Laughter and smiling facial expression modelling for the generation of virtual affective behavior

Laughter and smiling are significant facial expressions used in human to human communication. We present a computational model for the generation of facial expressions associated with laughter and smiling in order to facilitate the synthesis of such facial expressions in virtual characters. In addition, a new method to reproduce these types of laughter is proposed and validated using databases of generic and specific facial smile expressions. In particular, a proprietary database of laugh and smile expressions is also presented. This database lists the different types of classified and generated laughs presented in this work. The generated expressions are validated through a user study with 71 subjects, which concluded that the virtual character expressions built using the presented model are perceptually acceptable in quality and facial expression fidelity. Finally, for generalization purposes, an additional analysis shows that the results are independent of the type of virtual character’s appearance.

Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

Upskilling: Do Employers Demand Greater Skill When Workers Are Plentiful?

Using a proprietary database of online job postings, we find that education and experience requirements rose during the Great Recession. These increases were larger in states and occupations that experienced greater increases in the supply of available workers. This finding is robust to controlling for local demand conditions and firm [Formula: see text] job-title fixed effects and using a natural experiment arising from troop withdrawals as an exogenous shock to labor supply. Our results imply that the increase in unemployed workers during the Great Recession can account for 18% to 25% of the increase in skill requirements between 2007 and 2010.

Discussion of “What Turns the Taxman On? Tax Aggressiveness, Financial Statement Audits, and Tax Return Adjustments in Small Private Companies”

In this discussion of [Ojala, H., Kinnunen, J., Niemi. L, Troberg, P. and Collis, J. (2019). What turns the taxman on? Tax aggressiveness, financial statement audits and tax return adjustments in small private companies. Forthcoming, The International Journal of Accounting], we analytically show that tax evasion can be sustained in equilibrium in a rational expectations setting, and the analytical model to provide interesting future research directions. In addition, we also provide interesting future research directions using the proprietary database by closely examining the authors’ results.

Market Impact: A Systematic Study of Limit Orders

This paper is devoted to the important yet little explored subject of the market impact of limit orders. Our analysis is based on a proprietary database of metaorders — large orders that are split into smaller pieces before being sent to the market. We first address the case of aggressive limit orders and then that of passive limit orders. In both cases, we provide empirical evidence of a power law behavior for the temporary market impact. The relaxation of the price following the end of the metaorder is also studied, and the long-term impact is shown to stabilize at a level of approximately two-thirds of the maximum impact. Finally, a fair pricing condition during the life cycle of the metaorders is empirically validated.

The Behavior of Dealers and Clients on the European Corporate Bond Market: The Case of Multi-Dealer-to-Client Platforms

For the last two decades, most financial markets have undergone an evolution toward electronification. The market for corporate bonds is one of the last major financial markets to follow this unavoidable path. Traditionally quote-driven (i.e., dealer-driven) rather than order-driven, the market for corporate bonds is still mainly dominated by voice trading, but a lot of electronic platforms have emerged. These electronic platforms make it possible for buy-side agents to simultaneously request several dealers for quotes, or even directly trade with other buy-siders. The research presented in this paper is based on a large proprietary database of requests for quotes (RFQ) sent, through the multi-dealer-to-client (MD2C) platform operated by Bloomberg Fixed Income Trading, to one of the major liquidity providers in European corporate bonds. Our goal is (i) to model the RFQ process on these platforms and the resulting competition between dealers, and (ii) to use our model in order to implicit from the RFQ database the behavior of both dealers and clients on MD2C platforms.

Comparing Drug-Drug Interaction Severity Ratings between Bedside Clinicians and Proprietary Databases

Purpose. The purpose of this project was to compare DDI severity for clinician opinion in the context of the patient’s clinical status to the severity of proprietary databases. Methods. This was a single-center, prospective evaluation of DDIs at a large, tertiary care academic medical center in a 10-bed cardiac intensive care unit (CCU). A pharmacist identified DDIs using two proprietary databases. The physicians and pharmacists caring for the patients evaluated the DDIs for severity while incorporating their clinical knowledge of the patient. Results. A total of 61 patients were included in the evaluation and experienced 769 DDIs. The most common DDIs included: aspirin/clopidogrel, aspirin/insulin, and aspirin/furosemide. Pharmacists ranked the DDIs identically 73.8% of the time, compared to the physicians who agreed 42.2% of the time. Pharmacists agreed with the more severe proprietary database scores for 14.8% of DDIs versus physicians at 7.3%. Overall, clinicians agreed with the proprietary database 20.6% of the time while clinicians ranked the DDIs lower than the database 77.3% of the time. Conclusions. Proprietary DDI databases generally label DDIs with a higher severity rating than bedside clinicians. Developing a DDI knowledgebase for CDSS requires consideration of the severity information source and should include the clinician.