cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Remarks on the Gauss images of complete minimal surfaces in Euclidean four-space
Creators: name: Aiyama, Reiko
name: Akutagawa, Kazuo
name: Imagawa, Satoru
name: Kawakami, Yu
Publisher: name: arXiv
Year: 2016
Subjects: subject: Differential Geometry (math.DG)
subject: Complex Variables (math.CV)
subject: FOS: Mathematics
subject: 53A10, 30D35, 53C42

description: We perform a systematic study of the image of the Gauss map for complete minimal surfaces in Euclidean four-space. In particular, we give a geometric interpretation of the maximal number of exceptional values of the Gauss map of a complete orientable minimal surface in Euclidean four-space. We also provide optimal results for the maximal number of exceptional values of the Gauss map of a complete minimal Lagrangian surface in the complex two-space and the generalized Gauss map of a complete nonorientable minimal surface in Euclidean four-space.
description: 14 pages
Constrained mutational sampling of amino acids in HIV-1 protease evolution
Creators: name: Whitfield, T.W.
name: Schiffer, C.A.
name: Nachum, G.
name: Swanstrom, R.
name: Hollins, C., III
name: Zeldovich, K.B.
name: Luban, J.
name: Boucher, J.I.
name: Bolon, D.N.A.
name: Dauphin, A.
Publisher: name: Oxford University Press
Year: 2019

description: The evolution of HIV-1 protein sequences should be governed by a combination of factors including nucleotide mutational probabilities, the genetic code, and fitness. The impact of these factors on protein sequence evolution is interdependent, making it challenging to infer the individual contribution of each factor fromphylogenetic analyses alone. We investigated the protein sequence evolution of HIV-1 by determining an experimental fitness landscape of all individual amino acid changes in protease. We compared our experimental results to the frequency of protease variants in a publicly available data set of 32,163 sequenced isolates from drug-naý;ve individuals. The most common amino acids in sequenced isolates supported robust experimental fitness, indicating that the experimental fitness landscape captured key features of selection acting on protease during viral infections of hosts. Amino acid changes requiring multiple mutations from the likely ancestor were slightly less likely to support robust experimental fitness than single mutations, consistent with the genetic code favoring chemically conservative amino acid changes. Amino acids that were common in sequenced isolates were predominantly accessible by single mutations from the likely protease ancestor. Multiple mutations commonly observed in isolates were accessible by mutational walks with highly fit single mutation intermediates. Our results indicate that the prevalence of multiple-base mutations in HIV-1 protease is strongly influenced by mutational sampling. © 2019 The Author(s).
A tool for assessing the climate change mitigation and health impacts of environmental policies: the Cities Rapid Assessment Framework for Transformation (CRAFT)
Creators: name: Symonds, Phil
name: Milner, James
name: Rayeni, Nahid Mohajeri Pour
Publisher: name: figshare
Year: 2020
Subjects: subject: 90702 Environmental Engineering Modelling
subject: FOS: Environmental engineering

description: Supplementary File 2. This file contains estimated impact on health (i.e. theoretical premature number of deaths avoided in one year) and CO2 emissions due to ten key policy actions in London without interactions between policies)
Convolutional Dictionary Learning: Acceleration and Convergence
Creators: name: Chun, Il Yong
name: Fessler, Jeffrey A.
Publisher: name: arXiv
Year: 2017
Subjects: subject: Machine Learning (cs.LG)
subject: Optimization and Control (math.OC)
subject: FOS: Computer and information sciences
subject: FOS: Mathematics

description: Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.
description: 21 pages, 7 figures, submitted to IEEE Transactions on Image Processing
Open Design of Appropriate Technology for Sustainable Agriculture in the Peruvian Andes: A Sabbatical Presentation
Creators: name: Berg, Devin R.
Publisher: name: Zenodo
Year: 2020
Subjects: engineering
sabbatical
Perú

description: Dr. Devin Berg, Robert F. Cervenka School of Engineering, presents his year-long sabbatical and his experience traveling to and living in the Peruvian Andes, working with a woman owned organic farming collective, and running from a rapidly spreading global pandemic.
title: ABC de la inteligencia artificial
Creators: name: Universidad de Rosario. Escuela de Ingeniería, Ciencia y Tecnología
Publisher: name: Universidad del Rosario
Year: 2023

description: Como parte de nuestra temporada "Transforma la experiencIA académica: Conoce los retos de la inteligencia artificial e identifica posibilidades para incorporarla en tus prácticas académicas", la Universidad ha dispuesto esta cartilla con el ABC de la inteligencia artificial, que invita a los profesores y profesoras a identificar los aspectos más relevantes sobre el estado del arte de la inteligencia artificial y una de sus herramientas más usadas: el ChatGPT.
Convolutional Analysis Operator Learning: Acceleration and Convergence
Creators: name: Chun, Il Yong
name: Fessler, Jeffrey A.
Publisher: name: arXiv
Year: 2018
Subjects: subject: Image and Video Processing (eess.IV)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Machine Learning (cs.LG)
subject: Optimization and Control (math.OC)
subject: Machine Learning (stat.ML)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences
subject: FOS: Mathematics

description: Convolutional operator learning is gaining attention in many signal processing and computer vision applications. Learning kernels has mostly relied on so-called patch-domain approaches that extract and store many overlapping patches across training signals. Due to memory demands, patch-domain methods have limitations when learning kernels from large datasets -- particularly with multi-layered structures, e.g., convolutional neural networks -- or when applying the learned kernels to high-dimensional signal recovery problems. The so-called convolution approach does not store many overlapping patches, and thus overcomes the memory problems particularly with careful algorithmic designs; it has been studied within the "synthesis" signal model, e.g., convolutional dictionary learning. This paper proposes a new convolutional analysis operator learning (CAOL) framework that learns an analysis sparsifying regularizer with the convolution perspective, and develops a new convergent Block Proximal Extrapolated Gradient method using a Majorizer (BPEG-M) to solve the corresponding block multi-nonconvex problems. To learn diverse filters within the CAOL framework, this paper introduces an orthogonality constraint that enforces a tight-frame filter condition, and a regularizer that promotes diversity between filters. Numerical experiments show that, with sharp majorizers, BPEG-M significantly accelerates the CAOL convergence rate compared to the state-of-the-art block proximal gradient (BPG) method. Numerical experiments for sparse-view computational tomography show that a convolutional sparsifying regularizer learned via CAOL significantly improves reconstruction quality compared to a conventional edge-preserving regularizer. Using more and wider kernels in a learned regularizer better preserves edges in reconstructed images.
description: 22 pages, 11 figures, fixed incorrect math theorem numbers in fig. 3
Velocity-induced Heavy Quarkonium Dissociation using the gauge-gravity correspondence
Creators: name: Patra, Binoy Krishna
name: Khanchandani, Himanshu
name: Thakur, Lata
Publisher: name: arXiv
Year: 2015
Subjects: subject: High Energy Physics - Theory (hep-th)
subject: High Energy Physics - Phenomenology (hep-ph)
subject: Nuclear Theory (nucl-th)
subject: FOS: Physical sciences

description: Using the gauge-gravity duality we have obtained the potential between a heavy quark and an antiquark pair, which is moving perpendicular to the direction of orientation, in a strongly-coupled supersymmetric hot plasma. For the purpose we work on a metric in the gravity side, {\em viz.} OKS-BH geometry, whose dual in the gauge theory side runs with the energy and hence proves to be a better background for thermal QCD. The potential obtained has confining term both in vacuum and in medium, in addition to the Coulomb term alone, usually reported in the literature. As the velocity of the pair is increased the screening of the potential gets weakened, which may be understood by the decrease of effective temperature with the increase of velocity. The crucial observation of our work is that beyond a critical separation of the heavy quark pair, the potential develops an imaginary part which is nowadays understood to be the main source of dissociation. The imaginary part is found to vanish at small $r$, thus agrees with the perturbative result. Finally we have estimated the thermal width for the ground and first excited states and found that non-zero rapidities lead to an increase of thermal width. This implies that the moving quarkonia dissociate easier than the static ones, which agrees with other calculations. However, the width in our case is larger than other calculations due to the presence of confining terms.
description: 21 pages with 5 figures, Accepted in Physical Review D (in Press). arXiv admin note: text overlap with arXiv:1108.0684 by other authors
Hard and Soft Propaganda: Marketing Autocracy in Contemporary China
Creators: name: Du, Sharon
Publisher: name: Stanford Digital Repository
Year: 2023
Subjects: subject: Propaganda
subject: Propaganda, Chinese
subject: China
subject: Three Child Policy
subject: One Child Policy

description: Current scholarship has identified distinct styles of propaganda produced by the Chinese government. Huang (2015) describes “hard” propaganda as propaganda designed to signal state capacity rather than persuade; by contrast, Yang (2013) illustrates how “soft” propaganda advances narratives of everyday heroism to deflect from structural critique. However, literature on hard and soft propaganda remains bifurcated, even as both styles rapidly alternate in the Chinese information environment. My thesis offers the first empirical study of hard and soft propaganda, investigating whether distinct styles of propaganda produce different effects, whether propaganda variants interact with one another, and how this might contribute to autocratic resilience. I conduct an original survey experiment with over 650 mainland Chinese participants, who are treated with hard and/or soft propaganda based on the Three Child Policy. I find that citizens are significantly more likely to share and enjoy soft propaganda, and less likely to identify it as “political propaganda” in comparison to hard propaganda. Both hard and soft propaganda encourage citizens to seek redress through official channels, while soft propaganda also stirs an entrepreneurial response. Though hard and soft propaganda create political backlash, exposure to both varieties also makes it more difficult for participants to identify soft propaganda as political propaganda, and easier for participants to identify hard propaganda as political propaganda. I conclude that hard and soft propaganda result in distinct effects, and that a diverse informational strategy may reinforce autocracy over time.
D5.2 STARS4ALL Live Dashboards
Creators: name: STARS4ALL Project
Publisher: name: Figshare
Year: 2016
Subjects: subject: 80103 Computer Graphics
subject: FOS: Computer and information sciences
subject: Data Format
subject: Information Systems

description: This deliverable describes the graphical widgets
developed for this initial release. In this initial version, our effort has
been focused in the sensor network.
Distribution of some functionals for a Lévy process with matrix-exponential jumps of the same sign
Creators: name: Karnaukh, Ievgen
Publisher: name: arXiv
Year: 2014
Subjects: subject: Probability (math.PR)
subject: FOS: Mathematics
subject: G.3
subject: 60G51

description: This paper provides a framework for investigations in fluctuation theory for Lévy processes with matrix-exponential jumps. We present a matrix form of the components of the infinitely divisible factorization. Using this representation we establish generalizations of some results known for compound Poisson processes with exponential jumps in one direction and generally distributed jumps in the other direction.
description: 11 pages
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery
Creators: name: Chun, Il Yong
name: Fessler, Jeffrey A.
Publisher: name: arXiv
Year: 2018
Subjects: subject: Machine Learning (stat.ML)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: In "extreme" computational imaging that collects extremely undersampled or noisy measurements, obtaining an accurate image within a reasonable computing time is challenging. Incorporating image mapping convolutional neural networks (CNN) into iterative image recovery has great potential to resolve this issue. This paper 1) incorporates image mapping CNN using identical convolutional kernels in both encoders and decoders into a block coordinate descent (BCD) signal recovery method and 2) applies alternating direction method of multipliers to train the aforementioned image mapping CNN. We refer to the proposed recurrent network as BCD-Net using identical encoding-decoding CNN structures. Numerical experiments show that, for a) denoising low signal-to-noise-ratio images and b) extremely undersampled magnetic resonance imaging, the proposed BCD-Net achieves significantly more accurate image recovery, compared to BCD-Net using distinct encoding-decoding structures and/or the conventional image recovery model using both wavelets and total variation.
description: 5 pages, 3 figures
A tool for assessing the climate change mitigation and health impacts of environmental policies: the Cities Rapid Assessment Framework for Transformation (CRAFT)
Creators: name: Symonds, Phil
name: Milner, James
name: Mohajeri, Nahid
name: Aplin, Juliette
name: Hale, Jo
name: Lloyd, Simon J
name: HENRY FREMONT
name: Younkin, Sam
name: Shrubsole, Clive
name: Robertson, Lawrie
name: Taylor, Jonathon
name: Nici Zimmermann
name: Wilkinson, Paul
name: Davies, Mike
Publisher: name: figshare
Year: 2020
Subjects: subject: 90702 Environmental Engineering Modelling
subject: FOS: Environmental engineering

description: Supplementary File 1. This file contains assumptions; Calculation steps; Central estimates of percentage changes in GHGs emissions, indoor and outdoor air pollutants, housing-related risks, and travel-related physical activity due to ten current policy actions in London
The Measure and Mismeasure of Fairness
Creators: name: Corbett-Davies, Sam
name: Gaebler, Johann D.
name: Nilforoshan, Hamed
name: Shroff, Ravi
name: Goel, Sharad
Publisher: name: arXiv
Year: 2018
Subjects: subject: Computers and Society (cs.CY)
subject: FOS: Computer and information sciences

description: The field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last decade, several formal, mathematical definitions of fairness have gained prominence. Here we first assemble and categorize these definitions into two broad families: (1) those that constrain the effects of decisions on disparities; and (2) those that constrain the effects of legally protected characteristics, like race and gender, on decisions. We then show, analytically and empirically, that both families of definitions typically result in strongly Pareto dominated decision policies. For example, in the case of college admissions, adhering to popular formal conceptions of fairness would simultaneously result in lower student-body diversity and a less academically prepared class, relative to what one could achieve by explicitly tailoring admissions policies to achieve desired outcomes. In this sense, requiring that these fairness definitions hold can, perversely, harm the very groups they were designed to protect. In contrast to axiomatic notions of fairness, we argue that the equitable design of algorithms requires grappling with their context-specific consequences, akin to the equitable design of policy. We conclude by listing several open challenges in fair machine learning and offering strategies to ensure algorithms are better aligned with policy goals.
Validation of a total IC50 method which enables in vitro assessment of transporter inhibition under semi-physiological conditions
Creators: name: Kikuchi, Ryota
name: Peterkin, Vincent C.
name: Chiou, William J.
name: Morais, Sonia M. De
name: Bow, Daniel A. J.
Publisher: name: Figshare
Year: 2016
Subjects: subject: Biochemistry
subject: Medicine
subject: Physiology
subject: FOS: Biological sciences
subject: Pharmacology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics

description: 1. Accurate predictions of clinical transporter-mediated drug–drug interactions (DDI) from in vitro data can be challenging when compounds have poor solubility and/or high nonspecific binding. Additionally, current DDI predictions for compounds with high plasma–protein binding assume that the unbound fraction in plasma is 0.01, if the experimental value is less than 0.01 or cannot be determined. This approach may result in an overestimation of DDI risk. To overcome these challenges, it may be beneficial to conduct inhibition studies under physiologically relevant conditions. 2. Here, IC50 values, determined in the presence of 4% bovine serum albumin approximating human plasma albumin concentrations, were successfully used to predict DDI for uptake transporters, OATP1B1/1B3, OCT1/2, OAT1/3 and MATE1/2K. 3. The IC50 values of reference inhibitors with 4% bovine serum albumin, considered total IC50, were comparable to the predicted values based on nominal IC50 values determined under protein-free conditions and unbound fraction in plasma. Calculation of R-total and Cmax/IC50,total values using total plasma exposure and total IC50 values explained the clinical DDI or absence of it for these inhibitors. 4. These results suggest that IC50 determinations in the presence of 4% albumin can be used, in the context of clinical total exposure, to predict DDI involving uptake transporters.
Convolutional Analysis Operator Learning: Dependence on Training Data
Creators: name: Chun, Il Yong
name: Hong, David
name: Adcock, Ben
name: Fessler, Jeffrey A.
Publisher: name: arXiv
Year: 2019
Subjects: subject: Machine Learning (cs.LG)
subject: Machine Learning (stat.ML)
subject: FOS: Computer and information sciences

description: Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets. One can use many training images for CAOL, but a precise understanding of the impact of doing so has remained an open question. This paper presents a series of results that lend insight into the impact of dataset size on the filter update in CAOL. The first result is a general deterministic bound on errors in the estimated filters, and is followed by a bound on the expected errors as the number of training samples increases. The second result provides a high probability analogue. The bounds depend on properties of the training data, and we investigate their empirical values with real data. Taken together, these results provide evidence for the potential benefit of using more training data in CAOL.
description: 5 pages, 2 figures
Coherent $ρ^0$ photoproduction in ultra-peripheral Pb-Pb collisions at $\mathbf{\sqrt{\textit{s}_{\rm NN}}} = 2.76$ TeV
Creators: name: ALICE Collaboration
Publisher: name: arXiv
Year: 2015
Subjects: subject: Nuclear Experiment (nucl-ex)
subject: High Energy Physics - Experiment (hep-ex)
subject: FOS: Physical sciences

description: We report the first measurement at the LHC of coherent photoproduction of $ρ^0$ mesons in ultra-peripheral Pb-Pb collisions. The invariant mass and transverse momentum distributions for $ρ^0$ production are studied in the $π^+ π^-$ decay channel at mid-rapidity. The production cross section in the rapidity range $|y|<0.5$ is found to be $\mathrm{d}σ/\mathrm{d}y = 425 \pm 10 \, (\mathrm{stat.})$ $^{+42}_{-50} \, (\mathrm{sys.})$ mb. Coherent $ρ^0$ production is studied with and without requirement of nuclear breakup, and the fractional yields for various breakup scenarios are presented. The results are compared with those from lower energies and with model predictions.
description: 23 pages, 6 captioned figures, 2 tables, authors from page 18, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/1561
Algorithms for computing mixed multiplicities, mixed volumes and sectional Milnor numbers
Creators: name: Goel, Kriti
name: Mukundan, Vivek
name: Roy, Sudeshna
name: Verma, J. K.
Publisher: name: arXiv
Year: 2019
Subjects: subject: Commutative Algebra (math.AC)
subject: FOS: Mathematics
subject: Primary 13-04, 13A30, 13H15

description: We present a package 'MixedMultiplicity' for computing mixed multiplicities of ideals in a Noetherian ring which is either local or a standard graded algebra over a field. This enables us to find mixed volumes of convex lattice polytopes and sectional Milnor numbers of hypersurfaces with an isolated singularity. The algorithms make use of the defining equations of the multi-Rees algebra of ideals, which are obtained by generalising a result of D. Cox. K.-N. Lin, and G. Sosa.
description: Vivek Mukundan joined as a new co-author. The package has improved significantly from its earlier version
A tool for assessing the climate change mitigation and health impacts of environmental policies: the Cities Rapid Assessment Framework for Transformation (CRAFT)
Creators: name: Symonds, Phil
name: Milner, James
name: Mohajeri, Nahid
Publisher: name: figshare
Year: 2020
Subjects: subject: 90702 Environmental Engineering Modelling
subject: FOS: Environmental engineering

description: Supplementary File 1. This file contains assumptions; Calculation steps; Central estimates of percentage changes in GHGs emissions, indoor and outdoor air pollutants, housing-related risks, and travel-related physical activity due to ten current policy actions in London
Total Absorption Spectroscopy Study of $^{92}$Rb Decay: A Major Contributor to Reactor Antineutrino Spectrum Shape
Creators: name: Zakari-Issoufou, A. -A.
name: Fallot, M.
name: Porta, A.
name: Algora, A.
name: Tain, J. L.
name: Valencia, E.
name: Rice, S.
name: Bui, V. M
name: Cormon, S.
name: Estienne, M.
name: Agramunt, J.
name: Äystö, J.
name: Bowry, M.
name: Briz, J. A.
name: Caballero-Folch, R.
name: Cano-Ott, D.
name: Cucoanes, A.
name: Elomaa, V. -V.
name: Eronen, T.
name: Estévez, E.
name: Farrelly, G. F.
name: Garcia, A. R.
name: Gelletly, W.
name: Gomez-Hornillos, M. B
name: Gorlychev, V.
name: Hakala, J.
name: Jokinen, A.
name: Jordan, M. D.
name: Kankainen, A.
name: Karvonen, P.
name: Kolhinen, V. S.
name: Kondev, F. G
name: Martinez, T.
name: Mendoza, E.
name: Molina, F.
name: Moore, I.
name: Perez-Cerdán, A. B.
name: Podolyák, Zs.
name: Penttilä, H.
name: Regan, P. H.
name: Reponen, M.
name: Rissanen, J.
name: Rubio, B.
name: Shiba, T.
name: Sonzogni, A. A.
name: Weber, C.
name: collaboration, IGISOL
Publisher: name: arXiv
Year: 2015
Subjects: subject: Nuclear Experiment (nucl-ex)
subject: High Energy Physics - Experiment (hep-ex)
subject: FOS: Physical sciences

description: The antineutrino spectra measured in recent experiments at reactors are inconsistent with calculations based on the conversion of integral beta spectra recorded at the ILL reactor. $^{92}$Rb makes the dominant contribution to the reactor spectrum in the 5-8 MeV range but its decay properties are in question. We have studied $^{92}$Rb decay with total absorption spectroscopy. Previously unobserved beta feeding was seen in the 4.5-5.5 region and the GS to GS feeding was found to be 87.5(25)%. The impact on the reactor antineutrino spectra calculated with the summation method is shown and discussed.
description: 6 pages, 3 figures
Personal Responsibility and Personal Time
Creators: name: Doble, Rick
Publisher: name: Figshare
Year: 2016
Subjects: subject: Mental Health
subject: 170199 Psychology not elsewhere classified
subject: FOS: Psychology
subject: 170102 Developmental Psychology and Ageing

description: Personal responsibility is vital for gaining a true sense of one's own worth. People who avoid responsibility put themselves at arm's length from the world they live in. This paper explores these two notions.
A Statistical Analysis of Multipath Interference for Impulse Radio UWB Systems
Creators: name: Stanciu, Mihai I.
name: Azou, Stéphane
name: Rădoi, Emanuel
name: Şerbănescu, Alexandru
Publisher: name: arXiv
Year: 2014
Subjects: subject: Networking and Internet Architecture (cs.NI)
subject: FOS: Computer and information sciences

description: In this paper, we develop a statistical characterization of the multipath interference in an Impulse Radio (IR)-UWB system, considering the standardized IEEE 802.15.4a channel model. In such systems, the chip length has to be carefully tuned as all the propagation paths located beyond this limit can cause interframe/intersymbol interferences (IFI/ISI). Our approach aims at computing the probability density function (PDF) of the power of all multipath components with delays larger than the chip time, so as to prevent such interferences. Exact analytical expressions are derived first for the probability that the chip length falls into a particular cluster of the multipath propagation model and for the statistics of the number of paths spread over several contiguous clusters. A power delay profile (PDP) approximation is then used to evaluate the total interference power as the problem appears to be mathematically intractable. Using the proposed closed-form expressions, and assuming minimal prior information on the channel state, a rapid update of the chip time value is enabled so as to control the signal to interference plus noise ratio.
description: 17 pages, 9 figures; submitted to the Journal of the Franklin Institute on Sept. 24, 2013
Asymptotic behaviour of the one-dimensional "rock-paper-scissors" cyclic cellular automaton
Creators: name: de Menibus, Benjamin Hellouin
name: Borgne, Yvan Le
Publisher: name: arXiv
Year: 2019
Subjects: subject: Probability (math.PR)
subject: FOS: Mathematics
subject: 37B15, 92D25, 91A22, 60C05, 60G50

description: The one-dimensional three-state cyclic cellular automaton is a simple spatial model with three states in a cyclic "rock-paper-scissors" prey-predator relationship. Starting from a random configuration, similar states gather in increasingly large clusters; asymptotically, any finite region is filled with a uniform state that is, after some time, driven out by its predator, each state taking its turn in dominating the region (heteroclinic cycles). We consider the situation where each site in the initial configuration is chosen independently at random with a different probability for each state. We prove that the asymptotic probability that a state dominates a finite region corresponds to the initial probability of its prey. The proof methods are based on discrete probability tools, mainly particle systems and random walks.
description: 22 pages, figures in color
Out-of-sample generalizations for supervised manifold learning for classification
Creators: name: Vural, Elif
name: Guillemot, Christine
Publisher: name: arXiv
Year: 2015
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving way, while enhancing the separation between different classes in the learned embedding. Most nonlinear supervised manifold learning methods compute the embedding of the manifolds only at the initially available training points, while the generalization of the embedding to novel points, known as the out-of-sample extension problem in manifold learning, becomes especially important in classification applications. In this work, we propose a semi-supervised method for building an interpolation function that provides an out-of-sample extension for general supervised manifold learning algorithms studied in the context of classification. The proposed algorithm computes a radial basis function (RBF) interpolator that minimizes an objective function consisting of the total embedding error of unlabeled test samples, defined as their distance to the embeddings of the manifolds of their own class, as well as a regularization term that controls the smoothness of the interpolation function in a direction-dependent way. The class labels of test data and the interpolation function parameters are estimated jointly with a progressive procedure. Experimental results on face and object images demonstrate the potential of the proposed out-of-sample extension algorithm for the classification of manifold-modeled data sets.
Advisory Council Meeting: Tape 1
Creators: name: Council, The University Of Texas At Austin School Of Architecture Advisory
Publisher: name: The University of Texas at Austin
Year: 1987
Subjects: University of Texas at Austin. School of Architecture
Box, Hal
Austin (Tex.)
Architecture--Study and teaching
Alofsin, Anthony
Harvard University
Columbia University
Wright, Frank Lloyd, 1867-1959
University of Pennsylvania
Architectural design
interior decoration
Landscape architecture
tuition
scholarships
Rice University
Universities and colleges--Graduate work
Moore, Charles Willard, 1925-1993
University of Oxford.
Ford, O'Neil, 1905-1982
Speck, Lawrence W., 1949-
Deans (Education)

description: Audio files are EID restricted. Individuals without an EID should send an email request to apl-aaa@lib.utexas.edu.
Patient-specific flow descriptors and normalised wall index in peripheral artery disease: a preliminary study
Creators: name: Jaykrishna Singh
name: Brunner, Gerd
name: Morrisett, Joel D.
name: Ballantyne, Christie M.
name: Lumsden, Alan B.
name: Dipan J. Shah
name: Decuzzi, Paolo
Publisher: name: Figshare
Year: 2016
Subjects: subject: Medicine
subject: Cell Biology
subject: Physiology
subject: FOS: Biological sciences
subject: Biotechnology
subject: Sociology
subject: FOS: Sociology
subject: 69999 Biological Sciences not elsewhere classified
subject: Cancer
subject: Science Policy

description: Background and aims: MRI-based haemodynamics have been applied to study the relationship between time-averaged wall shear stresses (TAWSS), oscillatory shear index (OSI) and atherosclerotic lesions in the coronary arteries, carotid artery and human aorta. However, the role of TAWSS and OSI is poorly understood in lower extremity arteries. The aim of this work was to investigate the feasibility of haemodynamic assessment of the superficial femoral artery (SFA) in patients with peripheral artery disease (PAD) and we hypothesised that there is an association between TAWSS and OSI, respectively, and atherosclerotic burden expressed as a normalised wall index (NWI). Methods: Six cases of 3D vascular geometries of the SFA and related inlet/outlet flow conditions were extracted from patient-specific MRI data including baseline, 12 and 24 months. Blood flow simulations were performed to compute flow descriptors, including TAWSS and OSI, and NWI. Results: NWI was correlated positively with TAWSS (correlation coefficient: r = 0.592; p r = −0.310, p Conclusions: In this pilot study with a limited sample size, TAWSS was positively associated with NWI, a measure of plaque burden, whereas OSI showed an inverse relationship. However, our findings need to be verified in a larger prospective study. MRI-based study of haemodynamics is feasible in the superficial femoral artery.
On Low-rank Trace Regression under General Sampling Distribution
Creators: name: Hamidi, Nima
name: Bayati, Mohsen
Publisher: name: arXiv
Year: 2019
Subjects: subject: Machine Learning (cs.LG)
subject: Information Theory (cs.IT)
subject: Machine Learning (stat.ML)
subject: FOS: Computer and information sciences

description: In this paper, we study the trace regression when a matrix of parameters B* is estimated via the convex relaxation of a rank-regularized regression or via regularized non-convex optimization. It is known that these estimators satisfy near-optimal error bounds under assumptions on the rank, coherence, and spikiness of B*. We start by introducing a general notion of spikiness for B* that provides a generic recipe to prove the restricted strong convexity of the sampling operator of the trace regression and obtain near-optimal and non-asymptotic error bounds for the estimation error. Similar to the existing literature, these results require the regularization parameter to be above a certain theory-inspired threshold that depends on observation noise that may be unknown in practice. Next, we extend the error bounds to cases where the regularization parameter is chosen via cross-validation. This result is significant in that existing theoretical results on cross-validated estimators (Kale et al., 2011; Kumar et al., 2013; Abou-Moustafa and Szepesvari, 2017) do not apply to our setting since the estimators we study are not known to satisfy their required notion of stability. Finally, using simulations on synthetic and real data, we show that the cross-validated estimator selects a near-optimal penalty parameter and outperforms the theory-inspired approach of selecting the parameter.
description: 49 pages, 6 figure2
Post-Newtonian Cosmological Modelling
Creators: name: Sanghai, Viraj A. A.
name: Clifton, Timothy
Publisher: name: arXiv
Year: 2015
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences

description: We develop a new approach to building cosmological models, in which small pieces of perturbed Minkowski space are joined together at reflection-symmetric boundaries in order to form a global, dynamical space-time. Each piece of this patchwork universe is described using post-Newtonian gravitational physics, with the large-scale expansion of the universe being an emergent phenomenon. This approach to cosmology does not require any assumptions about non-local averaging processes. Our framework clarifies the relation between the weak-field limit of general relativity, and the cosmological solutions that result from solving Einstein's equations with a set of symmetry assumptions. It also allows the effects of structure formation on the large-scale expansion of the universe to be investigated in an unambiguous way. As an explicit example, we use this formalism to investigate the cosmological behaviour of a large number of regularly arranged point-like masses. In this case we find that the large-scale expansion is well modelled by a Friedmann-like equation that contains terms that take the form of dust, radiation, and spatial curvature. The radiation term, while small compared to the dust term, is purely a result of the non-linearity of Einstein's equations.
description: 22 pages, 5 figures, v2: Published version, v4: Corrections to published version
Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions
Creators: name: Kervadec, Hoel
name: Dolz, Jose
name: Yuan, Jing
name: Desrosiers, Christian
name: Granger, Eric
name: Ayed, Ismail Ben
Publisher: name: arXiv
Year: 2019
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: This study investigates imposing hard inequality constraints on the outputs of convolutional neural networks (CNN) during training. Several recent works showed that the theoretical and practical advantages of Lagrangian optimization over simple penalties do not materialize in practice when dealing with modern CNNs involving millions of parameters. Therefore, constrained CNNs are typically handled with penalties. We propose *log-barrier extensions*, which approximate Lagrangian optimization of constrained-CNN problems with a sequence of unconstrained losses. Unlike standard interior-point and log-barrier methods, our formulation does not need an initial feasible solution. The proposed extension yields an upper bound on the duality gap -- generalizing the result of standard log-barriers -- and yielding sub-optimality certificates for feasible solutions. While sub-optimality is not guaranteed for non-convex problems, this result shows that log-barrier extensions are a principled way to approximate Lagrangian optimization for constrained CNNs via implicit dual variables. We report weakly supervised image segmentation experiments, with various constraints, showing that our formulation outperforms substantially the existing constrained-CNN methods, in terms of accuracy, constraint satisfaction and training stability, more so when dealing with a large number of constraints.
The evolution of streams in a time-dependent potential
Creators: name: Buist, Hans J. T.
name: Helmi, Amina
Publisher: name: arXiv
Year: 2015
Subjects: subject: Astrophysics of Galaxies (astro-ph.GA)
subject: FOS: Physical sciences

description: We study the evolution of streams in a time-dependent spherical gravitational potential. Our goal is to establish what are the imprints of this time evolution on the properties of streams as well as their observability. To this end, we have performed a suite of numerical experiments for a host system that doubles its mass during the integration time and for a variety of initial conditions. In these experiments we found that the most striking imprint is a misalignment of 10 degrees in the angular location of the apocentres of the streams compared to the static case (and to the orbit of the centre of mass), which only becomes apparent for sufficiently long streams. We have also developed an analytic model using action-angle variables which allows us to explain this behaviour and to identify the most important signature of time evolution, namely a difference in the slope defined by the distribution of particles along a stream in frequency and in angle space. Although a difference in slope can arise when the present-day potential is not correctly modelled, this shortcoming can be by-passed because in this case, streams are no longer straight lines in angle space, but depict a wiggly appearance and an implausible energy gradient. The difference in slope due to time-evolution is small, typically $\sim10^{-2}$ and its amplitude depends on the growth rate of the potential, but nonetheless we find that it could be observable if accurate full-space information for nearby long streams is available.
description: 20 pages, 18 figures. Accepted for publication in A&A
Momentum-Net: Fast and convergent iterative neural network for inverse problems
Creators: name: Chun, Il Yong
name: Huang, Zhengyu
name: Lim, Hongki
name: Fessler, Jeffrey A.
Publisher: name: arXiv
Year: 2019
Subjects: subject: Image and Video Processing (eess.IV)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Machine Learning (cs.LG)
subject: Optimization and Control (math.OC)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences
subject: FOS: Mathematics

description: Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision. INNs combine regression NNs and an iterative model-based image reconstruction (MBIR) algorithm, often leading to both good generalization capability and outperforming reconstruction quality over existing MBIR optimization models. This paper proposes the first fast and convergent INN architecture, Momentum-Net, by generalizing a block-wise MBIR algorithm that uses momentum and majorizers with regression NNs. For fast MBIR, Momentum-Net uses momentum terms in extrapolation modules, and noniterative MBIR modules at each iteration by using majorizers, where each iteration of Momentum-Net consists of three core modules: image refining, extrapolation, and MBIR. Momentum-Net guarantees convergence to a fixed-point for general differentiable (non)convex MBIR functions (or data-fit terms) and convex feasible sets, under two asymptomatic conditions. To consider data-fit variations across training and testing samples, we also propose a regularization parameter selection scheme based on the "spectral spread" of majorization matrices. Numerical experiments for light-field photography using a focal stack and sparse-view computational tomography demonstrate that, given identical regression NN architectures, Momentum-Net significantly improves MBIR speed and accuracy over several existing INNs; it significantly improves reconstruction quality compared to a state-of-the-art MBIR method in each application.
description: 28 pages, 13 figures, 3 algorithms, 4 tables, submitted revision to IEEE T-PAMI
A passive seismic approach to estimating the thickness of sedimentary cover on the Nullarbor Plain, Western Australia
Creators: name: A. Scheib
name: P. Morris
name: R. Murdie
name: C. Delle Piane
Publisher: name: Figshare
Year: 2016
Subjects: subject: Space Science
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: Ecology
subject: FOS: Biological sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics

description: Passive seismic approaches, using a single-station, enable rapid, cost-effective and non-invasive estimates of the thickness of sedimentary rocks overlying crystalline basement. This approach was applied to estimate the Cenozoic and Cretaceous succession beneath the Nullarbor Plain in southeastern Western Australia. Passive seismic data acquired at the majority of the 94 sites show a single, strong resonance frequency peak between 0.4 and 0.6 Hz suggesting an impedance contrast of a single subsurface layer. Modelling these resonance frequencies against known stratigraphy at 12 drill holes shows that this impedance contrast corresponds to the contact of the base of the Cenozoic–Cretaceous sedimentary succession of the Eucla and Bight basins with the crystalline basement. Data from the remaining sites produced sediment thickness estimates ranging from only tens of metres near the western edge of the Nullarbor Plain to over 860 m near its southern margin. Near this margin, rapid thickening of the sedimentary cover is coincident with an interpreted paleosea-cliff or indicative of localised faulting. Beneath the Western Australian portion of the Nullarbor Plain the sedimentary cover is on average 320 m thick with the succession thinning gradually towards the margins of the basin. A passive seismic approach is thus seen as a useful screening tool for the mineral exploration industry in areas that are under cover allowing for better targeting and cost-reduction in greenfields exploration.
Finding the Dominant Zero of the Energy Probability Distribution
Creators: name: Carvalho, J. J.
name: Mota, A. L.
Publisher: name: arXiv
Year: 2019
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: FOS: Physical sciences

description: In this work, we present a method to locate the dominant zero of the Energy Probability Distribution (EPD) Zeros method applied to the study of phase transitions. The technique strongly reduces computer processing time and also increases the accuracy of the results. As an example, we apply it to the 2D Ising model, comparing with both the exact Onsager's results and the previous EPD zeros method results. We show that, for large lattices, the processing time is drastically reduced when compared to other EPD zeros search procedures, whereas for small lattices the gain in accuracy allows very accurate predictions in the thermodynamic limit.
description: 10 pages, 5 figures. Revised extended version
eHumanities Desktop - eine webbasierte Arbeitsumgebung für die geisteswissenschaftliche Fachinformatik
Creators: name: Mehler, Alexander
name: Waltinger, Ulli
Publisher: name: Kolloquium Wissensinfrastruktur
Year: 2010

description: Kolloquium Wissensinfrastruktur, Sommersemester 2010