cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Coeval intermediate-mass star formation in N4W
Creators: name: Chen, Zhiwei
name: Zhang, Shaobo
name: Zhang, Miaomiao
name: Jiang, Zhibo
name: Tamura, Motohide
name: Kwon, Jungmi
Publisher: name: arXiv
Year: 2016
Subjects: subject: Astrophysics of Galaxies (astro-ph.GA)
subject: FOS: Physical sciences

description: Protostars are mostly found in star-forming regions, where the natal molecular gas still remains. In about 5' west of the molecular bubble N4, N4W is identified as a star-forming clump hosting three Class II (IRS\,1\,--\,3), and one Class I (IRS\,4) young stellar objects (YSOs), as well as a submillimeter source SMM1. The near-IR polarization imaging data of N4W reveal two infrared reflection nebulae close to each other, which are in favor of the outflows of IRS\,1 and IRS\,2. The bipolar mid-IR emission centered on IRS\,4 and the arc-like molecular gas shell are lying on the same axis, indicating a bipolar molecular outflow from IRS\,4. There are two dust temperature distributions in N4W. The warmer one is widely distributed and has a temperature $T_\mathrm{d}\gtrsim28\,\mathrm{K}$, with the colder one from the embedded compact submillimeter source SMM1. N4W's mass is estimated to be $\sim2.5\times10^3\,M_\odot$, and the mass of SMM1 is $\sim5.5\times10^2\,M_\odot$ at $T_\mathrm{d}=15\,\mathrm{K}$, calculated from the CO\,$1-0$ emission and $870\,μ$m dust continuum emission, respectively. Based on the estimates of bolometric luminosity of IRS\,1\,--\,4, these four sources are intermediate-mass YSOs at least. SMM1 is gravitationally bound, and is capable of forming intermediate-mass stars or even possibly massive stars. The co-existence of the IR bright YSOs and the submillimeter source represents potential sequential star formation processes separated by $\sim0.5$\,Myr in N4W. This small age spread implies that the intermediate-mass star formation processes happening in N4W are almost coeval.
description: accepted for publication in Astrophysical Journal
BayesGmed: An R-package for Bayesian Causal Mediation Analysis
Creators: name: Yimer, Belay B
name: Lunt, Mark
name: Beasley, Marcus
name: Macfarlane, Gary J
name: McBeth, John
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computation (stat.CO)
subject: FOS: Computer and information sciences

description: The past decade has seen an explosion of research in causal mediation analysis. However, most analytic tools developed so far rely on frequentist methods which may not be robust in the case of small sample sizes. In this paper, we propose a Bayesian approach for causal mediation analysis based on Bayesian g-formula. We created BayesGmed, an R-package for fitting Bayesian mediation models in R. The application of the methodology (and software tool) is demonstrated by a secondary analysis of data collected as part of the MUSICIAN study, a randomised controlled trial of remotely delivered cognitive behavioural therapy (tCBT) for people with chronic pain. We tested the hypothesis that the effect of tCBT would be mediated by improvements in active coping, passive coping, fear of movement and sleep problems. The analysis of MUSICIAN data shows that tCBT has better-improved patients' self-perceived change in health status compared to treatment as usual (TAU). The adjusted log-odds of tCBT compared to TAU range from 1.491 (0.452, 2.612) when adjusted for sleep problems to 2.264 (1.063, 3.610) when adjusted for fear of movement. Higher scores of fear of movement (log-odds, -0.141 (-0.245, -0.048)), passive coping (log-odds, -0.217 (-0.351, -0.104)), and sleep problem (log-odds, -0.179 (-0.291, -0.078)) leads to lower odds of a positive self-perceived change in health status. The result of BayesGmed, however, shows that none of the mediated effects are statistically significant. We compared BayesGmed with the mediation R package, and the results were comparable. Finally, our probabilistic sensitivity analysis using the BayesGmed tool shows that the direct and total effect of tCBT persists even for a large departure in the assumption of no unmeasured confounding.
description: 3 figures. A missing appendix sections added and a text about prior specification added in section 4
Auslander-Reiten conjecture and finite injective dimension of Hom
Creators: name: Ghosh, Dipankar
name: Takahashi, Ryo
Publisher: name: arXiv
Year: 2021
Subjects: subject: Commutative Algebra (math.AC)
subject: FOS: Mathematics
subject: Primary 13D07, 13D05, 13H10

description: For a finitely generated module $ M $ over a commutative Noetherian ring $R$, we settle the Auslander-Reiten conjecture when at least one of ${\rm Hom}_R(M,R)$ and ${\rm Hom}_R(M,M)$ has finite injective dimension. A number of new characterizations of Gorenstein local rings are also obtained in terms of vanishing of certain Ext and finite injective dimension of Hom.
description: 11 pages, to appear in Kyoto J. Math
Monitoring edge-geodetic sets: hardness and graph products
Creators: name: Haslegrave, John
Publisher: name: arXiv
Year: 2022
Subjects: subject: Combinatorics (math.CO)
subject: Discrete Mathematics (cs.DM)
subject: FOS: Mathematics
subject: FOS: Computer and information sciences
subject: 05C12, 05C69 (Primary) 05C76, 68Q25 (Secondary)

description: Foucaud, Krishna and Lekshmi recently introduced the concept of monitoring edge-geodetic sets in graphs, and a related graph invariant. These are sets of vertices such that the removal of any edge changes the distance between some pair of vertices in the set. They studied the minimum possible size of such a set in a given graph, which we call the monitoring edge-geodetic number. We show that the decision problem for the monitoring edge-geodetic number is NP-complete. We also give best-possible upper and lower bounds for the Cartesian and strong products of two graphs. These bounds establish the exact value in many cases, including many new examples of graphs whose only monitoring edge-geodetic set is the whole vertex set.
description: 9 pages, 2 figures. Final version to appear in Discrete Applied MMathematics
Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses
Creators: name: Biggio, Luca
name: Vernardos, Georgios
name: Galan, Aymeric
name: Peel, Austin
Publisher: name: arXiv
Year: 2022
Subjects: subject: Instrumentation and Methods for Astrophysics (astro-ph.IM)
subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences

description: Strong gravitational lensing is a unique observational tool for studying the dark and luminous mass distribution both within and between galaxies. Given the presence of substructures, current strong lensing observations demand more complex mass models than smooth analytical profiles, such as power-law ellipsoids. In this work, we introduce a continuous neural field to predict the lensing potential at any position throughout the image plane, allowing for a nearly model-independent description of the lensing mass. We apply our method on simulated Hubble Space Telescope imaging data containing different types of perturbations to a smooth mass distribution: a localized dark subhalo, a population of subhalos, and an external shear perturbation. Assuming knowledge of the source surface brightness, we use the continuous neural field to model either the perturbations alone or the full lensing potential. In both cases, the resulting model is able to fit the imaging data, and we are able to accurately recover the properties of both the smooth potential and of the perturbations. Unlike many other deep learning methods, ours explicitly retains lensing physics (i.e., the lens equation) and introduces high flexibility in the model only where required, namely, in the lens potential. Moreover, the neural network does not require pre-training on large sets of labelled data and predicts the potential from the single observed lensing image. Our model is implemented in the fully differentiable lens modeling code Herculens.
Bipolar single-molecule electroluminescence and electrofluorochromism
Creators: name: Hung, Tzu-Chao
name: Robles, Roberto
name: Kiraly, Brian
name: Strik, Julian H.
name: Rutten, Bram A.
name: Khajetoorians, Alexander A.
name: Lorente, Nicolas
name: Wegner, Daniel
Publisher: name: arXiv
Year: 2022
Subjects: subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Understanding the fundamental mechanisms of optoelectronic excitation and relaxation pathways on the single-molecule level has only recently been started by combining scanning tunneling microscopy (STM) and spectroscopy (STS) with STM-induced luminescence (STML). In this paper, we investigate cationic and anionic fluorescence of individual zinc phthalocyanine (ZnPc) molecules adsorbed on ultrathin NaCl films on Ag(111) by using STML. They depend on the tip-sample bias polarity and appear at threshold voltages that are correlated with the onset energies of particular molecular orbitals, as identified by STS. We also find that the fluorescence is caused by a single electron tunneling process. Comparing with results from density functional theory calculations, we propose an alternative many-body picture to describe the charging and electroluminescence mechanism. Our study provides aspects toward well-defined voltage selectivity of bipolar electrofluorochromism, as well as fundamental insights regarding the role of transiently charged states of emitter molecules within OLED devices.
description: Accepted version of the manuscript. Published open access
Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space
Creators: name: Thomas, Josephine Maria
name: Muscoloni, Alessandro
name: Ciucci, Sara
name: Bianconi, Ginestra
name: Cannistraci, Carlo Vittorio
Publisher: name: arXiv
Year: 2016
Subjects: subject: Disordered Systems and Neural Networks (cond-mat.dis-nn)
subject: Artificial Intelligence (cs.AI)
subject: Machine Learning (cs.LG)
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences

description: Complex network topologies and hyperbolic geometry seem specularly connected, and one of the most fascinating and challenging problems of recent complex network theory is to map a given network to its hyperbolic space. The Popularity Similarity Optimization (PSO) model represents - at the moment - the climax of this theory. It suggests that the trade-off between node popularity and similarity is a mechanism to explain how complex network topologies emerge - as discrete samples - from the continuous world of hyperbolic geometry. The hyperbolic space seems appropriate to represent real complex networks. In fact, it preserves many of their fundamental topological properties, and can be exploited for real applications such as, among others, link prediction and community detection. Here, we observe for the first time that a topological-based machine learning class of algorithms - for nonlinear unsupervised dimensionality reduction - can directly approximate the network's node angular coordinates of the hyperbolic model into a two-dimensional space, according to a similar topological organization that we named angular coalescence. On the basis of this phenomenon, we propose a new class of algorithms that offers fast and accurate coalescent embedding of networks in the hyperbolic space even for graphs with thousands of nodes.
Higher order methylation features for clustering and prediction in epigenomic studies
Creators: name: Kapourani, Chantriolnt-Andreas
name: Sanguinetti, Guido
Publisher: name: arXiv
Year: 2016
Subjects: subject: Genomics (q-bio.GN)
subject: Quantitative Methods (q-bio.QM)
subject: FOS: Biological sciences

description: Motivation: DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-understood methylation-switch at CpG islands. Results: Here we use probabilistic machine learning to extract higher order features associated with the methylation profile across a defined region. These features quantitate precisely notions of shape of a methylation profile, capturing spatial correlations in DNA methylation across genomic regions. Using these higher order features across promoter-proximal regions, we are able to construct a powerful machine learning predictor of gene expression, significantly improving upon the predictive power of average DNA methylation levels. Furthermore, we can use higher order features to cluster promoter-proximal regions, showing that five major patterns of methylation occur at promoters across different cell lines, and we provide evidence that methylation beyond CpG islands may be related to regulation of gene expression. Our results support previous reports of a functional role of spatial correlations in methylation patterns, and provide a mean to quantitate such features for downstream analyses. Availability: https://github.com/andreaskapou/BPRMeth
description: 12 pages, 5 figures
Chilomicrus pecki Masner & Garcia 2002
Creators: name: Notton, David G.
Publisher: name: Zenodo
Year: 2014
Subjects: Biodiversity
Taxonomy
Animalia
Arthropoda
Insecta
Hymenoptera
Diapriidae
Chilomicrus
Chilomicrus pecki

description: Chilomicrus pecki Masner & García, 2002: 42, figs 32, 33.
Valid name
Chilomicrus pecki Masner & García, 2002.
Summary of types
Paratype ♂.
description: Published as part of Notton, David G., 2014, A catalogue of the types of Diapriinae (Hymenoptera, Diapriidae) at the Natural History Museum, London, pp. 1-123 in European Journal of Taxonomy 75 on page 82, DOI: 10.5852/ejt.2014.75, http://zenodo.org/record/3862773
Radio Pulsation Search and Imaging Study of SGR J1935+2154
Creators: name: Surnis, Mayuresh
name: Joshi, Bhal Chandra
name: Maan, Yogesh
name: Krishnakumar, M. A.
name: Manoharan, P. K.
name: Naidu, Arun
Publisher: name: arXiv
Year: 2016
Subjects: subject: Solar and Stellar Astrophysics (astro-ph.SR)
subject: High Energy Astrophysical Phenomena (astro-ph.HE)
subject: FOS: Physical sciences

description: We present the results obtained from imaging observations, and search for radio pulsations towards the magnetar SGR J1935+2154 made using the Giant Metrewave Radio Telescope, and the Ooty Radio Telescope. We present the high resolution radio image of the supernova remnant (SNR) G57.2+0.8, which is positionally associated with SGR J1935+2154. We did not detect significant periodic radio pulsations from the magnetar, with 8$σ$ upper limits on its flux density of 0.4, and 0.2 mJy at 326.5, and 610 MHz, respectively, for an assumed duty cycle of 10\%. The corresponding 6$σ$ upper limits at the two frequencies for any burst emission with an assumed width of 10 ms are 0.5 Jy, and 63 mJy, respectively. No continuum radio point source was detected at the position of SGR J1935+2154 with a 3$σ$ upper limit of 1.2 mJy. We also did not detect significant diffuse radio emission in a radius of 70 arc seconds in coincidence with the diffuse X-ray emission reported recently, with a 3$σ$ upper limit of 4.5 mJy. Using the archival HI spectra, we estimate the distance of SNR G57.2+0.8 to be 11.7 $\pm$ 2.8 kpc. Based on measured HI column density (N$_H$) along this line of sight, we argue that the magnetar could be physically associated with SNR G57.2+0.8. Based on present data, we can not rule out either a pulsar wind nebula or a dust scattering halo origin for the diffuse X-ray emission seen around the magnetar.
description: 20 pages, 4 figures. Accepted for publication in ApJ
Back to the Formula -- LHC Edition
Creators: name: Butter, Anja
name: Plehn, Tilman
name: Soybelman, Nathalie
name: Brehmer, Johann
Publisher: name: arXiv
Year: 2021
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: FOS: Physical sciences

description: While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. We show how symbolic regression trained on matrix-element information provides, for instance, optimal LHC observables in an easily interpretable form. We introduce the method using the effect of a dimension-6 coefficient on associated ZH production. We then validate it for the known case of CP-violation in weak-boson-fusion Higgs production, including detector effects.
Gas phase synthesis of non-bundled, small diameter single-walled carbon nanotubes with near-armchair chiralities
Creators: name: Mustonen, Kimmo
name: Laiho, Patrik
name: Kaskela, Antti
name: Zhu, Zhen
name: Reynaud, Olivier
name: Houbenov, Nikolay
name: Tian, Ying
name: Susi, Toma
name: Jiang, Hua
name: Nasibulin, Albert G.
name: Kauppinen, Esko I.
Publisher: name: arXiv
Year: 2016
Subjects: subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: FOS: Physical sciences

description: We present a novel floating catalyst synthesis route for individual, i.e. non-bundled, small diameter single-walled carbon nanotubes (SWCNTs) with a narrow chiral angle distribution peaking at high chiralities near the armchair species. An ex situ spark discharge generator was used to form iron particles with geometric number mean diameters of 3-4 nm and fed into a laminar flow chemical vapour deposition reactor for the continuous synthesis of long and high-quality SWCNTs from ambient pressure carbon monoxide. The intensity ratio of G/D peaks in Raman spectra up to 48 and mean tube lengths up to 4 microns were observed. The chiral distributions, as directly determined by electron diffraction in the transmission electron microscope, clustered around the (n,m) indices (7,6), (8,6), (8,7) and (9,6), with up to 70% of tubes having chiral angles over 20°. The mean diameter of SWCNTs was reduced from 1.10 to 1.04 nm by decreasing the growth temperature from 880 to 750 °C, which simultaneously increased the fraction of semiconducting tubes from 67 to 80%. Limiting the nanotube gas phase number concentration to approx. 100 000 per cubic centimetre successfully prevented nanotube bundle formation that is due to collisions induced by Brownian diffusion. Up to 80 % of 500 as-deposited tubes observed by atomic force and transmission electron microscopy were individual. Transparent conducting films deposited from these SWCNTs exhibited record low sheet resistances of 63 Ohms/sq. at 90 % transparency for 550 nm light.
Activating Alumni Engagement: The impact of an engaged alumni community on an institution’s strategic goals
Creators: Alison O’Connor
Publisher: name: University of Alberta Library
Year: 2022

Purpose: Engaged alumni have the potential to make a profound impact on an institution’s

strategic goals as mentors, teaching partners, advisors, lifelong learners, employers and donors.

Although alumni relations and engagement have been studied for decades, few researchers have

attempted to connect the impact of alumni involvement to the goals and objectives of an

institution’s strategic plan. This study aims to close that gap by asking, in what capacity do

alumni want to be involved in activities that may help advance the strategic goals of their alma

mater?

Method: Through semi-structured, one-on-one interviews with five engaged alumni from

the Southern Alberta Institute of Technology, this qualitative study connects alumni engagement

activities to the objectives outlined in the institution’s five-year strategic plan. Results: The

results strongly indicate that an engaged alumni population can significantly impact an

institution’s strategic goals and that alumni engagement professionals should be seen as valuable

partners on its road to success.

Value: The data presented in this study provide alumni

engagement offices with ideas for future alumni programming and research opportunities that

may inform those programs.
Hadronic matter phases and their application to rapidly rotating neutron stars
Creators: name: Endo, Tomoki
Publisher: name: arXiv
Year: 2015
Subjects: subject: High Energy Astrophysical Phenomena (astro-ph.HE)
subject: FOS: Physical sciences

description: Neutron stars are commonly considered as astronomical objects having highdensity interiors and an inner core region in which various hadronic matter phases are expected. Several studies show that the inner structures affect macroscopic phenomena of the star. However, we know that the inner structures of the star strongly depend on the equation of state (EOS). The EOS of high-density matter is still not clear and several recent observations indicate restrictions to EOSs. Theoretical studies should elucidate EOSs at high density and/or high temperature. For instance, many theoretical studies have attempted to account for the rotation effect of rapidly rotating neutron stars (i.e., pulsars). Accordingly, we also apply our EOSs to rapidly rotating stars. Furthermore, neutron stars generate a strong magnetic field. Several recent studies indicate that this magnetic field exerts restrictions on the EOS. In this paper, we focus on the investigation of the inner structures and the application of our EOSs to rotating stars. We find that one of our EOSs is consistent with observations, and another is inconsistent. We also find an important relation between the radius and rotation.
description: 5 pages, Topics in Astroparticle and Underground Physics 2015
Axion Quality Problem and Non-Minimal Gravitational Coupling in the Palatini Formulation
Creators: name: Cheong, Dhong Yeon
name: Hamaguchi, Koichi
name: Kanazawa, Yoshiki
name: Lee, Sung Mook
name: Nagata, Natsumi
name: Park, Seong Chan
Publisher: name: arXiv
Year: 2022
Subjects: subject: High Energy Physics - Theory (hep-th)
subject: High Energy Physics - Phenomenology (hep-ph)
subject: FOS: Physical sciences

description: In axion models, the global U(1) Peccei-Quinn (PQ) symmetry is explicitly broken by non-perturbative effects of gravity, such as axionic wormholes. The gravitational violation of the PQ symmetry due to wormholes is large enough to invalidate the PQ mechanism, which is entitled as the axion quality problem. Recently, a novel solution to this quality problem was suggested, where the non-minimal coupling of the axion field to gravity $ξ$ is introduced to suppress the wormhole contribution. In this work, we revisit the problem in a different but equally valid formulation of gravity, namely the Palatini formulation, where the Ricci scalar is solely determined by connection. We first find the axionic wormhole solution in the Palatini formulation, taking the full dynamical radial mode as well as the axial mode, then show that the quality problem is still resolved with the non-minimal coupling $ξ$. The requested lower bound of $ξ$ in the Palatini formulation turns out to be slightly higher than that in the metric formulation.
description: 7 pages, 6 figures, v2: matches published version in Phys. Rev. D
Uniqueness of the critical and supercritical Liouville quantum gravity metrics
Creators: name: Ding, Jian
name: Gwynne, Ewain
Publisher: name: arXiv
Year: 2021
Subjects: subject: Probability (math.PR)
subject: Mathematical Physics (math-ph)
subject: Metric Geometry (math.MG)
subject: FOS: Mathematics
subject: FOS: Physical sciences

description: We show that for each ${\mathbf c}_{\mathrm M} \in [1,25)$, there is a unique metric associated with Liouville quantum gravity (LQG) with matter central charge ${\mathbf c}_{\mathrm M}$. An earlier series of works by Ding-Dubédat-Dunlap-Falconet, Gwynne-Miller, and others showed that such a metric exists and is unique in the subcritical case ${\mathbf c}_{\mathrm M} \in (-\infty,1)$, which corresponds to coupling constant $γ\in (0,2)$. The critical case ${\mathbf c}_{\mathrm M} = 1$ corresponds to $γ=2$ and the supercritical case ${\mathbf c}_{\mathrm M} \in (1,25)$ corresponds to $γ\in \mathbb C$ with $|γ| = 2$.
 Our metric is constructed as the limit of an approximation procedure called Liouville first passage percolation, which was previously shown to be tight for $\mathbf c_{\mathrm M} \in [1,25)$ by Ding and Gwynne (2020). In this paper, we show that the subsequential limit is uniquely characterized by a natural list of axioms. This extends the characterization of the LQG metric proven by Gwynne and Miller (2019) for $\mathbf c_{\mathrm M} \in (-\infty,1)$ to the full parameter range $\mathbf c_{\mathrm M} \in (-\infty,25)$.
 Our argument is substantially different from the proof of the characterization of the LQG metric for $\mathbf c_{\mathrm M} \in (-\infty,1)$. In particular, the core part of the argument is simpler and does not use confluence of geodesics.
description: 109 pages, 24 figures; to appear in Proceedings of the London Mathematical Society
Estimating fine age structure and time trends in human contact patterns from coarse contact data: the Bayesian rate consistency model
Creators: name: Dan, Shozen
name: Chen, Yu
name: Chen, Yining
name: Monod, Melodie
name: Jaeger, Veronika K.
name: Bhatt, Samir
name: Karch, Andre
name: Ratmann, Oliver
Publisher: name: arXiv
Year: 2022
Subjects: subject: Applications (stat.AP)
subject: FOS: Computer and information sciences

description: Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), many contact surveys have been conducted to measure changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. These surveys were typically conducted longitudinally, using protocols that differ from those used in the pre-pandemic era. We present a model-based statistical approach that can reconstruct contact patterns at 1-year resolution even when the age of the contacts is reported coarsely by 5 or 10-year age bands. This innovation is rooted in population-level consistency constraints in how contacts between groups must add up, which prompts us to call the approach presented here the Bayesian rate consistency model. The model incorporates computationally efficient Hilbert Space Gaussian process priors to infer the dynamics in age- and gender-structured social contacts and is designed to adjust for reporting fatigue in longitudinal surveys. We demonstrate on simulations the ability to reconstruct contact patterns by gender and 1-year age interval from coarse data with adequate accuracy and within a fully Bayesian framework to quantify uncertainty. We investigate the patterns of social contact data collected in Germany from April to June 2020 across five longitudinal survey waves. We reconstruct the fine age structure in social contacts during the early stages of the pandemic and demonstrate that social contacts rebounded in a structured, non-homogeneous manner. We also show that by July 2020, social contact intensities remained well below pre-pandemic values despite a considerable easing of non-pharmaceutical interventions. This model-based inference approach is open access, computationally tractable enabling full Bayesian uncertainty quantification, and readily applicable to contemporary survey data as long as the exact age of survey participants is reported.
description: 39 pages, 16 figures
A novel route to a Hopf-bifurcation scenario in switched systems with dead zone
Creators: name: Kowalczyk, P.
Publisher: name: arXiv
Year: 2016
Subjects: subject: Chaotic Dynamics (nlin.CD)
subject: FOS: Physical sciences

description: Planar switched system with dead-zone are analyzed. In particular, we consider the effects of perturbation of the linear control law from purely positional to position-velocity control. This type of perturbation leads to a novel Hopf-like discontinuity induced bifurcation. We show that this bifurcation leads to the creation of small scale limit cycle attractors, which scale as the square root of the bifurcation parameter. We note that a Hopf-like bifurcation analyzed in non-smooth systems is characterized by a linear scaling law. We then investigate numerically a planar switched system with positional feedback law, dead-zone and time delay in the switching function. Using the same parameter values as for the switched system without delay in the switching function, we show numerically a Hopf-like bifurcation scenario which matches qualitatively and quantitatively with the scenario analyzed for the non-delayed system.
japplphysiol.00286.2016.pdf
Creators: name: Reynolds, Leryn J.
name: P. Credeur, Daniel
name: Manrique-Acevedo, Camila
name: Padilla, Jaume
name: Fadel, Paul J.
name: Thyfault, John
Publisher: name: Health Research Alliance
Year: 2023
Subjects: Cardiology (incl. cardiovascular diseases)

description: Increased endothelin-1 (ET-1) and reduced endothelial nitric oxide phosphorylation (peNOS) are hypothesized to reduce insulin-stimulated blood flow in type 2 diabetes (T2D), but studies examining these links in humans are limited. We sought to assess basal and insulin-stimulated endothelial signaling proteins (ET-1 and peNOS) in skeletal muscle from T2D patients. Ten obese T2D [glucose disposal rate (GDR): 6.6 ? 1.6 mg·kg lean body mass (LBM)?1 ·min?1 ] and 11 lean insulin-sensitive subjects (Lean GDR: 12.9 ? 1.2 mg·kg LBM?1 ·min?1 ) underwent a hyperinsulinemic-euglycemic clamp with vastus lateralis biopsies taken before and 60 min into the clamp. Basal biopsies were also taken in 11 medication-naïve, obese, non-T2D subjects. ET-1, peNOS (Ser1177), and eNOS protein and mRNA were measured from skel?etal muscle samples containing native microvessels. Femoral artery blood flow was assessed by duplex Doppler ultrasound. Insulin?stimulated blood flow was reduced in obese T2D (Lean: ?50.7 ? 6.5% baseline, T2D: ?20.8 ? 5.2% baseline, P ? 0.05). peNOS/eNOS content was higher in Lean under basal conditions and, although not increased by insulin, remained higher in Lean during the insulin clamp than in obese T2D (P ? 0.05). ET-1 mRNA and peptide were 2.25 ? 0.50- and 1.52 ? 0.11-fold higher in obese T2D com?pared with Lean at baseline, and ET-1 peptide remained 2.02 ? 1.9- fold elevated in obese T2D after insulin infusion (P ? 0.05) but did not increase with insulin in either group (P ? 0.05). Obese non-T2D subjects tended to also display elevated basal ET-1 (P ? 0.06). In summary, higher basal skeletal muscle expression of ET-1 and re?duced peNOS/eNOS may contribute to a reduced insulin-stimulated leg blood flow response in obese T2D patients.
Rank-based loss for learning hierarchical representations
Creators: name: Nolasco, Ines
name: Stowell, Dan
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (cs.LG)
subject: Sound (cs.SD)
subject: Audio and Speech Processing (eess.AS)
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: Hierarchical taxonomies are common in many contexts, and they are a very natural structure humans use to organise information. In machine learning, the family of methods that use the 'extra' information is called hierarchical classification. However, applied to audio classification, this remains relatively unexplored. Here we focus on how to integrate the hierarchical information of a problem to learn embeddings representative of the hierarchical relationships. Previously, triplet loss has been proposed to address this problem, however it presents some issues like requiring the careful construction of the triplets, and being limited in the extent of hierarchical information it uses at each iteration. In this work we propose a rank based loss function that uses hierarchical information and translates this into a rank ordering of target distances between the examples. We show that rank based loss is suitable to learn hierarchical representations of the data. By testing on unseen fine level classes we show that this method is also capable of learning hierarchically correct representations of the new classes. Rank based loss has two promising aspects, it is generalisable to hierarchies with any number of levels, and is capable of dealing with data with incomplete hierarchical labels.
description: This version corrects a bug in the baseline results
Core-collapse supernova simulations with reduced nucleosynthesis networks
Creators: name: Navó, Gerard
name: Reichert, Moritz
name: Obergaulinger, Martin
name: Arcones, Almudena
Publisher: name: arXiv
Year: 2022
Subjects: subject: High Energy Astrophysical Phenomena (astro-ph.HE)
subject: Solar and Stellar Astrophysics (astro-ph.SR)
subject: Nuclear Theory (nucl-th)
subject: FOS: Physical sciences

description: We present core-collapse supernova simulations including nuclear reaction networks that impact explosion dynamics and nucleosynthesis. The different composition treatment can lead to changes in the neutrino heating in the vicinity of the shock by modifying the number of nucleons and thus the neutrino-opacity of the region. This reduces the ram pressure outside the shock and allows an easier expansion. The energy released by the nuclear reactions during collapse also slows down the accretion and aids the shock expansion. In addition, nuclear energy generation in the postshocked matter produces up to $20\%$ more energetic explosions. Nucleosynthesis is affected due to the different dynamic evolution of the explosion. Our results indicate that the energy generation from nuclear reactions helps to sustain late outflows from the vicinity of the proto-neutron star, synthesizing more neutron-rich species. Furthermore, we show that there are systematic discrepancies between the ejecta calculated with in-situ and ex-situ reaction networks. These differences stem from the intrinsic characteristics of evolving the composition in hydrodynamic simulations or calculating it with Lagrangian tracer particles. The mass fractions of some Ca, Ti, Cr, and Fe isotopes are consistently underproduced in postprocessing calculations, leading to different nucleosynthesis paths. Our results suggest that large in-situ nuclear reaction networks are important for a realistic feedback of the energy generation, the neutrino heating, and a more accurate ejecta composition.
description: Submitted to ApJ. Received 2022 October 20; revised 2023 May 13; accepted 2023 May 15
Fermionic T-duality in massive type IIA supergravity on AdS_{10-k} x M_k
Creators: name: Bakhmatov, Ilya
Publisher: name: arXiv
Year: 2015
Subjects: subject: High Energy Physics - Theory (hep-th)
subject: FOS: Physical sciences

description: Fermionic T-duality transformation is studied for the N=1 supersymmetric solutions of massive type IIA supergravity with the metric AdS_{10-k} x M_k for k=3 and 5. We derive the Killing spinors of these backgrounds and use them as an input for the fermionic T-duality transformation. The resulting dual solutions form a large family of supersymmetric deformations of the original solutions by complex valued RR fluxes. We observe that the Romans mass parameter does not change under fermionic T-duaity, and prove its invariance in the k=3 case.
description: 31 pages
Mechanism for the stabilization of protein clusters above the solubility curve: the role of non-ideal chemical reactions
Creators: name: Lutsko, James F.
Publisher: name: arXiv
Year: 2016
Subjects: subject: Chemical Physics (physics.chem-ph)
subject: Soft Condensed Matter (cond-mat.soft)
subject: FOS: Physical sciences

description: Dense protein clusters are known to play an important role in nucleation of protein crystals from dilute solutions. While these have generally been thought to be formed from a metastable phase, the observation of similar, if not identical, clusters above the critical point for the dilute-solution/strong-solution phase transition has thrown this into doubt. Furthermore, the observed clusters are stable for relatively long times. Because protein aggregation plays an important role in some pathologies, understanding the nature of such clusters is an important problem. One mechanism for the stabilization of such structures was proposed by Pan, Vekilov and Lubchenko and was investigated using a DDFT model which confirmed the viability of the model. Here, we revisit that model and incorporate additional physics in the form of state-dependent reaction rates. We show by a combination of numerical results and general arguments that the state-dependent rates disrupt the stability mechanism. Finally, we argue that the state-depedent reactions correct unphysical aspects of the model with ideal (state-independent) reactions and that this necessarily leads to the failure of the proposed mechanism.
The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data
Creators: name: Schuster, Viktoria
name: Krogh, Anders
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis. The state of the art is currently represented by neural network models such as variational autoencoders (VAEs) which use a variational approximation of the likelihood for inference. We here present the Deep Generative Decoder (DGD), a simple generative model that computes model parameters and representations directly via maximum a posteriori (MAP) estimation. The DGD handles complex parameterized latent distributions naturally unlike VAEs which typically use a fixed Gaussian distribution, because of the complexity of adding other types. We first show its general functionality on a commonly used benchmark set, Fashion-MNIST. Secondly, we apply the model to multiple single-cell data sets. Here the DGD learns low-dimensional, meaningful and well-structured latent representations with sub-clustering beyond the provided labels. The advantages of this approach are its simplicity and its capability to provide representations of much smaller dimensionality than a comparable VAE.
The Umwelt of an Embodied Agent -- A Measure-Theoretic Definition
Creators: name: Ay, Nihat
name: Löhr, Wolfgang
Publisher: name: arXiv
Year: 2016
Subjects: subject: Information Theory (cs.IT)
subject: Probability (math.PR)
subject: Adaptation and Self-Organizing Systems (nlin.AO)
subject: FOS: Computer and information sciences
subject: FOS: Mathematics
subject: FOS: Physical sciences

description: We consider a general model of the sensorimotor loop of an agent interacting with the world. This formalises Uexküll's notion of a \emph{function-circle}. Here, we assume a particular causal structure, mechanistically described in terms of Markov kernels. In this generality, we define two $σ$-algebras of events in the world that describe two respective perspectives: (1) the perspective of an external observer, (2) the intrinsic perspective of the agent. Not all aspects of the world, seen from the external perspective, are accessible to the agent. This is expressed by the fact that the second $σ$-algebra is a subalgebra of the first one. We propose the smaller one as formalisation of Uexküll's \emph{Umwelt} concept. We show that, under continuity and compactness assumptions, the global dynamics of the world can be simplified without changing the internal process. This simplification can serve as a minimal world model that the system must have in order to be consistent with the internal process.
description: 16 pages
Automated feature extraction via deep neural network for support vector machine classifier applied in UAV-based human detection
Creators: name: DE OCAMPO, ANTON LOUISE
name: Dadios, Elmer P.
Publisher: name: figshare
Year: 2023
Subjects: Deep learning
Image processing
Video processing

description: In UAV-based monitoring for smart farms, one of the core tasks in the detection of farmers is the extraction of object-specific features sufficient to emphasize them from the vegetative environment. This article presents the evaluation of recent deep neural network architectures that can be assimilated as automated feature extractors for binary classifiers instead of being standalone. Seven classifiers are first evaluated to find the best candidate for the hybrid network which consists of cascaded DNN and binary classifier models. Support Vector Machine performed best on the farmer dataset (FDS) among the others based on F1-score: naïve bayes, tree classifier, linear discriminant, logistic regression, kNN, and various ensembles. For the selection of feature extractor, a Pareto analysis based on the following selection criteria is conducted: accuracy, precision, recall, F-measure, number of parameters, and the model size. From the 11 deep neural networks considered, only 10 are integrated with the SVM classifier. GoogleNet is excluded from the Pareto analysis since prior experimentation proved it to be performing better as a standalone classifier than as a feature extractor. But, the rest of the group has improved the classifier performance significantly when used as a feature extractor for the SVM classifier. ResNet-18 is identified as the most balanced feature extractor for linear SVM in terms of feature-length, performance on both INRIA and farmer dataset (FDS), the number of layers and parameters, and model size.
Elucidating Dicke Superradiance by quantum uncertainty
Creators: name: Santos, Eduardo M. dos
name: Duzzioni, Eduardo I.
Publisher: name: arXiv
Year: 2016
Subjects: subject: Quantum Physics (quant-ph)
subject: Statistical Mechanics (cond-mat.stat-mech)
subject: FOS: Physical sciences

description: Recently it was shown in Ref. [Phys. Rev. Lett. 112, 140402 (2014)] that in the idealized Dicke model of superradiance there is no entanglement among any partitions of the system during the total evolution time of the system. This result immediately conducts us to question if other measures from quantum information theory can explain the characteristic release of energy in a short time interval. In this work we identify the uncertainty of purely quantum origin as the property responsible for Dicke superradiance. The quantum uncertainty on the population of each emitter of the sample captured by the Wigner-Yanase skew information (WYSI) is proportional to the correlation radiation rate, which is the part of the total radiated power coming from dipole correlations and responsible for releasing in a short time a great intensity of radiation energy. We also show that the correlation measure called local quantum uncertainty, which is the minimization of the WYSI over all local observables, presents a double sudden change induced by environment. The time window between these two sudden changes is used to define the interval in which symmetric global observables of the system behave classically for $N \rightarrow \infty$, although the emitters remain strongly quantum correlated.
description: 5 pages, 3 figures, Comments are welcome
Mechanism for the stabilization of protein clusters above the solubility curve
Creators: name: Lutsko, James F.
name: Nicolis, Grégoire
Publisher: name: arXiv
Year: 2016
Subjects: subject: Soft Condensed Matter (cond-mat.soft)
subject: Biological Physics (physics.bio-ph)
subject: Chemical Physics (physics.chem-ph)
subject: FOS: Physical sciences

description: Pan, Vekilov and Lubchenko[\textit{J. Phys. Chem. B}, 2010, \textbf{114}, 7620] have proposed that dense stable protein clusters appearing in weak protein solutions above the solubility curve are composed of protein oligomers. The hypothesis is that a weak solution of oligomer species is unstable with respect to condensation causing the formation of dense, oligomer-rich droplets which are stabilized against growth by the monomer-oligomer reaction. Here, we show that such a combination of processes can be understood using a simple capillary model yielding analytic expressions for the cluster properties which can be used to interpret experimental data. We also construct a microscopic Dynamic Density Functional Theory model and show that it is consistent with the predictions of the capillary model. The viability of the mechanism is thus confirmed and it is shown how the radius of the stable clusters is related to physically interesting quantities such as the monomer-oligomer rate constants.
The influence of particle size distribution on measures of states for granular materials and implications for small strain stiffness
Creators: name: Liu, Deyun
Publisher: name: Imperial College London
Year: 2022

description: Gap-graded soils are poorly graded soils, which have a deficiency of certain particle sizes, one example is a mixture of coarse and finer sand grains with no intermediate-sized particles. While current knowledge of soil behaviour has been mainly established by considering the response of well-graded soils, natural soils encountered in geotechnical engineering practice can be gap-graded. In this research, both numerical and experimental approaches are applied to study the mechanical behaviour of gap-graded soils; materials with continuous particle size distributions are also investigated using the same analytical tools to provide a benchmark against which the gap-graded materials can be considered. Using discrete element method (DEM) simulations, the stress transmission within granular materials with a broad range of particle size distributions was investigated. The first series of analyses took a rather simplistic binary approach and considered grains to be either active or inactive grains. These analyses showed that for both continuous and gap-graded soils, the proportion by volume of inactive grains can be sufficiently large to influence widely used measures of state such as void ratio and bulk density. The nature of the stress transmission was then analysed, which shows that for materials with a continuous, linear gradation, the proportion of stress carried by a particle aligns with its particle volume; this is not the case in gap-graded soils. Using DEM, the small-strain stiffness of gap-graded soils was considered in detail. The results show that the particle-scale distribution of stress influences the stiffness of gap-graded soils. A new semi-empirical state variable, e_α, was proposed that weights the contribution of particles to the solids volume used in calculating e by the particle stress. The strong correlation of G_0 with e_α shows how the heterogeneity of stress influences stiffness. The difference between the stiffness obtained from the quasi-static small strain probes and the stiffness obtained from the dynamic bender element tests can qualitatively capture the proportion of inactive particles. The potential to use this observation in physical laboratory experiments was explored.
Quantum walk as a simulator of nonlinear dynamics: Nonlinear Dirac equation and solitons
Creators: name: Lee, Chang-Woo
name: Kurzyński, Paweł
name: Nha, Hyunchul
Publisher: name: arXiv
Year: 2015
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Quantum walk (QW) provides a versatile tool to study fundamental physics and also to make a variety of practical applications. We here start with the recent idea of {\it nonlinear} QW and show that introducing {\it nonlinearity} to QW can lead to a wealth of remarkable possibilities, e.g., simulating nonlinear quantum dynamics thus enhancing the applicability of QW above the existing level for a universal quantum simulator. As an illustration, we show that the dynamics of a nonlinear Dirac particle can be simulated on an optical nonlinear QW platform implemented with a measurement-based-feedforward scheme. The nonlinear evolution induced by the feed-forward introduces a self-coupling mechanism to (otherwise linear) Dirac particles, which accordingly behave as a \emph{soliton}. We particularly consider two kinds of nonlinear Dirac equations, one with a scalar-type self-coupling (Gross-Neveu model) and the other with a vector-type one (Thirring model), respectively. Using their known stationary solutions, we confirm that our nonlinear QW framework is capable of exhibiting characteristic features of a soliton. Furthermore, we show that the nonlinear QW enables us to observe and control an enhancement and suppression of the ballistic diffusion.