cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Pemetaan Topik Penelitian Seputar Akad Rahn (Gadai) pada Inklusi Keuangan Syariah: Studi Bibliometrik VOSviewer dan Literature Review
Creators: name: Ratnasari, Aprilia Dwi
name: Budianto, Eka Wahyu Hestya
name: Dewi, Nindi Dwi Tetria
Publisher: name: Zenodo
Year: 2023
Subjects: Akad Rahn
Lembaga Keuangan Syariah
Bibliometrik
VOSviewer
LIterature Review

description: Penelitian ini bertujuan untuk mengetahui peta perkembangan penelitian seputar Akad Rahn pada Lembaga Keuangan Syariah. Penelitian dilakukan dari tahun 2003 hingga 2022 dengan melakukan penelusuran pada jurnal nasional dan internasional terindeks Google Scholar, Sinta, dan Scopus melalui aplikasi Perish/Harzing, dengan kata kunci "Akad Rahn". Berdasarkan hasil penelusuran, terdapat 268 artikel penelitian, kemudian diinput ke dalam aplikasi VOSviewer dan dianalisis secara deskriptif melalui studi literature review. Hasil penelitian menunjukkan bahwa jumlah publikasi mengalami peningkatan secara signifikan setiap tahunnya. Dan berdasarkan hasil pemetaan menggunakan aplikasi VOSviewer, penelitian seputar Akad Rahn terbagi menjadi 5 kluster dan 155 item topik. Sedangkan berdasarkan hasil studi literature review, terdapat 10 tema utama seputar Akad Rahn pada Lembaga Keuangan Syariah.
Torsional oscillations of strange stars
Creators: name: Mannarelli, Massimo
Publisher: name: arXiv
Year: 2014
Subjects: subject: High Energy Astrophysical Phenomena (astro-ph.HE)
subject: High Energy Physics - Phenomenology (hep-ph)
subject: Nuclear Theory (nucl-th)
subject: FOS: Physical sciences

description: Strange stars are one of the hypothetical compact stellar objects that can be formed after a supernova explosion. The existence of these objects relies on the absolute stability of strange {\it collapsed} quark matter with respect to standard nuclear matter. We discuss simple models of strange stars with a bare quark matter surface, thus standard nuclear matter is completely absent. In these models an electric dipole layer a few hundreds Fermi thick should exist close to the star surface. Studying the torsional oscillations of the electrically charged layer we estimate the emitted power, finding that it is of the order of $10^{45}$ erg/s, meaning that these objects would be among the brightest compact sources in the heavens. The associated relaxation times are very uncertain, with values ranging between microseconds and minutes, depending on the crust thickness. Although part of the radiated power should be absorbed by the electrosphere surrounding the strange star, a sizable fraction of photons should escape and be detectable.
description: 8 pages, 6 figures, proceedings of QCD@work-International Workshop on QCD - Theory and Experiment 2014
Hausdorff dimensions of perturbations of a conformal iterated function system via thermodynamic formalism
Creators: name: Das, Tushar
name: Fishman, Lior
name: Simmons, David
name: Urbański, Mariusz
Publisher: name: arXiv
Year: 2020
Subjects: subject: Dynamical Systems (math.DS)
subject: Functional Analysis (math.FA)
subject: Number Theory (math.NT)
subject: FOS: Mathematics
subject: 37C45, 11K50, 37D35 (Primary), 11K60, 28A78, 37F35, 28A80, 37A45, 37B10, 37C30 (Secondary)

description: We consider small perturbations of a conformal iterated function system (CIFS) produced by either adding or removing some generators with small derivative from the original. We establish a formula, utilizing transfer operators arising from the thermodynamic formalism à la Sinai--Ruelle--Bowen, which may be solved to express the Hausdorff dimension of the perturbed limit set in series form: either exactly, or as an asymptotic expansion. Significant applications include strengthening Hensley's asymptotic formula from 1992, which improved on earlier bounds due to Jarník and Kurzweil, for the Hausdorff dimension of the set of real numbers whose continued fraction expansion partial quotients are all $\leq N$; as well as its counterpart for reals whose partial quotients are all $\geq N$ due to Good from 1941.
Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
Creators: name: Long, Teng
name: Fortunato, Nuno M.
name: Opahle, Ingo
name: Zhang, Yixuan
name: Samathrakis, Ilias
name: Shen, Chen
name: Gutfleisch, Oliver
name: Zhang, Hongbin
Publisher: name: arXiv
Year: 2020
Subjects: subject: Computational Physics (physics.comp-ph)
subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modelling of physical properties using machine learning. Applying the deep learning techniques, we have developed a generative model which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backwards propagator, both with their own advantages. Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range, and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium. The method can be extended to multicomponent systems for multi-objective optimization, which paves the way to achieve the inverse design of materials with optimal properties.
description: 23 pages, 3 figures
New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
Creators: name: Laber, Eric B.
name: Kosorok, Michael R.
name: Zhao, Ying-Qi
name: Zeng, Donglin
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized treatment selection into an either sequential or simultaneous classification problem, and can thus be applied by modifying existing machine learning techniques. The proposed methods are based on directly maximizing over all DTRs a nonparametric estimator of the expected long-term outcome; this is fundamentally different than regression-based methods, for example Q-learning, which indirectly attempt such maximization and rely heavily on the correctness of postulated regression models. We prove that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules. Simulation results suggest the proposed methods produce superior DTRs compared with Q-learning especially in small samples. We illustrate the methods using data from a clinical trial for smoking cessation.
title: Musculoskeletal causes of groin pain: A pictorial review
Creators: name: yusta-zato, Jose
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Trauma
subject: Imaging sequences
subject: MR
subject: Musculoskeletal system

description: Learning objectives Background Imaging findings OR Procedure Details Conclusion References Personal Information
description: Learning objectives: Groin pain is a commonly encountered entity in musculoskeletal radiology. The differential diagnosis is wide and includes adductor and rectus abdominis muscle/tendon and common adductor-rectus abdominis aponeurosis pathology, osteitis pubis,stress...
Leptogenesis from low-energy CP violation in minimal left-right symmetric model
Creators: name: Zhang, Xinyi
name: Yu, Jiang-Hao
name: Ma, Bo-Qiang
Publisher: name: arXiv
Year: 2020
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: FOS: Physical sciences

description: We perform a thermal unflavored leptogenesis analysis on minimal left-right symmetric models with discrete left-right symmetry identified as generalized parity or charge conjugation. When left-right symmetry is unbroken in the lepton Yukawa sector, the neutrino Dirac coupling matrix is completely determined by neutrino masses and mixing angles, allowing CP violation needed to generate leptogenesis totally resides in the low-energy sector. With two lepton asymmetry generation ways, both type I and mixed type I$+$II neutrino mass generation mechanisms are considered. After solving the Boltzmann equations numerically, we find that the low-energy CP phases in the lepton mixing matrix can successfully produce the observed baryon asymmetry, and in some cases, the Dirac CP phase can be the only source of CP violation. Finally, we discuss the interplay among low-energy CP phase measurements, leptogenesis, and neutrinoless double beta decay. We show that the viable models for successful leptogenesis can be probed in next-generation neutrinoless double-beta decay experiments.
description: 35 pages, 19 figures; reduce figure resolution for accessibility
Integral Reduction with Kira 2.0 and Finite Field Methods
Creators: name: Klappert, Jonas
name: Lange, Fabian
name: Maierhöfer, Philipp
name: Usovitsch, Johann
Publisher: name: arXiv
Year: 2020
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: High Energy Physics - Theory (hep-th)
subject: FOS: Physical sciences

description: We present the new version 2.0 of the Feynman integral reduction program Kira and describe the new features. The primary new feature is the reconstruction of the final coefficients in integration-by-parts reductions by means of finite field methods with the help of FireFly. This procedure can be parallelized on computer clusters with MPI. Furthermore, the support for user-provided systems of equations has been significantly improved. This mode provides the flexibility to integrate Kira into projects that employ specialized reduction formulas, direct reduction of amplitudes, or to problems involving linear system of equations not limited to relations among standard Feynman integrals. We show examples from state-of-the-art Feynman integral reduction problems and provide benchmarks of the new features, demonstrating significantly reduced main memory usage and improved performance w.r.t. previous versions of Kira.
description: 32 pages, v2: minor changes and clarifications, matches published version except for another fixed typo in Eq. (7)
An Overview of STAR Experimental Results
Creators: name: Xu, N.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Nuclear Experiment (nucl-ex)
subject: FOS: Physical sciences

description: With large acceptance and excellent particle identification, STAR is one of the best mid-rapidity collider experiments for studying high-energy nuclear collisions. The STAR experiment provides full information on initial conditions, properties of the hot and dense medium as well as the properties at freeze-out. In Au+Au collisions at $\sqrt{s_{NN}} = 200$ GeV, STAR's focus is on the nature of the sQGP produced at RHIC. In order to explore the properties of the QCD phase diagram, since 2010, the experiment has collected sizable data sets of Au+Au collisions at the lower collision energy region where the net-baryon density is large. At the 2014 Quark Matter Conference, the STAR experiment made 16 presentations that cover physics topics including {\it collective dynamics}, {\it electromagnetic probes}, {\it heavy flavor}, {\it initial state physics}, {\it jets}, {\it QCD phase diagram}, {\it thermodynamics and hadron chemistry}, and {\it future experimental facilities, upgrades, and instrumentation} [1-16]. In this overview we will highlight a few results from the STAR experiment, especially those from the recent measurements of the RHIC beam energy scan program. At the end, instead of a summary, we will discuss STAR's near future physics programs at RHIC.
description: 8 pages, 8 figures
title: Intra-abdominal lipomatous tumours presenting as hernias
Creators: name: Azzopardi, Christine
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Neoplasia
subject: Education
subject: MR
subject: CT
subject: Musculoskeletal soft tissue
subject: Anatomy

description: Learning objectives Background Imaging findings OR Procedure Details Conclusion References Personal Information
description: Learning objectives: To review the imaging characteristics of liposarcomas To demonstrate the anatomy of abdominal wall defects through a case series of liposarcomas presenting clinically as...
Machine Learning and Meta-Analysis Approach to Identify Patient Comorbidities and Symptoms that Increased Risk of Mortality in COVID-19
Creators: name: Aktar, Sakifa
name: Talukder, Ashis
name: Ahamad, Md. Martuza
name: Kamal, A. H. M.
name: Khan, Jahidur Rahman
name: Protikuzzaman, Md.
name: Hossain, Nasif
name: Quinn, Julian M. W.
name: Summers, Mathew A.
name: Liaw, Teng
name: Eapen, Valsamma
name: Moni, Mohammad Ali
Publisher: name: arXiv
Year: 2020
Subjects: subject: Quantitative Methods (q-bio.QM)
subject: Machine Learning (cs.LG)
subject: FOS: Biological sciences
subject: FOS: Computer and information sciences

description: Background: Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus is a significant global challenge. Many individuals who become infected have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative about individual risk of severe illness and mortality. Accurately determining how comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. Methods: To assess the interaction of patient comorbidities with COVID-19 severity and mortality we performed a meta-analysis of the published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Results: Our meta-analysis identified chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictor of mortality, in terms of symptom-comorbidity combinations, it was observed that Pneumonia-Hypertension, Pneumonia-Diabetes and Acute Respiratory Distress Syndrome (ARDS)-Hypertension showed the most significant effects on COVID-19 mortality. Conclusions: These results highlight patient cohorts most at risk of COVID-19 related severe morbidity and mortality which have implications for prioritization of hospital resources.
title: MR imaging features of arthritis and arthropathies involving shoulder joint
Creators: name: Prakash, Mahesh
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Arthritides
subject: Diagnostic procedure
subject: MR
subject: Musculoskeletal joint

description: Learning objectives Background Imaging findings OR Procedure Details Conclusion References Personal Information
description: Learning objectives: To highlight the magnetic resonance imaging(MRI) features of common arthritis and arthropathies involving the shoulder...
Additional file 2 of Effectiveness of a self-management mobile app on the quality of life of women with breast cancer: a study in a developing country
Creators: name: Mohammadzadeh, Zahra
name: Eghtedar, Samereh
name: Ayatollahi, Haleh
name: Jebraeily, Mohamad
Publisher: name: figshare
Year: 2022
Subjects: subject: Medicine
subject: Sociology
subject: FOS: Sociology
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: Developmental Biology
subject: Cancer
subject: Science Policy

description: Additional file 2.
Machine Learning Methods in Solving the Boolean Satisfiability Problem
Creators: name: Guo, Wenxuan
name: Yan, Junchi
name: Zhen, Hui-Ling
name: Li, Xijun
name: Yuan, Mingxuan
name: Jin, Yaohui
Publisher: name: arXiv
Year: 2022
Subjects: subject: Artificial Intelligence (cs.AI)
subject: Machine Learning (cs.LG)
subject: Logic in Computer Science (cs.LO)
subject: FOS: Computer and information sciences

description: This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques. Despite the great success of modern SAT solvers to solve large industrial instances, the design of handcrafted heuristics is time-consuming and empirical. Under the circumstances, the flexible and expressive machine learning methods provide a proper alternative to solve this long-standing problem. We examine the evolving ML-SAT solvers from naive classifiers with handcrafted features to the emerging end-to-end SAT solvers such as NeuroSAT, as well as recent progress on combinations of existing CDCL and local search solvers with machine learning methods. Overall, solving SAT with machine learning is a promising yet challenging research topic. We conclude the limitations of current works and suggest possible future directions.
Proper Use of Allele-Specific Expression Improves Statistical Power for cis -eQTL Mapping with RNA-Seq Data
Creators: name: Tzeng, Jung-Ying
name: Perou, Charles M.
name: Sun, Wei
name: Hu, Yi-Juan
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: Studies of expression quantitative trait loci (eQTLs) offer insight into the molecular mechanisms of loci that were found to be associated with complex diseases and the mechanisms can be classified into cis- and trans-acting regulation. At present, high-throughput RNA sequencing (RNA-seq) is rapidly replacing expression microarrays to assess gene expression abundance. Unlike microarrays that only measure the total expression of each gene, RNA-seq also provides information on allele-specific expression (ASE), which can be used to distinguish cis-eQTLs from trans-eQTLs and, more importantly, enhance cis-eQTL mapping. However, assessing the cis-effect of a candidate eQTL on a gene requires knowledge of the haplotypes connecting the candidate eQTL and the gene, which cannot be inferred with certainty. The existing two-stage approach that first phases the candidate eQTL against the gene and then treats the inferred phase as observed in the association analysis tends to attenuate the estimated cis-effect and reduce the power for detecting a cis-eQTL. In this article, we provide a maximum-likelihood framework for cis-eQTL mapping with RNA-seq data. Our approach integrates the inference of haplotypes and the association analysis into a single stage, and is thus unbiased and statistically powerful. We also develop a pipeline for performing a comprehensive scan of all local eQTLs for all genes in the genome by controlling for false discovery rate, and implement the methods in a computationally efficient software program. The advantages of the proposed methods over the existing ones are demonstrated through realistic simulation studies and an application to empirical breast cancer data from The Cancer Genome Atlas project.
Reinforcement Learning Trees
Creators: name: Zeng, Donglin
name: Zhu, Ruoqing
name: Kosorok, Michael R.
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: In this paper, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman, 2001) under high-dimensional settings. The innovations are three-fold. First, the new method implements reinforcement learning at each selection of a splitting variable during the tree construction processes. By splitting on the variable that brings the greatest future improvement in later splits, rather than choosing the one with largest marginal effect from the immediate split, the constructed tree utilizes the available samples in a more efficient way. Moreover, such an approach enables linear combination cuts at little extra computational cost. Second, we propose a variable muting procedure that progressively eliminates noise variables during the construction of each individual tree. The muting procedure also takes advantage of reinforcement learning and prevents noise variables from being considered in the search for splitting rules, so that towards terminal nodes, where the sample size is small, the splitting rules are still constructed from only strong variables. Last, we investigate asymptotic properties of the proposed method under basic assumptions and discuss rationale in general settings.
Largest small polygons: A sequential convex optimization approach
Creators: name: Bingane, Christian
Publisher: name: arXiv
Year: 2020
Subjects: subject: Optimization and Control (math.OC)
subject: FOS: Mathematics
subject: 52A10, 52A40, 52B55, 90C26

description: A small polygon is a polygon of unit diameter. The maximal area of a small polygon with $n=2m$ vertices is not known when $m\ge 7$. Finding the largest small $n$-gon for a given number $n\ge 3$ can be formulated as a nonconvex quadratically constrained quadratic optimization problem. We propose to solve this problem with a sequential convex optimization approach, which is an ascent algorithm guaranteeing convergence to a locally optimal solution. Numerical experiments on polygons with up to $n=128$ sides suggest that the optimal solutions obtained are near-global. Indeed, for even $6 \le n \le 12$, the algorithm proposed in this work converges to known global optimal solutions found in the literature.
title: Gout arthropathy with tophaceous involvement of the knee and patella – a clinical case
Creators: name: Lameiras, Raquel
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Arthritides
subject: Diagnostic procedure
subject: MR
subject: Musculoskeletal system
subject: Musculoskeletal soft tissue
subject: Musculoskeletal joint

description: Learning objectives Background Imaging findings OR Procedure Details Conclusion References Personal Information
description: Learning objectives: To review the radiological findings associated with Gout arthropathy. To present a case of Gout arthropathy with tophaceous involvement of the knee and...
Quantum Computation of Reactions on Surfaces Using Local Embedding
Creators: name: Gujarati, Tanvi P.
name: Motta, Mario
name: Friedhoff, Triet Nguyen
name: Rice, Julia E.
name: Nguyen, Nam
name: Barkoutsos, Panagiotis Kl.
name: Thompson, Richard J.
name: Smith, Tyler
name: Kagele, Marna
name: Brei, Mark
name: Jones, Barbara A.
name: Williams, Kristen
Publisher: name: arXiv
Year: 2022
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Modeling electronic systems is an important application for quantum computers. In the context of materials science, an important open problem is the computational description of chemical reactions on surfaces. In this work, we outline a workflow to model the adsorption and reaction of molecules on surfaces using quantum computing algorithms. We develop and compare two local embedding methods for the systematic determination of active spaces. These methods are automated and based on the physics of molecule-surface interactions and yield systematically improvable active spaces. Furthermore, to reduce the quantum resources required for the simulation of the selected active spaces using quantum algorithms, we introduce a technique for exact and automated circuit simplification. This technique is applicable to a broad class of quantum circuits and critical to enable demonstration on near-term quantum devices. We apply the proposed combination of active-space selection and circuit simplification to the dissociation of water on a magnesium surface using classical simulators and quantum hardware. Our study identifies reactions of molecules on surfaces, in conjunction with the proposed algorithmic workflow, as a promising research direction in the field of quantum computing applied to materials science.
description: 24 pages, 6 figures plus supplementary information
ELIXIR-UK data stewardship fellowship programme
Creators: name: Xènia  Pérez-Sitjà
name: Andrews, Robert
name: Khalil, Sara
name: Franicevic, Branka
name: Knox, Catherine
name: Munazah Andrabi
name: Shoaib Sufi
name: Susanna-Asunta Sansone
name: Goble, Carole
name: Poterlowicz, Krzysztof
Publisher: name: F1000 Research Limited
Year: 2023

description: In 2021, ELIXIR-UK was granted a UKRI award for the ELIXIR-UK DaSH Fellowship. The University of Bradford leads the project, along with a delivery team at Cardiff University, the Earlham Institute, the University of Manchester and the University of Oxford.

 The project aims to produce and deliver training in FAIR data stewardship and Research Data Management (RDM), using ELIXIR-UK knowledge and resources, such as the RDMkit and the FAIR Cookbook.

 It focuses on building capacity and professionalising Data Stewardship while leveraging the local expertise of diverse UK universities and research institutes – creating a data management culture from within the institutions.

 The project is looking to recruit a total of 20 Data Steward Fellows, who will act as RDM and FAIR ambassadors. The first cohort of nine experienced Fellows has already been meticulously selected to start working on: Creating short training videos – RDMbites Develop open RDM online courses catered for life science researchers Deliver RDM courses at their respective institutions to build capacity The leading and delivery teams of the grant will simultaneously focus on building a community of practice, overseeing and coordinating the training curriculum, and ensuring the sustainability of the Fellowship beyond the project.
Anomalous temperature and field behaviors of magnetization in cubic lattice frustrated ferromagnets
Creators: name: Ignatenko, A. N.
name: Katanin, A. A.
name: Irkhin, V. Yu.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: FOS: Physical sciences

description: Thermodynamic properties of cubic Heisenberg ferromagnets with competing exchange interactions are considered near the frustration point where the coefficient $D$ in the spin-wave spectrum $E_{\mathbf{k}}\sim D k^{2}$ vanishes. Within the Dyson-Maleev formalism it is found that at low temperatures thermal fluctuations stabilize ferromagnetism by increasing the value of $D$. For not too strong frustration this leads to an unusual "concave" shape of the temperature dependence of magnetization, which is in agreement with experimental data on the europium chalcogenides. Anomalous temperature behavior of magnetization is confirmed by Monte Carlo simulation. Strong field dependence of magnetization (paraprocess) at finite temperature is found near the frustration point.
description: 4 pages, 4 figures; proceedings of Moscow International Symposium on Magnetism (MISM-2014)
An account on links between Finsler and Lorentz Geometries for Riemannian Geometers
Creators: name: Javaloyes, Miguel Ángel
name: Pendás-Recondo, Enrique
name: Sánchez, Miguel
Publisher: name: arXiv
Year: 2022
Subjects: subject: Differential Geometry (math.DG)
subject: General Relativity and Quantum Cosmology (gr-qc)
subject: FOS: Mathematics
subject: FOS: Physical sciences
subject: 53C22, 53C50, 53C60, 53C80

description: Some links between Lorentz and Finsler geometries have been developed in the last years, with applications even to the Riemannian case. Our purpose is to give a brief description of them, which may serve as an introduction to recent references. As a motivating example, we start with Zermelo navigation problem, where its known Finslerian description permits a Lorentzian picture which allows for a full geometric understanding of the original problem. Then, we develop some issues including: (a) the accurate description of the Lorentzian causality using Finsler elements, (b) the non-singular description of some Finsler elements (such as Kropina metrics or complete extensions of Randers ones with constant flag curvature), (c) the natural relation between the Lorentzian causal boundary and the Gromov and Busemann ones in the Finsler setting, and (d) practical applications to the propagation of waves and firefronts.
description: 39 pages, 8 figures
title: MR evaluation of peripheral nerve abnormalities
Creators: name: Velonakis, George
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Trauma
subject: Neoplasia
subject: Inflammation
subject: Imaging sequences
subject: MR
subject: Neuroradiology peripheral nerve
subject: Musculoskeletal system
subject: Musculoskeletal soft tissue

description: Learning objectives Background Imaging findings OR Procedure Details Conclusion References Personal Information
description: Learning objectives: To discuss MR findings of peripheral nerve disease and to present characteristic cases of peripheral nerve...
title: Scrittura. Una pratica per decostruire la politica
Creators: name: Percoco , Laura
Publisher: name: Roma Tre-Press
Year: 2012

description: Il lavoro si propone di focalizzare la questione dell'alterità nella scrittura e nella filosofia. Gli autori di riferimento saranno Hélène Cixous, scrittrice, e Jacques Derrida, filosofo. Si cercherà, infatti, di muoversi proprio nel passaggio tra l'una e l'altra disciplina e, in senso lato, su quel terreno che non appartiene propriamente né all'una né all'altra essendo uno spazio di frontiera.

description: B@belonline vol. 13 Chaos/Kosmos
The Nonlinear Schrödinger-Airy equation in weighted Sobolev spaces
Creators: name: Castro, Alejandro J.
name: Jabbarkhanov, Khumoyun
name: Zhapsarbayeva, Lyailya
Publisher: name: arXiv
Year: 2022
Subjects: subject: Analysis of PDEs (math.AP)
subject: Mathematical Physics (math-ph)
subject: FOS: Mathematics
subject: FOS: Physical sciences
subject: 35A02, 35A02, 35G25, 35Q55

description: We study the persistence property of the solution for the nonlinear Schrödinger-Airy equation with initial data in the weighted Sobolev space $H^{1/4}(\mathbb{R})\cap L^2(|x|^{2m}dx)$, $0
description: 14 pages
Coupling between COVID-19 and seasonal influenza leads to synchronization of their dynamics
Creators: name: Rodríguez, Jorge P.
name: Eguíluz, Víctor M.
Publisher: name: arXiv
Year: 2020
Subjects: subject: Populations and Evolution (q-bio.PE)
subject: Physics and Society (physics.soc-ph)
subject: FOS: Biological sciences
subject: FOS: Physical sciences

description: Interactions between COVID-19 and other pathogens may change their dynamics. Specifically, this may hinder the modelling of empirical data when the symptoms of both infections are hard to distinguish. We introduce a model coupling the dynamics of COVID-19 and seasonal influenza, simulating cooperation, competition and asymmetric interactions. We find that the coupling synchronizes both infections, with a strong influence on the dynamics of influenza, reducing its time extent to a half.
Generalization of Fluctuation-Dissipation Theorem to Systems with Absorbing States
Creators: name: Padmanabha, Prajwal
name: Azaele, Sandro
name: Maritan, Amos
Publisher: name: arXiv
Year: 2022
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: FOS: Physical sciences

description: Systems that evolve towards a state from which they cannot depart are common in nature. But the fluctuation-dissipation theorem, a fundamental result in statistical mechanics, is mainly restricted to systems near-stationarity. In processes with absorbing states, the total probability decays with time, eventually reaching zero and rendering the predictions from the standard response theory invalid. In this article, we investigate how such processes respond to external perturbations and develop a new theory that extends the framework of the fluctuation-dissipation theorem. We apply our theory to two paradigmatic examples that span vastly different fields - a birth-death process in forest ecosystems and a targeted search on DNA by proteins. These systems can be affected by perturbations which increase their rate of extinction/absorption, even though the average or the variance of population sizes are left unmodified. These effects, which are not captured by the standard response theory, are exactly predicted by our framework. Our theoretical approach is general and applicable to any system with absorbing states. It can unveil important features of the path to extinction masked by standard approaches.
description: New Journal of Physics
Additional file 4 of Sex-split analysis of pathology and motor-behavioral outcomes in a mouse model of CLN8-Batten disease reveals an increased disease burden and trajectory in female Cln8mnd mice
Creators: name: Holmes, Andrew D.
name: White, Katherine A.
name: Pratt, Melissa A.
name: Johnson, Tyler B.
name: Likhite, Shibi
name: Meyer, Kathrin
name: Weimer, Jill M.
Publisher: name: figshare
Year: 2022
Subjects: subject: Medicine
subject: Neuroscience
subject: Physiology
subject: FOS: Biological sciences
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: Computational Biology

description: Additional file4. Table S1: Detailed animal n for each experiment (n=number of animals; Male/Female).
SPReM: Sparse Projection Regression Model For High-Dimensional Linear Regression
Creators: name: Sun, Qiang
name: Liu, Yufeng
name: Zhu, Hongtu
name: Ibrahim, Joseph G.
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods.
Search for Variable Sources Using Data of "Cold" Surveys
Creators: name: Majorova, E. K.
name: Zelenkova, O. P.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Astrophysics of Galaxies (astro-ph.GA)
subject: FOS: Physical sciences

description: We search for variable sources, using the data of the surveys conducted on the RATAN-600 radio telescope in 1980-1994 at 3.94 GHz. To test the radio sources of the RCR (RATAN Cold Refined) catalog for variability, we estimated the long-term variability indices V of the studied objects, their relative variability amplitudes V_chi, and the chi-square probabilities p. Out of about two hundred considered sources, 41 proved to have positive long-term variability indices, suggesting that these sources may be variable. Fifteen objects can be considered to be reliably variable according to the chi-square criterion p > 0.98, three of these sources have chi-square probabilities p > 0.999. T he corresponding probabilities for six sources lie in the 0.95 < p < 0.98 interval, and those of the remaining 20 objects in the 0.73 < p < 0.95 interval. Twenty-four of 41 objects are variable or possibly variable in the optical range, and five objects are known variable radio sources. We construct the light curves and spectra for the sources with positive long-term variability indices.
title: Dual energy CT for gout: mportant concepts and pitfalls
Creators: name: Kho, James
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Arthritides
subject: Experimental investigations
subject: Education
subject: CT
subject: Musculoskeletal system
subject: Musculoskeletal soft tissue
subject: Musculoskeletal joint

description: Learning objectives Background Imaging findings OR Procedure Details Conclusion References Personal Information
description: Learning objectives: This exhibit aims to 1) Illustrate appearances of monosodium urate deposition on dual energy CT 2) Review causes of false positive findings 3) Discuss potential causes of false negative findings, and important limitations of this...
Immiscible Color Flows in Optimal Transport Networks for Image Classification
Creators: name: Lonardi, Alessandro
name: Baptista, Diego
name: De Bacco, Caterina
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Machine Learning (cs.LG)
subject: Dynamical Systems (math.DS)
subject: Applied Physics (physics.app-ph)
subject: FOS: Computer and information sciences
subject: FOS: Mathematics
subject: FOS: Physical sciences

description: In classification tasks, it is crucial to meaningfully exploit the information contained in data. While much of the work in addressing these tasks is devoted to building complex algorithmic infrastructures to process inputs in a black-box fashion, less is known about how to exploit the various facets of the data, before inputting this into an algorithm. Here, we focus on this latter perspective, by proposing a physics-inspired dynamical system that adapts Optimal Transport principles to effectively leverage color distributions of images. Our dynamics regulates immiscible fluxes of colors traveling on a network built from images. Instead of aggregating colors together, it treats them as different commodities that interact with a shared capacity on edges. The resulting optimal flows can then be fed into standard classifiers to distinguish images in different classes. We show how our method can outperform competing approaches on image classification tasks in datasets where color information matters.
description: 23 pages, 13 figures, 2 tables
Cosmic-ray acceleration and gamma-ray signals from radio supernovae
Creators: name: Marcowith, A.
name: Renaud, M.
name: Dwarkadas, V.
name: Tatischeff, V.
Publisher: name: arXiv
Year: 2014
Subjects: subject: High Energy Astrophysical Phenomena (astro-ph.HE)
subject: FOS: Physical sciences

description: In this work the efficiency of particle acceleration at the forward shock right after the SN outburst for the particular case of the well-known SN 1993J is analyzed. Plasma instabilities driven by the energetic particles accelerated at the shock front grow over intraday timescales and drive a fast amplification of the magnetic field at the shock, that can explain the magnetic field strengths deduced from the radio monitoring of the source. The maximum particle energy is found to reach 1-10 PeV depending on the instability dominating the amplification process. We derive the time dependent particle spectra and the associated hadronic signatures of secondary particles arising from proton proton interactions. We find that the Cherenkov Telescope Array (CTA) should easily detect objects like SN 1993J in particular above 1 TeV, while current generation of Cherenkov telescopes such as H.E.S.S. could only marginally detect such events. The gamma-ray signal is found to be heavily absorbed by pair production process during the first week after the outburst. We predict a low neutrino flux above 10 TeV, implying a detectability horizon with a KM3NeT-type telescope of 1 Mpc only. We finally discuss the essential parameters that control the particle acceleration and gamma-ray emission in other type of SNe.
description: 7 pages, 3 figures (Note: conflict of .sty file version explains the problems with journal title and the abstract, apologies for any inconvenience). Appears as Nuclear Physics B Proceedings Supplement 2014 Proceedings of the workshop "Cosmic Ray Origin beyond the standard models", San Vito (2014), ed. by O.Tibolla, L. Drury
Demographic and Illness-Related Variables Associated With HIV-Related Fatigue
Creators: name: Harmon, James L.
name: Salahuddin, Naima
name: Pence, Brian Wells
name: Leserman, Jane
name: Barroso, Julie
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2008

description: Fatigue is one of the most debilitating symptoms suffered by those with HIV infection, yet little is known about its correlates. Our primary aims are to describe the degree to which fatigue affects daily functioning and the demographic and illness-related predictors of fatigue. The sample (n = 128) was composed of primarily poor, unemployed people of color. Fatigue most often interfered with the ability to think quickly, perform household chores, exercise, work, engage in recreational activities, walk, plan activities, and think clearly. The consequences of fatigue were highest for lowered motivation, difficulty concentrating, increased drowsiness, losing patience, and interference with work, family, and social life. Multiple linear regression analyses revealed statistically significant associations of employment status, monthly income, current antidepressant use, and number of years living with HIV infection as predictors of fatigue. These must be better understood in order to develop interventions to successfully ameliorate HIV-related fatigue.
Quantum Rényi Entropy Functionals for Bosonic Gaussian Systems
Creators: name: Lee, Junseo
name: Jeong, Kabgyun
Publisher: name: arXiv
Year: 2022
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: In this study, the quantum Rényi entropy power inequality of order $p>1$ and power $κ$ is introduced as a quantum analog of the classical Rényi-$p$ entropy power inequality. To derive this inequality, we first exploit the Wehrl-$p$ entropy power inequality on bosonic Gaussian systems via the mixing operation of quantum convolution, which is a generalized beam-splitter operation. This observation directly provides a quantum Rényi-$p$ entropy power inequality over a quasi-probability distribution for $D$-mode bosonic Gaussian regimes. The proposed inequality is expected to be useful for the nontrivial computing of quantum channel capacities, particularly universal upper bounds on bosonic Gaussian quantum channels, and a Gaussian entanglement witness in the case of Gaussian amplifier via squeezing operations.
description: 7 pages, 1 figure, 1 table; Close to published version
title: Key concepts in the 3D-CT evaluation of Bipolar Bone Loss in Shoulder Dislocations
Creators: name: Pai, Vivek
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Musculoskeletal system
subject: Musculoskeletal bone
subject: CT
subject: Education
subject: Education and training
subject: Outcomes
subject: Patterns of Care

description: Learning objectives Background Imaging findings OR Procedure Details Conclusion References Personal Information
description: Learning objectives: The aim of this presentation is to discuss the stabilizers of the glenohumeral joint and to review the pathophysiology of shoulder dislocation. We also present the concepts of Bipolar Bone Loss and Glenoid Tracking in this poster with details of...
Functors of actions
Creators: name: Ntelis, P.
name: Kernphysik, A. Morris Universitat Bonn - Helmholtz-Institut fur Strahlen und
name: Bonn
name: Germany
Publisher: name: arXiv
Year: 2020
Subjects: subject: General Physics (physics.gen-ph)
subject: FOS: Physical sciences

description: In this document, we introduce a novel formalism for any field theory and apply it to the effective field theories of large-scale structure. The new formalism is based on functors of actions composing those theories. This new formalism predicts the actionic fields. We discuss our findings in a cosmological gravitology framework. We present these results with a cosmological inference approach and give guidelines on how we can choose the best candidate between those models with some latest understanding of model selection using Bayesian inference.
description: 18 pages, 3 Figures, Paper published. Comments are welcome! This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10701-022-00628-z
A Two-Sample Conditional Distribution Test Using Conformal Prediction and Weighted Rank Sum
Creators: name: Hu, Xiaoyu
name: Lei, Jing
Publisher: name: arXiv
Year: 2020
Subjects: subject: Methodology (stat.ME)
subject: FOS: Computer and information sciences

description: We consider the problem of testing the equality of conditional distributions of a response variable given a vector of covariates between two populations. Such a hypothesis testing problem can be motivated from various machine learning and statistical inference scenarios, including transfer learning and causal predictive inference. We develop a nonparametric test procedure inspired from the conformal prediction framework. The construction of our test statistic combines recent developments in conformal prediction with a novel choice of conformity score, resulting in a weighted rank-sum test statistic that is valid and powerful under general settings. To our knowledge, this is the first successful attempt of using conformal prediction for testing statistical hypotheses beyond exchangeability. Our method is suitable for modern machine learning scenarios where the data has high dimensionality and large sample sizes, and can be effectively combined with existing classification algorithms to find good conformity score functions. The performance of the proposed method is demonstrated in various numerical examples.
description: 46 pages, 2 figures, 7 tables; to appear in Journal of the American Statistical Association
title: Narrow down the Marrow
Creators: name: Pai, Vivek
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Musculoskeletal system
subject: Musculoskeletal bone
subject: Musculoskeletal spine
subject: MR
subject: Education
subject: Neoplasia
subject: Infection
subject: Metabolic disorders

description: Learning objectives Background Imaging findings OR Procedure Details Conclusion References Personal Information
description: Learning objectives: To provide a systematic approach to the marrow pathologies encountered in musculoskeletal MRI. . To provide critical information of MR sequences used as problem-solving tools in the diagnosis of marrow...