Immediate Attention Please! What Matters To Customers Using A Social Network To Complain: Empirical Evidence From The Airline Industry

The popularity of social networks and smart mobile devices makes it convenient for customers to complain about unsatisfied service experiences by posting messages online, which needs immediate attention from service providers. Since the airline industry is one of the industries with lowest customer satisfaction and some airlines have been trying to use social networks for customer service, we collected tweets from five major airlines\u27 Twitter accounts to uncover the critical failure points complained by customers and to explore the missing links that cause the mismatches between airlines\u27 strategic intent and customers\u27 needs and expectations. Our findings revealed that customers\u27 complains mainly center on unsatisfied primary needs in five broad categories: explicit services, supporting facilities, implicit services, facilitating goods, and facilitating information. The top three most complained broad categories are explicit services, supporting facilities, and facilitating information. In addition, we identified that customers\u27 dissatisfaction is mainly due to the mismatch in three operation areas in the airline industry: (a) airlines\u27 emphasis on cost financial performance doesn\u27t match customers\u27 expectation on best values, (b) airlines\u27 focus on process-centered approach doesn\u27t match customers\u27 preferences on customer-centered approach, and (c) lack of information and unsynchronized communication doesn\u27t meet customers\u27 real time information and communication needs. Our findings can provide valuable insights for airline executives to improve their service operations

Directional diffusion models for graph representation learning

In recent years, diffusion models have achieved remarkable success in various domains of artificial intelligence, such as image synthesis, super-resolution, and 3D molecule generation. However, the application of diffusion models in graph learning has received relatively little attention. In this paper, we address this gap by investigating the use of diffusion models for unsupervised graph representation learning. We begin by identifying the anisotropic structures of graphs and a crucial limitation of the vanilla forward diffusion process in learning anisotropic structures. This process relies on continuously adding an isotropic Gaussian noise to the data, which may convert the anisotropic signals to noise too quickly. This rapid conversion hampers the training of denoising neural networks and impedes the acquisition of semantically meaningful representations in the reverse process. To address this challenge, we propose a new class of models called {\it directional diffusion models}. These models incorporate data-dependent, anisotropic, and directional noises in the forward diffusion process. To assess the efficacy of our proposed models, we conduct extensive experiments on 12 publicly available datasets, focusing on two distinct graph representation learning tasks. The experimental results demonstrate the superiority of our models over state-of-the-art baselines, indicating their effectiveness in capturing meaningful graph representations. Our studies not only provide valuable insights into the forward process of diffusion models but also highlight the wide-ranging potential of these models for various graph-related tasks

基于Kriging模型的地面气温空间插值研究

This paper aims to describe spatial interpolation methods to estimate surface air temperatures (SATs). The SAT at a particular location where SAT observations are not available is estimated through a Kriging interpolation between SAT measurements from 192 meteorological sites at which daily SAT observations have been obtained. A temporal de-trending method based on a Fourier series is used to model and remove the annual trend in original data in order to ensure the stationarity of de-trended data from which kriging parameters are determined. Furthermore, a spatial or surface de-trending in terms of geographic coordinates including altitude, latitude and longitude of each location is adopted in a Kriging model. Besides a Kriging model, an inverse distance weighting (IDW) interpolation method is tested as a comparison. The accuracies of both spatial interpolation approaches are assessed by calculating and comparing their mean absolute error (MAE) and root mean square error (RMSE) when taking each meteorological site as the target location in a cross-validation procedure. The results show that the Kriging model performs better than the IDW method at 174 sites. In addition, the temporal and spatial de-trending methods make the main contribution to the accurate capture of spatial correlations of SATs in the study area in a Kriging process

A redetermination of bis­(5,5′-diethyl­barbiturato)bis­(imidazole)cobalt(II)

The title complex, [Co(C8H12N2O3)2(C3H4N2)2], whose structure was first determined by Wang & Craven [(1971). J. Chem. Soc. D, pp. 290–291], has been redetermined with improved precision. A crystallographic twofold rotation axis passes through the Co atom, which is tetrahedrally coordinated by two N atoms from two barbital ligands and two N atoms from two imidazole ligands. The mol­ecules are self-assembled via inter­molecular N—H⋯O hydrogen-bonding inter­actions into a supra­molecular network

An unprecedented 2D covalent organic framework with an htb net topology.

A 2D imine-linked COF with a hitherto unreported htb type topology was synthesized from a linear diamine linker and a judiciously designed tetra-aldehyde building block. This work opens the door to the development of COFs with unprecedented topologies and may broaden the scope of COF functional materials by pore size and pore surface engineering

'Beibinghong': A new grape cultivar for brewing ice red wine

Research Not

Erratum

'Beibinghong': A new grape cultivar for brewing ice red wineVitis 53 (2), 85-89 (2014

Merging Experts into One: Improving Computational Efficiency of Mixture of Experts

Scaling the size of language models usually leads to remarkable advancements in NLP tasks. But it often comes with a price of growing computational cost. Although a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters (e.g., one expert) for each input, its computation escalates significantly if increasing the number of activated experts, limiting its practical utility. Can we retain the advantages of adding more experts without substantially increasing the computational costs? In this paper, we first demonstrate the superiority of selecting multiple experts and then propose a computation-efficient approach called \textbf{\texttt{Merging Experts into One}} (MEO), which reduces the computation cost to that of a single expert. Extensive experiments show that MEO significantly improves computational efficiency, e.g., FLOPS drops from 72.0G of vanilla MoE to 28.6G (MEO). Moreover, we propose a token-level attention block that further enhances the efficiency and performance of token-level MEO, e.g., 83.3\% (MEO) vs. 82.6\% (vanilla MoE) average score on the GLUE benchmark. Our code will be released upon acceptance. Code will be released at: \url{https://github.com/Shwai-He/MEO}.Comment: EMNLP 2023 Main Conference (Oral

Aqua­bis(1H-imidazole-κN 3)bis­(4-methyl­benzoato)-κO;κO,O′-nickel(II)

In the mononuclear title compound, [Ni(C8H7O2)2(C3H4N2)2(H2O)], the NiII atom is coordinated by three carboxylate O atoms (from a bidentate 4-methyl­benzoate ligand and a monodentate 4-methyl­benzoate ligand), two N atoms (from two imidazole ligands) and a water mol­ecule in an octa­hedral geometry. Inter­molecular O—H⋯O hydrogen-bonding inter­actions lead to infinite chains, which are further self-assembled into a supra­molecular network through inter­molecular N—H⋯O hydrogen-bonding inter­actions and π–π stacking [centroid–centroid distance = 3.717 (2) Å]