Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate and Ecosystems

How does life on our planet respond to--and shape--climate? This question has never been more urgent than it is today, when humans are faced with the daunting task of guiding adaptation to an inexorably changing climate. This concise, accessible, and authoritative book provides an unmatched introduction to the most reliable current knowledge about the complex relationship between living things and climate. Using an Earth System framework, David Schimel describes how organisms, communities of organisms, and the planetary biosphere itself react to and influence environmental change. While much about the biosphere and its interactions with the rest of the Earth System remains a mystery, this book explains what is known about how physical and chemical climate affect organisms, how those physical changes influence how organisms function as individuals and in communities of organisms, and ultimately how climate-triggered ecosystem changes feed back to the physical and chemical parts of the Earth System. An essential introduction, Climate and Ecosystems shows how Earth's living systems profoundly shape the physical world.
Effect of cognitive biases on human understanding of rule-based machine learning models

This thesis investigates to what extent do cognitive biases a ect human understanding of interpretable machine learning models, in particular of rules discovered from data. Twenty cognitive biases (illusions, e ects) are analysed in detail, including identi cation of possibly e ective debiasing techniques that can be adopted by designers of machine learning algorithms and software. This qualitative research is complemented by multiple experiments aimed to verify, whether, and to what extent, do selected cognitive biases in uence human understanding of actual rule learning results. Two experiments were performed, one focused on eliciting plausibility judgments for pairs of inductively learned rules, second experiment involved replication of the Linda experiment with crowdsourcing and two of its modi cations. Altogether nearly 3.000 human judgments were collected. We obtained empirical evidence for the insensitivity to sample size e ect. There is also limited evidence for the disjunction fallacy, misunderstanding of and , weak evidence e ect and availability heuristic. While there seems no universal approach for eliminating all the identi ed cognitive biases, it follows from our analysis that the e ect of many biases can be ameliorated by making rule-based models more concise. To this end, in the second part of thesis we propose a novel machine learning framework which postprocesses rules on the output of the seminal association rule classi cation algorithm CBA [Liu et al, 1998]. The framework uses original undiscretized numerical attributes to optimize the discovered association rules, re ning the boundaries of literals in the antecedent of the rules produced by CBA. Some rules as well as literals from the rules can consequently be removed, which makes the resulting classi er smaller. Benchmark of our approach on 22 UCI datasets shows average 53% decrease in the total size of the model as measured by the total number of conditions in all rules. Model accuracy remains on the same level as for CBA.
PENGARUH MANAJEMEN LABA DAN KONSERVATISME AKUNTANSI TERHADAP NILAI PERUSAHAAN DENGAN KINERJA PERUSAHAAN SEBAGAI VARIABEL PEMODERASI

Penelitian ini bertujuan untuk memberikan bukti empiris pengaruh manajemen laba dan konservatisme akuntansi terhadap nilai perusahaan dengan kinerja perusahaan sebagai variabel pemoderasi. Populasi penelitian pada perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia tahun 2016 – 2017. Pengambilan sampel dilakukan dengan metode purposive sampling. Jumlah populasi 168 yang memenuhi kriteria sebanyak 40 perusahaan, maka data penelitian 40 x 2 tahun yaitu 80 data. Pengolahan data penelitian untuk menguji hipotesis penelitian secara simultan dan parsial. Hasil penelitian menunjukkan bahwa manajemen laba berpengaruh signifikan terhadap nilai perusahaan –17,6%, konservatisme berpengaruh signifikan terhadap nilai perusahaan 21,3%, kinerja perusahaan berpengaruh sinifikan terhadap nilai perusahaan 22,7%, manajemen berpengaruh sinifikan laba terhadap nilai perusahaan dengan kinerja perusahaan sebagai variabel pemoderasi 9%. dan konservatisme berpengaruh sinifikan terhadap nilai perusahaan dengan kinerja perusahaan sebagai variabel pemoderasi 9%
Contribution to the calculation methods of the elastic properties and transport of heterogeneous media by the Fourier Transform

This work proposed some contributions to the homogenization methods with applications to composites materials, polycristals and porous media. The effective properties are determined by solving the unit cell problem and the corresponding Lippmann-Schwinger (LS) equation. The latter is solved by means of Neumann series. Different approaches are considered to evaluate each terms of the series using analytic or numerical approaches. In the first two chapters, we formulate a general class of LS equations for the polarization in the case of conductivity and then elasticity. The operator of the latter is optimized to obtain the best convergence of the associated Neumann series and then of the better estimate of the effective of the composite. The estimate is based on both a truncated Neumann series and an approximation of its residual. In chapter 3, we deal with the mass transport properties of porous media. Classically, the filtration law is given by the Darcy equation at the macroscopic scale. In the present work we compute the corrective terms of the Darcy equation in the situation of no strict scale separation. These corrective terms are determined numerically by solving a LS equation with a fast Fourier Transform (FFT) based iterative scheme. Finally, in chapter 4, we derivative numerically some bounds for the elastic properties of polycristals still by means of an FFT iterative scheme. The approach uses an exact description of the voronoi-unit cell geometry by using the shape functions of polygons and polyhedrons. The method is applied to polycristals constituted of cubic single crystals
Almost the Best of Three Worlds: Risk, Consistency and Optional Stopping for the Switch Criterion in Nested Model Selection

We study the switch distribution, introduced by Van Erven et al. (2012), applied to model selection and subsequent estimation. While switching was known to be strongly consistent, here we show that it achieves minimax optimal parametric risk rates up to a $\log\log n$ factor when comparing two nested exponential families, partially confirming a conjecture by Lauritzen (2012) and Cavanaugh (2012) that switching behaves asymptotically like the Hannan-Quinn criterion. Moreover, like Bayes factor model selection but unlike standard significance testing, when one of the models represents a simple hypothesis, the switch criterion defines a robust null hypothesis test, meaning that its Type-I error probability can be bounded irrespective of the stopping rule. Hence, switching is consistent, insensitive to optional stopping and almost minimax risk optimal, showing that, Yang's (2005) impossibility result notwithstanding, it is possible to `almost' combine the strengths of AIC and Bayes factor model selection.
ANTARES and IceCube Combined Search for Neutrino Point-like and Extended Sources in the Southern Sky

A search for point-like and extended sources of cosmic neutrinos using data collected by the ANTARES and IceCube neutrino telescopes is presented. The data set consists of all the track-like and shower-like events pointing in the direction of the Southern Sky included in the nine-year ANTARES point-source analysis, combined with the through-going track-like events used in the seven-year IceCube point-source search. The advantageous field of view of ANTARES and the large size of IceCube are exploited to improve the sensitivity in the Southern Sky by a factor $\sim$2 compared to both individual analyses. In this work, the Southern Sky is scanned for possible excesses of spatial clustering, and the positions of preselected candidate sources are investigated. In addition, special focus is given to the region around the Galactic Centre, whereby a dedicated search at the location of SgrA* is performed, and to the location of the supernova remnant RXJ 1713.7-3946. No significant evidence for cosmic neutrino sources is found and upper limits on the flux from the various searches are presented.
Forecasting the monsoon on daily to seasonal time‐scales in support of a field campaign

The successful planning and execution of a major field campaign relies on the availability and reliability of weather forecasts on a range of time‐scales. Here, we describe the wide range of forecast products generated in support of a field campaign that took place in India in 2016 as part of the Interaction of Convective Organization with Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS) project. We show examples of the suite of plots generated every day from the forecasts and supplied to the mission scientists, and describe how these were used to plan the flights. We highlight the benefits of having access to forecasts from a range of model resolutions and configurations; these allowed judgements to be made about uncertainty, particularly in the amount and location of deep convective rainfall, which is an important consideration for flight planning. Finally, we discuss the legacy of the forecasting activity, which has not only advanced our understanding of monsoon forecasting but also created a large database of targeted model forecast products for the whole of the 2016 monsoon season. These can be used by researchers for comparisons with in situ observations as well as future modelling studies.
Searches for steady neutrino emission from 3FHL blazars using eight years of IceCube data from the Northern hemisphere

Located at the South Pole, the IceCube Neutrino Observatory is the world largest neutrino telescope, instrumenting one cubic kilometre of Antarctic ice at a depth between 1450m to 2450m. In 2013 IceCube reported the first observations of a diffuse astrophysical high-energy neutrino flux. Although the IceCube Collaboration has identified more than 100 high-energy neutrino events, the origin of this neutrino flux is still not known. Blazars, a subclass of Active Galactic Nuclei and one of the most powerful classes of objects in the Universe, have long been considered promising sources of high energy neutrinos. A blazar origin of this high-energy neutrino flux can be examined using stacking methods testing the correlation between IceCube neutrinos and catalogs of hypothesized sources. Here we present the results of a stacking analysis for 1301 blazars from the third catalog of hard Fermi-LAT sources (3FHL). The analysis is performed on 8 years of through-going muon data from the Northern Hemisphere, recorded by IceCube between 2009 and 2016. No excess of neutrinos from the blazar position was found and first limits on the neutrino production of these sources will be shown
Measurement of cosmic ray proton spectrum with the Dark Matter Particle Explorer

The precise measurement of the energy spectrum of cosmic ray proton is very important for the study of cosmic ray physics. The DArk Matter Particle Explorer (DAMPE), a space-based high energy particle detector, has a very good potential to measure cosmic-ray protons up to 100 TeV. Since its successful launch on December 17, 2015, DAMPE has been operated on-orbit for more than three years. Here, the analysis of the cosmic-ray proton spectrum from 40GeV to 100TeV with DAMPE experiment will be reported.
Formulation Optimisation Of Rolled Pancake With Red Dragon Fruit’s Skin Using Response Surface Methodology

Kue Dadar (lit. pancake) is a green-coloured Indonesian cake, but in recent years it saw the colouring of the pancake is mainly using synthetic colouring. This type of colouring can be substituted with natural colourings such as Betalain, gathered from the skin of the red dragon fruit. The skin is also beneficial for human because it also contains fibre, pectin, and other nutritious contents. This research aims to obtain the formulation of red dragon skin pancake, using the factors of red dragon skin’s amount (X 1 ), egg’s amount (X 2 ), and thick coconut milk’s amount (X 3 ). This research will be conducted through experiments, utilising Response Surface Methodology (RSM) and Central Composite Design (CCD) . Data analysis will be assisted by Software Design Expert ver. 7.1. The results of this pancake formulation’s optimisation are 89.73 grams of red dragon fruit skin, 39.99 grams of egg, 167.78 ml of thick coconut milk and have a quadratic characteristic. Analysis results of R 2 and Adjusted R 2 by colour and texture response are 0.8720; 0.9500 and 0.9327; 0.9049 respectively. Response values on its optimal condition are 5.47 for colour and 5.62 for texture. Other quality values are 5.00 for shape (good shape), 5.23 for aroma (good smell), and 4.90 (sweet flavour).
Stratospheric Gravity Wave Products from Satellite Infrared Nadir Radiances in the Planning, Execution, and Validation of Aircraft Measurements during DEEPWAVE

Gravity wave perturbations in 15-μm nadir radiances from the Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) informed scientific flight planning for the Deep Propagating Gravity Wave Experiment (DEEPWAVE). AIRS observations from 2003 to 2011 identified the South Island of New Zealand during June–July as a “natural laboratory” for observing deep-propagating gravity wave dynamics. Near-real-time AIRS and CrIS gravity wave products monitored wave activity in and around New Zealand continuously within 10 regions of scientific interest, providing nowcast guidance and validation for flight planners. A novel technique used these gravity wave products to validate upstream forecasts of nonorographic gravity waves with 1–2-day lead times, providing time to plan flight intercepts as tropospheric westerlies brought forecast source regions into range. Postanalysis verifies the choice of 15 μm radiances for nowcasting, since 4.3-μm gravity wave products yielded spurious diurnal cycles, provided no altitude sensitivity, and proved relatively insensitive to deep gravity wave activity over the South Island. Comparisons of DEEPWAVE flight tracks with AIRS and CrIS gravity wave maps highlight successful repeated vectoring of the aircraft into regions of deep orographic and nonorographic gravity wave activity, and how background winds control the amplitude of waves in radiance perturbation maps. We discuss how gravity wave information in AIRS and CrIS radiances might be directly assimilated into future operational forecasting systems.
Utilizing general-purpose computing on graphic processing units for engineering algorithm speedup.

The goal of this thesis, is to investigate the possible benefits of using general-purpose computing on graphics processing units (GPGPU), in order to speed up the execution of calculations in engineering applications. The thesis focuses primarily on speeding up the process of analyzing laser scan point clouds, in software developed by TechnoSoft Inc. This is achieved by developing faster algorithms for solving the k-nearest neighbors (kNN), and All-kNN problem, optimized for point cloud data. A parallel brute-force algorithm is developed, which is capable of solving the kNN problem up to 70 times faster than a similar algorithm developed by Garcia et.al. [13], when working on comparable data. By utilizing the k-d tree data structure, a parallel tree-build and query algorithm is developed, suitable for solving the All-kNN problem. This algorithm improves the result obtained by the brute-force algorithm by up to 300 times. Utilizing general-purpose computing on graphic processing units for engineering algorithm speed up 3
Characteristics of brain tumor with optical coherence tomography

Brain tumor is resulting from the development of abnormal cells in the brain, probably leading to headache, vision or mental problems. Currently, surgery is the most effective solution to remove the tumor cells as possible. However, it is difficult to determine the safe margin during brain surgery. In this study, we implement optical coherence tomography (OCT) and OCT angiography (OCTA) to in vivo observe the growth of brain tumor and to evaluate the difference between normal and cancerous brain tissue. A swept-source OCT (SS-OCT) with a center wavelength of 1060 nm was developed for the study of brain tumor on the animal model and the angiographic algorithm was also demonstrated for simultaneously acquiring brain microcirculation. From the results, the change in the morphology of brain tissue during tumor growth can be identified. Moreover, the angiogenesis resulting from tumor growth can be observed. Moreover, the texture of OCT images and the angiogenesis are quantitatively evaluated. It can be noted that OCT/OCTA could be a potential tool for in vivo identification of brain tumor during brain surgery or treatments.
Exact predictive likelihood inference for Laplace distribution based on a time-constrained experiment

Abstract In this paper, we will first derive explicit expressions for the predictive maximum likelihood estimators (PMLEs) for Laplace distribution based on a time-constrained life-testing experiment. Next, we derive the exact conditional distribution of the likelihood predictor of the s-th failure through the use of exact conditional moment generating function (MGF). We develop three different exact prediction intervals for the future failure, and a Monte Carlo simulation study is then carried out to check the accuracy of the derived exact inference. Finally, an example is presented to illustrate all the inferential results developed here.
Multi-messenger interpretation of the neutrinos from TXS 0506+056

We discuss possible interpretations of the neutrinos observed from the AGN blazar TXS 0506+056 in the multi-messenger and multi-wavelength context, including both the 2014-15 and 2017 neutrino flares. While the neutrino observed in September 2017 has to describe contemporary data in e.g. the X-ray and VHE gamma-ray ranges, data at the 2014-15 excess are much sparser. We demonstrate that in both cases the simplest possible one-zone AGN blazar models face challenges. While the 2017 flare can be well interpreted by considering more sophisticated source geometries, the 2014-15 flare is much harder to describe with conventional models. One challenge is the energy injected into the electromagnetic cascade coming together with the neutrino production, which cannot be reconciled with the 13 observed neutrino events. We also speculate if a common interpretation of both flares is feasible.
A novel 4D chaotic system with multistability: Dynamical analysis, circuit implementation, control design

The purpose of this work is to introduce a novel 4D chaotic system and investigate its multistability. The novel system has an unstable origin and two stable symmetrical hyperbolic equilibria. When the parameter increases across a critical value, the equilibria lose their stability and double Hopf bifurcations occur with the appearance of limit cycles. A pair of point, periodic, chaotic attractors are observed in the system from different initial values for given parameters. The chaos of the system is yielded via period-doubling bifurcation. A double-scroll chaotic attractor is numerically observed as well. By using the electronic circuit, the chaotic attractor of the system is realized. The control problem of the system is reported. An effective controller is designed to stabilize the system.
An Invitation to Random Schr¨ odinger operators

Ces notes essayent de presenter les bases de la theorie des operateurs de Schrodinger aleatoires. Elles sont destinees a un public tres large et ne requierent que des connaissances minimales en analyse fonctionnelle et en theorie des probabilites. Neanmoins, on y donne une demonstration complete des asymptotiques de Lifshitz et de la localisation d'Anderson. Ces notes ont ete augmentees d'un appendice ecrit par F. Klopp, appendice presentant une preuve de la localisation d'Anderson suivant les idees d'Aizenman et Molchanov.
Social adaptability in ecotones: sea-level rise and climate change adaptation in Flushing and the Isles of Scilly, UK

Coastal zones and small islands are among the areas most impacted by global climate change and face great challenges for adaptation. While being considered as particularly vulnerable, many coastal communities, nevertheless, have long traditions of living not only by but with the sea. If such ecotones―places where ecosystems intersect―have features distinct from purely continental regions, the question is how life with the shore translates into adaptability towards environmental change. Life at the shore shapes emergent social relationships, local traditions, and collective memory. At the same time, issues such as tourism development, demographic change, and national and international administrations influence how environmental challenges in coastal areas are addressed. In this paper, I analyse how place-specific social structures and conflicting influences in ecotones affect adaptability to sea-level rise in coastal areas. This research draws on quantitative and qualitative data from a comparative study of two case studies, a coastal town and an archipelago, in Southwest England.
The Sensitivity of Spatial Regression Models to Network Misspecification

Spatial econometric models become increasingly popular in various subfields of political science. However, the necessity to specify the underlying network of dependencies, denoted by $\boldsymbol{W}$ , prior to estimation is a prevalent source of criticism since the true dependence structure is rarely known and theories mostly provide insufficient guidance. The present study investigates the effects of this network uncertainty which is a special case of model uncertainty that arises from uncertainty about the correct specification of $\boldsymbol{W}$ . It advocates Bayesian model averaging (BMA) as a superior approach to this problem, located at the intersection of theory and empirics. Conducting Monte Carlo experiments, I demonstrate that, while the effect estimates are robust toward a misspecification in the functional form of $\boldsymbol{W}$ , uncertainty in the neighborhood definition can bias the effect estimates derived from spatial autoregressive models. In contrast to alternative techniques, BMA directly addresses network uncertainty, correctly identifies the true network structure in the set of feasible alternatives, and provides unbiased effect estimates. Two replication studies from different subfields of the discipline illustrate the benefits of this approach for applied research.