Reviewerly/exHarmony huggingface dataset:
Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

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&amp;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

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

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 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 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

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

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

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

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

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 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 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

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

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

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

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.