Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Transferability of Features from Pretrained Deep Neural Networks for Lung Nodule Classification

Among most popular feature extractors, pretrained deep neural networks play a central role in transfer learning to extract high-level feature on small datasets. The transferable performance, however, cannot be guaranteed for the task of interest. To enhance the transferability, this paper employs fine-tuning and feature selection in a different way to improve the accuracy of lung nodule classification. The fine-tuning technique retrains the neural network using lung nodule dataset, while feature selection captures a useful subset of features for lung nodule classification. Preliminary experimental results on CT images from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) confirm that the classification accuracy on lung nodule can be significantly improved via finetuning and feature selection. Furthermore, the results outperform competitively handcrafted texture descriptors.
Finite-Sample Properties of GARCH Models in the Presence of Time-Varying Unconditional Variance: A Simulation Study

In this paper, the finite-sample properties of symmetric GARCH and asymmetric GJR-GARCH models in the presence of time-varying long term variance are considered. In particular, the deterministic spline-GARCH model is investigated by Monte-Carlo simulation, where the true parameter values are taken from estimated real equity index data. As a proxy for the behaviour of equity indices of developed countries, the S&P500 Index is estimated with the Quasi-Maximum-Likelihood (QML) method for dierent conditional heteroscedastic models (GARCH, GJR-GARCH, spline-GARCH and spline-GJR-GARCH). The estimated S&P500 parameter values are used to simulate a broad range of 6 dierent time-series lengths {100, 500, 1000, 5000, 10000, 25000} and 4 different numbers of spline knots {1,4,9,14}, combining to a total amount of 60 different model setups. To the best of my knowledge, there exist only a few limited simulation studies that focus on the spline-GARCH model. The main contribution of this paper is therefore to highlight the behaviour of the QML estimates when the long-term variance is implemented by the spline-GARCH model. Beside this, the paper provides a least-square approach to get useful starting values for the numerical estimation routine.
‘I Will Help but not Everybody’ – Donating to Charity in a Deficit vs. Growth Condition: The Importance of Well-Being

This study examined how the reason for donating influences the likelihood of charitable giving and whether well-being and happiness shape this relation. Students (N = 85) were asked to donate to either children struggling with learning (to reduce a deficit) or gifted children (to support growth). We expected that although generally people are more likely to offer money to reduce a deficit than to support growth, with an increase in happiness and well-being the difference in the odds of helping, resulting from these two motivations, would be diminished. The results showed that more people opted to help struggling children than gifted ones. Well-being and happiness were not related to willingness to help. They predicted the amount of support given, although the pattern of results was different for each psychological construct. The results are discussed with reference to a revised cost-reward model of intervention and concepts of wellbeing.
ANTECEDENTS AND OUTCOMES OF COOPERATION WITH RETAILERS FROM MANUFACTURERS’ PERSPECTIVE

The concept of cooperation is considered fundamental to fruitful business relationships. The way supply chains operate – how effective and efficient they are – could be driven by scope and quality of co-operation. It is also cooperation that underlies a number of theories that emerged recently in the wake of the explosive growth of social networks advocating co-creation and co-production with consumers as a superior way to achieve competitive advantage. In this study we look at the critical part of the supply chain of consumer products – the links between manufacturers and retailers. In particular, we investigate factors driving good cooperation and its expected benefits. The conceptual model for this study includes latent variables for the bargaining power of a retailer, type of relationship (pure cooperation, and coopetition), scope of cooperation, formality of relationship and cooperation benefits (joint and individual). Statistical analysis reported in the paper involved building a structural equation model that confirmed positive links between the type of relationship (TR), scope of cooperation (SC), formality of cooperation (FC) and cooperation benefits (CB). Bargaining power (BP) of the key retailer had mixed influence on TR and SC, displaying both positive and negative correlations with different aspects of TR and SC. Surprisingly, though, strong BP seemed to correspond to increased benefits from cooperation. This insight suggests that not always inequality in leverage between business partners has negative consequences for the weaker side. If fact, our research implies that, on balance, such inequality can bring about positive outcomes leading to enhanced performance of the weaker partner. The findings suggest that the cooperation mechanisms embedded in the model seem to be better suited to describing larger rather than medium manufacturing companies with 49% versus 28% of variance in CB explained. For both large and medium firms, the strongest driver of CB was cooperation in logistics and production. In comparison to medium businesses, large firms shown considerably more positive impacts of cooperation in marketing on CB.
Up then Down: The Bid-Price Trends in Revenue Management

In the classic revenue management (RM) problem of selling a fixed quantity of perishable inventories to price-sensitive non-strategic consumers over a finite horizon, the optimal pricing decision at any time depends on two important factors: consumer valuation and bid price. The former is determined exogenously by the demand side, while the latter is determined jointly by the inventory level on the supply side and the consumer valuations in the time remaining within the selling horizon. Because of the importance of bid prices in theory and practice of RM, this paper aims to enhance the understanding of the intertemporal behavior of bid prices in dynamic RM environments. We provide a probabilistic characterization of the optimal policies from the perspective of bid-price processes. We show that an optimal bid-price process has an upward trend over time before the inventory level falls to one and then has a downward trend. This intertemporal up-then-down pattern of bid-price processes is related to two fundamental static properties of the optimal bid prices: (1) At any given time, a lower inventory level yields a higher optimal bid price, which is referred to as the resource scarcity effect; (2) Given any inventory level, the optimal bid price decreases with time; that is referred to as the resource perishability effect. The demonstrated upward trend implies that the optimal bid-price process is mainly driven by the resource scarcity effect while the downward trend implies that the bid-price process is mainly driven by the resource perishability effect. We also demonstrate how optimal bid price and consumer valuation, as two competing forces, interact over time to drive the optimal-price process. The results are also extended to the network RM problems.
3DVPS: A 3D Point Cloud-Based Visual Positioning System

Visual positioning is a critical function in applications such as navigation and extended reality experiences. Recently, deep learning technologies, especially classification, had been implemented on the positioning task. However, to acquire a comprehensive positioning dataset and produce a high-performance neural network model is challenging. In this article, we propose to solve the issue by projecting training images from auto-generated 3D point cloud maps. By utilizing branch convolutional neural network (B-CNN) model, the “zoom-in” equivalent property results in favorable positioning accuracy and successful real-time implementation.
Analyzing the Effects of Temporal Resolution and Classification Confidence for Modeling Land Cover Change with Long Short-Term Memory Networks

Land cover change (LCC) is typically characterized by infrequent changes over space and time. Data-driven methods such as deep learning (DL) approaches have proven effective in many domains for predictive and classification tasks. When applied to geospatial data, sequential DL methods such as long short-term memory (LSTM) have yielded promising results in remote sensing and GIScience studies. However, the characteristics of geospatial datasets selected for use with these methods have demonstrated important implications on method performance. The number of data layers available, the rate of LCC, and inherent errors resulting from classification procedures are expected to influence model performance. Yet, it is unknown how these can affect compatibility with the LSTM method. As such, the main objective of this study is to explore the capacity of LSTM to forecast patterns that have emerged from LCC dynamics given varying temporal resolutions, persistent land cover classes, and auxiliary data layers pertaining to classification confidence. Stacked LSTM modeling approaches are applied to 17-year MODIS land cover datasets focused on the province of British Columbia, Canada. This geospatial data is reclassified to four major land cover (LC) classes during pre-processing procedures. The evaluation considers the dataset at variable temporal resolutions to demonstrate the significance of geospatial data characteristics on LSTM method performance in several scenarios. Results indicate that LSTM can be utilized for forecasting LCC patterns when there are few limitations on temporal intervals of the datasets provided. Likewise, this study demonstrates improved performance measures when there are classes that do not change. Furthermore, providing classification confidence data as ancillary input also demonstrated improved results when the number of timesteps or temporal resolution is limited. This study contributes to future applications of DL and LSTM methods for forecasting LCC.
Effect of Sustainable Procurement Practices on Organization Performance: A Review of Literature

1. Background Information Sustainable procurement draws its roots from the broad concept of sustainable development established as one of the current global agenda for the future (Argandona, 2014). The focus of sustainable procurement objective is to meet the varied requirements of all individuals in current and future societies, to promote private well-being, social cohesion and to create equal opportunities (CIPS, Abstract: Sustainable procurement is vested under the umbrella of sustainability, choices that retain the correct environmental, social and economic balance to guarantee long-term organizational success. The main objective of the study was to assess the effects of sustainable procurement practices on organization performance. The study was further guided by specific objectives such as to determine the effects of local buying practices, determining the effects of the adoption of eco-friendly packaging, assessing the effects of reverse logistics and identify the effects of ICT adoption in procurement on the organization performance. The study was anchored on stakeholder theory, Knowledge Based View, Technology Acceptance Theory and Resource Based Theory. A conceptual framework was formulated to show the relationship between the selected sustainability procurement practices on organization performance. The study was done using literature review whereby several relevant journals were reviewed randomly. The study revealed that local buying, eco-friendly packaging, reverse logistics and ICT adoption has a significant effect on organization performance. The study recommended the adoption of the selected variables for organizational sustainability. The study further poised for future researchers to use different research design to see whether similar results can be found
Epidemiological analysis on rabies surveillance in China from 2006 to 2008.

Objective To summarize the human rabies epidemiological characteristics and the factors influencing the epidemic by analyzing rabies surveillance data in China from 2006 to 2008.Methods The data of rabies epidemic in China were collected from National Disease Reporting Information System and National Rabies Surveillance System from 2006 to 2008 to conduct descriptive analysis.Results From 2006 to 2008,the increase of rabies incidence in China,which had lasted for 10 years,began to stop and decline despite some provinces still with severe epidemic.The cases mainly occurred in southern and middle eastern parts of China,the rabies cases in Guangxi,Guizhou,Guangdong and Hunan accounted for 52.8% of the national total.In this 3 years,up to 31.2% of the counties(districts) in China reported rabies cases.The cases among farmers,students and pre-school aged children accounted for 66.2%,14.7% and 6.8% of the total cases respectively.The incidence rates of children and elder people were higher than other age groups,and the cases in age group of 40-60 years accounted for 44.3% of the total.Among the 1604 cases reported through National Rabies Surveillance System,93.8% were caused by the exposure to dog or cat,21.8% did self wound care,and 12.7% received vaccination.Totally 55.8% of the cases were classified as grade III exposure and 5.3% of them were administrated rabies immunoglobulin(RIG) or anti-serum.The median of dog density was 15.0/100 persons and immunization rate of dogs was 12.0%. The amount of animal-bite victims visiting the rabies clinics under surveillance was found obviously increased,the people exposed to dog accounted for 83.0% and those exposed to cat accounted for 12.7%. The self wound care rate was 15.7% and the wound care rate in clinics was 85.7%,which decreased as the self wound care rate increased.Over 95.0% of the people exposed received vaccination in most rabies clinics and 33.1% of the people with grade III exposure were administrated RIG or anti-serum.The proportion of exposure grades varied by clinics and years.Conclusion From 2006 to 2008,the increase of the incidence of human rabies in China began to stop and decline,Most rabies cases were reported in rural area,and children and middle-aged people were mostly affected.Dog and cat remained the most important host animals of rabies in China.The high dog density and low immunization rate in rural area and poor post-exposure prophylaxis were the main reasons of high incidence of rabies in China,although the situation was better now,The clinicians' ability on post-exposure prophylaxis still needs to be improved.The training on post-exposure prophylaxis remains to be the priority to improve the prevention and control of rabies.
Multicentre Trials: Planning and Achieving TOTAL

This article provides an overview for those conducting multicentre rehabilitation trials. It aims to highlight potential problems and difficulties which may not be obvious initially. TOTAL (Trial of Occupational Therapy and Leisure) was a multicentre rehabilitation trial. The aim was to test whether a leisure-based intervention was more effective than activities of daily living (ADL) based therapy at improving the mood, leisure participation and independence in ADL of stroke patients. During the study period, 466 patients were recruited from five centres in the United Kingdom. Postal assessments were sent out at 6 and 12 months after discharge to evaluate mood, leisure independence and ADL performance. The possible difficulties in recruiting centres are presented. The problems encountered were staff shortages, insufficient numbers of patients, concern over aspects of the protocol and funding issues. Day-to-day problems in running the trial and how these may be resolved are also discussed, such as access to the trial coordinator, finance, the need for written agreements and regular communication with each centre. While the complexity of conducting a multicentre trial should not be underestimated, preparation, good planning and an awareness of potential problems makes it a feasible proposition.
Ready Species One: Exploring the Use of Augmented Reality to Enhance Systematic Biology with a Revision of Fijian Strumigenys (Hymenoptera: Formicidae)

Abstract Advances in technology are rapidly changing the way people transmit, view, and interact with information. These advances offer new opportunities for researchers to share scientific discoveries with each other and the general public as never before. The field of revisionary biology has audiences confined to small groups of specialists, but the core task of systematic biology—documenting the endless forms of nature—is particularly well suited to capitalize on innovations in the realm of virtual, mixed and augmented reality. Interactive three-dimensional (3D) digital models of biological specimens can help bridge barriers across scientific disciplines by circumventing technical jargon, and also promise to open exciting new vistas for public engagement. Here, we explore the potential of augmented reality for communicating the discovery of new species. As a test case, we revise a radiation of Strumigenys Smith (Hymenoptera: Formicidae) miniature trap-jaw ants in Fiji. In addition to traditional revisionary elements, we present the augmented reality application ‘Insects3D’ built specifically for this study. The application runs on mobile devices and allows users to interact with X-ray microtomography-derived 3D specimen models and visualize 3D geographic distribution maps. We recognize 23 species in Fiji, including 6 new species: S. anorak n. sp. , S. artemis n. sp. , S. avatar n. sp. , S. gunter n. sp. , S. oasis n. sp. , and S. parzival n. sp. This study demonstrates the potential of leveraging 3D data and technology for a more interactive systematic biology, and the need for research programs to develop robust and generalized tools to realize this potential.
New Evidence from Italy

We exploit a new dataset based on EU procurement award notices to investigate the relationship between the degree of centralization of public procurement and its performance. We focus on the case of Italy, where all levels of government, along with a number of other public institutions, are involved in procurement and are subject to the same EU regulation. We find that i) municipalities and utilities, which currently award the largest shares of contracts, perform worse than all other institutional categories; and ii) decentralization implies lower performance only when it comes with weak competences of procurement officials. The evidence seems to suggests that a re-organization of the procurement system, both in terms of partial centralization and better professionalization of procurement officials, would help improve overall procurement performance.
Single trajectory characterization via machine learning

In order to study transport in complex environments, it is extremely important to determine the physical mechanism underlying diffusion and precisely characterize its nature and parameters. Often, this task is strongly impacted by data consisting of trajectories with short length (either due to brief recordings or previous trajectory segmentation) and limited localization precision. In this paper, we propose a machine learning method based on a random forest architecture, which is able to associate single trajectories to the underlying diffusion mechanism with high accuracy. In addition, the algorithm is able to determine the anomalous exponent with a small error, thus inherently providing a classification of the motion as normal or anomalous (sub- or super-diffusion). The method provides highly accurate outputs even when working with very short trajectories and in the presence of experimental noise. We further demonstrate the application of transfer learning to experimental and simulated data not included in the training/test dataset. This allows for a full, high-accuracy characterization of experimental trajectories without the need of any prior information.
Birth weight thresholds in hospital reimbursement systems

Birth weight manipulation is common in per-case hospital reimbursement systems, in which hospitals receive more money for otherwise equal newborns with birth weight just below compared to just above specific birth weight thresholds. As hospitals receive more money for cases with weight below the thresholds, having a (reported) weight below a threshold could benefit the newborn. Also, these reimbursement thresholds overlap with diagnostic thresholds that have been shown to affect the quantity and quality of care that newborns receive. Based on the universe of hospital births in Germany from the years 2005–2011, we investigate whether weight below reimbursement relevant thresholds triggers different quantity and quality of care. We find that this is not the case, suggesting that hospitals’ financial incentives with respect to birth weight do not directly impact the care that newborns receive. JEL Codes: I110, I180,
The Sustainability of Tourism: Global Comparative Evidence

This study investigated the sustainability of global tourism in 163 countries for the period 2010 to 2015. Given the richness of the dataset, the data has been decomposed into 11 fundamental characteristics based on income levels, legal origins and openness to the sea. The empirical evidence was based on Generalised Method of Moments (GMM). Three main hypotheses were tested from which three broad findings were established. First, Hypothesis 1 is overwhelmingly valid because persistence in tourist arrivals is consistently an increasing function of income levels. Hypothesis 2 is not valid because French civil law countries are associated with comparatively higher levels of persistence in tourist arrivals. Hypothesis 3 is invalid because landlocked countries reflect higher levels of tourism persistence.
A COMPARATIVE STUDY OF THE GROWTH PROMOTER EFFECTIVENES FOR DIFFERENT DRUG FORMULATIONS, CONTAINING OREGANO OIL FOR BROILER CHICKENS

A comparative study for broiler chickens using different drug formulations, containing oregano essential oil was added daily: Ecodiar ® liquid 5% to the drinking water; Ecodiar ® powder 5% and oregano oil 1% to the complete feed from the 1 to the last day of the finishing period (day 41). The experiment included 80 clinically healthy broiler chickens of the both sexes, 1-day old, hybrid Cobb-500, bred to the last day of the starter (n = 40) and bred to the last day of the finishing period (n = 40). In the dynamics (during the starter and finishing period), the indicators were determined: body weight, average daily gain, feed intake per capita/per day, energy and protein intake, feed efficiency, energy efficiency and crude protein efficiency per 1 kg gain, weight of the carcass, liver, heart, gizzard, spleen and bursa of Fabricius. The our results showed that in body weight gain and carcass, liver, spleen and heart weights of the broilers without statistically significant differences were observed compared to the control group during the starter and the finishing periods In the gizzard weight of broilers that received Ecodiar liquid 5% and Ecodiar powder 5% with statistically significant differences were observed during the starter period, compared to the control group (p < 0.05). In weights of the bursa Fabricius in broilers that received Ecodiar liquid 5% and 1% of oregano oil (p < 0.05) with statistically significant difference was observed during the grower‐finisher period.