Essays on Service Information, Retrials and Global Supply Chain Sourcing

In many service settings, customers have to join the queue without being fully aware of the parameters of the service provider (for e.g., customers at check-out counters may not know the true service rate prior to joining). In such blind queues\u27\u27, customers typically make their decisions based on the limited information about the service provider\u27s operational parameters from past experiences, reviews, etc. In the first essay, we analyze a firm serving customers who make decisions under arbitrary beliefs about the service parameters. We show, while revealing the service information to customers improves revenues under certain customer beliefs, it may however destroy consumer welfare or social welfare. When consumers can self-organize the timing of service visits, they may avoid long queues and choose to retry later. In the second essay, we study an observable queue in which consumers make rational join, balk and (costly) retry decisions. Retrial attempts could be costly due to factors such as transportation costs, retrial hassle and visit fees. We characterize the equilibrium under such retrial behavior, and study its welfare effects. With the additional option to retry, consumer welfare could worsen compared to the welfare in a system without retrials. Surprisingly, self-interested consumers retry too little (in equilibrium compared to the socially optimal policy) when the retrial cost is low, and retry too much when the retrial cost is high. We also explore the impact of myopic consumers who may not have the flexibility to retry. In the third essay, we propose a comprehensive model framework for global sourcing location decision process. For decades, off-shoring of manufacturing to China and other low-cost countries was a no-brainer decision for many U.S. companies. In recent years, however, this trend is being challenged by some companies to re-shore manufacturing back to the U.S., or to near-shore manufacturing to Mexico. Our model framework incorporates perspectives over the entire life cycle of a product, i.e., product design, manufacturing and delivering, and after-sale service support, and we use it to test the validity of various competing theories on global sourcing. We also provide numerical examples to support our findings from the model

Blind Queues: The Impact of Consumer Beliefs on Revenues and Congestion

In many service settings, customers have to join the queue without being fully aware of the parameters of the service provider (e.g., customers at checkout counters may not know the true service rate before joining). In such “blind queues,” customers make their joining/balking decisions based on limited information about the service provider’s operational parameters (from past service experiences, reviews, etc.) and queue lengths. We analyze a firm serving customers making decisions under arbitrary beliefs about the service parameters in an observable queue for a service with a known price. By proposing an ordering for the balking threshold distributions in the customer population, we are able to compare the effects of customer beliefs on the queue. We show that, although revealing the service information to customers improves revenues under certain conditions, it may destroy consumer welfare or social welfare. Given a market size, consumer welfare can be significantly reduced when a fast server announces its true service parameter. When revenue is higher under some beliefs, one would expect the congestion to also be higher because more customers join, but we show that congestion may not necessarily increase

Deconfined quantum criticality and emergent symmetry in SrCu2(BO3)2

The deconfined quantum critical point (DQCP) represents a paradigm shift in theories of quantum matter, presenting a "beyond Landau" scenario for order-order transitions. Its experimental realization, however, has remained elusive. Here we demonstrate by high-pressure 11B NMR measurements on the quantum magnet SrCu2(BO3)2 that the magnetic field induced plaquette-singlet to antiferromagnetic transition above 1.8 GPa is proximate to a DQCP. We find a weak first-order transition between the two phases at a remarkably low temperature, Tc~0.07 K. Above Tc we observe quantum critical scaling at the highest pressure, 2.4 GPa. We explain the low first-order Tc values by a DQCP-induced emergent O(3) symmetry that is broken in the coexistence state. Our findings take the DQCP from a theoretical concept to a concrete experimental platform

Patterns generated by -order Markov chains

We derive an expression for the expected time for a pattern to appear in higher-order Markov chains with and without a starting sequence. This yields a result for directly calculating, the first time one of a collection of patterns appears, in addition to the probability, for each pattern, that it is the first to appear.Patterns Markov chains Waiting time

Music video affective understanding using feature importance analysis

Music video is a popular type of entertainment by viewers. Currently, the novel indexing and retrieval approach based on the affective cues contained in music videos becomes more and more attractive to users. Music video affective analysis and understanding is one of the most popular topics in current multimedia community. In this paper, we propose a novel feature importance analysis approach to select most representative arousal and valence features for arousal and valence modeling. Compared with state-of-the-art work by Zhang on music video affective analysis, our main contributions are in the following aspects: (1) Another 3 affect-related features are extracted to enrich the feature set and exploit their correlation with arousal and valence. (2) All extracted features are ordered via feature importance analysis, and then optimal feature subset is selected after ordering. (3) Different regression methods are compared for arousal and valence modeling in order to find the fittest estimation function. Our method achieves 33.39% and 42.17% deduction in terms of mean absolute error compared with Zhang's method. Experimental results demonstrate our proposed method has a considerable improvement on music video affective understanding

Correlation-based feature selection and regression

Music video is a well-known medium in music entertainment which contains rich affective information and has been widely accepted as emotion expressions. Affective analysis plays an important role in the content-based indexing and retrieval of music video. This paper proposes a general scheme for music video affective estimation using correlation based feature selection followed by regression. Arousal score and valence score with four grade scales are used to measure music video affective content in 2D arousal/valence space. The main contributions are in the following aspects: (1) correlation-based feature selection is performed after feature extraction to select representative arousal and valence features; (2) different regression methods including multiple linear regression and support vector regression with different kernels are compared to find the fittest estimation model. Significant reductions in terms of both mean absolute error and variation of absolute error compared with the state-of-the-art methods clearly demonstrate the effectiveness of our proposed method