Cross-cultural Comparison of Consumption Perceptions of Luxury Brands amongst American and Chinese Young Consumer

The global luxury markets have been fast developed in the past twenty-five years, in which the market in the United States and China are the two biggest ones. This study mainly examined influencing factors that affect Chinese and American young consumers’ perceptions towards luxury brands and further explore how cultural differences influence their perceptions. Data was collected both in China and America, and overall 16 interviewees were involved. By using a qualitative method and interpretive analyses, the results highlighted that Chinese young people lay much emphasis on social-oriented perceptions, while Americans tend to show more personal-oriented perceptions than Chinese, especially showing high inner-self to others. In addition, three cultural dimensions - individualism vs. collectivism, power distance, and long-term vs. short-term orientation - have an impact on their perceptions of luxury brands. Managerial implications are further discussed in the end

Research and application of management accounting in project performance evaluation based on PCA model

With the development of society and the continuous improvement of market economy, the economic environment of enterprises has changed greatly, the competition between enterprises is becoming more and more fierce, and the survival of enterprises has also been greatly challenged. In this context, the performance evaluation of enterprises has attracted the attention of the whole society, especially the scholars and experts in the business community, and the theory and method of enterprise performance analysis have also been further deepened and developed. With the help of the principal component analysis (PCA) model and management accounting tools, this paper aims to correctly evaluate the performance of enterprises, find the factors that affect the performance of enterprises, and then put forward some suggestions, hoping to provide reference for the performance evaluation of enterprises, improve the core competitiveness of enterprises, and help enterprises develop and grow better under the new situation

Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems

Type-based multiple access (TBMA) is a semantics-aware multiple access protocol for remote inference. In TBMA, codewords are reused across transmitting sensors, with each codeword being assigned to a different observation value. Existing TBMA protocols are based on fixed shared codebooks and on conventional maximum-likelihood or Bayesian decoders, which require knowledge of the distributions of observations and channels. In this letter, we propose a novel design principle for TBMA based on the information bottleneck (IB). In the proposed IB-TBMA protocol, the shared codebook is jointly optimized with a decoder based on artificial neural networks (ANNs), so as to adapt to source, observations, and channel statistics based on data only. We also introduce the Compressed IB-TBMA (CIB-TBMA) protocol, which improves IB-TBMA by enabling a reduction in the number of codewords via an IB-inspired clustering phase. Numerical results demonstrate the importance of a joint design of codebook and neural decoder, and validate the benefits of codebook compression.Comment: 5 pages, 3 figures, accepted by IEEE Signal Processing Letters (SPL

Federated Inference With Reliable Uncertainty Quantification Over Wireless Channels via Conformal Prediction

In this paper, we consider a wireless federated inference scenario in which devices and a server share a pre-trained machine learning model. The devices communicate statistical information about their local data to the server over a common wireless channel, aiming to enhance the quality of the inference decision at the server. Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision. With federated CP, devices communicate to the server information about the loss accrued by the shared pre-trained model on the local data, and the server leverages this information to calibrate a decision interval , or set , so that it is guaranteed to contain the correct answer with a pre-defined target reliability level. Previous work assumed noise-free communication, whereby devices can communicate a single real number to the server. In this paper, we study for the first time federated CP in a wireless setting. We introduce a novel protocol, termed wireless federated conformal prediction (WFCP), which builds on type-based multiple access (TBMA) and on a novel quantile correction strategy. WFCP is proved to provide formal reliability guarantees in terms of coverage of the predicted set produced by the server. Using numerical results, we demonstrate the significant advantages of WFCP against digital implementations of existing federated CP schemes, especially in regimes with limited communication resources and/or large number of devices

Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction

Consider a setting in which devices and a server share a pre-trained model. The server wishes to make an inference on a new input given the model. Devices have access to data, previously not used for training, and can communicate to the server over a common wireless channel. If the devices have no access to the new input, can communication from devices to the server enhance the quality of the inference decision at the server? Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision. With federated CP, devices communicate to the server information about the loss accrued by the shared pre-trained model on the local data, and the server leverages this information to calibrate a decision interval, or set, so that it is guaranteed to contain the correct answer with a pre-defined target reliability level. Previous work assumed noise-free communication, whereby devices can communicate a single real number to the server. In this paper, we study for the first time federated CP in a wireless setting. We introduce a novel protocol, termed wireless federated conformal prediction (WFCP), which builds on type-based multiple access (TBMA) and on a novel quantile correction strategy. WFCP is proved to provide formal reliability guarantees in terms of coverage of the predicted set produced by the server. Using numerical results, we demonstrate the significant advantages of WFCP against digital implementations of existing federated CP schemes, especially in regimes with limited communication resources and/or large number of devices.Comment: 33 pages, 6 figure

Cross-cultural Comparison of Consumption Perceptions of Luxury Brands amongst American and Chinese Young Consumer

The global luxury markets have been fast developed in the past twenty-five years, in which the market in the United States and China are the two biggest ones. This study mainly examined influencing factors that affect Chinese and American young consumers’ perceptions towards luxury brands and further explore how cultural differences influence their perceptions. Data was collected both in China and America, and overall 16 interviewees were involved. By using a qualitative method and interpretive analyses, the results highlighted that Chinese young people lay much emphasis on social-oriented perceptions, while Americans tend to show more personal-oriented perceptions than Chinese, especially showing high inner-self to others. In addition, three cultural dimensions - individualism vs. collectivism, power distance, and long-term vs. short-term orientation - have an impact on their perceptions of luxury brands. Managerial implications are further discussed in the end

Releasing Differentially Private Trajectories with Optimized Data Utility

The ubiquity of GPS-enabled devices has resulted in an abundance of data about individual trajectories. Releasing trajectories enables a range of data analysis tasks, such as urban planning, but it also poses a risk in compromising individual location privacy. To tackle this issue, a number of location privacy protection algorithms are proposed. However, existing works are primarily concerned with maintaining the trajectory data geographic utility and neglect the semantic utility. Thus, many data analysis tasks relying on utility, e.g., semantic annotation, suffer from poor performance. Furthermore, the released trajectories are vulnerable to location inference attacks and de-anonymization attacks due to insufficient privacy guarantee. In this paper, to design a location privacy protection algorithm for releasing an offline trajectory dataset to potentially untrusted analyzers, we propose a utility-optimized and differentially private trajectory synthesizer (UDPT) with two novel features. First, UDPT simultaneously preserves both geographical utility and semantic utility by solving a data utility optimization problem with a genetic algorithm. Second, UDPT provides a formal and provable guarantee against privacy attacks by synthesizing obfuscated trajectories in a differentially private manner. Extensive experimental evaluations on real-world datasets demonstrate UDPT’s outperformance against state-of-the-art works in terms of data utility and privacy

Leveling the Mountain Range of Excited-State Benchmarking through Multistate Density Functional Theory

The performance of multistate density functional theory (MSDFT) with nonorthogonal state interaction (NOSI) is assessed for 100 vertical excitation energies against the theoretical best estimates (TBE) extracted to the full configuration interaction accuracy on the database developed by Loos, P.F., at al. in 2018 (Loos2018). Two optimization techniques, namely block-localized excitation (BLE) and target state optimization (TSO), are examined along with two ways of estimating the transition density functional (TDF) for the correlation energy of the Hamiltonian matrix density functional. The results from the two optimization methods are similar. It was found that MSDFT-NOSI using the spin-multiplet degeneracy (SMD) constraint for the TDF of spin-coupling interaction, along with the M06-2X functional, yields a root-mean-square error (RMSE) of 0.22 eV, better than CIS(D_∞), CC2, and ADC(3) all of which have an RMSE of 0.28 eV, but somewhat less than STEOM-CCSD (RMSE of 0.14 eV) and CCSD (RMSE of 0.11 eV) wave function methods. Interestingly, MSDFT-NOSI performs noticeably better than TDDFT at an RMSE of 0.43 eV using the same functional and basis set on the Loos2018 database. In comparison with the ground state and the lowest triplet energies from KS-DFT calculations, it was found that the multistate DFT approach has little double counting of correlation. Importantly, there is no noticeable difference in the performance of MSDFT-NOSI on valence, Rydberg, singlet, triplet, and double-excitation states. Although the use of another hybrid functional PBE0 leads to a greater RMSE of 0.36 eV, the deviation is systematic with a linear regression slope of 0.994 against the results with M06-2X. The present benchmark reveals that density functional approximations developed for KS-DFT for the ground state with a non-interacting reference may be adopted in MSDFT calculations in which state interaction is key