cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
title: T2 Hyperintense soft tissue lesions
Creators: name: Domingues Madaleno, Raquel Rocha
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Musculoskeletal soft tissue
subject: MR
subject: Education
subject: Neoplasia
subject: Education and training

description: Learning objectives Background Imaging findings OR Procedure Details Conclusion References Personal Information
description: Learning objectives: To describe the imaging findings of soft tissue tumors with high signal intensity (SI) on in T2-weighted images. To depict an easy diagnostic approach, based on...
On Possibility of Detection of Variable Sources Using the Data of "Cold" Surveys Carried Out on RATAN-600
Creators: name: Majorova, E. K.
name: Zhelenkova, O. P.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Astrophysics of Galaxies (astro-ph.GA)
subject: FOS: Physical sciences

description: In this study we attempt to assess the possibility of detection of variable sources using the data of the 7.6-cm wavelength surveys carried out on the RATAN-600 radio telescope in the period from 1980 through 1994. Objects selected according to certain criteria from the RCR catalog are used to construct the calibration curves and to estimate the accuracy of the resulting calibration curves and determine the r.m.s. errors for the measured source flux densities. To check the calibration sources for the presence of variable objects, quantitative estimates are performed for a number of parameters that characterize variability, in particular, for the long-term variability index V and the chi-square probability p. The long-term variability index was found to be positive for 14 out of approximately 80 calibration sources, possibly indicating that these sources are variable. The most likely candidate variables are the three sources with the chi-square probability p > 0.95. Five sources have chi-square probabilities in the 0.85 < p < 0.95 interval, and the remaining six in the 0.6 < p < 0.8 interval. Nine out of 14 objects are possibly variable in the optical range. The light curves and spectra are determined for possible variable sources and a number of "non-variable" objects. We plan to use the results of this study in our future searches for variable radio sources using the data of the "Cold" surveys.
Mechanistic Hierarchical Gaussian Processes
Creators: name: Baker, Brent A.
name: Pandalai, Sudha P.
name: Dunson, David B.
name: Herring, Amy H.
name: Wheeler, Matthew W.
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2014

description: The statistics literature on functional data analysis focuses primarily on flexible black-box approaches, which are designed to allow individual curves to have essentially any shape while characterizing variability. Such methods typically cannot incorporate mechanistic information, which is commonly expressed in terms of differential equations. Motivated by studies of muscle activation, we propose a nonparametric Bayesian approach that takes into account mechanistic understanding of muscle physiology. A novel class of hierarchical Gaussian processes is defined that favors curves consistent with differential equations defined on motor, damper, spring systems. A Gibbs sampler is proposed to sample from the posterior distribution and applied to a study of rats exposed to non-injurious muscle activation protocols. Although motivated by muscle force data, a parallel approach can be used to include mechanistic information in broad functional data analysis applications.
MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis
Creators: name: Cai, Hanshu
name: Gao, Yiwen
name: Sun, Shuting
name: Li, Na
name: Tian, Fuze
name: Xiao, Han
name: Li, Jianxiu
name: Yang, Zhengwu
name: Li, Xiaowei
name: Zhao, Qinglin
name: Liu, Zhenyu
name: Yao, Zhijun
name: Yang, Minqiang
name: Peng, Hong
name: Zhu, Jing
name: Zhang, Xiaowei
name: Gao, Guoping
name: Zheng, Fang
name: Li, Rui
name: Guo, Zhihua
name: Ma, Rong
name: Yang, Jing
name: Zhang, Lan
name: Hu, Xiping
name: Li, Yumin
name: Hu, Bin
Publisher: name: arXiv
Year: 2020
Subjects: subject: Digital Libraries (cs.DL)
subject: Machine Learning (cs.LG)
subject: Neurons and Cognition (q-bio.NC)
subject: FOS: Computer and information sciences
subject: FOS: Biological sciences

description: According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis.
Some Statistical Strategies for DAE-seq Data Analysis: Variable Selection and Modeling Dependencies Among Observations
Creators: name: Ibrahim, Joseph G.
name: Rashid, Naim
name: Sun, Wei
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2014

description: In DAE (DNA After Enrichment)-seq experiments, genomic regions related with certain biological processes are enriched/isolated by an assay and are then sequenced on a high-throughput sequencing platform to determine their genomic positions. Statistical analysis of DAE-seq data aims to detect genomic regions with significant aggregations of isolated DNA fragments (“enriched regions”) versus all the other regions (“background”). However, many confounding factors may influence DAE-seq signals. In addition, the signals in adjacent genomic regions may exhibit strong correlations, which invalidate the independence assumption employed by many existing methods. To mitigate these issues, we develop a novel Autoregressive Hidden Markov Model (AR-HMM) to account for covariates effects and violations of the independence assumption. We demonstrate that our AR-HMM leads to improved performance in identifying enriched regions in both simulated and real datasets, especially in those in epigenetic datasets with broader regions of DAE-seq signal enrichment. We also introduce a variable selection procedure in the context of the HMM/AR-HMM where the observations are not independent and the mean value of each state-specific emission distribution is modeled by some covariates. We study the theoretical properties of this variable selection procedure and demonstrate its efficacy in simulated and real DAE-seq data. In summary, we develop several practical approaches for DAE-seq data analysis that are also applicable to more general problems in statistics.
Spatially Varying Coefficient Model for Neuroimaging Data With Jump Discontinuities
Creators: name: Kong, Linglong
name: Zhu, Hongtu
name: Fan, Jianqing
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2014

description: Motivated by recent work on studying massive imaging data in various neuroimaging studies, we propose a novel spatially varying coefficient model (SVCM) to capture the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) with a set of covariates. Two stylized features of neuorimaging data are the presence of multiple piecewise smooth regions with unknown edges and jumps and substantial spatial correlations. To specifically account for these two features, SVCM includes a measurement model with multiple varying coefficient functions, a jumping surface model for each varying coefficient function, and a functional principal component model. We develop a three-stage estimation procedure to simultaneously estimate the varying coefficient functions and the spatial correlations. The estimation procedure includes a fast multiscale adaptive estimation and testing procedure to independently estimate each varying coefficient function, while preserving its edges among different piecewise-smooth regions. We systematically investigate the asymptotic properties (e.g., consistency and asymptotic normality) of the multiscale adaptive parameter estimates. We also establish the uniform convergence rate of the estimated spatial covariance function and its associated eigenvalues and eigenfunctions. Our Monte Carlo simulation and real data analysis have confirmed the excellent performance of SVCM.
title: A Prospective Evaluation of Quality of Life After Palliative MRgFUS Treatment of Bone Metastases
Creators: name: Bazzocchi, Alberto
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Outcomes
subject: Metastases
subject: Cancer
subject: Outcomes analysis
subject: Experimental investigations
subject: Ablation procedures
subject: MR
subject: Oncology
subject: Musculoskeletal system
subject: Musculoskeletal bone

description: Purpose Methods and Materials Results Conclusion References Personal Information
description: Purpose: Bone is a common location for metastases in advanced cancer. Patients with bone metastases frequently suffer from significant pain that has a negative impact on patients’ well-being. Magnetic resonance-guided focused ultrasound surgery (MRgFUS)...
Targeted Local Support Vector Machine for Age-Dependent Classification
Creators: name: Wang, Yuanjia
name: Zeng, Donglin
name: Chen, Huaihou
name: Chen, Tianle
name: Marder, Karen
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2014

description: We develop methods to accurately predict whether pre-symptomatic individuals are at risk of a disease based on their various marker profiles, which offers an opportunity for early intervention well before definitive clinical diagnosis. For many diseases, existing clinical literature may suggest the risk of disease varies with some markers of biological and etiological importance, for example age. To identify effective prediction rules using nonparametric decision functions, standard statistical learning approaches treat markers with clear biological importance (e.g., age) and other markers without prior knowledge on disease etiology interchangeably as input variables. Therefore, these approaches may be inadequate in singling out and preserving the effects from the biologically important variables, especially in the presence of potential noise markers. Using age as an example of a salient marker to receive special care in the analysis, we propose a local smoothing large margin classifier implemented with support vector machine (SVM) to construct effective age-dependent classification rules. The method adaptively adjusts age effect and separately tunes age and other markers to achieve optimal performance. We derive the asymptotic risk bound of the local smoothing SVM, and perform extensive simulation studies to compare with standard approaches. We apply the proposed method to two studies of premanifest Huntington’s disease (HD) subjects and controls to construct age-sensitive predictive scores for the risk of HD and risk of receiving HD diagnosis during the study period.
title: Is MRI better than CT in the diagnosis of occult wrist fractures?
Creators: name: Luff, Thomas
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Comparative studies
subject: MR
subject: Musculoskeletal bone
subject: Trauma

description: Purpose Methods and Materials Results Conclusion References Personal Information
description: Purpose: Over treating occult wrist injuries can have significant impact on the patient with wrist immobilisation leading to potential difficulties in carrying out daily activities and loss of work time with subsequent financial impact.Follow-up data...
A many-body perturbation theory approach to energy band alignment at the crystalline tetracene-silicon interface
Creators: name: Klymenko, M. V.
name: Tan, L. Z.
name: Russo, S. P.
name: Cole, J. H.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Materials Science (cond-mat.mtrl-sci)
subject: Computational Physics (physics.comp-ph)
subject: FOS: Physical sciences

description: Hybrid inorganic-organic semiconductor interfaces are of interest for new photovoltaic devices operating above the Shockley-Queisser limit. Predicting energy band alignment at the interfaces is crucial for their design, but represents a challenging problem due to the large scales of the system, the energy precision required and a wide range of physical phenomena that occur at the interface. To tackle this problem, we use many-body perturbation theory in the non-self-consistent GW approximation, orbital relaxation corrections for organic semiconductors, and line-up potential method for inorganic semiconductors which allows for tractable and accurate computing of energy band alignment in crystalline van-der-Waals hybrid inorganic-organic semiconductor interfaces. In this work, we study crystalline tetracene physisorbed on the clean hydrogen-passivated 1x2 reconstructed (100) silicon surface. Using this computational approach, we find that the energy band alignment is determined by an interplay of the mutual dynamic dielectric screening of two materials and the formation of a dipole layer due to a weak hybridization of atomic/molecular orbitals at the interface. We also emphasize the significant role of the exchange-correlation effects in predicting band offsets for the hybrid inorganic-organic semiconductor interfaces.
Additional file 1 of Photolysis of caged cytokinin in single cells of Arabidopsis thaliana
Creators: name: Dow, Lachlan
name: Barrow, Russell A.
name: White, Rosemary G.
name: Mathesius, Ulrike
Publisher: name: figshare
Year: 2022
Subjects: subject: Plant Biology
subject: FOS: Biological sciences

description: Additional file 1: Figure S1. Structure of caged benzyladenine, showing atom numbering system. Figure S2. 1H NMR spectrum of Caged benzyladenine in CDCl3. Figure S3. 13C NMR spectrum of caged benzyladenine in CDCl3. Figure S4. Low resolution (A) and high resolution (B) ESI mass spectra of caged benzyladenine. Figure S5. Infrared spectrum of caged benzyladenine, as powder. Figure S6. Structure of caged adenine, showing atom numbering system. Figure S7. 1H NMR spectrum of caged adenine, in d6-DMSO. Figure S8. 13C NMR of caged adenine, in d6-DMSO. Figure S9. Low resolution (A) and high resolution (B) ESI mass spectra of caged adenine. Figure S10. IR spectrum of caged adenine, as powder. Table S1: Assessment of CBA uncaging efficiencies in vitro. Figure S11. UPLC chromatograms of benzyladenines. Table S2. Effect of UV wavelength on uncaging efficiency. Figure S12. Emission spectra of the three different UV lights utilised in S 13. Figure S13. ImageJ analysis of fluorescence from images shown in Fig. 3. Figure S14. Uncaging in small regions using a compound microscope. Figure S15. Uncaging CBA in vivo inhibits root growth. Figure S16. Detection of caged-fluorescein uncaging using a 720 nm (multiphoton) laser. Figure S17. Further images of successful GFP response from tissue-specific uncaging of CBA.
title: The use of ultrasound in the diagnosis of fractures in children: A short cut review
Creators: name: Facal de Castro, Fernando
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Trauma
subject: Diagnostic procedure
subject: Ultrasound
subject: Musculoskeletal bone

description: Purpose Methods and Materials Results Conclusion References Personal Information
description: Purpose: Ultrasound is a common diagnostic tool for different pathologies in children population, there is still not enough evidence on its use in bone imaging and fracture detection. It´s well known that bone is a natural obstacle to the transmission of...
An integral equation model for warm and hot dense mixtures
Creators: name: Starrett, C. E.
name: Saumon, D.
name: Daligault, J.
name: Hamel, S.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Plasma Physics (physics.plasm-ph)
subject: FOS: Physical sciences

description: In Starrett and Saumon [Phys. Rev. E 87, 013104 (2013)] a model for the calculation of electronic and ionic structures of warm and hot dense matter was described and validated. In that model the electronic structure of one "atom" in a plasma is determined using a density functional theory based average-atom (AA) model, and the ionic structure is determined by coupling the AA model to integral equations governing the fluid structure. That model was for plasmas with one nuclear species only. Here we extend it to treat plasmas with many nuclear species, i.e. mixtures, and apply it to a carbon-hydrogen mixture relevant to inertial confinement fusion experiments. Comparison of the predicted electronic and ionic structures with orbital-free and Kohn-Sham molecular dynamics simulations reveals excellent agreement wherever chemical bonding is not significant.
description: 10 pages, 4 figures
Gaussian-Process-based Robot Learning from Demonstration
Creators: name: Arduengo, Miguel
name: Colomé, Adrià
name: Lobo-Prat, Joan
name: Sentis, Luis
name: Torras, Carme
Publisher: name: arXiv
Year: 2020
Subjects: subject: Robotics (cs.RO)
subject: FOS: Computer and information sciences

description: Endowed with higher levels of autonomy, robots are required to perform increasingly complex manipulation tasks. Learning from demonstration is arising as a promising paradigm for transferring skills to robots. It allows to implicitly learn task constraints from observing the motion executed by a human teacher, which can enable adaptive behavior. We present a novel Gaussian-Process-based learning from demonstration approach. This probabilistic representation allows to generalize over multiple demonstrations, and encode variability along the different phases of the task. In this paper, we address how Gaussian Processes can be used to effectively learn a policy from trajectories in task space. We also present a method to efficiently adapt the policy to fulfill new requirements, and to modulate the robot behavior as a function of task variability. This approach is illustrated through a real-world application using the TIAGo robot.
description: 8 pages, 10 figures
Projections of Orbital Measures and Quantum Marginal Problems
Creators: name: Collins, Benoît
name: McSwiggen, Colin
Publisher: name: arXiv
Year: 2021
Subjects: subject: Mathematical Physics (math-ph)
subject: Probability (math.PR)
subject: Representation Theory (math.RT)
subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences
subject: FOS: Mathematics
subject: 15B52 (Primary) 81P45, 05E10 (Secondary)

description: This paper studies projections of uniform random elements of (co)adjoint orbits of compact Lie groups. Such projections generalize several widely studied ensembles in random matrix theory, including the randomized Horn's problem, the randomized Schur's problem, and the orbital corners process. In this general setting, we prove integral formulae for the probability densities, establish some properties of the densities, and discuss connections to multiplicity problems in representation theory as well as to known results in the symplectic geometry literature. As applications, we show a number of results on marginal problems in quantum information theory and also prove an integral formula for restriction multiplicities.
description: 40 pages, 1 figure. This version: minor updates in response to reviewer comments
Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling
Creators: name: Zeng, Donglin
name: Lin, Dan-Yu
name: Boerwinkle, Eric
name: Franceschini, Nora
name: North, Kari E.
name: Tao, Ran
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and utilizes all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project.
title: The effect of operator experience on scanning time in musculoskeletal ultrasound assessment of the shoulder
Creators: name: Murphy, Jennifer
Publisher: name: European Society of Muscoskeletal Radiology
Year: 2018
Subjects: subject: Musculoskeletal system
subject: Ultrasound
subject: Technical aspects
subject: Economics

description: Purpose Methods and Materials Results Conclusion References Personal Information
description: Purpose: Ultrasound is the second most commonly performed examination in diagnostic imaging [1]. The demand for ultrasound imaging continues to rise with an average growth per year of 5.3% since 2003 [2]. As a consequence, there is increasing pressure on...
Variations on a Higgs theme
Creators: name: Moretti, S.
Publisher: name: arXiv
Year: 2014
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: FOS: Physical sciences

description: We show how the $Z$ boson can be generated in gluon-gluon fusion and yield two photons, via $gg\to Z\to γγ$, through massive fermion loops only, thereby contributing events to a candidate Higgs sample in the di-photon channel. A sub-leading contribution also exists from $q\bar q\to Z\toγγ$ events. Assuming the Standard Model, the corresponding event rates are negligible at the LHC stages of 7, 8 TeV, given the luminosities collected therein (about 5 and 20 fb$^{-1}$, respectively). Conversely, at 14 TeV, the first process become accessible for luminosities of order 300 fb$^{-1}$. Finally, we show how additional fermion states entering such loops, in production, in decay or in both cases, could affect the predictions in this channel by curiously mimicking Higgs signals.
description: 18 pages, 1 table, 4 figures
Discrimination of dephasing channels
Creators: name: Rexiti, Milajiguli
name: Memarzadeh, Laleh
name: Mancini, Stefano
Publisher: name: arXiv
Year: 2022
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: The problem of dephasing channel discrimination is addressed for finite-dimensional systems. In particular, the optimization with respect to input states without energy constraint is solved analytically for qubit, qutrit and ququart. Additionally, it is shown that resorting to side entanglement assisted strategy is completely useless in this case.
Competing for Attention in Social Media under Information Overload Conditions
Creators: name: Feng, Ling
name: Hu, Yanqing
name: Li, Baowen
name: Stanley, H. Eugene
name: Havlin, Shlomo
name: Braunstein, Lidia A.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Physics and Society (physics.soc-ph)
subject: Social and Information Networks (cs.SI)
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences

description: Although the many forms of modern social media have become major channels for the dissemination of information, they are becoming overloaded because of the rapidly-expanding number of information feeds. We analyze the expanding user-generated content in Sina Weibo, the largest micro-blog site in China, and find evidence that popular messages often follow a mechanism that differs from that found in the spread of disease, in contrast to common believe. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that in contrast to epidemics, for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her. Thus the greater the number of friends an individual has the greater the number of repeated contacts needed to spread the message, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold. This is in contrast to the zero threshold that conventional wisdom derives from disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received, leading to only a handful of information contents that can be widely spread throughout the population.
description: 11 pages, 5 figures
Analysis of the Proportional Hazards Model With Sparse Longitudinal Covariates
Creators: name: Zeng, Donglin
name: Fine, Jason P.
name: Cao, Hongyuan
name: Churpek, Mathew M.
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: Regression analysis of censored failure observations via the proportional hazards model permits time-varying covariates which are observed at death times. In practice, such longitudinal covariates are typically sparse and only measured at infrequent and irregularly spaced follow-up times. Full likelihood analyses of joint models for longitudinal and survival data impose stringent modelling assumptions which are difficult to verify in practice and which are complicated both inferentially and computationally. In this article, a simple kernel weighted score function is proposed with minimal assumptions. Two scenarios are considered: half kernel estimation in which observation ceases at the time of the event and full kernel estimation for data where observation may continue after the event, as with recurrent events data. It is established that these estimators are consistent and asymptotically normal. However, they converge at rates which are slower than the parametric rates which may be achieved with fully observed covariates, with the full kernel method achieving an optimal convergence rate which is superior to that of the half kernel method. Simulation results demonstrate that the large sample approximations are adequate for practical use and may yield improved performance relative to last value carried forward approach and joint modelling method. The analysis of the data from a cardiac arrest study demonstrates the utility of the proposed methods.
Fairness in penalty shootouts: Is it worth using dynamic sequences?
Creators: name: Csató, László
name: Petróczy, Dóra Gréta
Publisher: name: arXiv
Year: 2020
Subjects: subject: Computer Science and Game Theory (cs.GT)
subject: General Economics (econ.GN)
subject: FOS: Computer and information sciences
subject: FOS: Economics and business
subject: 60J20, 91A80

description: The sequence of moves in a dynamic team tournament may distort the ex-ante winning probabilities and harm efficiency. This paper compares seven soccer penalty shootout rules that determine the kicking order, from a theoretical perspective. Their fairness is evaluated in a reasonable model of First Mover Advantage. We also discuss the probability of reaching the sudden death stage. In the case of stationary scoring probabilities, dynamic mechanisms are not better than static rules. However, it is worth compensating the second-mover by making it the first-mover in the sudden death stage. Our work has the potential to impact decision-makers who can guarantee fairer outcomes in dynamic tournaments by a carefully chosen sequence of actions.
description: 23 pages, 3 figures, 4 tables
Duality relations for hypergeometric series
Creators: name: Beukers, Frits
name: Jouhet, Frédéric
Publisher: name: arXiv
Year: 2014
Subjects: subject: Number Theory (math.NT)
subject: Combinatorics (math.CO)
subject: FOS: Mathematics

description: We explicitly give the relations between the hypergeometric solutions of the general hypergeometric equation and their duals, as well as similar relations for q-hypergeometric equations. They form a family of very general identities for hypergeometric series. Although they were foreseen already by N. M. Bailey in the 1930's on analytic grounds, we give a purely algebraic treatment based on general principles in general differential and difference modules.
description: 16 pages
COVID-19 is linked to changes in the time-space dimension of human mobility
Creators: name: Santana, Clodomir
name: Botta, Federico
name: Barbosa, Hugo
name: Privitera, Filippo
name: Menezes, Ronaldo
name: Di Clemente, Riccardo
Publisher: name: arXiv
Year: 2022
Subjects: subject: Physics and Society (physics.soc-ph)
subject: Computers and Society (cs.CY)
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences

description: Socio-economic constructs and urban topology are crucial drivers of human mobility patterns. During the coronavirus disease 2019 pandemic, these patterns were reshaped in their components: the spatial dimension represented by the daily travelled distance, and the temporal dimension expressed as the synchronization time of commuting routines. Here, leveraging location-based data from de-identified mobile phone users, we observed that, during lockdowns restrictions, the decrease of spatial mobility is interwoven with the emergence of asynchronous mobility dynamics. The lifting of restriction in urban mobility allowed a faster recovery of the spatial dimension compared with the temporal one. Moreover, the recovery in mobility was different depending on urbanization levels and economic stratification. In rural and low-income areas, the spatial mobility dimension suffered a more considerable disruption when compared with urbanized and high-income areas. In contrast, the temporal dimension was more affected in urbanized and high-income areas than in rural and low-income areas.
description: 29 pages, 14 figures. Peer-revied version: Santana, C., Botta, F., Barbosa, H. et al. COVID-19 is linked to changes in the time-space dimension of human mobility. Nat Hum Behav (2023)
A New Rational Algorithm for View Updating in Relational Databases
Creators: name: Delhibabu, Radhakrishnan
name: Behrend, Andreas
Publisher: name: arXiv
Year: 2014
Subjects: subject: Artificial Intelligence (cs.AI)
subject: Databases (cs.DB)
subject: FOS: Computer and information sciences

description: The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In order to apply the rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented in this paper, along with the concept of a generalized revision algorithm for knowledge bases (Horn or Horn logic with stratified negation). We show that knowledge base dynamics has an interesting connection with kernel change via hitting set and abduction. In this paper, we show how techniques from disjunctive logic programming can be used for efficient (deductive) database updates. The key idea is to transform the given database together with the update request into a disjunctive (datalog) logic program and apply disjunctive techniques (such as minimal model reasoning) to solve the original update problem. The approach extends and integrates standard techniques for efficient query answering and integrity checking. The generation of a hitting set is carried out through a hyper tableaux calculus and magic set that is focused on the goal of minimality.
description: arXiv admin note: substantial text overlap with arXiv:1301.5154
Bayesian Inference for Multivariate Meta-Regression With a Partially Observed Within-Study Sample Covariance Matrix
Creators: name: Shah, Arvind K.
name: Kim, Sungduk
name: Lin, Jianxin
name: Yao, Hui
name: Ibrahim, Joseph G.
name: Chen, Ming-Hui
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: Multivariate meta-regression models are commonly used in settings where the response variable is naturally multi-dimensional. Such settings are common in cardiovascular and diabetes studies where the goal is to study cholesterol levels once a certain medication is given. In this setting, the natural multivariate endpoint is Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). In this paper, we examine study level (aggregate) multivariate meta-data from 26 Merck sponsored double-blind, randomized, active or placebo-controlled clinical trials on adult patients with primary hypercholesterolemia. Our goal is to develop a methodology for carrying out Bayesian inference for multivariate meta-regression models with study level data when the within-study sample covariance matrix S for the multivariate response data is partially observed. Specifically, the proposed methodology is based on postulating a multivariate random effects regression model with an unknown within-study covariance matrix Σ in which we treat the within-study sample correlations as missing data, the standard deviations of the within-study sample covariance matrix S are assumed observed, and given Σ, S follows a Wishart distribution. Thus, we treat the off-diagonal elements of S as missing data, and these missing elements are sampled from the appropriate full conditional distribution in a Markov chain Monte Carlo (MCMC) sampling scheme via a novel transformation based on partial correlations. We further propose several structures (models) for Σ, which allow for borrowing strength across different treatment arms and trials. The proposed methodology is assessed using simulated as well as real data, and the results are shown to be quite promising.
Energy correlations of non-integrable Ising models: The scaling limit in the cylinder
Creators: name: Antinucci, Giovanni
name: Giuliani, Alessandro
name: Greenblatt, Rafael Leon
Publisher: name: arXiv
Year: 2020
Subjects: subject: Mathematical Physics (math-ph)
subject: Statistical Mechanics (cond-mat.stat-mech)
subject: FOS: Physical sciences

description: We consider a class of non-integrable 2D Ising models, whose Hamiltonian, in addition to the nearest neighbor couplings, includes weak multi-spin interactions, even under spin flip. We study the model in cylindrical domains of arbitrary aspect ratio and prove that, in the scaling limit, the multipoint energy correlations converge to the same limiting correlations as those of the nearest-neighbor Ising model in the cylinder with renormalized couplings, up to an overall multiplicative constant, independent of the shape and size of the domain. The proof is based on a representation of the generating function of correlations in terms of a non-Gaussian Grassmann integral, and a constructive Renormalization Group (RG) analysis thereof. A key technical novelty compared with previous works is a systematic analysis of the effect of the boundary corrections to the RG flow, in particular a proof that the scaling dimension of boundary operators is better by one dimension than their bulk counterparts. A cancellation mechanism based on an approximate image rule for the fermionic Green's function is of crucial importance for controlling the RG flow of the marginal boundary terms.
description: 89 pages. Substantial revisions throughout the manuscript
Clustering High-Dimensional Landmark-Based Two-Dimensional Shape Data
Creators: name: Styner, Martin
name: Huang, Chao
name: Zhu, Hongtu
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: An important goal in image analysis is to cluster and recognize objects of interest according to the shapes of their boundaries. Clustering such objects faces at least four major challenges including a curved shape space, a high-dimensional feature space, a complex spatial correlation structure, and shape variation associated with some covariates (e.g., age or gender). The aim of this paper is to develop a penalized model-based clustering framework to cluster landmark-based planar shape data, while explicitly addressing these challenges. Specifically, a mixture of offset-normal shape factor analyzers (MOSFA) is proposed with mixing proportions defined through a regression model (e.g., logistic) and an offset-normal shape distribution in each component for data in the curved shape space. A latent factor analysis model is introduced to explicitly model the complex spatial correlation. A penalized likelihood approach with both adaptive pairwise fusion Lasso penalty function and L2 penalty function is used to automatically realize variable selection via thresholding and deliver a sparse solution. Our real data analysis has confirmed the excellent finite-sample performance of MOSFA in revealing meaningful clusters in the corpus callosum shape data obtained from the Attention Deficit Hyperactivity Disorder-200 (ADHD-200) study.
The Effect of Decoherence on the Contextual and Nonlocal Properties of a Biphoton
Creators: name: Shaham, A.
name: Eisenberg, H. S.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Quantum contextuality is a nonintuitive property of quantum mechanics, that distinguishes it from any classical theory. A complementary quantum property is quantum nonlocality, which is an essential resource for many quantum information tasks. Here we experimentally study the contextual and nonlocal properties of polarization biphotons. First, we investigate the ability of the biphotons to exhibit contextuality by testing the violation of the KCBS inequality. In order to do so, we used the original protocol suggested in the KCBS paper, and adjusted it to the real scenario, where some of the biphotons are distinguishable. Second, we transmitted the biphotons through different unital channels with controlled amount of noise. We measured the decohered output states, and demonstrated that the ability to exhibit quantum contextuality using the KCBS inequality is more fragile to noise than the ability to exhibit nonlocality.
description: Main text: 5 pages, 2 figures. Supplementary material: 1 page, 1 figure, 1 table
Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors
Creators: name: Berg, Ewout van den
name: Minev, Zlatko K.
name: Kandala, Abhinav
name: Temme, Kristan
Publisher: name: arXiv
Year: 2022
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Noise in pre-fault-tolerant quantum computers can result in biased estimates of physical observables. Accurate bias-free estimates can be obtained using probabilistic error cancellation (PEC), which is an error-mitigation technique that effectively inverts well-characterized noise channels. Learning correlated noise channels in large quantum circuits, however, has been a major challenge and has severely hampered experimental realizations. Our work presents a practical protocol for learning and inverting a sparse noise model that is able to capture correlated noise and scales to large quantum devices. These advances allow us to demonstrate PEC on a superconducting quantum processor with crosstalk errors, thereby providing an important milestone in opening the way to quantum computing with noise-free observables at larger circuit volumes.
Superconductivity at 3.7 K in Ternary Silicide Li2IrSi3
Creators: name: Hirai, Daigorou
name: Kawakami, Rui
name: Magdysyuk, Oxana V.
name: Dinnebier, Robert E.
name: Yaresko, Alexander
name: Takagi, Hidenori
Publisher: name: arXiv
Year: 2014
Subjects: subject: Superconductivity (cond-mat.supr-con)
subject: Materials Science (cond-mat.mtrl-sci)
subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: FOS: Physical sciences

description: We report the discovery of superconductivity at Tc = 3.7 K in the new ternary lithium silicide Li2IrSi3. The crystal structure of Li2IrSi3 consists of IrSi6 antiprisms connected by Si triangles, giving rise to a three dimensional framework of covalent Si-Si and Si-Ir bonds. Electronic specific-heat in superconducting phase suggests that Li2IrSi3 is a BCS weak-coupling superconductor.
description: 15 pages, 3 figures, 1 table
The tensor Harish-Chandra--Itzykson--Zuber integral II: detecting entanglement in large quantum systems
Creators: name: Collins, Benoît
name: Gurau, Razvan
name: Lionni, Luca
Publisher: name: arXiv
Year: 2022
Subjects: subject: Mathematical Physics (math-ph)
subject: High Energy Physics - Theory (hep-th)
subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: We consider the recently introduced generalization of the Harish-Chandra--Itzykson--Zuber integral to tensors and discuss its asymptotic behavior when the characteristic size N of the tensors is taken to be large. This study requires us to make assumptions on the scaling with N of the external tensors. We analyze a two-parameter class of asymptotic scaling ansätze, uncovering several non-trivial asymptotic regimes. This study is relevant for analyzing the entanglement properties of multipartite quantum systems. We discuss potential applications of our results to this domain, in particular in the context of randomized local measurements.
description: 44 pages, 23 figures
Additional file 1 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 1.
IsoDOT Detects Differential RNA-Isoform Expression/Usage With Respect to a Categorical or Continuous Covariate With High Sensitivity and Specificity
Creators: name: Zou, Fei
name: Zhabotynsky, Vasyl
name: Liu, Yufeng
name: Wu, Yichao
name: Zhou, Hua
name: Miller, Darla
name: Sullivan, Patrick F.
name: Huang, Shunping
name: Kuan, Pei-Fen
name: Li, Yuan
name: McMillan, Leonard
name: Shaw, Ginger
name: Chu, Haitao
name: Sun, Wei
name: Chen, Ting-Huei
name: De Villena, Fernando Pardo-Manuel
name: Crowley, James J.
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: We have developed a statistical method named IsoDOT to assess differential isoform expression (DIE) and differential isoform usage (DIU) using RNA-seq data. Here isoform usage refers to relative isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control. The latter task is not an uncommon situation in practice, e.g., comparing the paternal and maternal alleles of one individual or comparing tumor and normal samples of one cancer patient. Simulation studies demonstrate the high sensitivity and specificity of IsoDOT. We apply IsoDOT to study the effects of haloperidol treatment on the mouse transcriptome and identify a group of genes whose isoform usages respond to haloperidol treatment.
A note on Cauchy's formula
Creators: name: Jing, Naihuan
name: Li, Zhijun
Publisher: name: arXiv
Year: 2022
Subjects: subject: Combinatorics (math.CO)
subject: Quantum Algebra (math.QA)
subject: FOS: Mathematics

description: We use the correlation functions of vertex operators to give a proof of Cauchy's formula \begin{align*} \prod^K_{i=1}\prod^N_{j=1}(1-x_iy_j)=\sum_{μ\subseteq [K\times N]}(-1)^{|μ|}s_μ\{x\}s_{μ'}\{y\}. \end{align*} As an application of the interpretation, we obtain an expansion of $\prod^\infty_{i=1}(1-q^i)^{i-1}$ in terms of half plane partitions.
description: 13 pages, no figure. Final version
Helioseismology in a bottle: modal acoustic velocimetry
Creators: name: Triana, Santiago Andrés
name: Zimmerman, Daniel S.
name: Nataf, Henri-Claude
name: Thorette, Aurélien
name: Lekic, Vedran
name: Lathrop, Daniel P.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Fluid Dynamics (physics.flu-dyn)
subject: Solar and Stellar Astrophysics (astro-ph.SR)
subject: Popular Physics (physics.pop-ph)
subject: FOS: Physical sciences

description: Measurement of the differential rotation of the Sun's interior is one of the great achievements of helioseismology, providing important constraints for stellar physics. The technique relies on observing and analyzing rotationally-induced splittings of p-modes in the star. Here we demonstrate the first use of the technique in a laboratory setting. We apply it in a spherical cavity with a spinning central core (spherical-Couette flow) to determine the mean azimuthal velocity of the air filling the cavity. We excite a number of acoustic resonances (analogous to p-modes in the Sun) using a speaker and record the response with an array of small microphones on the outer sphere. Many observed acoustic modes show rotationally-induced splittings, which allow us to perform an inversion to determine the air's azimuthal velocity as a function of both radius and latitude. We validate the method by comparing the velocity field obtained through inversion against the velocity profile measured with a calibrated hot film anemometer. This 'modal acoustic velocimetry' technique has great potential for laboratory setups involving rotating fluids in axisymmetric cavities. It will be useful especially in liquid metals where direct optical methods are unsuitable and ultrasonic techniques very challenging at best.
description: Accepted for publication on The New Journal Of Physics