489 research outputs found
The Effect of Retail Crowding Toward Consumer Emotion, Coping, and Satisfaction
No full textWith the growth of the overall economy, various kinds of new-type service industries have developed well and rooted in Taiwan around the past 20 years, and become the crucial industry in our society. To succeed under the keen competition, most the owners of the retail stores look forward to raise the level of the satisfaction of shopping from the material level to the spiritual one; they want to attract the consumers with the comfortable and cozy shopping environment they mold.
Crowding is a common phenomenon in Taiwan. The department stores and other retail stores are often packed with the crowd on weekends and special festivals. Such crowded shopping spaces always make consumers feel uncomfortable and even scare them away for that it is hard to enjoy the happiness of shopping under this kind of circumstance. Consumers can make themselves feel better by changing their behaviors or their mind set, which is called coping. The emotions caused by the crowdedness may influence the adoption of the coping behaviors, also, lead to the change of the consumer satisfaction. Many research have studied the implication of the coping behavior in the crowded situation, but few were focused on the retail crowding one; therefore, based on the theories of the coping behaviors and the results of the focus group interview, this study developed a set of operational definitions of the coping behaviors under the retail crowding situation. To measure the response of the consumers in the retail crowding situation, this study use two different levels of crowding pictures to design eight different scenarios. The results conclude a path which shows how does the retail crowding influence the consumer satisfaction through the motivation and the occasion of shopping, emotion, and the coping behaviors. The finding of the research is as followed.
\ue2\ua0. The more crowded the retailers are, the stronger sense of the negative emotions the consumers have. Besides, the negative emotions toward the others are especially stronger than the negative emotions toward the environment. Both type of the emotion have direct effects on the consumer satisfaction.
\ue2\ua1. The motivation and the occasion (holiday effect) for shopping can not be the moderators between perceived crowding and emotion.
\ue2\ua2. Consumers will adopt different kinds of coping behavior according to their emotion.
\ue2\ua3. Behavioral coping and emotional coping are the intervening factors between emotion and consumer satisfaction.
\ue2\ua4. The adoption of the behavioral coping will decrease the consumer satisfaction;
however, the adoption of the emotional coping can increase it
Doplor Sleep: Monitoring Hospital Soundscapes for Better Sleep Hygiene
No full textGood sleep is conducive to the recovery process of hospital patients - and yet, in many wards, sleep duration and quality can often be suboptimal, in part due to modifiable hospital-related sounds and noises. At the neurological ward of the Reinier de Graaf hospital in Delft, the Netherlands, we developed and evaluated a prototype information exchange system to raise awareness of specific sounds as disturbing patients' sleep. The system both classifies different relevant sound events and tracks sleep quality (using a Fitbit device). This information is then visualized for patients and staff to present the influence of the soundscape on patients' sleep hygiene in a friendly and comprehensive way. We discuss the design process, including a context study and various evaluations of the technology, interface, and created affordances. Our initial findings indicate that visualizing hospital soundscapes may, indeed, support both patients and staff in their efforts towards better sleep hygiene. Design AestheticsInternet of Thing
A Machine with Short-Term, Episodic, and Semantic Memory Systems
No full textInspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph. To evaluate this system and analyze the behavior of this agent, we designed and released our own reinforcement learning agent environment, the Room , where an agent has to learn how to encode, store, and retrieve memories to maximize its return by answering questions. We show that our deep Q-learning based agent successfully learns whether a short-term memory should be forgotten, or rather be stored in the episodic or semantic memory systems. Our experiments indicate that an agent with human-like memory systems can outperform an agent without this memory structure in the environment
Deep characteristics analysis on travel time of emergency traffic
No full textOwing to the rapid development of emergency rescue transportation in cities and the frequent emergencies, demand for emergency rescue is increasing drastically. How to select an emergency rescue route quickly and shorten the rescue travel time under the condition of limited urban road resources is of great significance. Based on the characteristics analysis of emergency rescue, this paper classifies priority levels of different emergency traffic, moreover, the travel times are also analysed with three scenarios: 1) emergency rescue vehicles encountering no queues; 2) encountered queues but lanes available; 3) encountered queues with no available lanes. Related case study shows that model in this paper can effectively shorten travel time of emergency traffic in the route and improve its efficiency.Accepted Author ManuscriptTransport and Plannin
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Incentive Design in the Machine Learning Age
No full textThis dissertation investigates the design of incentives in multi-agent systems where traditional assumptions -- such as fully rational agents and omniscient principals -- do not hold. As real-world systems increasingly involve learning-based decision-makers, either human or algorithmic, this work explores how learning alters the landscape of incentive design. The dissertation is organized into three parts.
The first part focuses on incentive design by learning principals, specifically in information and mechanism design settings. For information design, this part introduces novel algorithms that allow a principal to learn an agent's non-Bayesian belief updating process, such as a subjective prior or cognitive bias, via strategic interaction with the agent.
For mechanism design, this part examines how a coordinator can learn to compute Bayes correlated equilibria in non-truthful auctions using limited samples of agent types.
The second part studies incentive design for learning agents, who are modeled as boundedly rational learners rather than best responders. This part first presents, for a general class of principal-agent problems, a reduction from no-regret learning agents to approximately best-responding agents, enabling a precise analysis of the principal's performance. It then characterizes the convergence properties of multi-agent learning in first-price auction games, identifying when convergence to equilibrium is possible.
The third part explores incentive issues in deployed machine learning systems, with a case study on recommender systems. It demonstrates that the strategic behaviors by content creators can exacerbate polarization, even under diversity-promoting algorithms, and proposes alternative algorithmic designs that mitigate these effects.
Collectively, this dissertation lays foundational insights for designing systems that are robust to the incentives of learning-based, data-driven participants.Engineering and Applied Sciences - Computer Scienc
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Where do new ideas come from: New directions in science emerge from disconnection and discord
No full textSince the 1950s, citation number or “impact” has been the dominant metric by which science is quantitatively evaluated. But research contributions play distinct roles in the unfolding drama of scientific debate, agreement and advance, which are differentially valued by scientists and their institutions. Computational power, access to citation data and an array of modeling techniques have given rise to a widening portfolio of metrics that extract different signals regarding their contribution to scientific activities. Besides impact, the innovation of work and the way that it builds up the scientific discussions are taken into consideration. Here we unpacks the complex, temporally evolving relationship between citation impact alongside novelty and disruption, two emerging measures that capture the degree to which science not only influences, but transforms later work. Novelty is measured at the point of production and captures how research draws upon unusual combinations of prior work. Disruption is measured over time and captures how research comes to eclipse or amplify the prior work on which it builds. We theorize that novel papers will exhibit disruptive impact over time, and demonstrate how they are much more likely than conventional papers to disrupt current literature. Novel papers do not do so immediately, but often become “sleeping beauties”, accumulating surprising attention and citation impact over the long run. In summary, new directions for science are created from a lack of consensus. Finally, we show how novelty can be reformulated as the combination of ideas across knowledge space to reveal the combinatorial nature of advance. The evolution of knowledge space over time characterizes how yesterday’s novelty forms today’s scientific conventions, which condition the novelty—and surprise—of tomorrow’s breakthroughs
Computations of nonreacting flameholder flows with a zonal grid method
No full textThe "zonal grid method" is widely used to alleviate the difficulties for flow field calculations with complex geometry. In the present study, a patched grid method is employed in the computation of flow fields behind a two-ring flameholder which forms a multiple-connected region.
A standard K - ε model is used to close the system. The calculation is performed by using a SIMPLE type algorithm in two subdomains in a body-fitted coordinate system With nonstaggered grid arrangements. The concept of conservative interpolation technique is applied to treat the flux conservation across the interface. The effect of the distance between these two rings on the flow pattern is studied. It is found the distance of the rings either in the axial direction or in the radial direction does not change the strength of the recirculation zone, but alters the flow pattern. The predicted streamlines, the turbulence kinetic energy K, and the reverse mass flow rate are presented.Master of Scienc
Doplor Sleep: Friendly feedback towards a better hospital soundscape for sleep
No full textRecently in the Netherlands, researchers have found that sleep duration and quality were suboptimal in the hospital. Evidence proved that many modifiable hospitalrelated factors negatively associate with patients' sleep (JAMA Internal Medicine, 2018). The sound factor is the most significant sleep disturbance in the hospital. In this graduation project, collaborating with Reiner de Graaf hospital and Critical Alarms lab, an information exchange system was proposed to raise awareness of sound as sleep disturbance. The system captures the sound-producing events and visualizes them with visually attractive graphics. In this system, we use the smartphone as the sound captor. The recorded sounds are processed locally on the phone and converted into information such as sound level and the category it belongs to (alarm, speech, incidental sounds, or snore). Fitbit is implemented in the system to collect sleep information. To both patients and medical staff, The Doplor sleep system presents the influence of sound on sleep in a friendly and comprehensive way. During this project, a functioning prototype was developed. We have tested its functionality and user experience with the potential users
Machine Learning with Differentiable Physics Priors
No full textDifferentiable physics priors enable gradient-based learning systems to adhere to physical dynamics. By making physics simulations differentiable, we can backpropagate through the physical consequences of actions. This pipeline allows agents to quickly learn to achieve desired effects in the physical world and is an effective technique for solving inverse problems in physical or dynamical systems. This new programming paradigm bridges model-based and data-driven methods, mitigating data scarcity and model bias simultaneously.
My research focuses on developing scalable, powerful, and efficient differentiable physics simulators. We have created state-of-the-art differentiable physics for rigid bodies, cloth, fluids, articulated bodies, and deformable solids, achieving performance orders of magnitude better than existing alternatives. These differentiable simulators are applied to solve inverse problems, train control policies, and enhance reinforcement learning algorithms
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