arXiv subjects

Brian Lee

Publications and source records attributed to Brian Lee.

At least 37 records · Page 2Linked to original sources

arXiv2021

R2P2: Reactive Routing and Payment Protocol for Named Data Network using Blockchain

With the continuous emergence of new mobile devices which support new communication paradigms such as D2D and V2V, Internet users can take advantage of these devices to achieve better Internet connectivity and improve service quality. Meanwhile, packet forwarding brings extra costs to devices (e.g. electricity consumption), that hinders the realisation of successful ad-hoc networks. This paper proposes Reactive Routing and Payment Protocol (R2P2) to incentivise mobile devices to contribute idle networking resources and gain monetary returns. The routing and payment protocol is developed for Named-Data Network (NDN) because its content-centric nature can better support the intermittent and ephemeral communication requirements in ad-hoc networks. Blockchain is used as the settlement platform for transactions between devices because of its neutrality, robustness and trust. R2P2 is still an on-going project. The content of this paper focuses on the design of R2P2.

cs.NI↗

arXiv2021

Protecting Privacy and Transforming COVID-19 Case Surveillance Datasets for Public Use

Objectives: Federal open data initiatives that promote increased sharing of federally collected data are important for transparency, data quality, trust, and relationships with the public and state, tribal, local, and territorial (STLT) partners. These initiatives advance understanding of health conditions and diseases by providing data to more researchers, scientists, and policymakers for analysis, collaboration, and valuable use outside CDC responders. This is particularly true for emerging conditions such as COVID-19 where we have much to learn and have evolving data needs. Since the beginning of the outbreak, CDC has collected person-level, de-identified data from jurisdictions and currently has over 8 million records, increasing each day. This paper describes how CDC designed and produces two de-identified public datasets from these collected data. Materials and Methods: Data elements were included based on the usefulness, public request, and privacy implications; specific field values were suppressed to reduce risk of reidentification and exposure of confidential information. Datasets were created and verified for privacy and confidentiality using data management platform analytic tools as well as R scripts. Results: Unrestricted data are available to the public through Data.CDC.gov and restricted data, with additional fields, are available with a data use agreement through a private repository on GitHub.com. Practice Implications: Enriched understanding of the available public data, the methods used to create these data, and the algorithms used to protect privacy of de-identified individuals allow for improved data use. Automating data generation procedures allows greater and more timely sharing of data.

cs.CR↗

arXiv2020

Blockchain for Multi-Robot Collaboration to Combat COVID-19 and Future Pandemics

This conceptual paper overviews how blockchain technology is involving the operation of multi-robot collaboration for combating COVID-19 and future pandemics. Robots are a promising technology for providing many tasks such as spraying, disinfection, cleaning, treating, detecting high body temperature/mask absence, and delivering goods and medical supplies experiencing an epidemic COVID-19. For combating COVID-19, many heterogeneous and homogenous robots are required to perform different tasks for supporting different purposes in the quarantine area. Controlling and decentralizing multi-robot play a vital role in combating COVID-19 by reducing human interaction, monitoring, delivering goods. Blockchain technology can manage multi-robot collaboration in a decentralized fashion, improve the interaction among them to exchange information, share representation, share goals, and trust. We highlight the challenges and provide the tactical solutions enabled by integrating blockchain and multi-robot collaboration to combat COVID-19 pandemic. The framework of our conceptual proposed can increase the intelligence, decentralization, and autonomous operations of connected multi-robot collaboration in the blockchain network. We overview blockchain potential benefits to defining a framework of multi-robot collaboration applications to combat COVID-19 epidemics such as monitoring and outdoor and hospital End to End (E2E) delivery systems. Furthermore, we discuss the challenges and opportunities of integrated blockchain, multi-robot collaboration, and the Internet of Things (IoT) for combating COVID-19 and future pandemics.

cs.RO↗

arXiv2020

PPTP: Price-based Path-specified Transport Protocol for Named Data Network using Blockchain

Serving as a potential future Internet architecture, Named Data Network (NDN) offers superior information-centric architectural support for mobile ad-hoc networking. Using NDN as an underlying protocol, end-user devices (e.g. IoT device and smart phone) formulate a multi-hop (mesh) network, in which certain devices play a role of forwarding packets for others and/or act as gateways to access the Internet. Nevertheless, an autonomous (selfish) node in an ad-hoc network has two disincentives for forwarding packets for others: energy expenditure and possible delays for its own data. This paper introduces Price-based Path-specified Transport Protocol (PPT) for NDN, using blockchain as a payment platform to support money transfers between autonomous nodes thus to incentivise packet forwarding. In PPTP, routers advertise their expected prices for packet forwarding and consumers estimate the costs and select the appropriate paths for content downloading. PPTP is still an on-going project therefore this paper will present the design principle and planed functions, and show how PPTP are related to other existing blockchain-based networking solutions.

cs.NI↗

arXiv2020

Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations

Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma are at higher risk for severe illness from COVID-19. Thus, the relationship of comorbidity disease with COVID-19 may help to find repurposable drugs. To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs. First, we utilized knowledge of COVID-19 to construct a disease-gene-drug network (DGDr-Net) representing a COVID-19-centric interactome with components for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681 human genes and 2,173 drugs, and medical information for 18 common comorbidities. The DGDr-Net recommended candidate repurposable drugs for COVID-19 through network reinforcement driven scoring algorithms. The scoring algorithms determined the priority of recommendations by utilizing graph-based semi-supervised learning. From the predicted scores, we recommended 30 drugs, including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin, etc., as repurposable drugs for COVID-19, and the results were verified with drugs that have been under clinical trials. The list of drugs via a data-driven computational approach could help reduce trial-and-error in finding treatment for COVID-19.

q-bio.QM↗

arXiv2019

SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm

Objective. Recent advancements in electrode designs and micro-fabrication technology has allowed existence of microelectrode arrays with hundreds of channels for single-cell recordings. In such electrophysiological recordings, each implanted micro-electrode can record the activities of more than one neuron in its vicinity. Recording the activities of multiple neurons may also be referred to as multiple unit activity. However, for any further analysis, the main goal is to isolate the activity of each recorded neuron and thus called single-unit activity. This process may also be referred to as spike sorting or spike classification. Recent approaches to extract SUA are time consuming, mainly due to the requirement of human intervention at various stages of spike sorting pipeline. Lack of standardization is another drawback of the current available approaches. Therefore, in this study we proposed a standard spike sorter: SpikeDeep-Classifier, a fully automatic spike sorting algorithm. Approach. We proposed a novel spike sorting pipeline, based on a set of supervised and unsupervised learning algorithms. We used supervised, deep learning-based algorithms for extracting meaningful channels and removing background activities (noise) from the extracted channels. We also showed that the process of clustering becomes straight-forward, once the noise/artifact is completely removed from the data. Therefore, in the next stage, we applied a simple clustering algorithm (K-mean) with predefined maximum number of clusters. Lastly, we used a similarity-based criterion to keep distinct clusters and merge similar-looking clusters. Main results. We evaluated our algorithm on a dataset collected from two different species (humans and non-human primates (NHPs)) without any retraining. We also validated our algorithm on two publicly available labeled datasets.

q-bio.QM↗

arXiv2019

FPGA-accelerated machine learning inference as a service for particle physics computing

New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.

physics.data-an↗

arXiv2018

Universal Spike Classifier

In electrophysiology, microelectrodes are the primary source for recording neural data of single neurons (single unit activity). These microelectrodes can be implanted individually, or in the form of microelectrodes arrays, consisting of hundreds of electrodes. During recordings, some channels capture the activity of neurons, which is usually contaminated with external artifacts and noise. Another considerable fraction of channels does not record any neural data, but external artifacts and noise. Therefore, an automatic identification and tracking of channels containing neural data is of great significance and can accelerate the process of analysis, e.g. automatic selection of meaningful channels during offline and online spike sorting. Another important aspect is the selection of meaningful channels during online decoding in brain-computer interface applications, where threshold crossing events are usually for feature extraction, even though they do not necessarily correspond to neural events. Here, we propose a novel algorithm based on the newly introduced way of feature vector extraction and a supervised deep learning method: a universal spike classifier (USC). The USC enables us to address both above-raised issues. The USC uses the standard architecture of convolutional neural networks (Conv net). It takes the batch of the waveforms, instead of a single waveform as an input, propagates it through the multilayered structure, and finally classifies it as a channel containing neural spike data or artifacts. We have trained the model of USC on data recorded from single tetraplegic patient with Utah arrays implanted in different brain areas. This trained model was then evaluated without retraining on the data collected from six epileptic patients implanted with depth electrodes and two tetraplegic patients implanted with two Utah arrays, individually.

q-bio.NC↗

arXiv2018

PTP: Path-specified Transport Protocol for Concurrent Multipath Transmission in Named Data Networks

Named Data Networking (NDN) is a promising Future Internet architecture to support content distribution. Its inherent addressless routing paradigm brings valuable characteristics to improve the transmission robustness and efficiency, e.g. users are enabled to download content from multiple providers concurrently. However, multipath transmission NDN is different from that in Multipath TCP, i.e. the "paths" in NDN are transparent to and uncontrollable by users. To this end, the user controls the traffic on all transmission paths as an entirety, which leads to a noticeable problem of low bandwidth utilization. In particular, the congestion of a certain path will trigger the traffic reduction on the other transmission paths that are underutilized. Some solutions have been proposed by letting routers balance the loads of different paths to avoid congesting a certain path prematurely. However, the complexity of obtaining an optimal load balancing solution (of solving a Multi-Commodity Flow problem) becomes higher with the increasing network size, which limits the universal NDN deployments. This paper introduces a compromising solution - Path-specified Transport Protocol (PTP). PTP supports both the label switching and the addressless routing schemes. Specifically, the label switching scheme facilitates users to precisely control the traffic on each transmission path, and the addressless routing scheme maintains the valuable feature of retrieving content from any provider to guarantee robustness. As the traffic on a transmission path can be explicitly controlled by consumers, load balancing is no longer needed in routers, which reduce the computational burden of routers and consequently increase the system scalability. The experimental results show that PTP significantly increases the users' downloading rates and improved the network throughput.

cs.NI↗

arXiv2018

MVP2P: Layer-Dependency-Aware Live MVC Video Streaming over Peer-to-Peer Networks

Multiview video supports observing a scene from different viewpoints. The Joint Video Team (JVT) developed H.264/MVC to enhance the compression efficiency for multiview video, however, MVC encoded multiview video (MVC video) still requires high bitrates for transmission. This paper investigates live MVC video streaming over Peer-to-Peer (P2P) networks. The goal is to minimize the server bandwidth costs whist ensuring high streaming quality to peers. MVC employs intra-view and inter-view prediction structures, which leads to a complicated layer dependency relationship. As the peers' outbound bandwidth is shared while supplying all the MVC video layers, the bandwidth allocation to one MVC layer affects the available outbound bandwidth of the other layers. To optimise the utilisation of the peers' outbound bandwidth for providing video layers, a maximum flow based model is proposed which considers the MVC video layer dependency and the layer supplying relationship between peers. Based on the model, a layer dependency aware live MVC video streaming method over a BitTorrent-like P2P network is proposed, named MVP2P. The key components of MVP2P include a chunk scheduling strategy and a peer selection strategy for receiving peers, and a bandwidth scheduling algorithm for supplying peers. To evaluate the efficiency of the proposed solution, MVP2P is compared with existing methods considering the constraints of peer bandwidth, peer numbers, view switching rates, and peer churns. The test results show that MVP2P significantly outperforms the existing methods.

cs.MM↗

arXiv2017

Situational Awareness based Risk-Adapatable Access Control in Enterprise Networks

As the computing landscape evolves towards distributed architectures such as Internet of Things (IoT),enterprises are moving away from traditional perimeter based security models toward so called zero trust networking (ZTN) models that treat both the intranet and Internet as equally untrustworthy. Such security models incorporate risk arising from dynamic and situational factors, such as device location and security risk level risk, into the access control decision. Researchers have developed a number of risk models such as RAdAC (Risk Adaptable Access Control) to handle dynamic contexts and these have been applied to medical and other scenarios. In this position paper we describe our ongoing work to apply RAdAC to ZTN. We develop a policy management framework, FURZE, to facilitate fuzzy risk evaluation that also defines how to adapt to dynamically changing contexts. We also consider how enterprise security situational awareness (SSA) - which describes the potential impact to an organisations mission based on the current threats and the relative importance of the information asset under threat - can be incorporated into a RAdAC scheme

cs.CR↗

arXiv2017

Exploring the Brown Dwarf Desert: New Substellar Companions from the SDSS-III MARVELS Survey

Planet searches using the radial velocity technique show a paucity of companions to solar-type stars within ~5 AU in the mass range of ~10 - 80 M$_{\text{Jup}}$. This deficit, known as the brown dwarf desert, currently has no conclusive explanation. New substellar companions in this region help asses the reality of the desert and provide insight to the formation and evolution of these objects. Here we present 10 new brown dwarf and two low-mass stellar companion candidates around solar-type stars from the Multi-object APO Radial-Velocity Exoplanet Large-Area Survey (MARVELS) of the Sloan Digital Sky Survey III (SDSS-III). These companions were selected from processed MARVELS data using the latest University of Florida Two Dimensional (UF2D) pipeline, which shows significant improvement and reduction of systematic errors over previous pipelines. The 10 brown dwarf companions range in mass from ~13 to 76 M$_{\text{Jup}}$ and have orbital radii of less than 1 AU. The two stellar companions have minimum masses of ~98 and 100 M$_{\text{Jup}}$. The host stars of the MARVELS brown dwarf sample have a mean metallicity of [Fe/H] = 0.03 $\pm$ 0.08 dex. Given our stellar sample we estimate the brown dwarf occurrence rate around solar-type stars with periods less than ~300 days to be ~0.56%.

astro-ph.SR↗

arXiv2016

Very Low-Mass Stellar and Substellar Companions to Solar-like Stars From MARVELS VI: A Giant Planet and a Brown Dwarf Candidate in a Close Binary System HD 87646

We report the detections of a giant planet (MARVELS-7b) and a brown dwarf candidate (MARVELS-7c) around the primary star in the close binary system, HD 87646. It is the first close binary system with more than one substellar circum-primary companion discovered to the best of our knowledge. The detection of this giant planet was accomplished using the first multi-object Doppler instrument (KeckET) at the Sloan Digital Sky Survey (SDSS) telescope. Subsequent radial velocity observations using ET at Kitt Peak National Observatory, HRS at HET, the "Classic" spectrograph at the Automatic Spectroscopic Telescope at Fairborn Observatory, and MARVELS from SDSS-III confirmed this giant planet discovery and revealed the existence of a long-period brown dwarf in this binary. HD 87646 is a close binary with a separation of $\sim22$ AU between the two stars, estimated using the Hipparcos catalogue and our newly acquired AO image from PALAO on the 200-inch Hale Telescope at Palomar. The primary star in the binary, HD 87646A, has Teff = 5770$\pm$80K, log(g)=4.1$\pm$0.1 and [Fe/H] = $-0.17\pm0.08$. The derived minimum masses of the two substellar companions of HD 87646A are 12.4$\pm$0.7M$_{\rm Jup}$ and 57.0$\pm3.7$M$_{\rm Jup}$. The periods are 13.481$\pm$0.001 days and 674$\pm$4 days and the measured eccentricities are 0.05$\pm$0.02 and 0.50$\pm$0.02 respectively. Our dynamical simulations show the system is stable if the binary orbit has a large semi-major axis and a low eccentricity, which can be verified with future astrometry observations.

astro-ph.EP↗

arXiv2014

The Case for Cloud Service Trustmarks and Assurance-as-a-Service

Cloud computing represents a significant economic opportunity for Europe. However, this growth is threatened by adoption barriers largely related to trust. This position paper examines trust and confidence issues in cloud computing and advances a case for addressing them through the implementation of a novel trustmark scheme for cloud service providers. The proposed trustmark would be both active and dynamic featuring multi-modal information about the performance of the underlying cloud service. The trustmarks would be informed by live performance data from the cloud service provider, or ideally an independent third-party accountability and assurance service that would communicate up-to-date information relating to service performance and dependability. By combining assurance measures with a remediation scheme, cloud service providers could both signal dependability to customers and the wider marketplace and provide customers, auditors and regulators with a mechanism for determining accountability in the event of failure or non-compliance. As a result, the trustmarks would convey to consumers of cloud services and other stakeholders that strong assurance and accountability measures are in place for the service in question and thereby address trust and confidence issues in cloud computing.

cs.DC↗

arXiv2013

A Cautionary Tale: MARVELS Brown Dwarf Candidate Reveals Itself To Be A Very Long Period, Highly Eccentric Spectroscopic Stellar Binary

We report the discovery of a highly eccentric, double-lined spectroscopic binary star system (TYC 3010-1494-1), comprising two solar-type stars that we had initially identified as a single star with a brown dwarf companion. At the moderate resolving power of the MARVELS spectrograph and the spectrographs used for subsequent radial-velocity (RV) measurements (R ~ <30,000), this particular stellar binary mimics a single-lined binary with an RV signal that would be induced by a brown dwarf companion (Msin(i)~50 M_Jup) to a solar-type primary. At least three properties of this system allow it to masquerade as a single star with a very low-mass companion: its large eccentricity (e~0.8), its relatively long period (P~238 days), and the approximately perpendicular orientation of the semi-major axis with respect to the line of sight (omega~189 degrees). As a result of these properties, for ~95% of the orbit the two sets of stellar spectral lines are completely blended, and the RV measurements based on centroiding on the apparently single-lined spectrum is very well fit by an orbit solution indicative of a brown dwarf companion on a more circular orbit (e~0.3). Only during the ~5% of the orbit near periastron passage does the true, double-lined nature and large RV amplitude of ~15 km/s reveal itself. The discovery of this binary system is an important lesson for RV surveys searching for substellar companions; at a given resolution and observing cadence, a survey will be susceptible to these kinds of astrophysical false positives for a range of orbital parameters. Finally, for surveys like MARVELS that lack the resolution for a useful line bisector analysis, it is imperative to monitor the peak of the cross-correlation function for suspicious changes in width or shape, so that such false positives can be flagged during the candidate vetting process.

astro-ph.EP↗

arXiv2012

Very Low-mass Stellar and Substellar Companions to Solar-like Stars from MARVELS II: A Short-period Companion Orbiting an F Star with Evidence of a Stellar Tertiary And Significant Mutual Inclination

We report the discovery via radial velocity of a short-period (P = 2.430420 \pm 0.000006 days) companion to the F-type main sequence star TYC 2930-00872-1. A long-term trend in the radial velocities indicates the presence of a tertiary stellar companion with $P > 2000$ days. High-resolution spectroscopy of the host star yields T_eff = 6427 +/- 33 K, log(g) = 4.52 +/- 0.14, and [Fe/H]=-0.04 +/- 0.05. These parameters, combined with the broad-band spectral energy distribution and parallax, allow us to infer a mass and radius of the host star of M_1=1.21 +/- 0.08 M_\odot and R_1=1.09_{-0.13}^{+0.15} R_\odot. We are able to exclude transits of the inner companion with high confidence. The host star's spectrum exhibits clear Ca H and K core emission indicating stellar activity, but a lack of photometric variability and small v*sin(I) suggest the primary's spin axis is oriented in a pole-on configuration. The rotational period of the primary from an activity-rotation relation matches the orbital period of the inner companion to within 1.5 σ, suggesting they are tidally locked. If the inner companion's orbital angular momentum vector is aligned with the stellar spin axis, as expected through tidal evolution, then it has a stellar mass of M_2 ~ 0.3-0.4 M_\odot. Direct imaging limits the existence of stellar companions to projected separations < 30 AU. No set of spectral lines and no significant flux contribution to the spectral energy distribution from either companion are detected, which places individual upper mass limits of M < 1.0 M_\odot, provided they are not stellar remnants. If the tertiary is not a stellar remnant, then it likely has a mass of ~0.5-0.6 M_\odot, and its orbit is likely significantly inclined from that of the secondary, suggesting that the Kozai-Lidov mechanism may have driven the dynamical evolution of this system.

astro-ph.SR↗

arXiv2012

Accurate Group Delay Measurement for Radial Velocity Instruments Using the Dispersed Fixed Delay Interferometer Method

The dispersed fixed-delay Intereferometer (DFDI) method is attractive for its low cost, compact size, and multiobject capability in precision radial-velocity (RV) measurements. The phase shift of fringes of stellar absorption lines is measured and then converted to an RV shift via an important parameter, phase-to-velocity scale (PV scale), determined by the group delay (GD) of a fixed-delay interferometer. Two methods of GD measurement using a DFDI Doppler instrument are presented in this article: (1) GD measurement using white-light combs gen- erated by the fixed-delay interferometer and (2) GD calibration using an RV reference star. These two methods provide adequate precision of GD measurement and calibration, given the current RV precision achieved by a DFDI Doppler instrument. They can potentially be used to measure GD of an fixed-delay interferometer for submeter- precision Doppler measurement with a DFDI instrument. Advantages and limitations of each method are discussed in detail. The two methods can serve as standard procedures of PV-scale calibration for DFDI instruments and cross- checks for each other.

astro-ph.IM↗

arXiv2011

Eclipsing Binary Science Via the Merging of Transit and Doppler Exoplanet Survey Data - A Case Study With the MARVELS Pilot Project and SuperWASP

Exoplanet transit and Doppler surveys discover many binary stars during their operation that can be used to conduct a variety of ancillary science. Specifically, eclipsing binary stars can be used to study the stellar mass-radius relationship and to test predictions of theoretical stellar evolution models. By cross-referencing 24 binary stars found in the MARVELS Pilot Project with SuperWASP photometry, we find two new eclipsing binaries, TYC 0272-00458-1 and TYC 1422-01328-1, which we use as case studies to develop a general approach to eclipsing binaries in survey data. TYC 0272-00458-1 is a single-lined spectroscopic binary for which we calculate a mass of the secondary and radii for both components using reasonable constraints on the primary mass through several different techniques. For a primary mass of M_1 = 0.92 +/- 0.1 M_solar, we find M_2 = 0.610 +/- 0.036 M_solar, R_1 = 0.932 +/- 0.076 R_solar and R_2 = 0.559 +/- 0.102 R_solar, and find that both stars have masses and radii consistent with model predictions. TYC 1422-01328-1 is a triple-component system for which we can directly measure the masses and radii of the eclipsing pair. We find that the eclipsing pair consists of an evolved primary star (M_1 = 1.163 +/- 0.034 M_solar, R_1 = 2.063 +/- 0.058 R_solar) and a G-type dwarf secondary (M_2 = 0.905 +/- 0.067 M_solar, R_2 = 0.887 +/- 0.037 R_solar). We provide the framework necessary to apply this analysis to much larger datasets.

astro-ph.SR↗