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  1. Electron microscopy images of carbon nanotube (CNT) forests are difficult to segment due to the long and thin nature of the CNTs; density of the CNT forests resulting in CNTs touching, crossing, and occluding each other; and low signal-to-noise ratio electron microscopy imagery. In addition, due to image complexity, it is not feasible to prepare training segmentation masks. In this paper, we propose CNTSegNet, a dual loss, orientation-guided, self-supervised, deep learning network for CNT forest segmentation in scanning electron microscopy (SEM) images. Our training labels consist of weak segmentation labels produced by intensity thresholding of the raw SEM images and self labels produced by estimating orientation distribution of CNTs in these raw images. The proposed network extends a U-net-like encoder-decoder architecture with a novel two-component loss function. The first component is dice loss computed between the predicted segmentation maps and the weak segmentation labels. The second component is mean squared error (MSE) loss measuring the difference between the orientation histogram of the predicted segmentation map and the original raw image. Weighted sum of these two loss functions is used to train the proposed CNTSegNet network. The dice loss forces the network to perform background-foreground segmentation using local intensity features. The MSE loss guides the network with global orientation features and leads to refined segmentation results. The proposed system needs only a few-shot dataset for training. Thanks to itโ€™s self-supervised nature, it can easily be adapted to new datasets. 
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    Free, publicly-accessible full text available February 12, 2024
  2. Carbon nanotube (CNT) forests are imaged using scanning electron microscopes (SEMs) that project their multilayered 3D structure into a single 2D image. Image analytics, particularly instance segmentation is needed to quantify structural characteristics and to predict correlations between structural morphology and physical properties. The inherent complexity of individual CNT structures is further increased in CNT forests due to density of CNTs, interactions between CNTs, occlusions, and lack of 3D information to resolve correspondences when multiple CNTs from different depths appear to cross in 2D. In this paper, we propose CNT-NeRF, a generative adversarial network (GAN) for simultaneous depth layer decomposition and segmentation of CNT forests in SEM images. The proposed network is trained using a multi-layer, photo-realistic synthetic dataset obtained by transferring the style of real CNT images to physics-based simulation data. Experiments show promising depth layer decomposition and accurate CNT segmentation results not only for the front layer but also for the partially occluded middle and back layers. This achievement is a significant step towards automated, image-based CNT forest structure characterization and physical property prediction. 
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    Free, publicly-accessible full text available January 1, 2024
  3. Irfan Awan ; Muhammad Younas ; Jamal Bentahar ; Salima Benbernou (Ed.)
    Multi-site clinical trial systems face security challenges when streamlining information sharing while protecting patient privacy. In addition, patient enrollment, transparency, traceability, data integrity, and reporting in clinical trial systems are all critical aspects of maintaining data compliance. A Blockchain-based clinical trial framework has been proposed by lots of researchers and industrial companies recently, but its limitations of lack of data governance, limited confidentiality, and high communication overhead made data-sharing systems insecure and not efficient. We propose ๐–ฒ๐—ˆ๐—๐–พ๐—‹๐—‚๐–บ, a privacy-preserving smart contracts framework, to manage, share and analyze clinical trial data on fabric private chaincode (FPC). Compared to public Blockchain, fabric has fewer participants with an efficient consensus protocol. ๐–ฒ๐—ˆ๐—๐–พ๐—‹๐—‚๐–บ consists of several modules: patient consent and clinical trial approval management chaincode, secure execution for confidential data sharing, API Gateway, and decentralized data governance with adaptive threshold signature (ATS). We implemented two versions of ๐–ฒ๐—ˆ๐—๐–พ๐—‹๐—‚๐–บ with non-SGX deploys on AWS blockchain and SGX-based on a local data center. We evaluated the response time for all of the access endpoints on AWS Managed Blockchain, and demonstrated the utilization of SGX-based smart contracts for data sharing and analysis. 
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  4. null (Ed.)
  5. Free, publicly-accessible full text available May 1, 2024
  6. Abstract

    We search for gravitational-wave (GW) transients associated with fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst Project, during the first part of the third observing run of Advanced LIGO and Advanced Virgo (2019 April 1 15:00 UTCโ€“2019 October 1 15:00 UTC). Triggers from 22 FRBs were analyzed with a search that targets both binary neutron star (BNS) and neutron starโ€“black hole (NSBH) mergers. A targeted search for generic GW transients was conducted on 40 FRBs. We find no significant evidence for a GW association in either search. Given the large uncertainties in the distances of our FRB sample, we are unable to exclude the possibility of a GW association. Assessing the volumetric event rates of both FRB and binary mergers, an association is limited to 15% of the FRB population for BNS mergers or 1% for NSBH mergers. We report 90% confidence lower bounds on the distance to each FRB for a range of GW progenitor models and set upper limits on the energy emitted through GWs for a range of emission scenarios. We find values of order 1051โ€“1057erg for models with central GW frequencies in the range 70โ€“3560 Hz. At the sensitivity of this search, we find these limits to be above the predicted GW emissions for the models considered. We also find no significant coincident detection of GWs with the repeater, FRB 20200120E, which is the closest known extragalactic FRB.

     
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    Free, publicly-accessible full text available September 28, 2024
  7. Abstract The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org . The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages. 
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    Free, publicly-accessible full text available July 28, 2024
  8. Abstract We use 47 gravitational wave sources from the Third LIGOโ€“Virgoโ€“Kamioka Gravitational Wave Detector Gravitational Wave Transient Catalog (GWTCโ€“3) to estimate the Hubble parameter H ( z ), including its current value, the Hubble constant H 0 . Each gravitational wave (GW) signal provides the luminosity distance to the source, and we estimate the corresponding redshift using two methods: the redshifted masses and a galaxy catalog. Using the binary black hole (BBH) redshifted masses, we simultaneously infer the source mass distribution and H ( z ). The source mass distribution displays a peak around 34 M โŠ™ , followed by a drop-off. Assuming this mass scale does not evolve with the redshift results in a H ( z ) measurement, yielding H 0 = 68 โˆ’ 8 + 12 km s โˆ’ 1 Mpc โˆ’ 1 (68% credible interval) when combined with the H 0 measurement from GW170817 and its electromagnetic counterpart. This represents an improvement of 17% with respect to the H 0 estimate from GWTCโ€“1. The second method associates each GW event with its probable host galaxy in the catalog GLADE+ , statistically marginalizing over the redshifts of each eventโ€™s potential hosts. Assuming a fixed BBH population, we estimate a value of H 0 = 68 โˆ’ 6 + 8 km s โˆ’ 1 Mpc โˆ’ 1 with the galaxy catalog method, an improvement of 42% with respect to our GWTCโ€“1 result and 20% with respect to recent H 0 studies using GWTCโ€“2 events. However, we show that this result is strongly impacted by assumptions about the BBH source mass distribution; the only event which is not strongly impacted by such assumptions (and is thus informative about H 0 ) is the well-localized event GW190814. 
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    Free, publicly-accessible full text available June 1, 2024