435 research outputs found

    Dataset Distillation: A Comprehensive Review

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    Recent success of deep learning is largely attributed to the sheer amount of data used for training deep neural networks.Despite the unprecedented success, the massive data, unfortunately, significantly increases the burden on storage and transmission and further gives rise to a cumbersome model training process. Besides, relying on the raw data for training \emph{per se} yields concerns about privacy and copyright. To alleviate these shortcomings, dataset distillation~(DD), also known as dataset condensation (DC), was introduced and has recently attracted much research attention in the community. Given an original dataset, DD aims to derive a much smaller dataset containing synthetic samples, based on which the trained models yield performance comparable with those trained on the original dataset. In this paper, we give a comprehensive review and summary of recent advances in DD and its application. We first introduce the task formally and propose an overall algorithmic framework followed by all existing DD methods. Next, we provide a systematic taxonomy of current methodologies in this area, and discuss their theoretical interconnections. We also present current challenges in DD through extensive experiments and envision possible directions for future works.Comment: 23 pages, 168 references, 8 figures, under revie

    CFO Promotion-based Incentives and Earnings Management

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    This study examines whether CFO promotion-based incentives induce opportunistic reporting activities. We find that CFO promotion-based incentives, measured by the pay gap between the CEO and the CFO, are positively associated with accruals management and accounting misconduct in the pre-SOX period and the probability of meeting or beating analysts’ forecasts in both the pre- and post-SOX periods. Further analysis shows that CFO promotion-based incentives are negatively associated with real earnings management in both the pre- and post-SOX periods. In addition, we find some evidence that the association between CFO promotion-based incentives and opportunistic reporting activities is stronger before CEO turnovers. We also document that CFOs engage in more opportunistic financial reporting when the pay gap between the CFO and other VPs is greater. Overall, our findings suggest that CFO promotion-based incentives may encourage CFOs to engage in opportunistic reporting activities but mitigate real earnings management

    Estimation of silage density in bunker silos by drilling

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    Abstract Silage density is an important factor in silage making. High density of silage in a silo implies a higher storing capacity and a high density of silage can also decrease the risk of dry matter losses due to air penetration into the silage. However, the density is often difficult to measure under practical conditions. In this study a method based on weighing drilled silage samples was evaluated. The method was tested on bunker silos with mainly grass/clover silage in four different farms in mid-Sweden. The drilled samples were collected by two methods: drilling by a 23.2 mm diameter drill (Stickit) and a 39.8 mm diameter drill (JTI). As the Stickit was a new device it was used twice for sampling. The resulting densities (calculated from the weight of the drilled samples and the volume of the hole in the silage created by the sampling) was compared with the densities derived from weighing silage blocks of 300-500 kg fresh weight using silo block-cutters with 1.5 to 2.3 meters width. The results showed that the correlation between the drilling methods was high and that both the JTI and the Stickit drill estimated the fresh weight density, the dry matter density and the dry matter content of silage blocks satisfactory. It was further concluded that the dry matter density depends on the dry matter content and the depth on which the sample is taken. The drilling operation must therefore represent the full silo depth to be able to estimate the density of the entire silo
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