Miracle-dz/UniFAR-FTUs huggingface dataset:
Rita Sofia Ribeiro Monteiro

Cross-border Mergers and Acquisitions (M&As) in the European Union (EU) are rather frequent and represent around one third of the overall number of deals. Acquirers of targets located in the EU must comply with takeover rules set individually by member states, but also with European Council Directives. The most significant of these Directives in the context of M&As, and the one that will be further discussed in my research work, is the Takeover Bids Directive (TBD). The intent of the Directive is to ensure equal treatment for all companies launching takeover bids or that are subject to a change in control, providing minimum harmonization rules in view of creating a transparent environment for cross-border takeovers. This study examines the short-term stock price reaction to M&A announcements and the long-term post-announcement returns of firms involved in M&As in the EU to test if there are differences in the performance of domestic and international deals. Then, I study the impact of the enactment of the TBD on announcement returns of cross-border M&As. I also examine other determinants of cross-border deals. To test my hypotheses, I use the event study methodology, buy-and-hold abnormal returns, difference-in-differences techniques and probabilistic models. Using a sample of 2197 M&As conducted between 2000 and 2015, I do not find evidence that there are significant differences among domestic and international M&As in the shortor in the long-term. Furthermore, I conclude that the TBD did not have any significant impact on the short-term performance of M&As and that it also failed in the attempt to foster cross-border deals in the EU. Nevertheless, and consistent with previous literature, I do find that firms located in member states with lower investor protection and civil law origin are more likely to be targeted in
KEIO ECONOMIC STUDIES 34(2), 17-31 (1997) RELATIVE DEPRIVATION AND SATISFACTION ORDERINGS

The concern of this paper is the relationship between social welfare and relative deprivation/satisfaction. We first identify the class of social welfare functions whose orderings of alternative distributions of a fixed total income agree with the rankings generated by non-intersecting relative deprivation/satisfaction curves. We then show that for the variable mean income case the welfare ordering can be implemented by seeking a dominance relation in terms of the generalized satisfaction curve. Finally, the existence of a relationship between a summary measure of deprivation and a social welfare function is demonstrated. JEL Classification Numbers: D31, D63. Key-words : Relative deprivation, satisfaction, social welfare, ethical index.
AI Based Mobile Bill Payment System using Biometric Fingerprint

Corresponding Author: Ayyaswamy Kathirvel Department of CSE, Karunya Institute of Technology and Sciences, Coimbatore, India Email: kathirvel@karunya.edu Abstract: Billions of payment transactions occur in our day-to-day life, but every payment method depends on a material to carry. It is common for users to possess various materials like cash, credit cards and even mobiles to make payment. Meanwhile, it is easy for these materials to be robbed or lost. These instances result in a terrible trauma for the people. This study gives a detailed portrayal of a biometric payment application developed to introduce a concept of material-less payment. It enables the user to make a payment at any location by enrolling their fingertip without possessing any material. It involves a one-time registration of the User details upon all further transactions are validated and processed based on the user's fingerprint where the App takes care of the whole process. This implementation results in a novel payment method and avoids the risk of carrying valuable materials outdoors. This App creates an efficient and safe payment for society.
The “ inner values ” of an endoparasitic copepod – computer-based 3 D-reconstruction of Ismaila aliena

Knowledge about the Splanchnotrophidae, a family of endoparasitic copepods infesting opisthobranch sea slugs, currently is restricted to the external morphology. In contrast, their internal anatomy is still largely unknown and many questions concerning life-history traits remain unanswered. Therefore, the microanatomy of both sexes of Ismaila aliena Haumayr & Schrödl, 2003, a splanchnotrophid infesting the nudibranch Thecacera darwini Pruvot-Fol, 1950 in Chile, was studied using computer-based 3D-reconstruction methods on serial semithin histological sections. The body musculature comprises three paired longitudinal strands. Regarding the cephalic and thoracic appendages, besides the antennae only the first pair of male thoracopods is supplied with strong musculature. The digestive system consists of an oesophagus and a voluminous, sack-like midgut, while hindgut and anus are lacking. Structural, functional and observational evidences suggest that I. aliena and at least some other splanchnotrophids are body fluid rather than – eponymous – tissue feeders. The gonad of I. aliena is large in both sexes and neither antrum nor seminal receptacle was detected in the female. Compared to ectoparasitic copepods, the central nervous system of I. aliena is modified, especially in males. Microanatomical results of the present study are compared with available literature results on I. belciki Ho, 1987 (as I. monstrosa Bergh) and discussed regarding potential functions. Within an emerging functional and evolutionary framework we provide some new insights in the life history of the splanchnotrophid parasites.
Breast Milk on the Infant Faecal Microbiome: Components of Breast Milk Orchestrating the Establishment of Bifidobacteria Species

The initial colonization and early development of the gut microbiome during infancy are important to lifelong human health. Breast milk is the gold standard in infant nutrition. In addition to providing a source of nutrition for infants, breast milk has also been shown to play a role in the assembly of a wellbalanced and healthy infant gut microbiome, often dominated by Bifidobacterium species. Bifidobacteria are ubiquitous inhabitants of the gastrointestinal tract of mammals that are perceived as safe, beneficial microorganisms. Generally, bifidobacterial species that are commonly found in human intestines are referred to as human-residential bifidobacteria (HRB). Among HRB, infant-type HRB represent the major members of infant gut microbiota during breastfeeding period. It is increasingly evident that breast milk functions to orchestrating the establishment of bifidobacteria in infant gut. In this review, we aim to discuss about the functional role of the components in breast milk, particularly human milk oligosaccharides (HMOs) as well as the antimicrobial compounds, lysozyme, in the selection of the species of infant-type HRB as the exclusive members in infant gut microbiome.
Case study of integrated strategy development model for after-sales service of heavy machinery industrial

The heavy machinery is widely used from the agricultural applications to the majority of construction projects. The present study integrates the strategy analysis and propose a integrated strategy developmental model (ISDM) of after-sales service in heavy machinery industrial. The ISDM which developed in present study not only can apply in after-sales service, marketing, finance, R&D, and others fields of heavy machinery industrial in Taiwan, but also can apply in others countries.
A novel real-time control system for plasma cutting robot with xPC target

In this article, a novel inexpensive controller system for plasma cutting robots built based on MATLAB xPC is presented. The plasma cutting robot model with 5 degrees of freedom is constructed in the MATLAB/Simulink of the host machine, where the code is generated by real-time workshop and downloaded on the target machine. Two different types of proportional–integral–derivative controllers are applied on the experimental robot manipulator for the real-time plasma cutting process’s trajectory control using this hardware system. First, the performance of a standard proportional–integral–derivative controller is evaluated on this manipulator. Then, a novel proportional–integral–derivative controller structure is used to control the system, and it is compared with a standard proportional–integral–derivative controller. This study shows that the control system for a plasma cutting robot can be established inexpensively, and MATLAB xPC and different controllers can be applied on this system. Moreover, the study concludes that the novel proportional–integral–derivative controller used in this study gives better results for plasma cutting robots than the standard proportional–integral–derivative controller.
Defining and estimating effects in cluster randomized trials: A methods comparison

Across research disciplines, cluster randomized trials (CRTs) are commonly implemented to evaluate interventions delivered to groups of participants, such as communities and clinics. Despite advances in the design and analysis of CRTs, several challenges remain. First, there are many possible ways to specify the causal effect of interest (eg, at the individual‐level or at the cluster‐level). Second, the theoretical and practical performance of common methods for CRT analysis remain poorly understood. Here, we present a general framework to formally define an array of causal effects in terms of summary measures of counterfactual outcomes. Next, we provide a comprehensive overview of CRT estimators, including the t‐test, generalized estimating equations (GEE), augmented‐GEE, and targeted maximum likelihood estimation (TMLE). Using finite sample simulations, we illustrate the practical performance of these estimators for different causal effects and when, as commonly occurs, there are limited numbers of clusters of different sizes. Finally, our application to data from the Preterm Birth Initiative (PTBi) study demonstrates the real‐world impact of varying cluster sizes and targeting effects at the cluster‐level or at the individual‐level. Specifically, the relative effect of the PTBi intervention was 0.81 at the cluster‐level, corresponding to a 19% reduction in outcome incidence, and was 0.66 at the individual‐level, corresponding to a 34% reduction in outcome risk. Given its flexibility to estimate a variety of user‐specified effects and ability to adaptively adjust for covariates for precision gains while maintaining Type‐I error control, we conclude TMLE is a promising tool for CRT analysis.
Coordinating Momenta for Cross-Silo Federated Learning

Communication efficiency is crucial for federated learning (FL). Conducting local training steps in clients to reduce the communication frequency between clients and the server is a common method to address this issue. However, it leads to the client drift problem due to non-i.i. d. data distributions in different clients which severely deteriorates the performance. In this work, we propose a new method to improve the training performance in cross-silo FL via maintaining double momentum buffers. One momentum buffer tracks the server model updating direction, and the other tracks the local model updating direction. Moreover, we introduce a novel momentum fusion technique to coordinate the server and local momentum buffers. We also provide the first theoretical convergence analysis involving both the server and local standard momentum SGD. Extensive deep FL experimental results show a better training performance than FedAvg and existing standard momentum SGD variants.
Stochastic Localization Methods for Convex Discrete Optimization via Simulation

We develop and analyze a set of new sequential simulation-optimization algorithms for large-scale multi-dimensional discrete optimization via simulation problems with a convexity structure. The"large-scale"notion refers to that the decision variable has a large number of values to choose from on each dimension. The proposed algorithms are targeted to identify a solution that is close to the optimal solution given any precision level with any given probability. To achieve this target, utilizing the convexity structure, our algorithm design does not need to scan all the choices of the decision variable, but instead sequentially draws a subset of choices of the decision variable and uses them to"localize"potentially near-optimal solutions to an adaptively shrinking region. To show the power of the localization operation, we first consider one-dimensional large-scale problems. We propose the shrinking uniform sampling algorithm, which is proved to achieve the target with an optimal expected simulation cost under an asymptotic criterion. For multi-dimensional problems, we combine the idea of localization with subgradient information and propose a framework to design stochastic cutting-plane methods and the dimension reduction algorithm, whose expected simulation cost have a low dependence on the scale and the dimension of the problems. The proposed algorithms do not require prior information about the Lipschitz constant of the objective function and the simulation costs are upper bounded by a value that is independent of the Lipschitz constant. Finally, we propose an adaptive algorithm to deal with the unknown noise variance case under the assumption that the randomness of the system is Gaussian. We implement the proposed algorithms on both synthetic and queueing simulation optimization problems, and demonstrate better performances compared to benchmark methods.
A graphical multi-fidelity Gaussian process model, with application to emulation of expensive computer simulations

With advances in scientific computing and mathematical modeling, complex phenomena can now be reliably simulated. Such simulations can however be very timeintensive, requiring millions of CPU hours to perform. One solution is multi-fidelity emulation, which uses data of varying accuracies (or fidelities) to train an efficient predictive model (or emulator) for the expensive simulator. In complex problems, simulation data with different fidelities are often connected scientifically via a directed acyclic graph (DAG), which cannot be integrated within existing multi-fidelity emulator models. We thus propose a new Graphical Multi-fidelity Gaussian process (GMGP) model, which embeds this scientific DAG information within a Gaussian process framework. We show that the GMGP has desirable modeling traits via two Markov properties, and admits a scalable formulation for recursively computing the posterior predictive distribution along sub-graphs. We also present an experimental design framework over the DAG given a computational budget, and propose a nonlinear extension of the GMGP model via deep Gaussian processes. The advantages of the GMGP model over existing methods are then demonstrated via a suite of numerical experiments and an application to emulation of heavy-ion collisions, which can be used to study the conditions of matter in the Universe shortly after the Big Bang.
Cyber-Physical Co-Design of Wireless Control Systems

OF THE DISSERTATION Cyber-Physical Co-Design of Wireless Control Systems by Bo Li Doctor of Philosophy in Computer Science Washington University in St. Louis, 2015 Professor Chenyang Lu Wireless sensor-actuator network (WSAN) technology is gaining rapid adoption in process industries because of its advantages in lowering deployment and maintenance cost in challenging environments. While early success of industrial WSANs has been recognized, significant potential remains in exploring WSANs as unified networks for industrial plants. This thesis research explores a cyber-physical co-design approach to design wireless control systems. To enable holistic studies of wireless control systems, we have developed the Wireless Cyber-Physical Simulator (WCPS), an integrated co-simulation environment that integrates Simulink and our implementation of WSANs based on the industrial WirelessHART standard. We further develop novel WSAN protocols tailored for advanced control designs for networked control systems. WCPS now works as the first simulator that features both linear and nonlinear physical plant models, state-of-art WirelessHART protocol stack, and realistic wireless network characteristics. A realistic wireless structural control study sheds light on the challenges of WSC and the limitations of a traditional structural control approach under realistic wireless conditions. Systematic emergency control results demonstrate that x our real-time emergency communication approach enables timely emergency handling, while allowing regular feedback control loops to effectively share resources in WSANs during normal operations. A co-joint study of wireless routing and control highlights the importance of the co-design approach of wireless networks and control.
Repeated origins, widespread gene flow, and allelic interactions of target-site herbicide resistance mutations

Causal mutations and their frequency in agricultural fields are well-characterized for herbicide resistance. However, we still lack understanding of their evolutionary history: the extent of parallelism in the origins of target-site resistance (TSR), how long these mutations persist, how quickly they spread, and allelic interactions that mediate their selective advantage. We addressed these questions with genomic data from 19 agricultural populations of common waterhemp (Amaranthus tuberculatus), which we show to have undergone a massive expansion over the past century, with a contemporary effective population size estimate of 8 x 107. We found variation at seven characterized TSR loci, two of which had multiple amino acid substitutions, and three of which were common. These three common resistance variants show extreme parallelism in their mutational origins, with gene flow having shaped their distribution across the landscape. Allele age estimates supported a strong role of adaptation from de novo mutations, with a median age of 30 suggesting that most resistance alleles arose soon after the onset of herbicide use. However, resistant lineages varied in both their age and evidence for selection over two different timescales, implying considerable heterogeneity in the forces that govern their persistence. Two such forces are intra- and inter-locus allelic interactions; we report a signal of extended haplotype competition between two common TSR alleles, and extreme linkage with genome-wide alleles with known functions in resistance adaptation. Together, this work reveals a remarkable example of spatial parallel evolution in a metapopulation, with important implications for the management of herbicide resistance.
Wittgenstein’s Philosophy of Language The Philosophical Origins of Modern NLP Thinking

Ludwig Wittgenstein’s theory of language games introduced a philosophically unorthodox understanding to the meaning and the use of language. Modern natural language processing (NLP) approaches focus on context-derived models of meaning, avoidance of syntactically defined rules, and rely on large bodies of data to statistically approximate our real-world context. This paper traces the origin, development, and intersection of Wittgenstein intellectual legacy and its relevancy to NLP, assesses the ways his thoughts have influenced it, and examines how his work could be better applied in future directions of NLP.
Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation

Offshore wind farm modelling has been an area of rapidly increasing interest over the last two decades, with numerous analytical as well as computational-based approaches developed, in an attempt to produce designs that improve wind farm efficiency in power production. This work presents a Machine Learning (ML) framework for the rapid modelling of wind farm flow fields, using a Deep Neural Network (DNN) neural network architecture, trained here on approximate turbine wake fields, calculated on the state-of-the-art wind farm modelling software FLORIS. The constructed neural model is capable of accurately reproducing single wake deficits at hub-level for a 5MW wind turbine under yaw and a wide range of inlet hub speed and turbulence intensity conditions, at least an order of magnitude faster than the analytical wake-based solution method, yielding results with 1.5% mean absolute error. A superposition algorithm is also developed to construct flow fields over the whole wind farm domain by superimposing individual wakes. A promising advantage of the present approach is that its performance and accuracy are expected to increase even further when trained on high-fidelity CFD or real-world data through transfer learning, while its computational cost remains low.
Research on Simulation Method of UHVDC Corona Discharge

Ultra high voltage direct current (UHVDC) transmission has took a significant part in power grid. However, the increase of transmission efficiency hindered by electromagnetic environment problems caused by its corona discharge. Therefore, the corona discharge of UHVDC transmission line has been spotlighted. Considering the complex reaction of microscopic particles in the air during the discharge process, this paper integrates each split conductor as a discharge source located on the split centerline, and simplifies the discharge of the transmission line into a one-dimensional model of a coaxial cable. The study can help us to explore the physical process and microscopic particle motion characteristics of corona discharge deeply. Besides, it is an important guideline for studying and preventing electromagnetic environment problems from the root cause.
MSOME and IMSI: reasonable rationale, selective clinical value

Male infertility is a major etiology for infertility, accounting for 20-50% among couples referred for in vitro fertilization (IVF) [1]. The revolutionary introduction of intracytoplasmic sperm injection (ICSI) procedure a quarter of a century ago, supplied patients with severe male factor reasonable chance for biological parenthood and opened a new era in the field of assisted reproductive technology (ART). However, 3-5% of patients fail to conceive after IVF-ICSI [2], representing clinical challenge for fertility specialists. Advanced sperm selection techniques, e.g. sperm apoptosis, sperm birefringence, ability to bind to hyaluronic acid, and sperm morphology under ultra-high magnification, are increasingly being employed in ART, most commonly aiming to improve IVF-ICSI outcome. By a better selection of sperm for intracytoplasmic injection, it is assumed that a structurally intact and mature sperm with high DNA integrity will be chosen with the consequent improved IVF-ICSI outcome. A recent Cochrane review aiming to evaluate the impact of advanced sperm selection techniques on ART outcomes could not find sufficient evidence to allow review authors to determine whether sperm selected by hyaluronanic acid binding, sperm apoptosis, sperm birefringence or surface charge have any additive value over conventional selection [3]. They therefore concluded that further studies of suitable quality are required to evaluate whether any of these advanced sperm selection techniques can be recommended for use in clinical practice. Motile sperm organelle morphology examination (MSOME) is a real-time high magnification motile sperm examination performed using an inverted microscope equipped with high-power differential interference contrast (DIC) optics (magnification ×150) enhanced by digital imaging (magnification ×44) to achieve a total magnification of over ×6,000 [4]. This approach, primarily introduced by Bartoov et al. [5] is currently applied prior to performing intracytoplasmic morphologically selected sperm injection (IMSI), which consists of the intracytoplasmic injection of a spermatozoon that has been selected at high magnification. In their preliminary study of 24 normal-responder young (< 37 years) patients with previous failure of at least five consecutive routine cycles of IVF-