cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Optimal suppression of defect generation during a passage across a quantum critical point
Creators: name: Wu, Ning
name: Nanduri, Arun
name: Rabitz, Herschel
Publisher: name: arXiv
Year: 2014
Subjects: subject: Quantum Physics (quant-ph)
subject: Quantum Gases (cond-mat.quant-gas)
subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: FOS: Physical sciences

description: The dynamics of quantum phase transitions are inevitably accompanied by the formation of defects when crossing a quantum critical point. For a generic class of quantum critical systems, we solve the problem of minimizing the production of defects through the use of a gradient-based deterministic optimal control algorithm. By considering a finite size quantum Ising model with a tunable global transverse field, we show that an optimal power law quench of the transverse field across the Ising critical point works well at minimizing the number of defects, in spite of being drawn from a subset of quench profiles. These power law quenches are shown to be inherently robust against noise. The optimized defect density exhibits a transition at a critical ratio of the quench duration to the system size, which we argue coincides with the intrinsic speed limit for quantum evolution.
description: 5 pages, 4 figures. The first two authors contributed equally to this work
Assessment of renal pathophysiological processes and protective effect of quercetin on contrast-induced acute kidney injury in type 1 diabetic mice using diffusion tensor imaging
Creators: name: Wu, Ziqian
name: Hu, Jingyi
name: Li, Yanfei
name: Yao, Xiang
name: Ouyang, Siyu
name: Ren, Ke
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Biochemistry
subject: Microbiology
subject: FOS: Biological sciences
subject: Cell Biology
subject: Molecular Biology
subject: Physiology
subject: Chemical Sciences not elsewhere classified
subject: Immunology
subject: FOS: Clinical medicine
subject: Developmental Biology
subject: Marine Biology
subject: Science Policy

description: Purpose: To investigate the renal pathophysiological processes and protective effect of quercetin on contrast-induced acute kidney injury (CI–AKI) in mice with type 1 diabetic mellitus(DM) using diffusion tensor imaging(DTI). Methods: Mice with DM were divided into two groups. In the diabetic + contrast medium(DCA) group, the changes of the mice kidneys were monitored at 1, 24, 48, and 72 h after the injection of iodixanol(4gI/kg). The mice in the diabetic + contrast medium + quercetin(DCA + QE) group were orally given different concentrations of quercetin for seven days before injection of iodixanol. In vitro experiments, renal tubular epithelial (HK-2) cells exposed to high glucose conditions were treated with various quercetin concentrations before treatment with iodixanol(250 mgI/mL). Results: DTI-derived mean diffusivity(MD) and fractional anisotropy(FA) values can be used to evaluate CI-AKI effectively. Quercetin significantly increased the expression of Sirt 1 and reduced oxidative stress by increasing Nrf 2/HO-1/SOD1. The antiapoptotic effect of quercetin on CI-AKI was revealed by decreasing proteins level and by reducing the number of apoptosis-positive cells. In addition, flow cytometry indicated quercetin-mediated inhibition of M1 macrophage polarization in the CI-AKI. Conclusions: DTI will be an effective noninvasive tool in diagnosing CI-AKI. Quercetin attenuates CI-AKI on the basis of DM through anti-oxidative stress, apoptosis, and inflammation.
Introduction to session Preservation of Cultural Heritage Data: Challenges and opportunities during the life cycle of archaeological data
Creators: name: ASPÖCK, Edeltraud
name: GESER, Guntram
name: RICHARDS, Julian
Publisher: name: Zenodo
Year: 2022
Subjects: Archaeology
Re-use

description: Heritage actors have long been committed to digital transformation as a privileged mean for the long-term dissemination, conservation and valorisation of cultural heritage. Digitisation is also a key medium in shaping a more inclusive and sustainable society, especially in times marked by profound changes and crisis. The exponential amount and much varied nature of data produced in the context of archaeological interventions require the implementation of digital tools suitable to international scientific standards and procedures. ARIADNEplus (https://ariadne-infrastructure.eu) is a project funded by the European Commission to provide a digital infrastructure for archaeological datasets which enables sharing, discovery, and research across national and regional borders. The ARIADNEplus consortium comprises 41 partners from European and international national heritage authorities, archaeological research institutions and associations, data repository providers and technology partners. The project supports a more sustainable archaeological practice by promoting investment into long-term strategies for archaeological data preservation for the next generations with the help of technological solutions. The ARIADNEplus catalogue includes currently around 2 million records, comprising multiple archaeological data types and subdomains. The session invites papers to reflect on the development and application of standards during the archaeological data life cycle, with a focus on questions of interoperability and sustainability. What are the challenges during the several processing steps in a project from data creation to usage, long-term data storage and re-use? How do data record standards and vocabularies enhance access to datasets and what was the experience of ARIADNEplus data-providing partners and associated partners when aggregating their data in the project? How is it possible to use aggregated archaeological data for research across multiple providers and subdomains, and hence valorise individual datasets? In what ways do initiatives such as ARIADNE contribute to protect, research and valorise Cultural Heritage? Does the infrastructure lead to an increased sustainability of archaeological research practices? Which changes would be necessary to move closer to achieve these aims?
Homogenizing Entropy Across Different Environmental Conditions: A Universally Applicable Method for Transforming Continuous Variables
Creators: name: Peck, Joel R.
name: Waxman, David
Publisher: name: arXiv
Year: 2021
Subjects: subject: Information Theory (cs.IT)
subject: FOS: Computer and information sciences

description: In classical information theory, a causal relationship between two variables is typically modelled by assuming that, for every possible state of one of the variables, there exists a particular distribution of states of the second variable. Let us call these two variables the causal and caused variables, respectively. We shall assume that both variables are continuous and one-dimensional. In this work we consider a procedure to transform each variable, using transformations that are differentiable and strictly increasing. We call these increasing transformations. Any causal relationship (as defined here) is associated with a channel capacity, which is the maximum rate that information could be sent if the causal relationship was used as a signalling system. Channel capacity is unaffected when the two variables are changed by use of increasing transformations. For any causal relationship we show that there is always a way to transform the caused variable such that the entropy associated with the caused variable is independent of the value of the causal variable. Furthermore, the resulting universal entropy has an absolute value that is equal to the channel capacity associated with the causal relationship. This observation may be useful in statistical applications. Also, for any causal relationship, it implies that there is a 'natural' way to transform a continuous caused variable. We also show that, with additional constraints on the causal relationship, a natural increasing transformation of both variables leads to a transformed causal relationship that has properties that might be expected from a well-engineered measuring device.
description: 20 pages, 3 figures
What to do after immune-checkpoint inhibitors failure in advanced non-small cell lung cancer: an expert opinion and review
Creators: name: Agostara, Alberto Giuseppe
name: Roazzi, Laura
name: Villa, Federica
name: Romano’, Rebecca
name: Piscazzi, Daniele
name: Martinelli, Francesca
name: Ciarlo, Gabriele
name: Oresti, Sara
name: Travaglini, Francesca
name: Marando, Alessandro
name: Sartore-Bianchi, Andrea
name: Giannetta, Laura
name: Cerea, Giulio
name: Siena, Salvatore
name: Pizzutilo, Elio Gregory
name: Signorelli, Diego
Publisher: name: Taylor & Francis
Year: 2023
Subjects: Medicine
Pharmacology
Cancer
Hematology

description: Immune-checkpoint inhibitors (IO) have significantly improved outcomes of patients with non-oncogene-addicted non-small cell lung cancer (NSCLC), becoming the first-line agents for advanced disease. However, resistance remains a significant clinical challenge, limiting their effectiveness. Hereby, we addressed standard and innovative therapeutic approaches for NSCLC patients experiencing progression after IO treatment, discussing the emerging resistance mechanisms and the ongoing efforts to overcome them. In order to provide a complete overview of the matter, we performed a comprehensive literature search across prominent databases, including PubMed, EMBASE (Excerpta Medica dataBASE), and the Cochrane Library, and a research of the main ongoing studies on clinicaltrials.gov. The dynamics of progression to IO, especially in terms of time to treatment failure and burden of progressive disease, should guide the best subsequent management, together with patient clinical conditions. Long-responders to IO might benefit from continuation of IO beyond-progression, in combination with other treatments. Patients who experience early progression should be treated with salvage CT in case of preserved clinical conditions. Finally, patients who respond to IO for a considerable timeframe and who later present oligo-progression could be treated with a multimodal approach in order to maximize the benefit of immunotherapy.
PRIVAT Item Bank
Creators: name: Whaley, Paul
name: Hooijmans, Carlijn R.
Publisher: name: OSF Registries
Year: 2023
Subjects: subject: Medicine and Health Sciences
subject: Life Sciences
subject: study appraisal; in vitro research;

description: A method for literature-based discovery of assessment criteria for in vitro studies. An "item bank" of criteria that is the result of following the method
Bewältigungsstrategien von Lehramtsstudierenden (Bachelorstudium Lehramt Sekundarstufe) und deren Studien- und Berufszufriedenheit
Creators: name: Modl, Nicole
name: Krammer, Georg
Publisher: name: OSF Registries
Year: 2022
Subjects: subject: Teacher Education and Professional Development
subject: Education
subject: Berufszufriedenheit
subject: Bewältigungsstrategien
subject: Lehramt
subject: Studienzufriedenheit
subject: coping strategies
subject: job satisfaction
subject: study satisfaction
subject: teacher education

description: Diese Studie befasst sich mit den Bewältigungsstrategien von Studierenden des Bachelorstudiums Lehramt Sekundarstufe Allgemeinbildung. Angelehnt an die Studie von Eder (1987) wird erhoben, welche Strategien eingesetzt wurden, um das Studium positiv zu absolvieren. Weiters werden auch die Studienzufriedenheit und die Berufszufriedenheit der Proband:innen ermittelt, um der Frage nachgehen zu können, ob es einen signifkanten Zusammenhang zwischen den angewendeten Bewältigungsstrategien und der Studien- und Berufszufriedenheit gibt.
Engenheiro Metrologista - Ciclo de profissões
Creators: name: Santana, Márcio
Publisher: name: Zenodo
Year: 2023

description: Apresentação sobre a profissão de Engenheiro Metrologista no evento ciclo de Profissões no Colégio Albert Einstein, outubro de 2023. Presentation on the profession of Metrologist Engineer at the Professions cycle event at Colégio Albert Einstein, october, 2023.
Deep Learning Based Forecasting of Indian Summer Monsoon Rainfall
Creators: name: Kumar, Bipin
name: Abhishek, Namit
name: Chattopadhyay, Rajib
name: George, Sandeep
name: Singh, Bhupendra Bahadur
name: Samanta, Arya
name: Patnaik, B. S. V.
name: Gill, Sukhpal Singh
name: Nanjundiah, Ravi S.
name: Singh, Manmeet
Publisher: name: arXiv
Year: 2021
Subjects: subject: Atmospheric and Oceanic Physics (physics.ao-ph)
subject: FOS: Physical sciences

description: Accurate short range weather forecasting has significant implications for various sectors. Machine learning based approaches, e.g., deep learning, have gained popularity in this domain where the existing numerical weather prediction (NWP) models still have modest skill after a few days. Here we use a ConvLSTM network to develop a deep learning model for precipitation forecasting. The crux of the idea is to develop a forecasting model which involves convolution based feature selection and uses long term memory in the meteorological fields in conjunction with gradient based learning algorithm. Prior to using the input data, we explore various techniques to overcome dataset difficulties. We follow a strategic approach to deal with missing values and discuss the models fidelity to capture realistic precipitation. The model resolution used is (25 km). A comparison between 5 years of predicted data and corresponding observational records for 2 days lead time forecast show correlation coefficients of 0.67 and 0.42 for lead day 1 and 2 respectively. The patterns indicate higher correlation over the Western Ghats and Monsoon trough region (0.8 and 0.6 for lead day 1 and 2 respectively). Further, the model performance is evaluated based on skill scores, Mean Square Error, correlation coefficient and ROC curves. This study demonstrates that the adopted deep learning approach based only on a single precipitation variable, has a reasonable skill in the short range. Incorporating multivariable based deep learning has the potential to match or even better the short range precipitation forecasts based on the state of the art NWP models.
description: 14 pages, 14 figures. The manuscript is under review with journal 'Transactions on Geoscience and Remote Sensing'
Dephasing-assisted transport in linear triple quantum dots
Creators: name: Contreras-Pulido, L. D.
name: Bruderer, M.
name: Huelga, S. F.
name: Plenio, M. B.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Environmental noise usually hinders the efficiency of charge transport through coherent quantum systems; an exception is dephasing-assisted transport (DAT). We show that linear triple quantum dots in a transport configuration and subjected to pure dephasing exhibit DAT if the coupling to the drain reservoir exceeds a threshold. DAT occurs for arbitrarily weak dephasing and the enhancement can be directly controlled by the coupling to the drain. Moreover, for specific settings, the enhanced current is accompanied by a reduction in relative shot noise. We identify the quantum Zeno effect and long-distance tunnelling as underlying dynamical processes involved in dephasing-assisted and -suppressed transport. Our analytical results are obtained by using the density matrix formalism and the characteristic polynomial approach to full counting statistics.
description: To appear in New Journal of Physics, 20 pages, 5 figures
Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness
Creators: name: Green, Ben
Publisher: name: arXiv
Year: 2021
Subjects: subject: Computers and Society (cs.CY)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Efforts to promote equitable public policy with algorithms appear to be fundamentally constrained by the "impossibility of fairness" (an incompatibility between mathematical definitions of fairness). This technical limitation raises a central question about algorithmic fairness: How can computer scientists and policymakers support equitable policy reforms with algorithms? In this article, I argue that promoting justice with algorithms requires reforming the methodology of algorithmic fairness. First, I diagnose the problems of the current methodology for algorithmic fairness, which I call "formal algorithmic fairness." Because formal algorithmic fairness restricts analysis to isolated decision-making procedures, it leads to the impossibility of fairness and to models that exacerbate oppression despite appearing "fair." Second, I draw on theories of substantive equality from law and philosophy to propose an alternative methodology, which I call "substantive algorithmic fairness." Because substantive algorithmic fairness takes a more expansive scope of analysis, it enables an escape from the impossibility of fairness and provides a rigorous guide for alleviating injustice with algorithms. In sum, substantive algorithmic fairness presents a new direction for algorithmic fairness: away from formal mathematical models of "fair" decision-making and toward substantive evaluations of whether and how algorithms can promote justice in practice.
Explainable Deep Generative Models, Ancestral Fragments, and Murky Regions of the Protein Structure Universe
Creators: name: Draizen, Eli J.
name: Mura, Cameron
name: Bourne, Philip E.
Publisher: name: Zenodo
Year: 2022
Subjects: deep learning
protein structure
protein evolution

description: Modern proteins did not arise abruptly, as singular events, but rather over the course of at least 3.5 billion years of evolution. Can machine learning teach us how this occurred? The molecular evolutionary processes that yielded the intricate three-dimensional (3D) structures of proteins involve duplication, recombination and mutation of genetic elements, corresponding to short peptide fragments. Identifying and elucidating these ancestral fragments is crucial to deciphering the interrelationships amongst proteins, as well as how evolution acts upon protein sequences, structures & functions. Traditionally, structural fragments have been found using sequence and 3D structural alignment, but that becomes challenging when proteins have undergone extensive permutations-allowing two proteins to share a common architecture, though their topologies may drastically differ (a phenomenon termed the Urfold). We have designed a new framework to identify compact, potentially-discontinuous peptide fragments by combining (i) deep generative models of protein superfamilies with (ii) layer-wise relevance propagation (LRP) to identify atoms of great relevance in creating an embedding during an all superfamilies x all domains analysis. Our approach recapitulates known relationships amongst the evolutionarily ancient small beta-barrels (e.g. SH3 and OB folds) and P-loop-containing proteins (e.g. Rossmann and P-loop NTPases), previously established via manual analysis. Because of the generality of our deep model's approach, we anticipate that it can enable the discovery of new ancestral peptides. In a sense, our framework uses LRP as an `explainable AI' approach, in conjunction with a recent deep generative model of protein structure (termed DeepUrfold), in order to leverage decades worth of structural biology knowledge to decipher the underlying molecular bases for protein structural relationships-including those which are exceedingly remote, yet can be discovered via deep learning.
Topological superradiance in a degenerate Fermi gas
Creators: name: Pan, Jian-Song
name: Liu, Xiong-Jun
name: Zhang, Wei
name: Yi, Wei
name: Guo, Guang-Can
Publisher: name: arXiv
Year: 2014
Subjects: subject: Quantum Gases (cond-mat.quant-gas)
subject: FOS: Physical sciences

description: We predict the existence of a topological superradiant state in a two-component degenerate Fermi gas in a cavity. The superradiant light generation in the transversely driven cavity mode induces a cavity-assisted spin-orbit coupling in the system and opens a bulk gap at half-filling. This mechanism can simultaneously drive a topological phase transition in the system, yielding a topological superradiant phase. We map out the steady-state phase diagram of the system in the presence of an effective Zeeman field, and identify a critical quadracritical point beyond which the topological and the conventional superraidiant phase boundaries separate. We also propose to detect the topological phase transitions based on the unique signatures in the momentum-space density distribution.
description: 12 pages, 8 figures, latest version
Compressing Multisets with Large Alphabets using Bits-Back Coding
Creators: name: Severo, Daniel
name: Townsend, James
name: Khisti, Ashish
name: Makhzani, Alireza
name: Ullrich, Karen
Publisher: name: arXiv
Year: 2021
Subjects: subject: Information Theory (cs.IT)
subject: Machine Learning (cs.LG)
subject: Signal Processing (eess.SP)
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: Current methods which compress multisets at an optimal rate have computational complexity that scales linearly with alphabet size, making them too slow to be practical in many real-world settings. We show how to convert a compression algorithm for sequences into one for multisets, in exchange for an additional complexity term that is quasi-linear in sequence length. This allows us to compress multisets of exchangeable symbols at an optimal rate, with computational complexity decoupled from the alphabet size. The key insight is to avoid encoding the multiset directly, and instead compress a proxy sequence, using a technique called `bits-back coding'. We demonstrate the method experimentally on tasks which are intractable with previous optimal-rate methods: compression of multisets of images and JavaScript Object Notation (JSON) files. Code for our experiments is available at https://github.com/facebookresearch/multiset-compression.
A Preconditioned Hybrid SVD Method for Computing Accurately Singular Triplets of Large Matrices
Creators: name: Wu, Lingfei
name: Stathopoulos, Andreas
Publisher: name: arXiv
Year: 2014
Subjects: subject: Numerical Analysis (math.NA)
subject: Mathematical Software (cs.MS)
subject: FOS: Mathematics
subject: FOS: Computer and information sciences

description: The computation of a few singular triplets of large, sparse matrices is a challenging task, especially when the smallest magnitude singular values are needed in high accuracy. Most recent efforts try to address this problem through variations of the Lanczos bidiagonalization method, but they are still challenged even for medium matrix sizes due to the difficulty of the problem. We propose a novel SVD approach that can take advantage of preconditioning and of any well designed eigensolver to compute both largest and smallest singular triplets. Accuracy and efficiency is achieved through a hybrid, two-stage meta-method, PHSVDS. In the first stage, PHSVDS solves the normal equations up to the best achievable accuracy. If further accuracy is required, the method switches automatically to an eigenvalue problem with the augmented matrix. Thus it combines the advantages of the two stages, faster convergence and accuracy, respectively. For the augmented matrix, solving the interior eigenvalue is facilitated by a proper use of the good initial guesses from the first stage and an efficient implementation of the refined projection method. We also discuss how to precondition PHSVDS and to cope with some issues that arise. Numerical experiments illustrate the efficiency and robustness of the method.
description: 24 pages, 20 figures, and 8 tables. Accepted to SIAM Journal on Scientific Computing
Effect of Different Learning Environments on Students Who Game the System
Creators: name: Sales, Adam
name: Vanacore, Kirk
name: Gurung, Ashish
Publisher: name: OSF Registries
Year: 2023
Subjects: subject: Educational Psychology
subject: Science and Mathematics Education
subject: Education

description: Gaming the system – attempting to progress through a program without engaging in the necessary learning behaviors to benefit from the program – is a persistent problem within computer based learning platforms (Baker et al., 2008). Disengaged behavior amongst learners working on CBLPs is often referenced as gaming the system. Baker et al. (2008) define gaming behavior as "attempting to succeed in an educational environment by exploiting properties of the system rather than by learning the material and trying to use that knowledge to answer correctly.” Unsurprisingly the behavior is specifically associated with poorer learning (Pardos et al., 2014). Much effort has been exerterted to identify instances when students are engaging in gaming the system behaviors (Baker et al., 2006; Paquette, 2014; Paquette & Baker, 2019). The current study uses outputs of detention models to understand whether students who have a high propensity to game the system in a traditional computer based learning platform (i.e., problem sets with immediate hints and feedback) would benefit from other learning environments, such as problem sets with immediate hints and feedback or gamified learning environments.
Sign Structure, Electron Fractionalization, and Emergent Gauge Description of the Hubbard Model
Creators: name: Zhang, Long
name: Weng, Zheng-Yu
Publisher: name: arXiv
Year: 2014
Subjects: subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: FOS: Physical sciences

description: The Fermi sign structure plays a crucial role for a Landau's Fermi liquid. In this work, we identify the exact sign structure for the bipartite Hubbard model at an arbitrary strength of the on-site Coulomb repulsion $U$. This general sign structure naturally reproduces the conventional fermion signs in the small $U$ limit, and the phase string signs of the $t$-$J$ model in the large $U$ limit. We focus on the half-filling case as an example to illustrate why such a generic sign structure is important to understand the transition from the weakly correlated Fermi liquid regime to the strongly correlated Mott regime. In particular, we show that an electron fractionalization scheme with emergent partons and mutual Chern-Simons gauge fields provides a suitable framework to accurately handle the singular sign structure in two dimensions. A ground state ansatz at half-filling with incorporating the sign structure and the specific electron fractionalization is also proposed.
description: 8 pages main text + 4 pages appendices, 2 figures; v2: accepted by Phys. Rev. B
title: Machine learning for economics research: when, what and how
Creators: name: Desai, Ajit
Publisher: name: Bank of Canada
Year: 2023

description: This article reviews selected papers that use machine learning for economics research and policy analysis. Our review highlights when machine learning is used in economics, the commonly preferred models and how those models are used.
description: Nous passons en revue une sélection d’études tirant parti de l’apprentissage automatique à des fins de recherche économique et d’analyse de politiques. Notre examen fait ressortir les circonstances où l’apprentissage automatique est utilisé en économie, les modèles préférés en général, et la façon dont ils sont utilisés.
title: MACHINE LEARNING MODEL FOR EDUCATION LEVELLING IN MULTICULTURAL COUNTRIES USING UAE AS A CASE STUDY
Creators: name: Ghareeb, S
Publisher: name: Liverpool John Moores University
Year: 2024

description: This research proposed and developed a computational framework for such scenarios using Machine Learning (ML) techniques to help predict the most suitable levels for students when transferring between curricula, assigning these levels automatically, and holding students' data throughout their academic journey. Students' datasets were collected from their educational records for two consecutive academic years to fulfil this goal, and then pre-processing techniques were applied to the raw dataset. The research focused on how machine learning can predict students' levels using several models including Artificial Neural Network and Random Forest, alongside assembled classifiers. Extensive simulation results indicated that the Levenberg-Marquardt Neural Network method (LEVNN) has the best average results among the other applied methods. A user-friendly platform has been designed based on a web-based student management system to bring both perspectives together in one platform for schools and parents. The research would help education providers predict students' correct levels more efficiently without regular examinations, saving time and cost for schools, students, and parents.
An overview of mixing augmentation methods and augmentation strategies
Creators: name: Lewy, Dominik
name: Mańdziuk, Jacek
Publisher: name: arXiv
Year: 2021
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: Deep Convolutional Neural Networks have made an incredible progress in many Computer Vision tasks. This progress, however, often relies on the availability of large amounts of the training data, required to prevent over-fitting, which in many domains entails significant cost of manual data labeling. An alternative approach is application of data augmentation (DA) techniques that aim at model regularization by creating additional observations from the available ones. This survey focuses on two DA research streams: image mixing and automated selection of augmentation strategies. First, the presented methods are briefly described, and then qualitatively compared with respect to their key characteristics. Various quantitative comparisons are also included based on the results reported in recent DA literature. This review mainly covers the methods published in the materials of top-tier conferences and in leading journals in the years 2017-2021.
The $γ^* γ^*$ total cross section in next-to-leading order BFKL and LEP2 data
Creators: name: Ivanov, Dmitry Yu.
name: Murdaca, Beatrice
name: Papa, Alessandro
Publisher: name: arXiv
Year: 2014
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: FOS: Physical sciences

description: We study the total cross section for the collision of two highly-virtual photons at large energies, taking into account the BFKL resummation of energy logarithms with full next-to-leading accuracy. A necessary ingredient of the calculation, the next-to-leading order impact factor for the photon to photon transition, has been calculated by Balitsky and Chirilli using an approach based on the operator expansion in Wilson lines. We extracted the result for the photon impact factor in the original BFKL calculation scheme comparing the expression for the photon-photon total cross section obtained in BFKL with the one recently derived by Chirilli and Kovchegov in the Wilson-line operator expansion scheme. We perform a detailed numerical analysis, combining different, but equivalent in next-to-leading accuracy, representations of the cross section with various optimization methods of the perturbative series. We compare our results with previous determinations in the literature and with the LEP2 experimental data. We find that the account of Balitsky and Chirilli expression for the photon impact factor reduces the BFKL contribution to the cross section to very small values, making it impossible to describe LEP2 data as the sum of BFKL and leading-order QED quark box contributions.
description: 20 pages, 8 figures; two sentences and some references added, a few typos removed; version to be published on JHEP
Power Allocation for Uplink Communications of Massive Cellular-Connected UAVs
Creators: name: Cai, Xuesong
name: Kovács, István Z.
name: Wigard, Jeroen
name: Amorim, Rafhael
name: Tufvesson, Fredrik
name: Mogensen, Preben E.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Information Theory (cs.IT)
subject: Signal Processing (eess.SP)
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: Cellular-connected unmanned aerial vehicle (UAV) has attracted a surge of research interest in both academia and industry. To support aerial user equipment (UEs) in the existing cellular networks, one promising approach is to assign a portion of the system bandwidth exclusively to the UAV-UEs. This is especially favorable for use cases where a large number of UAV-UEs are exploited, e.g., for package delivery close to a warehouse. Although the nearly line-of-sight (LoS) channels can result in higher powers received, UAVs can in turn cause severe interference to each other in the same frequency band. In this contribution, we focus on the uplink communications of massive cellular-connected UAVs. Different power allocation algorithms are proposed to either maximize the minimal spectrum efficiency (SE) or maximize the overall SE to cope with severe interference based on the successive convex approximation (SCA) principle. One of the challenges is that a UAV can affect a large area meaning that many more UAV-UEs must be considered in the optimization problem, which is essentially different from that for terrestrial UEs. The necessity of single-carrier uplink transmission further complicates the problem. Nevertheless, we find that the special property of large coherent bandwidths and coherent times of the propagation channels can be leveraged. The performances of the proposed algorithms are evaluated via extensive simulations in the full-buffer transmission mode and bursty-traffic mode. Results show that the proposed algorithms can effectively enhance the uplink SEs. This work can be considered the first attempt to deal with the interference among massive cellular-connected UAV-UEs with optimized power allocations.
description: The final version can be found in IEEE Transactions on Vehicular Technology
Marine Ecosystem Credits: Advanced credit class design to scale ocean conservation finance
Creators: name: Filippi, Margaux
name: Wainstein, Martin
name: Shah, Ando
name: Constant, Axel
name: Fonseca, Maureen
name: Durkin, Louisa
Publisher: name: Zenodo
Year: 2022
Subjects: biodiversity crediting
ocean conservation
marine biodiversity
bbnj
marine protected areas

description: Ocean ecosystems are severely threatened by human activity, yet humanity directly depends on their health. A global goal has been set to protect 30% of ocean ecosystems by the year 2030, but the practice of ocean conservation does not have the funding mechanisms to support the scale of this effort. Marine Protected Areas, for example, are often legally established but governments lack the resources and incentives to provide robust surveillance and enforcement of these pristine environments. We present here the framework for a digitally native class of marine ecosystem credits, designed primarily as financial instruments to support funding at scale the protection and restoration of ocean ecosystems. The credit mechanism is conceived with principles of scientific integrity, social equity, and scalability. They are designed for fully digital implementations, considering advanced monitoring, reporting and verification practices, tokenization, and the use of integrated models for independent claim verifications. The initial types of credits covered by this framework include marine biodiversity, eutrophication, marine plastics, and blue carbon. The ecological crediting system proposed here is also developed with the potential to support nature-backed currencies, sovereign instruments that can further incorporate natural capital into the global economic system.
description: Website: https://www.openearth.org/projects/ocean-program
CFHTLenS: A Weak Lensing Shear Analysis of the 3D-Matched-Filter Galaxy Clusters
Creators: name: Ford, Jes
name: Van Waerbeke, Ludovic
name: Milkeraitis, Martha
name: Laigle, Clotilde
name: Hildebrandt, Hendrik
name: Erben, Thomas
name: Heymans, Catherine
name: Hoekstra, Henk
name: Kitching, Thomas
name: Mellier, Yannick
name: Miller, Lance
name: Choi, Ami
name: Coupon, Jean
name: Fu, Liping
name: Hudson, Michael J.
name: Kuijken, Konrad
name: Robertson, Naomi
name: Rowe, Barnaby
name: Schrabback, Tim
name: Velander, Malin
Publisher: name: arXiv
Year: 2014
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences

description: We present the cluster mass-richness scaling relation calibrated by a weak lensing analysis of >18000 galaxy cluster candidates in the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). Detected using the 3D-Matched-Filter cluster-finder of Milkeraitis et al., these cluster candidates span a wide range of masses, from the small group scale up to $\sim10^{15} M_{\odot}$, and redshifts 0.2 $\lesssim z\lesssim$ 0.9. The total significance of the stacked shear measurement amounts to 54$σ$. We compare cluster masses determined using weak lensing shear and magnification, finding the measurements in individual richness bins to yield 1$σ$ compatibility, but with magnification estimates biased low. This first direct mass comparison yields important insights for improving the systematics handling of future lensing magnification work. In addition, we confirm analyses that suggest cluster miscentring has an important effect on the observed 3D-MF halo profiles, and we quantify this by fitting for projected cluster centroid offsets, which are typically $\sim$ 0.4 arcmin. We bin the cluster candidates as a function of redshift, finding similar cluster masses and richness across the full range up to $z \sim$ 0.9. We measure the 3D-MF mass-richness scaling relation $M_{200} = M_0 (N_{200} / 20)^β$. We find a normalization $M_0 \sim (2.7^{+0.5}_{-0.4}) \times 10^{13} M_{\odot}$, and a logarithmic slope of $β\sim 1.4 \pm 0.1$, both of which are in 1$σ$ agreement with results from the magnification analysis. We find no evidence for a redshift-dependence of the normalization. The CFHTLenS 3D-MF cluster catalogue is now available at cfhtlens.org.
description: 3D-MF cluster catalog is NOW AVAILABLE at cfhtlens.org. Magnification-shear mass comparison in Figure 10. 19 pages, 10 figures. Accepted to MNRAS
Ein Wolfram-Szintillator-Kalorimeter zur Messung der Elektronenpolarisation bei HERA
Creators: name: Goetschel, Helene
Publisher: name: Deutsches Elektronen-Synchrotron, DESY, Hamburg
Year: 1990
Subjects: thesis
electron p: storage ring
electron: polarized beam
polarized beam: electron
polarization: measurement
measurement: polarization
radiation: laser
Compton scattering
calorimeter: spatial resolution
scintillation counter: sandwich
tungsten
calibration
DESY HERA Stor

description: Universität Hamburg, Diplomarbeit, 1990; 45 pp. (1990). = Universität Hamburg, Diplomarbeit, 1990
The Proton Radius from Bayesian Inference
Creators: name: Graczyk, Krzysztof M.
name: Juszczak, Cezary
Publisher: name: arXiv
Year: 2014
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: Nuclear Experiment (nucl-ex)
subject: Nuclear Theory (nucl-th)
subject: FOS: Physical sciences

description: The methods of Bayesian statistics are used to extract the value of the proton radius from the elastic $ep$ scattering data in a model independent way. To achieve that goal a large number of parametrizations (equivalent to neural network schemes) are considered and ranked by their conditional probability $P(\mathrm{parametrization}\,|\,\mathrm{data})$ instead of using the minimal error criterion. As a result the most probable proton radii values ($r_E^p=0.899\pm 0.003$ fm, $r_M^p=0.879\pm 0.007$ fm) are obtained and systematic error due to freedom in the choice of parametrization is estimated. Correcting the data for the two photon exchange effect turns out to be important to obtain the agreement between the $r_E^p$ and $r_M^p$ values. The results disagree with recent muonic atom measurements.
description: Two sentences are added in order to clarify that TPE corrections which are subtracted from the data were obtained from theoretical calculations (from box diagrams)
A Global Model of The Light Curves and Expansion Velocities of Type II-Plateau Supernovae
Creators: name: Pejcha, Ondrej
name: Prieto, Jose L.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Solar and Stellar Astrophysics (astro-ph.SR)
subject: High Energy Astrophysical Phenomena (astro-ph.HE)
subject: FOS: Physical sciences

description: We present a new self-consistent and versatile method that derives photospheric radius and temperature variations of Type II-Plateau supernovae based on their expansion velocities and photometric measurements. We apply the method to a sample of 26 well-observed, nearby supernovae with published light curves and velocities. We simultaneously fit ~230 velocity and ~6800 magnitude measurements distributed over 21 photometric passbands spanning wavelengths from 0.19 to 2.2 microns. The light curve differences among the Type II-Plateau supernovae are well-modeled by assuming different rates of photospheric radius expansion, which we explain as different density profiles of the ejecta and we argue that steeper density profiles result in flatter plateaus, if everything else remains unchanged. The steep luminosity decline of Type II-Linear supernovae is due to fast evolution of the photospheric temperature, which we verify with a successful fit of SN1980K. Eliminating the need for theoretical supernova atmosphere models, we obtain self-consistent relative distances, reddenings, and nickel masses fully accounting for all internal model uncertainties and covariances. We use our global fit to estimate the time evolution of any missing band tailored specifically for each supernova and we construct spectral energy distributions and bolometric light curves. We produce bolometric corrections for all filter combinations in our sample. We compare our model to the theoretical dilution factors and find good agreement for the B and V filters. Our results differ from the theory when the I, J, H, or K bands are included. We investigate the reddening law towards our supernovae and find reasonable agreement with standard R_V ~ 3.1 reddening law in UBVRI bands. Results for other bands are inconclusive. We make our fitting code publicly available.
description: 23 pages, 18 figures, 8 tables. Accepted to ApJ. Our results and the fitting tool are available at http://www.astro.princeton.edu/~pejcha/iip/
A strawberry harvest-aiding system with crop-transport co-robots: Design, development, and field evaluation
Creators: name: Peng, Chen
name: Vougioukas, Stavros
name: Slaughter, David
name: Fei, Zhenghao
name: Arikapudi, Rajkishan
Publisher: name: arXiv
Year: 2021
Subjects: subject: Robotics (cs.RO)
subject: Systems and Control (eess.SY)
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: Mechanizing the manual harvesting of fresh market fruits constitutes one of the biggest challenges to the sustainability of the fruit industry. During manual harvesting of some fresh-market crops like strawberries and table grapes, pickers spend significant amounts of time walking to carry full trays to a collection station at the edge of the field. A step toward increasing harvest automation for such crops is to deploy harvest-aid collaborative robots (co-bots) that transport the empty and full trays, thus increasing harvest efficiency by reducing pickers' non-productive walking times. This work presents the development of a co-robotic harvest-aid system and its evaluation during commercial strawberry harvesting. At the heart of the system lies a predictive stochastic scheduling algorithm that minimizes the expected non-picking time, thus maximizing the harvest efficiency. During the evaluation experiments, the co-robots improved the mean harvesting efficiency by around 10% and reduced the mean non-productive time by 60%, when the robot-to-picker ratio was 1:3. The concepts developed in this work can be applied to robotic harvest-aids for other manually harvested crops that involve walking for crop transportation.
The Strong Sensitivity of the Characteristics of Binary Stochastic Neurons Employing Low Barrier Nanomagnets to Small Geometrical Variations
Creators: name: Rahman, Rahnuma
name: Bandyopadhyay, Supriyo
Publisher: name: arXiv
Year: 2021
Subjects: subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: Applied Physics (physics.app-ph)
subject: FOS: Physical sciences

description: Binary stochastic neurons (BSNs) are excellent activators for machine learning. An ideal platform for implementing them are low- or zero-energy-barrier nanomagnets (LBMs) possessing in-plane anisotropy (e.g. circular or slightly elliptical disks) whose fluctuating magnetization encodes a probabilistic (p-) bit. Here, we show that such a BSN's activation function, the pinning current (which pins the output to a particular binary state), and the response time - all exhibit strong sensitivity to very slight geometric variations in the LBM's cross section. A mere 1% change in the diameter of a circular nanomagnet in any arbitrary direction can alter the response time by a factor of ~4 at room temperature and a 10% change can alter the pinning current by a factor of ~2. All this causes large device-to-device variation which is detrimental to integration. We also show that the energy dissipation is lowered but the response time is increased by replacing a circular cross-section with a slightly elliptical one and then encoding the p-bit in the magnetization component along the major axis. Encoding the p-bit in the magnetization component along the minor axis has the opposite effect. The energy-delay-product, however, is relatively independent of whether the cross-section is a circle or an ellipse and which magnetization component encodes the p-bit in the case of the ellipse.
description: This is the accepted version of an article in IEEE Transactions on Nanotechnology
Das Kalorimeter des HERA-Polarimeters: Installation, Kalibrierung, erste Messungen
Creators: name: Kaiser, Ralf
Publisher: name: Deutsches Elektronen-Synchrotron, DESY, Hamburg
Year: 1992
Subjects: thesis
electron: storage ring
electron: polarized beam
polarized beam: electron
polarization: transverse
transverse: polarization
polarization: measurement
measurement: polarization
resonance: depolarization
depolarization: resonance
radiation: laser
Compton scattering: backscatter
backscatter: Compton scattering
calorimeter
tungsten
scintillation counter: plastics
calibration
radiation: background
DESY HERA Stor

description: Westfälische Wilhelms-Universität Münster, Diplomarbeit, 1992; 105 pp. (1992). = Westfälische Wilhelms-Universität Münster, Diplomarbeit, 1992
Defect-induced magnetism in graphite through neutron irradiation
Creators: name: Wang, Yutian
name: Pochet, Pascal
name: Jenkins, Catherine A.
name: Arenholz, Elke
name: Bukalis, Gregor
name: Gemming, Sibylle
name: Helm, Manfred
name: Zhou, Shengqiang
Publisher: name: arXiv
Year: 2014
Subjects: subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: We have investigated the variation in the magnetization of highly ordered pyrolytic graphite (HOPG) after neutron irradiation, which introduces defects in the bulk sample and consequently gives rise to a large magnetic signal. We observe strong paramagnetism in HOPG, increasing with the neutron fluence. We correlate the induced paramagnetism with structural defects by comparison with density-functional theory calculations. In addition to the in-plane vacancies, the trans-planar defects also contribute to the magnetization. The lack of any magnetic order between the local moments is possibly due to the absence of hydrogen/nitrogen chemisorption, or the magnetic order cannot be established at all in the bulk form.
description: 10 pages, 8 figures