Industry Valuation Driven Earnings Management

This paper investigates whether industry valuation impacts firms’ earnings management decisions. Existing accounting literature assumes that industry valuation has a constant impact on this decision. We argue that a higher industry valuation increases the perceived benefits of earnings management at a time when the negative consequences associated with accrual reversal and the probability of detection are believed to be lower. Using a sample of quarterly data of U.S. firms from 1985 to 2005, we find that the four-quarter lagged industry valuation has a positive relationship with industry aggregate (current) discretionary accruals. More specific, one standard deviation increase in the aggregate industry valuation is associated with a significant increase of 2.4 cents in quarterly earnings per share. Our results are robust after controlling for several factors, including bubble years, size, leverage and performance.Industry valuation;Earnings management;Market to book ratio

Formation of hydroxylamine on dust grains via ammonia oxidation

The quest to detect prebiotic molecules in space, notably amino acids, requires an understanding of the chemistry involving nitrogen atoms. Hydroxylamine (NH$_2$OH) is considered a precursor to the amino acid glycine. Although not yet detected, NH$_2$OH is considered a likely target of detection with ALMA. We report on an experimental investigation of the formation of hydroxylamine on an amorphous silicate surface via the oxidation of ammonia. The experimental data are then fed into a simulation of the formation of NH$_2$OH in dense cloud conditions. On ices at 14 K and with a modest activation energy barrier, NH$_2$OH is found to be formed with an abundance that never falls below a factor 10 with respect to NH$_3$. Suggestions of conditions for future observations are provided.Comment: 9 pages, 9 figure

A totally laparoscopic associating liver partition and portal vein ligation for staged hepatectomy assisted with radiofrequency (radiofrequency assisted liver partition with portal vein ligation) for staged liver resection

In order to induce liver hypertrophy to enable liver resection in patients with a small future liver remnant, various methods have been proposed in addition to portal vein embolisation. Most recently, the ALPPS technique has gained significant international interest. This technique is limited by the high morbidity associated with an in-situ liver splitting and the patient undergoing two open operations. We present the case of a variant ALPPS technique performed entirely laparoscopically with no major morbidity or mortality. An increased liver volume of 57.9% was seen after 14 days. This technique is feasible to perform and compares favourably to other ALPPS methods whilst gaining the advantages of laparoscopic surgery

Multispecies Coalescent and its Applications to Infer Species Phylogenies and Cross-Species Gene Flow

Multispecies coalescent (MSC) is the extension of the single-population coalescent model to multiple species. It integrates the phylogenetic process of species divergences and the population genetic process of coalescent, and provides a powerful framework for a number of inference problems using genomic sequence data from multiple species, including estimation of species divergence times and population sizes, estimation of species trees accommodating discordant gene trees, inference of cross-species gene flow and species delimitation. In this review, we introduce the major features of the MSC model, discuss full-likelihood and heuristic methods of species tree estimation and summarize recent methodological advances in inference of cross-species gene flow. We discuss the statistical and computational challenges in the field and research directions where breakthroughs may be likely in the next few years

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations.Comment: MICCAI 2017 Camera Read