Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

Abstract: Abstract The assessment of glomerular lesions is a fundamental step toward the diagnosis of glomerular diseases. This requires diagnosis and fusion of information from all the glomeruli at multiple levels and stainings. The lack of research on multi‐level multistained glomerular identification and matching has resulted in renal pathologists devoting much time and attention to this time‐consuming and labor‐intensive process. This limits the overall efficiency of the diagnosis of glomerular diseases. This paper constructed a dataset consisting of 600 cases, each containing 12 levels of whole slide images from H&E, PAS, Masson trichrome, and PASM staining. The glomeruli identifying and matching was proposed. First, a multistained transformer‐based Mask R‐CNN is proposed to extract the position and contours of the glomeruli. Second, coherent point drift‐based coarse matching and hybrid feature‐based fine matching achieve pairwise matching. Finally, the voting‐based cross‐matching realizes 12‐level multistained matching. This system constitutes a practical human‐computer interface. Intensive experiments were conducted to validate the ability to identify and match 12‐level multistained glomeruli. The mAP@50 for detection and segmentation reached 95.40% and 95.70%, respectively. The basic and comprehensive matching rates of the glomeruli matching reached 98.25% and 74.59%, respectively. Visualization results further demonstrate that the model achieved accurate identification and matching. The proposed system achieves accurate identification and matching of 12 levels of multistained glomeruli and can serve as a tool for pathological diagnosis of glomerular diseases by simplifying the diagnostic process. More importantly, this system can lay the foundation for the fully automated assisted diagnosis of glomerular diseases.


Taming the Rotating Wave Approximation

Abstract: The interaction between light and matter is one of the oldest research areas of quantum mechanics, and a field that just keeps on delivering new insights and applications. With the arrival of cavity and circuit quantum electrodynamics we can now achieve strong light-matter couplings which form the basis of most implementations of quantum technology. But quantum information processing also has high demands requiring total error rates of fractions of percentage in order to be scalable (fault-tolerant) to useful applications. Since errors can also arise from modelling, this has brought into center stage one of the key approximations of quantum theory, the Rotating Wave Approximation (RWA) of the quantum Rabi model, leading to the Jaynes-Cummings Hamiltonian. While the RWA is often very good and incredibly useful to understand light-matter interactions, there is also growing experimental evidence of regimes where it is a bad approximation. Here, we ask and answer a harder question: for which experimental parameters is the RWA, although perhaps qualitatively adequate, already not good enough to match the demands of scalable quantum technology? For example, when is the error at least, and when at most, 1%? To answer this, we develop rigorous non-perturbative bounds taming the RWA. We find that these bounds not only depend, as expected, on the ratio of the coupling strength and the oscillator frequency, but also on the average number of photons in the initial state. This confirms recent experiments on photon-dressed Bloch-Siegert shifts. We argue that with experiments reporting controllable cavity states with hundreds of photons and with quantum error correcting codes exploring more and more of Fock space, this state-dependency of the RWA is increasingly relevant for the field of quantum computation, and our results pave the way towards a better understanding of those experiments.


GA-Net: Accurate and Efficient Object Detection on UAV Images Based on Grid Activations

Abstract: Object detection plays a crucial role in unmanned aerial vehicle (UAV) missions, where captured objects are often small and require high-resolution processing. However, this requirement is always in conflict with limited computing resources, vast fields of view, and low latency requirements. To tackle these issues, we propose GA-Net, a novel approach tailored for UAV images. The key innovation includes the Grid Activation Module (GAM), which efficiently calculates grid activations, the probability of foreground presence at grid scale. With grid activations, the GAM helps filter out patches without objects, minimize redundant computations, and improve inference speeds. Additionally, the Grid-based Dynamic Sample Selection (GDSS) focuses the model on discriminating positive samples and hard negatives, addressing background bias during training. Further enhancements involve GhostFPN, which refines Feature Pyramid Network (FPN) using Ghost module and depth-wise separable convolution. This not only expands the receptive field for improved accuracy, but also reduces computational complexity. We conducted comprehensive evaluations on DGTA-Cattle-v2, a synthetic dataset with added background images, and three public datasets (VisDrone, SeaDronesSee, DOTA) from diverse domains. The results prove the effectiveness and practical applicability of GA-Net. Despite the common accuracy and speed trade-off challenge, our GA-Net successfully achieves a mutually beneficial scenario through the strategic use of grid activations.


IASCAR: Incremental Answer Set Counting by Anytime Refinement

Abstract: Abstract Answer set programming (ASP) is a popular declarative programming paradigm with various applications. Programs can easily have many answer sets that cannot be enumerated in practice, but counting still allows quantifying solution spaces. If one counts under assumptions on literals, one obtains a tool to comprehend parts of the solution space, so-called answer set navigation . However, navigating through parts of the solution space requires counting many times, which is expensive in theory. Knowledge compilation compiles instances into representations on which counting works in polynomial time. However, these techniques exist only for conjunctive normal form (CNF) formulas, and compiling ASP programs into CNF formulas can introduce an exponential overhead. This paper introduces a technique to iteratively count answer sets under assumptions on knowledge compilations of CNFs that encode supported models. Our anytime technique uses the inclusion–exclusion principle to improve bounds by over- and undercounting systematically. In a preliminary empirical analysis, we demonstrate promising results. After compiling the input (offline phase), our approach quickly (re)counts.


Pistol: Pupil Invisible Supportive Tool in the Wild

Abstract: Abstract This paper is an in the wild evaluation of the eye tracking tool Pistol. Pistol supports Pupil Invisible projects and other eye trackers (Dikablis, Emke GmbH, Look, Pupil, and many more) in offline mode. For all eye tracking recordings, Pistol is able to compute pupil and iris ellipse, eye aperture, eye lids, 3D eye ball, optical vector estimation, and eye movement types. For Pupil Invisible projects, it also supports the detection of a special marker and allows the 2D and 3D gaze estimation mapped on the scene camera. In this extended journal paper for Pistol we focus on challenging in the wild situations, such as reflections on glasses, eye tracking during sport, changing lightning conditions, and this for two eye trackers. The tool Pistol can be downloaded here Link


A Study on Robust Finite-Time Visual Servoing with a Gyro-Stabilized Surveillance System

Abstract: This article presents the design and validation of a novel visual servoing scheme for a surveillance system. In this system, a two-axis gimbal mechanism operates the rotation of a camera which is able to provide visual information on the tracked target for the control system. The control objective is to bring the target’s projection to the center of the image plane with the smallest steady-state error and a smooth transient response, even with the unpredictable motion of the target and the influence of external disturbances. To fulfill these tasks, the proposed control scheme is designed consisting of two parts: (1) an observer estimates simultaneously the matched and unmatched disturbances; and (2) a motion control law guarantees the finite-time stability and visual servoing performance. Finally, experiments are conducted for validation and evaluation. The proposed control system shows its consistency and ought to perform better than previous approaches.


Optimizing Camera Motion with MCTS and Target Motion Modeling in Multi-Target Active Object Tracking

Abstract: In this work, we are dedicated to multi-target active object tracking (AOT), where the goal is to achieve continuous tracking of targets through real-time control of camera. This form of active camera control can be applied to unmanned aerial vehicles (UAV), intelligent robots, and sports events. Our work is conducted in an environment featuring multiple cameras and targets, where our goal is to maximize target coverage. Contrasting with previous research, our work introduces additional degrees of freedom for the cameras, allowing them not only to rotate but also to move along boundary lines. In addition, we model the motion of target to predict the future position of the target in environment. With target’s future position, we use Monte Carlo Tree Search (MCTS) method to find the optimal action of camera. Since the action space is large, we propose to leverage the action selection from multi-agent reinforcement learning (MARL) network to prune the search tree of Monte Carlo Tree Search method, so as to find the optimal action more efficiently. We establish a multi-target 2D environment to simulate several sports games, and experimental results demonstrate that our method can effectively improve the target coverage. The code is available at: http://github.com/HopeChanger/ActiveObjectTracking.


Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems

Abstract: Industrial control systems (ICSs) play a crucial role in managing and monitoring critical processes across various industries, such as manufacturing, energy, and water treatment. The connection of equipment from various manufacturers, complex communication methods, and the need for the continuity of operations in a limited environment make it difficult to detect system anomalies. Traditional approaches that rely on supervised machine learning require time and expertise due to the need for labeled datasets. This study suggests an alternative approach to identifying anomalous behavior within ICSs by means of unsupervised machine learning. The approach employs unsupervised machine learning to identify anomalous behavior within ICSs. This study shows that unsupervised learning algorithms can effectively detect and classify anomalous behavior without the need for pre-labeled data using a composite autoencoder model. Based on a dataset that utilizes HIL-augmented ICSs (HAIs), this study shows that the model is capable of accurately identifying important data characteristics and detecting anomalous patterns related to both value and time. Intentional error data injection experiments could potentially be used to validate the model’s robustness in real-time monitoring and industrial process performance optimization. As a result, this approach can improve system reliability and operational efficiency, which can establish a foundation for safe and sustainable ICS operations.


Transfer Reinforcement Learning for Combinatorial Optimization Problems

Abstract: Reinforcement learning is an important technique in various fields, particularly in automated machine learning for reinforcement learning (AutoRL). The integration of transfer learning (TL) with AutoRL in combinatorial optimization is an area that requires further research. This paper employs both AutoRL and TL to effectively tackle combinatorial optimization challenges, specifically the asymmetric traveling salesman problem (ATSP) and the sequential ordering problem (SOP). A statistical analysis was conducted to assess the impact of TL on the aforementioned problems. Furthermore, the Auto_TL_RL algorithm was introduced as a novel contribution, combining the AutoRL and TL methodologies. Empirical findings strongly support the effectiveness of this integration, resulting in solutions that were significantly more efficient than conventional techniques, with an 85.7% improvement in the preliminary analysis results. Additionally, the computational time was reduced in 13 instances (i.e., in 92.8% of the simulated problems). The TL-integrated model outperformed the optimal benchmarks, demonstrating its superior convergence. The Auto_TL_RL algorithm design allows for smooth transitions between the ATSP and SOP domains. In a comprehensive evaluation, Auto_TL_RL significantly outperformed traditional methodologies in 78% of the instances analyzed.


Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning

Abstract: Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifier using both labeled and pseudo-labeled samples. However, in data-scarce scenarios, reliance on labeled samples for initial classifier generation can degrade performance. Methods based on consistency regularization have shown promising results by encouraging consistent outputs for different semantic variations of the same sample obtained through diverse augmentation techniques. However, existing methods typically utilize only weak and strong augmentation variants, limiting information extraction. Therefore, a multi-augmentation contrastive semi-supervised learning method (MAC-SSL) is proposed. MAC-SSL introduces moderate augmentation, combining outputs from moderately and weakly augmented unlabeled images to generate pseudo-labels. Cross-entropy loss ensures consistency between strongly augmented image outputs and pseudo-labels. Furthermore, the MixUP is adopted to blend outputs from labeled and unlabeled images, enhancing consistency between re-augmented outputs and new pseudo-labels. The proposed method achieves a state-of-the-art performance (accuracy) through extensive experiments conducted on multiple datasets with varying numbers of labeled samples. Ablation studies further investigate each component’s significance.


Enhancing sparrow search algorithm with OCSSA: Integrating osprey optimization and Cauchy mutation for improved convergence and precision

Abstract: Abstract This study enhances the traditional sparrow search algorithm (SSA) to address its weaknesses, such as poor convergence rates and precision due to a tendency to fall into local optima. The improved version, OCSSA, integrates the Osprey optimization algorithm (OOA) and Cauchy mutation. Logistic chaotic mapping is used for initial population generation to increase genetic diversity. OOA boosts global exploration in the producer phase, and Cauchy mutation in the scroungers phase disrupts suboptimal solutions, enhancing the algorithm's ability to avoid local optima. OCSSA's performance, validated through ten benchmark functions and fault diagnosis optimization tasks, significantly improves convergence speed and accuracy, proving its effectiveness in complex optimization challenges.


Enhancing conceptual models with computational capabilities: A methodical approach to executable integrative modeling

Abstract: Abstract The lack of a common executable modeling framework that integrates systems engineering, software design, and other engineering domains is a major impediment to seamless product development processes. Our research aims to overcome this system‐software modeling gap by integrating computational, software‐related, and model execution capabilities into OPM‐based conceptual modeling, resulting in a holistic unified executable quantitative‐qualitative modeling framework. The gap is overcome via a Methodical Approach to Executable Integrative Modeling—MAXIM, an extension of OPM ISO 19450:2015, a standardization approvement given on 2015. We present the principles of MAXIM and demonstrate its operation within OPCloud—a web‐based collaborative conceptual OPM modeling framework. As a proof‐of‐concept, a model of an Airbus civil aircraft landing gear braking system is constructed and executed. Using MAXIM, engineers from five domains can collaborate at the very early phase of the system development and jointly construct a unified model that fuses qualitative and quantitative aspects of the various disciplines. This case study illustrates an important first step towards satisfying the critical and growing need to integrate systems engineering with software computations into a unified framework that enables a smooth transition from high‐level architecting to detailed, discipline‐oriented design. Such a framework is a key to agile yet robust future development of software‐intensive systems.


Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks

Abstract: The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput.


Integrating Blockchain in Smart Grids for Enhanced Demand Response: Challenges, Strategies, and Future Directions

Abstract: This research, conducted throughout the years 2022 and 2023, examines the role of blockchain technology in optimizing Demand Response (DR) within Smart Grids (SGs). It critically assesses a range of blockchain architectures, evaluating their impact on enhancing DR’s efficiency, security, and consumer engagement. Concurrently, it addresses challenges like scalability, interoperability, and regulatory complexities inherent in merging blockchain with existing energy systems. By integrating theoretical and practical viewpoints, it reveals the potential of blockchain technology to revolutionize Demand Response (DR). Findings affirm that integrating blockchain technology into SGs effectively enhances the efficiency and security of DR, and empirical data illustrate substantial improvements in both cases. Furthermore, key challenges include scalability and interoperability, and also identifying opportunities to enhance consumer engagement and foster system transparency in the adoption of blockchain within DR and SGs. Finally, this work emphasizes the necessity for further investigation to address development hurdles and enhance the effectiveness of blockchain technology in sustainable energy management in SGs.


The Geochemical Characteristics of Trace Elements in the Magnetite and Fe Isotope Geochemistry of the Makeng Iron Deposit in Southwest Fujian and Their Significance in Ore Genesis

Abstract: The Makeng iron deposit in southwest Fujian is a significant iron polymetallic deposit with various types of iron ore, including garnet magnetite, diopside magnetite, and quartz magnetite. The metallogenetic type of the deposit has been a subject of debate, in particular the genesis of magnetite and source of iron. In situ microanalysis of trace elements in magnetite from different ores shows relatively low levels of V, Ti, Cu, and Zn, with higher concentrations of Ca and Si, indicating the characteristics of a skarn type deposit. The δ57Fe values of the magnetite range from −0.091‰ to 0.317‰. Combining these data, whole-rock iron isotope analyses, including Juzhou and Dayang granites, diabase, and the Lower Carboniferous Lindi Formation sandstone, suggest that Fe in the magnetite primarily originates from granitic pluton, with potential contributions from diabase and the Lower Carboniferous Lindi Formation sandstone. Combined with field work, it is indicated that Makeng iron deposit is a skarn-type magnetite deposit associated with Yanshanian granitic intrusions. Therefore, the initial ore-forming fluid is postulated to be a high-temperature magmatic hydrothermal fluid with high oxygen fugacity. This fluid infiltrated spaces such as interlayer fracture zones between the Upper Carboniferous Jingshe Formation–Middle Permian Qixia Formation carbonate rocks and the Lower Carboniferous Lindi Formation sandstone, resulting in diverse magnetite ores due to metasomatism. The mineralization process of the Makeng iron deposit is basically the same and composed of typical skarn deposits. Magnetite was mainly formed during calcic skarn formation and continued to the initial stages of the retrograde alteration of skarns. In contrast, sulfide minerals, including molybdenite, sphalerite, and galena, precipitated during the quartz–sulfide stage.


Electron Probe Micro-Analyzer Studies and Raman Spectroscopic Characterization of Plagioclase in Basaltic Rocks from the Asal–Ghoubbet Area, Republic of Djibouti

Abstract: The Asal–Ghoubbet Rift area comprises basaltic rocks with similar compositions that were formed by volcanic eruptions. To gain insight into the magmatic processes of these volcanic formations, we investigated the mineral chemistry of plagioclase macrocrysts and microcrysts found in the basaltic rocks by using an electron probe micro-analyzer (EPMA). These basaltic rocks contain olivine, pyroxene, euhedral plagioclase macrocrysts, and euhedral to subhedral plagioclase microcrysts. These plagioclase macrocrysts reach up to 4 cm in length in the form of giant crystals as plagioclase ultraphyric basalts (PUBs). They have a mineral composition varying from bytownite to labradorite with anorthite content ranging from An53 to An86. Also, the microcrysts of all these volcanic rocks are characterized by labradorite and andesine compositions with An22–80. According to the calculated plagioclase thermobarometry, the crystallization temperature of the plagioclase macrocrysts and microcrysts is 1082 to 1216 °C and 1072 to 1203 °C, respectively, and the pressure is 3.92 to 14.51 kbar for the macrocrysts and 2.99 to 14.84 kbar for the microcrysts. Based on these thermobarometry results for the plagioclases, we conclude that the volcanic formations located in the Asal–Ghoubbet area would have come from different eruptions from a single magmatic chamber.


Concept logic trees: enabling user interaction for transparent image classification and human-in-the-loop learning

Abstract: Abstract Interpretable deep learning models are increasingly important in domains where transparent decision-making is required. In this field, the interaction of the user with the model can contribute to the interpretability of the model. In this research work, we present an innovative approach that combines soft decision trees, neural symbolic learning, and concept learning to create an image classification model that enhances interpretability and user interaction, control, and intervention. The key novelty of our method relies on the fusion of an interpretable architecture with neural symbolic learning, allowing the incorporation of expert knowledge and user interaction. Furthermore, our solution facilitates the inspection of the model through queries in the form of first-order logic predicates. Our main contribution is a human-in-the-loop model as a result of the fusion of neural symbolic learning and an interpretable architecture. We validate the effectiveness of our approach through comprehensive experimental results, demonstrating competitive performance on challenging datasets when compared to state-of-the-art solutions.


Wireless-Channel Key Distribution Based on Laser Synchronization

Abstract: We propose and experimentally demonstrate a wireless-channel key distribution scheme based on laser synchronization induced by a common wireless random signal. Two semiconductor lasers are synchronized under injection of the drive signal after electrical-optical conversion and emit irregular outputs that are used to generate shared keys. Our proof-of-concept experiment using a complex drive signal achieved a secure key generation rate of up to 150 Mbit/s with a bit error rate below 3.8 × 10−3. Numerical simulation results show that the proposed scheme has the potential to achieve a distribution distance of several hundred meters. It is believed that common-signal-induced laser synchronization paves the way for high-speed wireless physical-layer key distribution.


Bitterling fish optimization (BFO) algorithm

Abstract: Abstract The bitterling fish is a prime example of intelligent behavior in nature for survival. The bitterling fish uses the oyster spawning strategy as their babysitter. The female bitterling fish looks for a male fish stronger than other fish to find the right pair. In order to solve optimization issues, the Bitterling Fish Optimization (BFO) algorithm is modeled in this manuscript based on the mating behavior of these fish. The bitterling fish optimization algorithm is more accurate than the gray wolf optimization algorithm, whale optimization algorithm, butterfly optimization algorithm, Harris Hawks optimization algorithm, and black widow optimization algorithm, according to experiments and implementations on various benchmark functions. Data mining and machine learning are two areas where meta-heuristic techniques are frequently used. In trials, the MLP artificial neural network and a binary version of the BFO algorithm are used to lower the detection error for intrusion traffic. The proposed method's accuracy, precision, and sensitivity index for detecting network intrusion are 99.14%, 98.87%, and 98.85%, respectively, according to experiments on the NSL KDD data set. Compared to machine learning approaches like NNIA, DT, RF, XGBoot, and CNN, the proposed method is more accurate at detecting intrusion. The BFO algorithm is used for feature selection in the UNSW-NB15 dataset, and the tests showed that the accuracy of the proposed method is 96.72% in this dataset. The proposed method of the BFO algorithm is also used to improve Kmeans clustering, and the tests performed on the dataset of covid 19, diabetes, and kidney disease show that the proposed method performs better than iECA*, ECA*, GENCLUST + + (G + +) methods. Deep has KNN, LVQ, SVM, ANN, and KNN.


TMNIO:Triplet merged network with involution operators for improved few‐shot image classification

Abstract: Abstract Few‐shot learning enables machines to learn efficiently from limited labelled data. However, existing few‐shot learning methods may perform poorly when there is a lack of sufficient samples, and may encounter problems such as domain shift or overfitting when applied to new domains or tasks. To address the issues of poor fitting and insufficient generalization ability in new domains, a new method called triplet merged network with involution operators (TMNIO) is proposed. This method employs dual encoders that extract common and distinctive features from the prototype network, thereby enhancing the model's feature extraction capability. To further improve this ability, the traditional convolutional kernels are replaced with involution operators, which not only reduce the parameter count but also enlarge the receptive field to better extract local feature information. Additionally, this method employs a two‐stage training strategy, where triplet loss is used in the first stage to train the model and enhance its robustness and generalization ability. Extensive experiments on the miniImageNet, Omniglot, and Caltech‐UCSD Birds‐200 (CUB) datasets have shown that our proposed method achieved significant improvements in both training speed and accuracy, particularly on the miniImageNet dataset, where it achieved an outstanding 10% performance improvement.


The role of Artificial Intelligence in digital forensics: Case studies and future directions

Abstract: Key digested message The increase in digital evidence, especially in cases involving Indecent Images of Children (IIOC), presents a pressing challenge for law enforcement agencies. In this article, we discuss two of the most prominent types of Artificial Intelligence (AI) and how they can be used in digital forensic processes, providing examples, and highlighting potential challenges that are likely to be experienced in developing and adopting AI. The two main types are of Data-Driven Model (DDM) age classification and ModelBased Reasoning (MBR), and in this article, examples for both are provided and discussed in the contents of IIOC investigations.


Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Abstract: The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain.


Age and fluid source constraints for the Haoyaoerhudong orogenic gold deposit, North China: Evidence from geochronology and noble gas isotopes

Abstract: The Haoyaoerhudong gold deposit, located in the northwestern part of the North China craton (NCC), has produced over 120 metric tonnes (t) of gold since 2007. It has a total reserve of > 240 t at average gold grade of 0.62 g/t, making it one of the largest open pit gold mines in China. The steeply dipping, large-tonnage, low-grade, vein- or veinlet-type gold orebodies are hosted in strongly-deformed Mesoproterozoic carbonaceous schist of the Bayan Obo Group. The laminated/boudinaged veins/veinlets in the sinistral ductile–brittle shear zones are dominated by quartz, biotite, gold-bearing löllingite, pyrrhotite, (arseno)pyrite, with minor native gold, titanite and xenotime. In this paper, we present new in situ U–Pb geochronological data on magmatic zircon from the pre-ore dikes, on metamorphic and hydrothermal xenotime, and on hydrothermal titanite from the hydrothermally altered carbonaceous schist and auriferous quartz–sulfides veins/veinlets, as well as He-Ar isotopic analysis on gold-bearing (arseno)pyrite in the syn-ore stage. The metamorphic xenotime U–Pb age of 426 ± 6.0 Ma (2σ) records a regional metamorphic event, possibly related to the accretion of the Bainaimiao arc onto the NCC. Two pre-ore andesitic dikes yielded similar emplacement ages at ∼ 278 Ma constrained by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) U–Pb zircon data. Hydrothermal xenotime grains from the altered carbonaceous schist and auriferous quartz–sulfides veins yielded U–Pb ages of 256.0 ± 4.1 Ma (2σ) and 254.4 ± 2.1 Ma (2σ), respectively, overlapping with that of the hydrothermal titanite at 255.4 ± 0.8 Ma (2σ) from the laminated quartz–sulfides veinlets. This indicates that the gold mineralization occurred at ca. 255 Ma. The ∼ 255 Ma gold mineralization age is much younger than the previously reported Early–Middle Permian regional magmatic activity (ca. 291 Ma to 268 Ma), and may be associated with the regional sinistral strike-slip event in the late orogenic cycle related to the collision between the Siberian craton and the NCC. The 3He/4He (R/Ra) and 40Ar/36Ar values of the gold-bearing (arseno)pyrite are 0.04 to 0.09 (average = 0.07) and 375.8 to 2023 (average = 1045), which reveal the ore-forming fluids dominantly originated from the crustal rocks, with limited involvement from the mantle. Collectively, our new geochronological data, noble gas isotopic analyses, and geological evidence support a typical orogenic gold deposit model at Haoyaoerhudong


T‐Skeleton: Accurate scene text detection via instance‐aware skeleton embedding

Abstract: Abstract Existing segmentation‐based methods have made considerable progress in arbitrarily shaped text detection due to the advantage of dealing with shape variation. However, there still exist challenges to detecting accurate text instances with dense layouts, inaccurate annotations, and complex backgrounds. Many recent works have focused on improving arbitrary boundary prediction, but it may be difficult to accurately distinguish each instance of dense layouts because their boundary pixels may be mistakenly classified to produce inaccurate results (i.e., adhesive texts) with inaccurate annotation and complex backgrounds. Considering the local and long‐range dependencies, this paper proposes an efficient text detector, namely T‐Skeleton, to obtain more reliable segmentation detections. In the spirit of object skeletonization, we introduce the text instance skeleton highlighting the semantically significant structure (similar to the skeleton of a fish) to explicitly capture the long‐range dependencies of text instances. The key idea of T‐Skeleton is to calibrate the coarse text proposals by embedding text instance skeletons to separate crowd texts accurately and robustly. We further design a channel attention module to enlarge the performance margin between T‐Skeleton and the segmentation baseline. Experimental results on four publicly available datasets show the superiority of T‐Skeleton in handling long and curved texts.


A data driven approach to assess the petrophysical parametric sensitivity for lithology identification based on ensemble learning

Abstract: Lithology identification is essential for geological analysis and reservoir characterization of hydrocarbon bearing formation. Conventional approaches of lithology identification are not only inconvenient but also time-consuming and expensive. Machine learning (ML) algorithms have been introduced as an effective, reliable, and accurate method of lithology identification using well-log data due to their ability to recognize the nonlinear relationship between log responses and lithology. In this study, we employed the XGBoost classifier model to assess the parametric sensitivity of the logging features in identifying lithology. Sequential Forward Feature Selection (SFFS) approach was applied to determine the most significant set of logging features that yields maximum classification accuracy. Nine logging parameters were used as the input of the XGBoost model to classify five lithology classes. Hyper-parameters were tuned within appropriate tuning search range to optimize the model performance along with Grid Search and 5-fold cross validation. The training and testing accuracy obtained from the model are 96% and 90.97%, respectively. The optimized XGBoost model showed the lowest precision, recall, and F1 score for the clay class. The shale class, on the other side, had the highest precision, recall, and F1 score of 95%, 94%, and 94%, respectively. The best set of logging features for identifying formation lithology is determined to be [PEF, SP, LLD, CALI, MSFL, NPHI, LLS], with a classification accuracy of 92.10% for the testing dataset. The least significant logging parameters for identifying lithology were found to be DT and GR, as their addition with the best input feature set, resulted in a negative deviation in classification accuracy. Finally, based on the classification accuracy, we ranked the top three sets of logging features, those contributed significantly.


Statistical Perspective on the Petrological Utility of Polyphase Groundmass Compositions Inferred via Defocused Beam Electron Probe Microanalysis

Abstract: Polyphase groundmasses (micro‐scale minerals with or without glass) are generated from silicate liquids during the cooling of natural lavas often alongside larger minerals formed long before eruption. Many researchers have posited that compositions gleaned from the analysis of groundmasses closely approximate the compositions of the melts they were derived from, and these have been used frequently to model pre‐eruptive magma conditions. However, it is difficult to confidently identify and sample these groundmasses once they are formed. Using a sample of lava that exhibits a wide degree of textural variation (ranging from holocrystalline to hypohyaline) we show that compositions of groundmasses sampled using defocused electron beams are significantly different from glass compositions in terms of mean composition and covariance. Despite this, several groundmass compositions qualify as ‘in equilibrium’ with matrix/rim olivine. When processed using available thermometers, however, modelled equilibrium temperatures are significantly higher than those produced using glass data, on average. Because of this, we prescribe caution in using polyphase groundmass data generated using defocused beam analysis even as a rudimentary approach.


Gaussian quantum steering in multi-event horizon spacetime

Abstract: Abstract We study Gaussian quantum steering in the Schwarzschild–de Sitter (SdS) spacetime that is endowed with both a black hole event horizon (BEH) and a cosmological event horizon (CEH), giving rise to two different Hawking temperatures. It is shown that the Hawking effect of the black hole always reduces the quantum steering, but the Hawking effect of the expanding universe does not always play the same role. For the first time, we find that the Hawking effect can improve quantum steering. We also find that the observer who locates in the BEH has stronger steerability than the observer who locates in CEH. Further, we study the steering asymmetry, and the conditions for two-way, one-way and no-way steering in the SdS spacetime. Finally, we study the Gaussian quantum steering in the scenario of effective equilibrium temperature. We show that quantum steering reduces monotonically with the effective temperature but now increases monotonically with the Hawking temperature of the black hole, which banishes the belief that the Hawking effect can only destroy quantum steering.


Abnormal data detection and recovery of sensors network based on spatiotemporal deep learning methodology

Abstract: This paper proposes a novel deep-learning-enabled framework for abnormal data detection and recovery considering spatial-temporal correlation among sensors. The wavelet scattering transform is adopted to replace the first layer of one-dimension convolutional neural network classification model. The multiple typical sensor failure modes of sensor are identified including bias, drifting, gain, precision degradation, and three complete faults (constant; constant + noise; noise). The bidirectional long-short time memory (BLSTM) network is developed to recover abnormal data through positive and negative propagation. The BLSTM cell involving forget, input, and output gate is utilized to adaptively transmit valuable information of long-term data. Different sensor configurations are conducted to select the optimal input sensors of BLSTM model. The proposed models are then validated by three engineering cases. The results show that the failure mode diagnosis and recovery accuracy are over 90 %. Sensor selection has a large impact on the data reconstruction, and selecting sensors with higher correlation can achieve a higher accuracy. In addition, the data recovery accuracy of the proposed BLSTM model surpasses that of traditional long-short time memory models.


Contextual Feature Representation for Image-Based Insider Threat Classification

Abstract: With the rapid development of technology, insider threat incidents frequently occur in organizations. Detecting insider threats is an essential task in network infrastructure security. In this paper, we design an attention module to extract contextual features and augment abnormal features to generate high-quality images representing user behavior. Then, we use pre-trained ResNet and multi-source feature fusion on behavioral, psychological, and role features, intending to identify malicious insiders accurately. The proposed approaches are evaluated using the CMU-CERT Insider Threat Dataset. Experimental results show the effectiveness of methods and outperform other state-of-the-art methods.


Identifying optical microscope images of CVD-grown two-dimensional MoS2 by convolutional neural networks and transfer learning

Abstract: Background In Complementary Metal-Oxide Semiconductor (CMOS) technology, scaling down has been a key strategy to improve chip performance and reduce power losses. However, challenges such as sub-threshold leakage and gate leakage, resulting from short-channel effects, contribute to an increase in distributed static power. Two-dimensional transition metal dichalcogenides (2D TMDs) emerge as potential solutions, serving as channel materials with steep sub-threshold swings and lower power consumption. However, the production and development of these 2-dimensional materials require some time-consuming tasks. In order to employ them in different fields, including chip technology, it is crucial to ensure that their production meets the required standards of quality and uniformity; in this context, deep learning techniques show significant potential. Methods This research introduces a transfer learning-based deep convolutional neural network (CNN) to classify chemical vapor deposition (CVD) grown molybdenum disulfide (MoS 2 ) flakes based on their uniformity or the occurrence of defects affecting electronic properties. Acquiring and labeling a sufficient number of microscope images for CNN training may not be realistic. To address this challenge, artificial images were generated using Fresnel equations to pre-train the CNN. Subsequently, accuracy was improved through fine-tuning with a limited set of real images. Results The proposed transfer learning-based CNN method significantly improved all measurement metrics with respect to the ordinary CNNs. The initial CNN, trained with limited data and without transfer learning, achieved 68% average accuracy for binary classification. Through transfer learning and artificial images, the same CNN achieved 85% average accuracy, demonstrating an average increase of approximately 17%. While this study specifically focuses on MoS 2 structures, the same methodology can be extended to other 2-dimensional materials by simply incorporating their specific parameters when generating artificial images.


Complex network effects on the robustness of graph convolutional networks

Abstract: Abstract Vertex classification using graph convolutional networks is susceptible to targeted poisoning attacks, in which both graph structure and node attributes can be changed in an attempt to misclassify a target node. This vulnerability decreases users' confidence in the learning method and can prevent adoption in high-stakes contexts. Defenses have been proposed, focused on filtering edges before creating the model or aggregating information from neighbors more robustly. This paper considers an alternative: we investigate the ability to exploit network phenomena in the training data selection process to improve classifier robustness. We propose two alternative methods of selecting training data: (1) to select the highest-degree nodes and (2) to select nodes with many connections to the test data. In four real datasets, we show that changing the training set often results in far more perturbations required for a successful attack on the graph structure; often a factor of 2 over the random training baseline. We also run a simulation study in which we demonstrate conditions under which the proposed methods outperform random selection, finding that they improve performance most when homophily is higher, clustering coefficient is higher, node degrees are more homogeneous, and attributes are less informative. In addition, we show that the methods are effective when applied to adaptive attacks, alleviating concerns about generalizability.


Neural network crossover in genetic algorithms using genetic programming

Abstract: Abstract The use of genetic algorithms (GAs) to evolve neural network (NN) weights has risen in popularity in recent years, particularly when used together with gradient descent as a mutation operator. However, crossover operators are often omitted from such GAs as they are seen as being highly destructive and detrimental to the performance of the GA. Designing crossover operators that can effectively be applied to NNs has been an active area of research with success limited to specific problem domains. The focus of this study is to use genetic programming (GP) to automatically evolve crossover operators that can be applied to NN weights and used in GAs. A novel GP is proposed and used to evolve both reusable and disposable crossover operators to compare their efficiency. Experiments are conducted to compare the performance of GAs using no crossover operator or a commonly used human designed crossover operator to GAs using GP evolved crossover operators. Results from experiments conducted show that using GP to evolve disposable crossover operators leads to highly effectively crossover operators that significantly improve the results obtained from the GA.


“The sleep data looks way better than I feel.” An autoethnographic account and diffractive reading of sleep-tracking

Abstract: Sleep-tracking products are promising their users an improvement to their sleep by focusing on behavior change but often neglecting the contextual and individual factors contributing to sleep quality and quantity. Making good sleep for productive scheduling a personal responsibility does not necessarily lead to better sleep and may cause stress and anxiety. In an autoethnographic study, the first author of this paper tracked her sleep for one month using a diary, body maps and an Oura ring and compared her subjectively felt sleep experience with the data produced by the Oura app. A thematic analysis of the data resulted in four themes describing the relationship between the user-researcher and her wearable sleep-tracker: (1) good sleep scores are motivating, (2) experience that matches the data leads to sense-making, (3) contradictory information from the app leads to frustration, and (4) the sleep-tracker competes with other social agents. A diffractive reading of the data and research process, following Karen Barad's methodology, resulted in a discussion of how data passes through the analog and digital apparatus and what contextual factors are left out but still significantly impact sleep quality and quantity. We add to a canon of sleep research recommending a move away from representing sleep in terms of comparison and competition, uncoupling it from neoliberal capitalistic productivity and self-improvement narratives which are often key contributing factors to bad sleep in the first place.


A novel well log data imputation methods with CGAN and swarm intelligence optimization

Abstract: Well log data plays a vital role in decision-making, resource assessment, production optimization, and environmental management of oil and gas development. However, when exploring and developing deep and ultra-deep oil and gas, the performance of logging equipment is greatly challenged by high temperature, high pressure, and high corrosion wellbore conditions. Log data often needs to be completed or corrected. Data imputation technology has become a powerful tool to fill the missing in well log data. A well log data imputation method based on deep learning is proposed. The proposed method combines conditional generative adversarial networks (CGAN) with swarm intelligence optimization algorithms. The seismic layer velocity is used as a constraint to guide CGAN to generate well log data that matches geological features. Establish an objective function based on multiple conditions to evaluate the quality of generated samples. The swarm intelligence optimization algorithm is used to minimize the objective function. The case study shows that the proposed method obtains pseudo samples that meet specific scenarios and perform better than other algorithms. The proposed method provides a new approach for deep learning algorithms in sequence data prediction.


Does <scp>AI</scp>‐assisted creation of polyphonic music increase academic motivation? The <scp>DeepBach</scp> graphical model and its use in music education

Abstract: Abstract Background In the modern music industry, AI music generators have gained particular importance. The use of AI greatly simplifies the creation of polyphony. In addition, it can increase student motivation and interest. Aims This study focuses on the AI‐assisted creation of polyphonic music. The purpose of this study is to determine how creating polyphony through the Deep Bach model impacts the academic motivation of music university students. Materials & Methods Achieving this goal is possible by conducting an experimental training program based on the use of the above‐mentioned model. Results The results show that students in the experimental group have higher motivation than the control group participants. Therefore, AI‐based music creation has the potential to become a new trend in music education. Implications The findings of this study can be useful for music education experts, providing empirical data on the effectiveness of AI in music education. The use of the data can ultimately improve the learning process. Future research can focus on developing alternative AI models as well as investigating their effectiveness in music education.


Cellular geometric semantic genetic programming

Abstract: Abstract Among the different variants of Genetic Programming (GP), Geometric Semantic GP (GSGP) has proved to be both efficient and effective in finding good solutions. The fact that the operators of GSGP operate on the semantics of the individuals in a clear way provides guarantees on the way the search is performed. GSGP is not, however, free from limitations like the premature convergence of the population to a small–and possibly sub-optimal–area of the search space. One reason for this issue could be the fact that good individuals can quickly “spread” in the population suppressing the emergence of competition. To mitigate this problem, we impose a cellular automata (CA) inspired communication topology over GSGP. In CAs a collection of agents (as finite state automata) are positioned in a n -dimensional periodic grid and communicates only locally with the automata in their neighbourhoods. Similarly, we assign a location to each individual on an n -dimensional grid and the entire evolution for an individual will happen locally by considering, for each individual, only the individuals in its neighbourhood. Specifically, we present an algorithm in which, for each generation, a subset of the neighbourhood of each individual is sampled and the selection for the given cell in the grid is performed by extracting the two best individuals of this subset, which are employed as parents for the Geometric Semantic Crossover. We compare this cellular GSGP (cGSGP) approach with standard GSGP on eight regression problems, showing that it can provide better solutions than GSGP. Moreover, by analyzing convergence rates, we show that the improvement is observable regardless of the number of executed generations. As a side effect, we additionally show that combining a small-neighbourhood-based cellular spatial structure with GSGP helps in producing smaller solutions. Finally, we measure the spatial autocorrelation of the population by adopting the Moran’s I coefficient to provide an overview of the diversity, showing that our cellular spatial structure helps in providing better diversity during the early stages of the evolution.


Facilitating patient portal engagement: a channel expansion and behavior change wheel perspective

Abstract: Introduction Given the low patient portal adoption rates, the contradictory findings on the relationship between patient-provider communication and patient portal use, and the unclear mechanism of why doctor-patient communication might facilitate portal use as indicated in some existing studies, patient portal engagement warrants further examination. Methods Guided by the behavior change wheel framework and the channel expansion theory, this study examined the facilitators of patient portal engagement and tested the relationship between the facilitators (e.g., social opportunity and psychological capability) through analyzing the HINTS national survey data ( N = 1251). Results We found that patient portal access (a physical opportunity) and physician advocacy (a social opportunity) were two significant predictors of portal engagement while educational attainment was not. We did not find any direct correlation between patient-centered communication (PCC) and patient portal engagement, but instead, found a significant indirect relationship between the two. Discussion To the best of our knowledge, this is the first study to employ the behavior change wheel and channel expansion theory to explain patient portal engagement. Theoretically, our study extended the behavior change theory by further explaining the relationship between the key components (e.g., capability, opportunity) of behavior change. Practical strategies to increase patient portal engagement were proposed.


Prediction for nonlinear time series by improved deep echo state network based on reservoir states reconstruction

Abstract: Abstract With the aim to enhance prediction accuracy for nonlinear time series, this paper put forward an improved deep Echo State Network based on reservoir states reconstruction driven by a Self-Normalizing Activation (SNA) function as the replacement for the traditional Hyperbolic tangent activation function to reduce the model’s sensitivity to hyper-parameters. The Strategy was implemented in a two-state reconstruction process by first inputting the time series data to the model separately. Once, the time data passes through the reservoirs and is activated by the SNA activation function, the new state for the reservoirs is created. The state is input to the next layer, and the concatenate states module saves. Pairs of states are selected from the activated multi-layer reservoirs and input into the state reconstruction module. Multiple input states are transformed through the state reconstruction module and finally saved to the concatenate state module. Two evaluation metrics were used to benchmark against three other ESNs with SNA activation functions to achieve better prediction accuracy.


Networking concert halls, musicians, and interactive textiles: Interwoven Sound Spaces

Abstract: Interwoven Sound Spaces is an interdisciplinary project which brought together telematic music performance, interactive textiles, interaction design, and artistic research. A team of researchers collaborated with two professional contemporary music ensembles based in Berlin, Germany, and Piteå, Sweden, and four composers, with the aim of creating a telematic distributed concert taking place simultaneously in two concert halls and online. Central to the project was the development of interactive textiles capable of sensing the musicians’ movements while playing acoustic instruments, and generating data the composers used in their works. Musicians, instruments, textiles, sounds, halls, and data formed a network of entities and agencies that was reconfigured for each piece, showing how networked music practice enables distinctive musicking techniques. We describe each part of the project and report on a research interview conducted with one of the composers for the purpose of analysing the creative approaches she adopted for composing her piece.


Towards co-design of rehabilitation technologies: a collaborative approach to prioritize usability issues

Abstract: Introduction Early stakeholder engagement is critical to the successful development and translation of rehabilitation technologies, a pivotal step of which is usability testing with intended end-users. To this end, several methods employ end-user feedback to identify usability and implementation issues. However, the process of prioritizing identified issues seldom leverages the knowledge and expertise of the range of stakeholders who will ultimately affect the demand and supply of a device. This paper describes a novel method to prioritize end-user feedback using transdisciplinary stakeholder consultation and address it in subsequent product development. The proposed approach was demonstrated using a case study relating to the development of a novel technology for neural recovery after spinal cord injury. Method Feedback from five individuals with chronic spinal cord injury was collected during two-hour usability evaluation sessions with a fully functional high-fidelity system prototype. A think-aloud and semi-structured interview protocol was used with each participant to identify usability and acceptability issues relating to the system in a 3-phase approach. Phase 1 involved extracting usability issues from think-aloud and semi-structured interview data. Phase 2 involved rating the usability issues based on their significance, technical feasibility, and implementation priority by relevant internal and external stakeholders. Finally, Phase 3 involved aggregating the usability issues according to design and implementation elements to facilitate solution generation, and these solutions were then raised as action tasks for future design iterations. Results Sixty usability issues representing nine facets of usability were rated. Eighty percent of issues were rated to be of moderate to high significance, 83% were rated as being feasible to address, and 75% were rated as addressable using existing project resources. Fifty percent of the issues were rated to be a high priority for implementation. Evaluation of the grouped issues identified 21 tasks which were mapped to the product roadmap for integration into future design iterations. Discussion This paper presents a method for meaningful transdisciplinary stakeholder engagement in rehabilitation technology development that can extended to other projects. Alongside a worked example, we offer practical considerations for others seeking to co-develop rehabilitation technologies.


“Below 58 BPM,” involving real-time monitoring and self-medication practices in music performance through IoT technology

Abstract: The project presented in this paper illustrates the design process for the development of an IoT system that monitors a specific bio-metric parameter (heart rate) in real time and provides feedback for an opera singer, as well as adding effects that manipulate the sounds emitted by the body during a self-healing practice. This allows the singer to rest and alternate opera singing techniques (which is very demanding) with other less demanding singing techniques and even a self-healing session in case of necessity during a performance. The case study presented in this paper has been developed with and for Eleonora Amianto, an opera singer who suffered from a carotid aneurysm. We performed an idiographic design process, closely collaborating with Eleonora, and developed a wearable IoT that suited her health and artistic needs. In the design of the system, we explore the intersection between self-healthcare and performative arts, focusing on the use of an Internet of Musical Things (IoMusT) system to implement medical prevention and treatment practices in an art performance. The system is developed using open-source tools, allowing for easy replication and improvement, as well as reducing risks of obsolescence and costs of updating. We complement a formal evaluation session with field notes collected during the design phase. We could observe a positive effect of the system on Eleonora's practice and its potential applications within different performative scenarios.


An analysis of register variances between China and the US in international communications

Abstract: Translation of international communications plays an important role in facilitating the spread of a country’s voice and shaping its national image. This study employs multidimensional analysis to analyze China’s interpretation of international communications and compares it with the speeches from the White House press conferences to uncover the register variances between the two. The findings reveal that China and the U.S. differ in 4 dimensions and 46 individual linguistic features. The former is more informational with explicit written features and can be categorized into the register of learned exposition; the latter is more interactive and persuasive with prominent features of colloquialism, belonging to the register of involved persuasion. Through comparison with the source language, we find that the differences between the two are influenced by multiple factors such as source language, interpreting process, and political considerations. In conclusion, the interpretation of China’s international communications is a “third language code” with distinctive features from the language used by the U.S. in the political scenario, reflecting a meticulous, responsible but less interactive national image. These findings could shed light on the interpreting strategies adopted by government interpreters and further improve the effectiveness of international communications.


Evolutionary Reinforcement Learning: Hybrid Approach for Safety-Informed Fault-Tolerant Flight Control

Abstract: Recent research in artificial intelligence potentially provides solutions to the challenging problem of fault-tolerant and robust flight control. This paper proposes a novel Safety-Informed Evolutionary Reinforcement Learning algorithm (SERL), which combines Deep Reinforcement Learning (DRL) and neuroevolution to optimize a population of nonlinear control policies. Using SERL, the work has trained agents to provide attitude tracking on a high-fidelity nonlinear fixed-wing aircraft model. Compared to a state-of-the-art DRL solution, SERL achieves better tracking performance in nine out of ten cases, remaining robust against faults and changes in flight conditions, while providing smoother action signals.


Self-Prompting Tracking: A Fast and Efficient Tracking Pipeline for UAV Videos

Abstract: In the realm of visual tracking, remote sensing videos captured by Unmanned Aerial Vehicles (UAVs) have seen significant advancements with wide applications. However, there remain challenges to conventional Transformer-based trackers in balancing tracking accuracy and inference speed. This problem is further exacerbated when Transformers are extensively implemented at larger model scales. To address this challenge, we present a fast and efficient UAV tracking framework, denoted as SiamPT, aiming to reduce the number of Transformer layers without losing the discriminative ability of the model. To realize it, we transfer the conventional prompting theories in multi-model tracking into UAV tracking, where a novel self-prompting method is proposed by utilizing the target’s inherent characteristics in the search branch to discriminate targets from the background. Specifically, a self-distribution strategy is introduced to capture feature-level relationships, which segment tokens into distinct smaller patches. Subsequently, salient tokens within the full attention map are identified as foreground targets, enabling the fusion of local region information. These fused tokens serve as prompters to enhance the identification of distractors, thereby avoiding the demand for model expansion. SiamPT has demonstrated impressive results on the UAV123 benchmark, achieving success and precision rates of 0.694 and 0.890 respectively, while maintaining an inference speed of 91.0 FPS.


Gaming using different hand gestures using artificial neural network

Abstract:
INTRODUCTION: Gaming has evolved over the years, and one of the exciting developments in the industry is the integration of hand gesture recognition.
 OBJECTIVES: This paper proposes gaming using different hand gestures using Artificial Neural Networks which allows players to interact with games using natural hand movements, providing a more immersive and intuitive gaming experience.
 METHODS: Introduces two modules: recognition and analysis of gestures. The gesture recognition module identifies the gestures, and the analysis module assesses them to execute game controls based on the calculated analysis.
 RESULTS: The main results obtained in this paper are enhanced accessibility, higher accuracy and improved performance.
 CONCLUSION: To communicate with any of the traditional systems, physical contact is necessary. In the hand gesture recognition system, the same functionality can be interpreted by gestures without requiring physical contact with the interfaced devices.


The development of aesthetic experience through virtual and augmented reality

Abstract: Abstract Emerging technological innovations offer the potential for experiential engagement through virtual scenarios, yet the viability of this approach for educational purposes remains significantly underexplored. This study aims to assess the feasibility of Augmented Reality (AR) and Virtual Reality (VR) technologies in providing users with aesthetic experiences when visiting digital exhibitions. A total of 190 students participated in this investigation. The control group visited traditional exhibits at the Palace Museum in Beijing. This group underwent a survey to evaluate their acquired aesthetic experience. In contrast, the experimental group, comprising 96 students, engaged with VR/AR scenarios at the Palace Museum in Beijing. Accordingly, students in the experimental group were also surveyed to evaluate both their aesthetic experiences and, additionally, their user experiences. The survey results unveiled significant distinctions in aesthetic experiences between students in the control and experimental groups. Moreover, there were notable correlations between individual variables related to user and aesthetic experiences within the experimental group. Furthermore, the study revealed disparities in both user and aesthetic experiences among male and female students. The findings have implications for aesthetic education teachers and officials in the context of developing sound strategies for providing aesthetic experiences to their students. This information is also of interest to employees of museums, exhibitions, and other cultural facilities, who are interested in holding or hold digital exhibitions.


Complete characterization of a class of permutation trinomials in characteristic five

Abstract: Abstract In this paper, we address an open problem posed by Bai and Xia in [2]. We study polynomials of the form $$f(x)=x^{4q+1}+\lambda _1x^{5q}+\lambda _2x^{q+4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>f</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:msup> <mml:mi>x</mml:mi> <mml:mrow> <mml:mn>4</mml:mn> <mml:mi>q</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msub> <mml:mi>λ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:msup> <mml:mi>x</mml:mi> <mml:mrow> <mml:mn>5</mml:mn> <mml:mi>q</mml:mi> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msub> <mml:mi>λ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:msup> <mml:mi>x</mml:mi> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>+</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> over the finite field $${\mathbb F}_{5^{k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>5</mml:mn> <mml:mi>k</mml:mi> </mml:msup> </mml:msub> </mml:math> , which are not quasi-multiplicative equivalent to any of the known permutation polynomials in the literature. We find necessary and sufficient conditions on $$\lambda _1, \lambda _2 \in {\mathbb F}_{5^{k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>λ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>λ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>∈</mml:mo> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>5</mml:mn> <mml:mi>k</mml:mi> </mml:msup> </mml:msub> </mml:mrow> </mml:math> so that f ( x ) is a permutation monomial, binomial, or trinomial of $${\mathbb F}_{5^{2k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>5</mml:mn> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>k</mml:mi> </mml:mrow> </mml:msup> </mml:msub> </mml:math> .


Detecting multiple coexisting emotions from public emergency opinions

Abstract: To detect multiple coexisting emotions from public emergency opinions, this article proposes a novel two-stage multiple coexisting emotion-detection model. First, the text semantic feature extracted through bidirectional encoder representation from transformers (BERT) and the emotion lexicon feature extracted through the emotion dictionary are fused. Then, the emotion subjectivity judgement and multiple coexisting emotion detection are performed in two separate stages. In the first stage, we introduce synthetic minority oversampling technique (SMOTE) to enhance the balance of data distribution and select the optimal classifier to recognise opinion texts with emotion. In the second stage, the label powerset (LP)-SMOTE is proposed to increase the number of the minority category samples, and multichannel emotion classifiers and the decision mechanism are employed to recognise different types of emotions and determine the final coexisting emotion labels. Finally, the Weibo data about coronavirus disease 2019 (COVID-19) are collected to verify the effectiveness of the proposed model. Experiment results indicate that the proposed model outperforms state-of-the-art models, with the F1_macro of 0.8532, the F1_micro of 0.8333, and the hamming loss of 0.0476. The emotion detection results are conducive to decision-making for public emergency departments.


An empirical study of the personality factors and individual characteristics of older adult participation in third age education

Abstract: In China, the innovative development of education for older people is an important response to the aging population, and an important way to meet the spiritual, cultural, and social needs of older people. Current academic research mainly focuses on the social factors that affect the participation of older people in education and there are few empirical studies on the individual psychological characteristics of older adult learning behavior. This study takes a positive psychology approach to the exploration of the factors influencing the participation of older adults in education in a number of universities of the Third Age in Fujian Province, China. The study is based on curriculum preferences and frequency of participation in extracurricular activities. The results show that the personality traits of extraversion and neuroticism, as well as gender, age, level of educational attainment, and family economic status significantly affect frequency of participation in extracurricular activities and the curriculum preferences of older learners. The results of this study suggest that we can strengthen personality matching to promote the learning enthusiasm of older people in education, and adhere to the demand-oriented to promote the Third age education curriculum personalized. Furthermore, we should increase the scope for popularizing Third age education, improve the accessibility of education for the older people, accelerate the integration of older adult education with the Internet, innovate the form of older adult education, and then promote the development of customized curricula.


Application of Remote Sensing and Geographic Information System Technologies to Assess the Impact of Mining: A Case Study at Emalahleni

Abstract: This article presents an assessment of the impact of mining activities in the Emalahleni municipality, using GIS and RS technologies. The random forest algorithm was used to classify the land use and land cover in the Emalahleni municipality over a three-decade period (1990–2020). The classifications are settlement, water, mining area, vegetation, and bare land. The majority of the study area was found to be rocky ground, accounting for approximately 60% of the total study area. Change detection maps were created for vegetation and mining to assess the extent of land degradation in the study area over the three-decade period. The findings in this study highlight the importance of understanding the changes in land use and vegetation cover in the study area and its impact on the environment, as well as the local community. It is crucial to develop sustainable land management strategies that ensure that a reasonable balance concerning the economic development activities is achieved, such as mining with environmental management for its long-term viability for future generations. The data presented in this study provides a useful baseline for further research and can inform land-use planning and decision-making processes in Emalahleni.


An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network

Abstract: The process of iris recognition can result in a decline in recognition performance when the resolution of the iris images is insufficient. In this study, a super-resolution model for iris images, namely SwinGIris, which combines the Swin Transformer and the Generative Adversarial Network (GAN), is introduced. SwinGIris performs quadruple super-resolution reconstruction for low-resolution iris images, aiming to improve the resolution of iris images and thereby improving the recognition accuracy of iris recognition systems. The model utilizes residual Swin Transformer blocks to extract depth global features, and the progressive upsampling method along with sub-pixel convolution is conducive to focusing on the high-frequency iris information in the presence of more non-iris information. In order to preserve high-frequency details, the discriminator employs a VGG-style relative classifier to guide the generator in generating super-resolution images. In experimental section, we enhance low-resolution (56 × 56) iris images to high-resolution (224 × 224) iris images. Experimental results indicate that the SwinGIris model achieves satisfactory outcomes in restoring low-resolution iris image textures while preserving identity information.


Event-Based Guidance and Incremental Control with Application to Fixed-wing Unmanned Aerial Vehicle Perched Landing Maneuvers

Abstract: Abstract Considering the nonlinearity and unknown dynamics of fixed-wing unmanned aerial vehicles in perched landing maneuvers, an event-based online guidance and incremental control scheme is proposed. The guidance trajectory for perched landing must be dynamically feasible therefore an event-based trapezoidal collocation point optimization method is proposed. Introduction of the triggering mechanism for the rational use of computing resources to improve PL accuracy. Furthermore, a filter-based incremental nonlinear dynamic inverse (F-INDI) control with state transformation is proposed to achieve robust trajectory tracking under high angle of attack (AOA). The F-INDI uses low-pass filters to obtain incremental dynamics of the system, which simplifies the design process. The state transformation strategy is to convert the flight-path angle, AOA and velocity into two composite dynamics, which avoids the sign reversal problem of control gain under high AOA. The stability analysis shows that the original states can be controlled only by controlling the composite state. Simulation results show that the proposed scheme achieves high perched landing accuracy and a reliable trajectory tracking control.


PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs

Abstract: Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes—chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a form of cancer that begins in the lymphatic system, impacting lymphocytes, which are a specific type of white blood cell. This research addresses these challenges by proposing ensemble and non-ensemble transfer learning models employing pre-trained weights from VGG16, VGG19, DenseNet201, InceptionV3, and Xception. For the ensemble technique, this paper adopts a stack-based ensemble approach. It is a two-level classification approach and best suited for accuracy improvement. Testing on a multiclass dataset of CLL, FL, and MCL reveals exceptional diagnostic accuracy, with DenseNet201, InceptionV3, and Xception exceeding 90% accuracy. The proposed ensemble model, leveraging InceptionV3 and Xception, achieves an outstanding 99% accuracy over 300 epochs, surpassing previous prediction methods. This study demonstrates the feasibility and efficiency of the proposed approach, showcasing its potential in real-world medical applications for precise lymphoma diagnosis.


Generalized Weak Supervision for Neural Information Retrieval

Abstract: Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtain. To address this issue, one can train NRMs via weak supervision, where a large dataset is automatically generated using an existing ranking model (called the weak labeler) for training NRMs. Weakly supervised NRMs can generalize from the observed data and significantly outperform the weak labeler. This paper generalizes this idea through an iterative re-labeling process, demonstrating that weakly supervised models can iteratively play the role of weak labeler and significantly improve ranking performance without using manually labeled data. The proposed Generalized Weak Supervision (GWS) solution is generic and orthogonal to the ranking model architecture. This paper offers four implementations of GWS: self-labeling, cross-labeling, joint cross- and self-labeling, and greedy multi-labeling. GWS also benefits from a query importance weighting mechanism based on query performance prediction methods to reduce noise in the generated training data. We further draw a theoretical connection between self-labeling and Expectation-Maximization. Our experiments on four retrieval benchmarks suggest that our implementations of GWS lead to substantial improvements compared to weak supervision if the weak labeler is sufficiently reliable.


Stabilizer Formalism for Operator Algebra Quantum Error Correction

Abstract: We introduce a stabilizer formalism for the general quantum error correction framework called operator algebra quantum error correction (OAQEC), which generalizes Gottesman&apos;s formulation for traditional quantum error correcting codes (QEC) and Poulin&apos;s for operator quantum error correction and subsystem codes (OQEC). The construction generates hybrid classical-quantum stabilizer codes and we formulate a theorem that fully characterizes the Pauli errors that are correctable for a given code, generalizing the fundamental theorems for the QEC and OQEC stabilizer formalisms. We discover hybrid versions of the Bacon-Shor subsystem codes motivated by the formalism, and we apply the theorem to derive a result that gives the distance of such codes. We show how some recent hybrid subspace code constructions are captured by the formalism, and we also indicate how it extends to qudits.


On the validity of descriptors in <i>ACTFL Language Proficiency Guidelines</i>

Abstract: Abstract ACTFL Language Proficiency Guidelines (hereinafter called Guidelines) are a multi-lingual framework mainly based on the language proficiency development of several European languages. Little consideration is given to the peculiarity of Chinese. Efforts are made to assess the validity of the descriptors in Guidelines in comparison with Chinese Proficiency Grading Standards for International Chinese Language Education (hereinafter called Standards) . A total of five parameters from Guidelines that are not compatible with Standards have been selected and two questionnaires compiled by the authors are answered by local American Chinese language teachers to evaluate the validity of these descriptors. The study shows that the descriptors in Standards are more valid in differentiating language proficiency levels of Chinese. In accordance with this study, some revisions and amendments should be made to Guidelines .


Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms

Abstract: Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and outdoor images of a small wind turbine with healthy and damaged blades. Each model was tuned using different layers, image augmentations, and hyperparameter tuning to achieve optimal performance. The results showed that the proposed Transfer Xception outperformed other architectures by attaining 99.92% accuracy on the test data of this dataset. Furthermore, the performance of the investigated models was compared on a dataset containing faulty and healthy images of large-scale wind turbine blades. In this case, our results indicated that the best-performing model was also the proposed Transfer Xception, which achieved 100% accuracy on the test data. These accuracies show promising results in the adoption of machine learning for wind turbine blade fault identification.


Few-Shot Metering Anomaly Diagnosis with Variable Relation Mining

Abstract: Metering anomalies not only mean huge economic losses but also indicate the faults of equipment and power lines, especially within the substation. As a result, metering anomaly diagnosis is becoming one of the most important missions in smart grids. However, due to the insufficient and imbalanced anomaly cases, identifying the anomalies in smart meter data accurately and efficiently remains challenging. Existing methods usually employ few-shot learning models in computer vision directly, which requires the rich experience of human experts and sufficient abnormal cases for training. It blocks model generalizing to various application scenarios. To address these shortcomings, we propose a novel framework for metering anomaly diagnosis based on few-shot learning, named FSMAD. Firstly, we design a fault data injection model to emulate anomalies, so that no abnormal samples are required in the training phase. Secondly, we provide a learnable variable transformation to reveal inherent relationships among various smart meter data and help FSMAD extract more efficient features. Finally, the deeper metric network is equipped to support FSMAD in obtaining powerful comparison capability. Extensive experiments on a real-world dataset demonstrate the advantages of our FSMAD over state-of-the-art methods.


Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy

Abstract: Over the last decade, supervised denoising models, trained on extensive datasets, have exhibited remarkable performance in image denoising, owing to their superior denoising effects. However, these models exhibit limited flexibility and manifest varying degrees of degradation in noise reduction capability when applied in practical scenarios, particularly when the noise distribution of a given noisy image deviates from that of the training images. To tackle this problem, we put forward a two-stage denoising model that is actualized by attaching an unsupervised fine-tuning phase after a supervised denoising model processes the input noisy image and secures a denoised image (regarded as a preprocessed image). More specifically, in the first stage we replace the convolution block adopted by the U-shaped network framework (utilized in the deep image prior method) with the Transformer module, and the resultant model is referred to as a U-Transformer. The U-Transformer model is trained to preprocess the input noisy images using noisy images and their labels. As for the second stage, we condense the supervised U-Transformer model into a simplified version, incorporating only one Transformer module with fewer parameters. Additionally, we shift its training mode to unsupervised training, following a similar approach as employed in the deep image prior method. This stage aims to further eliminate minor residual noise and artifacts present in the preprocessed image, resulting in clearer and more realistic output images. Experimental results illustrate that the proposed method achieves significant noise reduction in both synthetic and real images, surpassing state-of-the-art methods. This superiority stems from the supervised model’s ability to rapidly process given noisy images, while the unsupervised model leverages its flexibility to generate a fine-tuned network, enhancing noise reduction capability. Moreover, with support from the supervised model providing higher-quality preprocessed images, the proposed unsupervised fine-tuning model requires fewer parameters, facilitating rapid training and convergence, resulting in overall high execution efficiency.


Light field image coding using a residual channel attention network–based view synthesis

Abstract: Purpose Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding. Design/methodology/approach This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN). Findings In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods. Originality/value (1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.


Exploring academic influence of algorithms by co-occurrence network based on full-text of academic papers

Abstract: Purpose In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network. Design/methodology/approach We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms. Findings The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms. Originality/value To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.


Seeing is Learning in High Dimensions: The Synergy Between Dimensionality Reduction and Machine Learning

Abstract: Abstract High-dimensional data are a key study object for both machine learning (ML) and information visualization. On the visualization side, dimensionality reduction (DR) methods, also called projections, are the most suited techniques for visual exploration of large and high-dimensional datasets. On the ML side, high-dimensional data are generated and processed by classifiers and regressors, and these techniques increasingly require visualization for explanation and exploration. In this paper, we explore how both fields can help each other in achieving their respective aims. In more detail, we present both examples that show how DR can be used to understand and engineer better ML models (seeing helps learning) and also applications of DL for improving the computation of direct and inverse projections (learning helps seeing). We also identify existing limitations of DR methods used to assist ML and of ML techniques applied to improve DR. Based on the above, we propose several high-impact directions for future work that exploit the analyzed ML-DR synergy.


Quantum Complexity of the Kronecker Coefficients

Abstract: Whether or not the Kronecker coefficients of the symmetric group count some set of combinatorial objects is a longstanding open question. In this work we show that a given Kronecker coefficient is proportional to the rank of a projector that can be measured efficiently using a quantum computer. In other words a Kronecker coefficient counts the dimension of the vector space spanned by the accepting witnesses of a QMA verifier, where QMA is the quantum analogue of NP. This implies that approximating the Kronecker coefficients to within a given relative error is not harder than a certain natural class of quantum approximate counting problems that captures the complexity of estimating thermal properties of quantum many-body systems. A second consequence is that deciding positivity of Kronecker coefficients is contained in QMA, complementing a recent NP-hardness result of Ikenmeyer, Mulmuley, and Walter. We obtain similar results for the related problem of approximating row sums of the character table of the symmetric group. Finally, we discuss an efficient quantum algorithm that approximates normalized Kronecker coefficients to inverse-polynomial additive error.Received 28 April 2023Accepted 17 January 2024DOI:https://doi.org/10.1103/PRXQuantum.5.010329Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithms & computationQuantum computationQuantum information theoryTechniquesComputational complexityQuantum Information, Science & Technology


Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools

Abstract: Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.


Comparing and Contrasting Near-Field, Object Space, and a Novel Hybrid Interaction Technique for Distant Object Manipulation in VR

Abstract: In this contribution, we propose a hybrid interaction technique that integrates near-field and object-space interaction techniques for manipulating objects at a distance in virtual reality (VR). The objective of the hybrid interaction technique was to seamlessly leverage the strengths of both the near-field and object-space manipulation techniques. We employed bimanual near-field metaphor with scaled replica (BMSR) as our near-field interaction technique, which enabled us to perform multilevel degrees-of-freedom (DoF) separation transformations, such as 1~3DoF translation, 1~3DoF uniform and anchored scaling, 1DoF and 3DoF rotation, and 6DoF simultaneous translation and rotation, with enhanced depth perception and fine motor control provided by near-field manipulation techniques. The object-space interaction technique we utilized was the classic Scaled HOMER, which is known to be effective and appropriate for coarse transformations in distant object manipulation. In a repeated measures within-subjects evaluation, we empirically evaluated the three interaction techniques for their accuracy, efficiency, and economy of movement in pick-and-place, docking, and tunneling tasks in VR. Our findings revealed that the near-field BMSR technique outperformed the object space Scaled HOMER technique in terms of accuracy and economy of movement, but the participants performed more slowly overall with BMSR. Additionally, our results revealed that the participants preferred to use the hybrid interaction technique, as it allowed them to switch and transition seamlessly between the constituent BMSR and Scaled HOMER interaction techniques, depending on the level of accuracy, precision and efficiency required.