cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Generalization of Weinberg's Compositeness Relations
Creators: name: Li, Yan
name: Guo, Feng-Kun
name: Pang, Jin-Yi
name: Wu, Jia-Jun
Publisher: name: arXiv
Year: 2021
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: High Energy Physics - Experiment (hep-ex)
subject: High Energy Physics - Lattice (hep-lat)
subject: Nuclear Theory (nucl-th)
subject: FOS: Physical sciences

description: We generalize the time-honored Weinberg's compositeness relations by including the range corrections through considering a general form factor. In Weinberg's derivation, he considered the effective range expansion up to $\mathcal{O}(p^2)$ and made two additional approximations: neglecting the non-pole term in the Low equation; approximating the form factor by a constant. We lift the second approximation, and work out an analytic expression for the form factor. For a positive effective range, the form factor is of a single-pole form. An integral representation of the compositeness is obtained and is expected to have a smaller uncertainty than that derived from Weinberg's relations. We also establish an exact relation between the wave function of a bound state and the phase of the scattering amplitude neglecting the non-pole term. The deuteron is analyzed as an example, and the formalism can be applied to other cases where range corrections are important.
description: 9 pages, 2 figures
Study on Transfer Learning Capabilities for Pneumonia Classification in Chest-X-Rays Image
Creators: name: Avola, Danilo
name: Bacciu, Andrea
name: Cinque, Luigi
name: Fagioli, Alessio
name: Marini, Marco Raoul
name: Taiello, Riccardo
Publisher: name: arXiv
Year: 2021
Subjects: subject: Image and Video Processing (eess.IV)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences

description: Over the last year, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants have highlighted the importance of screening tools with high diagnostic accuracy for new illnesses such as COVID-19. To that regard, deep learning approaches have proven as effective solutions for pneumonia classification, especially when considering chest-x-rays images. However, this lung infection can also be caused by other viral, bacterial or fungi pathogens. Consequently, efforts are being poured toward distinguishing the infection source to help clinicians to diagnose the correct disease origin. Following this tendency, this study further explores the effectiveness of established neural network architectures on the pneumonia classification task through the transfer learning paradigm. To present a comprehensive comparison, 12 well-known ImageNet pre-trained models were fine-tuned and used to discriminate among chest-x-rays of healthy people, and those showing pneumonia symptoms derived from either a viral (i.e., generic or SARS-CoV-2) or bacterial source. Furthermore, since a common public collection distinguishing between such categories is currently not available, two distinct datasets of chest-x-rays images, describing the aforementioned sources, were combined and employed to evaluate the various architectures. The experiments were performed using a total of 6330 images split between train, validation and test sets. For all models, common classification metrics were computed (e.g., precision, f1-score) and most architectures obtained significant performances, reaching, among the others, up to 84.46% average f1-score when discriminating the 4 identified classes. Moreover, confusion matrices and activation maps computed via the Grad-CAM algorithm were also reported to present an informed discussion on the networks classifications.
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation
Creators: name: Ommi, Yassaman
name: Yousefabadi, Matin
name: Faez, Faezeh
name: Sabour, Amirmojtaba
name: Baghshah, Mahdieh Soleymani
name: Rabiee, Hamid R.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (cs.LG)
subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: Graph data structures are fundamental for studying connected entities. With an increase in the number of applications where data is represented as graphs, the problem of graph generation has recently become a hot topic. However, despite its significance, conditional graph generation that creates graphs with desired features is relatively less explored in previous studies. This paper addresses the problem of class-conditional graph generation that uses class labels as generation constraints by introducing the Class Conditioned Graph Generator (CCGG). We built CCGG by injecting the class information as an additional input into a graph generator model and including a classification loss in its total loss along with a gradient passing trick. Our experiments show that CCGG outperforms existing conditional graph generation methods on various datasets. It also manages to maintain the quality of the generated graphs in terms of distribution-based evaluation metrics.
Modeling of Real Gas Flow Behavior in Porous Media
Creators: name: Chien, Tony
Publisher: name: The University of Texas at Austin
Year: 1993
Subjects: Porous media
Viscosity

description: Due to the highly nonlinear variation of gas density and viscosity with respect to pressure, no analytical solution to the real gas diffusivity equation has ever been presented in the literature. Analytical solutions used in gas well testing and pressure analysis are based on idealized assumptions, such as small and/or constant gas compressibility and constant hydraulic diffusivity. These solutions, though widely used and easily applied, are not accurate. This research presents a detailed investigation in the behavior of real gas flow through porous media. Starting from the most fundamental flow equation a new real gas potential is implemented to transform the nonlinear flow equation into a quasi-linear diffusivity equation. The effects of pressure-dependent fluid and rock properties such as gas viscosity, compressibility, porosity and permeability are included. Advanced analytical derivations with respect to nonconstant hydraulic diffusivity are performed, and an analytical solution method is successfully developed. Multiple-rate and multiple-well systems as well as bounded reservoirs of any shape are rigorously implemented by using the principle of superposition and an unsteady-state bounding technique. Validation of the model is favorably achieved by comparison with finite-difference simulation and type curve matching. Reservoir pressures and flowing bottomhole pressures calculated by the analytical solution presented in this study are more accurate than by those methods published in the literature. The new solution method is also applicable to a broad range of pressure changes and different flow periods. This is a significant contribution to transient pressure analysis, long-term well performance tests and production forecasts in natural gas reservoirs. Moreover, since the pressure-dependent porosity and permeability are included in this study, the general solution may be applied to abnormally pressured reservoirs and tight gas sands for improved predictions of gas reserves and flow performance.
Surface roughness prediction in end milling using multiple regression and adaptive neuro-fuzzy inference system
Creators: name: Maher, Ibrahem
name: M. E. H. Eltaib
name: R. M. El-Zahry
Publisher: name: figshare
Year: 2015
Subjects: subject: Statistics
subject: FOS: Mathematics
subject: Data Format
subject: FOS: Computer and information sciences
subject: 80309 Software Engineering
subject: Applied Computer Science
subject: Mechanical Engineering
subject: FOS: Mechanical engineering

description: Multiple regression and adaptive neuro-fuzzy inference system (ANFIS) were used to predict the surface roughness in the end milling process. Spindle speed, feed rate and depth of cut were used as predictor variables. Generalized bell memberships function (gbellmf) was adopted during the training process of ANFIS in this study. The predicted surface roughness using multiple regression and ANFIS were compared with measured data, the achieved accuracy were 91.9% and 94% respectively. These results indicate that the training of ANFIS with the gbellmf is accurate than multiple regression in the prediction of surface roughness.
Identification, Classification and Regulation of Newly Discovered Gas Condensate and Volatile Oil Reservoirs
Creators: name: Voorhis, David Bryan
Publisher: name: The University of Texas at Austin
Year: 1994
Subjects: Oil reservoir
Volatile oil

description: Volatile oil and gas condensate reservoirs are special reservoirs because of their potential for improved hydrocarbon liquid recovery through some form of pressure maintenance operation. These reservoirs are difficult to identify from surface production characteristics. Many times, the correct determination of the reservoir fluid type requires laboratory analysis of the reservoir fluid. Current regulations enforced by the Texas Railroad Commission do not specifically recognize volatile oil or gas condensate reservoirs. Single phase reservoirs are classified as being either oil or gas reservoirs, based on initial produced gas-oil ratios. As a consequence of the state's reservoir classification and the regulations associated with the classifications, many opportunities for improved hydrocarbon liquid recovery from newly discovered volatile oil and gas condensate reservoirs are lost. This research examines issues related to the identification, classification and regulation of newly discovered gas condensate and volatile oil reservoirs in Texas. A method for simulating surface production characteristics of different reservoir fluid types is developed and used to analyze the production differences attributed to the fluid type. Recommendations are made to change some existing regulations so that the chances are improved for increased hydrocarbon liquid recovery from newly discovered gas condensate and volatile oil reservoirs.
Modeling Foam Flow in Porous Media and Applications to Eor and Acidization
Creators: name: Zhou, Zuhui
Publisher: name: The University of Texas at Austin
Year: 1994
Subjects: Porous media
EOR process

description: Foams are used in the petroleum industry to divert gas in enhanced oil recovery (EOR) processes and divert acid in matrix acidization treatments. These processes are exceedingly complex, but a model based on the "limiting capillary pressure" of foam greatly simplifies the quantitative description and design of foam processes. This dissertation describes this model and applies it to EOR and well stimulation. It may be possible to bypass the daunting complexities of modeling the changes of foam texture and non-Newtonian foam rheology. By using the model of the "limiting capillary pressure", surprisingly simple and powerful conclusions can be drawn for foam plugging and flow in homogeneous reservoirs in radial geometry and in heterogeneous reservoirs with layers either isolated or in capillary equilibrium. One application of the model is in improving sweep efficiency and oil recovery in gas-injection EOR processes. With fractional-flow theory, one can estimate the velocities of the front and rear edges of a foam bank with various initial conditions. surfactant retention levels, and chase fluids behind the foam. Moreover, one can estimate the mobility of each bank and the extent of flow diversion between layers, in linear or radial geometry. Fractional-flow analysis based on extrapolated laboratory data suggests that for continuous-injection foam processes, the best swfactant is one that drives water saturation to the lowest possible value at the injected foam quality and is sensitive to permeability. In contrast, for water-alternating-gas processes, the best foam is one that degrades over a range of water saturations. Another application of the model is to predict foam diversion in matrix acidization of sandstones. This model predicts that foams can efficiently divert acid into low-permeability layers because capillary pressure makes foam less stable in these layers. The greatest diversion is obtained when foam is preceded by a swfactant preflush and followed by an acid slug that is compatible with the foam. The key to the success of such a process is the ability of a surfactant solution to immobilize the gas in previously injected foam. A process in which acid is injected continuously with foam can also achieve effective diversion. However, our model suggests that better performance is obtained by separating the foam and foam-compatible-acid slugs, and preceding the foam with a surfactant preflush.
Quantitative predictions of thermodynamic hysteresis: Temperature-dependent character of the phase transition in Pd-H
Creators: name: Rahm, J. Magnus
name: Löfgren, Joakim
name: Erhart, Paul
Publisher: name: arXiv
Year: 2021
Subjects: subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: The thermodynamics of phase transitions between phases that are size-mismatched but coherent differs from conventional stress-free thermodynamics. Most notably, in open systems such phase transitions are always associated with hysteresis. In spite of experimental evidence for the relevance of these effects in technologically important materials such as Pd hydride, a recipe for first-principles-based atomic-scale modeling of coherent, open systems has been lacking. Here, we develop a methodology for quantifying phase boundaries, hysteresis, and coherent interface free energies using density-functional theory, alloy cluster expansions, and Monte Carlo simulations in a constrained ensemble. We apply this approach to Pd--H and show that the phase transition changes character above approximately 400 K, occurring with an at all times spatially homogeneous hydrogen concentration, i.e., without coexistence between the two phases. Our results are consistent with experimental observations but reveal aspects of hydride formation in Pd nanoparticles that have not yet been accessible in experiment.
description: 11 pages, 6 figures
Universal finite-size amplitude and anomalous entanglement entropy of $z=2$ quantum Lifshitz criticalities in topological chains
Creators: name: Wang, Ke
name: Sedrakyan, T. A.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: FOS: Physical sciences

description: We consider Lifshitz criticalities with dynamical exponent $z=2$ that emerge in a class of topological chains. There, such a criticality plays a fundamental role in describing transitions between symmetry-enriched conformal field theories (CFTs). We report that, at such critical points in one spatial dimension, the finite-size correction to the energy scales with system size, $L$, as $\sim L^{-2}$, with universal and anomalously large coefficient. The behavior originates from the specific dispersion around the Fermi surface, $ε\propto \pm k^2$. We also show that the entanglement entropy exhibits at the criticality a non-logarithmic dependence on $l/L$, where $l$ is the length of the sub-system. In the limit of $l\ll L$, the maximally-entangled ground state has the entropy, $S(l/L)=S_0+2n(l/L)\log(l/L)$. Here $S_0$ is some non-universal entropy originating from short-range correlations and $n$ is half-integer or integer depending on the degrees of freedom in the model. We show that the novel entanglement originates from the long-range correlation mediated by a zero mode in the low energy sector. The work paves the way to study finite-size effects and entanglement entropy around Lifshitz criticalities and offers an insight into transitions between symmetry-enriched criticalities.
description: 22 pages, 4 figures
Capsule Graph Neural Networks with EM Routing
Creators: name: Lei, Yu
name: Zhang, Jing
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: To effectively classify graph instances, graph neural networks need to have the capability to capture the part-whole relationship existing in a graph. A capsule is a group of neurons representing complicated properties of entities, which has shown its advantages in traditional convolutional neural networks. This paper proposed novel Capsule Graph Neural Networks that use the EM routing mechanism (CapsGNNEM) to generate high-quality graph embeddings. Experimental results on a number of real-world graph datasets demonstrate that the proposed CapsGNNEM outperforms nine state-of-the-art models in graph classification tasks.
Understanding Dimensional Collapse in Contrastive Self-supervised Learning
Creators: name: Jing, Li
name: Vincent, Pascal
name: LeCun, Yann
name: Tian, Yuandong
Publisher: name: arXiv
Year: 2021
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Artificial Intelligence (cs.AI)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Self-supervised visual representation learning aims to learn useful representations without relying on human annotations. Joint embedding approach bases on maximizing the agreement between embedding vectors from different views of the same image. Various methods have been proposed to solve the collapsing problem where all embedding vectors collapse to a trivial constant solution. Among these methods, contrastive learning prevents collapse via negative sample pairs. It has been shown that non-contrastive methods suffer from a lesser collapse problem of a different nature: dimensional collapse, whereby the embedding vectors end up spanning a lower-dimensional subspace instead of the entire available embedding space. Here, we show that dimensional collapse also happens in contrastive learning. In this paper, we shed light on the dynamics at play in contrastive learning that leads to dimensional collapse. Inspired by our theory, we propose a novel contrastive learning method, called DirectCLR, which directly optimizes the representation space without relying on an explicit trainable projector. Experiments show that DirectCLR outperforms SimCLR with a trainable linear projector on ImageNet.
description: In Proceedings of the 10th International Conference on Learning Representations (ICLR) 2022
Finite Element Modeling of Annular Flows With Application to Slim Hole Drilling Hydraulics
Creators: name: Roberto Ribeiro, Paulo
Publisher: name: The University of Texas at Austin
Year: 1994
Subjects: Drilling hydraulic
Annular flow

description: During the exploration and exploitation phases of an oil field, it is necessary to drill wells at strategic points of the field in order to obtain information about the geology, lithology and oil bearing potential of the reservoir. Due to the financial and technical advantages of applying slim hole drilling and coring to exploration, increased effort in the analysis of the performance, safety and environmental impact of the operation has been observed. One of the major issues in slim hole drilling and coring operations is the accurate prediction of pressure losses in the annulus, in order to guarantee optimum operating conditions. As has been pointed out in the literature, the small annular clearance and the high rotational speeds of the drillstring are factors which preclude the vii common assumption of unidimensional axial flow in the annulus, which has been applied for conventional pressure drop computations. In order to address the effect of rotational speed, eccentricity of the drillstring, variation in borehole diameter due to cased and open-hole sections, and drillstring upsets, 2D and 3D finite element schemes were applied. The numerical modeling results showed good agreement with available laboratory data for laminar, axial-rotary flows of power law fluids in concentric annuli and axial flows in an eccentric annulus. The simulations revealed that the rotation of the inner cylinder causes a decrease in the pressure differential along the concentric annuli, which is due to the contribution of the tangential shear rate in the viscosity function of the generalized non-Newtonian constitutive equation for the fluid. The eccentricity caused a decrease in the pressure differential along the annulus for both purely axial and axial-rotary flows. The simulation of complex annular flow geometries revealed that the flow constraint imposed by the inner cylinder external upset introduces a significant pressure gradient increase. Despite the satisfactory performance of the numerical modeling under the scope of its assumptions of laminar regime and steady flow configuration, it was found to be limited in its ability to evaluate the pressure behavior in the annulus of the field case. This was due to the fact that in field operations, the pressure loss in the annulus is affected by flow turbulence and dynamic behavior of the drillstring.
Effects of Fluid Saturation and Stress State on the Mechanical and Chemical Properties of Shale
Creators: name: Mese, Ali Ihsan
Publisher: name: The University of Texas at Austin
Year: 1995
Subjects: Properties of shale
Stress effect

description: An experimental and theoretical investigation of water content and stress effect on mechanical, chemical and failure properties has been conducted using core samples from Barnett shale formation. Samples showed plastic deformation almost immediately with the initiation of loading. Therefore, in addition to the elastic static moduli, components of the plastic tangential compliance tensor were also obtained from triaxial stress cycling measurements for dry, partially saturated and fully saturated shale samples. Simultaneous measurements of compressional and shear wave velocities were carried out using pulse transmission technique in order to obtain the corresponding dynamic moduli. Static and dynamic Young's moduli decreased greatly in all Barnett shales tested with an increase in saturation, and increased with increasing at failure. The comparison of the experimental results with various failure surfaces indicated that partially and fully saturated shale samples are best represented by the Drager-Prager equation as compared to the von Mises, Mohr-Coulomb and Cap failure surfaces. A new saturation device was designed based on chemical potential theory in order to control saturation in the shale samples used. The method also helps to conduct measurements with strain-gauged shale samples which has not been successful prior to this work. A modified intermolecular force model is presented that provides a quantitative analysis of how intermolecular forces influence the expansion of clays and shales. Determination of electrostatic repulsion parameters from cation exchange capacity and specific surface area is described. Equilibrium is considered determined by the van der Waals attraction and electrostatic repulsion. Initial and equilibrium separation distances are calculated. Relations obtained between salt concentration, plate thickness and swelling which is calculated from initial and equilibrium separation distances agree reasonably well with available experimental swelling and swelling pressure data.
An Adaptation of Kota Bharu Community towards Monsoon Flood
Creators: name: Hua, Ang Kean
Publisher: name: figshare
Year: 2015
Subjects: Geography
Hydrology
Survey Results
Climate Science
Education

description: This study deals with how the community of Kota Bharu adapts to monsoon floods that strike in the Monsoon season. Malaysia is a developing country, but the flooding problem still cannot be prevented even with the application of science and technology. Monsoon flooding is a natural disaster. Therefore, this disaster is difficult to avoid using a variety of methods. So, the only way to react to a flood disaster is through understanding adaptation methods towards flooding. This research study is uses the quantitative methods, which is through a questionnaire study. A total of 400 respondents from the Kota Bharu community were willing to spend their time and managed to give the information by answering the questionnaire provided. The study area was set to involve the people living along and near the river in the district of Kota Bharu, Kelantan. The results indicated that continuous rains from upstream will provide big impact to the downstream. This happens when water flows from high to low areas, plus a large amount of water from elsewhere, such that the river cannot cope with the excessive water and overflows. These conditions cause monsoon floods to occur. So, the people of Kota Bharu have a variety of methods such as paying attention to the rain and flood warning system, preparing food before the food sources are cut off, keeping important items in a safe place, and using boats as a primary form of transport.
Space Information Time Theory
Creators: name: Kitchen, Kris
Publisher: name: figshare
Year: 2015
Subjects: subject: Interstellar and Intergalactic Matter
subject: Instrumentation, Techniques, and Astronomical Observations
subject: 20104 Galactic Astronomy
subject: FOS: Physical sciences
subject: Cosmology
subject: Planetary Geology
subject: Stellar Astronomy
subject: Stars, Variable Stars
subject: Astrophysics
subject: Planetary Science
subject: Solar System, Solar Physics, Planets and Exoplanets
subject: Extragalactic Astronomy
subject: Space Science

description: Space Information Time_- Quantium Imagination Theory
Critiquing-based Modeling of Subjective Preferences
Creators: name: Medlar, Alan
name: Li, Jing
name: Liu, Yang
name: Glowacka, Dorota
Publisher: name: arXiv
Year: 2021
Subjects: subject: Human-Computer Interaction (cs.HC)
subject: FOS: Computer and information sciences

description: Applications designed for entertainment and other non-instrumental purposes are challenging to optimize because the relationships between system parameters and user experience can be unclear. Ideally, we would crowdsource these design questions, but existing approaches are geared towards evaluation or ranking discrete choices and not for optimizing over continuous parameter spaces. In addition, users are accustomed to informally expressing opinions about experiences as critiques (e.g. it's too cold, too spicy, too big), rather than giving precise feedback as an optimization algorithm would require. Unfortunately, it can be difficult to analyze qualitative feedback, especially in the context of quantitative modeling. In this article, we present collective criticism, a critiquing-based approach for modeling relationships between system parameters and subjective preferences. We transform critiques, such as "it was too easy/too challenging", into censored intervals and analyze them using interval regression. Collective criticism has several advantages over other approaches: "too much/too little"-style feedback is intuitive for users and allows us to build predictive models for the optimal parameterization of the variables being critiqued. We present two studies where we model: (i) aesthetic preferences for images generated with neural style transfer, and (ii) users' experiences of challenge in the video game Tetris. These studies demonstrate the flexibility of our approach, and show that it produces robust results that are straightforward to interpret and inline with users' stated preferences.
Theory of Non-equilibrium "Hot" Carriers in Direct Band-gap Semiconductors Under Continuous Illumination
Creators: name: Sarkar, Subhajit
name: Un, Ieng-Wai
name: Sivan, Yonatan
name: Dubi, Yonatan
Publisher: name: arXiv
Year: 2021
Subjects: subject: Optics (physics.optics)
subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: Other Condensed Matter (cond-mat.other)
subject: Statistical Mechanics (cond-mat.stat-mech)
subject: FOS: Physical sciences

description: The interplay between the illuminated excitation of carriers and subsequent thermalization and recombination leads to the formation of non-equilibrium distributions for the "hot" carriers and to heating of both electrons, holes and phonons. In spite of the fundamental and practical importance of these processes, there is no theoretical framework which encompasses all of them and provides a clear prediction for the non-equilibrium carrier distributions. Here, a self-consistent theory accounting for the interplay between excitation, thermalization, and recombination in continuously-illuminated semiconductors is presented, enabling the calculation of non-equilibrium carrier distributions. We show that counter-intuitively, distributions deviate more from equilibrium under weak illumination than at high intensities. We mimic two experimental procedures to extract the carrier temperatures and show that they yield different dependence on illumination. Finally, we provide an accurate way to evaluate photoluminescence efficiency, which, unlike conventional models, predicts correctly the experimental results. These results provide a starting point towards examining how non-equilibrium features will affect properties hot-carrier based application.
description: Version accepted in New Journal of Physics
Phrase Pair Rescoring with Term Weighting for Statistical Machine Translation
Creators: name: Zhao, Bing
name: Vogel, Stephan
name: Waibel, Alex
name: Eck, Matthias
Publisher: name: Association for Computational Linguistics (ACL)
Year: 2004

description: We propose to score phrase translation pairs for statistical machine translation using term weight based models. These models employ $tf.idf$ to encode the weights of content and non-content words in phrase translation pairs. The translation probability is then modeled by similarity functions defined in a vector space. Two similarity functions are compared. Using these models in a statistical machine translation task shows significant improvements.
Ali, M., Ma, M., & Wong, G.K.-W. (2024). K–12 Pre-service Teachers' Perspectives on AI Models and Computational Thinking: The Insights from an Interpretive Research Inquiry. Proceedings of the 8th International Conference on Computational Thinking and STEM Education (CTE-STEM) 2024, 8(1), 66-71. https://doi.org/10.6084/m9.figshare.26008282.v1
Creators: name: N/A
Publisher: name: figshare
Year: 2024
Subjects: classificationCode: 460299
subject: Artificial intelligence not elsewhere classified
classificationCode: 390405
subject: Educational technology and computing
classificationCode: 460804
subject: Computing education
classificationCode: 390307
subject: Teacher education and professional development of educators

description: APA Citation: Ali, M., Ma, M., & Wong, G.K.-W. (2024). K–12 Pre-service Teachers' Perspectives on AI Models and Computational Thinking: The Insights from an Interpretive Research Inquiry. Proceedings of the 8th International Conference on Computational Thinking and STEM Education (CTE-STEM) 2024, 8(1), 66-71. https://doi.org/10.6084/m9.figshare.26008282.v1
Quantum Computation of Phase Transition in the Massive Schwinger Model
Creators: name: Thompson, Shane
name: Siopsis, George
Publisher: name: arXiv
Year: 2021
Subjects: subject: Quantum Physics (quant-ph)
subject: High Energy Physics - Lattice (hep-lat)
subject: High Energy Physics - Theory (hep-th)
subject: FOS: Physical sciences
subject: 81P68, 81T25

description: As pointed out by Coleman, physical quantities in the Schwinger model depend on a parameter $θ$ that determines the background electric field. There is a phase transition for $θ= π$ only. We develop a momentum space formalism on a lattice and use it to perform a quantum computation of the critical point of this phase transition on the NISQ device IMB Q Lima. After error mitigation, our results give strong indication of the existence of a critical point at $m/e\simeq 0.32$, where $m$ is the bare fermion mass and $e$ is the coupling strength, in good agreement with the classical numerical result $m/e \simeq 0.3335$.
description: 22 pages, 17 figures
Primary Depletion Reservoir Simulator for Real and Dry Gas
Creators: name: Al-Naim, Sami
Publisher: name: The University of Texas at Austin
Year: 1994
Subjects: Reservoir simulator
Hydraulic diffusivity

description: A real unsteady-state gas simulator has been developed using the new gas potential formulation presented by Chien. This simulator handles the non-linearity relationship of the PVT properties and pressure within a time step. It uses an iterative method to solve for the pressure. It can handle any reservoir shape and size. The simulator was validated for infinite and bounded reservoirs having different shape, well location, and rate schemes. It was shown that this simulator can be used as a disk-top personal computer tool for both gas reservoir simulation studies and gas well test analysis. This research introduces for the first time the concept of updating the bounding hydraulic diffusivity parameter. It was found that the bounding hydraulic diffusivity parameter must be updated every time the system is bounded using the average reservoir pressure. The model was optimized to run efficiently on disk-top personal computers. The log regression approximation which replaces the extensive computer time bounding rates calculations, was found to be valid only for ideal cases with regular boundaries and constant flow rates. A new empirical equation is presented to calculate the number of bounding calculations after which the bounding rate array size can be fixed. Also the Bisection iterative method was found to be the best method for the iterative procedure that solves for the potential. It reduces the computer time by 10% compared to the standard Successive Substitution iterative method. QR factorization method for solving 'the overdetermined matrix generated by the bounding equation was found to be the fastest, whereas Givens Rotations method was found to have the least memory requirement.
Palindromic factorization of rich words
Creators: name: Rukavicka, Josef
Publisher: name: arXiv
Year: 2021
Subjects: subject: Combinatorics (math.CO)
subject: Discrete Mathematics (cs.DM)
subject: FOS: Mathematics
subject: FOS: Computer and information sciences
subject: 68R15

description: A finite word $w$ is called \emph{rich} if it contains $\vert w\vert+1$ distinct palindromic factors including the empty word. For every finite rich word $w$ there are distinct nonempty palindromes $w_1, w_2,\dots,w_p$ such that $w=w_pw_{p-1}\cdots w_1$ and $w_i$ is the longest palindromic suffix of $w_pw_{p-1}\cdots w_i$, where $1\leq i\leq p$. This palindromic factorization is called \emph{UPS-factorization}. Let $luf(w)=p$ be \emph{the length of UPS-factorization} of $w$. In 2017, it was proved that there is a constant $c$ such that if $w$ is a finite rich word and $n=\vert w\vert$ then $luf(w)\leq c\frac{n}{\ln{n}}$. We improve this result as follows: There are constants $μ, π$ such that if $w$ is a finite rich word and $n=\vert w\vert$ then \[luf(w)\leq μ\frac{n}{e^{π\sqrt{\ln{n}}}}\mbox{.}\] The constants $c,μ,π$ depend on the size of the alphabet.
Understanding how fast black holes spin by analysing data from the second gravitational-wave catalogue
Creators: name: Hoy, Charlie
name: Fairhurst, Stephen
name: Hannam, Mark
name: Tiwari, Vaibhav
Publisher: name: arXiv
Year: 2021
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: FOS: Physical sciences

description: The Advanced LIGO and Virgo detectors have now observed approximately 50 black-hole-binary mergers, from which we can begin to infer how rapidly astrophysical black holes spin. The LIGO-Virgo Collaboration (LVC) analysis of detections up to the end of the first half of the third observing run (O3a) appeared to uncover a distribution of spin magnitudes that peaks at $\sim$0.2. This is surprising: is there a black-hole formation mechanism that prefers a particular, non-zero spin magnitude, or could this be the cumulative effect of multiple formation processes? We perform an independent analysis of the most recent gravitational-wave catalogue, and find that (a) the support for the LVC spin-magnitude is tenuous; in particular, adding or removing just one signal from the catalogue can remove the statistical preference for this distribution, and (b) we find potential evidence for two spin sub-populations in the observed black holes; one with extremely low spins and one with larger spin magnitudes. We make the connection that these spin sub-populations could be correlated with the mass of the binary, with more massive binaries preferring larger spin magnitudes, and argue that this may provide evidence for hierarchical mergers in the second gravitational-wave catalogue.
description: 12 pages, 6 figures. Update to match published version
Unbiased Graph Embedding with Biased Graph Observations
Creators: name: Wang, Nan
name: Lin, Lu
name: Li, Jundong
name: Wang, Hongning
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (cs.LG)
subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: Graph embedding techniques are pivotal in real-world machine learning tasks that operate on graph-structured data, such as social recommendation and protein structure modeling. Embeddings are mostly performed on the node level for learning representations of each node. Since the formation of a graph is inevitably affected by certain sensitive node attributes, the node embeddings can inherit such sensitive information and introduce undesirable biases in downstream tasks. Most existing works impose ad-hoc constraints on the node embeddings to restrict their distributions for unbiasedness/fairness, which however compromise the utility of the resulting embeddings. In this paper, we propose a principled new way for unbiased graph embedding by learning node embeddings from an underlying bias-free graph, which is not influenced by sensitive node attributes. Motivated by this new perspective, we propose two complementary methods for uncovering such an underlying graph, with the goal of introducing minimum impact on the utility of the embeddings. Both our theoretical justification and extensive experimental comparisons against state-of-the-art solutions demonstrate the effectiveness of our proposed methods.
description: Keywords: unbiased graph embedding, fairness, sensitive attributes, bias-free graph, node representation In Proceedings of the ACM Web Conference 2022 (WWW'22), April 25-29, 2022, Virtual Event, Lyon, France. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3485447.3512189
A π-phase-shifted fiber Bragg grating fabricated using a single phase mask
Creators: name: Yam, S. P.
name: Brodzeli, Z.
name: Kouskousis, B. P.
name: Rollinson, C. M.
name: Wade, Scott
name: Baxter, G. W.
name: Collins, S. F.
Publisher: name: Swinburne
Year: 2024

description: A fiber Bragg grating at twice the Bragg wavelength was fabricated by a standard phase mask technique and its two peaks/dips are attributed to the interleaved refractive index modulations along the fiber core that produced a pi-phase-shifted grating.
An accurate description of the structural and electronic properties of twisted bilayer graphene-boron nitride heterostructures
Creators: name: Long, Min
name: Pantaleón, Pierre A.
name: Zhan, Zhen
name: Guinea, Francisco
name: Silva-Guillén, Jose Ángel
name: Yuan, Shengjun
Publisher: name: arXiv
Year: 2021
Subjects: subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: FOS: Physical sciences

description: Twisted bilayer graphene (TBG) has taken the spotlight in the condensed matter community since the discovery of correlated phases at the so-called magic angle. Interestingly, the role of a substrate on the electronic properties of TBG has not been completely elucidated. Up to now, most of the theoretical works carried out in order to understand this effect have been done using continuum models. In this work, we have gone one step ahead and have studied heterostructures of TBG and hBN using an atomistic tight-binding model together with semi-classical molecular dynamics to take into account relaxation effects. We found that the presence of the hBN substrate has significant effects to the band structure of TBG even in the case where TBG and hBN are not aligned. Specifically, the substrate induces a large mass gap and strong pseudomagnetic fields which break the layer degeneracy. Interestingly, such degeneracy can be recovered with a second hBN layer. Finally, we have also developed a continuum model that describes the tight-binding band structure. Our results show that a real-space tight-binding model in combination with semi-classical molecular dynamics are a powerful tool to study the electronic properties of supermoiré systems and that using this real-space methodology could be key in order to explain certain experimental results in which the effect of the substrate plays an important role.
description: 13 pages, 6 figures
Comparative Explanations of Recommendations
Creators: name: Yang, Aobo
name: Wang, Nan
name: Cai, Renqin
name: Deng, Hongbo
name: Wang, Hongning
Publisher: name: arXiv
Year: 2021
Subjects: subject: Information Retrieval (cs.IR)
subject: Artificial Intelligence (cs.AI)
subject: Computation and Language (cs.CL)
subject: FOS: Computer and information sciences

description: As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i.e., comparative explanations about the recommended items. Ideally, after reading the explanations, a user should reach the same ranking of items as the system's. Unfortunately, little research attention has yet been paid on such comparative explanations. In this work, we develop an extract-and-refine architecture to explain the relative comparisons among a set of ranked items from a recommender system. For each recommended item, we first extract one sentence from its associated reviews that best suits the desired comparison against a set of reference items. Then this extracted sentence is further articulated with respect to the target user through a generative model to better explain why the item is recommended. We design a new explanation quality metric based on BLEU to guide the end-to-end training of the extraction and refinement components, which avoids generation of generic content. Extensive offline evaluations on two large recommendation benchmark datasets and serious user studies against an array of state-of-the-art explainable recommendation algorithms demonstrate the necessity of comparative explanations and the effectiveness of our solution.
description: 11 pages, 4 figures
Optimal Parameter Inflation to Enhance the Availability of Single-Frequency GBAS for Intelligent Air Transportation
Creators: name: Lee, Halim
name: Pullen, Sam
name: Lee, Jiyun
name: Park, Byungwoon
name: Yoon, Moonseok
name: Seo, Jiwon
Publisher: name: arXiv
Year: 2021
Subjects: subject: Systems and Control (eess.SY)
subject: Signal Processing (eess.SP)
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: Ground-based Augmentation System (GBAS) augments Global Navigation Satellite Systems (GNSS) to support the precision approach and landing of aircraft. To guarantee integrity, existing single-frequency GBAS utilizes position-domain geometry screening to eliminate potentially unsafe satellite geometries by inflating one or more broadcast GBAS parameters. However, GBAS availability can be drastically impacted in low-latitude regions where severe ionospheric conditions have been observed. Thus, we developed a novel geometry-screening algorithm in this study to improve GBAS availability in low-latitude regions. Simulations demonstrate that the proposed method can provide 5-8 percentage point availability enhancement of GBAS at Galeão airport near Rio de Janeiro, Brazil, compared to existing methods.
description: Submitted to IEEE Transactions on Intelligent Transportation Systems
Exterior Noise Prediction of Existing and Novel Tube-and-Wing Aircraft during Departure and Approach
Creators: name: Bertsch, Lothar
Publisher: name: Zenodo
Year: 2019
Subjects: airplane
aviation
noise
design
ICAO
DLR
PrADO
PANAM
aerolectures
aerolectures2019

description: Aviation connects people and business worldwide and enables undreamt-of distances to be overcome in shortest time. However, this form of mobility must be reconciled with the need for noise protection will. Aviation and politics have set themselves ambitious goals. Aircraft noise is to be greatly reduced by 2050 will. By 2020, new machines may only be half as loud as they were at the turn of the millennium. The presentation shows the state of research as well as new aircraft designs with a focus on noise emissions.
description: Hamburg Aerospace Lecture Series --- Collection of Presentations --- http://www.AeroLectures.de
Development of Three Dimensional Streamline Model (Utstream) and Its Application
Creators: name: Kurihara, Masanori
Publisher: name: The University of Texas at Austin
Year: 1995
Subjects: Utstream
Permeable media

description: Tracer test results can be analyzed to characterize flow through permeable media. Usually this is carried out by history matching with numerical models. However, a conventional streamline model, using line source and sink solutions, can be applied only to two dimensional (2D) areal geometry. Use of a numerical simulator for this purpose is limited because of numerical artifacts and large calculation time. We have developed a semianalytical three dimensional (3D) streamline model (UTSTREAM) for use with a steady-state velocity field. This model is based on the assumption that each directional velocity component varies linearly within a gridblock. This assumption allows analytical expressions of streamline trajectories and transit times of tracer particles along streamlines. Furthermore, the model incorporates the sub-programs that solve the convection-diffusion (C-D) equation with longitudinal and transverse dispersion, using newly introduced equivalent parameters that can convert a three phase problem vii to an equivalent single phase problem. The results of several test runs with and without dispersion showed good agreement with analytical solutions and UTCHEM results, and that the model requires quite small calculation time. As an application of the 3D streamline model, interwell tracer tests were simulated for characterizing residual hydrocarbon (HC) in a subsurface water zone. Sensitivities of the effluent partitioning tracer response were investigated for the reservoir parameters, including HC volumes, HC locations and completion intervals, that showed promise to successfully detect residual HC. UTSTREAM was next applied to the estimation of permeability fields and residual HC saturation distributions through automatic history matching with tracer test results. The program developed for this purpose, which incorporates the 3D streamline model and a program that stochastically generates permeability fields by the matrix decomposition method (MDM), solves the nonlinear least square problem by the hybrid methods of the Quasi-Newton, Levenverg-Marquardt and steepest descent. Although it is very difficult to obtain a unique solution for inversion problems in general, several trials were made to obtain the exact solution. Finally UTSTREAM was used to scale up fine grid permeability to a coarse grid system. As a result of the investigation of the tortuosity of streamlines drawn by UTSTREAM, it was revealed that the effective permeability of a coarse gridblock can be expressed as a function of the dimensionless parameters including a dimensionless correlation length, effective aspect ratio and a newly defined effective correlation length ratio. The effective permeability obtained from this relationship could be successfully applied to a scale up problem.
Which trial do we need? Evaluation of systemic antibiotics as primary prophylaxis in mechanically ventilated patients with burn injuries
Creators: name: Yahav D.
name: Williams F.N.
name: Mackow N.A.
name: van Duin D.
Publisher: name: Elsevier B.V.
Year: 2023

description: Burn injury is a frequent source of morbidity and mortality worldwide, and infection is a leading cause of death. In mechanically ventilated patients who sustain a burn injury, ventilator-associated pneumonia (VAP) is common and associated with poor outcomes and a high risk of recurrence.