of blood cells. This study uses a particular type of deep…

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…

Algorithms for Solving Misalignment Issues in Penalized PET/CT Reconstruction Using Anatomical Priors. (arXiv:1911.08012v1 [physics.med-ph])

Two algorithms for solving misalignment issues in penalized PET/CT reconstruction using anatomical priors are proposed. Both approaches are based on a recently published joint motion estimation and image reconstruction method. The first approach deforms the anatomical image to align it with the functional one while the second approach deforms both images to align them with…

Low-Rank Toeplitz Matrix Estimation via Random Ultra-Sparse Rulers. (arXiv:1911.08015v1 [cs.DS])

We study how to estimate a nearly low-rank Toeplitz covariance matrix $T$ from compressed measurements. Recent work of Qiao and Pal addresses this problem by combining sparse rulers (sparse linear arrays) with frequency finding (sparse Fourier transform) algorithms applied to the Vandermonde decomposition of $T$. Analytical bounds on the sample complexity are shown, under the…

Graph Learning for Spatiotemporal Signal with Long Short-Term Characterization. (arXiv:1911.08018v1 [cs.SI])

Mining natural associations from high-dimensional spatiotemporal signals have received significant attention in various fields including biology, climatology and financial analysis, etcetera. Due to the widespread correlation in diverse applications, ideas that taking full advantage of correlated property to find meaningful insights of spatiotemporal signals have begun to emerge. In this paper, we study the problem…

On the Price of Satisficing in Network User Equilibria. (arXiv:1911.07914v1 [cs.GT])

When network users are satisficing decision-makers, the resulting traffic pattern attains a satisficing user equilibrium, which may deviate from the (perfectly rational) user equilibrium. In a satisficing user equilibrium traffic pattern, the total system travel time can be worse than in the case of the PRUE. We show how bad the worst-case satisficing user equilibrium…

Strongly Budget Balanced Auctions for Multi-Sided Markets. (arXiv:1911.08094v1 [cs.GT])

In two-sided markets, Myerson and Satterthwaite’s impossibility theorem states that one can not maximize the gain-from-trade while also satisfying truthfulness, individual-rationality and no deficit. Attempts have been made to circumvent Myerson and Satterthwaite’s result by attaining approximately-maximum gain-from-trade: the double-sided auctions of McAfee (1992) is truthful and has no deficit, and the one by Segal-Halevi…

Cross-modal supervised learning for better acoustic representations. (arXiv:1911.07917v1 [cs.CV])

Obtaining large-scale human-labeled datasets to train acoustic representation models is a very challenging task. On the contrary, we can easily collect data with machine-generated labels. In this work, we propose to exploit machine-generated labels to learn better acoustic representations, based on the synchronization between vision and audio. Firstly, we collect a large-scale video dataset with…