Miracle-dz/UniFAR-FTUs huggingface dataset:
Daily ozone cycle in the stratosphere: global, regional and seasonal behaviour modelled with the Whole Atmosphere Community Climate Model

Abstract. The Whole Atmosphere Community Climate Model (WACCM) is utilised to study the daily ozone cycle and underlying photochemical and dynamical processes. The analysis is focused on the daily ozone cycle in the middle stratosphere at 5 hPa where satellite-based trend estimates of stratospheric ozone are most biased by diurnal sampling effects and drifting satellite orbits. The simulated ozone cycle shows a minimum after sunrise and a maximum in the late afternoon. Further, a seasonal variation of the daily ozone cycle in the stratosphere was found. Depending on season and latitude, the peak-to-valley difference of the daily ozone cycle varies mostly between 3 and 5% (0.4 ppmv) with respect to the midnight ozone volume mixing ratio. The maximal variation of 15% (0.8 ppmv) is found at the polar circle in summer. The global pattern of the strength of the daily ozone cycle is mainly governed by the solar zenith angle and the sunshine duration. In addition, we find synoptic-scale variations in the strength of the daily ozone cycle. These variations are often anti-correlated to regional temperature anomalies and are due to the temperature dependence of the rate coefficients k2 and k3 of the Chapman cycle reactions. Further, the NOx catalytic cycle counteracts the accumulation of ozone during daytime and leads to an anti-correlation between anomalies in NOx and the strength of the daily ozone cycle. Similarly, ozone recombines with atomic oxygen which leads to an anti-correlation between anomalies in ozone abundance and the strength of the daily ozone cycle. At higher latitudes, an increase of the westerly (easterly) wind cause a decrease (increase) in the sunshine duration of an air parcel leading to a weaker (stronger) daily ozone cycle.
SCREENING FOR ANTICANCER ACTIVITY OF LYCOPENE IN COMBINATION WITH VITAMIN E AGAINST MCF-7 AND HEP-2 CANCER CELL LINES

Numerous epidemiological studies have demonstrated that consumption of vegetables and fruits reduces the risk of many types of human cancers. Recently, there has been a growing interest in the role of the tomato carotenoid, lycopene, in cancer prevention and treatment. The hallmark of the cancer cell is the deregulation of molecular mechanisms responsible for control of cell growth. Therefore, possible mechanisms underlying the anticancer activity of lycopene has been investigated paying special attention to anticancer activity of lycopene in combination with vitamin E against MCF-7 and Hep-2 cancer cell lines. Cell viability was also analyzed by MTT assay. The combination of low concentrations of lycopene with vitamins exhibited a synergistic effect on cell proliferation and differentiation and an additive effect on cell cycle progression. Further research need to be explored to make use of lycopene as an effective anti-cancer drug.
Energy Efficiency of Connected Mobile Platforms in Presence of Background Traffic

In the last decade there has been an explosive growth in popularity of mobile computing platforms which include laptops, notebooks, tablets, cell phones, etc. However, the usability of these devices from end users point of view is directly associated with their battery life and the fact that they are powered by non-continuous energy sources imposes a serious limitations to these devices. As a result, a typical architecture design of such mobile computing platforms will include low power platform states for individual components (e.g. CPU, memory controller, hard-disk, etc.) or for the whole platform. These low power states can be sleep states where the components or the whole platform is in a non-operational mode (e.g. operating systems defined sleep states: standby, hibernate, etc.) or scaled states where they are operating at lower than peak performance (e.g. CPU dynamic voltage and frequency scaling (DVFS)). For example the Advanced Configuration and Power Interface (ACPI) specifies power management concepts and interfaces. ACPI integrates the operating system, device drivers, system hardware components, and applications for power management and defines several power states for each component ranging from fully powered on to fully powered-off with each successive state consuming the same or less amount of power.
Functional Readings and Wide-Scope Indefinites

Traditional formal semantics assumes that quantification in natural language op­ erates only on atomic entities. However, much recent work in natural language semantics has shown the advantages of more complex forms of quantification that involve jUnctions over atomic domains . Two areas of functional quantification have received special attention. One area concerns the so-called jUnctional and pair-list interpretation of questions and copular sentences . Another area deals with the wide scope interpretation of indefinite NPs. Quantification over SkolemjUnctions is used to model both kinds of phenomena. Some theories restrict the usage of Skolem functions to the simple case of choice jUnctions: Skolem functions that map any non-empty set to an entity in this set . Despite the similarity in the mechanisms that are used for describing func­ tional readings and wide scope indefinites, no attempt has so far been made to· bring them into one framework. This paper proposes such a ' unified mechanism, which is based on a similar type shifting operator to the one proposed by Jacobson (these proceedings) . Like Jacobson 's operator, the proposed mapping establishes a connection between functions from entities to entities on the one hand, and binary relations over entities , on the other hand. The mechanism accounts for the simi­ lar distribution of functional readings and wide-scope readings beyond islands with different NPs, and leads to a novel hypothesis about the class of quantifiers that license functional interpretations and the reasons for their restricted distribution. The structure of thi s paper is as follows . Section 2 briefly overviews the problems of functional readings and wide scope indefinites, gives necessary tech­ nical details about previous approaches and discusses the motivation for a unified analysis. Section 3 introduces the mapping that allows an extended theory to treat both phenomena and exemplifies its applications . Section 4 is a short note on the relations between Jacobson (these proceedings) and the present proposal . This paper is an abridged version of an unpublished work in preparatioq. that was prepublished as Winter (200 1b) and is available electronically.
Determination of bandgap states in p-type In0.49Ga0.51P grown on SiGe/Si and GaAs by deep level optical spectroscopy and deep level transient spectroscopy

The presence and properties of traps in p-type In0.49Ga0. 51P grown on low dislocation density, metamorphic Ge/SiGe/Si substrates and GaAs substrates were determined using deep level transient spectroscopy (DLTS) and deep level optical spectroscopy (DLOS) leading to the quantification of trap behavior throughout the entire 1.9 eV bandgap of the In0.49Ga0. 51P material as a function of substrate. Thermal emission-based DLTS revealed a single hole trap at Ev + 0.71 eV for growth on both lattice matched and mismatched substrates with similar concentrations. Complementary, optical emission-based DLOS measurements detected bandgap states at Ev + 1.18 eV, Ev + 1.36 eV, and Ev + 1.78 eV for p-type In0.49Ga0. 51P grown on both substrate types. The total concentration of the DLOS-detected states was found to comprise approximately 80% of the entire trap concentration in p-type In0.49Ga0. 51P bandgap. This relatively high concentration of above midgap levels may be of great significance for minority carrier devices tha.
A study on the effectiveness of the orientation process and cross-cultural training for the expatriate.

This paper focuses on the effectiveness of the orientation process and cross-cultural training (CCT) and its impact on cross-cultural adjustment for the expatriate. The objective of the study on which this paper is based is to evaluate the effectiveness of the orientation process for expatriates and to determine the need for a separate orientation and culture training. By improving the orientation process and identifying a need for culture specific training, the company can thus eliminate relocation and replacement costs. The main objective of this research is to design a guideline for the implementation of a culture specific orientation process for the expatriate. This will be done based on the recommendations made by the respondents of the survey. The paper reviews different writings in the areas of cross-cultural training, cross-cultural adjustment, the orientation process and the expatriate. The study highlights specific issues regarding cultural training, assignment failure and success, and the expatriate experiences. The research is motivated by the need to reduce assignment failure and the subsequent costs associated with engaging expatriation, and ensure smooth transition into a new culture. The research methodology utilized was qualitative, based on structured questionnaires and personal interviews. The study attempts to recommend, based on the findings, a culture centered orientation process for the expatriate.
Graph Multiview Canonical Correlation Analysis

Multiview canonical correlation analysis (MCCA) seeks latent low-dimensional representations encountered with multiview data of shared entities (a.k.a. common sources). However, existing MCCA approaches do not exploit the geometry of the common sources, which may be available a priori, or can be constructed using certain domain knowledge. This prior information about the common sources can be encoded by a graph, and be invoked as a regularizer to enrich the maximum variance MCCA framework. In this context, this paper's novel graph-regularized MCCA (GMCCA) approach minimizes the distance between the wanted canonical variables and the common low-dimensional representations, while accounting for graph-induced knowledge of the common sources. Relying on a function capturing the extent to which the low-dimensional representations of the multiple views are similar, a generalization bound of GMCCA is established based on Rademacher's complexity. Tailored for setups where the number of data pairs is smaller than the data vector dimensions, a graph-regularized dual MCCA approach is also developed. To further deal with nonlinearities present in the data, graph-regularized kernel MCCA variants are put forward too. Interestingly, solutions of the graph-regularized linear, dual, and kernel MCCA are all provided in terms of generalized eigenvalue decomposition. Several corroborating numerical tests using real datasets are provided to showcase the merits of the graph-regularized MCCA variants relative to several competing alternatives including MCCA, Laplacian-regularized MCCA, and (graph-regularized) PCA.
Turbulent Drag Reduction using Sinusoidal Riblets with Triangular Cross-Section

It is known that longitudinal ribs manufactured in a flat surface act to reduce turbulent skin-friction drag, providing a moderate drag reduction of 4 to 8%. It is shown in this paper that this value can be increased by at least 50% if sinusoidal-like rods are used instead of conventional straight riblets. Large Eddy Simulation of a turbulent flow over a riblet-covered surface is performed for three cases: straight riblets and sinusoidal riblets with two different values of wavelength. All riblets have triangular cross-section. It is found that drag reduction with sinusoidal riblets depend strongly on the wavelength, showing a benefit over straight riblets for a larger value of the wavelength, and an opposite trend for a smaller value. Different nature of the flow over straight and sinusoidal riblet surfaces is revealed by looking at crossflow motion in transverse planes, mean and instantaneous streamwise vorticity, and organized coherent structures. Turbulent statistics is compared between all three cases, crossflow turbulence intensity is reduced for sinusoidal riblets as opposed to straight riblets.
Super-Resolution Multi-Look Detection in SAR Tomography

Synthetic Aperture Radar (SAR) Tomography (TomoSAR) allows extending the 2-D focusing capabilities of SAR to the elevation direction, orthogonal to the azimuth and range. The multi-dimensional extension (along the time) also enables the monitoring of possible scatterer displacements. A key aspect of TomoSAR is the identification, in the presence of noise, of multiple persistent scatterers interfering within the same 2-D (azimuth range plane) pixel. To this aim, the use of multi-look has been shown to provide tangible improvements in the detection of single and double interfering persistent scatterers at the expense of a minor spatial resolution loss. Depending on the system acquisition characteristics, this operation may require also the detection of multiple scatterers interfering at distances lower than the Rayleigh resolution (super-resolution). In this work we further investigated the use of multi-look in TomoSAR for the detection of multiple scatterers located also below the Rayleigh resolution. A solution relying on the Capon filtering was first analyzed, due to its improved capabilities in the separation of the responses of multiple scatterers and sidelobe suppression. Moreover, in the framework of the Generalized Likelihood Ratio Test (GLRT), the single-look support based detection strategy recently proposed in the literature was extended to the multi-look case. Experimental results of tests carried out on two datasets acquired by TerraSAR-X and COSMO-SkyMED sensors are provided to show the performances of the proposed solution as well as the effects of the baseline span of the dataset for the detection capabilities of interfering scatterers.
SHINRA: Structuring Wikipedia by Collaborative Contribution

We are reporting the SHINRA project, a project for structuring Wikipedia with collaborative construction scheme. The goal of the project is to create a huge and well-structured knowledge base to be used in NLP applications, such as QA, Dialogue systems and explainable NLP systems. It is created based on a scheme of â€Resource by Collaborative Contribution (RbCC)â€. We conducted a shared task of structuring Wikipedia, and at the same, submitted results are used to construct a knowledge base. There are machine readable knowledge bases such as CYC, DBpedia, YAGO, Freebase Wikidata and so on, but each of them has problems to be solved. CYC has a coverage problem, and others have a coherence problem due to the fact that these are based on Wikipedia and/or created by many but inherently incoherent crowd workers. In order to solve the later problem, we started a project for structuring Wikipedia using automatic knowledge base construction shared-task. The automatic knowledge base construction shared-tasks have been popular and well studied for decades. However, these tasks are designed only to compare the performances of different systems, and to find which system ranks the best on limited test data. The results of the participated systems are not shared and the systems may be abandoned once the task is over. We believe this situation can be improved by the following changes: 1. designing the shared-task to construct knowledge base rather than evaluating only limited test data 2. making the outputs of all the systems open to public so that we can run ensemble learning to create the better results than the best systems 3. repeating the task so that we can run the task with the larger and better training data from the output of the previous task (bootstrapping and active learning) We conducted â€œSHINRA2018â€ with the above mentioned scheme and in this paper we report the results and the future directions of the project. The task is to extract the values of the pre-defined attributes from Wikipedia pages. We have categorized most of the entities in Japanese Wikipedia (namely 730 thousand entities) into the 200 ENE categories. Based on this data, the shared-task is to extract the values of the attributes from Wikipedia pages. We gave out the 600 training data and the participants are required to submit the attribute-values for all remaining entities of the same category type. Then 100 data out of them for each category are used to evaluate the system output in the shared-task. We conducted a preliminary ensemble learning on the outputs and found 15 F1 score improvement on a category and the average of 8 F1 score improvements on all 5 categories we tested over a strong baseline. Based on this promising results, we decided to conduct three tasks in 2019; multi-lingual categorization task (ML), extraction for the same 5 categories in Japanese with a larger training data (JP-5) and extraction for 34 new categories in Japanese (JP-34).
“Get Off My Internets”: How Anti-Fans Deconstruct Lifestyle Bloggers’ Authenticity Work

This paper examines the nature of authenticity labour in personal lifestyle blogging through a case study of travel bloggers. Specifically, it looks at how participants in the blogging anti-fan community Get Off My Internets (GOMI) identify and deconstruct lifestyle bloggers’ efforts to perform an ‘authentic’ persona. Within the broader context of online micro-celebrity, self-branding, and persona, I examine authenticity as a kind of labour that is necessary for lifestyle blogging ‘success,’ where success is measured by metrics like heavy website traffic and brand sponsorships. Lifestyle bloggers perform authenticity partly by narrating the process of cultivating personal authenticity through the ongoing process of selfimprovement towards an idealized goal. This personal authenticity is based on existentialist notions of ‘being true’ to one’s essential nature and personal commitments. In the GOMI community, bloggers’ representations of the inner life are frequently viewed with suspicion, and interpreted as ‘staged,’ and therefore inauthentic, performances of authenticity. Bloggers are also expected to demonstrate a commitment to ethical authenticity, and, subsequently, attempts to monetize their content through sponsorships and affiliate links are viewed with suspicion. Lastly, authenticity work in lifestyle blogging involves emphasizing one’s ordinariness alongside one’s extraordinariness, resulting in what I call ‘aspirational extra/ordinariness. ’ By observing trends in how travel bloggers perform authenticity and how anti-fans deconstruct these performances, it becomes apparent that critical publics identify inauthenticity in moments where the constructedness or performedness of authenticity is most apparent, indicating that while micro-celebrities rely on authenticity labour for their popularity, this very labour can detract from a persona’s perceived authenticity when it becomes obvious to publics.
Transformation of the Business Model in an Occupational Health Care Company Embedded in an Emerging Personal Data Ecosystem: A Case Study in Finland

Information technology has long been used as an enabler of exchange for goods and services. Services are evolving from generic to personalized, and the reverse use of customer data has been discussed in both academia and industry for the past few years. This article presents the results of an empirical case study in the area of preventive health care services. The primary data were gathered in workshops, in which future personal data-based services were conceptualized by analyzing future scenarios from a business perspective. The aim of this study is to understand business model transformation in emerging personal data ecosystems. The work was done as a case study in the context of occupational healthcare. The results have implications to theory and practice, indicating that adopting personal data management principles requires transformation of the business model, which, if successfully managed, may provide access to more resources, potential to offer better value, and additional customer channels. These advantages correlate with the broadening of the business ecosystem. Expanding the scope of this study to include more actors would improve the validity of the research. The results draw from existing literature and are based on findings from a case study and the economic properties of the healthcare industry in Finland. Abstract—Ecosystem, business model, personal data, preventive healthcare.Ecosystem, business model, personal data, preventive healthcare.
Evaluation of three lidar scanning strategies for turbulence measurements

Abstract. Several errors occur when a traditional Doppler beam swinging (DBS) or velocity–azimuth display (VAD) strategy is used to measure turbulence with a lidar. To mitigate some of these errors, a scanning strategy was recently developed which employs six beam positions to independently estimate the u, v, and w velocity variances and covariances. In order to assess the ability of these different scanning techniques to measure turbulence, a Halo scanning lidar, WindCube v2 pulsed lidar, and ZephIR continuous wave lidar were deployed at field sites in Oklahoma and Colorado with collocated sonic anemometers. Results indicate that the six-beam strategy mitigates some of the errors caused by VAD and DBS scans, but the strategy is strongly affected by errors in the variance measured at the different beam positions. The ZephIR and WindCube lidars overestimated horizontal variance values by over 60 % under unstable conditions as a result of variance contamination, where additional variance components contaminate the true value of the variance. A correction method was developed for the WindCube lidar that uses variance calculated from the vertical beam position to reduce variance contamination in the u and v variance components. The correction method reduced WindCube variance estimates by over 20 % at both the Oklahoma and Colorado sites under unstable conditions, when variance contamination is largest. This correction method can be easily applied to other lidars that contain a vertical beam position and is a promising method for accurately estimating turbulence with commercially available lidars.
Sintrack analysis for tracking components of musical signals

Musical signals often present close frequencies producing beats and the classical Fourier Transform does not achieve a sufficient resolution to estimate these components separately with accuracy. This paper presents a spectral analysis technique for estimating and tracking the frequencies, damping factors and amplitudes of each component. SINTRACK associates a Matrix Pencil high resolution method with a LMS adaptive algorithm. It detects changes in the signal underlying model (as the changing number of sinusoids for example) and can indicate the time variations of parameters. Results are shown for a signal recorded from a guitar.
Deep Haar Scattering Networks in Pattern Recognition: A promising approach

The aim of this paper is to discuss the use of Haar scattering networks, which is a very simple architecture that naturally supports a large number of stacked layers, yet with very few parameters, in a relatively broad set of pattern recognition problems, including regression and classification tasks. This architecture, basically, consists of stacking convolutional filters, that can be thought as a generalization of Haar wavelets, followed by non-linear operators which aim to extract symmetries and invariances that are later fed in a classification/regression algorithm. We show that good results can be obtained with the proposed method for both kind of tasks. We have outperformed the best available algorithms in 4 out of 18 important data classification problems, and have obtained a more robust performance than ARIMA and ETS time series methods in regression problems for data with strong periodicities.