Miracle-dz/UniFAR-FTUs huggingface dataset:
GPT-3 for Decision Logic Modeling

Operational decisions are an important part of knowledge-intensive organizations, as these are taken in a high volume on a daily basis. For this purpose, the Decision Model and Notation (DMN) standard describes an intuitive and user-friendly notation to model, communicate and execute business decisions. However, manually modeling the decisions in DMN remains a time-consuming task, as various textual sources need to be analyzed. As such, automation of decision modeling is beneficial for the decision process. This work investigates an automated approach to generating decision tables from natural language based on the GPT-3 large language model. Through a total of 72 experiments over six problem descriptions, this work evaluates GPT-3’s decision logic modeling and reasoning capabilities. While GPT-3 demonstrates promising abilities in extracting decision context and identifying relevant variables from natural language, further enhancements are needed to improve its decision table capabilities for efficient automation of DMN modeling.
The Lucas Asset Pricing Model

where ct is consumption of fruit per person, Lt is the population, Kt measures the stock of trees, and dt is the exogenous output of fruit that drops from each tree. In a given year, each tree produces exactly the same amount of fruit as every other tree, but dt varies from year to year depending on the weather. An economy like this, in which output arrives without any deliberate actions on the part of residents, is called an ‘endowment’ economy (or, sometimes, an ‘exchange’ economy).
An Assessment of Habitat Suitability for Pronghorn Populations of the Central Valley Region of California

An assessment of habitat suitability for pronghorn populations of the Central Valley region of California Virginia Burroughs Efforts to reintroduce and maintain populations of pronghorn (Antilocapra americana) to the California Central Valley, specifically the Carrizo Plain National Monument (CPNM) and the Mojave Desert (Antelope Valley) portion of Tejon Ranch, have largely been unsuccessful due to dwindling numbers of translocated animals. The objective of this study was to improve upon previous models for the CPNM using aerial survey data and then apply the model to the Tejon Ranch. Aerial survey data collected from 2000-2010 on the CPNM was used to establish " use " and " non-use " areas in the model. Model variables included vegetation type (forest, shrub, grassland, semi-desert scrub, crops, and bare areas), slope, and road density. Vegetation and road density variables were treated categorically and slope as a continuous variable. Kernel density estimation (KDE) was used to estimate utilization distributions and home ranges (Fieberg 2007). An 80% isopleth was used to define " used " and " unused " habitat areas within the study site. Binary logistic regression was used to detect correlations between habitat variables and habitat use by pronghorn. Results of the regression analysis indicated overall significance with a p-value of < 0.0001 (testing that all slopes = 0). Each habitat variable comparison was made after adjusting for the other variables (e.g., slope effects were evaluated after adjusting for road density and vegetation type) and was found to be significant. Each variable coefficient was then included in a predictive equation and entered into GIS to generate a map to predict where pronghorn would likely be observed. Similar layers were created for the Tejon Ranch and the predictive equation was run with the CPNM statistical analysis. Limited conclusions about habitat suitability on the CPNM or the Tejon Ranch can be made based on the habitat data available for this model. While slope, road density, and vegetation type are all significant habitat variables influencing pronghorn habitat use, further study is needed to understand the mechanisms driving these relationships. With additional data expansion of the current habitat suitability model would help to further define pronghorn habitat use, specifically the creation of a focused model of a particular season, life history period, or individual animal use to identify more detailed habitat use patterns.
Survey on Social Support of Higher Vocational College Students Based on SPSS

With the continuous development of social economy and the progress of science and technology, complex and diverse network information profoundly affects the values and thinking modes of higher vocational college students, and also has a certain impact on their mental health. Based on the social support theory, this paper makes an empirical study on the social support of higher vocational college students. SPSS26.0 software was used for descriptive statistics and t-test analysis. The results show that the social support of higher vocational college students is generally in good condition, but the support availability is relatively low. There is no signiﬁcant difference in social support between boys and girls; The social support degree of urban students is higher than that of rural students, and there are signiﬁcant differences. The only child has a higher degree of social support than the non-only child, and there are signiﬁcant differences.
World's Science, Technology & Medicine Open Access book publisher

The first section of the book entitled Clinical and Molecular Advances in Ankylosing Spondylitis is a review of the clinical manifestations of Ankylosing Spondylitis (AS) and Spondyloarthritis (SpA). The book includes chapters on Bone Mineral Density measurements, two chapters on the temporomandibular joints, axial fractures, clinical manifestations, diagnosis, and treatment. Molecular genetics and immune response are analyzed in the second section of the book; information on HLA-B*27, other MHC genes and the immune response of AS patients to bacteria is reviewed and updated. Two chapters are dedicated to recent information on non-MHC genes in AS susceptibility, and to new data on disease pathways generated from gene expression studies on peripheral blood.
Formative Assessment Based Students’ Recruitment Estimation: Neural Network Approach

Even though there is a strong focus on achieving the objectives of the education system, it is undoubtedly dependent on the knowledge, skills, and, most notably, the methodology of how teachers use qualitative and quantitative assessment techniques to assist learners. Teachers use formative assessment to monitor students’ progress, their level of knowledge, and their ability to self-assess. One of the critical outcomes following completion of a degree program is the ability of the student to obtain employment. More speciﬁcally, it focuses on acquiring knowledge, skills, and capacities that are then applied to real-life contexts. This paper presents a neural network approach to predicting students’ job placement basedonformativeassessment.TheapproachaidsHigherEducationInstitutionsin determiningtheprogressofindividualstudentsandareasforimprovementduring thegraduationprocess,increasingthelikelihoodofstudentsﬁndingemployment aftergraduation.Thepaperillustratesimportantparametersforcampusplacement selection, how embedding formative assessment and neural network modelling facilitate enhancing students’ knowledge, skills, and performance at the institute and fulﬁl its objectives of gaining meaningful employment.
World's Science, Technology Medicine Open Access book publisher

Maintenance of genomic stability is crucial in ensuring cellular homeostasis and perpetuation of life. Perpetuation of the genetic information relies upon faithful replication of the genome. Mutations, generated during DNA synthesis and/or cell division and induced by exposure to external chemical agents, are drivers of genetic and associated genomic instability believed to fuel malignant transformation. Curious‐ ly, pluripotent stem cells (PSCs) are characterized by a high degree of genomic instability of unknown origin, which resembles that observed in cancer cells. This peculiar feature of PSCs raises the questions of the reasons responsible for this apparent aberrant regulation and of how genome integrity is kept under control. Genomic instability of PSCs also raises important concerns about their use in regenerative medicine, which sets severe limitations in clinical applications. The aim of this chapter is to review current knowledge about the molecular grounds of genomic instability of PSCs of diverse origin, such as embryonic (ESCs), induced pluripotent (iPSCs), and adult (ASCs) stem cells. We will also review how these features undermine the use of PSCs in clinical applications and discuss new emerging perspectives aimed at reducing genomic instability so to improve their use in clinical applications.
( AISeL ) All Sprouts Content Sprouts 4-16-2011 Challenges When Digital Services for Sustainable EverydayTravel is Innovated

This short paper introduce and investigates challenges when digital services for sustainable everyday travel is innovated. The notions of sustainable innovation, energy informatics persuasive technology and service ecosystem is used as a basis for a vision that facilitate the development and evaluation of persuasive solutions for sustainable travel. Based in this vision different challenges for innovation of digital services is discovered and research questions presented.
AN A PRIORI MODEL FOR THE REDUCTION OF NUTATION OBSERVATIONS: A'SVi 994.3 NUTATION SERIES

r • Abst.ra. ct! We discuss the formulation of a new nutation series to be used 'in the reduction of modern space geodetic data. The motivation for devel: oping such a series is to develop a nutation series that has smaller short . period errors than the IAU 1980 nutat ion series and to provide a series that can be used wi th techniques such as the Global Positioning System (GPS) eo • that have sensitivity to nutations but can directly separate the effects of o M ' nutations from errors in the dynamical force models that effect the satellite ^ to ,$• orbits. A modern nuta t ion series should allow the errors in the force models ^ *o <? f o r GPS to be bet ter understood. The series is constructed by convolving 2 £ § the Kinoshi ta and Souchay rigid Earth nuta t ion series wi th an Earth response funct ion whose parameters are par t ly based on geophysical models ^ 't of the Earth and partly estimated from a long series (1979-1993) of very x. long baseline interferometry (VLBI) estimates of nutation angles. Secular 2 rates of change of the nutation angles to represent corrections to the precession constant and a secular change of the obliquity of the ecliptic are included in the theory. Time dependent amplitudes of the Free Core Nutation (FCN) that is most likely excited by variations in atmospheric pressure are included when the geophysical parameters are estimated. The complex components of the prograde annual nutation are estimated simultaneously with the geophysical parameters because of the large contribution to the nutation from the 5i atmospheric tide. The weighted root mean square (WRMS) scatter of the nutation angle estimates about this new model are 0.32 mas and the largest correction to the series when the amplitudes of the ten largest nutations are estimated is 0.17 ± 0.03 mas for the in phase component of the prograde 18.6 year nutation.
Fitting Complex Materials From A Single Image

Since its origin, Computer Graphics has aimed to simulate an illusion of reality. Rendering algorithms have been developed specifically to generate near-perfect images under realistic illumination conditions. It is often difficult to say if such images are realistic or not because there is no real reference such as a photograph. Moreover, the application may need to create novel viewpoints and/or novel illumination conditions from a sparse set of photographs. This is difficult to achieve without using image-based modeling and rendering algorithms. For example, suppose we want to insert a new synthetic object on top of a real anisotropic mirror inside a real scene. This operation clearly needs to take into account the interaction between the new object and its environment (especially this mirror). This is impossible to do, if we do not have an approximation of the reflectance properties of the real surfaces in the image. Therefore specific algorithms are necessary to recover these reflectance properties from the real images. Many authors have contributed to the resolution of this problem [13, 16, 24, 23, 25, 17, 18, 28, 7, 29, 14, 15, 22, 21, 10, 9, 20]. The algorithms that they have produced vary greatly and not all can be re-used for our applications. Considerable work has been done for the reflectance estimation of an isolated object in particular illumination conditions [13, 16, 24, 23, 25, 17, 18] . Although these techniques often bring very detailed reflectance information (i.e. a full BRDF in some cases), their goal is more to replace the use of an expensive gonioreflectometer rather than to be able to change the viewpoint and/or the illumination. Recently, several methods have been developed to take into account the interaction between objects inside a real scene, from a sparse set of photographs [7, 29, 14, 15]. Fournier [10] proposed a different approach but with the use of a single image. However, his technique was limited to perfect diffuse environments and was not able to take into account specular surfaces. Our method has the similar ambition to recover an approximation of the BRDF of the surfaces from a single image, including the processing of specular, isotropic or anisotropic surfaces. This is extremely difficult to achieve because it is not possible to compute a full BRDF correctly without having several images, except for trivial cases We propose a hierarchical and iterative technique that computes the best possible approximation of a real image, using the error computed between the rerendered image and the real one. Each of the new images is generated by making more and more complex assumptions about the reflectance properties of the real surfaces. It is rendered by a global illumination software that takes into account these reflectance changes. The main advantages of our approach are: it does not need any special device to capture the real image (a classical camera is enough), and it estimates the reflectances of all types of surfaces (including anisotropic mirrors) from a single image without any particular constraint
The Rise of Southern Republicans

1. The Southern Transformation 2. Confronting the Democratic Juggernaut 3. The Promising Peripheral South 4. The Impenetrable Deep South 5. The Democratic Smother 6. The Democratic Domination 7. Reagan's Realignment of White Southerners 8. A New Party System in the South 9. The Peripheral South Breakthrough 10. The Deep South Challenge 11. The Republican Surge 12. Competitive South, Competitive America Notes Index
ECE Senior Capstone Project 2021 Tech Notes Avalanche Photodiodes

By Isaac Pizarro, ECE ‘21 _____________________________________________________ Introduction When discussing electro-magnetic radiation, we often use the measurement of photons to describe the intensity of illumination. While using photons to describe illumination works well for theoretical calculations, measuring photons on an individual level is beyond the capability of most light detection systems. However, an avalanche photodiode (APD) utilizes a positive feedback mechanism to generate a measurable signal from a single photon. This is possible by imposing extreme conditions on the diode that causes a large current to arise from the absorption of a single photon.
The Capital Asset Pricing Model: An Overview of the Theory

Although the Capital Asset Pricing Model (CAPM) has been one of the most useful and frequently used theories in determining the required rate of return of a security, the application of this model has been controversial since early 1960s. The CAPM was introduced by Jack Treynor, William Sharpe, John Lintner and Jan Mossin independently, building on the earlier work of Harry Markowitz on diversification and modern portfolio theory. In theory, the capital asset pricing model is employed to set the investor required rate of return on a risky security given the non-diversifiable firm-specific risk, as the systematic risk will be eliminated in a well-diversified portfolio. This research aims to shed the light on this model by discussing the assumptions, the evolution of the Sharpe and Lintner model, and reviewing the literature on the relaxation of model assumptions and the critiques of the CAPM. Finally, the Arbitrage Pricing Model as an extension for the CAPM will be discussed.
KHABEER ( ريبخ ) AS A MACHINE TRANSLATION TOOL

: Khabeer is an Arabic expert system shell supports object oriented programming. This paper presents the use of Khabeer as a machine translation tool. Several phases of machine translation are demonstrated. These phases include lexical and morphological analysis, syntax analysis, knowledge representation and sentence generation. Due to the fact that Khabeer was developed to help in building Arabic-related applications, the paper emphasizes the use of Khabeer in dealing with Arabic sentences. This includes morphological analysis and syntax analysis of Arabic sentences and Arabic sentence generation.
Scholarly Horizons: University of Minnesota, Morris Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal Undergraduate Journal

Das et al. develop a method for analyzing the time complexity of concurrent, message-passing algorithms. Their method is based on adding timing information to datatypes. Specifically, they use a family of datatypes called session types ; these constrain the structure of interactions that may take place over a channel of communication. In Das’s system, the timing properties of an algorithm can be verified by a typechecker: if the timing information in the session types is mismatched, the computer will report a type error. In their paper, Das et al. develop the theory for such a typechecker, but do not provide an implementation.
Prostate Cancer Severity in Relation to Level of Food Processing

Background: The level of food processing has gained interest as a potential determinant of human health. The aim of this study was to assess the relationship between the level of food processing and prostate cancer severity. Methods: A sample of 120 consecutive patients were examined for the following: their dietary habits, assessed through validated food frequency questionnaires; their dietary intake of food groups, categorized according to the NOVA classification; and their severity of prostate cancer, categorized into risk groups according to European Association of Urology (EAU) guidelines. Uni- and multivariate logistic regression analyses were performed to test the association between the variables of interest. Results: Individuals reporting a higher consumption of unprocessed/minimally processed foods were less likely to have greater prostate cancer severity than those who consumed less of them in the energy-adjusted model (odds ratio (OR) = 0.38, 95% confidence interval (CI): 1.17–0.84, p = 0.017 and OR = 0.33, 95% CI: 0.12–0.91, p = 0.032 for medium/high vs. low grade and high vs. medium/low grade prostate cancers, respectively); however, after adjusting for potential confounding factors, the association was not significant anymore. A borderline association was also found between a higher consumption of ultra-processed foods and greater prostate cancer severity in the energy-adjusted model (OR = 2.11, 95% CI: 0.998–4.44; p = 0.051), but again the association was not significant anymore after adjusting for the other covariates. Conclusions: The level of food processing seems not to be independently associated with prostate cancer severity, while potentially related to other factors that need further investigation.
AMMVF-DTI: A Novel Model Predicting Drug–Target Interactions Based on Attention Mechanism and Multi-View Fusion

Accurate identification of potential drug–target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents significant challenges. In this study, we propose a novel end-to-end deep learning model called AMMVF-DTI (attention mechanism and multi-view fusion), which leverages a multi-head self-attention mechanism to explore varying degrees of interaction between drugs and target proteins. More importantly, AMMVF-DTI extracts interactive features between drugs and proteins from both node-level and graph-level embeddings, enabling a more effective modeling of DTIs. This advantage is generally lacking in existing DTI prediction models. Consequently, when compared to many of the start-of-the-art methods, AMMVF-DTI demonstrated excellent performance on the human, C. elegans, and DrugBank baseline datasets, which can be attributed to its ability to incorporate interactive information and mine features from both local and global structures. The results from additional ablation experiments also confirmed the importance of each module in our AMMVF-DTI model. Finally, a case study is presented utilizing our model for COVID-19-related DTI prediction. We believe the AMMVF-DTI model can not only achieve reasonable accuracy in DTI prediction, but also provide insights into the understanding of potential interactions between drugs and targets.
Event-Triggered Robust Model Predictive Control for Multi-Agent Systems Under Hybrid Attacks

In this paper, the consensus control problem for multi-agent systems (MASs) suffering hybrid attacks is discussed, in which the hybrid attacks include randomly occurring deception attacks on system dynamic behavior and denial-of-service attacks (DoS). An event-triggered robust model predictive control (RMPC) method is presented to achieve the consensus of MASs under hybrid attacks. It can be proved that the consensus errors can be asymptotic with prescribed H∞ performance, the sufficient condition for stability can be given in terms of linear matrix inequalities (LMIs), and the controller gains are obtained by solving an optimization problem involving some LMIs. Employing the event-triggered mechanism with maximum trigger interval, not only can not only save computation time and facilitate the efficiency of the shared resources but also can effectively be responsible for the negative impact of cyber-attacks on system performance. Finally, simulation results are given to illustrate the validity and effectiveness of the proposed method.