Explicit Kodaira-Spencer map over Hilbert modular varieties

The goal of this paper is to explicitly compute the Kodaira-Spencer maps over Hilbert-Siegel modular varieties and twisted Hilbert modular varieties and their effects on the metrics of the Hodge bundle. Our result is a generalization of the result in \cite{arXiv:2205.11334}.Comment: 27 page

“Ask Everyone?” Understanding How Social Q&A Feedback Quality Influences Consumers\u27 Purchase Intentions

Social question & answer (Q&A) feedback is a novel form of electronic word-of-mouth that allows customers to ask questions and share opinions with peer customers. Based on the stimulus-organism-response framework, this paper proposes a model to describe how social Q&A feedback quality affects consumers\u27 willingness to purchase by influencing their perceived risk, perceived usefulness, and use intention. We focused on the social Q&A feature named Ask Everyone in Taobao and collected 153 valid responses through an online survey. Canonical correlation analysis was used to identify the association between feedback characteristics and feedback quality. Then, PLS-SEM was conducted to test the proposed research model. Results show that feedback quality negatively associated with perceived risk, but had a positive impact on perceived usefulness, use intention, and purchase intention. Findings of this research has both theoretical and practical implications for facilitating social Q&A design in e-commerce platforms

Trial-and-Error Learning for MEMS Structural Design Enabled by Deep Reinforcement Learning

We present a systematic MEMS structural design approach via a "trial-and-error"learning process by using the deep reinforcement learning framework. This scheme incorporates the feedback from each "trial"to obtain sophisticated strategies for MEMS design optimizations. Disk-shaped MEMS resonators are selected as case studies and three remarkable advancements have been realized: 1) accurate overall performance predictions (97.9%) via supervised learning models; 2) efficient MEMS structural optimizations to guarantee targeted structural properties with an excellent generation accuracy of 97.7%; and 3) superior design explorations to achieve one order of magnitude performance enhancement than the training dataset. As such, the proposed scheme could facilitate a wide spectrum of MEMS applications with this data-driven inverse design methodology

Sampling-accelerated First-principles Prediction of Phonon Scattering Rates for Converged Thermal Conductivity and Radiative Properties

First-principles prediction of thermal conductivity and radiative properties is crucial. However, computing phonon scattering, especially for four-phonon scattering, could be prohibitively expensive, and the thermal conductivity even for silicon was still under-predicted and not converged in the literature. Here we propose a method to estimate scattering rates from a small sample of scattering processes using maximum likelihood estimation. The computational cost of estimating scattering rates and associated thermal conductivity and radiative properties is dramatically reduced by over 99%. This allows us to use an unprecedented q-mesh of 32*32*32 for silicon and achieve a converged thermal conductivity value that agrees much better with experiments. The accuracy and efficiency of our approach make it ideal for the high-throughput screening of materials for thermal and optical applications

From Hospitality to Hostility: The Impact of Polarity Difference in Managerial Responses on Subsequent Guest Satisfaction

Previous research has identified the effects of location bias in online product marketing and online communication. This study delves into these effects by analyzing sentiment polarity differences in managerial responses. Specifically, it investigates how hosts’ location bias towards local versus non-local guests affects guest satisfaction after booking within the context of P2P accommodation platforms. We collect actual host response data from Airbnb and employ a panel regression model with fixed effects to address our research questions. The empirical findings reveal that if hosts have more positive attitudes towards local guests compared to non-local guests, such location discrimination results in a reduction in ratings posted by subsequent guests. Furthermore, the depth of hosts’ descriptions and the number of listings moderate the impact of the polarity variance in managerial responses based on hosts’ location bias on guest satisfaction. These results carry important managerial implications, suggesting that hosts should actively address and minimize their location bias to enhance their reputation and marketing efforts

Correlation Enhanced Distribution Adaptation for Prediction of Fall Risk

With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults. Instead, there are multiple related but non-identical domain data with labels from the existing cohort or different institutions. Integrating different data sources with labeled and unlabeled samples to predict a patient\u27s condition poses a significant challenge. Traditional machine learning models assume that data for new patients follow a similar distribution. If the data does not satisfy this assumption, the trained models do not achieve the expected accuracy, leading to potential misdiagnosing risks. To address this issue, we utilize domain adaptation (DA) techniques, which employ labeled data from one or more related source domains. These DA techniques promise to tackle discrepancies in multiple data sources and achieve a robust diagnosis for new patients. In our research, we have developed an unsupervised DA model to align two domains by creating a domain-invariant feature representation. Subsequently, we have built a robust fall-risk prediction model based on these new feature representations. The results from simulation studies and real-world applications demonstrate that our proposed approach outperforms existing models

The impact of different sentiment in investment decisions: evidence from China’s stock markets IPOs

In this study, we used data on China’s initial public offerings (IPOs), market volatility and macro environment before and after two stock crashes during 2006–2016 to investigate how different investor sentiment affects IPO first-day flipping. The empirical results show that the expected returns of allocated investors are affected by sentiment, with allocated investors having higher psychological expectations of future returns during an optimistic bull market and their optimism discouraging first-day flipping, while higher risk-free interest rate levels and rising broad market indices also discourage first-day flipping and tend to sell in the future. The pessimistic bear market during which allocated investors have lower psychological expectations of future returns, their pessimism will promote first-day flipping, and the increase in the risk-free rate level will also promote first-day flipping, which is the opposite of the optimistic bull market, indicating that their risk aversion has increased and they tend to sell on the same day. We also found an anomaly that the greater the decline in the broad market index during a pessimistic bear market, the more inclined the allocated investors are to sell in the future when the broad market index rises in an attempt to gain higher returns. These findings help explain and understand the impact of market and macro index fluctuations on investor behavior under different investor sentiments

Joint Design of Access and Backhaul in Densely Deployed MmWave Small Cells

With the rapid growth of mobile data traffic, the shortage of radio spectrum resource has become increasingly prominent. Millimeter wave (mmWave) small cells can be densely deployed in macro cells to improve network capacity and spectrum utilization. Such a network architecture is referred to as mmWave heterogeneous cellular networks (HetNets). Compared with the traditional wired backhaul, The integrated access and backhaul (IAB) architecture with wireless backhaul is more flexible and cost-effective for mmWave HetNets. However, the imbalance of throughput between the access and backhaul links will constrain the total system throughput. Consequently, it is necessary to jointly design of radio access and backhaul link. In this paper, we study the joint optimization of user association and backhaul resource allocation in mmWave HetNets, where different mmWave bands are adopted by the access and backhaul links. Considering the non-convex and combinatorial characteristics of the optimization problem and the dynamic nature of the mmWave link, we propose a multi-agent deep reinforcement learning (MADRL) based scheme to maximize the long-term total link throughput of the network. The simulation results show that the scheme can not only adjust user association and backhaul resource allocation strategy according to the dynamics in the access link state, but also effectively improve the link throughput under different system configurations.Comment: 15 page