Showing papers by "Florida Atlantic University published in 2017"

Exploring the Sensitivity of Next Generation Gravitational Wave Detectors

Abstract: The second-generation of gravitational-wave detectors are just starting operation, and have already yielding their first detections. Research is now concentrated on how to maximize the scientific potential of gravitational-wave astronomy. To support this effort, we present here design targets for a new generation of detectors, which will be capable of observing compact binary sources with high signal-to-noise ratio throughout the Universe.

MGAE: Marginalized Graph Autoencoder for Graph Clustering

Abstract: Graph clustering aims to discovercommunity structures in networks, the task being fundamentally challenging mainly because the topology structure and the content of the graphs are difficult to represent for clustering analysis. Recently, graph clustering has moved from traditional shallow methods to deep learning approaches, thanks to the unique feature representation learning capability of deep learning. However, existing deep approaches for graph clustering can only exploit the structure information, while ignoring the content information associated with the nodes in a graph. In this paper, we propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering. The key innovation of MGAE is that it advances the autoencoder to the graph domain, so graph representation learning can be carried out not only in a purely unsupervised setting by leveraging structure and content information, it can also be stacked in a deep fashion to learn effective representation. From a technical viewpoint, we propose a marginalized graph convolutional network to corrupt network node content, allowing node content to interact with network features, and marginalizes the corrupted features in a graph autoencoder context to learn graph feature representations. The learned features are fed into the spectral clustering algorithm for graph clustering. Experimental results on benchmark datasets demonstrate the superior performance of MGAE, compared to numerous baselines.

Upper Limits on the Stochastic Gravitational-Wave Background from Advanced LIGO's First Observing Run

Abstract: A wide variety of astrophysical and cosmological sources are expected to contribute to a stochastic gravitational-wave background. Following the observations of GW150914 and GW151226, the rate and mass of coalescing binary black holes appear to be greater than many previous expectations. As a result, the stochastic background from unresolved compact binary coalescences is expected to be particularly loud. We perform a search for the isotropic stochastic gravitational-wave background using data from Advanced Laser Interferometer Gravitational Wave Observatory’s (aLIGO) first observing run. The data display no evidence of a stochastic gravitational-wave signal. We constrain the dimensionless energy density of gravitational waves to be Ω 0 < 1.7 × 10 − 7 with 95% confidence, assuming a flat energy density spectrum in the most sensitive part of the LIGO band (20–86 Hz). This is a factor of ∼ 33 times more sensitive than previous measurements. We also constrain arbitrary power-law spectra. Finally, we investigate the implications of this search for the background of binary black holes using an astrophysical model for the background.

Skin lesion classification from dermoscopic images using deep learning techniques

Abstract: The recent emergence of deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist the human expert in making better decisions about a patients health. In this paper we focus on the problem of skin lesion classification, particularly early melanoma detection, and present a deep-learning based approach to solve the problem of classifying a dermoscopic image containing a skin lesion as malignant or benign. The proposed solution is built around the VGGNet convolutional neural network architecture and uses the transfer learning paradigm. Experimental results are encouraging: on the ISIC Archive dataset, the proposed method achieves a sensitivity value of 78.66%, which is significantly higher than the current state of the art on that dataset.

Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks

Abstract: Recent studies on sequential attack schemes revealed new smart grid vulnerability that can be exploited by attacks on the network topology. Traditional power systems contingency analysis needs to be expanded to handle the complex risk of cyber-physical attacks. To analyze the transmission grid vulnerability under sequential topology attacks, this paper proposes a Q-learning-based approach to identify critical attack sequences with consideration of physical system behaviors. A realistic power flow cascading outage model is used to simulate the system behavior, where attacker can use the Q-learning to improve the damage of sequential topology attack toward system failures with the least attack efforts. Case studies based on three IEEE test systems have demonstrated the learning ability and effectiveness of Q-learning-based vulnerability analysis.

Closed-form tidal approximants for binary neutron star gravitational waveforms constructed from high-resolution numerical relativity simulations

Abstract: We construct closed-form gravitational waveforms (GWs) with tidal effects for the coalescence of binary neutron stars. The method relies on a new set of eccentricity-reduced and high-resolution numerical relativity (NR) simulations and is composed of three steps. First, tidal contributions to the GW phase are extracted from the time-domain NR data. Second, those contributions are employed to fix high-order coefficients in an effective and resummed post-Newtonian expression. Third, frequency-domain tidal approximants are built using the stationary phase approximation. Our tidal approximants are valid from the low frequencies to the strong-field regime. They can be analytically added to any binary black hole GW model to obtain a binary neutron star waveform, either in the time or in the frequency domain. This work provides simple, flexible, and accurate models ready to be used in both searches and parameter estimation of binary neutron star events.

Paper-based analytical devices for clinical diagnosis: recent advances in the fabrication techniques and sensing mechanisms.

Abstract: Introduction: There is a significant interest in developing inexpensive portable biosensing platforms for various applications including disease diagnostics, environmental monitoring, food safety, and water testing at the point-of-care (POC) settings. Current diagnostic assays available in the developed world require sophisticated laboratory infrastructure and expensive reagents. Hence, they are not suitable for resource-constrained settings with limited financial resources, basic health infrastructure, and few trained technicians. Cellulose and flexible transparency paper-based analytical devices have demonstrated enormous potential for developing robust, inexpensive and portable devices for disease diagnostics. These devices offer promising solutions to disease management in resource-constrained settings where the vast majority of the population cannot afford expensive and highly sophisticated treatment options.Areas covered: In this review, the authors describe currently developed cellulose and...

Promoting Drp1-mediated mitochondrial fission in midlife prolongs healthy lifespan of Drosophila melanogaster

Abstract: The accumulation of dysfunctional mitochondria has been implicated in aging, but a deeper understanding of mitochondrial dynamics and mitophagy during aging is missing. Here, we show that upregulating Drp1—a Dynamin-related protein that promotes mitochondrial fission—in midlife, prolongs Drosophila lifespan and healthspan. We find that short-term induction of Drp1, in midlife, is sufficient to improve organismal health and prolong lifespan, and observe a midlife shift toward a more elongated mitochondrial morphology, which is linked to the accumulation of dysfunctional mitochondria in aged flight muscle. Promoting Drp1-mediated mitochondrial fission, in midlife, facilitates mitophagy and improves both mitochondrial respiratory function and proteostasis in aged flies. Finally, we show that autophagy is required for the anti-aging effects of midlife Drp1-mediated mitochondrial fission. Our findings indicate that interventions that promote mitochondrial fission could delay the onset of pathology and mortality in mammals when applied in midlife. Mitochondrial fission and fusion are important mechanisms to maintain mitochondrial function. Here, the authors report that middle-aged flies have more elongated, or ‘hyper-fused’ mitochondria, and show that induction of mitochondrial fission in midlife, but not in early life, extends the health and life of flies.

Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis

Abstract: Currently, vibration analysis has been widely considered as an effective way to fulfill the fault diagnosis task of gearboxes in wind turbines (WTs) However, vibration signals are usually with abundant noise and characterized as nonlinearity and nonstationarity Therefore, it is quite challenging to extract robust and useful fault features from complex vibration signals to achieve an accurate and reliable diagnosis This paper proposes a novel feature representation learning approach, named stacked multilevel-denoising autoencoders (SMLDAEs), with the aim to learn robust and discriminative fault feature representations through a deep network architecture for diagnosis accuracy improvement In our proposed approach, we design an MLD training scheme, which uses multiple noise levels to train AEs It enables to learn more general and detailed fault feature patterns simultaneously at different scales from the complex frequency spectra of the raw vibration data, and therefore helps enhance the feature learning and fault diagnosis capability Furthermore, SMLDAE-based fault diagnosis is performed with an unsupervised representation learning procedure followed by a supervised fine-tuning process with label information for classification Our approach is evaluated by using the field vibration data collected from a self-designed WT gearbox test rig The results show that our proposed approach learned more robust and discriminative fault feature representations and achieved the best diagnosis accuracy compared with the traditional approaches

Metabolic Syndrome: Clinical and Policy Implications of the New Silent Killer

Abstract: The United States is experiencing its greatest life expectancy ever. Nonetheless, the general health of the US population is far from at an all-time high. An important contributor to the pandemic o...

Improved Sliding Mode Design for Load Frequency Control of Power System Integrated an Adaptive Learning Strategy

Abstract: Randomness from the power load demand and renewable generations causes frequency oscillations among interconnected power systems. Due to the requirement of synchronism of the whole grid, load frequency control (LFC) has become one of the essential challenges for power system stability and security. In this paper, by modeling the disturbances and parameter uncertainties into the LFC model, we propose an adaptive supplementary control scheme for the power system frequency regulation. An improved sliding mode control (SMC) is employed as the basic controller, where a new sliding mode variable is specifically proposed for the LFC problem. The adaptive dynamic programming strategy is used to provide the supplementary control signal, which is beneficial to the frequency regulation by adapting to the real-time disturbances and uncertainties. The stability analysis is also provided to guarantee the reliability of the proposed control strategy. For comparison, a particle swarm optimization-based SMC scheme is developed as the optimal parameter controller for the frequency regulation problem. Simulation studies are performed on single-area and multiarea benchmark systems, and comparative results illustrate the favorable performance of the proposed adaptive approach for the frequency regulation under load disturbances and parameter uncertainties.

Gravitational waves and mass ejecta from binary neutron star mergers: Effect of the mass ratio

Abstract: We present new $(3+1)\mathrm{D}$ numerical relativity simulations of the binary neutron star (BNS) merger and postmerger phase. We focus on a previously inaccessible region of the binary parameter space spanning the binary's mass ratio $q\ensuremath{\sim}1.00--1.75$ for different total masses and equations of state, and up to $q\ensuremath{\sim}2$ for a stiff BNS system. We study the mass ratio effect on the gravitational waves (GWs) and on the possible electromagnetic (EM) emission associated with dynamical mass ejecta. We compute waveforms, spectra, and spectrograms of the GW strain including all the multipoles up to $l=4$. The mass ratio has a specific imprint on the GW multipoles in the late-inspiral-merger signal, and it affects qualitatively the spectra of the merger remnant. The multipole effect is also studied by considering the dependency of the GW spectrograms on the source's sky location. Unequal mass BNSs produce more ejecta than equal mass systems with ejecta masses and kinetic energies depending almost linearly on $q$. We estimate luminosity peaks and light curves of macronova events associated with the mergers using a simple approach. For $q\ensuremath{\sim}2$ the luminosity peak is delayed for several days and can be up to 4 times larger than for the $q=1$ cases. The macronova emission associated with the $q\ensuremath{\sim}2$ BNS is more persistent in time and could be observed for weeks instead of a few days ($q=1$) in the near infrared. Finally, we estimate the flux of possible radio flares produced by the interaction of relativistic outflows with the surrounding medium. Also in this case a large $q$ can significantly enhance the emission and delay the peak luminosity. Overall, our results indicate that the BNS merger with a large mass ratio has EM signatures distinct from the equal mass case and more similar to black hole--neutron star binaries.

Effects of an Intervention to Reduce Hospitalizations From Nursing Homes: A Randomized Implementation Trial of the INTERACT Program.

Abstract: Importance Medicare payment initiatives are spurring efforts to reduce potentially avoidable hospitalizations. Objective To determine whether training and support for implementation of a nursing home (NH) quality improvement program (Interventions to Reduce Acute Care Transfers [INTERACT]) reduced hospital admissions and emergency department (ED) visits. Design, Setting, and Participants This analysis compared changes in hospitalization and ED visit rates between the preintervention and postintervention periods for NHs randomly assigned to receive training and implementation support on INTERACT to changes in control NHs. The analysis focused on 85 NHs (36 717 NH residents) that reported no use of INTERACT during the preintervention period. Interventions The study team provided training and support for implementing INTERACT, which included tools that help NH staff identify and evaluate acute changes in NH resident condition and document communication between physicians; care paths to avoid hospitalization when safe and feasible; and advance care planning and quality improvement tools. Main Outcomes and Measures All-cause hospitalizations, hospitalizations considered potentially avoidable, 30-day hospital readmissions, and ED visits without admission. All-cause hospitalization rates were calculated for all resident-days, high-risk days (0-30 days after NH admission), and lower-risk days (≥31 days after NH admission). Results We found that of 85 NHs, those that received implementation training and support exhibited statistically nonsignificant reductions in hospitalization rates compared with control NHs (net difference, −0.13 per 1000 resident-days; P = .25), hospitalizations during the first 30 days after NH admission (net difference, −0.37 per 1000 resident-days; P = .48), hospitalizations during periods more than 30 days after NH admission (net difference, −0.09 per 1000 resident-days; P = .39), 30-day readmission rates (net change in rate among hospital discharges, −0.01; P = .36), and ED visits without admission (net difference, 0.02 per 1000 resident-days; P = .83). Intervention NHs exhibited a reduction in potentially avoidable hospitalizations overall (net difference, −0.18 per 1000 resident-days, P = .01); however, this effect was not robust to a Bonferroni correction for multiple comparisons. Conclusions and Relevance Training and support for INTERACT implementation as carried out in this study had no effect on hospitalization or ED visit rates in the overall population of residents in participating NHs. The results have several important implications for implementing quality improvement initiatives in NHs. Trial Registration clinicaltrials.gov Identifier:NCT02177058

Getting Leopards to Change their Spots: Co-creating a New Professional Role Identity

Abstract: We investigated how professional role identity change can be accomplished in highly institutionalized contexts characterized by resiliency. We show that the collective professional role identity of...

BIKE: Bit Flipping Key Encapsulation

Abstract: HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. BIKE: Bit Flipping Key Encapsulation Nicolas Aragon, Paulo Barreto, Slim Bettaieb, Loïc Bidoux, Olivier Blazy, Jean-Christophe Deneuville, Philippe Gaborit, Shay Gueron, Tim Guneysu, Carlos Aguilar Melchor, et al.

Rps26 directs mRNA-specific translation by recognition of Kozak sequence elements.

Abstract: We describe a novel approach to separate two ribosome populations from the same cells and use this method in combination with RNA-seq to identify mRNAs bound to Saccharomyces cerevisiae ribosomes with and without Rps26, a protein linked to the pathogenesis of Diamond-Blackfan anemia (DBA). These analyses reveal that Rps26 contributes to mRNA-specific translation by recognition of the Kozak sequence in well-translated mRNAs and that Rps26-deficient ribosomes preferentially translate mRNA from select stress-response pathways. Surprisingly, exposure of yeast to these stresses leads to the formation of Rps26-deficient ribosomes and to the increased translation of their target mRNAs. These results describe a novel paradigm: the production of specialized ribosomes, which play physiological roles in augmenting the well-characterized transcriptional stress response with a heretofore unknown translational response, thereby creating a feed-forward loop in gene expression. Moreover, the simultaneous gain-of-function and loss-of-function phenotypes from Rps26-deficient ribosomes can explain the pathogenesis of DBA.

Post-Quantum Cryptography on FPGA Based on Isogenies on Elliptic Curves

Abstract: To the best of our knowledge, we present the first hardware implementation of isogeny-based cryptography available in the literature Particularly, we present the first implementation of the supersingular isogeny Diffie-Hellman (SIDH) key exchange, which features quantum-resistance We optimize this design for speed by creating a high throughput multiplier unit, taking advantage of parallelization of arithmetic in $\mathbb {F}_{p^{2}}$ , and minimizing pipeline stalls with optimal scheduling Consequently, our results are also faster than software libraries running affine SIDH even on Intel Haswell processors For our implementation at 85-bit quantum security and 128-bit classical security, we generate ephemeral public keys in 1655 million cycles for Alice and 1490 million cycles for Bob We generate the shared secret in an additional 1510 million cycles for Alice and 1312 million cycles for Bob On a Virtex-7, these results are approximately 15 times faster than known software implementations running the same 512-bit SIDH Our results and observations show that the isogeny-based schemes can be implemented with high efficiency on reconfigurable hardware

User profile preserving social network embedding

Abstract: This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPPSNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.

A post-quantum digital signature scheme based on supersingular isogenies

Abstract: We present the first general-purpose digital signature scheme based on supersingular elliptic curve isogenies secure against quantum adversaries in the quantum random oracle model with small key sizes. This scheme is an application of Unruh’s construction of non-interactive zero-knowledge proofs to an interactive zero-knowledge proof proposed by De Feo, Jao, and Plut. We implement our proposed scheme on an x86-64 PC platform as well as an ARM-powered device. We exploit the state-of-the-art techniques to speed up the computations for general C and assembly. Finally, we provide timing results for real world applications.