When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios Engineering

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

Guest Editorial:Special Issue on Intelligent Interpretation of Remote Sensing Images: Theory, Methods and Applications

This special edition of the Journal of Geodesy and Geoinformation Science(JGGS)presents a curated selection of papers that offer novel techniques in the realm of intelligent interpretation of remote sensing images.In recent years,tremendous efforts have been made across a wide spectrum including theory,models,algorithms,applications and datasets,etc.Such advancements have demonstrated their stunning performance in a variety of remote sensing topics spanning from general challenges like remote image classification and object detection,to highly specialized tasks such as autonomous situation awareness and disaster response.Moreover,such achievements have redefined disciplinary boundaries and also reshaped our education.

Prescribed Performance Evolution Control for Quadrotor Autonomous Shipboard Landing

The shipboard landing problem for a quadrotor is addressed in this paper,where the ship trajectory tracking control issue is transformed into a stabilization control issue by building a relative position model.To guarantee both transient performance and steady-state landing error,a prescribed performance evolution control(PPEC)method is developed for the relative position control.In addition,a novel compensation system is proposed to expand the performance boundaries when the input saturation occurs and the error exceeds the predefined threshold.Considering the wind and wave on the relative position model,an adaptive sliding mode observer(ASMO)is designed for the disturbance with unknown upper bound.Based on the dynamic surface control framework,a shipboard landing controller integrating PPEC and ASMO is established for the quadrotor,and the relative position control error is guaranteed to be uniformly ultimately bounded.Simulation results have verified the feasibility and effectiveness of the proposed shipboard landing control scheme.

Interpretable machine learning optimization(InterOpt)for operational parameters:A case study of highly-efficient shale gas development

An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning,and is demonstrated via optimization of shale gas development.InterOpt consists of three parts:a neural network is used to construct an emulator of the actual drilling and hydraulic fracturing process in the vector space(i.e.,virtual environment);:the Sharpley value method in inter-pretable machine learning is applied to analyzing the impact of geological and operational parameters in each well(i.e.,single well feature impact analysis):and ensemble randomized maximum likelihood(EnRML)is conducted to optimize the operational parameters to comprehensively improve the efficiency of shale gas development and reduce the average cost.In the experiment,InterOpt provides different drilling and fracturing plans for each well according to its specific geological conditions,and finally achieves an average cost reduction of 9.7%for a case study with 104 wells.

Channel-Feedback-Free Transmission for Downlink FD-RAN:A Radio Map Based Complex-Valued Precoding Network Approach

As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network costs.However,with the decoupling of uplink base stations and downlink base stations in FDRAN,the traditional transmission mechanism,which relies on real-time channel feedback,is not suitable as the receiver is not able to feedback accurate and timely channel state information to the transmitter.This paper proposes a novel transmission scheme without relying on physical layer channel feedback.Specifically,we design a radio map based complex-valued precoding network(RMCPNet)model,which outputs the base station precoding based on user location.RMCPNet comprises multiple subnets,with each subnet responsible for extracting unique modal features from diverse input modalities.Furthermore,the multimodal embeddings derived from these distinct subnets are integrated within the information fusion layer,culminating in a unified representation.We also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss function.We evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16%and 76%performance improvements over the conventional real-valued neural network and statistical codebook approach,respectively.

Discovery of Partial Differential Equations from Highly Noisy and Sparse Data with Physics-Informed Information Criterion

Data-driven discovery of partial differential equations(PDEs)has recently made tremendous progress,and many canonical PDEs have been discovered successfully for proof of concept.However,determining the most proper PDE without prior references remains challenging in terms of practical applications.In this work,a physics-informed information criterion(PIC)is proposed to measure the parsimony and precision of the discovered PDE synthetically.The proposed PIC achieves satisfactory robustness to highly noisy and sparse data on 7 canonical PDEs from different physical scenes,which confirms its ability to handle difficult situations.The PIC is also employed to discover unrevealed macroscale governing equations from microscopic simulation data in an actual physical scene.The results show that the discovered macroscale PDE is precise and parsimonious and satisfies underlying symmetries,which facilitates understanding and simulation of the physical process.The proposition of the PIC enables practical applications of PDE discovery in discovering unrevealed governing equations in broader physical scenes.

What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence

THE well-known ancient Chinese philosopher Lao Tzu(老子)or Laozi(6th~4th century BC during the Spring and Autumn period)started his classic Tao Teh Ching《道德经》or Dao De Jing(see Fig.1)with six Chinese characters:"道(Dao)可(Ke)道(Dao)非(Fei)常(Chang)道(Dao)",which has been traditionally interpreted as“道可道,非常道”or"The Dao that can be spoken is not the eternal Dao".

SST-V:A Scalable Semantic Transmission Framework for Video

The emerging new services in the sixth generation(6G)communication system impose increasingly stringent requirements and challenges on video transmission.Semantic communications are envisioned as a promising solution to these challenges.This paper provides a highly-efficient solution to video transmission by proposing a scalable semantic transmission algorithm,named scalable semantic transmission framework for video(SST-V),which jointly considers the semantic importance and channel conditions.Specifically,a semantic importance evaluation module is designed to extract more informative semantic features according to the estimated importance level,facilitating high-efficiency semantic coding.By further considering the channel condition,a cascaded learning based scalable joint semanticchannel coding algorithm is proposed,which autonomously adapts the semantic coding and channel coding strategies to the specific signalto-noise ratio(SNR).Simulation results show that SST-V achieves better video reconstruction performance,while significantly reducing the transmission overhead.

Drogue detection for autonomous aerial refueling via hybrid pigeon-inspired optimized color opponent and saliency aggregation

Drogue detection is one of the challenging tasks in autonomous aerial refueling due to the requirement for accuracy and rapidity.Saliency detection based on image intrinsic cues can achieve fast detection,but with poor accuracy.Recent studies reveal that optimization-based methods provide accurate and quick solutions for saliency detection.This paper presents a hybrid pigeon-inspired optimization method,the optimized color opponent,that aims to adjust the weight of color opponent channels to detect the drogue region.It can optimize the weights in the selected aerial refueling scene offline,and the results are applied for drogue detection in the scene.A novel algorithm aggregated by the optimized color opponent and robust background detection is presented to provide better precision and robustness.Experimental results on benchmark datasets and aerial refueling images show that the proposed method successfully extracts the saliency region or drogue and exhibits superior performance against the other saliency detection methods with intrinsic cues.The algorithm designed in this paper is competent for the drogue detection task of autonomous aerial refueling.

Dynamic targets searching assistance based on virtual camera priority

Background When a user walks freely in an unknown virtual scene and searches for multiple dynamic targets,the lack of a comprehensive understanding of the environment may have a negative impact on the execution of virtual reality tasks.Previous studies can help users with auxiliary tools,such as top view maps or trails,and exploration guidance,for example,automatically generated paths according to the user location and important static spots in virtual scenes.However,in some virtual reality applications,when the scene has complex occlusions,and the user cannot obtain any real-time position information of the dynamic target,the above assistance cannot help the user complete the task more effectively.Methods We design a virtual camera priority-based assistance to help the user search dynamic targets efficiently.Instead of forcing users to go to destinations,we provide an optimized instant path to guide them to places where they are more likely to find dynamic targets when they ask for help.We assume that a certain number of virtual cameras are fixed in virtual scenes to obtain extra depth maps,which capture the depth information of the scene and the locations of the dynamic targets.Our method automatically analyzes the priority of these virtual cameras,chooses the destination,and generates an instant path to assist the user in finding the dynamic targets.Our method is suitable for various virtual reality applications that do not require manual supervision or input.Results A user study is designed to evaluate the proposed method.The results indicate that compared with three conventional navigation methods,such as the top-view method,our method can help users find dynamic targets more efficiently.The advantages include reducing the task completion time,reducing the number of resets,increasing the average distance between resets,and reducing user task load.Conclusions We presented a method for improving dynamic target searching efficiency in virtual scenes by virtual camera priority-based path guidance.Compared with three conventional navigation methods,such as the top-view method,this method can help users find dynamic targets more effectively.

Flexible Hf_(0.5)Zr_(0.5)O_(2)ferroelectric thin films on polyimide with improved ferroelectricity and high flexibility

Flexible memory devices are promising for information storage and data processing applications in portable,wearable,and smart electronics operating under curved conditions.In this work,we realized high-performance flexible ferroelectric capacitors based on Hf_(0.5)Zr_(0.5)O_(2)(HZO)thin film by depositing a buffer layer of Al_(2)O_(3)on polyimide(PI)substrates using atomic layer deposition(ALD).The flexible ferroelectric HZO films exhibit high remnant polarization(Pr)of 21μC/cm^(2).Furthermore,deterioration of polarization,retention,and endurance performance was not observed even at a bending radius of 2 mm after 5,000 bending cycles.This work marks a critical step in the development of high-performance flexible HfO_(2)-based ferroelectric memories for next-generation wearable electronic devices.

Microscopic mechanism of imprint in hafnium oxide-based ferroelectrics

Hafnia-based ferroelectrics have greatly revived the field of ferroelectric memory(FeRAM),but certain reliability issues must be satisfactorily resolved before they can be widely applied in commercial memories.In particular,the imprint phenomenon severely jeopardizes the read-out reliability in hafnia-based ferroelectric capacitors,but its origin remains unclear,which hinders the development of its recovery schemes.In this work,we have systematically investigated the imprint mechanism in TiN/Hf_(0.5)Zr_(0.5)O_(2)(HZO)/TiN ferroelectric capacitors using experiments and first-principles calculations.It is shown that carrier injection-induced charged oxygen vacancies are at the heart of imprint in HZO,where other mechanisms such as domain pinning and dead layer are less important.An imprint model based on electron de-trapping from oxygen vacancy sites has been proposed that can satisfactorily explain several experimental facts such as the strong asymmetric imprint,leakage current variation,and so forth.Based on this model,an effective imprint recovery method has been proposed,which utilizes unipolar rather than bipolar voltage inputs.The remarkable recovery performances demonstrate the prospect of improved device reliability in hafnia-based FeRAM devices.

Exploring Frontier Technologies in Video-Based Person Re-Identification:A Survey on Deep Learning Approach

Video-based person re-identification(Re-ID),a subset of retrieval tasks,faces challenges like uncoordinated sample capturing,viewpoint variations,occlusions,cluttered backgrounds,and sequence uncertainties.Recent advancements in deep learning have significantly improved video-based person Re-ID,laying a solid foundation for further progress in the field.In order to enrich researchers’insights into the latest research findings and prospective developments,we offer an extensive overview and meticulous analysis of contemporary video-based person ReID methodologies,with a specific emphasis on network architecture design and loss function design.Firstly,we introduce methods based on network architecture design and loss function design from multiple perspectives,and analyzes the advantages and disadvantages of these methods.Furthermore,we provide a synthesis of prevalent datasets and key evaluation metrics utilized within this field to assist researchers in assessing methodological efficacy and establishing benchmarks for performance evaluation.Lastly,through a critical evaluation of the experimental outcomes derived from various methodologies across four prominent public datasets,we identify promising research avenues and offer valuable insights to steer future exploration and innovation in this vibrant and evolving field of video-based person Re-ID.This comprehensive analysis aims to equip researchers with the necessary knowledge and strategic foresight to navigate the complexities of video-based person Re-ID,fostering continued progress and breakthroughs in this challenging yet promising research domain.