Reviewerly/exHarmony huggingface dataset:
Quantum Dicke battery supercharging in the bound-luminosity state

Quantum batteries, which are quantum systems to be used for the storage and transformation of energy, have been recently attracting research interest. A promising candidate for their investigation is the Dicke model, which describes an ensemble of two-level systems interacting with a single-mode electromagnetic wave in a resonator cavity. In order to charge the battery, a coupling between the ensemble of two-level systems and resonator cavity should be turned off at a certain moment of time. This moment of time is chosen in such a way that the energy gets fully stored in an ensemble of two-level systems. In our previous works we have investigated a bound-luminosity superradiant state of the extended Dicke model and found analytical expressions for the dynamics of coherent energy transfer between the superradiant condensate and the ensemble of two-level systems. Here, using our previous results, we have derived analytically the superlinear law for the quantum battery charging power $P\ensuremath{\sim}{N}^{3/2}$ as a function of the number $N$ of two-level systems in the battery, and also the $N$ dependence for the charging time ${t}_{c}\ensuremath{\sim}{N}^{\ensuremath{-}1/2}$. The $N$ exponent $3/2$ of the charging power is in quantitative correspondence with the recent result $1.541$ obtained numerically by other authors. The physics of Dicke quantum battery charging is considered in detail.
Quantum Computing and Machine Learning on an Integrated Photonics Platform

Integrated photonic chips leverage the recent developments in integrated circuit technology, along with the control and manipulation of light signals, to realize the integration of multiple optical components onto a single chip. By exploiting the power of light, integrated photonic chips offer numerous advantages over traditional optical and electronic systems, including miniaturization, high-speed data processing and improved energy efficiency. In this review, we survey the current status of quantum computation, optical neural networks and the realization of some algorithms on integrated optical chips.
On the reasons for the removal of A.T. Tvardovsky from the position of head of the editorial office of the magazine “New World”

The subject of the article is the activities of the “New World” journal in the 1950-1960s. The author examines the role of the magazine at the beginning of the reform of Russian literature; the position of the editor-in-chief of “New World” regarding the function of literature designed to reflect reality; the reasons why A. Tvardovsky was removed from the post of editorial director. The work indicates that A. Tvardovsky served as editor-in-chief of the “New World” for 16 years, but during his career he faced dismissal twice. The article reflects the direction and main ideas of liberal thought of the New World magazine, which in the 1950s and 1960s was under the leadership of the editor-in-chief A. Tvardovsky. When studying the problems associated with the magazine’s position on the need to provide literary creativity with a certain freedom. When considering the activities of the magazine and its editor in certain historical and sociocultural conditions, a comparative historical method was used. The main conclusion of the study is that A. Tvardovsky, through his activities as editor-in-chief of the publication “New World” and his principled position on the freedom of literary creativity, made it possible to outline the beginning of the Thaw period in the Soviet literary process. The novelty of the research lies in the fact that the article systematizes information about the reasons for the removal of A. Tvardovsky from the post of editorial director and proves the role of the periodical in question in the process of making a turn in Russian literature towards the realistic tradition. A. Tvardovsky even published those works that were rejected by other leading journals due to the discrepancy between the author’s ideas and the ideas of the ruling party.
GraphSAGE++: Weighted Multi-scale GNN for Graph Representation Learning

Abstract Graph neural networks (GNNs) have emerged as a powerful tool in graph representation learning. However, they are increasingly challenged by over-smoothing as network depth grows, compromising their ability to capture and represent complex graph structures. Additionally, some popular GNN variants only consider local neighbor information during node updating, ignoring the global structural information and leading to inadequate learning and differentiation of graph structures. To address these challenges, we introduce a novel graph neural network framework, GraphSAGE++. Our model extracts the representation of the target node at each layer and then concatenates all layer weighted representations to obtain the final result. In addition, the strategies combining double aggregations with weighted concatenation are proposed, which significantly enhance the model’s discernment and preservation of structural information. Empirical results on various datasets demonstrate that GraphSAGE++ excels in vertex classification, link prediction, and visualization tasks, surpassing existing methods in effectiveness.
Countermeasure Strategies to Address Cybersecurity Challenges Amidst Major Crises in the Higher Education and Research Sector: An Organisational Learning Perspective

Purpose: The purpose of this research paper was to analyse the counterstrategies to mitigate cybersecurity challenges using organisational learning loops amidst major crises in the Higher Education and Research Sector (HERS). The authors proposed the learning loop framework revealing several counterstrategies to mitigate cybersecurity issues in HERS. The counterstrategies are explored, and their implications for research and practice are discussed. Methodology: The qualitative methodology was adopted, and semi-structured interviews with cybersecurity experts and top managers were conducted. Results: This exploratory paper proposed the learning loop framework revealing introducing new policies and procedures, changing existing systems, partnership with other companies, integrating new software, improving employee learning, enhancing security, and monitoring and evaluating security measures as significant counterstrategies to ensure the cyber-safe working environment in HERS. These counterstrategies will help to tackle cybersecurity in HERS, not only during the current major crisis but also in the future. Implications: The outcomes provide insightful implications for both theory and practice. This study proposes a learning framework that prioritises counterstrategies to mitigate cybersecurity challenges in HERS amidst a major crisis. The proposed model can help HERS be more efficient in mitigating cybersecurity issues in future crises. The counterstrategies can also be tested, adopted, and implemented by practitioners working in other sectors to mitigate cybersecurity issues during and after major crises. Future research can focus on addressing the shortcomings and limitations of the proposed learning framework adopted by HERS.
AI on the Road: NVIDIA Jetson Nano-Powered Computer Vision-Based System for Real-Time Pedestrian and Priority Sign Detection

Advances in information and signal processing, driven by artificial intelligence techniques and recent breakthroughs in deep learning, have significantly impacted autonomous driving by enhancing safety and reducing the dependence on human intervention. Generally, prevailing ADASs (advanced driver assistance systems) incorporate costly components, making them financially unattainable for a substantial portion of the population. This paper proposes a solution: an embedded system designed for real-time pedestrian and priority sign detection, offering affordability and universal applicability across various vehicles. The suggested system, which comprises two cameras, an NVIDIA Jetson Nano B01 low-power edge device and an LCD (liquid crystal system) display, ensures seamless integration into a vehicle without occupying substantial space and provides a cost-effective alternative. The primary focus of this research is addressing accidents caused by the failure to yield priority to other drivers or pedestrians. Our study stands out from existing research by concurrently addressing traffic sign recognition and pedestrian detection, concentrating on identifying five crucial objects: pedestrians, pedestrian crossings (signs and road paintings separately), stop signs, and give way signs. Object detection was executed using a lightweight, custom-trained CNN (convolutional neural network) known as SSD (Single Shot Detector)-MobileNet, implemented on the Jetson Nano. To tailor the model for this specific application, the pre-trained neural network underwent training on our custom dataset consisting of images captured on the road under diverse lighting and traffic conditions. The outcomes of the proposed system offer promising results, positioning it as a viable candidate for real-time implementation; its contributions are noteworthy in advancing the safety and accessibility of autonomous driving technologies.
Distinguishing Geogenic Load and Anthropogenic Contribution to Soil Contamination in Mineralised Mountain Landscape of Ore Mountains (Czech Republic) Using Cumulative Distribution Functions

In ore regions impacted by mining and metal smelting emissions, distinguishing between geogenic anomalies and anthropogenic contamination poses a significant challenge. In a study from two areas with different mining histories in the Ore Mountains, Czech Republic, we demonstrate that sampling of topsoils and subsoils respecting local geology and correct soil data treatment respecting soil textural variability effects are indispensable to the correct construction and interpretation of geochemical maps and identification of anthropogenic contamination by As, Cu, Pb, and Zn. The list of analysed elements must include both potentially toxic and lithogenic elements, otherwise natural controls of soil composition cannot be deciphered. By using empirical cumulative distribution functions, we found that local backgrounds for As/Fe and Pb/Ti are naturally elevated (5.7 to 9.8 times and 2.1 to 2.7 times higher, respectively) compared to the global averages. We constructed geochemical maps with topsoil minus subsoil concentrations to show the main directions of spreading anthropogenic contamination. The anthropogenic diffuse contamination contribution was calculated and expressed as an enrichment relative to the local background (16% and 12% for As/Fe and 17% and 14% for Pb/Ti, respectively). This corresponds to topsoil enrichment by ca. 15 and 14 mg kg−1 for As and ca. 35 and 42 mg kg−1 for Pb in the two study areas. The obtained estimates were comparable to the results from the local peat archives. The approach we used is efficient in deciphering natural and anthropogenic controls of PTEs in geochemically complicated areas.
Far-field far-subwavelength spatial resolution using relative motion with structured illumination

Relative motion of structured optical illumination with respect to an object and far-field measurement of intensity are presented as a means to obtain far-subwavelength spatial resolution with a direct imaging arrangement. The principle behind this approach is that the variable interaction of an object with a background field generates information about nanometer-scale features that is encoded in the propagating plane-wave spectrum, allowing far-field data that are modulated with motion according to the nanostructure. Information theory supports this superresolution mechanism and illustrates sensitivity with respect to the illumination and detection arrangements. Simulations indicate that available lasers and detectors would enable a resolution of $\ensuremath{\lambda}/1000$ with modest signal-to-noise requirements and single-pixel detection. Relative motion in structured fields is shown to enhance spatial resolution achievable using data inversion with constraints. Importantly, far-subwavelength sensitivity is shown to be achievable even when the illuminating field is unknown. These results suggest applications that include material defect detection and unlabeled protein sensing, and direct extensions to estimating geometrical features at unprecedented spatial resolution become possible.
A genre-oriented analysis of <i>TikTok</i> instructional discourse

Abstract This study intends to contribute to the analysis of digital instructional discourse in order to gain an insight into how the EFL classroom has in some sense shifted online in the hands of amateur experts ( Tolson 2010 ; Bhatia 2018 ), who create an informal learning environment by drawing on their discursive competence, disciplinary knowledge and professional practice. By incorporating quantitative, qualitative, and case study data, this article considers the value of genre analysis in educational social media research. It focuses, particularly, on the rhetorical and lexico-grammatical features of a selection of TikTok video mini-lessons targeting Italian speakers of English as a foreign language. Analysis of the data reveals that digital language teaching discourse on TikTok is a structured event with recurrent rhetorical patterns and linguistic features for achieving both pedagogical and promotional communicative purposes. The research bears considerable relevance given the need for the analysis of transformation processes in instructional discourse amid the widespread use of information and communication technologies and the advance of online learning.
Union-find quantum decoding without union-find

The union-find decoder is a leading algorithmic approach to the correction of quantum errors on the surface code, achieving code thresholds comparable to minimum-weight perfect matching (MWPM) with amortized computational time scaling near-linearly in the number of physical qubits. This complexity is achieved via optimizations provided by the disjoint-set data structure. We demonstrate, however, that the behavior of the decoder at scale underutilizes this data structure for twofold analytic and algorithmic reasons, and that improvements and simplifications can be made to architectural designs to reduce resource overhead in practice. To reinforce this, we model the behavior of erasure clusters formed by the decoder and show that there does not exist a percolation threshold within the data structure for any mode of operation. This yields a linear-time worst-case complexity for the decoder at scale, even with a naive implementation omitting popular optimizations.
Unraveling the Potential of ChatGPT: Investigating the Efficacy of Reading Text Adaptation

The purpose of this study is to investigate the effectiveness of ChatGPT in reading text adaptation. Its primary focus is on examining ChatGPT's ability to adapt texts with particular attention to its contextual understanding and limitations. Employing a mixed-methods approach, the research combines quantitative analysis and qualitative evaluation. Quantitative measures assess the consistency of ChatGPT's responses across various text adaptation scenarios, whereas qualitative evaluations involve the assessment of language teachers' attitudes and perceptions towards the use of ChatGPT for text adaptation in their teaching practices. Ethical considerations pertaining to potential biases and misinformation in the model's output are also discussed. The outcomes of this investigation contribute to understanding ChatGPT's strengths and limitations in reading text adaptation. The research has practical implications for domains such as language education, content creation, and information retrieval, where accurate and adaptable text comprehension is crucial.
Research on Spatiotemporal Heterogeneity of the Impact of Earthquakes on Global Copper Ore Supply Based on Geographically Weighted Regression

The large and super large copper deposits worldwide are mainly distributed in the Pacific Rim and Gondwana metallogenic domains, and they are highly coupled with the spatial location of seismically active zones. Major copper-producing countries such as Chile are located in areas with high seismic activity. Earthquakes often cause copper mines to shut down, copper prices to soar, and even mining disasters, affecting the stable supply of global copper. In order to study the impact of earthquakes on the global copper ore supply, information on resource endowments, production, and earthquake data from the past 30 years are collected. This article mainly analyzes (1) the spatial correlation between earthquakes and copper mine projects, and the spatial characteristics of earthquakes’ impact on copper mine production, using correlation analysis and geographically weighted regression (GWR); (2) the impact of sudden earthquake events on the export volume and price of copper mines from the perspective of time, using the earthquake index constructed based on the magnitude of the earthquake, the depth of the epicenter, and the distance from the copper mine. The results indicate that the regions with high spatial correlation between copper mine projects and earthquakes are mainly located along the Pacific coast of South and North Americas. Earthquakes can lead to an increase in copper prices, but they will fall significantly in the short term. The impact of earthquakes on export volume generally has a delay period of 1–2 months, resulting in a short-term decrease in export volume. This article quantitatively evaluates the impact of earthquake risk on the supply chain from both spatial and temporal dimensions, providing reference for supply chain risk monitoring, risk impact assessment, and prediction. On the basis of the research results of this article, mineral resource management departments can quantitatively evaluate the spatiotemporal impact of natural risks such as earthquakes on the supply chain, adjust management policies in a timely manner, and improve the level of refined management of supply chain risks.
Two methods for breaking down a quantum algorithm

Despite the promise that fault-tolerant quantum computers can efficiently solve classically intractable problems, it remains a major challenge to find quantum algorithms that may reach computational advantage in the present era of noisy, small-scale quantum hardware. Thus, there is a substantial ongoing effort to create new quantum algorithms (or adapt existing ones) to accommodate depth and space restrictions. By adopting a hybrid query perspective, we identify and characterize two methods of breaking down quantum algorithms into rounds of lower (query) depth, designating these approaches as ``parallelization'' and ``interpolation.'' To the best of our knowledge, these had not been explicitly identified and compared side by side, although one can find instances of them in the literature. We apply them to two problems with known quantum speedup: calculating the $k$-threshold function and computing a NAND tree. We show that for the first problem parallelization offers the best performance, while for the second interpolation is the better choice. This illustrates that no approach is strictly better than the other, and that there is more than one good way to break down a quantum algorithm into a hybrid quantum-classical algorithm.
EAO: Enhanced aquila optimizer for solving optimization problem

The Aquila optimization (AO) algorithm has the drawbacks of local optimization and poor optimization accuracy when confronted with complex optimization problems. To remedy these drawbacks, this paper proposes an Enhanced aquila optimization (EAO) algorithm. To avoid elite individual from entering the local optima, the elite opposition-based learning strategy is added. To enhance the ability of balancing global exploration and local exploitation, a dynamic boundary strategy is introduced. To elevate the algorithm’s convergence rapidity and precision, an elite retention mechanism is introduced. The effectiveness of EAO is evaluated using CEC2005 benchmark functions and four benchmark images. The experimental results confirm EAO’s viability and efficacy. The statistical results of Freidman test and the Wilcoxon rank sum test are confirmed EAO’s robustness. The proposed EAO algorithm outperforms previous algorithms and can useful for threshold optimization and pressure vessel design.
Sparse Random Hamiltonians Are Quantumly Easy

Identification of a large class of Hamiltonians that are easy to solve on quantum computers but difficult on classical ones provides a possible path to practical quantum advantage in the simulation of quantum systems.
Shapley value: from cooperative game to explainable artificial intelligence

Abstract With the tremendous success of machine learning (ML), concerns about their black-box nature have grown. The issue of interpretability affects trust in ML systems and raises ethical concerns such as algorithmic bias. In recent years, the feature attribution explanation method based on Shapley value has become the mainstream explainable artificial intelligence approach for explaining ML models. This paper provides a comprehensive overview of Shapley value-based attribution methods. We begin by outlining the foundational theory of Shapley value rooted in cooperative game theory and discussing its desirable properties. To enhance comprehension and aid in identifying relevant algorithms, we propose a comprehensive classification framework for existing Shapley value-based feature attribution methods from three dimensions: Shapley value type, feature replacement method, and approximation method. Furthermore, we emphasize the practical application of the Shapley value at different stages of ML model development, encompassing pre-modeling, modeling, and post-modeling phases. Finally, this work summarizes the limitations associated with the Shapley value and discusses potential directions for future research.
Classification Of Adaptive Back Propagation Neural Network Along with Fuzzy Logic in Chronic Kidney Disease

A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98% accurate in terms of efficiency.