3,438 research outputs found

    Earnings Management Of Mergers And Acquisitions Of Target Candidates And Deal Withdrawn

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    In this paper, we investigate the tendencies of target candidate companies to manage earnings, which affects financial reporting quality, in order to increase transaction value, and the withdrawal of deals as a result of low financial reporting quality in M&A in a sample of 316 mergers and acquisitions in South Korea between 2002 and 2011. Using the accruals quality measure developed by Dechow and Dichev (2002) as a proxy for financial reporting quality, we find the following. First, the financial reporting quality of target candidate firms is lower than that of non-target candidate firms because target candidate firms engage in earnings management prior to M&A. Second, low-quality financial reporting of target firms is positively related to the likelihood of deal withdrawal as a result of poor financial reporting quality

    Effects and Pharmacological Use of Alkaloids on the Eyes

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    Alkaloids can have a variety of effects on the eyes. Some alkaloids are used as a treatment for eye diseases, such as keratoconjunctivitis, but they are also toxic to the retina. Other alkaloids are known to protect neuroretina from damage caused by oxidative stress. Numerous ophthalmic drugs, such as glaucoma and antibiotic eye drops, have long been developed through alkaloids. In this chapter, we will introduce the beneficial and detrimental effects of alkaloids on the eye. In addition, the action of alkaloids as existing eye drops and the possibility of developing them as drugs in the future will be discussed

    Development of an LCD-Based Visual Field System

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    Background: The present study investigated the diagnostic effectiveness of an LCD-based visual field testing system (LVF) in comparison with the standard automated perimetry Humphrey Field Analyzer II-750i (HFA). Methods: A randomized controlled crossover study was conducted with 202 normal and 128 glaucomatous eyes using both LVF and HFA. The visual field testing systems were compared in terms of mean deviation (MD), pattern standard deviation (PSD), and area under the receiver operating characteristics curve (AUC) of MD and PSD differentiating the normal and glaucomatous eyes. Results: Significant correlations were found between MD measurements from LVF and those from HFA for normal eyes (r = 0.342) and glaucomatous eyes (r = 0.796); slightly higher significant correlations were identified between PSD measurements from LVF and those from HFA for normal eyes (r = 0.363) and glaucomatous eyes (r = 0.828). Furthermore, high AUCs of MD were found as 0.786 for LVF and 0.868 for HFA and AUCs of PSD as 0.913 for LVF and 0.932 for HFA. Conclusion: The comparison results of the present study support the competence of LVF compared with HFA in visual field testing for early detection of glaucoma.11Ysciescopuskc

    Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning

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    Object-centric learning (OCL) aspires general and compositional understanding of scenes by representing a scene as a collection of object-centric representations. OCL has also been extended to multi-view image and video datasets to apply various data-driven inductive biases by utilizing geometric or temporal information in the multi-image data. Single-view images carry less information about how to disentangle a given scene than videos or multi-view images do. Hence, owing to the difficulty of applying inductive biases, OCL for single-view images remains challenging, resulting in inconsistent learning of object-centric representation. To this end, we introduce a novel OCL framework for single-view images, SLot Attention via SHepherding (SLASH), which consists of two simple-yet-effective modules on top of Slot Attention. The new modules, Attention Refining Kernel (ARK) and Intermediate Point Predictor and Encoder (IPPE), respectively, prevent slots from being distracted by the background noise and indicate locations for slots to focus on to facilitate learning of object-centric representation. We also propose a weak semi-supervision approach for OCL, whilst our proposed framework can be used without any assistant annotation during the inference. Experiments show that our proposed method enables consistent learning of object-centric representation and achieves strong performance across four datasets. Code is available at \url{https://github.com/object-understanding/SLASH}
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