High band gap 2-6 and 3-5 tunneling junctions for silicon multijunction solar cells

A multijunction silicon solar cell of high efficiency is provided by providing a tunnel junction between the solar cell junctions to connect them in series. The tunnel junction is comprised of p+ and n+ layers of high band gap 3-5 or 2-6 semiconductor materials that match the lattice structure of silicon, such as GaP (band gap 2.24 eV) or ZnS (band gap 3.6 eV). Each of which has a perfect lattice match with silicon to avoid defects normally associated with lattice mismatch

Method for growing low defect, high purity crystalline layers utilizing lateral overgrowth of a patterned mask

A method for growing a high purity, low defect layer of semiconductor is described. This method involves depositing a patterned mask of a material impervious to impurities of the semiconductor on a surface of a blank. When a layer of semiconductor is grown on the mask, the semiconductor will first grow from the surface portions exposed by the openings in the mask and will bridge the connecting portions of the mask to form a continuous layer having improved purity, since only the portions overlying the openings are exposed to defects and impurities. The process can be iterated and the mask translated to further improve the quality of grown layers

The determinants of earnings management by the acquirer: The case of french corporate takeovers.

This paper analyses the determinants of earnings management by the acquirer of 60 takeover-bids that occurred between 1998 and 2008 on the French market. Four main findings are shown in this study. First, we find strong evidence that bidder firms manipulate earnings prior to takeovers suggesting that target managers have incentive to detect earnings manipulation to ensure the interests of the target shareholders. Second, managers manipulate their earnings either downward or upward and regardless of the method of payment. Third, bidder toehold influences negatively and significantly the discretionary accruals. Finally, the relationship between discretionary accruals and managerial ownership is negatively significant. This practice favors a resource transfer to the bidder controlling shareholder at the expense of minority shareholders.Corporate takeover, Earnings management, Information asymmetry, Discretionary accruals, Method of payment

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis

Text preprocessing is often the first step in the pipeline of a Natural Language Processing (NLP) system, with potential impact in its final performance. Despite its importance, text preprocessing has not received much attention in the deep learning literature. In this paper we investigate the impact of simple text preprocessing decisions (particularly tokenizing, lemmatizing, lowercasing and multiword grouping) on the performance of a standard neural text classifier. We perform an extensive evaluation on standard benchmarks from text categorization and sentiment analysis. While our experiments show that a simple tokenization of input text is generally adequate, they also highlight significant degrees of variability across preprocessing techniques. This reveals the importance of paying attention to this usually-overlooked step in the pipeline, particularly when comparing different models. Finally, our evaluation provides insights into the best preprocessing practices for training word embeddings.Comment: Blackbox EMNLP 2018. 7 page