Miracle-dz/UniFAR-FTUs huggingface dataset:
Nowcasting GDP With a Large Factor Model Space

We propose a novel time-varying parameters mixed-frequency dynamic factor model which is integrated into a dynamic model averaging framework for macroeconomic nowcasting. Our suggested model can efficiently deal with the nature of the real-time data flow as well as parameter uncertainty and time-varying volatility. In addition, we develop a fast estimation algorithm. This enables us to generate nowcasts based on a large factor model space. We apply the suggested framework to nowcast German GDP. Our recursive out-of-sample forecast evaluation results reveal that our framework is able to generate forecasts superior to those obtained from a naive and more competitive benchmark models. These forecast gains seem to emerge especially during unstable periods, such as the Great Recession, but also remain over more tranquil periods.
The determinants of degree programme satisfaction

ABSTRACT Using a unique European dataset for graduates, this paper examines the determinants of college satisfaction and investigates the relative importance of human capital, course composition, and subsequent employment outcomes. We find that aspects of the degree programme related to the mode of teaching and personal development were considerably more important in determining college satisfaction, relative to the field of study, or the subsequent jobs that they obtained. Our results highlight that practical learning approaches and an experience that enhances non-cognitive personal development (NCPD) are some of the course attributes most valued by students retrospectively. Therefore, universities can enhance the quality of student experiences, and also equip them with aspects of human capital allowing them to prosper within the labour market, with an increased focus on both applied learning experiences and non-cognitive personal development.
A Dynamic Genetic-Based Context Modeling Approach in Internet of Things Environments

Internet of Things (IoTs) enables entities every day to communicate and collaborate with each other for providing information, data and services to inhabitants and users. IoTs consists of a large number of smart devices that can generate immense amount of data with different types. These sensors raw data needs to be modeled in a certain structure before filtering and processing to provision context information. This process is called context modeling. Context modeling provides definition of how context data are structured and maintained through context aware system. However, employing model for every context type through context aware application is static and is specified by the application developer. The main problem in IoTs is that the structure of context data changes overtime, therefore static modeling cannot be adaptable for modeling these changes. In this paper, a new dynamic approach for context modeling based on genetic algorithm and satisfaction factor is proposed. Firstly, the proposed approach uses genetic algorithm to find the best matching between a set of contexts and a set of available context models. Secondly, it uses a satisfaction factor to calculate the satisfaction degree for each context with each available context model and select the context model with high satisfaction degree as the structure model of this context, dynamically. In addition, flexibility indicator property and context based are defined to measure the performance of the proposed approach. The results of conducted simulations show that the proposed approach achieves higher performance than static approach for context modeling.
The role of health locus of control in value co-creation for standardized screening services

Purpose Despite the availability and accessibility of standardized screening services, such as preventative health services, many individuals avoid participation. The extant health literature has indicated that health locus of control (HLOC) influences engagement and uptake of health services. The purpose of this paper is to explore how the microfoundation, HLOC, contributes to value co-creation via service-generated and self-generated activities in standardized screening services. Design/methodology/approach A qualitative study of 25 consumers who have experienced one of the three standardized screening services in Australia was undertaken, followed by thematic analysis of the data. Findings Service-generated activities elicit reactive responses from consumers – compliance and relinquishing control – but when customers lead co-creation activities, their active responses emphasize protecting self and others, understanding relationship needs and gaining control. Consumers with high internal HLOC are more likely to take initiative for their health, take active control of the process and feel empowered through participating. Consumers with low internal HLOC, in contrast, require more motivation for participation, including encouragement from powerful others through promotion or interpersonal dialogue. Social implications These findings can be used by policymakers and providers of preventative health services for the betterment of citizen health. Originality/value The integration of the DART framework, customer value co-creation activities, and the delineation of self-generated and service-generated activities provides a holistic framework to understand the influence of HLOC on the co-creation of value in standardized screening services.
Self-Organizing Slot Access for Neighboring Cooperation in UAV Swarms

This article focuses on the slot access problem for neighboring cooperation in unmanned aerial vehicle (UAV) swarms. To avoid the slot access process being hindered by unavailable topology information or information exchanges, a self-organized collision discovery mechanism is proposed. Each broadcaster can know whether its transmission is successful through the mechanism which provides the basic knowledge for finding the slot access strategy. Considering the distributed feature, the slot access problem is formulated as two game models. Both games are proved to have at least one Nash Equilibrum (NE) and the best NE is the optimum of the problem. Two distributed and synchronous algorithms are proposed to reach the NE. The first algorithm converges fast which satisfies the dynamic feature of UAV swarms and the second one converges to the optimum asymptotically. Moreover, to enhance the time efficiency of UAV swarms, the total number of required slots is investigated in some typical topologies and then conjectured to general ones. Simulation results verify that the proposed method is effective and the conjecture is true in almost all topologies.
Perceived social support and mental health states in a clinic-based sample of older HIV positive adults

ABSTRACT Despite a high prevalence of mental health problems in older, HIV+ adults (aged ≥ 50), few studies have examined the relationship between perceived social support and mental health burdens in this group. In a clinic-based sample of 100 adults, 23% and 29% met criteria for one and more than one mental health burden, respectively. In multivariable binary logistic models, perceived receipt of positive and emotional social support as well as overall support was inversely associated with the presence of mental health burdens. There is a need for additional investigation of how social support can help mitigate mental health burdens among HIV+ older adults.
Network medicine characterisation of genetic disorders by propagation of disease phenotypic similarities

The elucidation of the genetic causes of diseases is central to understanding the mechanisms of action of a pathology and the development of treatments. Disease gene prediction methods streamline the discovery of the molecular basis for a disease by prioritizing genes for experimental validation. Technological advances, such as high throughput sequencing and screening techniques, have led to an increasing accumulation of genomic data. Despite this growth, the mechanisms of action through which genomic variants drive disease development are not fully understood. Earlier approaches to find non-experimental disease gene associations such as linkage analysis or genome-wide association studies, produce either limited results or hundreds of candidates, making experimental validation expensive and time consuming. Modern biological networks have been exploited to capture significant features of the highly complex protein interactions, leading to the rise of computational methods in network medicine. Recent network medicine based approaches bypass the lack of functional annotation by drawing inferences from interaction data. My approach, called Cardigan (ChARting DIsease Gene AssociatioNs), is based on a semi-supervised algorithm that propagates labels on the interactome. These labels integrate disease phenotypic information expressed as a similarity measure between diseases, which is obtained by mining and comparing MeSH terms relevant for each disease on the MeSH ontology. Thorough experimentation shows that Cardigan vastly outperforms state-of-the-art disease gene prioritisation methods. This work additionally presents an exploratory extension of the approach, which, to the best of my knowledge, allows network methods for the first time to handle protein interfaces. As a ramification of disease characterisation, this work presents an analysis of viral induced lymphoid malignancies on mice. In particular, the characterisation of the clonality of viral insertions to classify different stages of lymphomagenesis. The results show that we can identify rare driver mutations from late stage samples, with infrequent occurrences as clonal mutations, by adding statistical support of their occurrence as subclonal mutations. Several known rare cancer drivers were found to appear as subclonal mutations in late stage cancer samples more often than expected by random chance. Another research ramification focuses on a pipeline to infer
Imaging Glial Cells in Alzheimer’s Disease: From Light Microscopy to Multiplexed Ion Beam Imaging

accumulation, s glial cell phenotypes, neuroinﬂammation, and neuron death, has been slowed by single-plex imaging technologies and staining technologies. This paper will take a historical approach to the evolution of neuroimaging of glial cells in the context of AD, where the limitations of traditional light microscopy (LM) and immunohistochemical (IHC) techniques will be contrasted to the promising application of the novel imaging technology, High-Dimensional Multiplexed Ion Beam
Two-Stage ICI Suppression in the Downlink of Asynchronous URLLC

Applying the concept of virtual cell together with open-loop communications has been recently shown to achieve ultra-reliable and low-latency communication (uRLLC) in vehicular networks. Nevertheless, losing perfect synchronization due to proactive communications creates difficulty in mitigating multiple access interference (MAI). Multiple carrier frequency offsets (CFOs) resulting from different oscillators at different access points (APs) incur serious inter-carrier interference (ICI) to further complicate downlink MAI. Asynchronous multiuser detection (MUD) with ICI-Whitening was shown leading to satisfactory performance, but the whitening scheme needs the covariance matrix of ICI that is practically hard to obtain for downlink receivers. We develop a two-stage ICI suppression method to resolve this challenge. The first-stage processing is Pseudo-ICI-Whitening (P-ICI-W) or its simplified version Pseudo-Truncate ICI-W (PT-ICI-W), which does not rely on the estimation of ICI covariance and is suitable for asynchronous downlink. In terms of post-processing signal-to-interference-plus-noise ratio (SINR) and bit-error rate (BER), our proposed mechanism can approach ICI-Whitening. The second-stage processing is based on the recently proposed generalized linear minimum mean square error (LMMSE) projection to further cancel some ICI terms. Moreover, our proposed mechanism is compatible with space-time-block-coded signals, namely Alamouti coding and Complex Interleaved Orthogonal Design (CIOD), to yield more reliable proactive wireless communications.
Variation of Rumen Bacterial Diversity in Steers after the Beginning of Grazing

Part of the Plant Sciences Commons, and the Soil Science Commons This document is available at https://uknowledge.uky. edu/igc/22/1-8/24 The XXII International Grassland Congress (Revitalising Grasslands to Sustain Our Communities) took place in Sydney, Australia from September 15 through September 19, 2013. Proceedings Editors: David L. Michalk, Geoffrey D. Millar, Warwick B. Badgery, and Kim M. Broadfoot Publisher: New South Wales Department of Primary Industry, Kite St., Orange New South Wales, Australia
STREETLIGHT OBJECTS RECOGNITION BY REGION AND HISTOGRAM FEATURES IN AN AUTONOMOUS VEHICLE SYSTEM

In this paper Streetlight object identification is addressed using the notion of image processing. An approach based on Image Processing Techniques is proposed for selection and processing of features from the images. Histogram and Region was applied on the extracted images. Histogram and Region features were then extracted and employed to train the Support Vector Machine (SVM) classifier for streetlight recognition. Experimental results shows 99.1%, 84% and 100% for histogram, region features and combination of both respectively. Experimental results have proved that the proposed method is robust, accurate, and powerful in object recognition.
ANALISIS DAN IMPLEMENTASI SISTEM TRADING OTOMATIS BITCOIN DENGAN MENGGUNAKAN RECURRENT REINFORCEMENT LEARNING

Abstrak Bitcoin adalah salah satu cryptocurrency yang diminati untuk menjadi media investasi dalam mencapai keuntungan finansial. Meskipun investasi menggunakan Bitcoin sangat populer, investasi jenis ini memiliki risiko yang perlu dipertimbangkan. Untuk mengantisipasi risiko dalam berinvestasi menggunakan Bitcoin, sistem perdagangan diperlukan untuk berdagang secara otomatis. Sistem dibangun menggunakan dua metode komputasi, yaitu Recurrent Neural Network dan metode Reinforcement Learning yang kemudian disebut Recurrent Reinforcement Learning. Metode ini memerlukan nilai parameter yang tepat untuk memaksimalkan nilai sharpe ratio. Nilai sharpe ratio digunakan untuk mengukur kelebihan pengembalian, atau premi risiko, per unit deviasi dalam aset investasi atau strategi perdagangan. Dalam penelitian tugas akhir ini, dilakukan analisis terhadap parameter yang mempengaruhi kinerja sistem. Hasil yang diperoleh dari analisis yang telah dilakukan menyatakan bahwa sistem mendapatkan nilai sharpe ratio 0,10963. Nilai sharpe ratio yang didapatkan masih relatif tinggi karena suatu investasi dinilai beresiko rendah jika nilai sharpe ratio nya satu keatas. Kata Kunci:Bitcoin, Trading, Recurrent Reinforcement Learning,RRL Abstract Bitcoin is one of the cryptocurrency in demand to be an investment medium in achieving financial returns. Although investing using Bitcoin is very popular, this type of investment has risks that need to be considered. To anticipate risks in investing using Bitcoin, a trading system is needed to trade automatically. The system is built using two computational methods, namely Recurrent Neural Network and Reinforcement Learning method which is then called Recurrent Reinforcement Learning. This method requires the right parameter values to maximize the sharpe ratio value. Sharpe ratio values are used to measure excess returns, or risk premiums, per unit deviation in investment assets or trading strategies. In this final project research, an analysis of parameters that affect system performance is carried out. The results obtained from the analysis that has been done states that the system gets a sharpe ratio value of 0,10963. The value of the sharpe ratio obtained is still relatively high because an investment is considered low risk if the value of the sharpe ratio is one and above. Keywords: Bitcoin, Trading, Recurrent Reinforcement Learning, RRL
Projeto via LMIs de Controlador H∞ Chaveado para Sistemas Incertos, Aplicado ao Sistema 2 DOF Ball Balancer com D-estabilidade

: The project proposes the study, simulation and implementation of an 𝑯 ∞ controller applied in an unstable plant with polytopic uncertainties. These uncertainties are related to failures, which recurrently occur in the practical implementations of engineering. Thus, the designed controller should ensure high reliability for such systems. The switched control law chooses the gain of the controller that does state feedback in order to minimize the derivative of the Lyapunov function of the quadratic type, guaranteeing a low-cost performance, being the project based on Linear Matrix Inequalities - LMIs. The theories covered in this topic are contemporary and promise high reliability and efficiency that will be confirmed with practical implementation in a 2 DOF Ball Balancer system
On the Integration of Knowledge Graphs into Deep Learning Models for a More Comprehensible AI - Three Challenges for Future Research

Deep learning models contributed to reaching unprecedented results in prediction and classification tasks of Artificial Intelligence (AI) systems. However, alongside this notable progress, they do not provide human-understandable insights on how a specific result was achieved. In contexts where the impact of AI on human life is relevant (e.g., recruitment tools, medical diagnoses, etc.), explainability is not only a desirable property, but it is -or, in some cases, it will be soon-a legal requirement. Most of the available approaches to implement eXplainable Artificial Intelligence (XAI) focus on technical solutions usable only by experts able to manipulate the recursive mathematical functions in deep learning algorithms. A complementary approach is represented by symbolic AI, where symbols are elements of a lingua franca between humans and deep learning. In this context, Knowledge Graphs (KGs) and their underlying semantic technologies are the modern implementation of symbolic AI—while being less flexible and robust to noise compared to deep learning models, KGs are natively developed to be explainable. In this paper, we review the main XAI approaches existing in the literature, underlying their strengths and limitations, and we propose neural-symbolic integration as a cornerstone to design an AI which is closer to non-insiders comprehension. Within such a general direction, we identify three specific challenges for future research—knowledge matching, cross-disciplinary explanations and interactive explanations.
Models for Elementary Particles, Dark Matter, Dark Energy, and Galaxies

We show theory that spans tiny and vast aspects of physics. We suggest descriptions for new elementary particles, dark matter, and dark energy. We use those descriptions to explain data regarding dark matter effects, dark energy effects, and galaxy formation. Our mathematics-based modeling, descriptions, and explanations embrace and augment standard physics theory and modeling. One basis for our modeling is an extension to mathematics for harmonic oscillators.
A Stepwise Approach to Assess the Occupancy State of Larval Lampreys in Streams

Pacific Lamprey Entosphenus tridentatus is an ecologically and culturally important anadromous species of conservation concern for which fisheries managers use information on occupancy state in streams to assess species status and inform stream management decisions. Here we developed a stepwise approach that incorporates the potential for nondetection and a preselected expected maximum probability of stream occupancy if field crews do not document larval Pacific Lamprey during sampling. Our approach includes seven steps: define the occupancy question; select the maximum acceptable probability of occupancy, if the species is not documented during sampling; define an assumed detection probability for the target organism; calculate required sampling effort; select sampling units; conduct sampling; and interpret sampling results into probabilistic occupancy conclusions. We examined detection probability of our approach for larval lamprey using data from multiple occupied streams in the Pacific Northwest. We illustrated our approach by evaluating Balm Grove Dam as a barrier to Pacific Lamprey migration in Gales Creek, Oregon. Bayesian estimates of detection probability in occupied streams ranged from 0.15 to 0.94, with an overall median of 0.70 (95% credible interval: 0.60–0.79). Assuming detection probability is at least 0.15 (i.e., lowest estimate), 19 reaches are required for the expected maximum probability of occupancy to be not more than 0.05, if the species is not documented through our sampling approach. Although detected downstream, we detected no larvae upstream of Balm Grove Dam; thus, we conclude that the maximum probability of occupancy upstream of Balm Grove Dam was not more than 0.05 at an assumed detection probability of 0.4, suggesting the dam as a barrier to adult migration. We provide an occupancy assessment tool with standardized sampling requirements that incorporates the potential for nondetection and the flexibility to select an expected maximum probability of occupancy if researchers document no larvae, to aid management and restoration in a single stream.
The impact of Medicaid expansion on utilization of mental health services in west Louisville, Kentucky.

vi LIST OF TABLES . xi CHAPTER I: INTRODUCTION . 1 The Burden of Mental Illness . 1 Disparities in Risk and Access to Mental Health Care . 5 The Role of Health Insurance in Health . 9 U.S. Health Reform. 10 Mental Health Parity . 11 The Affordable Care Act . 13 Medicaid Expansion. 14 Proposed Study . 16 Operational Definitions . 19 CHAPTER II: REVIEW OF THE LITERATURE . 23 Introduction . 23 Epidemiology of Mental Health. 24
Epidemiology of Traumatic Brain Injury over the World: A Systematic Review

We identified 60 reports from 29 countries with data on Traumatic brain injury (TBI) epidemiology in the published literature. Men were at higher risk of TBI than women. The average age at the time of TBI ranged from 27 to 59.67 years while the median age ranged from 29 to 45 years. The incidence of TBI in Sweden, Italy and Norway was decreased while the incidence of TBI in Spain and Taiwan was increased. The countries with the incidence of TBI from high to low were New Zealand, United States, Spain, Sweden, South Africa, Austria, France, Italy, Germany, Canada, Norway, Australia, Portugal, Finland, China, Iran, Switzerland and Belgium. The overall mild: moderate: severe ratio was 55: 27.7: 17.3 based on Glasgow Coma Scale (GCS). In patients with moderate and severe TBI, death was the most common outcome. In TBI patients with all severities, good recovery was the major clinical outcome. Motor vehicle collision (MVC) was the leading cause of TBI in China, Pakistan, Japan, Australia, France, Spain, Austria, England, Croatia, Slovakia, Bosnia, Macedonia, Netherland and Italy, whereas fall was the leading cause in United States, Canada, New Zealand, Sweden, Scotland, Norway and Finland. The MVC-related TBIs were the most common causes in developing countries, whereas the fall-related TBIs were the most common causes in developed countries. The percentage of MVC-related TBIs was the highest in Asia. Europe had the highest percentage of fall-related TBIs and work-related TBIs. North America, followed by Oceania, had the highest percentage of sport-related TBIs.