Reviewerly/exHarmony huggingface dataset:
Using Crank-Nicolson Scheme for Continuous Hopfield Network Equilibrium

The Continuous Hopfield Network (CHN) so far remained a concrete example of recurrent neural networks. The use of CHN in various applications is excellent, which generally leads to better results. The continuous Hopfield network has an energy function associated with it, and the network is bound to converge if the activity of each neuron with respect to time is given by a differential equation. The resolution of this differential equation of CHN in most works is based on the Euler method. In this work, we try to use another method for the solution of the CHN differential equation and rely on the Crank-Nicolson method. This work aims to improve and develop the performance of CHN using the Crank-Nicolson method. For this purpose, we have carried out an analytical and comparative study between the two methods of solving the CHN differential equation Euler and Crank-Nicolson applied to the problem of task assignment.
Beyond Algorithmic Solutions: The Significance of Academic Debate for Learning Assessment and Skill Cultivation in the AI World

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Securing Images Using High Dimensional Chaotic Maps and DNA Encoding Techniques

With the growing need for secure multimedia data transmission, image encryption has become an important research area. Traditional encryption algorithms like RSA are not well-suited for this purpose, leading researchers to explore new approaches such as chaotic maps. The present study introduces a new image encryption algorithm that utilizes an improved Rossler system as a keystream generator. The improved Rossler system is an enhanced version of the original Rossler system, which has been optimized for better chaotic behavior and improved security. For the confusion part, we combine DNA encoding techniques with Baker maps to ensure high levels of security. Various performance metrics, including NPCR, UACI, correlation coefficient, histogram analysis, and key sensitivity analysis, were used to evaluate the proposed scheme. The results showed that the proposed method surpassed several existing image encryption methods in terms of both security and efficiency.
Quantum Semantic Communications for Metaverse: Principles and Challenges

The Metaverse is a connected virtual space that has recently emerged as a new frontier for human interaction and experiences. For ideal immersive Metaverse experiences, perpetual data traffic poses stringent requirements on computation latency, communication bandwidth, data privacy, and transmission delay. Semantic communication with machine learning provides a nascent paradigm shift from context-agnostic communication toward semantics-centric communication to address these underlying challenges simultaneously. However, classical limitations of this computing-intensive communication partially serve the purposes of semantic extraction, privacy protection, and data-driven processing for Metaverse applications. In this article, we investigate the potential benefits of incorporating quantum anonymous communication and variational quantum computing to develop quantum semantic communication that enables Metaverse users to interact with virtual environments more reliably and securely. Specifically, quantum embedding and quantum machine learning are employed to extract and encode semantic messages of classical data into quantum states on high-dimensional Hilbert spaces. These semantic quantum states are then anonymously teleported to the receiving party using the quantum teleportation protocol that exploits anonymous entanglement and broadcast. We detail this anonymous quantum semantic communication framework that fully capitalizes on unique quantum resources and properties for the Metaverse, including fundamental principles and deployment challenges.
Comparing Photovoltaic Power Prediction: Ground-Based Measurements vs. Satellite Data Using an ANN Model

Accurate prediction of photovoltaic (PV) power output is crucial for assessing the feasibility of early-stage projects in relation to specific site weather conditions. While various mathematical models have been used in the past for PV power prediction, most of them only consider irradiance and ambient temperature, neglecting other important meteorological parameters. In this article, a 1-year dataset from a high-precision meteorological station at the Green Energy Research facility is utilized, along with electrical parameters from a polycrystalline silicon c-Si PV module exposed during the study period, to forecast PV power. In addition, the accuracy of using satellite data for PV power forecasting is investigated, considering the growing trend of its utilization in recent research. Regression techniques, such as linear regression with interaction, tree regression, Gaussian process regression, ensemble learning for regression, response surface methodology, SVM cubic, and artificial neural network (ANN), are employed for PV power prediction, using both ground measurement data and satellite data. Comparatively lower accuracies are observed when using satellite data across all regression methods, in contrast to the higher accuracies achieved with ground-based measurements. Notably, the Gaussian process regression method demonstrates high accuracy ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.25 for satellite data and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.94 for ground-based data). Furthermore, the ANN approach further enhances the accuracy of PV power forecasting, yielding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.42 for satellite data and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.96 for ground-based data. These findings emphasize the need for caution when relying on satellite data for PV power forecasting, even when employing advanced ANN approaches. It underscores the importance of considering ground-based measurements for more reliable and accurate predictions.
A Deep Learning-Based Novel Class Discovery Approach for Automatic Modulation Classification

The existing Automatic Modulation Classification (AMC) methods require the training and testing datasets share a common set of modulation categories. However, the AMC model may encounter the need to discriminate novel classes in non-cooperative environments. To the best of our knowledge, the research reporting AMC in the class-disjoint environment has not been addressed yet. In this letter, a novel class discovery method is proposed for AMC leveraging the information contained in the labeled training dataset. Specifically, a 3-stage deep learning method is introduced to recognize samples of the known classes and cluster samples of novel classes. The extracted features and the pairwise similarity relationship are considered as the common knowledge between the two class-disjoint datasets and are utilized to help the construction and training of the classifier for novel classes. The simulation results validate the effectiveness and performance of the proposed method.
Performance comparison among VGG16, InceptionV3, and resnet on galaxy morphology classification

Abstract This article introduces the structures of three classical convolutional neural networks: VGG16, InceptionV3, and ResNet50, and compares their performance on galaxy morphology classification. The different structures of these networks give them some distinct features, leading to different results. The dataset used was Galaxy10 DECals created by the Galaxy Zoo project. The models were compared in the same way, using one fully-connected layer only without modifying its original architecture, evaluated by accuracy, precision, recall rate, and f1 score. The experimental results show that InceptionV3 gave the best classification results from all aspects. The inception module performs well on the galaxy morphology classification problem.
Satellite Manoeuvre Detection with Multistatic Radar

Abstract Traditional radar sensors used for surveillance rely on monostatic radar principles. However, recently the use of remote radio frequency telescopes as bistatic receivers represents an interesting way to reuse existing facilities while providing additional information to improve tracking accuracy. In this paper we study the benefits of using such a system for the task of manoeuvre detection in satellites in LEO and MEO. We investigate the conditions in which a multistatic radar is advantageous for this purpose, and show concrete results based on simulated data. Moreover, we propose novel manoeuvre detection methods, and compare their accuracy to methods found in the literature. A more general way of assessing the accuracy of these manoeuvre detection methods is also proposed, with the aim of taking into account that the parameters of the manoeuvre that actually takes place also have an effect on the accuracy. These can be split into optimal control based methods, and statistical methods. We found the addition of multistatic radar to allow considerable improvement in the accuracy of the manoeuvre detection process, an improvement that is shown to be greater the greater the baseline, i.e., the distance of the receiver to the transmitter. Furthermore, the manoeuvre detection methods that accurately model the uncertainty in the measurements were found to be the most accurate.
Author’s agency in a research article: From the grammar of language to the grammar of communication

Introduction . Writing for publication has been developed for two last decades as an independent field of research to help authors publish their research articles. Starting from the assessment that preparing the publication in a journal is an academic activity that requires learning to construct the author’s positioning in the manuscript, this paper points out the shortage of professionals to assume institutional organisation of training courses in Russia. Aim. This paper aims to suggest a framework for informal practices based on autonomy and the collective organisation of authors to construct an authorial position in the manuscript or author’s agency toward a target journal. Methodology and research methods . Various approaches applied in this paper are based on activity theory: situated learning, Change Laboratory, and expansive learning. A review of Russian and foreign research in writing for publication shows that only one of the three types of authorial agency, that is, transformative agency, might help the author to prepare a manuscript for publication in a target journal. The framework needed for emerging author’s transformative agency re-conceptualises the model of the Change Laboratory for situated learning that aims at the author’s socialisation in the discursive practices of a target journal. Situated learning for writing for publication is based on the principles of the Change Laboratory: collectivity; double stimulation; overcoming the contradiction by ascending from the abstract to the concrete. Results. A framework for three cycles of expansive learning aimed at the emergence of the author’s agency toward a target journal has been developed. The first cycle allows selecting a journal and revealing its implicit rules for manuscript production. The second cycle is focused on step-by-step manuscript revisions. Finally, a change of journal and/or author whose manuscript is discussed involves the third cycle of expansive learning. Scientific novelty . The paper considers manuscript production for publishing in a journal as an academic activity requiring deliberate training in native and foreign languages. The framework for three cycles of expansive learning in writing for publication conceptualises the publication as the author’s participation in social practices involving the journal as a discursive community. Practical significance . The framework suggested in this paper for emerging transformative agency in writing for publication might imply an alternative to the institutional training courses. Three cycles of expansive learning aim to develop the author’s publication competence and improve the quality of research articles.
Semantic road segmentation based on adapted Poly-YOLO

Abstract With artificial intelligence continuing to change people’s everyday life in profound ways, the desire to endow vehicles with the ability to drive autonomously has emerged for years. Thus, autonomous driving has become a popular field. The autonomous driving task can be divided into three general procedures: perception, planning, and locomotion. The first and foremost part of these general procedures is the perception task. Among those perception methods, the most prevailing one is semantic segmentation, which is annotating and predicting the object located at the pixel level, meaning nearly all pixels should be classified into certain categories. However, this method provides enough accuracy while bringing a considerable computational burden. Thus, implementing real-time road semantic segmentation on autonomous driving vehicles is still a costly task. In this paper, an adapted model improved upon the Poly-YOLO baseline model is proposed, which is a well-developed object detection algorithm providing bounding polygons to enclose the target object, forming a polygon mask similar to that of semantic segmentation. This paper endeavors to enhance the model’s accuracy in detecting variously sized targets greatly and to fine-tune the model to generate more proximate enclosing polygons. The adapted model has experienced a leap in performance compared to the baseline Poly-YOLO model.
Based on Gaussian filter to improve the effect of the images in Gaussian noise and pepper noise

Abstract Gaussian filter is one of the important research topics. Researchers find that Gaussian filter can suppress noise, but there is still a gap in the completeness of Gaussian filter for denoising. Therefore, the author optimized the Gaussian filter to achieve better filtering effect. By collecting SNR and PSNR data and comparing and analysing different noise types of the data under the same condition, the author explored the denoising effect of Gaussian filter method on Gaussian noise and Salt and pepper noise, and improved the Gaussian filter method. According to the comparison of SNR and PSNR data, the author found that under the default condition, the SNR value of Gaussian noise after optimization is 57.69201, and the SNR value of Salt and pepper noise is 31.5896. The PSNR value of Gaussian noise is 31.5021, and the PSNR value of Salt and pepper noise is 19.7872. The SNR and PSNR values of Gaussian noise are much larger than those of Salt and pepper noise, indicating that the denoising effect of Gaussian filter on Gaussian noise is obviously better than that of Salt and pepper noise under the same condition. The SNR value and PSNR value of Gaussian noise in the two groups of experiments are larger than Salt and pepper noise, which indicates that under the same condition, Gaussian filter is conducive to suppressing Gaussian noise and has a more significant impact on Gaussian noise.
Complex-valued trainable activation function hardware using a TCO/silicon modulator

Artificial neural network-based electro-optic chipsets constitute a very promising platform because of its remarkable energy efficiency, dense wavelength parallelization possibilities and ultrafast modulation speeds, which can accelerate computation by many orders of magnitude. Furthermore, since the optical field carries information in both amplitude and phase, photonic hardware can be leveraged to naturally implement complex-valued neural networks (CVNNs). Operating with complex numbers may double the internal degrees of freedom as compared with real-valued neural networks, resulting in twice the size of the hardware network and, thus, increased performance in the convergence and stability properties. To this end, the present work revolves on the concept of CVNNs by offering a design, and simulation demonstration, for an electro-optical dual phase and amplitude modulator implemented by integrating a transparent conducting oxide (TCO) in a silicon waveguide structure. The design is powered by the enhancement of the optical-field confinement effect occurring at the epsilon-near-zero (ENZ) condition, which can be tuned electro-optically in TCOs. Operating near the ENZ resonance enables large changes on the real and imaginary parts of the TCO’s permittivity. In this way, phase and amplitude (dual) modulation can be achieved in single device. Optimal design rules are discussed in-depth by exploring device’s geometry and voltage-dependent effects of carrier accumulation inside the TCO film. The device is proposed as a complex-valued activation function for photonic neural systems and its performance tested by simulating the training of a photonic hardware neural network loaded with our custom activation function.
Time-domain photonic image processor based on speckle projection and reservoir computing

Abstract High-speed image processing is essential for many real-time applications. On-chip photonic neural network processors have the potential to speed up image processing, but their scalability is limited in terms of the number of input/output channels because high-density integration is challenging. Here, we propose a photonic time-domain image processing approach, where real-world visual information is compressively acquired through a single input channel. Thus, large-scale processing is enabled even when using a small photonic processor with limited input/output channels. The drawback of the time-domain serial operation can be mitigated using ultrahigh-speed data acquisition based on gigahertz-rate speckle projection. We combine it with a photonic reservoir computer and demonstrate that this approach is capable of dynamic image recognition at gigahertz rates. Furthermore, we demonstrate that this approach can also be used for high-speed learning-based imaging. The proposed approach can be extended to diverse applications, including target tracking, flow cytometry, and imaging of sub-nanosecond phenomena.
Diffractive neural networks built from spatial light modulators for laser beam shaping in the NIR and VIS

Diffractive neural networks enable robust three-dimensional beam shaping by treating systems of cascading diffractive optical elements as artificial neural networks, enabling the usage of neural network training methods for the design. Here, we demonstrate how this can be used to create intensity distributions with a large depth-of-field in an experimental realization through spatial light modulators.
Discrete Isothermic Nets Based on Checkerboard Patterns

Abstract This paper studies the discrete differential geometry of the checkerboard pattern inscribed in a quadrilateral net by connecting edge midpoints. It turns out to be a versatile tool which allows us to consistently define principal nets, Koenigs nets and eventually isothermic nets as a combination of both. Principal nets are based on the notions of orthogonality and conjugacy and can be identified with sphere congruences that are entities of Möbius geometry. Discrete Koenigs nets are defined via the existence of the so-called conic of Koenigs. We find several interesting properties of Koenigs nets, including their being dualizable and having equal Laplace invariants. Isothermic nets can be defined as Koenigs nets that are also principal nets. We prove that the class of isothermic nets is invariant under both dualization and Möbius transformations. Among other things, this allows a natural construction of discrete minimal surfaces and their Goursat transformations.
Quantum State Tomography for Kerr Parametric Oscillators

Kerr parametric oscillators (KPOs) implemented in the circuit quantum electrodynamic architecture can operate as qubits. Their applications to quantum annealing and universal quantum computation have been studied intensively. For these applications, the readout of the state of KPOs is of practical importance. We develop a scheme of state tomography for KPOs with reflection measurement. Although it is known that the reflection coefficient depends on the state of the KPO, it is unclear whether tomography of a qubit encoded into a KPO can be performed in a practical way mitigating decoherence during the measurement, and how accurate it is. We show that the reflection coefficient has a one-to-one correspondence with a diagonal element of the density matrix of the qubit when a probe frequency is properly chosen and an additional single-photon drive is introduced. Thus, our scheme offers a novel way to read out the qubit along an axis of the Bloch sphere, and therefore the reflection measurement and single-qubit gates can constitute state tomography.
Three-Dimensional Mineral Prospectivity Modeling with the Integration of Ore-Forming Computational Simulation in the Xiadian Gold Deposit, Eastern China

Finding new, effective predictive variables for 3D mineral prospectivity modeling is both important and challenging. The 3D ore-forming numerical modeling quantitively characterizes the complex coupling-mineralization process of the structure, fluid, heat, and wall rock, which may be potential indicators for mineral exploration. We here conducted 3D mineral prospectivity modeling with the integration of ore-forming computational simulation information in the Xiadian orogenic gold deposit, China, to examine whether the simulation data input can improve the reliability of prospectivity modeling. First, we constructed the 3D models of the orebody and fault to extract the fault geometric features using spatial analysis, as they are always considered to be the crucial controls of gold distribution. Second, we performed 3D numerical modeling of the deformation–fluid–heat-coupling process of the structurally controlled hydrothermal Au system using the FLAC3D platform. Finally, the fault-geometry features (buffer, dip, dip variation, and undulation) and the ore-formation-simulation indices (volume strain, shear strain, temperature variation, and fluid flux) were integrated using Bayesian decomposition modeling, which has a promising nonlinear model ability and a flexible variable-integration ability. The prospectivity modeling results demonstrated that the model generated by combining geometry and simulation variables achieved significantly higher AUC, precision, accuracy, Kappa, and F1 scores compared to other models using a single-predictor-variable dataset. This suggests that the joint use of geometry and simulation variables construct a comprehensive association between gold and its ore-controlling factors, thereby resulting in a highly reliable prospectivity model. Thus, the approach of 3D mineral prospectivity modeling aided by ore-forming numerical simulation proves to be more useful in guiding mineral exploration, especially in the condition of fewer variables. Based on the prospectivity modeling outcomes, we identified four gold targets at depth in the Xiadian district that warrant focused exploration efforts.