Miracle-dz/UniFAR-FTUs huggingface dataset:
ERDC/EL TR-07-10, Development of a Distributed Source Contaminant Transport, Transformation, and Fate (CTT&F) Sub-Model for Military Installations

One of the responsibilities of the U.S. military is to operate munitions test and live-fire training ranges. As a result of this testing and training, many explosives and their degradation products persist in the environment. Ecosystem management by military installations can be met only by providing the tools necessary to actively manage watersheds. Water quality and related aquatic ecosystems are major end-points and are insufficiently understood components of natural resource management. Numerical modeling, as part of watershed management, is not new. Considerable advances have been made in physically based distributed watershed hydrologic modeling in the past few years; however, few physically based, distributed models simulate contaminant transformation and transport processes. This report summarizes the development of overland and channel contaminant fate and transport modules for linkage with a variety of hydraulic and hydrologic modeling systems. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR. ERDC/EL TR-07-10 iii
COVID-19 Information trust, Situation Awareness and Adoption of Health Protective Behaviors in China

COVID-19 spreads across China and other countries in a matter of weeks. It is uncertain how people response towards health-protective behaviors in this pandemic. This study aims to evaluate the influence of information sources on situation awareness to adopt health-protective behaviors such as hand hygiene, face-mask use, and social distancing. In total, 288 Chinese completed a questionnaire in July 2020. Data on health-protective behaviors, trust in (formal/informal) information, understanding, perceived susceptibility, and worry were collected. Structural equation model (SEM) was used to identify associations between trust in different information sources, situation awareness and health-protective behaviors. Results suggest that trust in formal information was associated with greater reported understanding of COVID-19 (β=0.33), which in turn was associated with more social distancing (β=0.10). Trust in formal information was inversely associated with perceived susceptibility (β=-0.29), which in turn was negatively associated with social distancing (β=-0.24). Trust in informal information has a positive association with worry (β=0.12), which was positively associated with face-mask use (β=0.22). Trust in informal information was inversely associated with understanding of COVID-19 (β=-0.27), which was positively associated with hand hygiene (β=0.15). Therefore, it can be concluded that trust in formal information was more strongly associated with greater understanding of COVID-19 and social distancing, whereas trust in informal information was strongly associated with worry and face-mask use. Trust in formal information was also associated with less perceived susceptibility and less social distancing.
SUBALPINE SNOWPACK-CLIMATE MANIPULATION AND MODELING EXPERIMENT AT NIWOT RIDGE, CO AND VALLES CALDERA NATIONAL PRESERVE, NM by LEAH MEROMY

Meromy, Leah (M.A., Geography) Subalpine snowpack-climate manipulation and modeling experiment at Niwot Ridge, CO and Valles Caldera National Preserve, NM Thesis directed by Professor Noah P. Molotch At Niwot Ridge, CO, a global warming experiment using near-infrared (IR) heaters is being conducted. Investigation of snow accumulation, snowmelt, and soil microclimate found the heaters to influence these variables at the subalpine experimental site. These changes were compared to an environmentally similar yet naturally warmer subalpine snowpack in the Valles Caldera National Preserve, NM. Over the 2010 and 2011 snow seasons, snow accumulated 42% lower on average and melted out 1-37 days earlier in the warmer plots (CO heated and NM) compared to the CO controls. Soil temperature was 2.6 °C greater on average in warmer plots compared to controls. Peak soil moisture was 0-12% lower in warmer plots versus controls. In order to estimate differences in energy and mass balance exchange at the snow surface in control versus warmer plots, the one-dimensional, physically based snowmelt model, SNOWPACK, was used. Energy and mass fluxes in control cases were compared to heated, NM, and synthetically warmer cases. Model results found that the heaters alter radiative, turbulent and mass fluxes by amounts comparable to differences between CO and NM fluxes. The sign and magnitude of energy and mass exchanges were similar between the control and synthetic models. The proportion of the energy flux associated with latent heat was 11-30% of the total energy flux in heated and NM models compared to 2-9% in control and synthetic models during snowmelt, and subsequently the associated evaporative loss to the atmosphere was much lower in control cases relative to NM cases. These results indicate that warmer conditions (i.e. increased average air
A SIMPLE CONDITION OF FERMAT WILES THEOREM MAINLY LED BY COMBINATORICS

This paper gives the simple and necessary condition of Fermat Wiles Theorem with mainly providing one method to analyze natural numbers and the formula X + Y n = Z logically and geometrically, which is positioned in combinatorial design theory. The condition is gcd(X,E) = X − E ∧ gcd(Y,E) = Y − E in ¬(n | XY ), or gcd(X,E)/n = X −E ∧ gcd(Y,E) = Y −E in n | X ∧¬(n | Y ). Provided that E denotes E = X + Y −Z, n is a prime number equal to or more than 2, and X,Y, Z are coprime numbers.
Chaotic system and special cases in restricted three-body problem

This paper mainly analyzes the chaotic systems behind the three-body problem, establishing the relationship between initial parameters of the three stars and the degree of chaos, classifying and comparing the Lagrangian (some or famous) points with computer simulation and mathematical demonstration. Moreover, this paper also presents the analysis for the special cases of three-body systems in two-dimensional plane with stabilized patterns of every specific solution with computer simulation and theoretical demonstrations.
DROP: Dimensionality Reduction Optimization for Time Series

Dimensionality reduction is a critical step in analytics pipelines for high-volume, high-dimensional time series. Principal Component Analysis (PCA) is frequently the method of choice for many applications, yet is often prohibitively expensive for large datasets. Many theoretical means of accelerating PCA via sampling have recently been proposed, but these techniques typically treat PCA as a reusable statistical operator, independent of downstream analytics workflows. We demonstrate how accounting for downstream analytics operations during dimensionality reduction via PCA allows stochastic methods to efficiently operate over very small (e.g., 1%) subsamples of input data, thus reducing computational overhead and end-to-end runtime. This enables end-to-end optimization over both dimensionality reduction and analytics tasks. By combining techniques spanning progressive sampling, approximate query processing, and cost-based optimization, our proposed dimensionality reduction optimizer enables end-to-end speedups of up to 13.9 times over classic SVD-based PCA techniques, and achieves parity with and can exceed conventional approaches like FFT and PAA by up to 3 times in end-to-end workflows.
All Rights Reserved STATISTICAL METHODS TO DETECT DIFFERENTIALLY METHYLATED REGIONS WITH NEXT-GENERATION SEQUENCING DATA By

FENGJIAO HU Statistical Methods to Detect Differentially Methylated Regions with NextGeneration Sequencing Data (Under the direction of Dr. VARGHESE GEORGE) Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases, including various types of cancer. We propose three different approaches to test for differentially methylated regions (DMRs) associated with complex traits, while accounting for correlations within and among CpG sites in the DMRs. One approach is a nonparametric method using a kernel distance statistic and the second one is a likelihood-based method using a binomial spatial scan statistic. Both of these approaches detect differential methylation regions between cases and controls along the genome. The kernel distance method uses the kernel function, while the binomial scan statistic approach uses a mixed effect model to incorporate correlations among CpG sites. Extensive simulations show that both approaches have excellent control of type I error, and both have reasonable statistical power. The binomial scan statistic approach appears to have higher power, while the kernel distance method is computationally faster. We also propose a third method under the Bayesian framework for comparing methylation rates when disease status is classified into ordinal multinomial categories (e.g., stages of cancer). The DMRs are detected using moving windows along the genome. Within each window, the Bayes factor is calculated to compare the two models corresponding to constant vs. monotonic methylation rates among the groups. As in the case of the scan statistic approach, the correlations between the sites are incorporated using a mixed effect model. Results from extensive simulation indicate that the Bayesian method is statistically valid and reasonably powerful to detect DMRs associated with disease severity. The proposed methods are demonstrated using data from a chronic lymphocytic leukemia (CLL) study.
Diﬀerential Siamese Network for the Avoidance of Moving Obstacles

Ever since the public roads are getting increasingly crowded, a demand arises for autonomously driven vehicles. The ability to avoid obstacles is a basic necessity for it to become a viable option for the consumer. It is one of the reasons that obstacle avoidance is a very active research topic in the ﬁeld of computer vision. Our research primarily focuses on the avoidance of moving obstacles. Existing approaches to this problem often make use of multiple sensors (e.g. LIDAR and RADAR) in addition to one or more cameras. Here, we propose a Diﬀerential Siamese Network (DSN) which utilises a monocular RGB camera as its sole input sensor. Since the focus lies on the avoidance of moving obstacles, work from other researchers serve as a baseline for our research. The work from Khan and Parker [KP19] is selected due to its performance and robustness, and provides a convolutional neural network architecture to manoeuvre in an environment with static obstacles. The DSN is assessed in an environment with both static and moving obstacles to determine whether there is a signiﬁcant diﬀerence in trajectories between the DSN and baseline. Virtual environments, constructed with CoppeliaSim, are utilised to ensure a fair comparison. From the experiments came forth that the DSN’s ability to avoid static obstacles is not inferior to the baseline. The distance to obstacles is measured during every run and stated that they both keep a similar distance to all obstacles. Moreover, the DSN passes moving obstacles more eﬃcient than the baseline. The DSN is able to recognise the movement of an obstacle and readjust his trajectory accordingly. So would the DSN turn left when approaching an obstacle moving from left to right, while the baseline would turn right and hereafter encounters the obstacle again.
Student satisfaction with feedback in a third year Nutrition unit: A strategic approach

The feedback received by students on assessment tasks is a major source of their dissatisfaction with feedback generally, explaining why models of assessment continue to evolve to prioritise provision of useful feedback. Boud’s notion of sustainable assessment is an example. We argue for conceptualising the sustainability of assessment practices from teachers’ point of view as well. Assessment is a major component of teaching academics’ workloads. The time and effort required to support particular practices should be considered relative to how well these are evidenced to support student learning outcomes and enhance their satisfaction with feedback. We report on a trial of this approach, from the second phase of action research examining student and staff experiences of targeted feedback on a draft assessment in a large, final year Nutrition unit. The aim of our intervention was primarily to improve student satisfaction with the extent to which feedback in the unit assisted them to achieve learning outcomes, but also to support student learning. Our findings indicate that, in some contexts, impactful practices like providing feedback on drafts alone may not enhance student satisfaction with feedback. Reflection on our findings led us to reframe the next phase of the project more strategically, by advocating for collaboration on course-wide, programmatic assessment, as a sustainable teaching practice and to support sustainable assessment. We call for models of assessment in higher education and course and unit evaluation to be strengthened through further research examining the sustainability of assessment practices for academics, relative to learner outcomes.
Efficient Implementation of Spiking Neural Networks on the Loihi Neuromorphic Processor

ion, giving to the user the possibility to model large ensembles of neurons and build large networks. However, it is also possible to fine-tune the characteristics of each neuron and synapse present in the network, leading to a deep control over the whole simulation. Different simulators are available with such a scope: Nengo [40], BRIAN2 [41], PyNN [42] are all software toolkit written in python that let to implement large networks of neurons with a high level of detail in the neuron model description. These simulators can run locally and can be used to execute simulations of complex SNNs. Specifically related to Loihi neuromorphic chip, a dedicated software toolkit called Intel NxSDK [43] is available. This python API allows to build network models with deep controls of neurons characteristics, and directly deploy the model on Loihi hardware. The software API gives the user a complete control over the neuromorphic chip, allowing a full exploitation of the hardware. Concerning the ANN to SNN conversion, specific software tools need to be employed. Some simulators have their own conversion toolkit, like Nengo-DL [44] for the Nengo simulator. However, a more universally compatible conversion toolkit is SNN-ToolBox [7]. This software takes as input ANNs that have been trained with different backends, like Keras [45], and convert them into SNNs models compatible with different SNN simulators, like PyNN, BRIAN2 and a built-in simulator called INI [7]. Moreover, SNN-ToolBox offers a full compatibility with Intel NxSDK, allowing to build converted SNNs that can be directly deployed on the Loihi platform. 27
Effects of Mandatory Military Service on Wages and Other Socioeconomic Outcomes

IZA DP No. 14352 MAY 2021 Effects of Mandatory Military Service on Wages and Other Socioeconomic Outcomes In this paper, we estimate the effects of mandatory military service by exploiting the post-cold war decrease in the need for soldiers causing a substantial number of potential conscripts not to be drafted into the German military. Specifically, using previously unavailable information on degree of fitness in the military’s medical exam as a control variable, we test for the effects of mandatory military service on wages; employment; marriage/partnership status; and satisfaction with work, financial situation, health, family life, friends, and life in general. We find almost no statistically significant effects of this 6 to 9 month career interruption for young German men, with the exception of hourly wage, which shows a negative point estimate of -15 percent with a large confidence interval of between -30 and -0.2 percent. This interval estimate is consistent with previous findings for the United States, Denmark, and the Netherlands. JEL Classification: J12, J24, J47
This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics

In web search on debated topics, algorithmic and cognitive biases strongly influence how users consume and process information. Recent research has shown that this can lead to a search engine manipulation effect (SEME): when search result rankings are biased towards a particular viewpoint, users tend to adopt this favored viewpoint. To better understand the mechanisms underlying SEME, we present a pre-registered, 5 x 3 factorial user study investigating whether order effects (i.e., users adopting the viewpoint pertaining to higher-ranked documents) can cause SEME. For five different debated topics, we evaluated attitude change after exposing participants with mild pre-existing attitudes to search results that were overall viewpoint-balanced but reflected one of three levels of algorithmic ranking bias. We found that attitude change did not differ across levels of ranking bias and did not vary based on individual user differences. Our results thus suggest that order effects may not be an underlying mechanism of SEME. Exploratory analyses lend support to the presence of exposure effects (i.e., users adopting the majority viewpoint among the results they examine) as a contributing factor to users' attitude change. We discuss how our findings can inform the design of user bias mitigation strategies.
Prototype as a Representation in Establishing and Maintaining a Rhetorical Participation Structure

Representations are considered to be means for better communication and collaboration in determining software requirements. Uses of representations have normally been studied in artificially created situations characterized by a harmonic common interest. Representations then can be seen as props for hearing the users' voice in requirements analysis. Two episodes in a 'real world' requirements determination session are analyzed not only in a responsive but also in a rhetoric light. A high-fidelity prototype as a representation in concert with a free flow of control could be seen to hinder the democratic determination of the requirements, and to enable rhetorical persuasion.
Electrophilic Aromatic Substitution: theoretical insight into halonium ion self-existence in catalytic system

Potential Energy Scan (PES) has already proven to be a powerful tool in computational chemistry to detect critical points in the energy path of a system, such as transition states and local minima/maxima in energy convergence. Previous studies showed a wide application of PES in many different fields of physical-chemical sciences, such as materials, supramolecular, and catalysis chemistry. Moreover, the evaluation of the basic PES algorithms at a reasonably affordable level of theory has in principle revealed good basic statistical relationships that allow further investigations in this research area. Herein, a simple and fast graphical method for accurate PES evaluation was proposed, performed at the PM7 semiempirical level of theory for catalytic systems in electrophilic aromatic substitution processes. The results presented in this case study showed a relative error ranging from 1.5 to 27.1% for most catalytic-electrophiloid systems. The treatment of such systems with PES algorithms led to novel iron(V) species and opened a completely new field in tandem transition metal-nonmetal catalysis, implying entirely new insights. Moreover, the basic statistical analysis showed that there are no significant outliers, and therefore it can be concluded that the graphical analysis approach can be used in further detailed treatment of PES results in the search for saddle points and prediction of transition state properties under known conditions in the DFT and MP2 functions discussed here. The novel graphical methodology has been introduced by two applied graphical methods, and its accuracy demonstrated in semiempirical methods provides solid results in view of future development and application in a wide range of chemical sciences.
EFFECT OF TAX INCENTIVES ON FOREIGN DIRECT INVESTMENTS INFLOWS IN KENYA SIMON MUNENE KAMAU A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF MASTERS IN TAX AND CUSTOMS

The study sought to determine the effect of tax incentives on foreign direct investments inflows in Kenya. The objectives were: to determine the effect of farm work deductions, Industrial building allowances, investment deductions and wear and tear allowances on foreign direct investment inflows in Kenya. The study was conducted at the macro level and therefore was looking at tax incentives and foreign direct investment inflows for the country annually. Secondary data was collected for a period of 10 years (2008 to 2017) on an annual basis. The study employed an explanatory research design. The researcher also conducted inferential statistics specifically correlation and regression analysis. A multiple linear regression model was used to analyze the relationship between tax incentives and foreign direct investment inflows. Statistical package for social sciences version 22 was used for data analysis purposes. F test and t test were applied to test the significance of the overall model and individual parameters respectively. Diagnostic tests were carried out on the collected data to ensure it is reliable and stable for the analyses. From the results, the R-square value was 0.633 which can be translated to mean that 63.3% of the variations in foreign direct investment inflows in Kenya are attributable to the four selected independent variables and the 36.7 percent remainder are attributable to other factors beyond the scope of this research. The study also revealed a strong connection of predictor variables and foreign direct investment inflows (R=0.796). Analysis of variance (ANOVA) results at 5% significance level show an F statistic of 2.160 which was less than the critical value and hence the model was found not statistically significant. Additionally, the results showed that, industrial building allowances and investment deductions are statistically significant factors affecting foreign direct investment inflows while farm works deductions and wear and tear allowances do not substantially determine foreign direct investment inflows in Kenya. The recommendation made by the study was that more focus should be placed by policy makers to the current levels of industrial building allowances and investment deductions since they have a significant influence on foreign direct investments inflows in the Kenya. The study recommends the need for further studies to focus on the other variables that determine foreign direct investment inflows in Kenya.