Miracle-dz/UniFAR-FTUs huggingface dataset:
The Clustering of DESI-like Luminous Red Galaxies Using Photometric Redshifts

We present measurements of the redshift-dependent clustering of a DESI-like luminous red galaxy (LRG) sample selected from the Legacy Survey imaging dataset, and use the halo occupation distribution (HOD) framework to fit the clustering signal. The photometric LRG sample in this study contains 2.7 million objects over the redshift range of 0.4 < z < 0.9 over 5655 sq. degrees. We have developed new photometric redshift (photo-z) estimates using the Legacy Survey DECam and WISE photometry, with σNMAD = 0.02 precision for LRGs. We compute the projected correlation function using new methods that maximize signal-to-noise while incorporating redshift uncertainties. We present a novel algorithm for dividing irregular survey geometries into equal-area patches for jackknife resampling. For a 5-parameter HOD model fit using the MultiDark halo catalog, we find that there is little evolution in HOD parameters except at the highest-redshifts. The inferred large-scale structure bias is largely consistent with constant clustering amplitude over time. In an appendix, we explore limitations of MCMC fitting using stochastic likelihood estimates resulting from applying HOD methods to N-body catalogs, and present a new technique for finding best-fit parameters in this situation. Accompanying this paper we have released the PRLS (Photometric Redshifts for the Legacy Surveys) catalog of photo-z’s obtained by applying the methods used in this work to the full Legacy Survey Data Release 8 dataset. This catalog provides accurate photometric redshifts for objects with z < 21 over more than 16,000 square degrees of sky.
Guided-mode resonance filter extended-cavity diode laser

We characterize a semiconductor external cavity diode laser whose optical feedback is provided by a guided mode resonance filter (GMRF). We focus on the spectral properties. The wavelength of operation falls in the telecom range (1506 nm). The GMRF acting both as a wavelength intracavity filter and feedback mirror allows for a compact laser design. The single-mode operation is verified in a wide range of driving currents. We finely tune the cavity length to adjust the frequency by 14 GHz without mode-hops in agreement with the expected free-spectral range of the resonator ∼20 GHz. The compactness of the cavity allows fast frequency sweeps when modulating the current (90 MHz mA−1 at 100 kHz, the modulation bandwidth). The frequency noise (366 kHz white-noise contribution) is also analysed to evaluate the potential of our design for high-resolution applications.
Searching for AGN and pulsar candidates in 4FGL unassociated sources using machine learning

In the fourth Fermi Large Area Telescope source catalog (4FGL), 5064 γ-ray sources are reported, including 3207 active galactic nuclei (AGNs), 239 pulsars, 1336 unassociated sources, 92 sources with weak association with blazars at low Galactic latitudes and 190 other sources. We employ two different supervised machine learning classifiers, combined with the direct observation parameters given by the 4FGL fits table, to search for sources potentially classified as AGNs and pulsars in the 1336 unassociated sources. In order to reduce the error caused by the large difference in the sizes of samples, we divide the classification process into two separate steps in order to identify the AGNs and the pulsars. First, we select the identified AGNs from all of the samples, and then select the identified pulsars from the remaining cases. Using the 4FGL sources associated or identified as AGNs, pulsars and other sources with the features selected through the K-S test and the random forest (RF) feature importance measurement, we trained, optimized and tested our classifier models. Then, the models are applied to classify the 1336 unassociated sources. According to the calculation results of the two classifiers, we report the sensitivity, specificity, accuracy in each step and the class of unassociated sources given by each classifier. The accuracy obtained in the first step is approximately 95%; in the second step, the obtained overall accuracy is approximately 80%. Combining the results of the two classifiers, we predict that there are 583 AGN-type candidates, 115 pulsar-type candidates, 154 other types of γ-ray candidates and 484 of uncertain types.
Colour in Online Advertising: Going for Trust, Which Blue is a Must?

Abstract In an online environment the customer relies heavily on cues that indicate that an electronic vendor (e-vendor), can be trusted (especially in comparison with an offline shopping environment). Several studies investigated the factors that reduce mistrust in an online environment. However, little is known about the effects of colour on the process of establishing trust between e-vendor and consumer, and purchase intention. The current body of studies on the effects of, specifically, the colour blue on trust in an online environment also show contrasting results. Further, the literature review revealed that (value) variations of colours have different effects on human behaviour, or perception. It was also argued that the effect of blue on trust and behaviour would be greater for people from cultures that are generally more risk averse. The aim of the present study, therefore, was to investigate the effect of two different values of blue (i.e. lighter and darker valued) on trust and the intention to book an accommodation, differentiated by culture. In an online experiment 91 Chinese and 125 Dutch respondents (average age 27 years) were presented with an accommodation offer displayed within either a predominantly darker, or lighter valued blue environment. The results showed an indirect (mediating) effect of trust on the relationship between the predominantly darker coloured blue environment and booking intention. There was no evidence for the hypothesized moderating effect of culture. To conclude, this study contributes to a better understanding of the effects of (value) variations of colour in an online environment on human behaviour, and perception. The use of dark blue colour schemes in an online environment can be recommended to e-vendors. Especially in online environments in which the customer is more involved (e.g. financially) or vulnerable in the (purchasing) process, and thus more cues are needed to win a visitor’s trust.
Reliability as an Element of Process in Managing a Fleet of Railway Vehicles

Abstract Safe and economical devices, which are constantly growing in demand, lead to improvement of operational strategies applied in railway enterprises. Economic analysis and safety, and hence reliability and readiness, are the most important parameters in the operation of railway vehicles. The article presents a strategy for maintaining railway vehicles based on reliability indicators. The strategy was verified based on empirical research in a transport company.
Some further results of the laplace transform for variable–order fractional difference equations

Abstract The Laplace transform is important for exact solutions of linear differential equations and frequency response analysis methods. In comparison with the continuous–time systems, less results can be available for fractional difference equations. This study provides some fundamental results of two kinds of fractional difference equations by use of the Laplace transform. Some discrete Mittag–Leffler functions are defined and their Laplace transforms are given. Furthermore, a class of variable–order and short memory linear fractional difference equations are proposed and the exact solutions are obtained.
’I Urge You To See This...’. Clickbait as One of the Dominant Features of Contemporary Online Headlines

Abstract With the arrival of the Internet the already-existing mass media have undergone a complete revolution. Among the most affected subtypes one could easily distinguish the press, which had to find its own place within the new medium. The fierce competition in the realm of online publishing has engendered a number of idiosyncratic linguistic devices used to lure the readers. One of the most popular ones is the phenomenon recognized as clickbait, i.e. an umbrella term for a number of techniques used to attract attention and arouse curiosity. In the following paper, we shall investigate the presence of the said phenomenon in online headlines. In order to do that we shall perform a corpus-based analysis of the data acquired from the most popular American social news outlets on the Internet, namely Buzzfeed, TMZ and E! Online. Apart from establishing the extent to which clickbait has dominated online headlines, we shall also pinpoint and discuss the specific linguistic techniques used to attract potential readers.
Lagrangian solver for vector fractional diffusion in bounded anisotropic aquifers: Development and application

Abstract Fractional-derivative models (FDMs) are promising tools for characterizing non-Fickian transport in natural geological media. Hydrologic applications of FDMs, however, have been limited in the last two decades, due to the lack of feasible models and solvers to quantify multi-dimensional anomalous diffusion for pollutants in bounded aquifers. This study develops and applies FDM tools to capture vector fractional dispersion for both conservative and reactive pollutants in fractional Brownian motion (fBm) random fields with bounded domains. A d-dimensional anisotropic fBm field for hydraulic conductivity (K) is first generated numerically. A particle-tracking based, fully Lagrangian solver is then developed to approximate particle dynamics in the fBm K fields under various boundary conditions, where the governing equation is the vector FDM subordination to regional flow. Numerical experiments show that the Lagrangian solver can combine nonlocal anomalous transport and local aquifer properties to quantify pollutant transport in bounded aquifers. Application analyses further reveal that the K correlation can significantly enhance the spreading of conservative pollutant particles, and increase the reaction rate by enhancing the mobility and mixing of reactant particles undergoing bimolecular reactions. Extension of the Lagrangian solver is also discussed, including modeling transient flow, generalizing boundary conditions, and capturing complex chemical reactions. This study therefore provides the hydrologic community an efficient Lagrangian solver to model reactive anomalous transport in bounded anisotropic aquifers with any dimension, size, and boundary conditions.
Sodium Nitroprusside Stimulates Micropropagation and TDZ Induces Adventitious Shoots Regeneration in Red Flesh Apple Malus niedzwetzkyana Koehne Dieck ex

Abstract The aim of the present investigation was to optimize protocols for micropropagation and adventitious shoot regeneration from leaf explants of two wild ecotypes of red flesh apple Malus niedzwetzkyana for future breeding programs. At the proliferation stage, different concentrations of sodium nitroprusside (SNP) and triacontanol (TRIA) were compared. To optimize shoot regeneration from leaf explants, interactive effects of 1-phenyl-3-(1,2,3-thidiazol-5-yl)-urea – thidiazuron (TDZ), indole-3-butyric acid (IBA) and two explant types were investigated. At rooting stage, the effect of exposure time of microshoots to darkness and exposure time to different concentrations of IBA and α-naphthalene acetic acid (NAA) were compared. The results showed that SNP affected the growth rate significantly and the maximum multiplication rates per explant (9.6 in the first ecotype and 8.8 in the second) were produced in the Quoirin and Lepoivre medium containing 17 SNP µM, in addition to 4 µm 6-benzylaminopurine (BAP) and 3 µm gibberellic acid (GA3). IBA and TDZ affected the adventitious shoot regeneration from leaf explants significantly, the highest number of regenerated shoots (18.3 per explant) was obtained from the basal section of the leaves cultured on the medium containing 2 μM IBA and 15 μM TDZ. At rooting stage, the maximum rooting (88.6%) was obtained in the result of one weak exposure to darkness on medium containing 3 μM IBA.
The Tax Burden CGE Analysis for Slovakia and Slovenia

Abstract In the paper, the static computable general equilibrium model for Slovakia and Slovenia is used for a tax burden analysis. There was considered simultaneous 1% increase in taxes on primary factors, on firms’ and government domestic and imported purchases, on import taxes, on output (or income) tax, on private domestic and imported consumption taxes and export subsidies. The direct tax burden as well as the allocative efficiency effects of a tax, the welfare effects and welfare decomposition of such change for both countries is analysed. The most sensitive sectors on tax rate changes is heavy manufacturing and processed food and the most distorting effect has the tax increase on private consumption tax. The government’s tax increase should generate return at least 105.75% of its costs in Slovakia and 101.92% in Slovenia, otherwise the welfare will decline.
Haploid Induction via In Vitro Gynogenesis in Persian Shallot (Allium hirtifolium)

Abstract Haploid induction using in vitro cultures of unpollinated flowers has been recognized as an important tool to produce homozygous plants for genetic studies and breeding programs. In this study the potential of gynogenic haploid induction in four ecotypes of Allium hirtifolium under different combinations of benzylaminopurine (BAP) with 2,4-dichlorophenoxyacetic acid (2,4-D), or α-naphthaleneacetic acid (NAA) was investigated. Unpollinated flower buds were excised from an umbel 5 to 3 days before anthesis, and cultured onto B5 medium containing 7.5% sucrose and 2 mg·dm−3 BAP with auxin. The experiments revealed that NAA increased the percentage of gynogenesis induction and number of gynogenic embryos per flower in all ecotypes. Somatic organogenesis from basal callus or other floral parts was most effective on the media containing 2,4-D. Plants obtained by gynogenesis were haploid in 70–77% and plants from somatic tissue were mostly diploid.
On the topology of partial metric spaces

Abstract In this paper, we give some necessary and sufficient conditions under which the topology generated by a partial metric is equivalent to the topology generated by a suitably defined metric. Next, we study some new extensions of the Generalized Banach Contraction Principle to partial metric spaces. Moreover, we draw a particular attention to the space of all sequences showing, in particular, that some well-known fixed point theorems for ultrametric spaces, can be used for operators acting in that space. We illustrate our considerations by suitable examples and counterexamples.
Phase-coupled simultaneous coherent perfect absorption and controllable hot-electron photodetection in Schottky junction metamaterial

Abstract We report a new type of coherent perfect absorption that is determined by the phase coupling between metaatoms and is referred to as the phase-coupled simultaneous coherent perfect absorption (PC-SCPA) for antisymmetric and symmetric incidences and especially the PC-SCPA for antisymmetric and symmetric incidences can be simultaneously achieved in the same bi-layered Schottky junction metamaterial possessing the phase coupling. Our proposed mechanism exploits the phase coupling between metaatoms, which is in contrast with the existing mechanism which depends on the near-field coupling. The theory of PC-SCPA is provided using coupled mode theory with the phase coupling. The operating wavelengths of PC-SCPA are insensitive to the variations of the spacing distances between metaatoms in the lateral and vertical directions. An infrared PC-SCPA-based hot-electron photodetection with dynamically switchable operating wavelengths and dynamically tunable bandwidth is theoretically and numerically verified in the same bi-layered Schottky junction metamaterial. The peak of spectrum of responsivity for antisymmetric and symmetric incidences can be switched to the same wavelength only by altering the phase coupling. Our study may build the bridge among the new type of PC-SCPA, metamaterial, and hot electron and may find potential and significant applications in hot-electron photodetection.
RHOA and mDia1 Promotes Apoptosis of Breast Cancer Cells Via a High Dose of Doxorubicin Treatment

Abstract Background Transforming RhoA proteins (RHOA) and their downstream Diaphanous homolog 1 proteins (DIAPH1) or mDia1 participate in the regulation of actin cytoskeleton which plays critical role in cells, i.e., morphologic changes and apoptosis. Methodology To determine the cell viability the real time cell analysis (RTCA) and flow cytometry were used. To perform proteomic analysis, the label-free quantitative method and post-translation modification by the nano-HPLC and ESI-MS ion trap mass analyser were used. Results The results of the cell viability showed an increase of dead cells (around 30 %) in MCF-7/DOX-1 (i.e., 1μM of doxorubicin was added to MCF-7/WT breast cancer cell line) compared to MCF-7/WT (control) after 24 h doxorubicin (DOX) treatment. The signalling pathway of the Regulation of actin cytoskeleton (p<0.0026) was determined, where RHOA and mDia1 proteins were up-regulated. Also, post-translational modification analysis of these proteins in MCF-7/DOX-1 cells revealed dysregulation of the actin cytoskeleton, specifically the collapse of actin stress fibbers due to phosphorylation of RHOA at serine 188 and mDia1 at serine 22, resulting in their deactivation and cell apoptosis. Conclusion These results pointed to an assumed role of DOX to dysregulation of actin cytoskeleton and cell death.
TF-MF: Improving Multiview Representation for Twitter User Geolocation Prediction

Twitter user geolocation detection can inform and benefit a range of downstream geospatial tasks such as event and venue recommendation, local search, and crisis planning and response. In this paper, we take into account user shared tweets as well as their social network, and run extensive comparative studies to systematically analyze the impact of a variety of language-based, network-based, and hybrid methods in predicting user geolocation. In particular, we evaluate different text representation methods to construct text views that capture the linguistic signals available in tweets that are specific to and indicative of geographical locations. In addition, we investigate a range of network-based methods, such as embedding approaches and graph neural networks, in predicting user geolocation based on user interaction network. Our findings provide valuable insights into the design of effective and efficient geolocation identification engines. Finally, our best model, called TF-MF, substantially outperforms state-of-the-art approaches under minimal supervision.
Predicting Public Opinion on Drug Legalization: Social Media Analysis and Consumption Trends

In this paper, we focus on the collection and analysis of relevant Twitter data on a state-by-state basis for (i) measuring public opinion on marijuana legalization by mining sentiment in Twitter data and (ii) determining the usage trends for six distinct types of marijuana. We overcome the challenges posed by the informal and ungrammatical nature of tweets to analyze a corpus of 306,835 relevant tweets collected over the four-month period, preceding the November 2015 Ohio Marijuana Legalization ballot and the four months after the election for all states in the US. Our analysis revealed two key insights: (i) the people in states that have legalized recreational marijuana express greater positive sentiments about marijuana than the people in states that have either legalized medicinal marijuana or have not legalized marijuana at all; (ii) the states that have a high percentage of positive sentiment about marijuana is more inclined to authorize (e.g., by allowing medical marijuana) or broaden its legal usage (e.g., by allowing recreational marijuana in addition to medical marijuana). Our analysis shows that social media can provide reliable information and can serve as an alternative to traditional polling of public opinion on drug use and epidemiology research.
Vertex-Weighted Measures for Link Prediction in Hashtag Graphs

Communications on the popular social networking platform, Twitter, can be mapped in terms of a hashtag graph, where vertices correspond to hashtags, and edges correspond to co-occurrences of hashtags within the same distinct tweet. Furthermore, a vertex in hashtag graphs can be weighted with the number of tweets a hashtag has occurred in, and edges can be weighted with the number of tweets both hashtags have co-occurred in. In this paper, we describe additions to some well-known link prediction methods that allow the weights of both vertices and edges in a weighted hashtag graph to be taken into account. We base our novel predictive additions on the assumption that more popular hashtags have a higher probability to appear with other hashtags in the future. We then apply these improved methods to 3 sets of Twitter data with the intent of predicting hashtags co-occurences in the future. Experimental results on real-life data sets consisting of over 3, 000, 000 combined unique Tweets and over 250, 000 unique hashtags show the effectiveness of the proposed models and algorithms on weighted hashtag graphs.
Online Feature Selection for Multi-label Classification in Multi-objective Optimization Framework

The current paper addresses the online feature selection problem in multi-label classification framework where multi-labelled data with features arriving in an online fashion is considered as input. The proposed approach works in two phases, in the first phase, the best subset of features is selected from the initial available set of features using a multi-objective optimization (MOO) based feature selection technique. In the second phase of the proposed feature selection technique, a newly arrived feature is accepted or rejected based on redundancy with respect to the already selected set of features and relevancy of the arrived feature with respect to the class labels. In order to show the efficacy of the proposed algorithm, it is tested on 7 various types of multi-label datasets of different domains such as text, biology, and audio. The obtained results outperform the results obtained by state-of-the-art approaches in majority of the cases.
Epidemic Threshold and Lifetime Distribution for Information Diffusion on Simultaneously Growing Networks

We study information diffusion modeled by epidemic models on a class of growing preferential attachment networks. We show through a thorough simulation study that there is a fundamental difference in the nature of the epidemic process on growing temporal networks in comparison to the same process on static networks. The empirical distribution of the epidemic lifetime on growing networks has a considerably heavier, and possibly infinite, tail. Furthermore, the notion of the epidemic threshold has only minor significance in this context, since network growth reduces the critical value of the corresponding static graph.