Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging a Participatory Process for Restoration Return on Investment: The Nature Conservancy's Floodplain Investment Tool

In this study, we develop the Floodplain Investment Tool (FPIT) to help stakeholders prioritize conservation efforts to maximize return on investments (e.g., ensure the most “bang for the buck”). We applied the FPIT for the 177 mi2 (458 km2) Paulins Kill watershed located in northwestern New Jersey, as a case study focusing on nutrient loading. The resultant FPIT reflected independently assessed habitat and ecosystem impairment scores across the watershed. Nutrient load (both total phosphorus [TP] and total nitrogen [TN]) estimates made with the FPIT were in relative agreement across scales with a more robust modeling approach and allowed for development of several application scenarios. For example, we estimated a well‐targeted budget of $2 million could reduce annual nutrient load to be approximately 530 lbs (240 kg) of TP and 5,835 lbs (2,647 kg) of TN for the watershed. Where the clear value for the FPIT can be seen is in the process by which it was pilot‐tested for application and stakeholder voices were integrated into its development. This allows for a level of transparency not available from more top‐down approaches where stakeholders are involved only at the end of the development process.
Impact of Undersampling on the Predictive Performance of Logistic Regression and Machine Learning Algorithms A Simulation Study

e42 | www.epidem. com © 2020 Wolters Kluwer Health, Inc. All rights reserved. To the Editor: Machine learning techniques may improve risk prediction and disease screening. Class imbalance (ratio of noncases to cases > 1) routinely occurs in epidemiologic data and may degrade the predictive performance of machine learning algorithms.1–4 Of the many techniques developed to address class imbalance,5,6 here, we investigated simple undersampling. This method is straightforward and accessible, but evidence on its performance is mixed and practical guidance is needed. Using simulated data, we assessed the predictive performance of the ensemble machine learning algorithm SuperLearner and logistic regression in imbalanced and undersampled data to investigate whether undersampling alters predictive accuracy.
ZU Scholars

Alzheimer’s disease (AD) is a neurodegenerative condition that affects the central nervous system and represents 60% to 70% of all dementia cases. Due to an increased aging population, the number of patients diagnosed with AD is expected to exceed 131 million worldwide by 2050. The disease is characterized by various clinical symptoms and pathological features that deﬁne three main sequential decline stages, namely, early/mild, intermediate/moderate and late/severe stages. Although it is considered irreversible, early diagnosis of AD is highly desirable to help preserve cognitive function. However, early diagnosis is difﬁcult due to different factors, including the patient-speciﬁc development of AD. The main contribution of the proposed work is to present a personalized (i.e., local/brain regional) computer-aided diagnosis (CAD) system for early diagnosis of AD from two perspectives, functional and structural to assist diagnosis. In other words, the proposed system uniquely yields local/regional diagnosis by combining 11 C PiB positron emission tomography ( 11 C PiB PET), which provides functional diagnosis, with structural magnetic resonance imaging (sMRI), which provides structural diagnosis. To the best of our knowledge, this is the ﬁrst work to combine sMRI and the 11 C PiB PET for local/regional early diagnosis of AD. The system processes the two modalities through a number of steps: pre-processing, brain labeling (parcellation), feature extraction, and diagnosis. A local/regional diagnosis is presented for each modality separately, followed by the ﬁnal global diagnosis obtained by integrating the results from the two modalities. Evaluation of the proposed system shows average results of 97 . 5% , 100% , and 96 . 77% for accuracy, speciﬁcity, and sensitivity, respectively. With further development, it is envisioned that this system could contribute to the early diagnosis of AD in the clinical setting.
Intersection and decomposition algorithms for planar arrangements

This book presents a study of various problems related to arrangements of lines, segments, or curves in the plane. The first problem is a proof of almost tight bounds on the length of (n,s)-Davenport-Schinzel sequences, a technique for obtaining optimal bounds for numerous algorithmic problems. Then the intersection problem is treated. The final problem is improving the efficiency of partitioning algorithms, particularly those used to construct spanning trees with low stabbing numbers, a very versatile tool in solving geometric problems. A number of applications are also discussed.
I Transmitter Design for LMS-MIMO-MCCDMA Systems with Pilot Channel Estimates and Zero Forcing Equalizer

We propose a downlink multiple-input multipleoutput (MIMO) multi-carrier code division multiple access (MCCDMA) system with adaptive beamforming algorithm for smart antennas. The algorithm used in this paper is based on the Least Mean Square (LMS), with pilot channel estimation (PCE) and the zero forcing equalizer (ZFE) in the receiver, requiring reference signal and no knowledge channel. MC-CDMA is studied in a multiple antenna context in order to efficiently exploit robustness against multipath effects and multi-user flexibility of MC-CDMA and channel diversity offered by MIMO systems for radio mobile channels. Computer simulations, considering multi-path Rayleigh Fading Channel, interference inter symbol and interference are presented to verify the performance. Simulation results show that the scheme achieves good performance in a multi-user system. Keywords—Adaptive Beamforming, LMS Algorithm, MCCDMA, MIMO System, Smart Antenna.
Sensory invariance driven action (SIDA) framework for understanding the meaning of neural spikes

Sensory Invariance Driven Action (SIDA) Framework for Understanding the Meaning of Neural Spikes. (May 2004) Sarvani Kumar Bhamidipati, B.E. , Gujarat University, India Chair of Advisory Committee: Dr.Yoonsuck Choe What does the spike of a sensory neuron mean? This is a fundamental question in computational neuroscience. Conventional approaches provide an answer based on correlation between spike pattern and the stimulus that caused it. However, these approaches do not satisfactorily explain how the brain, which does not have direct knowledge of the world or the stimuli, can achieve this task. This thesis frames the problem in terms of a task for a simulated agent and provides a solution based on an approach which regards action as necessary for acquiring the meaning of neural spikes. This approach differs from some others in that it proposes a new criterion called the sensory invariance criterion, which can be used to associate meaning to spike patterns in terms of action sequences the agent generates. This criterion forms the basis of the Sensory Invariance Driven Action (SIDA) framework presented in this thesis. This framework is implemented in a reinforcement learning agent and the results indicate that the agent can successfully learn to associate meaning to the sensor activity in terms of specific actions which reflect the properties of the stimulus. Further behavioral experiments on the agent show that this framework allows the agent to learn the meaning of complex (spatio-temporal) spike patterns. The successful learning exhibited by the agent raises hopes that SIDA can be used to build agents with natural semantics.
Genome-guided transcript assembly from integrative analysis of RNA sequence data

The identification of full length transcripts entirely from short-read RNA sequencing data (RNA-seq) remains a challenge in genome annotation pipelines. Here we describe an automated pipeline for genome annotation that integrates RNA-seq and gene-boundary data sets, which we call generalized RNA integration tool, or GRIT. By applying GRIT to Drosophila melanogaster short-read RNA-seq, cap analysis of gene expression (CAGE) and poly(A)-site-seq data collected for the modENCODE project, we recover the vast majority of previously annotated transcripts and double the total number of transcripts cataloged. We find that 20% of protein coding genes encode multiple protein-localization signals
Comparison of Evolutionary Computation and Empirical Penman-Monteith Equation for Daily and Monthly Reference Evapotranspiration Estimation in Tropical Region

Evapotranspiration is the combination of evaporation and transpiration processes that give means the process of water loss to the atmosphere. Reference evapotranspiration (ETo) estimation is part of water cycle that importance for planning and management of irrigation purposes and water resource systems. Due to its importance, the accurate modeling of ETo is of vital importance to estimate crop water requirement and its availability. This research presents a system identification and differential evolution approach by using Differential Evolution and System Identification (DESI) and Modified Genetic Algorithm (MGA) approach for modeling daily and monthly ETo in peninsular of Malaysia. The data set comprising air temperature, humidity, wind speed, and solar radiation was utilized for estimating ETo using FAO56 Penman Monteith (PM) equation as the reference. The modeling results were analyzed and compared with the traditional Penman Monteith method. Based on the analyses, the approach used was found that the models of ETo is adequate and understandable, and suited to estimate the dynamics of the evapotranspiration process. The performance of the model is comparable with that of the PM method.
INUIT PERCEPTIONS OF LEARNING AND FORMAL EDUCATION IN THE CANADIAN ARCTIC

INUIT PERCEPTIONS OF LEARNING AND FORMAL EDUCATION IN THE CANADIAN ARCTIC Geneviève L. Lalonde Advisor: University of Guelph, 2017 Dr. Tristan Pearce There is a longstanding desire among some Inuit and northern educators to better integrate Inuit culture and modes of learning in education. At present, efforts to include Inuit culture in education can be described as ad hoc or add-ons to a Euro-North American schooling system, which puts many Inuit in internal conflict trying to live according to two value systems that in some ways are contradictory. This thesis reports on research conducted with Inuit in the western Canadian Arctic to examine what aspects of culture and modes of learning Inuit desire to have included in education, beyond those identified a priori by non-Inuit educators. A conceptual framework for the cultural negotiation of Indigenous education is empirically applied in a case study of Ulukhaktok, NWT to identify what Inuit think Inuit youth should learn, how they should learn it, where they should learn it and from whom, and why it is important for them to learn it. Data were collected using semi-structured interviews (n=31), free-lists and participant observation. Findings show that Inuit desire to have native languages, subsistence knowledge, skills and values, and understanding of the local environment included in education, which not only builds competence in subsistence but also provides students with capacity to cope with challenges in the modern world. This involves on-the-land hands-on learning with a skilled person and practical everyday use of native languages in learning. Inuit perceive school as a place for “learning” and the research identifies opportunities to negotiate this space to better integrate Inuit culture and modes of learning.
MIT Open Access

—The domain of human factors considers how to improve system design by considering the human within the design process, rather than designing a system and then considering the effects on the human after the design is completed. There have been decades of work in the domain of human factors and human– system integration in applications for complex systems to understand concepts within the physical domain (e.g., system sizing and injury risk) and cognitive domain (e.g., workload, situation awareness, and automation mode confusion). Exosystem technologies are reaching the point where the transition to an operational environment is within reach. This paper presents human factors principles related to the cognitive domain. We present examples of these concepts in the context of exosystem design for operational environments and to aid the development of system evaluation standards. We also review current exosystem evaluation methods and present how performance metrics and tasks can be expanded to better describe system function in the context of known human factors complexities.
Filesystem Performance and Scalability in Linux 2.4.17

The Linux kernel is unique in that it supports a wide variety of high-quality filesystems. For server systems, the most commonly used are Ext2, Ext3, ReiserFS, XFS and JFS. This paper compares the performance of these filesystems using Linux 2.4. 17 and three benchmarks: pgmeter, an open source implementation of the Intel Iometer benchmark; filemark (a version of postmark); and AIM Benchmark Suite VII. The benchmarks were run on three different systems ranging in size from a contemporary single-user workstation to a 28-processor ccNUMA machine. Although the best-performing filesystem varies depending on the benchmark and system used, some larger trends are evident in the data. On the smaller systems, the best-performing file system is often Ext2, Ext3 or ReiserFS. For the larger systems and higher loads, XFS can provide the best overall performance.
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—The increased adoption of mobile devices and social networking is drastically changing the way people monitor and share knowledge about their environment. Here, information and communication technologies (ICT) offer signiﬁcant new ways to support social activism in cities by providing residents with new digital tools to articulate projects and mobilize activities. However, the development of ICT for activism is still in its infancy, with activists using basic tools stitched together in an ad hoc manner for their needs. Still, Internet-based technologies and related software architectures feature various enablers for civic action beyond base social networking. To that end, this paper discusses the vision and initial details of AppCivist, a platform that builds on cross-domain research among social scientists and computer scientists to revisit service-oriented architecture and relevant services to further social activism. We discuss the ICT challenges inherent in this project and present our recent work to address them.
Hydrocarbons in soil and meltwater stream sediments near Artigas Antarctic Research Station: origin, sources and levels

Abstract Aliphatic and polycyclic aromatic hydrocarbons (AHs and PAHs) were investigated in soil and meltwater stream sediments near the Uruguayan Artigas Research Station (BCAA). Gas chromatography with flame ionization detection and gas chromatography mass spectrometry were used to determine the composition of AHs and PAHs, respectively. Total AH concentrations were in the range 0.57–2333 µg g-1, while total PAH concentrations were in the range 1.36–51 650 ng g-1. Based on AH and PAH concentrations, sites in the service area and next to the boat storeroom are highly contaminated, while the other sites sampled have moderate to low contamination levels or are not impacted. High unresolved complex mixture concentrations indicate the occurrence of previous petrogenic contamination, but the dominance of low molecular weight and alkyl PAHs indicate recent oil introductions. Anthropogenic hydrocarbons dominate and are related to diesel fuel and organic residue combustion, fuel storage and boat traffic. Petrogenic contamination is relatively high, as at other Antarctic stations, but is restricted to the boat storeroom and the service area where the incinerator, the generator room and fuel tanks are located. Improvements made in fuel management procedures and the dismantling of the old fuel tanks will reduce the risk of diesel leaks and their impact on the environment near the BCAA. This study provides reference information for future environmental monitoring.
Green's theorem in seismic imaging across the scales

Abstract. The earthquake seismology and seismic exploration communities have developed a variety of seismic imaging methods for passive- and active-source data. Despite the seemingly different approaches and underlying principles, many of those methods are based in some way or another on Green's theorem. The aim of this paper is to discuss a variety of imaging methods in a systematic way, using a specific form of Green's theorem (the homogeneous Green's function representation) as a common starting point. The imaging methods we cover are time-reversal acoustics, seismic interferometry, back propagation, source–receiver redatuming and imaging by double focusing. We review classical approaches and discuss recent developments that fully account for multiple scattering, using the Marchenko method. We briefly indicate new applications for monitoring and forecasting of responses to induced seismic sources, which are discussed in detail in a companion paper.
Incremental learning strategies for credit cards fraud detection: Extended abstract

Every second, Fraud Detection Systems (FDS) check streams of thousands of credit or debit card transactions. Most of the models in production and in the literature rely on batch learning, wasting part of the most recent data. Incremental learning may be beneficial in terms of compu-tational/architectural cost and confidentiality (e.g. avoiding the storage of sensitive data). The full paper focuses on two questions related to the use of incremental learning for FDS: (1) Can incremental learning be as competitive as batch and retraining approaches? (2) Can combining incremental, ensembles, diversity, and transfer learning lead to efficient models under concept drift? We investigate those elements and provide an experimental evaluation on a real-life case study including more than 150 days of e-commerce transactions (or 50 million transactions).
Taking the Ocean’s Pulse: A Vision for the Canadian Biogeochemical Argo Program

The international Argo Program, described in the NYTimes as one of the scientific triumphs of our time (Gillis, J., 12 August 2014, In the Ocean, Clues to Change, New York Times, D3), has fundamentally transformed our ability to measure interior properties of the ocean. Argo is a global array of almost 4,000 autonomous profilers measuring temperature and salinity of the upper 2,000 m. These observations are relayed by satellite and available within hours of collection. While traditional sampling from research vessels is important, it is too infrequent and geographically limited to observe the dynamic and rapidly changing ocean adequately. Satellites provide broad-scale views, but are limited to the surface and in terms of the properties they can observe. Over the past two decades, Argo floats have collected over a million profiles of temperature and salinity, twice the number obtained by research vessels during all of the 20th century. These unprecedented views of the ocean’s interior physical properties have enabled quantification of ocean warming due to climate change, and resulted in significant improvements in ocean and weather forecasting. The ocean’s biogeochemical properties are also changing rapidly, with profound impacts on ecosystems and climate, but to date, our ability to observe these changes is limited. The proposed biogeochemical extension of the Argo network (BGC-Argo) would revolutionize our ability to observe the ocean’s changing biogeochemical state by enabling us to observe seasonal to decadal-scale variability in biological productivity, ocean acidification, and ocean deoxygenation, as well as allowing quantification of the ocean’s uptake of CO2. In 2016, the Science Plan for BGC-Argo was formulated by an international group of experts; the plan calls for an additional 1000 biogeochemical floats that would measure pH, oxygen, nitrate, chlorophyll, suspended particles and light.
Temporal Variations in Tree-Ring Growth Response to Climate of European Larch (Larix Decidua Mill.): An Example from Sudetes, Poland

The temporal stability of climate-growth relationship is a basic assumption and requirement of climate reconstruction using tree rings. However, many recent studies showed change in tree-ring growth response to climate for the last decades. The temporal stability of relationship mentioned above can depend on the local factors, e.g. slope aspect. In presented study the tree-ring growth response to climate of larch from Polish part of Sudetes was analysed over time. The aim was to investigate its temporal variability, existence of trends and influence of local factors. The relation between tree-ring growth of larches from six sites located at comparable altitudes (about 700-750 m.a.s.l.) and climatic factors that mainly control the tree-ring growth of larches in the study area was analysed over time. The Pearson correlation between tree-ring width site chronologies and climatic data for the study area (the average monthly air temperature and total monthly precipitation series from high-resolution gridded dataset CRU TS v.4.01) was calculated in 40-years moving windows for the period 1901-2010. The results showed variations in the tree-ring growth response to climate over the analysed time period. Although in general, similar trends were observed for all sites, some differences in trends and magnitudes among sites were also spotted. Interesting is, that the response of sites became more consistent and similar for the last decades. The discussion of the received results with respect to currently observed climatic trends in the region and local factors that can explain the differences in response between particular sites is presented.
The Team Software Process SM (TSP SM )

ix 1 Software Quality 1 2 How the TSP Was Developed 3 2.1 Engineering Teamwork 3 2.2 The Conditions for Teamwork 4 2.3 Effective Teams 5 2.4 Building Effective Teams 6 3 An Operational Team Process 7 4 The Structure of the TSP 9 5 Launching a TSP Team 11 6 The TSP Teamworking Process 15 6.1 Leading the Team 15 6.2 Communication 16 6.3 Maintaining the Plan 16 6.4 Rebalancing Team Workload 18 7 TSP Quality Management 19 7.1 The Quality Plan 19 7.2 Identifying Quality Problems 23 7.3 Finding and Preventing Quality Problems 26 8 TSP Introduction 29
The Humidity of the Volcanic Soils and Their Impact on the Processes of Mass Removal in Colombia

This chapter describes some aspects of the formation of soils derived from volcanic ash, especially soils classified according to the soil taxonomy as Andisols of the mountains of the central mountain range in Colombia, cultivated with pastures for the production of milk, meat, and potatoes. General erosion, caving, mudslides, and landslides reach and cover large urban and rural territories characterized by a high rainfall regime with decadal records of 227 days of rain per year and with total loss of arable, agricultural, and productive layers. This chapter summarizes aspects of the research carried out by the author in these soils, through the description of profiles on 52 pits, field and laboratory analysis of their physical and some chemical properties for the understanding of the moisture storage capacity, and explains the mechanisms that govern their physical properties, composition, and interaction between particles and fluids and, consequently, their intense erodability and high moisture-retention capacity as a detonating mechanism of the processes of erosion and mass removal of secular occurrence in the soils of the region with high population densities causing hundreds of deaths and incalculable economic losses.