Miracle-dz/UniFAR-FTUs huggingface dataset:
Conformal defects in two-dimensional conformal field theories

We study conformal defects in two-dimensional conformal field theories (CFTs). These are one-dimensional objects across which the difference between the holomorphic and antiholomorphic parts of the stress-energy tensor is continuous. Such defects may exist within a CFT as well as between two different CFTs. There are two subclasses of conformal defects that are well-known: topological defects, which preserve the holomorphic and antiholomorphic parts of the stress-energy tensor separately, and factorising defects, which can be considered as products of conformal boundary conditions separating the theory along the defect. In this thesis, we call conformal defects, which do not fall into either of the aforementioned subclasses, non-trivial conformal defects. The primary focus of this thesis is studying the non-trivial conformal defect present in a unitary Virasoro minimal model which was first predicted by Kormos, Runkel, and Watts [98]. As a first step, we calculate the reflection and transmission coefficients, which were first defined in [91], of these defects using the leading-order perturbative calculation. We then consider conformal defects in the tri-critical Ising model as a concrete example. We revisit the construction of super-conformal defects proposed by Gang and Yamaguchi [94] and give a more systematic construction of such defects using super W-algebras. In addition, we propose a topological interface separating the super-conformal and bosonic theories, from which conformal defects in the latter theory can be obtained from the former one. Using the topological interfaces and superconformal defects, we obtain non-topological and non-factorising defects in the bosonic tri-critical Ising model.
On a multilattice analogue of a hypersequent S5 calculus

In this paper, we present a logic MML S5 n which is a combination of multilattice logic and modal logic S5. MML S5 n is an extension of Kamide and Shramko’s modal multilattice logic which is a multilattice analogue of S4. We present a cut-free hypersequent calculus for MML S5 n in the spirit of Restall’s one for S5 and develop a Kripke semantics for MML S5 n , following Kamide and Shramko’s approach. Moreover, we prove theorems for embedding MML S5 n into S5 and vice versa. As a result, we obtain completeness, cut elimination, decidability, and interpolation theorems for MML S5 n . Besides, we show the duality principle for MML S5 n . Additionally, we introduce a modification of Kamide and Shramko’s sequent calculus for their multilattice version of S4 which (in contrast to Kamide and Shramko’s original one) proves the interdefinability of necessity and possibility operators. Last, but not least, we present Hilbert-style calculi for all the logics in question as well as for a larger class of modal multilattice logics.
Propagation of cosmic rays and their secondaries in the intracluster medium

Abstract We present results of the propagation of high-energy cosmic rays (CRs) and their secondaries in the intracluster medium (ICM). To this end, we employ three-dimensional cosmological magnetohydrodynamical simulations of the turbulent intergalactic medium to explore the propagation of CRs with energies between 1014 and 1019 eV. We study the interaction of test particles with this environment considering all relevant electromagnetic, photohadronic, photonuclear, and hadronuclear processes. Finally, we discuss the consequences of the confinement of high-energy CRs in clusters for the production of gamma rays and neutrinos.
A Flexible Zero-Inflated Poisson Regression Model

OF DISSERTATION A Flexible Zero-Inflated Poisson Regression Model A practical problem often encountered with observed count data is the presence of excess zeros. Zero-inflation in count data can easily be handled by zero-inflated models, which is a two-component mixture of a point mass at zero and a discrete distribution for the count data. In the presence of predictors, zero-inflated Poisson (ZIP) regression models are, perhaps, the most commonly used. However, the fully parametric ZIP regression model could sometimes be restrictive, especially with respect to the mixing proportions. Taking inspiration from some of the recent literature on semiparametric mixtures of regressions models for flexible mixture modeling, we propose a semiparametric ZIP regression model. We present an “EM-like” algorithm for estimation and a summary of asymptotic properties of the estimators. The proposed semiparametric models are then applied to a data set involving clandestine methamphetamine laboratories and Alzheimer’s disease.
Status and performance results from NectarCam -- a camera for CTA medium sized telescopes

The Cherenkov Telescope Array (CTA) will be the first ground-based observatory for gamma-ray astronomy. With more than a hundred of 4th generation of Imaging Atmospheric Cherenkov Telescopes (IACTs) distributed in two large arrays, CTA will reach unprecedented sensitivity, angular resolution, and spectral coverage. Three classes of IACT -- 40 Medium-Sized Telescopes (MSTs), 8 Large-Sized Telescopes (LSTs) and 70 Small-Sized Telescopes (SSTs) -- are required to cover the full CTA energy range (20 GeV to 300 TeV). NectarCAM is a Cherenkov camera which is designed to equip medium sized telescopes of CTA, covering the central energy range from 100 GeV to 30 TeV, with a field of view of 8 degrees. It's based on a modular design with data channels using the NECTAr chip, which are equipped with both GHz sampling Switched Capacitor Array and 12-bit Analog to Digital Converter (ADC). The camera will be equipped of 265 modules, each module consisting of 7 photomultiplier Tubes (PMTs) and a Front End Board performing the data capture, sending the data over the Ethernet after the trigger decision at rates up to 10 kHz. This contribution provides an overview of the status of the first NectarCAM camera currently under integration in Saclay. Furthermore, we will discuss the calibration strategies and present performance results from the Saclay test bench and from the first data taken under a real sky on the prototype of medium sized telescope (MST) structure in Adlershof.
VERITAS detection of VHE emission from the optically bright quasar OJ 287

We report on the VERITAS detection of very-high-energy (VHE, $E > 100~\mathrm{GeV}$) $\gamma$-ray emission from the optically bright quasar OJ~287 which is located at a redshift of z = 0.306. OJ~287 has been observed to display regular optical outbursts with a period of approximately 12 years, with the last major optical outburst having occurred in 2015. To explain this periodicity, models involving a binary supermassive black hole system at the core of OJ~287, or a helical jet, have been developed. Motivated by elevated {\it Swift}-XRT count rates, VERITAS observed OJ~287 in February 2017, and detected the object at >5 standard deviations above background. This detection prompted further VERITAS, {\it Swift}-XRT and multiwavelength observations of the object. The results of the VERITAS observational campaign are presented.
100 Years of Progress on Mountain Meteorology Research

Mountains significantly influence weather and climate on Earth, including disturbed surface winds; altered distribution of precipitation; gravity waves reaching the upper atmosphere; and modified global patterns of storms, fronts, jet streams, and climate. All of these impacts arise because Earth’s mountains penetrate deeply into the atmosphere. This penetration can be quantified by comparing mountain heights to several atmospheric reference heights such as density scale height, water vapor scale height, airflow blocking height, and the height of natural atmospheric layers. The geometry of Earth’s terrain can be analyzed quantitatively using statistical, matrix, and spectral methods. In this review, we summarize how our understanding of orographic effects has progressed over 100 years using the equations for atmospheric dynamics and thermodynamics, numerical modeling, and many clever in situ and remote sensing methods. We explore how mountains disturb the surface winds on our planet, including mountaintop winds, severe downslope winds, barrier jets, gap jets, wakes, thermally generated winds, and cold pools. We consider the variety of physical mechanisms by which mountains modify precipitation patterns in different climate zones. We discuss the vertical propagation of mountain waves through the troposphere into the stratosphere, mesosphere, and thermosphere. Finally, we look at how mountains distort the global-scale westerly winds that circle the poles and how varying ice sheets and mountain uplift and erosion over geologic time may have contributed to climate change.
HaloSat: A CubeSat to Study the Hot Galactic Halo

HaloSat is a small satellite (CubeSat) designed to map soft X-ray oxygen line emission across the sky in order to constrain the mass and spatial distribution of hot gas in the Milky Way. The goal of HaloSat is to help determine if hot gas gravitationally bound to individual galaxies makes a significant contribution to the cosmological baryon budget. HaloSat was deployed from the International Space Station in 2018 July and began routine science operations in 2018 October. We describe the goals and design of the mission, the on-orbit performance of the science instrument, and initial observations.
The Factors Influencing Market Price Of Sago Starch In Meranti Islands By Linear Regression

One of the primary food from Indonesia is sago, and one of the sago producers is the Meranti islands. In addition to processed sago products into foodstuffs, sago products are also sold as sago starch. This study discusses the three factors that influence the market price of sago starch are the number of sago farmers, the area of sago land and annual production. Machine learning approach used is the linear regression. After training data, it can be observed that there are two factors that have a significant influence on the market price of sago starch. The results can be reference material for the industrial and trading offices of Meranti islands in observing the annual sago price so that the farmers and sago entrepreneurs can get the appropriate price of sago starch.
Wind turbines power regulation using a low-complexity linear parameter varying-model predictive control approach

In this paper, the model predictive control (MPC) approach is utilized to stabilize the output power of the wind turbines at the region above the rated wind speed. The controller is designed based on two different approaches and results have been compared. First, by putting the advantages of the MPC approach into practice, the optimal output power regulation of the wind turbine is obtained using a control oriented linear parameter varying (LPV) model of the wind turbine. However, this method inherently requires high computational cost and thus powerful hardware and processors. To cope with this limitation, an efficient suboptimal approach is proposed that significantly reduces the online computational complexity of the controller. In this approach, the main part of the controller design procedure is done off-line prior to the closed-loop wind turbine power generation and a set of optimal controllers were designed using the MPC scheme. Then, a convex combination of the calculated controllers is used for online power regulation of the wind turbine. It is noted that the selected wind turbine is a horizontal axis wind turbine operating at various speeds ranging from 10-25 m/s. Finally, using a set of simulation results we investigate the performance of the proposed approach.
Pricing dynamic fund protection under a Regime-switching Jump-diffusion model with stochastic protection level

In this paper, we investigate the valuation of dynamic fund protections under the assumption that the market value of the basic fund and the protection level follow regime-switching processes with jumps. The price of the dynamic fund protection (DFP) is associated with the Laplace transform of the first passage time. We derive the explicit formula for the Laplace transform of the DFP under the regime-switching, hyper-exponential jump-diffusion process. By using the Gaver-Stehfest algorithm, we present some numerical results for the price of the DFP.
Inverse Gaussian–based model with measurement errors for degradation analysis

To build more credible degradation models, it is necessary to consider measurement errors in degradation analysis. This article proposes an inverse Gaussian-based state space model with measurement errors that can capture the unit-to-unit variability of the degradation rate by incorporating a random effect. Then, the lifetime distribution and alarm probabilities are derived. Under the non-Gaussian assumptions, conventional parameter estimation algorithms cannot be applied directly. Therefore, an improved expectation–maximization algorithm that is combined with particle methods is developed to estimate parameters. Finally, this article concludes with a simulation study and two case applications to demonstrate the applicability and advantages of the proposed model.
On the notion of optimality in the stochastic multi-armed bandit problems

The topics addressed in this thesis lie in statistical machine learning and sequential statistic. Our main framework is the stochastic multi-armed bandit problems. In this work we revisit lower bounds on the regret. We obtain non-asymptotic, distribution-dependent bounds and provide simple proofs based only on well-known properties of Kullback-Leibler divergence. These bounds show in particular that in the initial phase the regret grows almost linearly, and that the well-known logarithmic growth of the regret only holds in a final phase. Then, we propose algorithms for regret minimization in stochastic bandit models with exponential families of distributions or with distribution only assumed to be supported by the unit interval, that are simultaneously asymptotically optimal (in the sense of Lai and Robbins lower bound) and minimax optimal. We also analyze the sample complexity of sequentially identifying the distribution whose expectation is the closest to some given threshold, with and without the assumption that the mean values of the distributions are increasing. This work is motivated by phase I clinical trials, a practically important setting where the arm means are increasing by nature. Finally we extend Fano's inequality, which controls the average probability of (disjoint) events in terms of the average of some Kullback-Leibler divergences, to work with arbitrary unit-valued random variables. Several novel applications are provided, in which the consideration of random variables is particularly handy. The most important applications deal with the problem of Bayesian posterior concentration (minimax or distribution-dependent) rates and with a lower bound on the regret in non-stochastic sequential learning.
On-sky performance verification of near infrared eAPD technology for wavefront sensing at ground based telescopes, demonstration of e-APD pixel performance to improve the sensitivity of large science focal planes and possibility to use this technology in space

Ground based near infrared adaptive optics as well as fringe tracking for coherent beam combination in optical interferometry required the development of high-speed sensors. Because of the high speed, a large analog bandwidth is needed. The short exposure times result in small signal levels which require noiseless detection. Both of these conflicting requirements cannot be met by state-of-the-art conventional CMOS technology of near infrared arrays as has been attempted previously[1][2]. The HgCdTe electron avalanche photo diode (eAPD) technology is the only way to overcome the limiting CMOS noise barrier of near infrared sensors. Therefore, ESO funded the development of the near infrared SAPHIRA 320x256 pixel e-APD arrays at LEONARDO [3][4][5][6][7]. SAPHIRA arrays have now become the devices of choice for control loops with unsurpassed performance [21]. This has also been demonstrated by the four wavefront sensors and the fringe tracker deployed in the VLTI instrument GRAVITY which set a new sensitivity standard in infrared interferometry [8][9]. It has also been demonstrated that APD arrays have extremely low dark current (1E-3 electrons/s/pixel) and may outperform conventional CMOS arrays for 100 second integrations when operated with moderate APD gains. For AO systems of extremely large telescopes and for co-phasing segmented mirror telescopes larger formats are needed. Therefore, a 512x512 pixel SAPHIRA array with 64 parallel video outputs optimized for pyramid wavefront sensing is in development. Since the SAPHIRA array has successfully passed radiation hardness testing it soon may be used for future instruments in space. Apart from the large array common voltage for high APD gain it can also be operated with voltages compatible with the space qualified SIDECAR ASIC [10].
Wavelet Autocorrelation and Denoising of Spread Spectrum Signal for Narrowband Wireless Indoor Positioning

Channel noise is a key factor that affects the measurement accuracy of narrowband wireless indoor positioning. To reduce noise effects, it is often required to effectively filter the received spread spectrum signals. However, current filtering methods in the time or frequency domain can only filter high frequency band noise, making the measurement accuracy low. In this paper, a low frequency band noise reduction algorithm based on the threshold segmentation of the rectangular window is proposed. The method is based on thresholding on time-frequency wavelet domain by variance analysis and signal despreading is achieved by wavelet autocorrelation at low frequency band after denoising with reference to locally generated spread spectrum signal without added noise at the same subband. Simulation results show that compared with other filtering methods, the proposed method by wavelet autocorrelation and denoising can eliminate the noise effects at the receiver, leading to improving the positioning accuracy significantly.
MDI-SS: matched filter detection with inverse covariance matrix-based spectrum sensing in cognitive radio

Spectrum sensing has been a major issue while dealing with cognitive radio (CR) networks. Predominantly the situation arises where noise is falsely interpreted as primary user signal called as probability of false alarm (Pfa). The user presence is estimated based on the parameters Pfa, probability of detection (PD) and receiver operating characteristics (ROC). Matched filter detection (MFD) and Neyman Pearson (NP) observer approaches are existing methods used to identify the Pfa. MFD measured ROC with different algorithms and suggests NP observer to improve the PD and minimise the Pfa. This paper proposes a novel method of matched filter detection with inverse covariance matrix-based spectrum sensing (MDI-SS). The ROC is measured, compared between MDI-SS and MFD. Next by comparing NP observer and MDI-SS, the affected samples of Pfa are identified, tabulated for different SNR levels. Finally, comparative analysis has been proposed between MDI-SS with NP observer for PD and Pfa.
Building a Lexico-Semantic Resource Collaboratively

The motto of EURALEX 2018 was “Lexicography in global contexts”, emphasising changes in the field of lexicography related to digital transformation, and the associated need to bring together lex-icographic efforts on a global level. This has been done in recent years through the Globalex initia-tive, a constellation of lexicographic associations that includes representatives from all continental associations of lexicography: Afrilex, Asialex, Australex, Dictionary Society of North America, and Euralex. Similar development can be witnessed in the decision of European Commission in 2017 to fund a four-year project dedicated to the establishment of the European Lexicographic Infrastructure (ELEXIS), which was also presented at the congress. This volume of proceedings includes congress papers submitted in three categories: papers, posters, and software demonstrations. During the review process each submitted contribution was evaluated by two independent blind referees. In case of doubt, a third independent opinion was involved.
InFuse data fusion methodology for space robotics, awareness and machine learning

Autonomous space vehicles such as orbital servicing satellites and planetary exploration rovers must be comprehensively aware of their environment in order to make appropriate decisions. Multi-sensor data fusion plays a vital role in providing these autonomous systems with sensory information of different types, from different locations, and at different times. The InFuse project, funded by the European Commission's Horizon 2020 Strategic Research Cluster in Space Robotics, provides the space community with an open-source Common Data Fusion Framework (CDFF) by which data may be fused in a modular fashion from multiple sensors. In this paper, we summarize the modular structure of this CDFF and show how it is used for the processing of sensor data to obtain data products for both planetary and orbital space robotic applications. Multiple sensor data from field testing that includes inertial measurements, stereo vision, and scanning laser range information is first used to produce robust multi-layered environmental maps for path planning. This information is registered and fused within the CDFF to produce comprehensive three-dimensional maps of the environment. To further explore the potential of the CDFF, we illustrate several applications of the CDFF that have been evaluated for orbital and planetary use cases of environmental reconstruction, mapping, navigation, and visual tracking. Algorithms for learning of maps, outlier detection, localization, and identification of objects are available within the CDFF and some early results from their use in space analogue scenarios are presented. These applications show how the CDFF can be used to provide a wide variety of data products for use by awareness and machine learning processes in space robots.
Synthetic and Empirical Capsicum Annuum Image Dataset

This dataset consists of per-pixel annotated synthetic (10500) and empirical images (50) of Capsicum annuum, also known as sweet or bell pepper, situated in a commercial greenhouse. Furthermore, the source models to generate the synthetic images are included. The aim of the datasets are to facilitate bootstrapping agricultural semantic segmentation computer vision models with synthetic data that fine-tune and test on empirical images.
A SURVEY AUTOMATIC TEXT SUMMARIZATION

Text summarization is compress the source text into a diminished version conserving its information content and overall meaning. Because of the great amount of the information we are provided it and thanks to development of Internet Technologies, text summarization has become an important tool for interpreting text information. Text summarization methods can be classified into extractive and abstractive summarization. An extractive summarization method involves selecting sentences of high rank from the document based on word and sentence features and put them together to generate summary. The importance of the sentences is decided based on statistical and linguistic features of sentences. An abstractive summarization is used to understanding the main concepts in a given document and then expresses those concepts in clear natural language. In this paper, gives comparative study of various text summarization techniques.