Miracle-dz/UniFAR-FTUs huggingface dataset:
Participation and Performance in Accountable Care Organizations

This paper studies provider participation and performance in Medicare’s Accountable Care Organizations (ACOs). I build and estimate a two-stage structural model in which potential ACO participants first choose which, if any, ACO to join based on the characteristics of an ACO and the net income they expect to earn from participating in that ACO. In the second stage, participants in an ACO act strategically, choosing their contribution to ACO savings and quality to maximize their payoff, hence determining overall ACO performance and the net income from participating. The model is estimated with public ACO-level performance data. Estimation provides strong evidence that Medicare providers are more likely to participate in ACOs that earn more, with an additional $100,000 in ACO income increasing participation in that ACO by over 1%. I also find that primary care physicians have a much higher cost of improving quality of care and increasing savings than specialists, and all providers face a strong trade-off between these two objectives. One counterfactual policy experiment shows that two-sided ACOs could increase the cost-savings of the Medicare Shared Savings Program by $50 million per year, or 9%. Another counterfactual experiment finds that cost-savings is maximized when 53% of savings are paid back to ACOs, which is strikingly close to current policy. Under perfect coordination of ACO participants, program savings would increase by $1.26 billion.
Prosedur Pelayanan Terhadap Kepuasan Pelanggan Pada Indomaret Tugu Cimanggis

This final report discusses the procedure of service to customer satisfaction at Indomaret Tugu Cimanggis, by doing observation and interview to Indomaret employee and compare it with theory that discuss about service procedure to customer satisfaction. Based on the observations that the author has done this aims to improve service to customer satisfaction Indomaret Tugu Cimanggis. Although there are still many problems encountered, but the problems faced are not too significant so only need a little evaluation to further improve the service to customer satisfaction. The data used are primary data collected and interview to Indomaret Tugu Cimanngis.
Submucous fibrosis in non-betel nut chewing subjects.

: Six patients with the typical features of submucous fibrosis (SF) but without a history of betel nut chewing or an abnormal intake of chillies are described. All had palpable fibrous bands in their mouths and limited mouth opening as well as a variety of symptoms associated with SF. Biopsy specimens of the cheeks showed the classic features of SF, an atrophic epithelium and a very dense lamina propria. Also seen were hyperkeratosis, atypia and signet cell-like degeneration of the epithelium and chronic inflammation of the lamina propria. In 3 cases the fibrosis extended into the submucosa. As no reason for the disease could be demonstrated the question is asked whether people with a genetic predisposition can develop SF spontaneously.
Methodological Lapses in Plant and Equipment Valuation among Lagos Valuers

Machinery and equipment valuation for whatever purpose is to address the problem posed by the client. The aim of this paper is to evaluate the level of adherence to valuation processes and standard in machinery and equipment valuation by practitioners in the study area. The study is carried out through the distribution of 120 structured questionnaires to valuation practitioners in Lagos metropolis to find out their approach, level of training, experience, knowledge of standard etc. This is corroborated with the content analysis of 40 valuation reports on machinery and equipment to determine the degree of compliance to establish process and standard of practice.Findings from the study indicate that majority of reports are scanty with regard to methodology standard, functional and economical obsolescence, machine capacity, serial number, model number, etc. The non-compliance to valuation process and standard could be a major source of inaccuracy in valuation. Standardization is critical to the promotion of uniformity, consistency, reliability, public trust and international acceptability of valuation reports considering the effect of globalization on property market.
A Research on the Perception Level of the Success Criteria of Learning Organizations among a Group of Physicians and Nurses

According to results in research considering learning organizations the humankind’s unused potential can be brought to light if employees achieve personal development. Making the continuous development a way of life will improve the performance of employees and the performance of the organization in particular. A company’s philosophy based on continuous learning will lead to an advantage for the learning organizations, which constantly increase their abilities of directing their future in the competition. We believe that the ‘learning organizations’ is a management model that should be employed particularly in the health care institutions. This way, each topic can be examined, tested for improvement and continuous development can be maintained. Thus, the purpose of our project is to make the common vision, personal ability, learning in a team and systematic thinking accepted as basic skills and to define the perception level of the success criteria of the learning organizations among the groups of physicians and nurses working in the health care organizations, which should be learning organizations. This project consists of two parts: a theoretical part and a fieldwork. The theoretical part uses scientific books, articles, and researches. As fieldwork, a survey verifies whether physicians and nurses attending learning organizations match the criteria of success. The survey is conducted in a group of private hospitals in the city of Istanbul, Turkey.
Mycetomas with pulmonary dissemination

: Three mycetoma cases with pulmonary dissemination are presented. Two of them caused by N. brasiliensis and one by N. asteroides. The dissemination in one of them was by contiguity and the two other by hematogenous way. We point out the sequence of treatments used, emphasizing the excellent therapeutic result achieved with the association of sulfamethoxazole-trimethoprim and amikacin endovenously. We propose a therapeutic trial in a greater number of severe cases of mycetoma with the last antibiotic association in order to evaluate its therapeutic efficacy.
Purchase Decisions for Bookish Boxes: Influence of Instagram

There is no question that the internet, and by extension, social media has changed the way people communicate in the world today. Not only has the rapid advancement in technology affected how people communicate with each other, but also with businesses. Businesses have been forced to start marketing their products digitally in order to compete with others in their respective markets. The entire e-commerce industry has formed because of these technological advancements and changes in consumer tastes and preferences. To some small businesses, the emergence of social media has been a great benefit because they now have access to a marketing tool that can reach more people at a lower cost. One industry that has been taking advantage of social media, and more specifically, Instagram, is the bookish subscription box companies. These companies offer book-loving people boxes full of things ranging from books to book-related merchandise on a subscription basis. This study explores marketing tactics used by these companies by looking at the presence they have on the internet and various social media platforms. A survey was sent to the book community on Instagram, known as the #bookstagram community, to discover the motivation behind the purchases of bookish boxes and the influence social media has on those purchase decisions. The results reveal the strong influence of social media on consumer purchase decisions when it comes to bookish boxes. Most participants indicated that they look towards Instagram to find out information on these companies. The fear of missing out (FOMO) and the fandoms represented in the products were also highly influential and have grown as a result of social media. PURCHASE DECISIONS FOR BOOKISH BOXES 3
Workplace Isolation Occurring in Remote Workers

Workplace Isolation Occurring in Remote Workers by Adam Hickman MBA, Walden University, 2015 BA, Hiram College, 2012 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Management Walden University May 2019 Abstract Organizational leaders and managers may not have the management acumen, organizational awareness, or leadership expertise to construct and implement effective strategies, policies, and procedures to help reduce the frequency of the phenomenon of workplace isolation. The purpose of this qualitative, exploratory, multiple case study was to gain a common understanding about how workplace isolation may influence a remote employee’s performance in a customer service organization in the United States. Emerson’s social exchange theory was used as the conceptual framework. A series of semistructured interviews with 21 remote workers that consisted of 4 different divisions at the same organization was conducted to yield thematic results. Data analysis includedOrganizational leaders and managers may not have the management acumen, organizational awareness, or leadership expertise to construct and implement effective strategies, policies, and procedures to help reduce the frequency of the phenomenon of workplace isolation. The purpose of this qualitative, exploratory, multiple case study was to gain a common understanding about how workplace isolation may influence a remote employee’s performance in a customer service organization in the United States. Emerson’s social exchange theory was used as the conceptual framework. A series of semistructured interviews with 21 remote workers that consisted of 4 different divisions at the same organization was conducted to yield thematic results. Data analysis included holistic and pattern coding. The most common understandings that emerged into themes were the need of social interaction, manager communication, and peer-to-peer interactions that had an influence on job performance. The knowledge acquired in this study can affect social change by providing insights for leaders, managers, and practitioners to create policies and strategies to improve the engagement, performance, and well-being of remote workers who may experience workplace isolation. Workplace Isolation Occurring in Remote Workers by Adam Hickman MBA, Walden University, 2015 BA, Hiram College, 2012 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Management
What Effect Do Rumors Have on Publicly Traded Sports Teams

This paper explores the effects of rumors on stock prices. It targets publicly traded soccer teams to see what impact rumors of players transferring, in and out of teams, has on their respective stock returns. A total of 56 rumors are being used in the analysis. An event study is conducted around the dates of each transfer rumor in order to examine what types of abnormalities exist in the returns of the stock. To further investigate this study, a cross sectional analysis is being performed to see whether player reputation, player status, and future validity of the rumors had more of an effect on the stock’s returns. My statistical analysis indicates that abnormal market returns occur with strong statistical significance between 3 and 1 day before the publication of the transfer rumors in the sample. Univariate tests show no statistical significance in any of the cross sectional analyses performed. A regression controlling for each factor, in addition to the team’s ranking, also showed no statistical significance. Market value, used as a control variable for a player’s popularity in the regression analysis, was found to have a statistically significant relationship with abnormal returns.
Planning for Transportation Problems

Today, a vast amount of resources is spent globally on ineffective transportation planning. Using techniques of automated planning, we study simplified variants of logistic problems, where items are delivered to their destinations using a fleet of vehicles moving on an oriented, non-negatively weighted graph that represents a road network. We propose several planning systems for the effective solution of such problems. Experiments conducted on original planning competition data show that our planners are able to improve plan quality when compared to domain-independent planners from the competition. Last but not least, we developed TransportEditor, a visualizer and editor of transportation problems, for efficient problem analysis, planner construction, and plan introspection.
Strategies for protecting intellectual resources in a company

Presently, innovations and advanced technologies are the basis for the development of companies in Poland. Their creation and growth require not only expenditures and advanced and extensive organizational structures, but also exploratory and executive behavioural skills of their employees. Thanks to these skills, intellectual resources are created. The purpose of this paper is to present strategy for protecting intellectual resources in the company. In these deliberations, the authors present their model for the protection of intellectual resources that, in practical perspective, is a component of the broadly understood business development strategy. The point of reference for the purpose and scope of the paper was research conducted in the period from May to July 2015 on a sample of 137 companies classified into small and medium-sized enterprises (SMEs).
Learning Context-aware Task Reasoning for Efficient Meta-reinforcement Learning

Despite recent success of deep network-based Reinforcement Learning (RL), it remains elusive to achieve human-level efficiency in learning novel tasks. While previous efforts attempt to address this challenge using meta-learning strategies, they typically suffer from sampling inefficiency with on-policy RL algorithms or meta-overfitting with off-policy learning. In this work, we propose a novel meta-RL strategy to address those limitations. In particular, we decompose the meta-RL problem into three sub-tasks, task-exploration, task-inference and task-fulfillment, instantiated with two deep network agents and a task encoder. During meta-training, our method learns a task-conditioned actor network for task-fulfillment, an explorer network with a self-supervised reward shaping that encourages task-informative experiences in task-exploration, and a context-aware graph-based task encoder for task inference. We validate our approach with extensive experiments on several public benchmarks and the results show that our algorithm effectively performs exploration for task inference, improves sample efficiency during both training and testing, and mitigates the meta-overfitting problem.
Meta-Embeddings Based On Self-Attention

Creating meta-embeddings for better performance in language modelling has received attention lately, and methods based on concatenation or merely calculating the arithmetic mean of more than one separately trained embeddings to perform meta-embeddings have shown to be beneficial. In this paper, we devise a new meta-embedding model based on the self-attention mechanism, namely the Duo. With less than 0.4M parameters, the Duo mechanism achieves state-of-the-art accuracy in text classification tasks such as 20NG. Additionally, we propose a new meta-embedding sequece-to-sequence model for machine translation, which to the best of our knowledge, is the first machine translation model based on more than one word-embedding. Furthermore, it has turned out that our model outperform the Transformer not only in terms of achieving a better result, but also a faster convergence on recognized benchmarks, such as the WMT 2014 English-to-French translation task.
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion

While many works focus on 3D reconstruction from images, in this paper, we focus on 3D shape reconstruction and completion from a variety of 3D inputs, which are deficient in some respect: low and high resolution voxels, sparse and dense point clouds, complete or incomplete. Processing of such 3D inputs is an increasingly important problem as they are the output of 3D scanners, which are becoming more accessible, and are the intermediate output of 3D computer vision algorithms. Recently, learned implicit functions have shown great promise as they produce continuous reconstructions. However, we identified two limitations in reconstruction from 3D inputs: 1) details present in the input data are not retained, and 2) poor reconstruction of articulated humans. To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans. Our work differs from prior work in two crucial aspects. First, instead of using a single vector to encode a 3D shape, we extract a learnable 3-dimensional multi-scale tensor of deep features, which is aligned with the original Euclidean space embedding the shape. Second, instead of classifying x-y-z point coordinates directly, we classify deep features extracted from the tensor at a continuous query point. We show that this forces our model to make decisions based on global and local shape structure, as opposed to point coordinates, which are arbitrary under Euclidean transformations. Experiments demonstrate that IF-Nets outperform prior work in 3D object reconstruction in ShapeNet, and obtain significantly more accurate 3D human reconstructions. Code and project website is available at https://virtualhumans.mpi-inf.mpg. de/ifnets/.
Leveraging the Super Instruction Architecture to Develop Massively Parallel Computational and Environmental Chemistry Applications

The task of developing high performing parallel software must be made easier and more cost effective in order to fully exploit existing and emerging large scale computer systems for the advancement of science. The Super Instruction Architecture is a parallel programming platform geared towards applications that need to manage large amounts of data stored in potentially sparse multidimensional arrays during calculations. The SIA platform was originally designed for the Quantum Chemistry software package ACESIII. More recently, the SIA was reimplemented to overcome limitations in the original ACESIII program. It has now been successfully employed in the new Aces4 Quantum Chemistry software package and the development of the atmospheric transport application MATLOC, thus demonstrating the versatility of the SIA approach. MATLOC calculates transport and dispersion of mass over regions in the range of 100-1000s of square kilometers and is a significant improvement over existing community codes. This paper describes results from both the transport and dispersion application as well as some difficult Quantum Chemistry open shell coupled cluster benchmark calculations using Aces4.
DEEVA: A Deep Learning and IoT Based Computer Vision System to Address Safety and Security of Production Sites in Energy Industry

When it comes to addressing the safety/security related needs at different production/construction sites, accurate detection of the presence of workers, vehicles, equipment important and formed an integral part of computer vision-based surveillance systems (CVSS). Traditional CVSS systems focus on the use of different computer vision and pattern recognition algorithms overly reliant on manual extraction of features and small datasets, limiting their usage because of low accuracy, need for expert knowledge and high computational costs. The main objective of this paper is to provide decision makers at sites with a practical yet comprehensive deep learning and IoT based solution to tackle various computer vision related problems such as scene classification, object detection in scenes, semantic segmentation, scene captioning etc. Our overarching goal is to address the central question of What is happening at this site and where is it happening in an automated fashion minimizing the need for human resources dedicated to surveillance. We developed Deep ExxonMobil Eye for Video Analysis (DEEVA) package to handle scene classification, object detection, semantic segmentation and captioning of scenes in a hierarchical approach. The results reveal that transfer learning with the RetinaNet object detector is able to detect the presence of workers, different types of vehicles/construction equipment, safety related objects at a high level of accuracy (above 90%). With the help of deep learning to automatically extract features and IoT technology to automatic capture, transfer and process vast amount of realtime images, this framework is an important step towards the development of intelligent surveillance systems aimed at addressing myriads of open ended problems in the realm of security/safety monitoring, productivity assessments and future decision making.
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning

The proper setting of contention window (CW) values has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is not scalable enough to maintain stable throughput for an increasing number of stations, yet it remains the default method of channel access for 802.11ax single-user transmissions. Therefore, we propose a new method of CW control, which leverages deep reinforcement learning (DRL) principles to learn the correct settings under different network conditions. Our method, called centralized contention window optimization with DRL (CCOD), supports two trainable control algorithms: deep Q-network (DQN) and deep deterministic policy gradient (DDPG). We demonstrate through simulations that it offers efficiency close to optimal (even in dynamic topologies) while keeping computational cost low.
Improved Simulation of Quantum Circuits by Fewer Gaussian Eliminations

We show that the cost of strong simulation of quantum circuits using $t$ $T$ gate magic states exhibits non-trivial reductions on its upper bound for $t=1$, $t=2$, $t=3$, and $t=6$ with odd-prime-qudits. This agrees with previous numerical bounds found for qubits. We define simulation cost by the number of terms that require Gaussian elimination of a $t \times t$ matrix and so capture the cost of simulation methods that proceed by computing stabilizer inner products or evaluating quadratic Gauss sums. Prior numerical searchs for qubits were unable to converge beyond $t=7$. We effectively increase the space searched for these non-trivial reductions by $>10^{10^4}$ and extend the bounds to $t=14$ for qutrits. This is accomplished by using the Wigner-Weyl-Moyal formalism to algebraically find bounds instead of relying on numerics. We find a new reduction in the upper bound from the $12$-qutrit magic state of ${3^{\sim 0.469t}}$, which improves on the bound obtained from the $6$-qutrit magic state of ${3^{\sim 0.482t}}$.