Essays on Sales Force Career Incentives

This dissertation uses game theoretic models in a principal-agent framework to study how firms optimally manage long term career related incentives for their sales people. When sales people put sales effort they face incentives not only from short term incentives like commissions and bonuses but also from long term rewards associated with progression in their career. In particular, sales people are often motivated to get promoted and avoid being laid off, to get selected to managerial positions and to form stronger relationships with customers so that they can bargain for higher wages in the future, respectively. Three different essays examine each of these three career related incentives and how firms can optimally manage them. Essay 1 (Chapter 2) studies why and how firms use a type of promotion and layoff policy, called the Forced Ranking policy, to provide optimal long term career incentives to sales people. Findings from the essay suggests that when sales people are ambiguity averse and there is economic uncertainty regarding promotions and layoffs, firms are likely to commit to a promotion policy but may or may not commit to a layoff policy as part of Forced Ranking. Interestingly, it is shown that firms enjoying higher margins are more likely to commit to both promotion and layoffs, consistent with observations from industry practice. Results also suggest that in absence of costs from promoting and laying off employees, firms should use an up-or-out contract to motivate the sales force. Essay 2 (Chapter 3) investigates how career incentives associated with promotion of sales employees to sales management roles may interfere with selection of the right sales managers. The essay was motivated by the common observation that organizations often promote their best sales people to sales managerial roles but after promotion find that the sales people are not as good as they were expected to be in their new roles, a phenomenon called Peter Principle. An alternative explanation for this phenomenon of adverse selection is provided and possible solutions are analyzed as part of the essay. In essay 3 (Chapter 4) long term career incentives that sales reps face when they can form relationships with their customers are considered. Loyalty generated from customer-salesperson relationships is often owned by the sales person and it can be lost if the sales person moves to another firm. Therefore, firms compete for both customers as well as sales reps with the objective of poaching customers that are loyal to the sales reps. The essay analyzes how firms can deal with such a competition. Findings suggest that contrary to general beliefs, the presence of anti-employee poaching regulations like Non-Compete clauses, or tacit collusion to not poach each other\u27s employees may hurt firm profits under some conditions. Overall, the dissertation answers how firms can manage sales force career incentives to maximize profits

Presumed bilateral branch retinal vein occlusions secondary to antiepileptic agents

A 61-year-old man presented to the ophthalmology department having developed bilateral branch retinal vein occlusions. Baseline blood tests revealed no abnormality; however, subsequent investigations showed a raised plasma homocysteine (HC) level. The patient has been treated for refractory epilepsy for a number of years. Although antiepileptic medications have been shown to reduce folate levels and result in a raised HC level, this has not previously been shown to be to a level causing a retinal vascular event

Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets

[EN] In the last years, the control of online user generated content is becoming a priority, because of the increase of online aggressiveness and hate speech legal cases. Considering the complexity and the importance of this issue, this paper presents an approach that combines the deep learning framework with linguistic features for the recognition of aggressiveness in Mexican tweets. This approach has been evaluated relying on a collection of tweets released by the organizers of the shared task about aggressiveness detection in the context of the Ibereval 2018 evaluation campaign. The use of a benchmark corpus allows to compare the results with those obtained by Ibereval 2018 participant systems. However, looking at the achieved results, linguistic features seem not to help the deep learning classification for this task.The work of Simona Frenda and Paolo Rosso was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).Frenda, S.; Banerjee, S.; Rosso, P.; Patti, V. (2020). Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets. Computación y Sistemas. 24(2):633-643. https://doi.org/10.13053/CyS-24-2-3398S63364324

Intravitreal Ranibizumab in the Treatment of Butterfly-Shaped Pattern Dystrophy Associated with Choroidal Neovascularization: A Case Report

Purpose: To present and document the effectiveness of intravitreal ranibizumab in the treatment of patients with choroidal neovascularization due to butterfly-shaped pattern dystrophy (PD) of the macula. Methods: Three intravitreal ranibizumab injections of 0.5 mg/0.05 ml in monthly intervals were given to a patient with a previously diagnosed butterfly-shaped PD who subsequently developed subfoveal choroidal neovascularization on the right eye. The patient had previously received a combination of verteporfin/photodynamic therapy for a juxtafoveal choroidal neovascular membrane on the left eye. Results: At the end of the treatment course, there was significant improvement of the patient’s vision and the appearance of the macula on optic coherence tomography and fluorescein angiography. Best-corrected visual acuity improved from 6/12 to 6/6 and retinal thickness at the macula decreased from 323 to 247 µm. No subretinal fluid remained. The patient is clinically stable over a 12-month follow-up period. Conclusions: Intravitreal ranibizumab seems to be an effective and safe option for the treatment of subfoveal choroidal neovascularization in patients with butterfly-shaped PD

A randomized controlled open label comparative clinical study of cephalexin versus doxycycline in patients with acne vulgaris in a hospital based population of South India

Background: Oral antibiotics are frequently used for acne vulgaris. Cephalexin has documented some success against acne vulgaris in earlier studies. Here the efficacy and safety of Cephalexin have been compared against the standard therapy of Doxycyline.Methods: From September 2010 to June 2011, 100 patients of moderate Acne vulgaris were randomized into two groups to receive oral Doxycyline (100mg once daily) or Cephalexin (500mg thrice daily) in an open label manner for eight weeks. All participants were allowed to use topical 5%Benzoyl peroxide gel twice daily. Efficacy was measured in terms of reduction in the number of facial comedones and inflammatory lesions from baseline after eight weeks.Results: 44 patients from Cephalexin group and48 patients from Doxycyline group completed the study. Both drugs have significantly decreased comedone count as well as the inflammatory lesion count after eight weeks. However, Doxycycline appeared better in terms of Comedone count (14.5±3.07 versus 12.9±4.31, p=0.045) as well as inflammatory lesion count (8.64.1±2.14 versus 7.67±2.46, p=0.047) at the end. The total adverse event was slightly more with Cephalexin (6.81% versus 6.25%, p= 0.912), where Diarrhoea remained the commonest adverse effect (4.54%).Conclusions: Although for the first time oral Cephalexin has displayed efficacy against moderate acne vulgaris in a prospective clinical study, it appeared inferior to Doxycycline over eight weeks. Therefore, it becomes an option only when other oral antibiotics are contraindicated or not tolerated

Hate Speech and Offensive Language Detection in Bengali

Social media often serves as a breeding ground for various hateful and offensive content. Identifying such content on social media is crucial due to its impact on the race, gender, or religion in an unprejudiced society. However, while there is extensive research in hate speech detection in English, there is a gap in hateful content detection in low-resource languages like Bengali. Besides, a current trend on social media is the use of Romanized Bengali for regular interactions. To overcome the existing research's limitations, in this study, we develop an annotated dataset of 10K Bengali posts consisting of 5K actual and 5K Romanized Bengali tweets. We implement several baseline models for the classification of such hateful posts. We further explore the interlingual transfer mechanism to boost classification performance. Finally, we perform an in-depth error analysis by looking into the misclassified posts by the models. While training actual and Romanized datasets separately, we observe that XLM-Roberta performs the best. Further, we witness that on joint training and few-shot training, MuRIL outperforms other models by interpreting the semantic expressions better. We make our code and dataset public for others.Comment: Accepted at AACL-IJCNLP 202

Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach

[EN] Before the advent of the Internet era, code-mixing was mainly used in the spoken form. However, with the recent popular informal networking platforms such as Facebook, Twitter, Instagram, etc., in social media, code-mixing is being used more and more in written form. User-generated social media content is becoming an increasingly important resource in applied linguistics. Recent trends in social media usage have led to a proliferation of studies on social media content. Multilingual social media users often write native language content in non-native script (cross-script). Recently Banerjee et al. [9] introduced the code-mixed cross-script question answering research problem and reported that the ever increasing social media content could serve as a potential digital resource for less-computerized languages to build question answering systems. Question classification is a core task in question answering in which questions are assigned a class or a number of classes which denote the expected answer type(s). In this research work, we address the question classification task as part of the code-mixed cross-script question answering research problem. We combine deep learning framework with feature engineering to address the question classification task and enhance the state-of-the-art question classification accuracy by over 4% for code-mixed cross-script questions.The work of the third author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project.Banerjee, S.; Kumar Naskar, S.; Rosso, P.; Bandyopadhyay, S. (2018). Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach. Journal of Intelligent & Fuzzy Systems. 34(5):2959-2969. https://doi.org/10.3233/JIFS-169481S2959296934