Essays on pricing of cardinality bundles

This dissertation studies the pricing of cardinality bundles, where firms set prices that depend only on the size of the purchased bundle, a practice that is increasingly being adopted by industry. The first essay develops a fast combinatorial technique to obtain the optimal prices for cardinality bundles. The second essay extend the basic model to solve the problem when there exists fixed costs or economies of scale. The third essay relax a key assumption in cardinality bundling literature, which restricts each consumer to purchase no more than one bundle

Varying-coefficient functional linear regression

Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or multiple-response linear regression models is a regression parameter function in one or two arguments. If, in addition, one has scalar predictors, as is often the case in applications to longitudinal studies, the question arises how to incorporate these into a functional regression model. We study a varying-coefficient approach where the scalar covariates are modeled as additional arguments of the regression parameter function. This extension of the functional linear regression model is analogous to the extension of conventional linear regression models to varying-coefficient models and shares its advantages, such as increased flexibility; however, the details of this extension are more challenging in the functional case. Our methodology combines smoothing methods with regularization by truncation at a finite number of functional principal components. A practical version is developed and is shown to perform better than functional linear regression for longitudinal data. We investigate the asymptotic properties of varying-coefficient functional linear regression and establish consistency properties.Comment: Published in at http://dx.doi.org/10.3150/09-BEJ231 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Klotho inhibits growth and promotes apoptosis in human lung cancer cell line A549

<p>Abstract</p> <p>Background</p> <p>Klotho, as a new anti-aging gene, can shed into circulation and act as a multi-functional humoral factor that influences multiple biological processes. Recently, published studies suggest that klotho can also serve as a potential tumor suppressor. The aim of this study is to investigate the effects and possible mechanisms of action of klotho in human lung cancer cell line A549.</p> <p>Methods</p> <p>In this study, plasmids encoding klotho or klotho specific shRNAs were constructed to overexpress or knockdown klotho in vitro. A549 cells were respectively treated with pCMV6-MYC-KL or klotho specific shRNAs. The MTT assay was used to evaluate the cytotoxic effects of klotho and flow cytometry was utilized to observe and detect the apoptosis of A549 cells induced by klotho. The activation of IGF-1/insulin signal pathways in A549 cells treated by pCMV6-MYC-KL or shRNAs were evaluated by western blotting. The expression levels of bcl-2 and bax transcripts were evaluated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR).</p> <p>Results</p> <p>Overexpression of klotho reduced the proliferation of lung cancer A549 cells, whereas klotho silencing in A549 cells enhanced proliferation. Klotho did not show any effects on HEK-293 cells. Klotho overexpression in A549 cells was associated with reduced IGF-1/insulin-induced phosphorylation of IGF-1R (IGF-1 receptor)/IR (insulin receptor) (<it>P </it>< 0.01). Overexpression of klotho can promote the apoptosis of A549 cells (<it>P </it>< 0.01). Overexpression of klotho, a bcl family gene bax, was found up-regulated and bcl-2, an anti-apoptosis gene, was found down-regulated (<it>P </it>< 0.01). In contrast, bax and bcl-2 were found down-regulated (<it>P </it>< 0.05) and up-regulated (<it>P </it>< 0.01), respectively when silencing klotho using shRNAs.</p> <p>Conclusions</p> <p>Klotho can inhibit proliferation and increase apoptosis of A549 cells, this may be partly due to the inhibition of IGF-1/insulin pathways and involving regulating the expression of the apoptosis-related genes bax/bcl-2. Thus, klotho can serve as a potential tumor suppressor in A549 cells.</p

DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL

Deep reinforcement learning (DRL) is becoming increasingly popular in implementing traffic signal control (TSC). However, most existing DRL methods employ fixed control strategies, making traffic signal phase duration less flexible. Additionally, the trend of using more complex DRL models makes real-life deployment more challenging. To address these two challenges, we firstly propose a two-stage DRL framework, named DynamicLight, which uses Max Queue-Length to select the proper phase and employs a deep Q-learning network to determine the duration of the corresponding phase. Based on the design of DynamicLight, we also introduce two variants: (1) DynamicLight-Lite, which addresses the first challenge by using only 19 parameters to achieve dynamic phase duration settings; and (2) DynamicLight-Cycle, which tackles the second challenge by actuating a set of phases in a fixed cyclical order to implement flexible phase duration in the respective cyclical phase structure. Numerical experiments are conducted using both real-world and synthetic datasets, covering four most commonly adopted traffic signal intersections in real life. Experimental results show that: (1) DynamicLight can learn satisfactorily on determining the phase duration and achieve a new state-of-the-art, with improvement up to 6% compared to the baselines in terms of adjusted average travel time; (2) DynamicLight-Lite matches or outperforms most baseline methods with only 19 parameters; and (3) DynamicLight-Cycle demonstrates high performance for current TSC systems without remarkable modification in an actual deployment. Our code is released at Github.Comment: 9 pages, 5figure

ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning

Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification, and the introduction of intelligent wearable ECG devices has enabled daily monitoring. However, due to the need for professional expertise in the ECGs interpretation, general public access has once again been restricted, prompting the need for the development of advanced diagnostic algorithms. Classic rule-based algorithms are now completely outperformed by deep learning based methods. But the advancement of smart diagnostic algorithms is hampered by issues like small dataset, inconsistent data labeling, inefficient use of local and global ECG information, memory and inference time consuming deployment of multiple models, and lack of information transfer between tasks. We propose a multi-resolution model that can sustain high-resolution low-level semantic information throughout, with the help of the development of low-resolution high-level semantic information, by capitalizing on both local morphological information and global rhythm information. From the perspective of effective data leverage and inter-task knowledge transfer, we develop a parameter isolation based ECG continual learning (ECG-CL) approach. We evaluated our model's performance on four open-access datasets by designing segmentation-to-classification for cross-domain incremental learning, minority-to-majority class for category incremental learning, and small-to-large sample for task incremental learning. Our approach is shown to successfully extract informative morphological and rhythmic features from ECG segmentation, leading to higher quality classification results. From the perspective of intelligent wearable applications, the possibility of a comprehensive ECG interpretation algorithm based on single-lead ECGs is also confirmed.Comment: 10 page