NewEvery arXiv paper, its researchers & institutions — mapped.
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#machine learning

45 results
cs.HC2019

Cultural association based on machine learning for team formation

Hrishikesh Kulkarni, Bradly Alicea

The paper proposes a machine‑learning approach that uses a Graphical Association Method to quantify cultural similarity between individuals and apply this measure to form effective…

#cultural association#team formation#graph-based modeling#machine learning
astro-ph.IM2019

Using Artificial Intelligence to Augment Science Prioritization for Astro2020

Brian Thomas, Harley Thronson, Andrew Adrian +5

The paper proposes using artificial intelligence and machine learning to help the Astro2020 Decadal Survey panel analyze and prioritize scientific objectives by automatically revie…

#decadal survey#science prioritization#artificial intelligence#machine learning
astro-ph.IM2019

German-Russian Astroparticle Data Life Cycle Initiative

Andreas Haungs, Igor Bychkov, Julia Dubenskaya +19

The paper describes the German‑Russian Astroparticle Data Life Cycle Initiative, which aims to build a prototype data‑life‑cycle platform for astroparticle experiments, enabling di…

#data life cycle#distributed storage#open science#multi-messenger analysis
math.OC2019

Ambulance Emergency Response Optimization in Developing Countries

Justin J. Boutilier, Timothy C. Y. Chan

The paper develops a robust optimization framework, combined with machine‑learning demand and travel‑time models, to improve the placement and routing of emergency medical vehicles…

#ambulance location#vehicle routing#robust optimization#machine learning
cs.NI2019

A Survey on Recent Advances in Transport Layer Protocols

Michele Polese, Federico Chiariotti, Elia Bonetto +3

The paper reviews recent developments in transport layer protocols, focusing on new congestion‑control methods, user‑space transport designs, and multipath capabilities.

#transport protocols#congestion control#multipath transport#machine learning
cs.LG2019

Kernels on fuzzy sets: an overview

Jorge Guevara, Roberto Hirata, Stéphane Canu

The paper presents kernels defined on fuzzy sets—[0,1]-valued membership functions—as similarity measures, describing several kernel families and their use in machine‑learning task…

#fuzzy sets#kernel methods#similarity measures#uncertainty modeling
cs.AR2019

Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design

Hosein Mohammadi Makrani, Farnoud Farahmand, Hossein Sayadi +6

The paper introduces Pyramid, a machine‑learning framework that predicts the optimal clock frequency and resource usage of high‑level synthesis (HLS) designs by calibrating HLS rep…

#high-level synthesis#machine learning#performance estimation#FPGA
q-bio.QM2019

Development of a Fragment-Based Machine Learning Algorithm for Designing Hybrid Drugs Optimized for Permeating Gram-Negative Bacteria

Rachael A. Mansbach, Inga V. Leus, Jitender Mehla +6

The paper introduces a fragment‑based machine‑learning method called Hunting FOX that identifies chemical fragments that improve a compound’s ability to cross the outer membrane of…

#antibiotic design#gram-negative bacteria#fragment-based screening#machine learning
cs.DL2019

What Do Citation Counts Measure? An Updated Review of Studies on Citations in Scientific Documents Published between 2006 and 2018

Iman Tahamtan, Lutz Bornmann

This paper reviews 41 studies from 2006‑2018 on what citation counts measure, focusing on citation content, context, and motivations, and highlights recent computational methods fo…

#citation analysis#citation context#machine learning#scholarly communication
cs.CY2019

Artificial Intelligence and the Future of Psychiatry: Insights from a Global Physician Survey

P. Murali Doraiswamy, Charlotte Blease, Kaylee Bodner

The paper presents results from a global survey of 791 psychiatrists on how likely they think AI/ML will replace various psychiatric tasks, finding mixed expectations and highlight…

#artificial intelligence#machine learning#psychiatry#physician attitudes
cs.CR2019

Machine Learning for Intelligent Authentication in 5G-and-Beyond Wireless Networks

He Fang, Xianbin Wang, Stefano Tomasin

The paper proposes using machine‑learning techniques to improve authentication in 5G and future wireless networks by exploiting physical‑layer features for continuous, model‑free d…

#5g#authentication#machine learning#physical layer security
cs.CR2019

Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement?

He Fang, Angie Qi, Xianbin Wang

The paper proposes AI‑driven methods for fast, lightweight authentication and adaptive, progressive authorization in large‑scale IoT networks, using machine learning at gateways an…

#iot security#authentication#authorization#machine learning
cs.LG2019

An Experiment on Measurement of Pavement Roughness via Android-Based Smartphones

Piyasak Thiandee, Boonsap Witchayangkoon, Sayan Sirimontree +1

The paper evaluates how three Android smartphones can record vibration acceleration data to estimate road roughness, comparing simple RMS calculations and machine‑learning models a…

#pavement roughness#smartphone sensors#acceleration data#machine learning
q-fin.ST2019

Investigating the effect of competitiveness power in estimating the average weighted price in electricity market

Naser Rostamni, Tarik A. Rashid

The paper studies how measures of market competitiveness, especially the Residual Supply Index, affect the accuracy of daily electricity price forecasts using a neural network mode…

#electricity market#price forecasting#market power#machine learning
quant-ph2019

Repetitive Readout Enhanced by Machine Learning

Genyue Liu, Mo Chen, Yi-Xiang Liu +2

The paper demonstrates that machine‑learning analysis of the full time‑trace from repetitive quantum‑non‑demolition readout can identify measurement back‑action and improve the fid…

#repetitive readout#machine learning#quantum non-demolition measurement#readout fidelity
cs.SE2019

A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques

Xiaotao Song, Hailong Sun, Xu Wang +1

The paper surveys research on automatically generating source code comments, reviewing challenges, algorithm categories, design principles, evaluation methods, and future research…

#code comment generation#software documentation#machine learning#natural language generation
astro-ph.EP2019

Classifying Exoplanet Candidates with Convolutional Neural Networks: Application to the Next Generation Transit Survey

Alexander Chaushev, Liam Raynard, Michael R. Goad +13

The paper applies convolutional neural networks to automatically classify exoplanet candidates from the Next Generation Transit Survey, achieving high accuracy and reducing manual…

#exoplanet detection#transit surveys#convolutional neural networks#machine learning
cs.NI2019

Localization in Ultra Narrow Band IoT Networks: Design Guidelines and Trade-Offs

Hazem Sallouha, Alessandro Chiumento, Sreeraj Rajendran +1

The paper proposes an RSSI‑based fingerprinting method that uses a few GPS‑enabled sensors to localize other devices in ultra‑narrow‑band IoT networks like Sigfox, achieving up to…

#localization#ultra narrow band#sigfox#rssi fingerprinting
hep-ph2019

Higgs Assisted Razor Search for Higgsinos at a 100 TeV pp Collider

Adarsh Pyarelal, Shufang Su

The paper proposes a razor‑based search strategy, enhanced with machine‑learning techniques, to discover Higgsino‑like supersymmetric particles at a future 100 TeV proton‑proton co…

#supersymmetry#higgsinos#100 tev collider#razor search
stat.AP2019

Comparative Analysis of User Behavior of Dock-Based vs. Dockless Bikeshare and Scootershare in Washington, D.C

Kiana Roshan Zamir, Iryna Bondarenko, Arefeh Nasri +2

The paper compares how users of dock-based bikeshare, dockless bikeshare, and dockless scootershare in Washington, D.C. travel, using logistic regression and random forest models t…

#dockless bikeshare#dockless scootershare#user behavior#urban mobility
physics.ins-det2019

A Novel Energy Resolved X-Ray Semiconductor Detector

Tengfei Yan, Chunlei Yang, Xiaodong Cui

The paper proposes a semiconductor detector architecture that uses energy‑dependent X‑ray absorption to achieve low‑cost, energy‑resolved (hyperspectral) X‑ray imaging, with spectr…

#x-ray imaging#semiconductor detector#energy-resolved detection#hyperspectral imaging
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