Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation and Adaptive Online Correction of Systematic Errors in the Global Forecast System (GFS) Using Analysis Increments

Title of dissertation: ESTIMATION AND ADAPTIVE ONLINE CORRECTION OF SYSTEMATIC ERRORS IN THE GLOBAL FORECAST SYSTEM (GFS) USING ANALYSIS INCREMENTS Kriti Bhargava Doctor of Philosophy, 2019 Dissertation directed by: Professor Eugenia Kalnay and Professor James A. Carton Department of Atmospheric and Oceanic Science Numerical Weather prediction models have improved drastically in the last few decades with advances in data assimilation, improved parameterizations, and ensemble forecasting. Despite these developments, the performance of numerical weather prediction models like the Global Forecast System (GFS) are still limited by errors in the model forecasts. These errors arise from inaccuracies in the initial condition and models inability to accurately represent physics, dynamics and chemical processes. Operation centers generally use an offline correction schemes that corrects the forecast error after the forecast is generated. Past research has shown that another class of correction schemes, the online correction schemes that correct for the forecast errors during the model integration have certain advantages over offline schemes. However, the online schemes tested so far are prohibitive for operation use. The goal of this work is to introduce and test an “adaptive online correction scheme based on the methodology developed by Danforth et al. (2007) that is suitable for operational use is introduced and implemented. As a first step towards correcting tendency equation, the model errors are estimated using the 6-hr Analysis Increments (AIs). Assuming initial linear error growth and absence of any residual bias in the analysis, 6-hr AIs provide a measure of model errors that can later be used to estimate model tendency errors. Seasonal means of 6-hr AIs during the period from 2012-2016 indicate robust model biases despite the changes in the model and data assimilation during that period. Apart from the season mean error, GFS also has significant periodic errors that are dominated by errors in the diurnal and semi-diurnal cycle. An adaptive online correcting scheme that uses 6-hr AIs, averaged over a moving training period to compute the bias correction term to be added in model integration equation is then implemented with GFS. The scheme is tested using training periods of different lengths ranging from past 7 to 28 days. This scheme is remarkably stable and reduces the forecasts errors significantly in forecasts all over the globe at lead times of 1 day and shorter and over the tropics at longer lead times. An offline correction scheme was also tested but found to be less effective than the online correction scheme especially at lead times longer than 1-day. ESTIMATION AND ADAPTIVE ONLINE CORRECTION OF SYSTEMATIC ERRORS IN THE GLOBAL FORECAST SYSTEM (GFS) USING ANALYSIS INCREMENTS
Energy Efficient Routing and Network Coding in Core Networks

We propose network coding as an energy efficient data transmission technique in core networks with non-bypass and bypass routing approaches. The improvement in energy efficiency is achieved through reduction in the traffic flows passing through intermediate nodes. A mixed integer linear program (MILP) is developed to optimize the use of network resources, and the results show that our proposed network coding approach introduces up to 33% power savings for the non-bypass case compared with the conventional architectures. For the bypass case, 28% power savings are obtained considering futuristic network components power consumption. A heuristic based on the minimum hop count routing shows power savings comparable to the MILP results. Furthermore, we study how the change in network topology affects the savings produced by network coding. The results show that the savings are proportional to the average hop count of the network topology. We also derive power consumption analytic bounds and closed form expressions for networks that implement network coding and thus also verify the results obtained by the MILP model.
Testing-Based Community Detection Methods for Complex Networks

JOHN J. PALOWITCH: Testing-Based Community Detection Methods for Complex Networks (Under the direction of Andrew B. Nobel) Community detection is an exploratory method of grouping strongly connected nodes in a network, in most cases using only the network edge structure as a guide. Using discovered communities for downstream analyses can be crucial for real-world decision-making and inference. Recent approaches to community detection include testing-based community extraction, a process in which communities are refined one-by-one via analysis of graph statistics. However, to date, testing-based extraction methods are tied to the configuration model as a null, which applies only to single-layer, binary graphs. In this thesis, testing-based extraction is generalized to arbitrary networks types with a framework called Node-Set Testing (NST). The NST framework defines the broader statistical elements of an approach that uses hypothesis testing to detect communities in complex networks. The NST framework is applied to (i) weighted networks and (ii) bipartite correlation networks, resulting in novel community detection algorithms. In particular, new null models and test statistics are specified to apply iterative hypothesis-testing algorithms on these types of networks. Detailed analyses of the empirical and theoretical properties of the proposed methods are provided. Other chapters in this thesis, while not explicitly involving testing-based algorithms, support the discussion of community detection in heterogeneous networks. One chapter provides a consistency analysis of a significance-based score for community extraction in multilayer networks. In another chapter, preceding the discussion of the NST method for bipartite correlation networks, an application area called eQTL analysis is discussed. In particular, a new model for estimating the effect size and regression correlation of the links in an eQTL network is introduced and studied.
Basilisk: Bayesian hierarchical inference of the galaxy–halo connection using satellite kinematics – I. Method and validation

We present a Bayesian hierarchical inference formalism (Basilisk) to constrain the galaxy–halo connection using satellite kinematics. Unlike traditional methods, Basilisk does not resort to stacking the kinematics of satellite galaxies in bins of central luminosity, and does not make use of summary statistics, such as satellite velocity dispersion. Rather, Basilisk leaves the data in its raw form and computes the corresponding likelihood. In addition, Basilisk can be applied to flux-limited, rather than volume-limited samples, greatly enhancing the quantity and dynamic range of the data. And finally, Basilisk is the only available method that simultaneously solves for halo mass and orbital anisotropy of the satellite galaxies, while properly accounting for scatter in the galaxy–halo connection. Basilisk uses the conditional luminosity function to model halo occupation statistics, and assumes that satellite galaxies are a relaxed tracer population of the host halo’s potential with kinematics that obey the spherical Jeans equation. We test and validate Basilisk using mocks of varying complexity, and demonstrate that it yields unbiased constraints on the galaxy–halo connection and at a precision that rivals galaxy–galaxy lensing. In particular, Basilisk accurately recovers the full PDF of the relation between halo mass and central galaxy luminosity, and simultaneously constrains the orbital anisotropy of the satellite galaxies. Basilisk ’s inference is not affected by potential velocity bias of the central galaxies, or by slight errors in the inferred, radial profile of satellite galaxies that arise as a consequence of interlopers and sample impurity.
Supernova lightCURVE POPulation Synthesis II: Validation against supernovae with an observed progenitor

Abstract We use the results of a supernova light-curve population synthesis to predict the range of possible supernova light curves arising from a population of single-star progenitors that lead to type IIP supernovae. We calculate multiple models varying the initial mass, explosion energy, nickel mass and nickel mixing and then compare these to type IIP supernovae with detailed light curve data and pre-explosion imaging progenitor constraints. Where a good fit is obtained to observations, we are able to achieve initial progenitor and nickel mass estimates from the supernova lightcurve that are comparable in precision to those obtained from progenitor imaging. For 2 of the 11 IIP supernovae considered our fits are poor, indicating that more progenitor models should be included in our synthesis or that our assumptions, regarding factors such as stellar mass loss rates or the rapid final stages of stellar evolution, may need to be revisited in certain cases. Using the results of our analysis we are able to show that most of the type IIP supernovae have an explosion energy of the order of log(Eexp/ergs) = 50.52 ± 0.10 and that both the amount of nickel in the supernovae and the amount of mixing may have a dependence on initial progenitor mass.
Timing-induced quantum collapse of wave-particle duality in a two-path interferometer

We propose an experiment of two-path interference in which the optical path difference between the two interferometer arms is much larger than the spatial spread of single-photon pulses, thereby enabling the "which-path" information of an individual photon to be identified, without disturbing its passage at all, by measuring its time of flight. This apparently simple experiment poses a few conceptual puzzles, including the suggestion of inevitability of the von Neumann-Wigner interpretation of quantum mechanics.
DIAGNOSTIC AND INTENSIVE CARE IN ACUTE PANCREATITIS Evaluation of volemic status during combined extracorporeal detoxification in patients with severe acute pancreatitis … : a retrospective observational study

INTRODUCTION: The morbidity and mortality of severe acute pancreatitis (SAP) remains high. Intensive therapy aimed at stopping hypovolemia, systemic endotoxico-sis is a debatable problem. OBJECTIVE: Improving treatment results in patients with SAP by assessing the dynamics of volumetric criteria and the degree of fluid therapy (FT). MATERIALS AND METHODS: The study included 25 patients. First, study group of 9 patient, used standard therapy in conjunction with selective hemoperfusion and continuous veno-venous hemofiltration. Second, control group of 16 patients, who received standard intensive care. FT in the groups was 2.5–3.5 ml/kg/h. thermodilution. From the 1 st to the 3 rd day, Global end-diastol-ic volume index increased from 345 (328–412) to 648 (590– 690) ml/m 2 in group 1, and from 375 (348–413) to 654 (599– 701) ml/m 2 in group 2 ( p < 0.05). Intra thoracic blood volume index increased from 440 (420–510) to 780 (750–840) ml/ m 2 in group 1 and 430 (417.5–465) to 750 (665–780) ml/m 2 in group 2 ( p < 0.05). Extravascular lung water index and pulmonary vascular permeability index by 5 days in group 2 increased by 1.8 — 11 (10.5–11.8) and 2.2 times — 6.5 (5.75– 7), respectively ( p < 0.05). CONCLUSIONS: FT 3.5 ml/kg/h, reaches isovolemia by the 3 rd day of therapy. The use of extracorporeal detoxification methods in complex therapy is accompanied by an improvement in clinical and laboratory parameters. Evaluation volemic status during combined extracorporeal detoxification in patients with severe acute pancreatitis : a retrospective observational study. Annals of
Equilibrium and stability of two-dimensional pinned drops

Superhydrophobicity relies on the stability of drops's interfaces pinned on sharp edges to sustain non-wetting (Cassie-Baxter) equilibrium states. Gibbs already pointed out that equilibrium is possible as long as the pinning angle at the edge falls between the equilibrium contact angles corresponding to the flanks of the edge. However, the lack of stability can restrict further the realizable equilibrium configurations. To find these limits we analyze here the equilibrium and stability of two-dimensional drops bounded by interfaces pinned on mathematically sharp edges. We are specifically interested on how the drop's stability depends on its size, which is measured with the Bond number $Bo = (\mathcal{W}_d/\ell_c)^2$, defined as the ratio of the drop's characteristic length scale $\mathcal{W}_d$ to the capillary length $\ell_c = \sqrt{\sigma/\rho g}$. Drops with a fixed volume become more stable as they shrink in size. On the contrary, open drops, i.e. capable of exchanging mass with a reservoir, are less stable as their associated Bond number decreases.
Polly Want a Cracker: Analyzing Performance of Parroting on Paraphrase Generation Datasets

Paraphrase generation is an interesting and challenging NLP task which has numerous practical applications. In this paper, we analyze datasets commonly used for paraphrase generation research, and show that simply parroting input sentences surpasses state-of-the-art models in the literature when evaluated on standard metrics. Our findings illustrate that a model could be seemingly adept at generating paraphrases, despite only making trivial changes to the input sentence or even none at all.
An Assessment of Historical Traffic Forecast Accuracy and Sources of Forecast Error

OF THESIS AN ASSESSMENT OF HISTORICAL TRAFFIC FORECAST ACCURACY AND SOURCES OF FORECAST ERROR Transportation infrastructure improvement projects are typically huge and have significant economic and environmental effects. Forecasts of demand of the facility in the form of traffic level help size the project as well as choose between several alternatives. Inaccuracy in these forecasts can thus have a great impact on the efficiency of the operational design and the benefits accrued from the project against the cost. Despite this understanding, evaluation of traffic forecast inaccuracy has been too few, especially for un-tolled roads in the United States. This study, part of a National Cooperative Highway Research Program (NCHRP) funded project, bridges this gap in knowledge by analyzing the historical inaccuracy of the traffic forecasts based on a database created as part of the project. The results show a general over-prediction of traffic with actual traffic deviating from forecast by about 17.29% on an average. The study also compares the relative accuracy of forecasts on several categorical variables. Besides enumerating the error in forecasts, this exploration presents the potential factors influencing accuracy. The results from this analysis can help create an uncertainty window around the forecast based on the explanatory variables, which can be an alternate risk analysis technique to sensitivity testing.
BAT AGN Spectroscopic Survey – XIII. The nature of the most luminous obscured AGN in the low-redshift universe

We present a multiwavelength analysis of 28 of the most luminous low-redshift narrow-line, ultra-hard X-ray-selected active galactic nuclei (AGN) drawn from the 70-month Swift/BAT all-sky survey, with bolometric luminosities of $\log (L_{\rm bol} /{\rm erg\, s}^{-1}) \gtrsim 45.25$. The broad goal of our study is to determine whether these objects have any distinctive properties, potentially setting them aside from lower luminosity obscured AGN in the local Universe. Our analysis relies on the first data release of the BAT AGN Spectroscopic Survey (BASS/DR1) and on dedicated observations with the VLT, Palomar, and Keck observatories. We find that the vast majority of our sources agree with commonly used AGN selection criteria which are based on emission line ratios and on mid-infrared colours. Our AGN are pre-dominantly hosted in massive galaxies (9.8 ≲ log (M*/M⊙) ≲ 11.7); based on visual inspection of archival optical images, they appear to be mostly ellipticals. Otherwise, they do not have distinctive properties. Their radio luminosities, determined from publicly available survey data, show a large spread of almost four orders of magnitude – much broader than what is found for lower X-ray luminosity obscured AGN in BASS. Moreover, our sample shows no preferred combination of black hole masses (MBH) and/or Eddington ratio (λEdd), covering 7.5 ≲ log (MBH/M⊙) ≲ 10.3 and 0.01 ≲ λEdd ≲ 1. Based on the distribution of our sources in the λEdd−NH plane, we conclude that our sample is consistent with a scenario where the amount of obscuring material along the line of sight is determined by radiation pressure exerted by the AGN on the dusty circumnuclear gas.
A new approach for parameter estimation of the single-diode model for photovoltaic cells/modules

Solar energy has become a popular renewable energy source, leading to wide use of photovoltaic (PV) cells/modules in energy production. For this reason, realistic modeling of PVs and determining the equivalent circuit parameters is of great importance in terms of planning and operation. Hence, in this study, an analytical model for identifying the single-diode equivalent circuit parameters; series resistance (Rs ), shunt resistance (Rp ), diode ideality factor (a), diode reverse-saturation current (Io ), and photon current (Ipv ) for PV cells/modules is developed without neglecting any term. In order to test the accuracy of the model, a number of PV modules from different manufacturers are taken into account and the results are compared with those obtained by using such analytical models given in the literature. Current-voltage (I-V ) characteristics of the PV modules, which are studied here, are also simulated by comparing with the experimental I-V curves provided by the manufacturers. Results show that the values of the parameters obtained for the PV modules are consistent with those extracted by using other analytical models. In addition, I-V curves created by using the obtained parameters are in full agreement with the experimental data. The curves also show a high degree of compatibility with the ones created by using the optimal parameters of the two-diode models given in the literature. Moreover, the proposed model provides a great advantage in estimating equivalent circuit parameters in terms of ease of use, requirements for input data, dependency on initial conditions as well as considering the parameters which are neglected in such methods given in the literature.
Learnability scaling of quantum states: Restricted Boltzmann machines

Generative modeling with machine learning has provided a new perspective on the data-driven task of reconstructing quantum states from a set of qubit measurements. As increasingly large experimental quantum devices are built in laboratories, the question of how these machine learning techniques scale with the number of qubits is becoming crucial. We empirically study the scaling of restricted Boltzmann machines (RBMs) applied to reconstruct ground-state wavefunctions of the one-dimensional transverse-field Ising model from projective measurement data. We define a learning criterion via a threshold on the relative error in the energy estimator of the machine. With this criterion, we observe that the number of RBM weight parameters required for accurate representation of the ground state in the worst case - near criticality - scales quadratically with the number of qubits. By pruning small parameters of the trained model, we find that the number of weights can be significantly reduced while still retaining an accurate reconstruction. This provides evidence that over-parametrization of the RBM is required to facilitate the learning process.
Validation in the Development of Cost Accounting Practicum Module of Guided Discovery Methods

This study aims to validate the development of cost accounting practice modules so that they are more effectively used by students in the learning process. This type of research is descriptive research that will describe the validation process from module development to finding results about module validity that has been compiled. The source of research data was obtained from module validation sheets which were assessed based on each indicator by two experts. Data analysis techniques were carried out qualitatively and quantitatively. Data from the module validation sheet were analyzed quantitatively, and then descriptive techniques were used to draw qualitative conclusions. The results of the study indicated that the modules that have been designed were eligible to be used in the learning process of cost accounting practice courses at STKIP PGRI West Sumatra. These results were based on the results of material validation with an assessment of four indicators, namely the suitability of the material with competency standards, the accuracy of the material, supporting learning material and material updating with an average score of 3.2 by each expert. Expert advice for module improvement before practicing practice is that it is necessary to add information in the example of problem calculation and improvement in language writing.
Learning memory management with C‐Sim: A C‐based visual tool

Nowadays, Computer Science (CS) students must cope with continuous challenges related to programming skill acquisition. In some occasions, they have to deal with the internals of memory management (pointers, pointer arithmetic, and heap management) facing a vision of programming from the low abstraction level offered by C. Even using C++ and references, not all scenarios where objects or collections of objects need to be managed can be covered. Based on the difficulties identified when dealing with such low‐level abstractions, the C‐Sim application, aimed at learning these concepts in an easy way, has been developed. To support the tool, the C programming language was selected. It allows showing concepts, remaining as close as possible both to the hardware and the operating system. To validate C‐Sim, pre and posttests were filled in by a group of 60 first‐year CS students, who employed the tool to learn about memory management. Grades of students using C‐Sim were also obtained and compared with those that did not use the tool the former academic year. As main outcomes, 82.26% of students indicated that they had improved programming and memory management knowledge, and 83.64% pointed out that the use of this type of tools improves the understanding and quality of the practice lessons. Furthermore, the marks of students have significantly increased. Finally, C‐Sim was designed from the ground up as a learning aid and can be useful for lecturers, who can complement their lessons using interactive demonstrations. Students can also employ it to experiment and learn autonomously.
DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network

Odometry is of key importance for localization in the absence of a map. There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have brought novel approaches to VO, which directly learn salient features from raw images. These learning-based approaches have led to more accurate and robust VO systems. However, they have not been well applied to point cloud data yet. In this work, we investigate how to exploit deep learning to estimate point cloud odometry (PCO), which may serve as a critical component in point cloud-based downstream tasks or learning-based systems. Specifically, we propose a novel end-to-end deep parallel neural network called DeepPCO, which can estimate the 6-DOF poses using consecutive point clouds. It consists of two parallel sub-networks to estimate 3D translation and orientation respectively rather than a single neural network. We validate our approach on KITTI Visual Odometry/SLAM benchmark dataset with different baselines. Experiments demonstrate that the proposed approach achieves good performance in terms of pose accuracy.