Reviewerly/exHarmony huggingface dataset:
QuakeFlow: a scalable machine-learning-based earthquake monitoring workflow with cloud computing

Earthquake monitoring workflows are designed to detect earthquake signals and to determine source characteristics from continuous waveform data. Recent developments in deep learning seismology have been used to improve tasks within earthquake monitoring workflows that allow the fast and accurate detection of up to orders of magnitude more small events than are present in conventional catalogs. To facilitate the application of machine-learning algorithms to large-volume seismic records, we developed a cloud-based earthquake monitoring workflow, QuakeFlow, that applies multiple processing steps to generate earthquake catalogs from raw seismic data. QuakeFlow uses a deep learning model, PhaseNet, for picking P/S phases and a machine learning model, GaMMA, for phase association with approximate earthquake location and magnitude. Each component in QuakeFlow is containerized, allowing straightforward updates to the pipeline with new deep learning/machine learning models, as well as the ability to add new components, such as earthquake relocation algorithms. We built QuakeFlow in Kubernetes to make it auto-scale for large datasets and to make it easy to deploy on cloud platforms, which enables large-scale parallel processing. We used QuakeFlow to process three years of continuous archived data from Puerto Rico, and found more than a factor of ten more events that occurred on much the same structures as previously known seismicity. We applied Quakeflow to monitoring frequent earthquakes in Hawaii and found over an order of magnitude more events than are in the standard catalog, including many events that illuminate the deep structure of the magmatic system. We also added Kafka and Spark streaming to deliver real-time earthquake monitoring results. QuakeFlow is an effective and efficient approach both for improving realtime earthquake monitoring and for mining archived seismic data sets.
Multi-label emotion classification in texts using transfer learning

Social media is a widespread platform that provides a massive amount of user-generated content that can be mined to reveal the emotions of social media users. This has many potential benefits, such as getting a sense of people’s pulse on various events or news. Emotion classification from social media posts is challenging, especially when it comes to detecting multiple emotions from a short piece of text, as in multi-label classification problem. Most of the previous work on emotion detection has focused on deep neural networks such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) Networks. However, none of them has utilized multiple attention mechanisms and Recurrent Neural Networks (i.e., specialized attention networks for each emotion) nor utilized the recently introduced Transformer Networks such as XLNet, DistilBERT, and RoBERTa for the task of classifying emotions with multiple labels. The proposed multiple attention mechanism reveals the contribution of each word on each emotion, which has not been investigated before. In this study, we investigate both the use of LSTMs and the fine-tuning of Transformer Networks through Transfer Learning along with a single-attention network and a multiple-attention network for multi-label emotion classification. The experimental results show that our novel transfer learning models using pre-trained transformers with and without multiple attention mechanisms were able to outperform the current state-of-the-art accuracy (58.8% - Baziotis et al., 2018) in the SemEval-2018 Task-1C dataset. Our best-performing RoBERTa-MA (RoBERTa-Multi-attention) model outperformed the state-of-the-art and achieved 62.4% accuracy (3.6% gain over the state-of-the-art) on the challenging SemEval-2018 E-c: Detecting Emotions (multi-label classification) dataset for English. Moreover, the XLNet-MA (XLNet-Multi-attention) model outperformed the other proposed models by achieving 45.6% accuracy on the Ren-CECps dataset for Chinese.
Reliably assessing the electronic structure of cytochrome P450 on today’s classical computers and tomorrow’s quantum computers

An accurate assessment of how quantum computers can be used for chemical simulation, especially their potential computational advantages, provides important context on how to deploy these future devices. To perform this assessment reliably, quantum resource estimates must be coupled with classical computations attempting to answer relevant chemical questions and to define the classical algorithms simulation frontier. Herein, we explore the quantum computation and classical computation resources required to assess the electronic structure of cytochrome P450 enzymes (CYPs) and thus define a classical-quantum advantage boundary. This is accomplished by analyzing the convergence of density matrix renormalization group plus n-electron valence state perturbation theory (DMRG+NEVPT2) and coupled-cluster singles doubles with noniterative triples [CCSD(T)] calculations for spin gaps in models of the CYP catalytic cycle that indicate multireference character. The quantum resources required to perform phase estimation using qubitized quantum walks are calculated for the same systems. Compilation into the surface code provides runtime estimates to compare directly to DMRG runtimes and to evaluate potential quantum advantage. Both classical and quantum resource estimates suggest that simulation of CYP models at scales large enough to balance dynamic and multiconfigurational electron correlation has the potential to be a quantum advantage problem and emphasizes the important interplay between classical computations and quantum algorithms development for chemical simulation.
Make chatbots more adaptive: Dual pathways linking human-like cues and tailored response to trust in interactions with chatbots

As one of the most popular AI applications, chatbots are creating new ways and value for businesses to interact with their customers, and their adoption and continued use will depend on users’ trust. However, due to the non-transparent of AI-related technology and the ambiguity of application boundaries, it is difficult to determine which aspects enhance the adaptation of chatbots and how they interactively affect human trust. Based on the theory of task-technology fit, we developed a research model to investigate how two conversational cues of chatbots, human-like cues and tailored responses, influence human trust toward chatbots and to explore appropriate boundary conditions (individual characteristics and task characteristics) in interacting with chatbots. One survey and two experiments were performed to test the research model, and the results indicated that (1) perceived task solving competence and social presence mediate the pathway from conversational cues to human trust, which was validated in the context of e-commerce and education; (2) the extent of users’ ambiguity tolerance moderates the effects of two conversational cues on social presence; and (3) when performing high-creative tasks, the human-like chatbot induces higher perceived task solving competence. Our findings not only contribute to the AI trust-related literature but also provide practical implications for the development of chatbots and their assignment to individuals and tasks.
Source-free domain adaptation for image segmentation

Domain adaptation (DA) has drawn high interest for its capacity to adapt a model trained on labeled source data to perform well on unlabeled or weakly labeled target data from a different domain. Most common DA techniques require concurrent access to the input images of both the source and target domains. However, in practice, privacy concerns often impede the availability of source images in the adaptation phase. This is a very frequent DA scenario in medical imaging, where, for instance, the source and target images could come from different clinical sites. We introduce a source-free domain adaptation for image segmentation. Our formulation is based on minimizing a label-free entropy loss defined over target-domain data, which we further guide with weak labels of the target samples and a domain-invariant prior on the segmentation regions. Many priors can be derived from anatomical information. Here, a class-ratio prior is estimated from anatomical knowledge and integrated in the form of a Kullback-Leibler (KL) divergence in our overall loss function. Furthermore, we motivate our overall loss with an interesting link to maximizing the mutual information between the target images and their label predictions. We show the effectiveness of our prior-aware entropy minimization in a variety of domain-adaptation scenarios, with different modalities and applications, including spine, prostate and cardiac segmentation. Our method yields comparable results to several state-of-the-art adaptation techniques, despite having access to much less information, as the source images are entirely absent in our adaptation phase. Our straightforward adaptation strategy uses only one network, contrary to popular adversarial techniques, which are not applicable to a source-free DA setting. Our framework can be readily used in a breadth of segmentation problems, and our code is publicly available: https://github.com/mathilde-b/SFDA.
Artificial Intelligence, Blockchain Technology, and Risk-Taking Behavior in the 4.0IR Metaverse Era: Evidence from Bangladesh-Based SMEs

This study investigates the variables affecting the adoption of blockchain technology (BT) among small and medium-sized enterprises (SMEs) with the application of artificial intelligence (AI) via the mediating lens of risk-taking behavior. As an initial sample, 150 owners/top managers from 150 SMEs (one informant from each) in Dhaka, Bangladesh, were chosen. A stratified random sample was employed for this cross-sectional study. Applying structural equation modeling, the combined influence of internal and external variables influencing the intention to adopt BT is explored. Results show that: (1) knowledge of artificial intelligence has a positive and significant effect on the adoption of blockchain technology; (2) the relevant advantage of artificial intelligence has a positive and significant effect on the adoption of blockchain technology; (3) perceived ease of use of artificial intelligence has a positive and significant effect on the adoption of blockchain technology; (4) risk-taking behavior mediates the relationship between knowledge of artificial intelligence and adoption of blockchain technology; (5) risk-taking behavior does not mediate the relationship between relevant advantage and perceived ease of use of artificial intelligence with the adoption of blockchain technology. The current study is one of the few empirical investigations relating to SMEs using artificial intelligence and blockchain technologies for business operations. The study&rsquo;s limitations are the small sample size and use of a single informant. However, the findings on the adoption of blockchain technology have applications for boosting the competitiveness of SMEs. This study&rsquo;s originality stems from two factors: the novelty of blockchain technology and its potential to upend SMEs&rsquo; conventional mode of operation. It highlights the need to consider the key variables affecting SMEs&rsquo; adoption of blockchain technology with artificial intelligence.
Explaining deep neural networks: A survey on the global interpretation methods

A substantial amount of research has been carried out in Explainable Artificial Intelligence (XAI) models, especially in those which explain the deep architectures of neural networks. A number of XAI approaches have been proposed to achieve trust in Artificial Intelligence (AI) models as well as provide explainability of specific decisions made within these models. Among these approaches, global interpretation methods have emerged as the prominent methods of explainability because they have the strength to explain every feature and the structure of the model. This survey attempts to provide a comprehensive review of global interpretation methods that completely explain the behaviour of the AI models. We present a taxonomy of the available global interpretations models and systematically highlight the critical features and algorithms that differentiate them from local as well as hybrid models of explainability. Through examples and case studies from the literature, we evaluate the strengths and weaknesses of the global interpretation models and assess challenges when these methods are put into practice. We conclude the paper by providing the future directions of research in how the existing challenges in global interpretation methods could be addressed and what values and opportunities could be realized by the resolution of these challenges.
Opportunities and Challenges for Large-Scale Phase-Change Material Integrated Electro-Photonics

Programmable photonics have the potential to completely transform a range of emerging applications, including optical computing, optical signal processing, light detecting and ranging, and quantum applications. However, implementing energy-efficient and large-scale systems remains elusive because commonly used programmable photonic approaches are volatile and energy-hungry. Recent results on nonvolatile phase-change material (PCM) integrated photonics present a promising opportunity to create truly programmable photonics. The ability to drastically change the refractive index of the PCMs in a nonvolatile fashion allows creating programmable units with zero-static energy. By taking advantage of the electrical control, nonvolatile reconfiguration, and zero crosstalk between each unit, PCMs can enable extra large-scale integrated (ELSI) photonics. In this Perspective, we briefly review the recent progress in PCM photonics and discuss the challenges and limitations of this emerging technology. We argue that energy efficiency is a more critical parameter than the operating speed for programmable photonics, making PCMs an ideal candidate. This has the potential for a disruptive paradigm shift in the reconfigurable photonics research philosophy, as slow but energy-efficient and large index modulation can provide a better solution for ELSI photonics than fast but power-hungry, small index tuning methods. We also highlight the exciting opportunities to leverage wide bandgap PCMs for visible-wavelength applications, such as quantum photonics and optogenetics, and for rewritable photonic integrated circuits (PICs) using nanosecond pulsed lasers. The latter can dramatically reduce the fabrication cost of PICs and democratize the PIC manufacturing process for rapid prototyping.
Robust thermal infrared tracking via an adaptively multi-feature fusion model

When dealing with complex thermal infrared (TIR) tracking scenarios, the single category feature is not sufficient to portray the appearance of the target, which drastically affects the accuracy of the TIR target tracking method. In order to address these problems, we propose an adaptively multi-feature fusion model (AMFT) for the TIR tracking task. Specifically, our AMFT tracking method adaptively integrates hand-crafted features and deep convolutional neural network (CNN) features. In order to accurately locate the target position, it takes advantage of the complementarity between different features. Additionally, the model is updated using a simple but effective model update strategy to adapt to changes in the target during tracking. In addition, a simple but effective model update strategy is adopted to adapt the model to the changes of the target during the tracking process. We have shown through ablation studies that the adaptively multi-feature fusion model in our AMFT tracking method is very effective. Our AMFT tracker performs favorably on PTB-TIR and LSOTB-TIR benchmarks compared with state-of-the-art trackers.
Social Presence and Imagery Processing as Predictors of Chatbot Continuance Intention in Human-AI-Interaction

What factors drive consumers to use artificial intelligence (AI)-powered chatbots? The current study examined the associations among AI-powered chatbots’ anthropomorphism (human-likeness, animacy, and intelligence), social presence, imagery processing, psychological ownership, and continuance intention in the context of Human-AI-Interaction. Results from a path analysis using LISREL 8.54 show that consumers’ perceived human-likeness of AI-powered chatbots is a positive predictor of social presence and imagery processing. Imagery processing is a positive predictor of psychological ownership of the products (fashion industry) and services (tourism industry) promoted by the chatbots. Most importantly, social presence and imagery processing are positive predictors of AI-chatbot continuance intention. These empirical findings entail practical implications for AI-powered chatbot developers and managerial implications for commercial brands such that (1) increasing anthropomorphism of chatbots and inducing the sense of being co-present with the chatbots are important factors AI-chatbot designers and developers need to consider and (2) inducing vivid visualization of the products endorsed by the chatbots is an important variable marketers need to understand.
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs

Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where the superior performance is mainly established when natural node features are available. However, it is not well understood how GNNs work without natural node features, especially regarding the various ways to construct artificial ones. In this paper, we point out the two types of artificial node features, i.e., positional and structural node features, and provide insights on why each of them is more appropriate for certain tasks, i.e., positional node classification, structural node classification, and graph classification. Extensive experimental results on 10 benchmark datasets validate our insights, thus leading to a practical guideline on the choices between different artificial node features for GNNs on non-attributed graphs. The code is available at https://github.com/zjzijielu/gnn-positional-structural-node-features.
Continuous entanglement distribution over a transnational 248 km fiber link

Abstract Reliable long-distance distribution of entanglement is a key technique for many quantum applications, most notably quantum key distribution. Here, we present a continuously working, trusted-node free international link between Austria and Slovakia, directly distributing polarization-entangled photon pairs via 248 km of deployed telecommunication fiber. Despite 79 dB loss, we observe stable detected pair rates of 9 s −1 over 110 h. We mitigate multi-pair detections with strict temporal filtering, enabled by nonlocal compensation of chromatic dispersion and superconducting nanowire detectors. Fully automatized active polarization stabilization keeps the entangled state’s visibility at 86% for altogether 82 h. In a quantum cryptography context, this corresponds to an asymptotic secure key rate of 1.4 bits/s and 258 kbit of total key, considering finite-key effects. Our work paves the way for low-maintenance, ultra-stable quantum communication over long distances, independent of weather conditions and time of day, thus constituting an important step towards the quantum internet.
A survey: object detection methods from CNN to transformer

Abstract Object detection is the most important problem in computer vision tasks. After AlexNet proposed, based on Convolutional Neural Network (CNN) methods have become mainstream in the computer vision field, many researches on neural networks and different transformations of algorithm structures have appeared. In order to achieve fast and accurate detection effects, it is necessary to jump out of the existing CNN framework and has great challenges. Transformer’s relatively mature theoretical support and technological development in the field of Natural Language Processing have brought it into the researcher’s sight, and it has been proved that Transformer’s method can be used for computer vision tasks, and proved that it exceeds the existing CNN method in some tasks. In order to enable more researchers to better understand the development process of object detection methods, existing methods, different frameworks, challenging problems and development trends, paper introduced historical classic methods of object detection used CNN, discusses the highlights, advantages and disadvantages of these algorithms. By consulting a large amount of paper, the paper compared different CNN detection methods and Transformer detection methods. Vertically under fair conditions, 13 different detection methods that have a broad impact on the field and are the most mainstream and promising are selected for comparison. The comparative data gives us confidence in the development of Transformer and the convergence between different methods. It also presents the recent innovative approaches to using Transformer in computer vision tasks. In the end, the challenges, opportunities and future prospects of this field are summarized.
Virtual tourism atmospheres: The effects of pleasure, arousal, and dominance on the acceptance of virtual tourism

Few studies have explored the factors influencing continuous usage intention toward virtual tours, such as dominance, pleasure, and arousal. To address this research gap, this study constructed an integrated framework and structural model based on the pleasure–arousal–dominance model to explore how certain factors (atmospheric and affective) affect consumers' continuous usage intention and word-of-mouth communication related to virtual tourism. This study examined the effects of ubiquity, telepresence, vividness, interactivity, and background music–environment congruency on users' feelings of pleasure, dominance, and arousal. The effects of these factors on word of mouth and continuous usage intention were also examined. A questionnaire was administered, and 509 valid questionnaire responses were used in the analysis; 63.5% of the respondents were women, and the respondents' mean age was 36.16 years. The overall fit of our structural model was acceptable, and the Cronbach's alpha values indicated adequate reliability. All the research hypotheses were supported, revealing that each factor affects the virtual tourism experience. This study also revealed that telepresence and vividness are related to pleasure derived from virtual tourism; this result is consistent with those of other studies. Telepresence and vividness also affect word-of-mouth communication and continuous usage intention. Thus, designers of virtual tours should pay particular attention to telepresence and vividness to ensure a positive virtual tourism experience. Researchers should examine other factors that affect usage intention and consumers' behavior.