bluuebunny/crossref_metadata_2025_split huggingface dataset:
Health technology assessment to employ COVID-19 serological tests as companion diagnostics in the vaccination campaign against SARS-CoV-2
Authors: Tomaiuolo Rossella , Restelli Umberto , Faggiano Francesco Cosimo , Di Resta Chiara , Al Bitar Nehme Sami , Giuliani Francesco , Derrico Pietro , Ricciardi Walter , Banfi Giuseppe , Ritrovato Matteo 
Published: June 2022

Abstract

Objectives
In scenarios of vaccine scarcity or contexts of organizational complexity, it is necessary to define prioritization strategies for allocating vaccine doses in compliance with the criterion of equity and efficiency of health resources. In this context, the COVIDIAGNOSTIX project, based on the health technology assessment (HTA), assessed the role of SARS-CoV-2 serological tests as a companion diagnostic in the definition of the vaccination strategies for the vaccine administration. To guarantee evidence support for health policy choices, two different vaccine strategies were analyzed, one based on administering the vaccine booster dose to the entire population (VACCINE strategy) and the other based on allocation criteria (TEST&VACCINE strategy).

Methods
The decision-oriented HTA (DoHTA) method, integrated with specific modeling and simulation techniques, helped define the perimeter to make health policy choices.

Results
The processing of the scores attributed to the key performance indicators concerning all the evaluation domains shows a performance of 94.34% for the TEST&VACCINE strategy and 83.87% for the VACCINE strategy.

Conclusions
TEST&VACCINE strategy can be the most advantageous in various scenarios due to greater speed from an operational and an economic point of view. The assessment schemes defined by COVIDIAGNOSTIX (i.e., technologies/intended use/settings) can easily and quickly be exported and adapted to respond to similar health “policy questions”.
Repository of intra- and inter-run variations of quantitative autoantibody assays: a European multicenter study
Authors: Dragon-Durey Marie-Agnès , Bizzaro Nicola , Senant Marie , Andreeva Hristina , Bogdanos Dimitrios P. , Bonroy Carolien , Bossuyt Xavier , Eriksson Catharina , Fabien Nicole , Heijnen Ingmar , Herold Manfred , Musset Lucile , Kuhi Liisa , Lopez-Hoyos Marcos , Berki Tímea , Roozendaal Caroline , Sack Ulrich , Sundic Tatjana , Taylor Lorna , Kuna Andrea Tesija , Damoiseaux Jan 
Published: June 2022

Abstract

Objectives
No reference data are available on repositories to measure precision of autoantibody assays. The scope of this study was to document inter- and intra-run variations of quantitative autoantibody assays based on a real-world large international data set.

Methods
Members of the European Autoimmunity Standardisation Initiative (EASI) group collected the data of intra- and inter-run variability obtained with assays quantifying 15 different autoantibodies in voluntary participating laboratories from their country. We analyzed the impact on the assay performances of the type of immunoassay, the number of measurements used to calculate the coefficient of variation (CVs), the nature and the autoantibody level of the internal quality control (IQC).

Results
Data were obtained from 64 laboratories from 15 European countries between February and October 2021. We analyzed 686 and 1,331 values of intra- and inter-run CVs, respectively. Both CVs were significantly dependent on: the method of immunoassay, the level of IQC with higher imprecision observed when the antibody levels were lower than 2-fold the threshold for positivity, and the nature of the IQC with commercial IQCs having lower CVs than patients-derived IQCs. Our analyses also show that the type of autoantibody has low impact on the assay’ performances and that 15 measurements are sufficient to establish reliable intra- and inter-run variations.

Conclusions
This study provides for the first time an international repository yielding values of intra- and inter-run variation for quantitative autoantibody assays. These data could be useful for ISO 15189 accreditation requirements and will allow clinical diagnostic laboratories to assure quality of patient results.
Linguistic synesthesia is metaphorical: a lexical-conceptual account
Authors: Zhao Qingqing , Ahrens Kathleen , Huang Chu-Ren 
Published: June 2022

AbstractThis study seeks to clarify the nature of linguistic synesthesia using a lexical-conceptual account. Based on a lexical analysis of Mandarin synesthetic usages, we find that (1) linguistic synesthesia maps the metaphorical meaning between two domains; and (2) linguistic synesthetic mappings and conceptual metaphoric mappings have similar behaviors when sense modalities are treated as conceptual domains that contain a set of mappings constrained by Mapping Principles. This lexical-conceptual account is designed to capture the fact that linguistic synesthesia involves mapping between lexicalized concepts of sensory properties, instead of the real-time sensory input that is processed in neurological synesthesia. The incorporation of a lexical semantic view with the framework of Conceptual Metaphor Theory not only offers a coherent and comprehensive account for the nature of linguistic synesthesia, but also handles aspects of linguistic synesthesia previously only accounted for by non-metaphorical accounts. These design features make this proposal the most comprehensive account to fit the current data. Furthermore, by showing linguistic synesthesia as a type of metaphor, our study strengthens the role of conceptual metaphors as the link between the perceived world and our conceptualization of that world.
Super-resolution optical microscopy using cylindrical vector beams
Authors: Liu Min , Lei Yunze , Yu Lan , Fang Xiang , Ma Ying , Liu Lixin , Zheng Juanjuan , Gao Peng 
Published: June 2022

Abstract
Super-resolution optical microscopy, which gives access to finer details of objects, is highly desired for fields of nanomaterial, nanobiology, nanophotonics, etc. Many efforts, including tip optimization and illumination optimization etc., have been made in both near-field and far-field super-resolution microscopy to achieve a spatial resolution beyond the diffraction limit. The development of vector light fields opens up a new avenue for super-resolution optical microscopy via special illumination modes. Cylindrical vector beam (CVB) has been verified to enable resolution improvement in tip-scanning imaging, nonlinear imaging, stimulated emission depletion (STED) microscopy, subtraction imaging, superoscillation imaging, etc. This paper reviews recent advances in CVB-based super-resolution imaging. We start with an introduction of the fundamentals and properties of CVB. Next, strategies for CVB based super-resolution imaging are discussed, which are mainly implemented by tight focusing, depletion effect, plasmonic nanofocusing, and polarization matching. Then, the roadmap of super-resolution imaging with CVB illumination in the past two decades is summarized. The typical CVB-based imaging techniques in fields of both near-field and far-field microscopy are introduced, including tip-scanning imaging, nonlinear imaging, STED, subtraction imaging, and superoscillation imaging. Finally, challenges and future directions of CVB-illuminated super-resolution imaging techniques are discussed.
Negativ privilegiert statt deklassiert
Authors: Rhein Philipp 
Published: June 2022

ZusammenfassungDer Zusammenhang zwischen Ressentiments und Rechtspopulismus stellt sich empirisch anders dar, als häufig vermutet. Zu diesem Ergebnis kommt dieser Beitrag, der aufbauend auf Ergebnissen und Erfahrungen einer eigenen Interviewstudie mit AfD-WählerInnen und anhand exemplarischer Feldnotizen und Interviewpassagen die Bedeutung von Ressentiments bei RechtspopulistInnen untersucht. Ins Zentrum wird die ForscherIn-Beforschten Interaktionsschnittstelle gerückt. An ihr zeigt sich im Kern, dass AfD-WählerInnen weniger Deklassierungserfahrungen oder Abstiegserwartungen verarbeiten und solche Erfahrungen im Feld auch nicht in Gestalt von Ressentiments gegen den Forscher auftauchen. Vielmehr hegen sie einen Groll gegen die Erwartung, dass der Forscher dieses Deutungsschema anwenden könnte. Demgegenüber präsentieren sie ein Selbstbild, in dem vor allem ein Opfer- und Privilegierten-Selbstverständnis gleichermaßen eine Rolle spielen.
Propuesta de medición y categorización de la ruralidad en Costa Rica
Authors: Chacón Orozco Esteban 
Published: June 2022

Históricamente, la ruralidad ha estado relacionada con actividades económicas que dependen del uso de los recursos naturales y llevadas a cabo en lugares remotos. Adicionalmente, esta se ha visto cómo un concepto y un imaginario dependiente de lo urbano, ya que es solamente porque este no lo es; conceptos donde lo rural es explicado como lo restante, lo que empieza cuando lo urbano termina.
Es entonces a partir de estas premisas que este artículo hace un repaso sobre algunos trabajos que analizan la ruralidad, desde su definición conceptual, pasando por la evolución histórica de cómo se han estudiado y entendido las relaciones socioeconómicas de sus pobladores, hasta varias metodologías para procurar su medición y su clasificación.
A partir de esta revisión bibliográfica, se crea una propuesta propia sobre cómo sería una posible medición y categorización de lo rural y de lo urbano, incluyendo un enfoque geoespacial diferente. La propuesta adiciona una subcategoría que no ha sido observada por el autor en otros trabajos previamente realizados en la categorización de ruralidad.
Parameters of the Blood Oxidant/Antioxidant System in Elderly Patients with Acute Poisoning
Authors: Simonova A. Yu. , Ilyashenko K. K. , Klychnikova E. V. , Evseev A. K. , Potskhveriya M. M. , Belova M. V. , Tazina E. V. , Shabanov A. S. , Kuzovlev A. N. 
Published: June 2022

The aim of the study was to assess the oxidant/antioxidant status in elderly patients in the early period of acute poisoning by psychotropic drugs or corrosive substances.Material and methods. An open prospective observational study with retrospective control was conducted in 80 patients (age ≥ 60 years) with acute poisoning, of which 49 patients aged 72.1±9.55 years had psychotropic drug poisoning (PDP) and 31 subjects aged 73.0±10.3 years had corrosive substance poisoning (CSP). Patients with mild poisoning were excluded from the study. The control group consisted of 39 volunteers aged 68.3±6.3 years. Total antioxidant status (TAS), blood levels of malondialdehyde (MDA), stable nitric oxide metabolites (nitrite/nitrate, NOx), and oxidative stress index (MDA/TAS) were measured on days 1, 3 and 5 after hospital admission.Results. When analyzing the changes in the parameters of the oxidant/antioxidant system, we observed lower values of the studied parameters in patients with both PDP and CSP compared to the control group. In patients with PDP, several parameters were reduced: MDA by 1.2 times on days 1 and 3 (P=0.002; P=0.008, respectively), NOx by 1.7 times (P<0.001) at all stages of the study, MDA/TAS by 2.4–2.9 times (P<0.001). In patients with CSP, MDA level decreased by 1.1–1.2 times at all study timepoints (P=0.003; P=0.010; P=0.046, respectively), NOx dropped 1.4–1.6-fold (P=0.012; P=0.004; P=0.023, respectively), and MDA/TAS decreased by 2.3–2.4 times (P<0.001). While comparing patients with favorable and fatal outcome, we found that in survived patients an increase of MDA/TAS along with growing NOx level was seen by day 5 with no signiﬁcant changes of MDA and TAS, while in non-survivors MDA/TAS dropped continuously due to progressive fall of NOx level, reaching values 2.8–2.9 times (P<0.001) lower than those of the controls.Conclusion. In elderly patients with acute poisonings due to psychotropic drugs or corrosive substances, an inadequate response of the oxidant/antioxidant system occurs manifesting as a reduced blood level of peroxidation products with simultaneous normal or slightly decreased concentration of antioxidant protection system components. Thus, the oxidative stress develops, which contributes to the death of the patients.
Coupled Dictionary Learning for Unsupervised Feature Selection
Authors: Zhu Pengfei , Hu Qinghua , Zhang Changqing , Zuo Wangmeng 
Published: June 2022

Unsupervised feature selection (UFS) aims to reduce the time complexity and storage burden, as well as improve the generalization performance. Most existing methods convert UFS to supervised learning problem by generating labels with specific techniques (e.g., spectral analysis, matrix factorization and linear predictor). Instead, we proposed a novel coupled analysis-synthesis dictionary learning method, which is free of generating labels. The representation coefficients are used to model the cluster structure and data distribution. Specifically, the synthesis dictionary is used to reconstruct samples, while the analysis dictionary analytically codes the samples and assigns probabilities to the samples. Afterwards, the analysis dictionary is used to select features that can well preserve the data distribution. The effective L2p-norm (0 < p <1) regularization is imposed on the analysis dictionary to get much sparse solution and is more effective in feature selection.We proposed an iterative reweighted least squares algorithm to solve the L2p-norm optimization problem and proved it can converge to a fixed point. Experiments on benchmark datasets validated the effectiveness of the proposed method
A Study of Compact Reserve Pricing Languages
Authors: Bateni MohammadHossein , Esfandiary Hossein , Mirrokni Vahab , Seddighin Saeed 
Published: June 2022

Online advertising allows advertisers to implement fine-tuned targeting of users. While such precise targeting leads to more effective advertising, it introduces challenging multidimensional pricing and bidding problems for publishers and advertisers. In this context, advertisers and publishers need to deal with an exponential number of possibilities. As a result, designing efficient and compact multidimensional bidding and pricing systems and algorithms are practically important for online advertisement. Compact bidding languages have already been studied in the context of multiplicative bidding. In this paper, we study the compact pricing problem.
A Unified Convex Surrogate for the Schatten-<i>p</i> Norm
Authors: Xu Chen , Lin Zhouchen , Zha Hongbin 
Published: June 2022

The Schatten-p norm (0 < p < 1) has been widely used to replace the nuclear norm for better approximating the rank function. However, existing methods are either 1) not scalable for large scale problems due to relying on singular value decomposition (SVD) in every iteration, or 2) specific to some p values, e.g., 1/2, and 2/3. In this paper, we show that for any p, p1, and p2 > 0 satisfying 1/p = 1/p1 + 1/p2, there is an equivalence between the Schatten-p norm of one matrix and the Schatten-p1 and the Schatten-p2 norms of its two factor matrices. We further extend the equivalence to multiple factor matrices and show that all the factor norms can be convex and smooth for any p > 0. In contrast, the original Schatten-p norm for 0 < p < 1 is non-convex and non-smooth. As an example we conduct experiments on matrix completion. To utilize the convexity of the factor matrix norms, we adopt the accelerated proximal alternating linearized minimization algorithm and establish its sequence convergence. Experiments on both synthetic and real datasets exhibit its superior performance over the state-of-the-art methods. Its speed is also highly competitive.
Explicit Defense Actions Against Test-Set Attacks
Authors: Alfeld Scott , Zhu Xiaojin , Barford Paul 
Published: June 2022

Automated learning and decision making systems in public-facing applications are vulnerable to malicious attacks. Examples of such systems include spam detectors, credit card fraud detectors, and network intrusion detection systems. These systems are at further risk of attack when money is directly involved, such as market forecasters or decision systems used in determining insurance or loan rates. In this paper, we consider the setting where a predictor Bob has a fixed model, and an unknown attacker Alice aims to perturb (or poison) future test instances so as to alter Bob's prediction to her benefit. We focus specifically on Bob's optimal defense actions to limit Alice's effectiveness. We define a general framework for determining Bob's optimal defense action against Alice's worst-case attack. We then demonstrate our framework by considering linear predictors, where we provide tractable methods of determining the optimal defense action. Using these methods, we perform an empirical investigation of optimal defense actions for a particular class of linear models -- autoregressive forecasters -- and find that for ten real world futures markets, the optimal defense action reduces the Bob's loss by between 78 and 97%.
Generalization Analysis for Ranking Using Integral Operator
Authors: Liu Yong , Liao Shizhong , Lin Hailun , Yue Yinliang , Wang Weiping 
Published: June 2022

The study on generalization performance of ranking algorithms is one of the fundamental issues in ranking learning theory. Although several generalization bounds have been proposed based on different measures, the convergence rates of the existing bounds are usually at most O(√1/n), where n is the size of data set. In this paper, we derive novel generalization bounds for the regularized ranking in reproducing kernel Hilbert space via integral operator of kernel function. We prove that the rates of our bounds are much faster than (√1/n). Specifically, we first introduce a notion of local Rademacher complexity for ranking, called local ranking Rademacher complexity, which is used to measure the complexity of the space of loss functions of the ranking. Then, we use the local ranking Rademacher complexity to obtain a basic generalization bound. Finally, we establish the relationship between the local Rademacher complexity and the eigenvalues of integral operator, and further derive sharp generalization bounds of faster convergence rate.
Achieving Privacy in the Adversarial Multi-Armed Bandit
Authors: Tossou Aristide , Dimitrakakis Christos 
Published: June 2022

In this paper, we improve the previously best known regret bound to achieve ε-differential privacy in oblivious adversarial bandits from O(T2/3 /ε) to O(√T lnT/ε). This is achieved by combining a Laplace Mechanism with EXP3. We show that though EXP3 is already differentially private, it leaks a linear amount of information in T. However, we can improve this privacy by relying on its intrinsic exponential mechanism for selecting actions. This allows us to reach O(√ ln T)-DP, with a a regret of O(T2/3) that holds against an adaptive adversary, an improvement from the best known of O(T3/4). This is done by using an algorithm that run EXP3 in a mini-batch loop. Finally, we run experiments that clearly demonstrate the validity of our theoretical analysis.
Extracting Urban Microclimates from Electricity Bills
Authors: Vu Thuy , Parker D. Stott 
Published: June 2022

Sustainable energy policies are of growing importance in all urban centers.Climate — and climate change — will play increasingly important roles in these policies.Climate zones defined by the California Energy Commissionhave long been influential in energy management.For example, recently a two-zone division of Los Angeles(defined by historical temperature averages) was introduced for electricity rate restructuring.The importance of climate zones has been enormous,and climate change could make them still more important. AI can provide improvements on the ways climate zones are derived and managed.This paper reports on analysis of aggregate household electricity consumption (EC) data from local utilities in Los Angeles,seeking possible improvements in energy management. In this analysis we noticed that EC data permits identificationof interesting geographical zones — regions having EC patterns that are characteristically different from surrounding regions.We believe these zones could be useful in a variety of urban models.
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
Authors: Chen Pin-Yu , Sharma Yash , Zhang Huan , Yi Jinfeng , Hsieh Cho-Jui 
Published: June 2022

Recent studies have highlighted the vulnerability of deep neural networks (DNNs) to adversarial examples — a visually indistinguishable adversarial image can easily be crafted to cause a well-trained model to misclassify. Existing methods for crafting adversarial examples are based on L2 and L∞ distortion metrics. However, despite the fact that L1 distortion accounts for the total variation and encourages sparsity in the perturbation, little has been developed for crafting L1-based adversarial examples. In this paper, we formulate the process of attacking DNNs via adversarial examples as an elastic-net regularized optimization problem. Our elastic-net attacks to DNNs (EAD) feature L1-oriented adversarial examples and include the state-of-the-art L2 attack as a special case. Experimental results on MNIST, CIFAR10 and ImageNet show that EAD can yield a distinct set of adversarial examples with small L1 distortion and attains similar attack performance to the state-of-the-art methods in different attack scenarios. More importantly, EAD leads to improved attack transferability and complements adversarial training for DNNs, suggesting novel insights on leveraging L1 distortion in adversarial machine learning and security implications of DNNs.
Truthful and Near-Optimal Mechanisms for Welfare Maximization in Multi-Winner Elections
Authors: Bhaskar Umang , Dani Varsha , Ghosh Abheek 
Published: June 2022

Mechanisms for aggregating the preferences of agents in elections need to balance many different considerations, including efficiency, information elicited from agents, and manipulability. We consider the utilitarian social welfare of mechanisms for preference aggregation, measured by the distortion. We show that for a particular input format called threshold approval voting, where each agent is presented with an independently chosen threshold, there is a mechanism with nearly optimal distortion when the number of voters is large. Threshold mechanisms are potentially manipulable, but place a low informational burden on voters. We then consider truthful mechanisms. For the widely-studied class of ordinal mechanisms which elicit the rankings of candidates from each agent, we show that truthfulness essentially imposes no additional loss of welfare. We give truthful mechanisms with distortion O(√m log m) for k-winner elections, and distortion O(√m log m) when candidates have arbitrary costs, in elections with m candidates. These nearly match known lower bounds for ordinal mechanisms that ignore the strategic behavior. We further tighten these lower bounds and show that for truthful mechanisms our first upper bound is tight. Lastly, when agents decide between two candidates, we give tight bounds on the distortion for truthful mechanisms.