Reviewerly/exHarmony huggingface dataset:
Waveguide QED with Quadratic Light-Matter Interactions

Quadratic light-matter interactions are nonlinear couplings such that quantum emitters interact with photonic or phononic modes exclusively via the exchange of excitation pairs. Implementable with atomic and solid-state systems, these couplings lead to a plethora of phenomena that have been characterized in the context of cavity QED, where quantum emitters interact with localized bosonic modes. Here, we explore quadratic interactions in a waveguide QED setting, where quantum emitters interact with propagating fields confined in a one-dimensional environment. We develop a general scattering theory under the Markov approximation and discuss paradigmatic examples for spontaneous emission and scattering of biphoton states. Our analytical and semianalytical results unveil fundamental differences with respect to conventional waveguide QED systems, such as the spontaneous emission frequency-entangled photon pairs or the full transparency of the emitter to single-photon inputs. This unlocks new opportunities in quantum information processing with propagating photons. As a striking example, we show that a single quadratically coupled emitter can implement a two-photon logic gate with unit fidelity, circumventing a no-go theorem derived for conventional waveguide-QED interactions.2 MoreReceived 21 March 2023Accepted 19 July 2023DOI:https://doi.org/10.1103/PRXQuantum.4.030326Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum engineeringQuantum gatesScattering theorySuperconducting quantum opticsCondensed Matter, Materials & Applied PhysicsAtomic, Molecular & OpticalQuantum Information
Multispin Clifford codes for angular momentum errors in spin systems

The physical symmetries of a system play a central role in quantum error correction. In this work we encode a qubit in a collection of systems with angular momentum symmetry (spins), extending the tools developed by Gross [J. A. Gross, Phys. Rev. Lett. 127, 010504 (2021)] for single large spins. By considering large spins present in atomic systems and focusing on their collective symmetric subspace, we develop codes with octahedral symmetry capable of correcting errors up to second order in angular momentum operators. These errors include the most physically relevant noise sources such as microwave control errors and optical pumping. We additionally explore qubit codes that exhibit distance scaling commensurate with the surface code while permitting transversal single-qubit Clifford operations.
Recurrent generation of maximally entangled single-particle states via quantum walks on cyclic graphs

While earlier research has predicted that maximally entangled single-particle states (MESPS), i.e., entanglement between different degrees of freedom of the same particle, can occur using quantum walk schemes with two or more coins or with a coin and identity operation, the authors analytically predict that MESPS can be generated using only a single coin on cyclic graphs. The generated MESPS occurs at recurring time steps, an advantage the authors exploit in a quantum cryptographic protocol.
Penalized model-based clustering of complex functional data

Abstract High dimensional data, large-scale data, imaging and manifold data are all fostering new frontiers of statistics. These type of data are commonly considered in Functional Data Analysis where they are viewed as infinite-dimensional random vectors in a functional space. The rapid development of new technologies has generated a flow of complex data that have led to the development of new modeling strategies by scientists. In this paper, we basically deal with the problem of clustering a set of complex functional data into homogeneous groups. Working in a mixture model-based framework, we develop a flexible clustering technique achieving dimensionality reduction schemes through an $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> penalization. The proposed procedure results in an integrated modelling approach where shrinkage techniques are applied to enable sparse solutions in both the means and the covariance matrices of the mixture components, while preserving the underlying clustering structure. This leads to an entirely data-driven methodology suitable for simultaneous dimensionality reduction and clustering. The proposed methodology is evaluated through a Monte Carlo simulation study and an empirical analysis of real-world datasets showing different degrees of complexity.
Super-Resolution Reconstruction of Remote Sensing Data Based on Multiple Satellite Sources for Forest Fire Smoke Segmentation

Forest fires are one of the most devastating natural disasters, and technologies based on remote sensing satellite data for fire prevention and control have developed rapidly in recent years. Early forest fire smoke in remote sensing images, on the other hand, is thin and tiny in area, making it difficult to detect. Satellites with high spatial resolution sensors can collect high-resolution photographs of smoke, however the impact of the satellite’s repeat access time to the same area means that forest fire smoke cannot be detected in time. Because of their low spatial resolution, photos taken by satellites with shorter return durations cannot capture small regions of smoke. This paper presents an early smoke detection method for forest fires that combines a super-resolution reconstruction network and a smoke segmentation network to address these issues. First, a high-resolution remote sensing multispectral picture dataset of forest fire smoke was created, which included diverse years, seasons, areas, and land coverings. The rebuilt high-resolution images were then obtained using a super-resolution reconstruction network. To eliminate data redundancy and enhance recognition accuracy, it was determined experimentally that the M11 band (2225–2275 nm) is more sensitive to perform smoke segmentation in VIIRS images. Furthermore, it has been demonstrated experimentally that improving the accuracy of reconstructed images is more effective than improving perceptual quality for smoke recognition. The final results of the super-resolution image segmentation experiment conducted in this paper show that the smoke segmentation results have a similarity coefficient of 0.742 to the segmentation results obtained using high-resolution satellite images, indicating that our method can effectively segment smoke pixels in low-resolution remote sensing images and provide early warning of forest fires.
Hybrid Multiscale Search for Dynamic Planning of Multi-Agent Drone Traffic

Unmanned aerial vehicles or drones are widely used or proposed to carry out various tasks in low-altitude airspace. To safely integrate drone traffic into congested airspace, the current concept of operations for drone traffic management will reserve a static traffic volume for the whole planned trajectory, which is safe but inefficient. In this paper, we propose a dynamic traffic volume reservation method for the drone traffic management system based on a multiscale A* algorithm. The planning airspace is represented as a multiresolution grid world, where the resolution will be coarse for the area on the far side. Therefore, each drone only needs to reserve a temporary traffic volume along the finest flight path in its local area, which helps release the airspace back to others. Moreover, the multiscale A* can run nearly in real-time due to a much smaller search space, which enables dynamically rolling planning to consider updated information. To handle the infeasible corner cases of the multiscale algorithm, a hybrid strategy is further developed, which can maintain a similar optimal level to the classic A* algorithm while still running nearly in real-time. The presented numerical results support the advantages of the proposed approach.
Towards Enhancing the Media Industry Through AI-Driven Image Recommendations

In a fast-changing media ecosystem, professionals and enterprises in the News and Media industry face new challenges that they should address to maximize their productivity and improve their services. The rise of alternative news sources, such as social media, the leading news source, especially for young people, has led to emerging requirements in the News and Media industry. A core requirement is publishing articles as fast as possible on various platforms, combining visual and textual content. Accompanying news with images raises the readers’ interest, improves engagement, and recall. Therefore, the News and Media industry professionals must adapt their publication strategies to meet this requirement and the media consumers’ expectations. However, the selection of the appropriate images is a time-consuming and manual task. Towards this direction, we propose VIREO, which addresses this challenge by providing professionals (e.g., journalists) with an integrated digital solution that automatically recommends a collection of images that could accompany an article. VIREO implements text and image analysis and matching processes leveraging AI techniques in real time to achieve this. VIREO aims to benefit both professionals (e.g., journalists) by suggesting appealing images that accompany the textual content of their articles and create breath-taking stories and the media consumers (e.g., readers) by delivering an enhanced reading experience, engagement, and recall.
Comprehensive survey of the solving puzzle problems

Solving puzzle problems using computer-aided methods is becoming more common with applications in forensic science, restoration, banking system, and multimedia. However, only a few surveys have been published on this topic, the most recent being more than a decade old. The scope of 2D puzzle problems is extensive, and the number of computer-aided methods has increased in recent years. In this paper, we have presented a comprehensive survey to pave a roadmap for researchers dealing with puzzle problems. This study classifies 2D puzzle problems in a novel way, considering many examples such as dissection, combinatorial and double-sided puzzles and reclassifies computer-aided methods to cover the studies carried out in recent years. Various strategies (pre-grouping and global consistency approach) have been investigated to solve the puzzle problem effectively. The computer-aided methods have been examined deeply, including many recent methods related to squared jigsaw puzzles, torn photographs, banknotes, and fragmented documents, and they are compared to each other. In addition, new topics such as combining mosaic pieces and Islamic architectural motif puzzle problems have been proposed to the interest of researchers. In conclusion, our study shows many research opportunities that are not yet solved by any computer-aided method.
An experimental study on the performance of collaborative filtering based on user reviews for large-scale datasets

Collaborative filtering (CF) approaches generate user recommendations based on user similarities. These similarities are calculated based on the overall (explicit) user ratings. However, in some domains, such ratings may be sparse or unavailable. User reviews can play a significant role in such cases, as implicit ratings can be derived from the reviews using sentiment analysis, a natural language processing technique. However, most current studies calculate the implicit ratings by simply aggregating the scores of all sentiment words appearing in reviews and, thus, ignoring the elements of sentiment degrees and aspects of user reviews. This study addresses this issue by calculating the implicit rating differently, leveraging the rich information in user reviews by using both sentiment words and aspect-sentiment word pairs to enhance the CF performance. It proposes four methods to calculate the implicit ratings on large-scale datasets: the first considers the degree of sentiment words, while the second exploits the aspects by extracting aspect-sentiment word pairs to calculate the implicit ratings. The remaining two methods combine explicit ratings with the implicit ratings generated by the first two methods. The generated ratings are then incorporated into different CF rating prediction algorithms to evaluate their effectiveness in enhancing the CF performance. Evaluative experiments of the proposed methods are conducted on two large-scale datasets: Amazon and Yelp. Results of the experiments show that the proposed ratings improved the accuracy of CF rating prediction algorithms and outperformed the explicit ratings in terms of three predictive accuracy metrics.
Location‐aware scalable service composition

Abstract The problem of service composition is the process of assigning resources to services from a pool of available ones in the shortest possible time so that the overall quality of service is maximized. This article provides solutions for the composition problem that takes into account its scalability, services' locations, and users' restrictions, which are key for the management of applications using state‐of‐the‐art technologies. The provided solutions use different techniques, including genetic algorithms and heuristics. We provide an extensive experimental evaluation, which shows the pros and cons of each of them, and allows us to characterize the preferred option for each specific problem. Since no solution dominates the others, we propose a decision tree, based on our results, to select the best composition algorithm in each situation.
Does capacity utilization crowd‐out research activity? Cross‐national evidence from firm‐level data

Abstract Focusing on a relatively under‐researched aspect, this paper examines the impact of capacity utilization by firms on their propensities to engage in research and development (R&amp;D). Capacity utilization is related to a firm's current operations, while R&amp;D is forward‐looking, related to the generation of new products and processes. Using a simple theoretical model to set up the hypothesis that greater capacity utilization lowers the incentives of firms to engage in research, our empirical research, based on survey data from thousands of firms across scores of nations, supports this hypothesis. Other results point to the importance of firm characteristics, as well as the perceptions about corruption and the informal sector. Some implications for policy are discussed.
MDIW-13: a New Multi-Lingual and Multi-Script Database and Benchmark for Script Identification

Abstract Script identification plays a vital role in applications that involve handwriting and document analysis within a multi-script and multi-lingual environment. Moreover, it exhibits a profound connection with human cognition. This paper provides a new database for benchmarking script identification algorithms, which contains both printed and handwritten documents collected from a wide variety of scripts, such as Arabic, Bengali (Bangla), Gujarati, Gurmukhi, Devanagari, Japanese, Kannada, Malayalam, Oriya, Roman, Tamil, Telugu, and Thai. The dataset consists of 1,135 documents scanned from local newspaper and handwritten letters as well as notes from different native writers. Further, these documents are segmented into lines and words, comprising a total of 13,979 and 86,655 lines and words, respectively, in the dataset. Easy-to-go benchmarks are proposed with handcrafted and deep learning methods. The benchmark includes results at the document, line, and word levels with printed and handwritten documents. Results of script identification independent of the document/line/word level and independent of the printed/handwritten letters are also given. The new multi-lingual database is expected to create new script identifiers, present various challenges, including identifying handwritten and printed samples and serve as a foundation for future research in script identification based on the reported results of the three benchmarks.
Analysis of outage performance in a 6G‐V2X communications system utilising free‐space optical quantum key distribution

Abstract Quantum‐based technologies will provide system engineers with new capabilities for securing data communications. The UK AirQKD project has implemented a Free‐Space Optical Quantum Key Distribution (QKD) system to enable the continuous generation of symmetric encryption keys. One of the use cases for the generated keys is to secure Vehicle‐to‐Everything (V2X) communications. V2X applications would benefit from the certificate‐free security provided by QKD for a post‐quantum society. How FSO‐QKD could integrate into a V2X architecture is examined. An overview of V2X is provided with the role that FSO‐QKD could secure V2X data though some obstacles exist. One of the issues with 6G communications is the potential line‐of‐sight (LOS) considerations between the V2X devices. The modelling required for LOS is examined to analyse the outage performance of the building to infrastructure links in the 6G architecture. The results from the model show that further work is required if 6G LOS communications are going to be relied upon for future safety‐critical V2X applications.
Modeling Long and Short Term User Preferences by Leveraging Multi-Dimensional Auxiliary Information for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has shown great value for both users and providers in location-based services. Existing methods mainly rely on partial information in users’ check-in sequences, and are brittle to users with few interactions. Moreover, they ignore the impact of multi-dimensional auxiliary information such as user check-in frequency, POI category on user preferences modeling and the impact of dynamic changes in user preferences over different time periods on recommendation performance. To address the above limitations, we propose a novel method for next POI recommendation by modeling long and short term user preferences with multi-dimensional auxiliary information. In particular, the proposed model includes a static LSTM module to capture users’ multi-dimensional long term static preferences and a dynamic meta-learning module to capture users’ multi-dimensional dynamic preferences. Furthermore, we incorporate a POI category filter into our model to comprehensively simulate users’ preferences. Experimental results on two real-world datasets demonstrate that our model outperforms the state-of-the-art baseline methods in two commonly used evaluation metrics.
Near-term quantum algorithm for computing molecular and materials properties based on recursive variational series methods

Determining the properties of molecules and materials is one of the premier applications of quantum computing. A major question in the field is: how might we use imperfect near-term quantum computers to solve problems of practical value? We propose a quantum algorithm to estimate the properties of molecules using near-term quantum devices. The method is a recursive variational series estimation method, where we expand an operator of interest in terms of Chebyshev polynomials and evaluate each term in the expansion using a variational quantum algorithm. We test our method by computing the one-particle Green's function in the energy domain and the autocorrelation function in the time domain.
GIS-based mineral prospectivity mapping using machine learning methods: A case study from Zhuonuo ore district, Tibet

The Zhunuo ore concentration area (ZOCA) is the most potential prospective area of Cu-Au (Mo) in the west of the southern subterrane, Tibet. Single traditional prospective methods (e.g., stream sedimentary geochemistry) often produced larger area and false abnormal information in the Gangdese orogenic belt because of the high altitude and the intense weather and erosion, which can not meet the urgent demand of the current situation for Cu resources. In this study, we combined a mineral system approach with GIS-based machine learning approachs to obtain geologically meaningful mineral prospective maps. The detail steps include: (i) establishing the mineral system conception model of porphyry copper deposits (PCDs); (ii) transforming the targeted porphyry metallogenic system components into spatial proxies associated with the crucial ore-forming processes; (iii) extracting the spatial proxies: proximity to intrusive rocks (source), NE orientation faults (transport and/or physical trap), Fe-oxide and propylitization hydrothermal alterations zone (hydrothermal fluids) and the metallogenic strength diagram of Cu-Mo-W-Bi -Au-Ag-Pb-Zn (deposition); (iv) Radial Basis Functions Link Networks (RBFLN), Random forests (RF) Supervised and Fuzzy Clustering (FC) unsupervised machine learning methods were applied to capture the complex and crucial mineralization information between known deposit types and evidence layers; (vi) model estimation and delineating prospective potential targets: Receiver operating characteristic curve (ROC), predictive-area (P-A) plotting and normalised density (Nd) were used to evaluate the predictive models results. The results indicate that the RBFLN model, RF model, and FC model show high predictive accuracy. The AUC values under the ROC area of the RBFLN model, RF model, and FC model are 0.99, 0.96, and 0.94, respectively. The RBFLN model outperforms the RF model and FC model, the predictive-area plotting of RBFLN occupies 12% of the study area containing 88% of the known deposits. The predictive-area plotting of the RF model and FC model showed that 14% and 21% of the study area contained 86% and 79% of the known deposits, respectively. The normalized density (Nd) of a layer is defined as the ratio of the prediction success rate (Pr) of the P-A plotting to the corresponding area (Oa). The normalized density of the RBFLN model, the RF model, and the FC model are 7.33, 6.14, and 3.76, respectively, which revealed that the results of the three predictive models all have positive indications. These studies show that RBFLN supervised machine learning method is a more robustness and generalization capability. The predictive results also provide prospective potential targets (e.g., northern Cimabanshuo, northwest Wubaduolai, and southwestern and western Zhunuo PCD) for further exploration, and this method can be also applicable to other mineral systems and districts.
The role of chatbots’ human-like characteristics in online shopping

Despite their importance for online shopping, little is known about what motivates customers to trust and use chatbots. This study fills this gap by investigating factors leading customers to trust chatbots, eventually leading to a willingness to use them. We examined the antecedents of trust from two perspectives: first, the customer perspective, represented by innovativeness and optimism, and second, the chatbots’ human-like characteristics, represented by anthropomorphism, empathy, and social presence. The model was examined using a representative sample of 395 online shopping customers. Our findings show that customer’s readiness characteristics (optimism and innovativeness), and the human-like characteristics of chatbots (empathy and social presence) have a positive influence on customers’ trust in chatbots, while perceived anthropomorphism has a negative influence on trust. In addition, our results indicate that customers’ trust significantly affects their willingness to use this new technology. The results of the study provide key insights for chatbot developers and marketing managers on how to build trust in chatbots, which will, in turn, lead to an increase in customers’ willingness to use this technology in the online shopping context.