Reviewerly/exHarmony huggingface dataset:
Raman Scattering Errors in Stimulated-Raman-Induced Logic Gates in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mmultiscripts><mml:mrow><mml:msup><mml:mrow><mml:mi>Ba</mml:mi></mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:mrow><mml:mprescripts /><mml:none /><mml:mrow><mml:mn>133</mml:mn></mml:mrow></mml:mmultiscripts></mml:mrow></mml:math>

^{133}Ba^{+} is illuminated by a laser that is far detuned from optical transitions, and the resulting spontaneous Raman scattering rate is measured. The observed scattering rate is lower than previous theoretical estimates. The majority of the discrepancy is explained by a more accurate treatment of the scattered photon density of states. This work establishes that, contrary to previous models, there is no fundamental atomic physics limit to laser-driven quantum gates from laser-induced spontaneous Raman scattering.
Quality and Reliability of YouTube Videos on Myocardial Infarction: A Cross-Sectional Study

This study aims to assess the quality and reliability of the disease information available on YouTube (www.youtube.com) about "heart attacks" or myocardial infarctions, using a Global Quality Score (GQS) for quality, a DISCERN score for reliability, and a Video Power Index (VPI) for popularity.In this cross-sectional observational study, the YouTube videos were analyzed in terms of the type of uploader, content, and other factors. The GQS, DISCERN score, and Video Power Index (VPI) were utilized to assess the quality, reliability, and popularity of the information, respectively.The majority of the videos (78.44%) were uploaded over a year ago. Only 33.34% and 7.84% were uploaded by doctors and healthcare organizations, respectively. Around 72.55% of the videos contained information about symptoms, 66.67% discussed the causes, 52.94% covered treatment, and 47.06% focused on prevention. Additionally, 41.18% provided details on investigations, while only 19.61% touched upon mortality. Patient-created videos accounted for 19.61% of the content, and 15.69% of the videos included promotional material.The main outcome of our study indicates that the YouTube videos examined regarding myocardial infarctions exhibit high-quality content, as supported by a higher average GQS score. The consistent quality of information discovered in our study suggests that YouTube can serve as an additional platform for sharing knowledge and educating individuals about this important health condition. By raising awareness and delivering accurate information, these videos can help in early detection, prevention, and better outcomes for individuals who are at risk of experiencing a myocardial infarction.
RZCR: Zero-shot Character Recognition via Radical-based Reasoning

The long-tail effect is a common issue that limits the performance of deep learning models on real-world datasets. Character image datasets are also affected by such unbalanced data distribution due to differences in character usage frequency. Thus, current character recognition methods are limited when applied in the real world, especially for the categories in the tail that lack training samples, e.g., uncommon characters. In this paper, we propose a zero-shot character recognition framework via radical-based reasoning, called RZCR, to improve the recognition performance of few-sample character categories in the tail. Specifically, we exploit radicals, the graphical units of characters, by decomposing and reconstructing characters according to orthography. RZCR consists of a visual semantic fusion-based radical information extractor (RIE) and a knowledge graph character reasoner (KGR). RIE aims to recognize candidate radicals and their possible structural relations from character images in parallel. The results are then fed into KGR to recognize the target character by reasoning with a knowledge graph. We validate our method on multiple datasets, and RZCR shows promising experimental results, especially on few-sample character datasets.
Virtual learning environments and digital twins: Enhancing accessibility, diversity, and flexibility in training secondary educational administrators

&lt;p&gt;This study investigates the potential of Virtual Learning Environments (VLEs) and digital twins to overcome geographical, scheduling, and diversity barriers in the training of secondary educational administrators. Recognizing the limitations in traditional in-person visits to schools-particularly for graduate students in rural areas and those working fulltime with dependents-and the current ethnic composition of educational administrators, where White individuals comprise 64.5% of the population in the US (71.01% for principals), this research explores how VLEs can democratize access and foster diversity in educational leadership training. Over the academic year 2022–2023, pre- and post-engagement surveys were administered to students in a Visionary and Innovative Leadership course that employed a digital twin of a middle school. The virtual environment allowed students to explore the school and interact with its mission and vision principles at their own pace, from any location, and as frequently as required. These digital tools not only offered a homogenous and repeatable experience but also enabled a deeper, self-directed investigation into how an institution’s mission and vision are operationalized within its physical environment. Findings from the study indicate that VLEs and digital twins offer considerable potential in terms of accessibility, flexibility, and diversity in educational leadership training. Through the integration of these innovative technologies, future leaders can experience immersive, interactive, and inclusive learning environments, contributing to a more diverse and effective educational leadership landscape.&lt;/p&gt;
Digging Into Uncertainty-Based Pseudo-Label for Robust Stereo Matching

Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and generalize poorly to others. Such domain shift issue is usually addressed by substantial adaptation on costly target-domain ground-truth data, which cannot be easily obtained in practical settings. In this paper, we propose to dig into uncertainty estimation for robust stereo matching. Specifically, to balance the disparity distribution, we employ a pixel-level uncertainty estimation to adaptively adjust the next stage disparity searching space, in this way driving the network progressively prune out the space of unlikely correspondences. Then, to solve the limited ground truth data, an uncertainty-based pseudo-label is proposed to adapt the pre-trained model to the new domain, where pixel-level and area-level uncertainty estimation are proposed to filter out the high-uncertainty pixels of predicted disparity maps and generate sparse while reliable pseudo-labels to align the domain gap. Experimentally, our method shows strong cross-domain, adapt, and joint generalization and obtains <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1st</b> place on the stereo task of Robust Vision Challenge 2020. Additionally, our uncertainty-based pseudo-labels can be extended to train monocular depth estimation networks in an unsupervised way and even achieves comparable performance with the supervised methods.
Body-Pose-Guided Action Recognition with Convolutional Long Short-Term Memory (LSTM) in Aerial Videos

The accurate detection and recognition of human actions play a pivotal role in aerial surveillance, enabling the identification of potential threats and suspicious behavior. Several approaches have been presented to address this problem, but the limitation still remains in devising an accurate and robust solution. To this end, this paper presents an effective action recognition framework for aerial surveillance, employing the YOLOv8-Pose keypoints extraction algorithm and a customized sequential ConvLSTM (Convolutional Long Short-Term Memory) model for classifying the action. We performed a detailed experimental evaluation and comparison on the publicly available Drone Action dataset. The evaluation and comparison of the proposed framework with several existing approaches on the publicly available Drone Action dataset demonstrate its effectiveness, achieving a very encouraging performance. The overall accuracy of the framework on three provided dataset splits is 74%, 80%, and 70%, with a mean accuracy of 74.67%. Indeed, the proposed system effectively captures the spatial and temporal dynamics of human actions, providing a robust solution for aerial action recognition.
Variational Quantum‐Neural Hybrid Error Mitigation

Abstract Quantum error mitigation (QEM) is crucial for obtaining reliable results on quantum computers by suppressing quantum noise with moderate resources. It is a key factor for successful and practical quantum algorithm implementations in the noisy intermediate scale quantum (NISQ) era. Since quantum‐classical hybrid algorithms can be executed with moderate and noisy quantum resources, combining QEM with quantum‐classical hybrid schemes is one of the most promising directions toward practical quantum advantages. This work shows how the variational quantum‐neural hybrid eigensolver (VQNHE) algorithm, which seamlessly combines the expressive power of a parameterized quantum circuit with a neural network, is inherently noise resilient with a unique QEM capacity, which is absent in vanilla variational quantum eigensolvers (VQE). The study carefully analyzes and elucidates the asymptotic scaling of this unique QEM capacity in VQNHE from both theoretical and experimental perspectives. Finally, a variational basis transformation is proposed for the Hamiltonian to be measured under the VQNHE framework, yielding a powerful tri‐optimization setup, dubbed as VQNHE++. VQNHE++ can further enhance the quantum‐neural hybrid expressive power and error mitigation capacity.
Ricci Curvature-Based Graph Sparsification for Continual Graph Representation Learning

Memory replay, which stores a subset of historical data from previous tasks to replay while learning new tasks, exhibits state-of-the-art performance for various continual learning applications on the Euclidean data. While topological information plays a critical role in characterizing graph data, existing memory replay-based graph learning techniques only store individual nodes for replay and do not consider their associated edge information. To this end, based on the message-passing mechanism in graph neural networks (GNNs), we present the Ricci curvature-based graph sparsification technique to perform continual graph representation learning. Specifically, we first develop the subgraph episodic memory (SEM) to store the topological information in the form of computation subgraphs. Next, we sparsify the subgraphs such that they only contain the most informative structures (nodes and edges). The informativeness is evaluated with the Ricci curvature, a theoretically justified metric to estimate the contribution of neighbors to represent a target node. In this way, we can reduce the memory consumption of a computation subgraph from O(dL) to O(1) and enable GNNs to fully utilize the most informative topological information for memory replay. Besides, to ensure the applicability on large graphs, we also provide the theoretically justified surrogate for the Ricci curvature in the sparsification process, which can greatly facilitate the computation. Finally, our empirical studies show that SEM outperforms state-of-the-art approaches significantly on four different public datasets. Unlike existing methods, which mainly focus on task incremental learning (task-IL) setting, SEM also succeeds in the challenging class incremental learning (class-IL) setting in which the model is required to distinguish all learned classes without task indicators and even achieves comparable performance to joint training, which is the performance upper bound for continual learning.
TFGNet: Traffic Salient Object Detection Using a Feature Deep Interaction and Guidance Fusion

Emergency prediction and driver attention prediction are fundamental tasks within the realm of self-driving vehicles and assistant driving systems. The utilization of visual saliency detection in these tasks has garnered considerable attention, owing to its inherent advantages. However, current research on traffic saliency detection primarily focuses on emulating the human visual system for attention allocation in traffic scenes, neglecting the detection of complete salient objects. In this paper, we propose the Traffic Salient Object Detection Using a Feature Deep Interaction and Guidance Fusion Network (TFGNet). Different from previous methods, our method detects the complete objects that attract human attention in natural traffic scenes, rather than a certain point without object semantic information, which can provide assistance for target recognition tasks in the domain of intelligent driving. Moreover, we propose a traffic salient object detection framework based on feature interaction and guidance fusion, enabling the detection of salient objects across varying scales. Experimental results on multiple benchmark datasets demonstrate that, compared to the state-of-the-art methods, our method exhibits superior performance in terms of precision, recall, and error rate.
Information theoretic perspective on sample complexity

The statistical supervised learning framework assumes an input–output set with a joint probability distribution that is reliably represented by the training dataset. The learning system is then required to output a prediction rule learned from the training dataset’s input–output pairs. In this work, we investigate the relationship between the sample complexity, the empirical risk and the generalization error based on the asymptotic equipartition property (AEP) (Shannon, 1948). We provide theoretical guarantees for reliable learning under the information-theoretic AEP, with respect to the generalization error and the sample size in different settings.
E-HRNet: Enhanced Semantic Segmentation Using Squeeze and Excitation

In the field of computer vision, convolutional neural network (CNN)-based models have demonstrated high accuracy and good generalization performance. However, in semantic segmentation, CNN-based models have a problem—the spatial and global context information is lost owing to a decrease in resolution during feature extraction. High-resolution networks (HRNets) can resolve this problem by keeping high-resolution processing layers parallel. However, information loss still occurs. Therefore, in this study, we propose an HRNet combined with an attention module to address the issue of information loss. The attention module is strategically placed immediately after each convolution to alleviate information loss by emphasizing the information retained at each stage. To achieve this, we employed a squeeze-and-excitation (SE) block as the attention module, which can seamlessly integrate into any model and enhance the performance without imposing significant parameter increases. It emphasizes the spatial and global context information by compressing and recalibrating features through global average pooling (GAP). A performance comparison between the existing HRNet model and the proposed model using various datasets show that the mean class-wise intersection over union (mIoU) and mean pixel accuracy (MeanACC) improved with the proposed model, however, there was a small increase in the number of parameters. With cityscapes dataset, MeanACC decreased by 0.1% with the proposed model compared to the baseline model, but mIoU increased by 0.5%. With the LIP dataset, the MeanACC and mIoU increased by 0.3% and 0.4%, respectively. The mIoU also decreased by 0.1% with the PASCAL Context dataset, whereas the MeanACC increased by 0.7%. Overall, the proposed model showed improved performance compared to the existing model.
Steering through multiple waypoints without model-based trajectory planning

Humans and other animals are capable of moving at high speeds through densely cluttered environments, avoiding obstacles, squeezing through narrow openings, and following winding paths to reach their goals. To perform such tasks, it is often not enough to focus entirely on the most proximal goal. Skillful navigation also relies on the ability to look farther ahead and anticipate the need to reach goals that lie just beyond the immediate future. For example, when steering through multiple waypoints, the actor may adapt how they approach the first waypoint in anticipation of having to steer through the second waypoint. Such behavior is often described as path planning and assumed to rely on forward and inverse internal models. However, the possibility that such behavior could also be captured by information-based control without resorting to internal models has not been seriously considered. In this study, we introduce a new information-based control strategy for steering through multiple waypoints that captures anticipation. The strategy relies on currently available information about the curvature and tangent direction of the constant radius path that passes through the next two waypoints. Agents that follow this strategy move so as to null the difference between the current heading direction and the optically specified tangent direction of the constant-radius path. In simulations of this strategy, we found that the agent adapts how it approaches the upcoming waypoint in anticipation of the subsequent waypoint. We also compared model trajectories with those generated by humans in a waypoint steering task as well as those generated by other models of steering. Taken together, the findings provide an existence proof that human-like anticipatory steering is possible based on information-based control without resorting to internal models.
Enhancing Face Mask Detection Using Convolutional Neural Networks: A Comparative Study

Detecting face masks is essential for maintaining public safety and preventing the spread of contagious illnesses. In this article, we give a thorough inves- tigation into how Convolutional Neural Networks (CNNs) may improve face mask identification. The goal of this work is to provide a reliable and robust CNN-based method for identifying people who are wearing masks in practical situations. We start by outlining the CNN architecture, which has a sequen- tial structure made up of convolutional layers, activation functions, pooling layers, and fully linked layers, and is utilized for facemask identification. The architecture is made to recognize masked and unmasked faces with accuracy and learn hierarchical representations of input photos. Layers are pooled for downsampling, fully linked layers are used for high-level representations, and activation functions are used to induce non-linearities. We use a number of measures, including accuracy, precision, recall, and F1-score, to assess the performance of our CNN model. The accuracy of our experimental findings is encouraging, with a 95% overall accuracy in identifying people wearing masks. The accuracy in accurately detecting both positive and negative cases is balanced, as seen by the precision and recall values, which are determined to be 92% and 96%, respectively. We also assess the model’s effectiveness in other scenarios, such as those involving several people spread out across a wide area. Our findings show that even when people are at different distances from one another, there is constant performance with a high accuracy rate of above 90%. This demonstrates the model’s capacity to identify masks regardless of the distance that people are from the camera. We compare the performance of our CNN-based approach to current mask recognition algorithms and show how it outperforms them, outperforming more conventional approaches that generally had accuracy levels of 70–80%.
Comparative Study of Transformer and LSTM Network with Attention Mechanism on Image Captioning

In a globalized world at the present epoch of generative intelligence, most of the manual labour tasks are automated with increased efficiency. This can support businesses to save time and money. A crucial component of generative intelligence is the integration of vision and language. Consequently, image captioning become an intriguing area of research. There have been multiple attempts by the researchers to solve this problem with different deep learning architectures, although the accuracy has increased, but the results are still not up to standard. This study buckles down to the comparison of Transformer and LSTM with attention block model on MS-COCO dataset, which is a standard dataset for image captioning. For both the models we have used pre-trained Inception-V3 CNN encoder for feature extraction of the images. The Bilingual Evaluation Understudy score (BLEU) is used to checked the accuracy of caption generated by both models. Along with the transformer and LSTM with attention block models, CLIP-diffusion model, M2-Transformer model and the X-Linear Attention model have been discussed with state of the art accuracy.
Web Phishing Classification using Combined Machine Learning Methods

Phishing is a crime that uses social engineering techniques, both in deceptive statements and technically, to steal consumers' personal identification data and financial account credentials. With the new Phishing machine learning approach, websites can be recognized in real-time. K-Nearest Neighbor(KNN) and Naïve Bayes (NB) are popular machine learning approaches. KNN and NB have their own strengths and weaknesses. By combining the two, deficiencies can be covered. So this study proposes to combine K-Nearest Neighbor with Naïve Bayes to classify phishing websites. Based on the results of the accuracy test of the combination of KNN with k=8 and Naïve Bayes, a maximum accuracy of 93.44% is produced. This result is 6.25% superior compared to using only one classifier.
On exploring node-feature and graph-structure diversities for node drop graph pooling

Graph Neural Networks (GNNs) have been successfully applied to graph-level tasks in various fields such as biology, social networks, computer vision, and natural language processing. For the graph-level representations learning of GNNs, graph pooling plays an essential role. Among many pooling techniques, node drop pooling has garnered significant attention and is considered as a leading approach. However, existing node drop pooling methods, which typically retain the top-k nodes based on their significance scores, often overlook the diversity inherent in node features and graph structures. This limitation leads to suboptimal graph-level representations. To overcome this, we introduce a groundbreaking plug-and-play score scheme, termed MID. MID comprises a Multidimensional score space and two key operations: flIpscore and Dropscore. The multidimensional score space depicts the significance of nodes by multiple criteria; the flipscore process promotes the preservation of distinct node features; the dropscore compels the model to take into account a range of graph structures rather than focusing on local structures. To evaluate the effectiveness of our proposed MID, we have conducted extensive experiments by integrating it with a broad range of recent node drop pooling methods, such as TopKPool, SAGPool, GSAPool, and ASAP. In particular, MID has proven to bring a significant average improvement of approximately 2.8% over the four aforementioned methods when tested on 17 real-world graph classification datasets. Code is available at https://github.com/whuchuang/mid.