Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterising Dutch forests, wetlands and cultivated lands on the basis of phytolith assemblages

Abstract Palaeoecological reconstructions in the Netherlands are commonly based on pollen and macrofossil analysis, but can be limited if the preservation of organic material is poor. Phytoliths, biogenic silica, do not have this limitation and preserve in settings where other macro- and microfossils do not. Little is known about how phytolith assemblages preserved in soils and sediments reflect the parent vegetation in north-western European systems, so it is currently difficult to contextualise past environments. Here, we characterise phytolith assemblages for soil samples recovered from three major vegetation types in the Netherlands to provide reference data for future reconstructions of past vegetation change. We collected 42 soil surface samples from forests, wetlands and agricultural fields across the Netherlands and characterised the phytolith assemblages they contained. We identified the different phytolith morphotypes and quantified the percentages and concentrations (#phytoliths/cm3 soil) in each sample. We used non-metric multidimensional scaling to assess the variation in phytolith assemblage composition within, and between, the three vegetation types. The phytolith assemblages analysed from the forests, wetlands and agricultural fields were clearly distinguishable from each other. Agricultural fields were dominated by four phytolith morphotypes of grass silica short cells (GSSCs): rondel (tabular), cross type 1 (>15 µm), rondel (elongated) and disturbance or crop phytoliths. Forests settings had significantly higher amounts of different arboreal phytoliths (large and small spheroid rugose) compared with other vegetation types. Wetlands could be identified by significantly higher amounts of Cyperaceae phytoliths (papillate) and other GSSCs (saddle and bilobates with thick castula). Phytolith assemblages could distinguish different subtypes of vegetation within forest and wetland areas, while differences between agricultural systems could not be identified. Our study demonstrates that phytoliths preserved in soils or sediments can be used to separate major vegetation types across the Netherlands. Thus, these results support the hypothesis that phytoliths can be used to infer past environmental conditions in palaeoecological reconstructions. We suggest that future work should: (1) focus on characterising which phytolith types are produced by the commonest tree, wetland, shrub and herb species in the Netherlands and (2) characterise phytolith assemblages across a wider array of vegetation types in north-western European systems to increase the capability for quantitative reconstructions using phytolith assemblages.
Design and Implementation of Dual Band Aperture Coupled Antenna Array

Light-weight antenna arrays require utilizing thesame antenna aperture to provide multiple functions (e.g., communications and radar) in separate frequency bands. In this paper, we present a novel antenna element design for a dual band array, comprising interleaved printed dipoles spaced to avoid grating lobes in each band. The folded dipoles are designed to be resonant at octave -separated frequency bands (1 and 2 GHz), and inkjetprinted on photographic paper. Each dipole is gapfed by voltage induced electromagnetically from a microstrip line on the other side of the substrate. Keywords— Dual band antenna; dual frequency; inkjetprinting; multifunctional antennas; nested antennas; phased array; printed dipole; shared aperture design.
Modeling suitability, movement, and interaction

"The Esri Guide to GIS Analysis, Volume 3: Modeling Suitability, Movement, and Interaction" describes practical applications of modeling concepts in a geographic information system (GIS). This book shows readers how they can explore spatial interaction, site selection, routing, and scheduling, and explains how to best interpret the results of the analysis. With full-color maps and illustrations and sample applications, this book will help students studying GIS and professional GIS analysts better use models to evaluate locations and analyze movement.
Accurate PKa Computation Using Matched Interface and Boundary (MIB) Method Based Poisson-Boltzmann Solver

The pKa values are important quantities characterizing the ability of protein active sites to give up protons. pKa can be measured using NMR by tracing chemicalshifts of some special atoms, which is however expensive and time-consuming. Alternatively, pKa can be calculated numerically by electrostatic free energy changes subject to the protonation and deprotonation of titration sites. To this end, the PoissonBoltzmann (PB) model is an effective approach for the electrostatics. However, numerically solving PB equation is challenging due to the jump conditions across the dielectric interfaces, irregular geometry of the molecular surface, and charge singularities. Our recently developed matched interface and boundary (MIB) method treats these challenges rigorously, resulting in a solid second order MIBPB solver. Since the MIBPB solver uses Green’s function based regularization of charge singularities by decomposing the solution into a singular component and a regularized component, it is particularly efficient in treating the accuracy-sensitive, numerous, and complicated charge distributions from the pKa calculation. Our numerical results demonstrate that accurate free energies and pKa values are achieved at coarse grid rapidly. In addition, the resulting software, which pipelines the entire pKa calculation procedure, is available to all potential users from the greater bioscience community. AMS subject classifications: 92C40, 35J66
A feasibility study for Deep Learning Image Guided Guidewire Tracking for Image-guided Interventions

A current challenge in real-time magnetic resonance imaging (MRI) guided minimally invasive images is needle tracking and planning. We propose a pipeline for automatic object detection using a state-of-the-art object detection network. Predictions by the object detection network were used to translate the MRI plane to keep a guidewire tip in a plane. We evaluated the pipeline on displacement error between the prediction and the actual location of the guidewire tip in a setup with an anthropomorphic blood vessel. For this setup, we hypothesized that the network should be able to correctly predict the actual location within a margin of 10 mm, at least within 1000 ms. Results show that the pipeline can accurately track the guidewire tip in real-time (within 458 ms), with a mean displacement error of 7 mm (σ = 4). Based on this evidence, we have demonstrated the feasibility of deep learning assisted image-guided interventions, creating possibilities for other deep learning guided interventions. Our proposed method shows potential for cryoablation. During these types of minimally invasive procedures tracking needles can be a challenge. Keywords— Image guided interventions, magnetic resonance imaging, object detection, object tracking, deep learning, real-time inference
A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study

Background The data missing from patient profiles in intensive care units (ICUs) are substantial and unavoidable. However, this incompleteness is not always random or because of imperfections in the data collection process. Objective This study aimed to investigate the potential hidden information in data missing from electronic health records (EHRs) in an ICU and examine whether the presence or missingness of a variable itself can convey information about the patient health status. Methods Daily retrieval of laboratory test (LT) measurements from the Medical Information Mart for Intensive Care III database was set as our reference for defining complete patient profiles. Missingness indicators were introduced as a way of representing presence or absence of the LTs in a patient profile. Thereafter, various feature selection methods (filter and embedded feature selection methods) were used to examine the predictive power of missingness indicators. Finally, a set of well-known prediction models (logistic regression [LR], decision tree, and random forest) were used to evaluate whether the absence status itself of a variable recording can provide predictive power. We also examined the utility of missingness indicators in improving predictive performance when used with observed laboratory measurements as model input. The outcome of interest was in-hospital mortality and mortality at 30 days after ICU discharge. Results Regardless of mortality type or ICU day, more than 40% of the predictors selected by feature selection methods were missingness indicators. Notably, employing missingness indicators as the only predictors achieved reasonable mortality prediction on all days and for all mortality types (for instance, in 30-day mortality prediction with LR, we achieved area under the curve of the receiver operating characteristic [AUROC] of 0.6836±0.012). Including indicators with observed measurements in the prediction models also improved the AUROC; the maximum improvement was 0.0426. Indicators also improved the AUROC for Simplified Acute Physiology Score II model—a well-known ICU severity of illness score—confirming the additive information of the indicators (AUROC of 0.8045±0.0109 for 30-day mortality prediction for LR). Conclusions Our study demonstrated that the presence or absence of LT measurements is informative and can be considered a potential predictor of in-hospital and 30-day mortality. The comparative analysis of prediction models also showed statistically significant prediction improvement when indicators were included. Moreover, missing data might reflect the opinions of examining clinicians. Therefore, the absence of measurements can be informative in ICUs and has predictive power beyond the measured data themselves. This initial case study shows promise for more in-depth analysis of missing data and its informativeness in ICUs. Future studies are needed to generalize these results.
On Computing Optimal Repairs for Conditional Independence

This thesis focuses on the concept of Conditional Independence (CI) and its testing, which holds immense significance across various fields, including economics, social sciences, and biomedical research. Notably, within computer science, CI has become an integral part of building probabilistic and causal models. It aids efficient inference and plays a key role in uncovering causal relationships. The primary aim of this thesis is to broaden the scope of CI beyond its testing aspect. We introduce the pioneering problem of data repair, designed to adhere to particular CI constraints. The value and pertinence of this problem are highlighted through two contrasting applications. The first application is debiasing data and developing fair machine learning (ML) models. As fairness becomes a central issue in machine learning, exploring techniques for debiasing data to construct more equitable models is crucial. The proposed data repair methodology supports this, assisting in creating fairer models. The second application is about improving data quality and cleaning processes. Maintaining data quality is a continuous challenge across various fields, and our repair methods present a novel way to address this, enhancing the overall quality and reliability of the data. The proposed repairs use optimal transport (OT) and Earth Mover’s distance as dissimilarity measures. This approach ensures the preservation of the underlying probability distribution’s geometry. In the context of fairness, this contributes to increased downstream model accuracy. In the realm of data cleaning, it offers a robust method for error detection. To facilitate the efficient generation of the repairs, we present novel techniques, including relaxed OT and block coordinate descent methods. The effectiveness of the repair methodologies is validated through experiments conducted on synthetic and real-world datasets. This comprehensive exploration highlights the potential of data repair in addressing critical issues in machine learning and data quality, offering a new perspective on using CI in these fields.
Data Visualization in Online Educational Research

This chapter presents a general and practical guideline that is intended to introduce the traditional visualization methods (word clouds), and the advanced visualization methods including interactive visualization (heatmap matrix) and dynamic visualization (dashboard), which can be applied in quantitative, qualitative, and mixed-methods research. This chapter also presents the potentials of each visualization method for assisting researchers in choosing the most appropriate one in the web-based research study. Graduate students, educational researchers, and practitioners can contribute to take strengths from each visual analytical method to enhance the reach of significant research findings into the public sphere. By leveraging the novel visualization techniques used in the web-based research study, while staying true to the analytical methods of research design, graduate students, educational researchers, and practitioners will gain a broader understanding of big data and analytics for data use and representation in the field of education.
The Growth of Vocational High School Students' 4C Skills on the Use of PjBL STEM-Based Physics Digital Module

The Partnership for 21st Century Learning (P21) developed the Framework for 21st Century Learning to help education practitioners integrate skills into lesson teaching. This framework describes the skills, knowledge, and expertise that students must master in order to succeed in work and life. One of the skills that must be mastered by students in facing the 21st century is the 4C skill which consists of critical thinking, communication, collaboration, and creativity skills. This study aims to analyze the growth of the 4C skills of SMK students as the effect of implementing the use of PjBL STEM-based digital physics modules. This research is an experimental study with the before-after method, which compares the conditions before and after the implementation of learning activities. Data was collected by filling out a questionnaire with a Likert scale by students. The results showed that the 4C skills of vocational high school students grew significantly.
AN EXPERT?

islative districts in this country changes sufficiently that they need to be reapportioned. On the face of it, that task seems a simple matter. All that needs to be done is to draw a specified number of districts so that each has the same number of people, or as close to the same number as is practical. The ideal electoral district population is the total state population divided by the number of seats in the legislature. So defined, the problem is technical, not political, since determining the ideal population is an arithmetic exercise and figuring the size of the districts requires knowledge of and skill in the use of census data. One would think that a problem of this sort should not provoke a great deal of controversy; either the districts have equal populations or they do not. By contrast, the practice of redistricting is quite complicated. A great deal of time and money is spent on drawing and analyzing plans. Reapportionment staffs collect immense amounts of data and build or purchase sophisticated computer systems to aid them in their tasksr The legislators themselves sit through numerous meetings, argu-
FAST IMPLEMENTATIONS OF CURVE25519 ON INTEL SKYLAKE

Curve25519 is an important primitive for elliptic curve Difﬁe-Hellman key exchange. We implement it using Intel’s AVX2 instructions set in a 4-way vectorized fashion, targeting Intel’s Skylake architecture. We explore using integer arithmetic and ﬂoating point arithmetic, applying 4-way FMA units to elliptic cryptography as a novel idea. Our designs manage to beat the performance of the currently fastest implementations in the SUPERCOP benchmarking suite
CDSBen: Benchmarking the Performance of Storage Services in Cloud-native Database System at ByteDance

In this work, we focus on the performance benchmarking problem of storage services in cloud-native database systems, which are widely used in various cloud applications. The core idea of these systems is to separate computation and storage in traditional monolithic OLTP databases. Specifically, we first present the characteristics of two representative real I/O workloads at the storage tier of ByteDance's cloud-native database veDB. We then elaborate the limitations of using standard benchmarks such as TPC-C and YCSB to resemble these workloads. To overcome these limitations, we devise a learning-based I/O workload benchmark called CDS-Ben. We demonstrate the superiority of CDSBen by deploying it at ByteDance and showing that its generated I/O traces accurately resemble the real I/O traces in production. Additionally, we verify the accuracy and flexibility of CDSBen by generating a wide range of I/O workloads with different I/O characteristics.
Visions along the Trail: Community Action and Visitor Employed Photography in Two Native American Communities.

Rural community development is undergoing changing visions, activities, and methodologies. Factors impacting this change include decentralization, budget reduction in the public sector, and globalization and downsizing in the private sector. Community "building" (community-generated change and emphasis on capacities rather than deficiencies) must replace the concept of community "develOpment. " In this paper, visitor-employed photography (VEP) is explored as an appropriate new tool in community building. Nineteen research participants from the Winnebago and Omaha tribes were given cameras and asked to take photographs of self-selected positive and negative aspects of their environment and share and explain their perceptions. Analysis of VEP data.showed community strengths, directions for change, and resident priorities and vision. Omaha participants were concerned about their lack of inclusion on the local school board and consequent lack of control over curriculum and other matters. Positive VEP images showed individual teachers who encouraged study of Indian culture despite the antagonistic school board, while.negative images showed fences that prevented viewing of school sports events by Indians who could not afford admission. Winnebago participants focused on their community's need for adequate affordable housing. VEP interviews singled out community leaders and provided the means for directing efforts toward community building and action. (SAS) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************************************************************** VISIONS ALONG THE TRAIL: COMMUNITY ACTION AND VISITOR EMPLOYED PHOTOGRAPHY IN TWO NATIVE AMERICAN COMMUNITIES Dorothy I. Jenkins School of Landscape Architecture Louisiana State University Quentin A.L. Jenkins Departments of Sociology and Rural Sociology Louisiana State University U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement ED p CATIONAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. 0 Minor changes have been made to improve reproduction quality, Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. 1 PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY JeArki ? As TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) Note: The research reported in this paper was supported by the North Central Rural Development Center, Louisiana State University and Iowa State University
Study Skills for Students with Dyslexia

Managing Your Workload - Jamie Crabb, Jane Davis and Sandra Hargreaves Understanding Your Preferred Learning Style - Sandra Hargreaves and Paula Baty Note-Taking and Note-Making - Kay McEachran Reading Strategies and Speed-Reading - Peri Batliwala and Judith Cattermole Answering Essay Questions - Sandra Hargreaves Structuring Different Writing Genres - Helen Birkmyre The Dissertation - Cheri Shone Improving Your Grammar, Spelling and Punctuation - Sandra Hargreaves and John Brennan Improving Mathematics Skills and Using Statistics - Judith Cattermole and John Brennan Examination Techniques - Sandra Hargreaves Collaborative Learning - Paula Baty
Constrained Delaunay Tetrahedrization: A Robust and Practical Approach

We present a numerically robust algorithm for computing the constrained Delaunay tetrahedrization (CDT) of a piecewise-linear complex, which has a 100% success rate on the 4408 valid models in the Thingi10k dataset. We build on the underlying theory of the well-known tetgen software, but use a floating-point implementation based on indirect geometric predicates to implicitly represent Steiner points: this new approach dramatically simplifies the implementation, removing the need for ad-hoc tolerances in geometric operations. Our approach leads to a robust and parameter-free implementation, with an empirically manageable number of added Steiner points. Furthermore, our algorithm addresses a major gap in tetgen's theory which may lead to algorithmic failure on valid models, even when assuming perfect precision in the calculations. Our output tetrahedrization conforms with the input geometry without approximations. We can further round our output to floating-point coordinates for downstream applications, which almost always results in valid floating-point meshes unless the input triangulation is very close to being degenerate.
Powerful CEOs and their Impact on Corporate Performance *

Executives can only impact firm outcomes if they have influence over crucial decisions. Based on this idea we develop and test the hypothesis that firms whose CEOs have more decision-making power should experience more variability in performance. Focusing primarily on the power the CEO has over the board and other top executives as a consequence of his formal position and titles, status as a founder, and status as the board’s sole insider, we find that stock returns are more variable for firms run by powerful CEOs. These findings suggest that the interaction between executive characteristics and organizational variables has important consequences for firm performance.