Product Market Competition, Labor Mobility, and the Cross-Section of Stock Returns

This is the author accepted manuscript. The final version is available on open access from Oxford University Press via the DOI in this recordThis paper explores the impact of product market competition on the positive relation between labor mobility (LM) and future returns. We develop a production-based model and formalize the intuition that low exposure to systematic risk in a concentrated industry limits LM’s amplifying effect on operating leverage. Therefore, the model predicts a stronger positive relation between LM and expected returns for firms in competitive industries. Consistent with the model’s prediction, we empirically find that LM predicts returns only among firms in competitive industries. This evidence suggests that the intensity of competition in firms’ product market potentially drives the positive LM-return relation

Entanglement for a Bimodal Cavity Field Interacting with a Two-Level Atom

Negativity has been adopted to investigate the entanglement in a system composed of a two-level atom and a two-mode cavity field. Effects of Kerr-like medium and the number of photon inside the cavity on the entanglement are studied. Our results show that atomic initial state must be superposed, so that the two cavity field modes can be entangled. Moreover, we also conclude that the number of photon in the two cavity mode should be equal. The interaction between modes, namely, the Kerr effect, has a significant negative contribution. Note that the atom frequency and the cavity frequency have an indistinguishable effect, so a corresponding approximation has been made in this article. These results may be useful for quantum information in optics systems.Comment: Accepted by Commun. Theor. Phy

The Role of Solar Wind Hydrogen in Space Weathering: Insights from Laboratory-Irradiated Northwest Africa 12008

Micrometeoroid impacts, solar wind plasma interactions, and regolith gardening drive the complicated and nuanced mechanism of space weathering (or optical maturation); a process by which a materials optical properties are changed as a result of chemical and physical alterations at the surface of grains on airless bodies. Reddened slopes, attenuated absorption bands, and an overall reduction in albedo in the visible and near-IR wavelength ranges are primarily the result of native iron nanoparticle (npFe0) production within glassy rims that form from sputtering and vaporization. The sizes and abundance of these particles provide information about the relative surface exposure age of a particular grain. In addition, many studies have indicated that composition greatly affects the rate at which optical maturation occurs. Despite our understanding of how npFe0 affects optical signatures, the relative roles of micrometeoroid bombardment and solar wind interactions remains undetermined. To simulate the early effects of weathering by the solar wind and to determine thresholds for optical change with respect to a given mineral phase, we irradiated a fine-grained lunar basalt with 1 keV H+ to a fluence of 6.4 x 1016 H+ per sq.cm. Surface alterations within four phases have been evaluated using transmission electron microscopy (TEM). We found that for a given fluence of H+, the extent of damage acquired by each grain was dependent on its composition. No npFe(0) was produced in any of the phases evaluated in this study. These results are consistent with many previous studies conducted using ions of similar energy, but they also provide valuable information about the onset of space weathering and the role of the solar wind during the early stages of optical maturation

Polo-like kinase 1 siRNA-607 induces mitotic arrest and apoptosis in human nasopharyngeal carcinoma cells

Polo-like kinase (Plk) 1 is overexpressed in many human malignancies including nasopharyngeal carcinoma, indicating its potential as a therapeutic target. Recently, using a simple cellular morphologybased strategy, we have identified several novel effective siRNAs against Plk1 including Plk1 siRNA- 607. In this study, we further investigated the effects of Plk1 siRNA-607 in human nasopharyngeal carcinoma cell line, HNE-1. Real time RT-PCR and Western blot indicated that Plk1 siRNA-607 transfection resulted in a significant inhibition in Plk1 expression in the HNE-1 cells. Furthermore, cell cycle, cell growth and apoptosis analysis clearly indicated that Plk1 siRNA-607 caused a dramatic mitotic cell cycle arrest followed by massive apoptotic cell death, and eventually resulted in a significant decrease in growth and viability of the nasopharyngeal carcinoma cells. Given that Plk1 has been widely accepted as a novel efficient target for cancer therapy, these results suggested that Plk1 siRNA-607 could be further developed for the treatment of human nasopharyngeal carcinoma.Key words: Nasopharyngeal carcinoma, Plk1, RNA silencing, cell cycle, apoptosis

Seminar Users in the Arabic Twitter Sphere

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

Decoding Pixel-Level Image Features from Two-Photon Calcium Signals of Macaque Visual Cortex

Images of visual scenes comprise essential features important for visual cognition of the brain. The complexity of visual features lies at different levels, from simple artificial patterns to natural images with different scenes. It has been a focus of using stimulus images to predict neural responses. However, it remains unclear how to extract features from neuronal responses. Here we address this question by leveraging two-photon calcium neural data recorded from the visual cortex of awake macaque monkeys. With stimuli including various categories of artificial patterns and diverse scenes of natural images, we employed a deep neural network decoder inspired by image segmentation technique. Consistent with the notation of sparse coding for natural images, a few neurons with stronger responses dominated the decoding performance, whereas decoding of ar tificial patterns needs a large number of neurons. When natural images using the model pretrained on artificial patterns are decoded, salient features of natural scenes can be extracted, as well as the conventional category information. Altogether, our results give a new perspective on studying neural encoding principles using reverse-engineering decoding strategies

Shape Retrieval of Non-rigid 3D Human Models

3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods are compared

Upregulated sirtuin 1 by miRNA-34a is required for smooth muscle cell differentiation from pluripotent stem cells

© 2015 Macmillan Publishers Limited. All rights reserved. microRNA-34a (miR-34a) and sirtuin 1 (SirT1) have been extensively studied in tumour biology and longevityaging, but little is known about their functional roles in smooth muscle cell (SMC) differentiation from pluripotent stem cells. Using well-established SMC differentiation models, we have demonstrated that miR-34a has an important role in SMC differentiation from murine and human embryonic stem cells. Surprisingly, deacetylase sirtuin 1 (SirT1), one of the top predicted targets, was positively regulated by miR-34a during SMC differentiation. Mechanistically, we demonstrated that miR-34a promoted differentiating stem cells' arrest at G0G1 phase and observed a significantly decreased incorporation of miR-34a and SirT1 RNA into Ago2-RISC complex upon SMC differentiation. Importantly, we have identified SirT1 as a transcriptional activator in the regulation of SMC gene programme. Finally, our data showed that SirT1 modulated the enrichment of H3K9 tri-methylation around the SMC gene-promoter regions. Taken together, our data reveal a specific regulatory pathway that miR-34a positively regulates its target gene SirT1 in a cellular context-dependent and sequence-specific manner and suggest a functional role for this pathway in SMC differentiation from stem cells in vitro and in vivo

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-