Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Probabilistic Prediction and Uncertainty Quantification of Hurricane Surge and Inundation

: This study proposes a methodology for efficient probabilistic prediction of near-landfall hurricane-driven storm surge, tide, and inundation. We perturb forecasts of hurricane track, intensity, and size according to quasi-random low-discrepancy Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations, and the resulting set of maximum water surface elevations are used as a training set to develop a Polynomial Chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with 3rd order PCs using Elastic Net penalized regression with Leave-One-Out cross-validation provides the most robust fit across training and validation sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate at 48-hour lead time for three past U.S. landfalling hurricanes (Irma 2017, Florence 2018, and Laura 2020) are found to be reliable when compared to best-track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. This methodology is built into an open-source Python framework available from https://github.com/noaa-ocs-modeling/EnsemblePerturbation . This purpose of this study is develop and evaluate a methodol- ogy that can be used to provide high-quality probabilistic predictions of hurricane-induced storm surge and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors 31 using quasi-random sequences combined with machine learning techniques that fit polynomial 32 functions to the data are well-suited to this task. The polynomial functions also have the benefit 33 of producing exact sensitivity indices of storm surge and inundation to the forecasted hurricane 34 properties such as path, intensity, and size, which can be used for uncertainty estimation. The code 35 implementing the presented methodology is publicly available on Github. 36 Statistical and along a of the ensemble HSOFS modeling system. The ensemble generation and PC analysis methodology implemented in an open-source called EnsemblePerturbation
Influence of using Pomegranate Peel Silage in Rations of Dairy Cows on their Productive Performance

Influence of using Pomegranate Peel Silage in Rations of Dairy Cows on their Performance. Abstract This study was implemented to evaluate the effect of a partial replacement of the whole corn plant silage (WCS) with the pomegranate peel silage (PPS) treated with enzymes mixed (ZYMOGEN) or lactic acid bacterial inoculants (Inoculant 1188) on the nutrients digestibility and the productive performance of dairy cows. In the first experiment, two stacks from PPS and WCS prepared, and WCS replaced by PPS at 25:75, 50:50, and 75:25, respectively, for forming three rations. The second experiment was carried out with twenty lactating crossbred Friesian cows in four similar groups (5 cows/per group): the control group fed on as fed a ratio consisting of WCS. G1 (WCS replaced with untreated PPS at level 50:50), G2 (WCS replaced with PPS treated with bacterial inoculants at level 50:50), and G3 (WCS replaced with PPS treated with ZYMOGEN at level 50:50). The results showed CP, other groups. It concluded that using treated or untreated pomegranate peel is safe in dairy cows feeding at a level of 20 %. Supplementation of ZYMOGEN or inoculant 1188 to PPS improved the fermentation and nutritive quality of silage.
Assessing Cognitive Bias in Korean and Indonesian Scientists: Considering Sociocultural Factors in Judgment and Choice

There has been the growing concern about excessive numbers of false-positive results published in the scientific literature. Cognitive bias plays a considerable role in trigger-ing false findings that involve indirect and unwitting self-deception by scientists. This study considers the sociocultural differences in cognitive bias between Korean and Indonesian scientists. A cognitive bias assessment (CBA) was developed and adminis-tered to 184 professors, lecturer assistants, doctoral students, and master’s students in South Korea and Indonesia. The CBA results revealed some similar response patterns between Korean and Indonesian scientists. Additionally, the detection of 19 potential differential item functionings (DIFs) demonstrates the influence of sociocultural factors on how scientists interpret to each item. Finally, the Indonesian scientists scored significantly higher in optimism and belief bias. This study discusses the importance of awareness of cognitive biases, particularly the role of science education to reduce biases through systematic thinking, reasoning, and judgment by understanding scientific methods.
AN APPLICATION OF LINEAR MIXED MODELS ON CD4+ COUNT PROGRESSION OF HIV+ PATIENTS UNDER ANTIRETROVIRAL THERAPY: A CASE OF DEBRE BERHAN REFERRAL HOSPITAL,

: In studying the progression of HIV-positive patients under ART, CD4+ count should be measured repeatedly per individual to decide about progression of patients to AIDS and monitoring the success of Therapy. The main aim of the study was modeling the progression of CD4+ counts of HIV + patients under ART in Debre Berhan Referral Hospital, Ethiopia. A retrospective cohort study was used and out of a population of HIV-patients who were taking ART in the hospital during September,2013toFebruary,2019,data on 322 patients was taken to the study; in which225 (69.88%) were females and 97(30.12%) were males. Linear Mixed Models used as data analysis which incorporate subject-specific random effects. The average baseline CD4+ count after patients initiated to ART was 335.7 and it changed to 408.6over specified period of time. Females‟CD4+ count were better progression than males with an estimate, (β=0.7602, p=0.0215). Age had negative time interaction effect, (β=-0.01698, p=0.0092), implied older patients have lower mean change in CD4+ count than younger patients. Functional status, regimen class, age, marital status, WHO clinical stages had significant effect on patient‟s CD4+ count progression. The random intercept and random slope models have significant separate estimation with ( β = 6.652, p=0.001) and (β=1.101, p=0.001) respectively. As conclusion, HIV-positive patients advised to start ART early with higher CD4+ counts as baseline and slope models have positive correlation estimate, 0.6621 implied patients who have higher baseline CD4+ count tend to have higher rate of increment than those with lower baseline CD4+ count.
An Analysis of Paediatric Cd4 Counts for Acquired Immune Deficiency Syndrome Using Flexible Random Curves

SUMMARY In this paper we analyse CD4 counts from infants born to mothers who are infected with the human immunodeficiency virus. A random effects model with linear or low order polynomials in time is unsatisfactory for these longitudinal data We develop an alternative approach based on a flexible family of models for which both the fixed and the random effects are linear combinations of B-splines. The fixed and random parts are smooth functions of time and the covariance structure is parsimonious. The procedure allows estimates of each individual's smooth trajectory over time to be exhibited. Model selection, estimation and computation are discussed. Centile curves are presented that take into account the longitudinal nature of the data. We emphasize a graphical approach to the presentation of results.
E ﬀ ect of Domestic and Global Environmental Events on Environmental Concern and Environmental Responsibility among University Students

: Recently, both global and domestic environmental events have been occurring more frequently, bringing catastrophic consequences to humans and the environment. These adverse events have caused widespread concern among the general public. In positive terms, these devastating events could potentially enhance people’s environmental concern, which, in turn, could instill a greater sense of environmental responsibility. This study aims to examine how global and domestic environmental concerns mediate the e ﬀ ect of environmental knowledge and attitudes on environmental responsibility. Students of King Mongkut’s University of Technology Thonburi in Bangkok, Thailand, were selected as the participants. A simple random technique was applied for selecting the participants. Questionnaire surveys with 863 students were carried out during September–October 2019. A path analysis was performed to test relationships among environmental knowledge, environmental attitudes, global and domestic environmental concerns, and environmental responsibility. The results demonstrated that domestic environmental concerns, taken alone, contributed less to the students’ sense of environmental responsibility. Domestic environmental concerns had a stronger e ﬀ ect on environmental responsibility when taken together with global environmental concerns. In addition, both domestic and global environmental concerns could help transform environmental knowledge and attitudes into environmental responsibility. Only environmental attitudes had no direct e ﬀ ect on responsibility. These results show that domestic and global catastrophic environmental events could raise students’ levels of concern for the environment, and, ultimately, enhance their sense of moral responsibility to protect the environment.
HEAO-1 observations of gamma ray bursts

A search of data from the High Energy X-Ray and Low Energy Gamma Ray Experiment on HEAO-1 uncovered 14 gamma ray bursts. Nine of these events are reported for the first tiome. Except for the faintest events, all of the bursts detected by this experiment have been measured above an MeV, thereby confirming the hard spectral character of gamma ray burst spectra reported by SMM. Results give a burst rate of at least 105 per year above 6 times 10 to the minus 7th power ergs, which is consistent with previous measurements of burst frequency.
A Study of Multi-Cloud Marketplace Model using Cooperative and Non-cooperative Game Theoretic Frameworks

In the ﬁrst part of this thesis, we analyze a multi-cloud marketplace model consisting of brokers with multi-unit demands and cloud providers with multi-unit supplies. Using tools from cooperative game theory we show the existence of a core allocation which also maximizes the market surplus. We do this by ﬁnding an optimal coalition structure and formulate an integer linear programming problem to maximize the overall market surplus. The optimal coalition lies in the core which gives it stability and no incentive to deviate to any other sub-coalition. We then study our model in the real-world scenario in which the brokers don’t reveal their valuations for the cloud providers. For this, we use the auction algorithm due to Bertsekas [2] which leads to a core allocation without requiring the brokers to reveal their utilities. In the second part of this thesis, we study how an oligopolist inﬂuences the coalition structure in federated cloud markets. We consider two price offering strategies for an oligopolist: non-adaptive and adaptive. In the non-adaptive strategy, an oligopolist makes a price offer to all the cloud providers simultaneously. It can be noted that the oligopolist can buy-out all the cloud providers by making a price offer which is equal to a core allocation and the total price offer made by the oligopolist is equal to the value of the grand coalition. In the adaptive strategy, the oligopolist approaches the cloud providers one after another in a sequential manner. We show that by using the adaptive strategy, the oligopolist can buy-out all the cloud providers at a total price offer which is less than that of the non-adaptive strategy.
Survey: Bias in NLP

In this paper, we present a comprehensive study of societal biases that result from the application of standard NLP tasks, focusing on how data and techniques contribute to biases and the progress towards detecting them. We look at different metrics for measurement of bias. We analyze how social biases with respect to different demo-graphics get reflected on corpora of major entertainment source like movies and multilingual social media posts.
A New Approach to Model Machine Learning by Using Complex Bipolar Intuitionistic Fuzzy Information

Many industries are developing robust models, capable of analyzing huge and complex data by using machine learning (ML) while delivering faster and more accurate results on vast scales. ML is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. ML tools enable organizations to swiftly identify profitable opportunities and potential risks. Besides these uses, ML also has a wide range of applications in our daily lives. So, the development in ML is most important in this age of digital system to solve more complex problems. In order to further develop ML and diminish the uncertainties to improve accuracy, an innovative concept of complex bipolar intuitionistic fuzzy sets (CBIFSs) is introduced in this article. Further, the Cartesian product of two CBIFSs is defined. Moreover, the complex bipolar intuitionistic fuzzy relations (CBIFRs) and their types with suitable examples are defined. In addition, some important results and properties are also presented. The proposed modeling techniques are used to study different ML factors and their interrelationship, so that the functionality of ML might be enhanced. Furthermore, the advantages and benefits of proposed methods are described by their side to side comparison with preexisting frameworks in the literature.
A study of Automatic Segmentation of 3-D Brain MRI and its application to Deformable Registration

Automated segmentation of cortical and non-cortical human brain structures has been hitherto approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume. The obtained mean Dice coefficient varied according to the number of labels, for example, it is 0.844 0.031 and 0.743 0.019 for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state-of-the-art methods on nearly all datasets, at a reduced computational time. The consistently good performance of the proposed method across datasets and no requirement for registration make it attractive for many applications where reduced computational time is necessary. Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In the second part of the thesis, we examine the question of how features learnt using various Deep Learning (DL) frameworks for segmentation can be used for deformable registration and whether this feature learning is necessary or not. We investigate the use of features learned by different DL methods in the current state-of-the-art discrete registration framework and analyze its performance on 2 publicly available datasets. We draw insights about the type of DL framework useful for feature learning. We consider the impact, if any, of the complexity of different DL models and brain parcellation methods on the performance of discrete registration. Our results indicate that the registration performance with DL features and SSC are comparable and stable across datasets whereas this does not hold for low level features.
Imaging through turbid media via sparse representation: imaging quality comparison of three projection matrices

The incident light will be scattered away due to the inhomogeneity of the refractive index in many materials which will greatly reduce the imaging depth and degrade the imaging quality. Many exciting methods have been presented in recent years for solving this problem and realizing imaging through a highly scattering medium, such as the wavefront modulation technique and reconstruction technique. The imaging method based on compressed sensing (CS) theory can decrease the computational complexity because it doesn't require the whole speckle pattern to realize reconstruction. One of the key premises of this method is that the object is sparse or can be sparse representation. However, choosing a proper projection matrix is very important to the imaging quality. In this paper, we analyzed that the transmission matrix (TM) of a scattering medium obeys circular Gaussian distribution, which makes it possible that a scattering medium can be used as the measurement matrix in the CS theory. In order to verify the performance of this method, a whole optical system is simulated. Various projection matrices are introduced to make the object sparse, including the fast Fourier transform (FFT) basis, the discrete cosine transform (DCT) basis and the discrete wavelet transform (DWT) basis, the imaging performances of each of which are compared comprehensively. Simulation results show that for most targets, applying the discrete wavelet transform basis will obtain an image in good quality. This work can be applied to biomedical imaging and used to develop real-time imaging through highly scattering media.
Reproducibility Report for ACM SIGMOD 2022 Paper: “TreeToaster: Towards an IVM-Optimized Compiler”

4 REPRODUCIBILITY EVALUATION 4.1 Process Downloading the Docker image to our evaluation machine was unexpectedly difficult: Google Drive does not support easy wget or curl download for large files. Instead, a special client like Gdown is required. However, such clients can only download “open access” files. Hence, we had to download the Docker image to a desktop machine first and then copy it over to our evaluation machine. This is quite cumbersome since we are in a home-office due to the pandemic situation. Next time, please consider providing the Docker image on a different file hoster or host the file yourself and guard it with an . htaccess. Apart from acquiring the Docker image, the overall evaluation is very straight-forward and well documented. A single script main_script. sh automatically downloads the required data sets, runs the benchmarks, and produces the charts presented in the paper.
Capturing Self-Regulated Learning During Search

Researchers in the learning sciences have demonstrated the benefits of effective self-regulated learning (SRL) in improving learning outcomes. The search-as-learning community aims to improve learning outcomes during search, but offers limited research exploring the impact of SRL on learning during search. Current limited research in search-as-learning explores only perceptions of SRL processes after the search process [1]. Results from such analyses are limited in that SRL is a dynamic, active process and participant perceptions of SRL can be unreliable [2, 3]. In this paper, we propose the implementation of an SRL coding framework to capture SRL processes as they unfold throughout a search session. Additionally, we offer several implications for future work using the proposed methodology.
The Wintertime Covariation of CO 2 and Criteria Pollutants in an Urban Valley of the Western United States

Numerous mountain valleys experience wintertime particulate pollution events, when persistent cold air pools (PCAPs) develop and inhibit atmospheric mixing, leading to the accumulation of pollutants. Here we examine the relationships between trace gases and criteria pollutants during winter in Utah ’ s Salt Lake Valley, in an effort to better understand the roles of transport versus chemical processes during differing meteorological conditions as well as insights into how targeted reductions in greenhouse gases will impact local air quality in varying meteorological conditions. CO 2 is a chemically inert gas that is coemitted during fossil fuel combustion with pollutants. Many of these coemitted pollutants are precursors that react chemically to form secondary particulate matter. Thus, CO 2 can serve as a stable tracer and potentially help distinguish transport versus chemical in ﬂ uences on pollutants. During the winter of 2015 – 2016, we isolated enhancements in CO 2 over baseline levels due to urban emissions ( “ CO 2ex ” ). CO 2ex was paired with similar excesses in other pollutant concentrations. These relationships were examined during different wintertime conditions and stages of pollution episodes: (a) Non-PCAP, (b) beginning, and (c) latter stages of an episode. We found that CO 2ex is a good indicator of the presence of gaseous criteria pollutants and a reasonable indicator of PM 2.5 . Additionally, the relationships between CO 2ex and criteria pollutants differ during different phases of PCAP events which provide insight into meteorological and transport processes. Lastly, we found a slight overestimation of CO:CO 2 emission ratios and a considerable overestimation of NO x :CO 2 by existing inventories for the Salt Lake Valley. chamber ” in order to probe the CO 2 -pollutant relationships and the meteorological conditions that drive them. This work builds upon a previous paper by Pataki et al. (2005), in which SLV CO 2 observations were used to establish the use of CO 2 as an inert tracer of atmospheric transport, as well as the study by Gorski et al. (2015), which used CO 2 as an indicator for anthropogenic combustion in relation to distinct isotopic signatures of urban water vapor during PCAP conditions. This work expands beyond the Pataki et al. (2005) and Gorski et al. (2015) analyses in several key ways: (1) a signi ﬁ cantly larger suite of pollutant observations colocated with CO 2 that were collected for a longer period of time; (2) addition of a new CO 2 site ( “ Suncrest ” ) at higher elevation; (3) availability of enhanced meteorological observations; and (4) comparisons against emission inventories.