To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is networ...
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This paper addresses bird song scene analysis focusing on location of birds and acoustic features of bird songs. Such a research area usually requires manual annotation related to positions and/or vocalization types o...
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This paper addresses bird song scene analysis focusing on location of birds and acoustic features of bird songs. Such a research area usually requires manual annotation related to positions and/or vocalization types of the target animals for a large amount of observed data. However, this manual annotation has two problems. One is that it is tough to annotate data observed in real environments because environmental noise exist and sound is reflected by trees and the ground, and also several birds at different locations may sing at the same time. The other is that it is inevitable that manual annotation produces inaccurate and inconsistent labels due to human errors and annotators' individual differences. For the first problem, we propose a Spatial-Cue-Based Probabilistic Model (SCBPM), which is a probabilistic model to estimate the maximum likelihood result for a bird song scene analysis by integrating sound source detection, localization, separation and identification based on spatial information of sound sources. For the second problem, we employ a semiautomatic annotation approach, in which a semi-supervised training method is deduced for SCBPM. This method decreases the amount of manual annotation. Preliminary experiments using recorded bird song data from the wild revealed that our system outperformed a conventional bird song scene analysis system by simply connecting sound source detection, localization, separation and identification in a cascade way in terms of identification accuracy.
Chronic Kidney Disease ("CKD") and its comorbidities, diabetes, hypertension and cardiovascular disease ("CVD"), are frequently measured by routine procedures and lab tests, creating a large amount...
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Chronic Kidney Disease ("CKD") and its comorbidities, diabetes, hypertension and cardiovascular disease ("CVD"), are frequently measured by routine procedures and lab tests, creating a large amount of historical data about this patient population. In this paper, we conducted a retrospective study based on Electronic Health Records ("EHR") data, in order to identify patterns in the development of CKD. In particular, we used a clustering approach to quantifiably identify diabetic patients who are at risk of progressing to advanced stages of CKD. Using values from routine measurements and lab tests such as systolic blood pressure, diastolic blood pressure, body mass index ("BMI"), Hemoglobin A1c ("HbAlc"), triglycerides and high density lipid cholesterol ("HDL cholesterol"), patients were classified into four clusters with a distinct separation between the cluster with the best value for each lab test and a cluster with the worst value for each lab test. We used lab values from each subsequent visit to calculate a progression score using the distance to the best and worst clusters, which indicated whether a patient's health was improving or deteriorating. We believe that this approach holds promise for future tools, as it is able to provide an ordered list of patients who are at greater risk of deterioration and should benefit from intervention by healthcare providers. The conclusions made in this paper are aimed at enabling timely monitoring and earlier intervention for patients that are associated with higher possibility of CKD progression.
This report provides an overview of recent work that harnesses the Big data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on...
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As robotic systems are integrated into mission operations, they provide key benefits over traditional manned systems such as increased endurance, versatility, and risk reduction for personnel. In order for robotic sys...
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As robotic systems are integrated into mission operations, they provide key benefits over traditional manned systems such as increased endurance, versatility, and risk reduction for personnel. In order for robotic systems to become fully integrated into United States Army missions, they will need to gain a level of autonomy that is closer to that of human personnel. Under Mission Command, the current system of issuing orders, an Operational Order (OPORD) contains the information required to execute a mission. The structure allows for authors of OPORDs to leave many details of the operation open to the discretion of the reader, making task interpretation and execution difficult for an autonomous system. The goal of this project, and one step of many in the process of automating systems to read OPORDs, is to show how OPORDs can be annotated with relevant information (such as locations, coordinates, and organizations) using the natural language processing techniques of tokenization and named-entity recognition (NER). Using A-fold cross validation of a Conditional Random Field (CRF) sequence model on 9 OPORDs containing 38,551 tokens, we were able to extract entities with an overall precision of 0.702, recall of 0.478, and F-measure of 0.569.
Some species of semiaquatic arthropods including water striders and springtails can jump from the water surface to avoid sudden dangers like predator attacks. It was reported recently that the jump of medium-sized wat...
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Some species of semiaquatic arthropods including water striders and springtails can jump from the water surface to avoid sudden dangers like predator attacks. It was reported recently that the jump of medium-sized water striders is a result of surface-tension-dominated interaction of thin cylindrical legs and water, with the leg movement speed nearly optimized to achieve the maximum takeoff velocity. Here we describe the mathematical theories to analyze this exquisite feat of nature by combining the review of existing models for floating and jumping and the introduction of the hitherto neglected capillary forces at the cylinder tips. The theoretically predicted dependence of body height on time is shown to match the observations of the jumps of the water striders and springtails regardless of the length of locomotory appendages. The theoretical framework can be used to understand the design principle of small jumping animals living on water and to develop biomimetic locomotion technology in semiaquatic environments.
We can significantly reduce the time required to realize designs if it is possible to find limits to the performance of an embedded system, solely based on high-level system specifications. For that purpose, we presen...
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This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual g...
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Extended results on the cosmic-ray electron + positron spectrum from 11 GeV to 4.8 TeV are presented based on observations with the Calorimetric Electron Telescope (CALET) on the International Space Station utilizing ...
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Extended results on the cosmic-ray electron + positron spectrum from 11 GeV to 4.8 TeV are presented based on observations with the Calorimetric Electron Telescope (CALET) on the International Space Station utilizing the data up to November 2017. The analysis uses the full detector acceptance at high energies, approximately doubling the statistics compared to the previous result. CALET is an all-calorimetric instrument with a total thickness of 30 X0 at normal incidence and fine imaging capability, designed to achieve large proton rejection and excellent energy resolution well into the TeV energy region. The observed energy spectrum in the region below 1 TeV shows good agreement with Alpha Magnetic Spectrometer (AMS-02) data. In the energy region below ∼300 GeV, CALET’s spectral index is found to be consistent with the AMS-02, Fermi Large Area Telescope (Fermi-LAT), and Dark Matter Particle Explorer (DAMPE), while from 300 to 600 GeV the spectrum is significantly softer than the spectra from the latter two experiments. The absolute flux of CALET is consistent with other experiments at around a few tens of GeV. However, it is lower than those of DAMPE and Fermi-LAT with the difference increasing up to several hundred GeV. The observed energy spectrum above ∼1 TeV suggests a flux suppression consistent within the errors with the results of DAMPE, while CALET does not observe any significant evidence for a narrow spectral feature in the energy region around 1.4 TeV. Our measured all-electron flux, including statistical errors and a detailed breakdown of the systematic errors, is tabulated in the Supplemental Material in order to allow more refined spectral analyses based on our data.
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