How electronic word of mouth dynamically influences product sales and supplies: an evidence from China film industry

As an important part of e-commerce, online reviews play a significant role in consumers’ purchase decisions. This study investigated the dynamic effects of electronic word of mouth (eWOM) and the number of people wishing to watch a movie on movie sales and supplies in the Chinese movie market. Using a dynamic simultaneous equation system and data of 76 films, this study analyzed the interrelationships between eWOM and movie sales and supplies. Our findings showed that both the volume and valence of eWOM affected movie sales and supplies significantly. The number of people who wanted to watch a movie had an opposite effect on movie sales and supply; eWOM volume had a positive impact on movie sales and supplies; and eWOM valence had a negative impact on movie sales and a positive impact on movie supplies. The number of people who wish to watch a movie was another important variable for movie sales and supplies, and it had a negative impact on the daily movie sales but a positive impact on the daily movie supplies. This study provided a detailed explanation of these results and thus contributed to improving the efficiency of movie suppliers’ utilization of online reviews

Towards Interpretable Machine Learning in Medical Image Analysis

Over the past few years, ML has demonstrated human expert level performance in many medical image analysis tasks. However, due to the black-box nature of classic deep ML models, translating these models from the bench to the bedside to support the corresponding stakeholders in the desired tasks brings substantial challenges. One solution is interpretable ML, which attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, interpretability is not a property of the ML model but an affordance, i.e., a relationship between algorithm and user. Thus, prototyping and user evaluations are critical to attaining solutions that afford interpretability. Following human-centered design principles in highly specialized and high stakes domains, such as medical image analysis, is challenging due to the limited access to end users. This dilemma is further exacerbated by the high knowledge imbalance between ML designers and end users. To overcome the predicament, we first define 4 levels of clinical evidence that can be used to justify the interpretability to design ML models. We state that designing ML models with 2 levels of clinical evidence: 1) commonly used clinical evidence, such as clinical guidelines, and 2) iteratively developed clinical evidence with end users are more likely to design models that are indeed interpretable to end users. In this dissertation, we first address how to design interpretable ML in medical image analysis that affords interpretability with these two different levels of clinical evidence. We further highly recommend formative user research as the first step of the interpretable model design to understand user needs and domain requirements. We also indicate the importance of empirical user evaluation to support transparent ML design choices to facilitate the adoption of human-centered design principles. All these aspects in this dissertation increase the likelihood that the algorithms afford interpretability and enable stakeholders to capitalize on the benefits of interpretable ML. In detail, we first propose neural symbolic reasoning to implement public clinical evidence into the designed models for various routinely performed clinical tasks. We utilize the routinely applied clinical taxonomy for abnormality classification in chest x-rays. We also establish a spleen injury grading system by strictly following the clinical guidelines for symbolic reasoning with the detected and segmented salient clinical features. Then, we propose the entire interpretable pipeline for UM prognostication with cytopathology images. We first perform formative user research and found that pathologists believe cell composition is informative for UM prognostication. Thus, we build a model to analyze cell composition directly. Finally, we conduct a comprehensive user study to assess the human factors of human-machine teaming with the designed model, e.g., whether the proposed model indeed affords interpretability to pathologists. The human-centered design process is proven to be truly interpretable to pathologists for UM prognostication. All in all, this dissertation introduces a comprehensive human-centered design for interpretable ML solutions in medical image analysis that affords interpretability to end users

Physical Regimes of Electrostatic Wave-Wave nonlinear interactions generated by an Electron Beam Propagation in Background Plasma

Electron-beam plasma interaction has long been a topic of great interest. The validities of Quasi-Linear (QL) theory and Weak Turbulence (WT) theory are limited by the requirement of sufficiently dense mode spectrum and small wave amplitude. In this paper, by performing a large number of high resolution two-dimensional (2D) particle-in-cell (PIC) simulations and using analytical theories, we extensively studied the collective processes of a mono-energetic electron beam emitted from a thermionic cathode propagating through a cold plasma. We show that initial two-stream instability between the beam and background cold electrons is saturated by wave trapping rather than QL theory. Further evolution occurs due to strong wave-wave nonlinear processes. We show that the beam-plasma interaction can be classified into four different physical regimes in the parameter space for the plasma and beam parameters. The differences between the different regimes are analyzed in detail. For the first time, we identified a new regime in strong Langmuir turbulence featured by what we call Electron Modulational Instability (EMI) that creates a local Langmuir wave packet faster than ion frequency ({\omega}_pi) and ions initially do not respond to EMI in the initial growing stage. On a longer timescale, the action of the ponderomotive force produces very strong ion density perturbations so that the beam-plasma wave interaction stops being resonant. Consequently, in this EMI regime beam-plasma interaction is a periodic burst (intermittent) process. The beams are strongly scattered, and the Langmuir wave spectrum is significantly broadened, which gives rise to the strong heating of bulk electrons. Some interesting phenomena in the strong turbulent regime are also discussedComment: 65 pages, 19 figure

Electron Modulation Instability in the Strong Turbulent Regime for Electron Beam Propagation in Background Plasma

We study collective processes for an electron beam propagating through a background plasma using simulations and analytical theory. A new regime where the instability of a Langmuir wave packet can grow locally much faster than ion frequency ({\omega}_pi) is clearly identified. The key feature of this new regime is an Electron Modulational Instability that rapidly creates a local Langmuir wave packet, which in its turn produces local charge separation and strong ion density perturbations because of the action of the ponderomotive force, such that the beam-plasma wave interaction stops being resonant. Three evolution stages of the process and observed periodic burst features are discussed. Different physical regimes in the plasma and beam parameter space are clearly demonstrated for the first time.Comment: 19 pages, 3 figure

When Urban Region Profiling Meets Large Language Models

Urban region profiling from web-sourced data is of utmost importance for urban planning and sustainable development. We are witnessing a rising trend of LLMs for various fields, especially dealing with multi-modal data research such as vision-language learning, where the text modality serves as a supplement information for the image. Since textual modality has never been introduced into modality combinations in urban region profiling, we aim to answer two fundamental questions in this paper: i) Can textual modality enhance urban region profiling? ii) and if so, in what ways and with regard to which aspects? To answer the questions, we leverage the power of Large Language Models (LLMs) and introduce the first-ever LLM-enhanced framework that integrates the knowledge of textual modality into urban imagery profiling, named LLM-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining (UrbanCLIP). Specifically, it first generates a detailed textual description for each satellite image by an open-source Image-to-Text LLM. Then, the model is trained on the image-text pairs, seamlessly unifying natural language supervision for urban visual representation learning, jointly with contrastive loss and language modeling loss. Results on predicting three urban indicators in four major Chinese metropolises demonstrate its superior performance, with an average improvement of 6.1% on R^2 compared to the state-of-the-art methods. Our code and the image-language dataset will be released upon paper notification

Oriented Graphene Nanoribbons Embedded in Hexagonal Boron Nitride Trenches

Graphene nanoribbons (GNRs) are ultra-narrow strips of graphene that have the potential to be used in high-performance graphene-based semiconductor electronics. However, controlled growth of GNRs on dielectric substrates remains a challenge. Here, we report the successful growth of GNRs directly on hexagonal boron nitride substrates with smooth edges and controllable widths using chemical vapour deposition. The approach is based on a type of template growth that allows for the in-plane epitaxy of mono-layered GNRs in nano-trenches on hexagonal boron nitride with edges following a zigzag direction. The embedded GNR channels show excellent electronic properties, even at room temperature. Such in-plane hetero-integration of GNRs, which is compatible with integrated circuit processing, creates a gapped channel with a width of a few benzene rings, enabling the development of digital integrated circuitry based on GNRs.Comment: 32 pages, 4 figures, Supplementary informatio