Physical Information Research Articles

Consistent social information perceived in animated backgrounds improves ensemble perception of facial expressions.

Observers can rapidly extract the mean emotion from a set of faces with remarkable precision, known as ensemble coding. Previous studies have demonstrated that matched physical backgrounds improve the precision of ongoing ensemble tasks. However, it remains unknown whether this facilitation effect still occurs when matched social information is perceived from the backgrounds. In two experiments, participants decided whether the test face in the retrieving phase appeared more disgusted or neutral than the mean emotion of the face set in the encoding phase. Both phases were paired with task-irrelevant animated backgrounds, which included either the forward movement trajectory carrying the "cooperatively chasing" information, or the backward movement trajectory conveying no such chasing information. The backgrounds in the encoding and retrieving phases were either mismatched (i.e., forward and backward replays of the same trajectory), or matched (i.e., two identical forward movement trajectories in Experiment 1, or two different forward movement trajectories in Experiment 2). Participants in both experiments showed higher ensemble precisions and better discrimination sensitivities when backgrounds matched. The findings suggest that consistent social information perceived from memory-related context exerts a context-matching facilitation effect on ensemble coding, and more importantly, this effect is independent of consistent physical information.

Physics-informed neural networks for an optimal counterdiabatic quantum computation

A novel methodology that leverages physics-informed neural networks to optimize quantum circuits in systems with NQ qubits by addressing the counterdiabatic (CD) protocol is introduced. The primary purpose is to employ physics-inspired deep learning techniques for accurately modeling the time evolution of various physical observables within quantum systems. To achieve this, we integrate essential physical information into an underlying neural network to effectively tackle the problem. Specifically, the imposition of the solution to meet the principle of least action, along with the hermiticity condition on all physical observables, among others, ensuring the acquisition of appropriate CD terms based on underlying physics. This approach provides a reliable alternative to previous methodologies relying on classical numerical approximations, eliminating their inherent constraints. The proposed method offers a versatile framework for optimizing physical observables relevant to the problem, such as the scheduling function, gauge potential, temporal evolution of energy levels, among others. This methodology has been successfully applied to 2-qubit representing H2 molecule using the STO-3G basis, demonstrating the derivation of a desirable decomposition for non-adiabatic terms through a linear combination of Pauli operators. This attribute confers significant advantages for practical implementation within quantum computing algorithms.

Expert's experience-informed hierarchical kriging method for aerodynamic data modeling

Data-driven models, such as kriging, have gained popularity in aerospace engineering due to their capability of predicting multidimensional, nonlinear aerodynamic characteristics of an aircraft. However, they are still suffering from the problem associated with poor extrapolation capability and physical interpretability, which in turn has great impacts on aerodynamic performance and flight safety. To address this problem, this article proposes to incorporate an empirical aerodynamic model obtained from expert's understanding of aerodynamics in the fitting process of a kriging model. First, empirical aerodynamic models based on expert's understanding and experience are derived. Second, the regression term of a kriging model is replaced by the expert's experience-informed model so that the global trend can be consistent with physical laws and provides extra knowledge in the subregion(s) without training data, especially for the extrapolation region(s). Finally, the expert's experience-informed hierarchical kriging (EEI-HK) model is built in a sequential way. The proposed method is validated with analytical test examples and demonstrated by aerodynamic data modeling of an AGARD-B missile and an FDL-5A hypersonic flight vehicle. Results show that, compared with ordinary and universal kriging models, the proposed EEI-HK model can dramatically improve the prediction accuracy in the extrapolation domain and slightly enhance the interpolation accuracy, with assistance from physical information provided by an empirical model. In consequence, it can be a promising approach for aerodynamic data extrapolation and saving the cost of establishing an aerodynamic database.

Classical information entropy of parton distribution functions and an application in searching gluon saturation

Classical information entropy of parton distribution functions and an application in searching gluon saturation

Hypersonic inlet flow field reconstruction dominated by shock wave and boundary layer based on small sample physics-informed neural networks

High Mach number inlets face complex challenges such as shock wave/boundary layer interference, adversely impacting the aerodynamic characteristics and stable operating boundaries. Traditional computational fluid dynamics (CFD) simulations for performance and flow field analysis are slow, and data-driven deep learning technologies struggle with predicting flow fields in complex aerodynamic phenomena like shock wave/boundary layer interactions, boundary layer separation, and Mach disks. This study introduces a rapid, robust method for reconstructing hypersonic viscous inlet flow fields using inlet geometry design parameters. This method is called shock wave and boundary layer dominated two-dimensional multiphysical field reconstruction model based on multi-scale sensory field fusion residual physical information neural network (PIMSRF_ResNet), ensuring high-precision training data. Using the optimal Pareto solution set from multi-objective optimization with a small sample intelligence method, 150 sets of multi-physics fields under Ma10 conditions with varying geometric design parameters are calculated. Compared to traditional pure data-driven models, PIMSRF_ResNet's structural similarity in the test set reaches 0.9773, with a peak signal-to-noise ratio close to 30 dB and a correlation coefficient exceeding 98%. These experimental results demonstrated that the proposed method is a promising tool for real-time prediction of complex internal flow fields dominated by high Mach number shock waves and boundary layers.

RGB‐D road segmentation based on cross‐modality feature maintenance and encouragement

AbstractDeep images can provide rich spatial structure information, which can effectively exclude the interference of illumination and road texture in road scene segmentation and make better use of the prior knowledge of road area. This paper first proposes a new cross‐modal feature maintenance and encouragement network. It includes a quantization statistics module as well as a maintenance and encouragement module for effective fusion between multimodal data. Meanwhile, for the problem that if the road segmentation is performed directly using a segmentation network, there will be a lack of supervised guidance with clear physical meaningful information and poor interpretability of learning features, this paper proposes two road segmentation models based on prior knowledge of deep image: disparity information and surface normal vector information. Then, a two‐branch neural network is used to process the colour image and the processed depth image separately, to achieve the full utilization of the complementary features of the two modalities. The experimental results on the KITTI road dataset and Cityscapes dataset show that the method in this paper has good road segmentation performance and high computational efficiency.

Prediction and causal inference of cardiovascular and cerebrovascular diseases based on lifestyle questionnaires

Cardiovascular and cerebrovascular diseases (CCVD) are prominent mortality causes in Japan, necessitating effective preventative measures, early diagnosis, and treatment to mitigate their impact. A diagnostic model was developed to identify patients with ischemic heart disease (IHD), stroke, or both, using specific health examination data. Lifestyle habits affecting CCVD development were analyzed using five causal inference methods. This study included 473,734 patients aged ≥ 40 years who underwent specific health examinations in Kanazawa, Japan between 2009 and 2018 to collect data on basic physical information, lifestyle habits, and laboratory parameters such as diabetes, lipid metabolism, renal function, and liver function. Four machine learning algorithms were used: Random Forest, Logistic regression, Light Gradient Boosting Machine, and eXtreme-Gradient-Boosting (XGBoost). The XGBoost model exhibited superior area under the curve (AUC), with mean values of 0.770 (± 0.003), 0.758 (± 0.003), and 0.845 (± 0.005) for stroke, IHD, and CCVD, respectively. The results of the five causal inference analyses were summarized, and lifestyle behavior changes were observed after the onset of CCVD. A causal relationship from ‘reduced mastication’ to ‘weight gain’ was found for all causal species theory methods. This prediction algorithm can screen for asymptomatic myocardial ischemia and stroke. By selecting high-risk patients suspected of having CCVD, resources can be used more efficiently for secondary testing.

Deep Learning-Based Design Method for Acoustic Metasurface Dual-Feature Fusion.

Existing research in metasurface design was based on trial-and-error high-intensity iterations and requires deep acoustic expertise from the researcher, which severely hampered the development of the metasurface field. Using deep learning enabled the fast and accurate design of hypersurfaces. Based on this, in this paper, an integrated learning approach was first utilized to construct a model of the forward mapping relationship between the hypersurface physical structure parameters and the acoustic field, which was intended to be used for data enhancement. Then a dual-feature fusion model (DFCNN) based on a convolutional neural network was proposed, in which the first feature was the high-dimensional nonlinear features extracted using a data-driven approach, and the second feature was the physical feature information of the acoustic field mined using the model. A convolutional neural network was used for feature fusion. A genetic algorithm was used for network parameter optimization. Finally, generalization ability verification was performed to prove the validity of the network model. The results showed that 90% of the integrated learning models had an error of less than 3 dB between the real and predicted sound field data, and 93% of the DFCNN models could achieve an error of less than 5 dB in the local sound field intensity.

Giant electron-mediated phononic nonlinearity in semiconductor-piezoelectric heterostructures.

Efficient and deterministic nonlinear phononic interactions could revolutionize classical and quantum information processing at radio frequencies in much the same way that nonlinear photonic interactions have at optical frequencies. Here we show that in the important class of phononic materials that are piezoelectric, deterministic nonlinear phononic interactions can be enhanced by orders of magnitude via the heterogeneous integration of high-mobility semiconductor materials. To this end, a lithium niobate and indium gallium arsenide heterostructure is utilized to produce the most efficient three- and four-wave phononic mixing to date, to the best of our knowledge. We then show that the conversion efficiency can be further enhanced by applying semiconductor bias fields that amplify the phonons. We present a theoretical model that accurately predicts the three-wave mixing efficiencies in this work and extrapolate that these nonlinearities can be enhanced far beyond what is demonstrated here by confining phonons to smaller dimensions in waveguides and optimizing the semiconductor material properties.

Characterising the Hierarchy of Multi-time Quantum Processes with Classical Memory

Memory is the fundamental form of temporal complexity: when present but uncontrollable, it manifests as non-Markovian noise; conversely, if controllable, memory can be a powerful resource for information processing. Memory effects arise from/are transmitted via interactions between a system and its environment; as such, they can be either classical or quantum. From a practical standpoint, quantum processes with classical memory promise near-term applicability: they are more powerful than their memoryless counterpart, yet at the same time can be controlled over significant timeframes without being spoiled by decoherence. However, despite practical and foundational value, apart from simple two-time scenarios, the distinction between quantum and classical memory remains unexplored. Here, we analyse multi-time quantum processes with memory mechanisms that transmit only classical information forward in time. Complementing this analysis, we also study two related – but simpler to characterise – sets of processes that could also be considered to have classical memory from a structural perspective, and demonstrate that these lead to remarkably distinct phenomena in the multi-time setting. Subsequently, we systematically stratify the full hierarchy of memory effects in quantum mechanics, many levels of which collapse in the two-time setting, making our results genuinely multi-time phenomena.

Optimal superdense coding capacity in the non-Markovian regime

Superdense coding (SDC) is a significant technique widely used in quantum information processing. Indeed, it consists of sending two bits of classical information using a single qubit, leading to faster and more efficient quantum communication. In this paper, we propose a model to evaluate the effect of backflow information in a SDC protocol through a non-Markovian dynamics. The model considers a qubit interacting with a structured Markovian environment. In order to generate a non-Markovian dynamic, an auxiliary qubit contacts a Markovian reservoir in such a way that the non-Markovian regime can be induced. By varying the coupling strength between the central qubit and the auxiliary qubit, the two dynamical regimes can be switched interchangeably. An enhancement in non-Markovian effects corresponds to an increase in this coupling strength. Furthermore, we conduct an examination of various parameters, namely temperature weight, and decoherence parameters in order to explore the behaviors of SDC, quantum fisher information (QFI), and local quantum uncertainty using an exact calculation. The obtained results show a significant relationship between non-classical correlations and QFI since they behave similarly, allowing them to detect what is beyond entanglement. In addition, the presence of non-classical correlations enables us to detect the optimal SDC capacity in a non-Markovian regime.

Abstract PO1-11-03: Women with Metastatic Disease: Modeling a Clear and Well-Defined Announcement Pathway

Abstract Introduction: The announcement of the diagnosis of metastatic breast cancer is a key step in enabling the patient to fully understand her disease and the various treatment options, and thus to be more involved in her management and follow-up. However, according to the « Croyances et réalités 2 » survey conducted in 2021 among 269 French patients with metastatic breast cancer, 38% of them felt that they "did not fully understand" the information given by their doctor during the announcement consultation. Objective: The objective of this work is to model an optimal pathway for the announcement of metastatic breast cancer based on precise guidelines and good coordination between the different actors, in order to ensure that all patients have the necessary information and support. Method: A scientific committee composed of 4 French oncologists from 4 different centers initiated a collaborative process to propose, after discussions, precise guidelines for the announcement of metastatic breast cancer. Results: A set of 13 concrete guidelines were suggested by the expert group (Table 1). The central aspect is to build a specialized and flexible support, adapted to the needs of the patient, without ever hiding the truth about her disease. The announcement process must be adapted to the patient's history (recurrent or immediately metastatic), but also to the organization and resources available in the health care institution. The experts recommend a model of announcement process in two stages, separated by a few days. First, the oncologist announces what the entry into metastatic disease represents, followed by a repeat of the announcement by a nurse trained in the specificities of metastatic breast cancer, in order to have the patient reformulate her understanding of the disease and treatment. In a second phase, a new consultation allows to present the next therapeutic steps and ensure a coordinated intervention of the different health care professionals involved: doctors, nurses, pharmacists and supportive care specialists as needed. The offer of supportive care should be flexible and it is recommended that questionnaires should be made available to the patient to help identify her precise needs. The experts recommend that this second consultation be conducted jointly by an oncologist and a nurse. Subsequently, the town physician and pharmacist must be contacted to ensure the relay in the accompaniment of the patients in partnership with the cancer care center, and thus to ensure a good sharing of information on the patient's follow-up. To help the establishment of this dynamic announcement model, the experts gave advice on implementation: training the professionals involved in the process through theoretical training and role-playing, increasing the amount of time spent on the announcement consultation, and setting up a library of online or physical information materials to share with the patient. Finally, it is recommended that the financing of this process be prepared at the institutional level before its effective implementation in order to ensure its sustainability. Conclusion: Organizing a consultation for the announcement of metastatic breast cancer is essential to optimize the experience of the announcement and support. It must be a structured, funded and coordinated process between different health professionals specially trained for this approach. Table 1: Summary of guidelines for a metastatic breast cancer notification pathway. Citation Format: Florence Dalenc, Séverine Guiu, Marie-Ange Mouret-Reynier, Nathalie Quenel-Tueux. Women with Metastatic Disease: Modeling a Clear and Well-Defined Announcement Pathway [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-11-03.

Method for rock fracture prediction and early warning: Insight from fusion of multi-physics field information

Understanding the precursors leading to rock fracture is crucial for ensuring safety in mining and geotechnical engineering projects. To effectively discern these precursors, a collaborative monitoring approach that integrates multiple sources of information is imperative. This paper considered a rock multi-parameter monitoring loading system, incorporating infrared radiation and acoustic emission monitoring technologies to simultaneously track the rock fracture process. The study delves into the spatiotemporal evolution patterns of infrared radiation and acoustic emission in rock under loading. Utilizing stress, cumulative acoustic emission count, and average infrared radiation temperature (AIRT), the paper establishes a comprehensive evaluation model termed “acoustic-thermal-stress” fusion information, employing principal component analysis (PCA). The research reveals that the sensitivity to rock sample damage response follows the sequence of cumulative acoustic emission count, AIRT, and stress. Furthermore, a novel method for identifying rock fracture precursors is proposed, based on the first derivative of the comprehensive evaluation model. This method addresses the limitations of single physical field information, enhancing the robustness of monitoring data. It determines the average stress level of fracture precursors to be 0.77σmax. Subsequently, the study defines the probability function of rock damage during loading and fracture, enabling the realization of probability-based warnings for rock fracture. This approach introduces a new perspective on rock fracture prediction, significantly contributing to safety monitoring and warning systems in mine safety and geotechnical engineering. The findings of this research hold paramount engineering significance, offering valuable insights for enhancing safety measures in such projects.

Hybrid Integrated Silicon Photonics Based on Nanomaterials

Integrated photonic platforms have rapidly emerged as highly promising and extensively investigated systems for advancing classical and quantum information technologies, since their ability to seamlessly integrate photonic components within the telecommunication band with existing silicon-based industrial processes offers significant advantages. However, despite this integration facilitating the development of novel devices, fostering fast and reliable communication protocols and the manipulation of quantum information, traditional integrated silicon photonics faces inherent physical limitations that necessitate a challenging trade-off between device efficiency and spatial footprint. To address this issue, researchers are focusing on the integration of nanoscale materials into photonic platforms, offering a novel approach to enhance device performance while reducing spatial requirements. These developments are of paramount importance in both classical and quantum information technologies, potentially revolutionizing the industry. In this review, we explore the latest endeavors in hybrid photonic platforms leveraging the combination of integrated silicon photonic platforms and nanoscale materials, allowing for the unlocking of increased device efficiency and compact form factors. Finally, we provide insights into future developments and the evolving landscape of hybrid integrated photonic nanomaterial platforms.

Quantum driven deep learning for enhanced diabetic retinopathy detection

Traditional convolutional neural networks (CNNs) have shown potential for recognizing retinopathy caused by diabetes (DR). However, developing quantum computing has the possibility for improved feature representation. We propose a hybrid approach that combines classical CNNs with quantum circuits to capitalize on both classical and quantum information for DR classification. Using the Keras and Qiskit frameworks, our model encodes picture features into quantum states, allowing for richer representations. Through experiments on a collection of retinal pictures, our model displays competitive performance, with excellent reliability and precision in categorizing DR severity levels. This combination of classical and quantum paradigms offers a fresh approach to enhancing DR diagnosis and therapy.

Exploring Physics-Informed Neural Networks for the Generalized Nonlinear Sine-Gordon Equation

The nonlinear sine-Gordon equation is a prevalent feature in numerous scientific and engineering problems. In this paper, we propose a machine learning-based approach, physics-informed neural networks (PINNs), to investigate and explore the solution of the generalized non-linear sine-Gordon equation, encompassing Dirichlet and Neumann boundary conditions. To incorporate physical information for the sine-Gordon equation, a multiobjective loss function has been defined consisting of the residual of governing partial differential equation (PDE), initial conditions, and various boundary conditions. Using multiple densely connected independent artificial neural networks (ANNs), called feedforward deep neural networks designed to handle partial differential equations, PINNs have been trained through automatic differentiation to minimize a loss function that incorporates the given PDE that governs the physical laws of phenomena. To illustrate the effectiveness, validity, and practical implications of our proposed approach, two computational examples from the nonlinear sine-Gordon are presented. We have developed a PINN algorithm and implemented it using Python software. Various experiments were conducted to determine an optimal neural architecture. The network training was employed by using the current state-of-the-art optimization methods in machine learning known as Adam and L-BFGS-B minimization techniques. Additionally, the solutions from the proposed method are compared with the established analytical solutions found in the literature. The findings show that the proposed method is a computational machine learning approach that is accurate and efficient for solving nonlinear sine-Gordon equations with a variety of boundary conditions as well as any complex nonlinear physical problems across multiple disciplines.

Optimal quantum key distribution networks: capacitance versus security

The rate and security of quantum communications between users placed at arbitrary points of a quantum communication network depend on the structure of the network, on its extension and on the nature of the communication channels. In this work we propose a strategy for the optimization of trusted-relays based networks that intertwines classical network approaches and quantum information theory. Specifically, by suitably defining a quantum communication efficiency functional, we identify the optimal quantum communication connections through the network by balancing security and the quantum communication rate. The optimized network is then constructed as the network of the maximal quantum communication efficiency connections and its performance is evaluated by studying the scaling of average properties as functions of the number of nodes and of the network spatial extension.

Land-use interactions, Oil-Field infrastructure, and natural processes control hydrocarbon and arsenic concentrations in groundwater, Poso Creek Oil Field, California, USA

Like many hydrocarbon production areas in the U.S., the Poso Creek Oil Field in California includes and is adjacent to other land uses (agricultural and other developed lands) that affect the hydrology and geochemistry of the aquifer overlying and adjacent to oil development. We hypothesize that the distributions of hydrocarbons and arsenic in groundwater in such areas will be controlled by complex interactions between mixed land uses, oil-field infrastructure, and natural processes. In 2020–2021, samples of groundwater and surface water were collected and analyzed for a large suite of inorganic and organic chemicals and isotope and gas tracers to test this hypothesis. Those data are supplemented with ancillary data on historical geochemistry, hydrology, geology, and oil-field infrastructure. Hydrocarbons in groundwater (e.g., methane through pentane gases and benzene) are associated with natural processes (e.g., fault offsets or transition in sediment depositional environment) and oil-field infrastructure (e.g., fluid-migration pathways associated with uncemented annulus in oil wells or unlined pits). Arsenic concentrations >10 μg per liter (μg/L; maximum concentration 12.9 μg/L) are associated with natural processes in old, high-pH groundwater, and more recent recharge of water from natural and/or engineered recharge processes. Along the southwest margin of the oil field, pumping for drinking-water and irrigation supplies in combination with engineered groundwater recharge produce a depression in groundwater elevations where groundwater with elevated sulfate concentrations from agricultural areas and groundwater with hydrocarbons from the oil field mix to produce a zone of sulfate reduction that removes hydrocarbons and arsenic from groundwater but produces elevated sulfide (S2-) concentrations (maximum concentration 29 mg per liter, mg/L). In this study, multiple approaches were required to resolve the overlapping effects of land uses, oil-field infrastructure, and natural processes on the distributions of hydrocarbons and arsenic in groundwater. The combined use of geographic, historical, physical, chemical, isotopic, and other information to constrain processes could be a useful approach for studies in other hydrocarbon-production areas. This is particularly important where land uses affect aquifer hydrology to an extent that causes mixing of groundwaters with different chemical compositions.

Surface current prediction based on a physics-informed deep learning model

Sea surface current is an important factor that affects various aspects of oceanic climate, energy, and shipping. Traditional methods for simulating current based on ocean dynamic equations are computationally complex, while existing data-driven deep learning methods overlook the constraints of actual physical processes, limiting the further improvement of prediction accuracy. In this paper, we address the issue of sea surface current prediction by proposing a deep learning model that incorporates physical processes. The model combines physical information with deep learning techniques to overcome the limitations of low computational efficiency in numerical models and the lack of interpretability in data-driven deep learning methods. This integration allows us to improve the prediction accuracy of sea surface current. Furthermore, we propose a weighted loss function into the model to address the challenge of accurately predicting high-magnitude current. The research results demonstrate that our proposed prediction model for sea surface current outperforms the baseline model, particularly in predicting high-magnitude current. Simultaneously, the model demonstrates favorable generalization performance and lower data dependency. This paper provides a new and effective method and strategy for sea surface current prediction.

Adaptive Whale Optimization Algorithm–DBiLSTM for Autonomous Underwater Vehicle (AUV) Trajectory Prediction

AUVs are autonomous underwater robots equipped with advanced sensors and navigation systems. Due to the complexity and uncertainty of the marine environment, AUVs are susceptible to the effects of the marine environment and may experience communication delays or even accidents. Based on the aforementioned issues, this paper proposes a prediction method for lost AUVs based on an adaptive optimization depth BiLSTM (AWOA-DBiLSTM) neural network model. To enhance prediction accuracy, AWOA-DBiLSTM employs a double BiLSTM to extract AUV features from positional information and physical attitude. Additionally, AWOA-DBiLSTM utilizes a gating mechanism to filter and reset physical attitude feature information to obtain features associated with positional information. After undergoing filtering operations, the physical attitude information of the AUV is fused with the position information to achieve trajectory prediction. For the first time, the differentiation and stratified extraction of AUV data features are presented in this paper. The experimental results demonstrate that the model achieves significant improvements in prediction accuracy and generalization, and the present study is of great significance for application in the task of predicting the trajectories of lost AUVs.