Miracle-dz/UniFAR-FTUs huggingface dataset:
Particulate matter monitoring – past, present and future

The health problems caused by exposure to airborne Particulate Matter (PM) beyond safe limits have been studied for many years. Government regulatory agencies have adapted and updated the safe exposure limits as more progress is made both in policy developments and detection system design. Bulky PM detectors, though very accurate do not provide sufficient spatial and temporal resolution, and are static and expensive. Current much smaller commercial PM sensors are mobile but still mostly too expensive and largely still too big for real-time continuous personal use. They also must be calibrated to convert their counts to mass concentration despite their variation from unit to unit. The continuous drive towards having a cheaper, smaller, yet more effective PM sensors for personal exposure analysis and indoor environments is pushing the current boundaries of current techniques. Emerging PM sensing techniques must now achieve this, while also linking to other structured source apportionment and semantic analysis of air quality data aimed at providing useful information about user activities mostly provided via the internet. This review highlights research on PM detection and monitoring, covering methods and principle of operation of detection instruments, emerging trends and future outlooks. Further, this work reviews PM detection challenges, measurement interpretation and possible solutions going forward.
Application of Computational Methods to the Study of Stylistics in China

The application of computational methods to the study of Stylistics in China began in 1970s,at which time it mainly concerned the verification of the authority and the study of certain writer's language style.This method soon received wide support in the field of rhetoric and there were also some scholars attempting to build the mathematical model for it.Recently,there are many studies carried out in this field,which can still be applied in many other ways,such as the study of language itself and social intelligence.
Interactive comment on “Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and statistical consistency” by Diana Lucatero et al

In the present study we analyze the effect of bias adjustments in both meteorological and streamflow forecasts on skill and reliability of monthly average streamflow and low flow forecasts. Both raw and pre-processed meteorological 10 seasonal forecast from the European Center for Medium-Range Weather Forecasts (ECMWF) are used as inputs to a spatially distributed, coupled surface – subsurface hydrological model based on the MIKE SHE code in order to generate streamflow predictions up to seven months in advance. In addition to this, we postprocess streamflow predictions using an empirical quantile mapping that adjusts the predictive distribution in order to match the observed one. Bias, skill and statistical consistency are the qualities evaluated throughout the forecast generating strategies and we analyze where the 15 different strategies fall short to improve them. ECMWF System 4-based streamflow forecasts tend to show a lower accuracy level than those generated with an ensemble of historical observations, a method commonly known as Ensemble Streamflow Prediction (ESP). This is particularly true at longer lead times, for the dry season and for streamflow stations that exhibit low hydrological model errors. Biases in the mean are better removed by postprocessing that in turn is reflected in the higher level of statistical consistency. However, in general, the reduction of these biases is not enough to ensure a higher level of 20 accuracy than the ESP forecasts. This is true for both monthly mean and minimum yearly streamflow forecasts. We highlight the importance of including a better estimation of the initial state of the catchment, which will increase the capability of the system to forecast streamflow at longer leads.
Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios

Abstract. Worldwide, groundwater resources are under a constant threat of overexploitation and pollution due to anthropogenic and climatic pressures. For sustainable management and policy making a reliable prediction of groundwater levels for different future scenarios is necessary. Uncertainties are present in these groundwater-level predictions and originate from greenhouse gas scenarios, climate models, conceptual hydro(geo)logical models (CHMs) and groundwater abstraction scenarios. The aim of this study is to quantify the individual uncertainty contributions using an ensemble of 2 greenhouse gas scenarios (representative concentration pathways 4.5 and 8.5), 22 global climate models, 15 alternative CHMs and 5 groundwater abstraction scenarios. This multi-model ensemble approach was applied to a drought-prone study area in Bangladesh. Findings of this study, firstly, point to the strong dependence of the groundwater levels on the CHMs considered. All groundwater abstraction scenarios showed a significant decrease in groundwater levels. If the current groundwater abstraction trend continues, the groundwater level is predicted to decline about 5 to 6 times faster for the future period 2026–2047 compared to the baseline period (1985–2006). Even with a 30 % lower groundwater abstraction rate, the mean monthly groundwater level would decrease by up to 14 m in the southwestern part of the study area. The groundwater abstraction in the northwestern part of Bangladesh has to decrease by 60 % of the current abstraction to ensure sustainable use of groundwater. Finally, the difference in abstraction scenarios was identified as the dominant uncertainty source. CHM uncertainty contributed about 23 % of total uncertainty. The alternative CHM uncertainty contribution is higher than the recharge scenario uncertainty contribution, including the greenhouse gas scenario and climate model uncertainty contributions. It is recommended that future groundwater-level prediction studies should use multi-model and multiple climate and abstraction scenarios.
Efficient sampling geometries and reconstruction algorithms for estimation of diffusion propagators

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy EFFICIENT SAMPLING GEOMETRIES AND RECONSTRUCTION ALGORITHMS FOR ESTIMATION OF DIFFUSION PROPAGATORS By Wenxing Ye May 2012 Chair: Baba C. Vemuri Cochair: Alireza Entezari Major: Electrical and Computer Engineering Diffusion weighted magnetic resonance imaging (DW-MRI) is a non-invasive techniques that can reveal the anatomical structure of living organism. Through measuring directional diffusion of water molecules in tissue rich in fiber structure, DW-MRI can provide invaluable in vivo information about the neuronal connectivity patterns. This kind of information is extremely important for many clinical applications, especially when brain areas which consist of abundant white matter fibers is concerned. Diffusion propagator is the most widely used measure to describe the water diffusion quantitatively. It gives the probability of water molecule motion along a vector in fixed MR setting. The main aim of this dissertation is to investigate better ways of sampling and reconstructing the diffusion propagator from a signal processing point of view. This dissertation shows a model free framework for diffusion propagator reconstruction with special focus on increasing the total reconstruction accuracy through optimizing the sampling geometry. The reconstruction framework is proposed through gradually introducing the concepts of optimal sampling lattice, interlaced mult-shell sampling scheme, multi-dimensional sinc basis representation and box-spline basis representation. The main difficulty in diffusion propagator reconstruction lies in the fact that sampling of diffusion signal is very time consuming and the time is linearly proportional
Contributions to Sparse Methods for Complex Data Analysis

This document is organized around three chapters.that summarize my research activity since 2008, that is, after my PhD thesis. The first chapter provides motivations for my research work. I first depict informally the kind of data statisticians have to deal with in recent application problems. I build on the example of genomics, with which I am familiar, in order to extract the most striking characteristics of modern data that strongly jeopardize the common way of doing statistics. I exhibit important statistical challenges associated with such data and motivate the use of particular tools at the heart of my research preoccupations, which are at the edge of statistics, optimization and machine learning. I then briefly present the main themes of my research and set them in the landscape of the statistical learning community. The second chapter proposes an overview of my contributions to Gaussian Graphical Models (GGM), in terms of modeling and more importantly in terms of inference of the conditional structure associated with such models. After a quick outline of the existing literature and an introduction to the most popular methods in the community,I present my contribution to this field. These contributions were motivated by the need to account for some special features characterizing genomics data and biological networks. The third chapter is dedicated to my works on structured sparse methods. After a brief overview of the basic computational and statistical tools related to sparse regularization, I present four of my contributions to this field. I wish to demonstrate the diversity of these contributions, some being related to algorithmic and computational considerations, while some others are focused on the statistical properties of the methods. All emerged from motivations anchored in applications. A comprehensive curriculum vitae is finally given in the appendix.
Holotype skulls, stratigraphy, bone taphonomy and excavation history in the Zoolithen Cave and new theory about Esper’s “great deluge”

Abstract. Kurzfassung: Die Zoolithenhohle liegt entlang des Wiesenttals (Oberfranken, Bayern, Sud-Deutschland) und hat eine lange „Spatenforschungs“-Historie. Die Fundstelle ist von internationaler Bedeutung aufgrund ihrer funf validen Holotypen-Eiszeittier-Schadeln des „Hohlenbaren“ sowie Top-Pradatoren (Eiszeit-Lowe, -Hyane, -Wolf und -Rotwolf). Das grose Hohlensystem entwickelte sich auf drei Etagen und hat drei fluviale Haupt-Sedimentsequenzen inklusive zwei Haupt-Speleothem-Genesephasen unter wechselnden Ponor-, Trocken- und Nassphasen wahrend des Oligozan/Miozan (Neogen) uber das Pliozan/Fruhpleistozan bis hin zum Spat-Pleistozan. Die ersten Hohlenbaren-Populationen Oberfrankens mit Ursus deningeri nutzen die Hohle als Horst bereits im MIS 6–8 (Holstein-Interglazial/Saale-Glazial). Isolierte P4 Zahn- sowie die Schadelmorphotypen (= “Hohlenbaren-Uhr”) datieren verschiedene Hohlenbaren-Arten/Unterarten (U. spelaeus eremus/spelaeus, U. ingressus) in das Spat-Pleistozan (MIS 3–5d). Ihre Knochen wurden in etlichen Fallen zuerst durch Top-Pradatoren beschadigt. Letztendlich wurden die Knochen aller pleistozanen Grossauger wahrend des Hochglazials (= Last Glacial Maximum, Post-U. deninger-Zeit oder Postglazial) von zwei ehemaligen Eingangsbereichen in Hohenlagen um 455 m NHN bis zu 30 Meter in tiefere Hohlenbereiche besonders uber die Vertikalschafte durch Hochflutereignisse verschwemmt. Dieses wurde in historischer Zeit als „biblische Sintflut“ interpretiert. Die jungsten „Flussterrassen-Dolomitkiese“ in Hohlenlagen um 455 m NHN werden als Reliktsedimente in verschiedenen Hohlen um Muggendorf angetroffen und konnen in solchen extremen Hohenlagen 130 Meter uber der heutigen Wiesent nicht mehr mit „naturlicher Erosion/Flussterrassenstratigraphie“ erklart werden. Sie mussen im noch unklaren glazialen Kontext stehen. Letztendlich hinterliesen Eisenzeit-La Tene-Menschen Sekundarbestattungen (Schadel, Langknochen, Keramik und Jenseits-Nahrungs-Haustier-Beigaben) nur im ersten tiefen Vertikalschaft (= Aufzugschacht), ahnlich wie in der nahegelegenden Esperhohle.
Self-assessment in higher education: empirical evidence from the Department of Business Administration of Shahjalal University of Science and Technology, Bangladesh

The paper aims to explore the self-assessment practices in higher education in Bangladesh with special reference to the Department of Business Administration of Shahjalal University of Science and Technology. For self-assessment purpose, the researchers have collected opinions from students, alumni, employers and faculty members on eight areas. In collecting data, the study used a semi-structured questionnaire on four-point scales. To test the internal consistency, the researchers have used Cronbach's alpha test of coefficient. The study found that the department is in a very weak position almost in all aspects of self-assessment. The study suggested the concerned authority to provide sufficient funds and logistic facilities for ensuring the quality of higher education in the department.
IBM SPSS Modeler Cookbook

Over 60 practical recipes to achieve better results using the experts' methods for data mining Overview Go beyond mere insight and build models than you can deploy in the day to day running of your business Save time and effort while getting more value from your data than ever before Loaded with detailed step-by-step examples that show you exactly how its done by the best in the business In Detail IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide. What you will learn from this book Use and understand the industry standard CRISP_DM process for data mining. Assemble data simply, quickly, and correctly using the full power of extraction, transformation, and loading (ETL) tools. Control the amount of time you spend organizing and formatting your data. Develop predictive models that stand up to the demands of real-life applications. Take your modeling to the next level beyond default settings and learn the tips that the experts use. Learn why the best model is not always the most accurate one. Master deployment techniques that put your discoveries to work making the most of your business most critical resources. Challenge yourself with scripting for ultimate control and automation - its easier than you think! Approach This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts. It is loaded with step-by-step examples explaining the process followed by the experts. Who this book is for If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics.
Low levels of pollen and seed flow in a riparian forest fragment of the dioecious tropical tree Genipa Americana L

Tropical forests contain some of the greatest diversity of tree species in the world, making them vital for the maintenance of the planet’s biodiversity. In tropical ecosystems, the diversity of tree species provides habitats and environmental conditions necessary for the survival of thousands of species of animals and plants.1 As a result of indiscriminate harvesting, the advancement of agriculture, and urban sprawl, these ecosystems are among the most devastated in the world; the destruction of this habitat has become synonymous with species loss.2 The survival of tropical species is thus dependent on their ability to persist in fragmented landscapes or isolated nature reserves.
Studying the effects of deficit irrigation as an on-farm strategy on carrot yield in arid environment

The deficit irrigation (DI) as an on-farm water management strategy was tested for 3-years to determine the water requirements and productivity, soil salinity and yield for carrot irrigated with saline water (3.6 dS/m) under actual farming conditions in southern Tunisia. The irrigation regimes were full (I100) and deficit (I80 and I60) irrigated with levels of 100, 80 and 60% of ETc when the readily available water, 35% of total available water (TAW), in the I100 treatment was depleted, and traditional farmer practice (Farmer). Regulated DI regime where 40% reduction is applied only during ripening stage (I60-ripening) was also used . Higher soil salinity is observed at harvest under I60 and Farmer treatments compared to I100. Carrot yield was highest over 3 years for the I100 scheduling technique, (29.5, 28.7 and 26.8 t/ha) although no significant differences were observed with the I60-ripening strategy . However, the I80 and I60 treatments caused significant reductions in carrot yields through a reduction in roots number and weight in comparison with I100. The farmer’s method not only caused significant reductions in yield but also resulted in using 43 to 57 % more water and increased soil salinity. For all treatments, carrot yields were higher in first than the two following years. Water productivity (WP) values reflected this difference and varied between absolute extremes of 3.3 and 9.7 kg/m 3 . WP was the greatest in the I60 treatment and the least in the Farmer's strategy. I100 irrigation strategy provides significant advantage on yield and WP compared to farmer’s method in carrot production. Therefore, for water-saving purposes, the I100 irrigation scheduling is recommended to optimize the use of saline water in carrot production and to control soil salinity. Nonetheless, under water scarcity, irrigation of carrot could be scheduled using I60-ripening and I80 deficit strategies, with some yield reductions (4-15%).
Personalized large scale classification of public tenders on hadoop

This project was completed as part of an innovation partnership with Fujitsu Canada and Universite Laval. The needs and objectives of the project were centered on a business problem defined jointly with Fujitsu. Our project aimed to classify a corpus of electronic public tenders based on state of the art Hadoop big data technology. The objective was to identify with high recall public tenders relevant to the IT services business of Fujitsu Canada. A small scale prototype based on the BNS algorithm (Bi-Normal Separation) was empirically shown to classify with high recall (93%) the public tender corpus. The prototype was then re-implemented on a full scale Hadoop cluster using Apache Pig for the data preparation pipeline and using Apache Mahout for classification. Our experimentation show that the large scale system not only maintains high recall (91%) on the classification task, but can readily take advantage of the massive scalability gains made possible by Hadoop’s distributed architecture.
Scan statistics for the online discovery of locally anomalous subgraphs

Identifying anomalies in computer networks is a challenging and complex problem. Often, anomalies occur in extremely local areas of the network. Locality is complex in this setting, since we have an underlying graph structure. To identify local anomalies, we introduce a scan statistic for data extracted from the edges of a graph over time. In the computer network setting, the data on these edges are multivariate measures of the communications between two distinct machines, over time. We describe two shapes for capturing locality in the graph: the star and the k-path. While the star shape is not new to the literature, the path shape, when used as a scan window, appears to be novel. Both of these shapes are motivated by hacker behaviors observed in real attacks. A hacker who is using a single central machine to examine other machines creates a star-shaped anomaly on the edges emanating from the central node. Paths represent traversal of a hacker through a network, using a set of machines in sequence. To identify local anomalies, these shapes are enumerated over the entire graph, over a set of sliding time windows. Local statistics in each window are compared with their historic behavior to capture anomalies within the window. These local statistics are model-based. To capture the communications between computers, we have applied two different models, observed and hidden Markov models, to each edge in the network. These models have been effective in handling various aspects of this type of data, but do not completely describe the data. Therefore, we also present ongoing work in the modeling of host-to-host communications in a computer network. Data speeds on larger networks require online detection to be nimble. We describe a full anomaly detection system, which has been applied to a corporate sized network and achieves better than real-time analysis speed. We present results on simulated data whose parameters were estimated from real network data. In addition, we present a result from our analysis of a real, corporate-sized network data set. These results are very encouraging, since the detection corresponded to exactly the type of behavior we hope to detect.
A Cluster-based Approach Towards Detecting and Modeling Network Dictionary Attacks

In this paper, we provide an approach to detect network dictionary attacks using a data set collected as flows based on which a clustered graph is resulted. These flows provide an aggregated view of the network traffic in which the exchanged packets in the network are considered so that more internally connected nodes would be clustered. We show that dictionary attacks could be detected through some parameters namely the number and the weight of clusters in time series and their evolution over the time. Additionally, the Markov model based on the average weight of clusters,will be also created. Finally, by means of our suggested model, we demonstrate that artificial clusters of the flows are created for normal and malicious traffic. The results of the proposed approach on CAIDA 2007 data set suggest a high accuracy for the model and, therefore, it provides a proper method for detecting the dictionary attack.