Miracle-dz/UniFAR-FTUs huggingface dataset:
An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations

Name ambiguity in the context of bibliographic citations is a difficult problem which, despite the many efforts from the research community, still has a lot of room for improvement. In this article, we present a heuristic-based hierarchical clustering method to deal with this problem. The method successively fuses clusters of citations of similar author names based on several heuristics and similarity measures on the components of the citations (e.g., coauthor names, work title, and publication venue title). During the disambiguation task, the information about fused clusters is aggregated providing more information for the next round of fusion. In order to demonstrate the effectiveness of our method, we ran a series of experiments in two different collections extracted from real-world digital libraries and compared it, under two metrics, with four representative methods described in the literature. We present comparisons of results using each considered attribute separately (i.e., coauthor names, work title, and publication venue title) with the author name attribute and using all attributes together. These results show that our unsupervised method, when using all attributes, performs competitively against all other methods, under both metrics, loosing only in one case against a supervised method, whose result was very close to ours. Moreover, such results are achieved without the burden of any training and without using any privileged information such as knowing a priori the correct number of clusters. © 2010 Wiley Periodicals, Inc.
Principled and Data Efﬁcient Support Vector Machine Training Using the Minimum Description Length Principle, with Application in Breast Cancer Prediction

Support vector machines (SVMs) are established as highly successful classiﬁers in a broad range of applications, including numerous medical ones. Nevertheless, their current employment is restricted by a limitation in the manner in which they are trained, most often the training-validation-test or k - fold cross-validation approaches, which are wasteful both in terms of the use of the available data as well as computational resources. This is a particularly important consideration in many medical problems, in which data availability is low (be it because of the inherent difﬁculty in obtaining suf-ﬁcient data, or because of practical reasons, e.g. pertaining to privacy and data sharing). In this paper we propose a novel approach to training SVMs which does not suffer from the aforementioned limitation, which is at the same time much more rigorous in nature, being built upon solid information theoretic grounds. Speciﬁcally, we show how the training process, that is the process of hyperparameter inference, can be formulated as a search for the optimal model under the minimum description length (MDL) criterion, allowing for theory rather than empiricism driven selection and removing the need for validation data. The effectiveness and superiority of our approach are demonstrated on the Wisconsin Diagnostic Breast Cancer Data Set.
HBert: A Long Text Processing Method Based on BERT and Hierarchical Attention Mechanisms

With the emergence of a large-scale pre-training model based on the transformer model, the effect of all-natural language processing tasks has been pushed to a new level. However, due to the high complexity of the transformer's self-attention mechanism, these models have poor processing ability for long text. Aiming at solving this problem, a long text processing method named HBert based on Bert and hierarchical attention neural network is proposed. Firstly, the long text is divided into multiple sentences whose vectors are obtained through the word encoder composed of Bert and the word attention layer. And the article vector is obtained through the sentence encoder that is composed of transformer and sentence attention. Then the article vector is used to complete the subsequent tasks. The experimental results show that the proposed HBert method achieves good results in text classification and QA tasks. The F1 value is 95.7% in longer text classification tasks and 75.2% in QA tasks, which are better than the state-of-the-art model longformer.
The necessity and benefits of multiple feedback for robust biological development

Robust development of biological organisms is known to involve a variety of inhibitors whose specific roles in downregulating the undesirably excessive signaling activities are reasonably well understood. Empirical evidence points to existence of feedback mechanisms for upregulating inhibitory agents. At least two conventional feedback models have been found by model analysis to be ineffective or biologically inappropriate. In this paper, we explore more fully the recently formulated new type of regulatory feedback for robustness to examine all such feedback processes possible in a three‐component basic extracellular model for ligand signaling. Acting alone, all processes will again be shown to be ineffective for promoting robustness. The principal finding of the present theoretical analysis is that a concurrent implementation of some combinations of two of these feedback processes actually renders the signaling gradient robust with respect to genetic and epi‐genetic perturbations. Moreover, different multifeedback combinations achieve robustness in different ways. These findings provide a possible explanation for the concurrent presence of several such individually ineffective feedback processes in robust signaling gradients.
INFLUENCE OF VR / AR ON LEARNING TECHNOLOGIES AND EDUCATIONAL PRACTICES

The development of modern society leads to rapid changes in the conditions of its existence, contributing to the processes of evolution and transformation, which, in turn, becomes a source of creation and implementation of innovations in education and training. The growing digitalization cannot bypass the education sector; the study of the impact and prospects of using digital technologies both in higher and secondary schools is actualizing. The didactic capabilities of modern technologies and their application in education is
Distributed energy resources for a zero-energy neighbhourhood

—Zero energy buildings are on the increasing trend. They are perceived as appropriate technology to reducing CO 2 emissions, improving energy efficiency and alleviating energy poverty. The main goal is that a grid-connected building produces enough energy on site to equal or exceed its annual energy requirement while using the grid as a buffer. Many municipalities see this concept as a prospective solution for developing future neighborhoods and thereby aim to develop a neighborhood with net zero energy concept. This paper proposes passive designs measures and distributed power generations required in designing such a neighborhood.
Pan-Sharpening Using an Efficient Bidirectional Pyramid Network

Pan-sharpening is an important preprocessing step for remote sensing image processing tasks; it fuses a low-resolution multispectral image and a high-resolution (HR) panchromatic (PAN) image to reconstruct a HR multispectral (MS) image. This paper introduces a new end-to-end bidirectional pyramid network for pan-sharpening. The overall structure of the proposed network is a bidirectional pyramid, which permits the network to process MS and PAN images in two separate branches level by level. At each level of the network, spatial details extracted from the PAN image are injected into the upsampled MS image to reconstruct the pan-sharpened image from coarse resolution to fine resolution. Subpixel convolutional layers and the enhanced residual blocks are used to make the network efficient. Comparison of the results obtained with our proposed method and the results using other widely used state-of-the-art approaches confirms that our proposed method outperforms the others in visual appearance and objective indexes.
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—Ordering the selection of training data using active learning can lead to improvements in learning efﬁ-ciently from smaller corpora. We present an exploration of active learning approaches applied to three grounded language problems of varying complexity in order to analyze what methods are suitable for improving data efﬁciency in learning. We present a method for analyzing the complexity of data in this joint problem space, and report on how characteristics of the underlying task, along with design decisions such as feature selection and classiﬁcation model, drive the results. We observe that representativeness, along with diversity, is crucial in selecting data samples.
Influence of pro-algesic foods on chronic pain conditions

This review examines current knowledge about putative “pro-algesic” dietary components, and discusses whether limiting the intake of these substances can help improve chronic pain. Although there is a common impression that numerous food components, natural and synthetic, can cause or worsen pain symptoms, very few of these substances have been investigated. This article focuses on four substances, monosodium glutamate, aspartame, arachidonic acid, and caffeine, where research shows that overconsumption may induce or worsen pain. For each substance, the mechanism whereby it may act to induce pain is examined, and any clinical trials examining the effectiveness of reducing the intake of the substance discussed. While all four substances are associated with pain, decreased consumption of them does not consistently reduce pain.
viability of balancing wind generation with storage

- This paper studies the impact of balancing wind generation with storage on the thermal plant mix and load for different levels of installed wind and storage, and under different operational strategies. Moreover, the optimal time frame to be used for the optimization of the system operation is studied and the possible revenue that can be generated by the system with wind and storage is calculated for different scenarios. It is shown that the introduction of intermittent energy resources reduces the participation of the base-load plants and increases the peaking plants, and the increasing storage dramatically increases the participation of the mid-merit plants. Furthermore, the mid-merit strategy and 24 hours time frame resulted in the best use of the system with wind and storage.
Projecting the course of COVID-19 in Turkey: A probabilistic Projecting the course of COVID-19 in Turkey: A probabilistic modeling approach modeling approach

and no drastic changes were seen in diagnosis or management protocols, the epidemic curve would tend to decrease in this time interval. Also, the predictive validity analysis suggests that the proposed model projections should have a PI around 95% for the first 12 days of the projections. Conclusion: We expect that drastic changes in the course of COVID-19 in Turkey will cause the model to suffer in predictive validity, and this can be used to monitor the epidemic. We hope that the discussion on these projections and the limitations of the epidemiological forecasting will be beneficial to the medical community, and policy makers.
Tilburg University Multi-stage Adjustable Robust Mixed-Integer Optimization via Iterative Splitting of the Uncertainty set

In this paper we propose a methodology for constructing decision rules for integer and continuous decision variables in multiperiod robust linear optimization problems. This type of problems finds application in, for example, inventory management, lot sizing, and manpower management. We show that by iteratively splitting the uncertainty set into subsets one can differentiate the later-period decisions based on the revealed uncertain parameters. At the same time, the problem’s computational complexity stays at the same level as for the static robust problem. This holds also in the non-fixed recourse situation. In the fixed recourse situation our approach can be combined with linear decision rules for the continuous decision variables. We provide theoretical results how to split the uncertainty set by identifying sets of uncertain parameter scenarios to be divided for an improvement in the worst-case objective value. Based on this theory, we propose several splitting heuristics. Numerical examples entailing a capital budgeting and a lot sizing problem illustrate the advantages of the proposed approach.
Learning Text Patterns for Web Information Extraction and Assessment

Learning text patterns that suggest a desired type of information is a common strategy for extracting information from unstructured text on the Web. In this paper, we introduce the idea that learned patterns can be used as both extractors (to generate new information) and discriminators (to assess the truth of extracted information). We demonstrate experimentally that a Web information extraction system (KnowItAll) can be improved (in terms of coverage and accuracy) through the addition of a simple pattern-learning algorithm. By using learned patterns as extractors, we are able to boost recall by 50% to 80%; and by using such patterns as discriminators we are able to reduce classification errors by 28% to 35%. In addition, the paper reports theoretical results on optimally selecting and ordering discriminators, and shows that this theory yields a heuristic that further reduces classification errors by an additional 19% to 35% – giving an overall error reduction of 47% to 53%.
Author's response to reviews Title: Knowledge of modifiable risk factors of Coronary Atherosclerotic Heart Disease (CASHD) among a sample in India Authors:

Background: The prevalence of Coronary Atherosclerotic Heart Disease (CASHD) is iincreasing in India. Several modifiable risk factors contribute directly to this disease burden. Public knowledge of such risk factors among the urban Indian population is largely unknown. This investigation attempts to quantify the knowledge of modifiable risk factors of CASHD as sampled among an Indian population at a large metropolitan hospital. Methods: A hospital basedhospital-based, cross sectional study was conducted at All India Institute of Medical Sciences (AIIMS), a major tertiary care hospital in New Delhi, India. Participants (n=217) were recruited from patient waiting areas in the emergency room and were provided with standardised questionnaires to assess their knowledge of modifiable risk factors of CASHD. The risk factors specifically included smoking, hypertension, elevated cholesterol levels, dDiabetes mMellitus and obesity. Identifying three 3 or less risk factors was regarded as a poor knowledge level of knowledge, and whereas identifying four 4 or more risk factors was regarded as a good knowledge level of knowledge. A multiple logistic regression model was used to isolate independent demographic markers predictive of a participant’s level of
Heterogeneity and the Non-Parametric Analysis of Consumer Choice: Conditions for

This paper considers structural non-parametric random utility models for continuous choice vari ables with unobserved heterogeneity. We provide sufficient conditions on random preferences to yield reduced-form systems of non-parametric stochastic demand functions that allow global invertibility be tween demands and non-separable unobserved heterogeneity. Invertibility is essential for global identi fication of structural consumer demand models, for the existence of well-specified probability models of choice and for the non-parametric analysis of revealed stochastic preference. We distinguish between new classes of models in which heterogeneity is separable and non-separable in the marginal rates of substitution, respectively.
Comptes Rendus Physique

The coarse-graining of amorphous plasticity from the atomistic to the mesoscopic scale is studied inthe frameworkofasimplescalarelasto-plasticmodel. Buildingonrecentresults obtainedontheatomistic scale, we discuss the interest of a disordered landscape-informed threshold disorder to reproduce the physicsofamorphousplasticity.Wedeveloparejuvenationscenariothatreproducesquasi-quantitativelythe evolution of the mean local yield stress and the localization behavior. We emphasize the crucial role of two dimensionless parameters: the relative strength of the yield stress disorder with respect to the typical stress drops associated with a
Reconstitution des événements climatiques extrêmes (crues et tempêtes) au cours de l'holocène dans le golfe d’Aigues-Mortes (Sud de la France)

Les effets du changement climatique sur les evenements extremes sont difficiles a estimer car ces phenomenes presentent une large variabilite, et par consequent il est delicat d'identifier des tendances significatives compte tenu de l'absence de longues series de donnees instrumentales. Dans cette etude, nous presentons un enregistrement de ces tempetes extremes sur la cote Mediterraneenne francaise au cours des 7000 dernieres annees a partir de carottes sedimentaires prelevees dans un systeme lagunaire du Golfe du Lion. L'utilisation d'une approche multi-traceur a haute resolution avec des analyses granulometriques, faunistiques, geochimiques (XRF), en mineraux argileux associees a des donnees chronologiques de radiocarbone et de 210Pb, 137Cs ; nous a permis de reconstruire l'histoire, au cours de l'Holocene, des depots lagunaires en relation avec l'activite des tempetes. Bien que les parametres morphologiques de cet environnement lagunaire aient change durant l'Holocene, ne permettant pas de comparer ces evenements au cours du temps en terme d'intensite, nous enregistrons six periodes d'augmentation de l'activite des tempetes datees a 6200, 5500, 4400, 3200, 1500 an cal B.P. et durant le Petit Age Glaciaire (de 450 a 100 an cal B.P.). Ces changements de l'activite hydrodynamique cotiere, en antiphase avec les periodes d'aridifications en Mediterranee de l'Ouest, sont correlees avec celles observees dans le bassin Nord Atlantique et correspondent aux evenements de refroidissement Holocene. L'augmentation de l'activite des tempetes dans la cette region de la Mediterranee durant les periodes froides, associee a une diminution des temperatures au pole Nord, est probablement liee a l'augmentation du gradient thermique qui, par interaction barocline, favorise les cycles d'evolution des tempetes dans toute l'Europe et la Mediterranee de l'Ouest. Cette etude demontre ainsi que la temperature dans la region Nord Atlantique influence l'activite des tempetes extremes et donc du climat Mediterraneen a l'echelle de l'Holocene.
European economic governance the Berlin-Washington consensus

This paper argues that the European Union has gone farther than any other country or institution in internalizing the prescriptions of the Washington Consensus. Embedding neo-liberal principles in the treaties defining its governance, the EU has enshrined a peculiar doctrine within its constitution. We further argue that this “Berlin-Washinghton Consensus” has serious empirical and theoretical flaws, as its reliance on Pareto optimality leads to neglect the crucial links between current and potential growth. We show by means of a simple model that the call for structural reforms as an engine for growth may be controversial, once current and potential output are related. We claim that adherence to the Consensus may go a long way in explaining the poor growth performance of the European economy in the past two decades, because of the constraints that it imposed on fiscal and monetary policies. The same constraints have deepened the eurozone crisis that started in 2007, putting unwarranted emphasis on austerity and reform. Challenging the Consensus becomes a precondition for avoiding the implosion of the euro, and recovering growth.
Project Title: Sustainable Adaptations to Drought and Climate Variability in Agricultural Production Systems Across Nebraska

• Dr. Cody L. Knutson, National Drought Mitigation Center, University of Nebraska-Lincoln, 820 Hardin Hall, Lincoln, NE 68583-0988, cknutson1@unl.edu, (402) 472-6718 • Dr. Michael J. Hayes, National Drought Mitigation Center, University of Nebraska-Lincoln, 823 Hardin Hall, Lincoln, NE 68583-0988, mhayes2@unl.edu, (402) 472-4271 • Robert K. Hitchcock, Department of Anthropology, Michigan State University, 354 Baker Hall, East Lansing, MI, 48824, (517) 353-2950 • Jeffrey Peake, DSC 263, Department of Geography and Geology, University of Nebraska-Omaha, Omaha, NE, 68182-0199, (402) 554-2726 • Martin Kleinschmit, Center for Rural Affairs, Hartington, Nebraska, 68739, (402) 254-6893, MartinK@cfra.org
Local explosions and extinction of continuous state branching processes with logistic competition

We study by duality methods the extinction and explosion times of continuous-state branching processes with logistic competition (LCSBPs) and identify the local time at ∞ of the process when it is instantaneously reﬂected at ∞ . The main idea is to introduce a certain “bidual” process V of the LCSBP Z . The latter is the Siegmund dual process of the process U , that was introduced in [Fou19] as the Laplace dual of Z . By using both dualities, we shall relate local explosions and the extinction of Z to local extinctions and the explosion of the process V . The process V being a one-dimensional diﬀusion on [0 , ∞ ], many results on diﬀusions can be used and transfered to Z . A concise study of Siegmund duality for regular one-dimensional diﬀusions is also provided.
Cardiopulmonary Resuscitation Training Disparities in the United States

Background Bystander cardiopulmonary resuscitation (CPR) is associated with increased survival from cardiac arrest, yet bystander CPR rates are low in many communities. The overall prevalence of CPR training in the United States and associated individual‐level disparities are unknown. We sought to measure the national prevalence of CPR training and hypothesized that older age and lower socioeconomic status would be independently associated with a lower likelihood of CPR training. Methods and Results We administered a cross‐sectional telephone survey to a nationally representative adult sample. We assessed the demographics of individuals trained in CPR within 2 years (currently trained) and those who had been trained in CPR at some point in time (ever trained). The association of CPR training and demographic variables were tested using survey weighted logistic regression. Between September 2015 and November 2015, 9022 individuals completed the survey; 18% reported being currently trained in CPR, and 65% reported training at some point previously. For each year of increased age, the likelihood of being currently CPR trained or ever trained decreased (currently trained: odds ratio, 0.98; 95% CI, 0.97–0.99; P<0.01; ever trained: OR, 0.99; 95% CI, 0.98–0.99; P=0.04). Furthermore, there was a greater then 4‐fold difference in odds of being currently CPR trained from the 30–39 to 70–79 year old age groups (95% CI, 0.10–0.23). Factors associated with a lower likelihood of CPR training were lesser educational attainment and lower household income (P<0.01 for each of these variables). Conclusions A minority of respondents reported current training in CPR. Older age, lesser education, and lower income were associated with reduced likelihood of CPR training. These findings illustrate important gaps in US CPR education and suggest the need to develop tailored CPR training efforts to address this variability.