Reviewerly/exHarmony huggingface dataset:
A Fault-Tolerant Honeycomb Memory

Recently, Hastings &amp; Haah introduced a quantum memory defined on the honeycomb lattice. Remarkably, this honeycomb code assembles weight-six parity checks using only two-local measurements. The sparse connectivity and two-local measurements are desirable features for certain hardware, while the weight-six parity checks enable robust performance in the circuit model.In this work, we quantify the robustness of logical qubits preserved by the honeycomb code using a correlated minimum-weight perfect-matching decoder. Using Monte Carlo sampling, we estimate the honeycomb code's threshold in different error models, and project how efficiently it can reach the "teraquop regime" where trillions of quantum logical operations can be executed reliably. We perform the same estimates for the rotated surface code, and find a threshold of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>0.2</mml:mn><mml:mi mathvariant="normal">&amp;#x0025;</mml:mi><mml:mo>&amp;#x2212;</mml:mo><mml:mn>0.3</mml:mn><mml:mi mathvariant="normal">&amp;#x0025;</mml:mi></mml:math> for the honeycomb code compared to a threshold of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>0.5</mml:mn><mml:mi mathvariant="normal">&amp;#x0025;</mml:mi><mml:mo>&amp;#x2212;</mml:mo><mml:mn>0.7</mml:mn><mml:mi mathvariant="normal">&amp;#x0025;</mml:mi></mml:math> for the surface code in a controlled-not circuit model. In a circuit model with native two-body measurements, the honeycomb code achieves a threshold of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1.5</mml:mn><mml:mi mathvariant="normal">&amp;#x0025;</mml:mi><mml:mo>&amp;#x003C;</mml:mo><mml:mi>p</mml:mi><mml:mo>&amp;#x003C;</mml:mo><mml:mn>2.0</mml:mn><mml:mi mathvariant="normal">&amp;#x0025;</mml:mi></mml:math>, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>p</mml:mi></mml:math> is the collective error rate of the two-body measurement gate - including both measurement and correlated data depolarization error processes. With such gates at a physical error rate of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>10</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>&amp;#x2212;</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math>, we project that the honeycomb code can reach the teraquop regime with only <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>600</mml:mn></mml:math> physical qubits.
Fast CU Decision-Making Algorithm Based on DenseNet Network for VVC

The joint video expert team (JVET) is currently developing a new video coding standard called H.266/Versatile Video Coding (VVC). Compared with High Efficiency Video Coding (HEVC), VVC has added a variety of coding tools. These tools have greatly improved video compression efficiency and maintained a high level video quality. However, due to the increase in computational complexity, the encoding time is much longer than HEVC. We propose a prediction tool based on DenseNet (a convolutional neural network) to decrease the VVC coding complexity. We predict the probability that the edge of 4×4 blocks in each 64×64 block is the division boundary by Convolutional Neural Networks (CNN). Then, we skip the unnecessary rate distortion optimization (RDO) and speed up the coding by probability vectors in advance. The proposed method can reduce the coding complexity of 46.10% in VTM10.0 intra coding, while Bjøntegaard delta bit rate (BDBR) only increases by 1.86%. In the sequence with a resolution greater than 1080P, the acceleration efficiency can be at 64.81%, the BDBR loss only increased by 1.92%.
Genuine quantum networks with superposed tasks and addressing

Abstract We show how to make quantum networks, both standard and entanglement-based, genuine quantum by providing them with the possibility of handling superposed tasks and superposed addressing. This extension of their functionality relies on a quantum control register, which specifies not only the task of the network, but also the corresponding weights in a coherently superposed fashion. Although adding coherent control to classical tasks, such as sending or measuring—or not doing so—is in general impossible, we introduce protocols that are able to mimick this behavior under certain conditions. We achieve this by always performing the classical task, either on the desired state or a properly chosen dummy state. We provide several examples, and show that externally controlling quantum superposition of tasks offers additional possibilities and advantages over usually considered single functionality. For instance, superpositions of different target state configurations shared among different nodes of the network can be prepared, or quantum information can be sent among a superposition of different paths or to different destinations.
Does workplace spirituality influence knowledge-sharing behavior and work engagement in work? Trust as a mediator

Management scholars view workplace spirituality as the main factor behind building trust among employees and playing a pivotal role in enhancing the organization's positive outcomes, i.e., knowledge sharing behavior &amp; work engagement. Underpinning social exchange theory, we explored the linkage between workplace spirituality, knowledge sharing behavior, and work engagement. We further studied to look at the mediating effect of trust between workplace spirituality and positive outcomes. Data was collected from six private companies, the total number of respondents was (n=196). The study's analysis showed that workplace spirituality substantially positively impacts knowledge sharing behavior and work engagement. Furthermore, the link between workplace spirituality, knowledge sharing behavior, and work engagement is positively and statistically significantly mediated by trust. Thus, this work contributes significantly to the research paradigm by presenting workplace spirituality as a solution for high-rise trust among employees, fostering employee engagement in their work, and improving the capacity of knowledge-sharing behavior. Additionally, at the end of this study, theoretical and managerial suggestions, future avenues, and limitations are stated.
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning

Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains.Existing approaches only learn class-specific semantic features and intermediate representations from source domains.This affects generalizability to unseen target domains, resulting in suboptimal performances.To this end, we present CONTAINER, a novel contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER.Instead of optimizing class-specific attributes, CONTAINER optimizes a generalized objective of differentiating between token categories based on their Gaussian-distributed embeddings.This effectively alleviates overfitting issues originating from training domains.Our experiments in several traditional test domains (OntoNotes, CoNLL'03, WNUT '17, GUM) and a new large scale Few-Shot NER dataset (Few-NERD) demonstrate that, on average, CONTAINER outperforms previous methods by 3%-13% absolute F1 points while showing consistent performance trends, even in challenging scenarios where previous approaches could not achieve appreciable performance.
Simple Entity-Centric Questions Challenge Dense Retrievers

Open-domain question answering has exploded in popularity recently due to the success of dense retrieval models, which have surpassed sparse models using only a few supervised training examples.However, in this paper, we demonstrate current dense models are not yet the holy grail of retrieval.We first construct EntityQuestions, a set of simple, entityrich questions based on facts from Wikidata (e.g., "Where was Arve Furset born?"), and observe that dense retrievers drastically underperform sparse methods.We investigate this issue and uncover that dense retrievers can only generalize to common entities unless the question pattern is explicitly observed during training.We discuss two simple solutions towards addressing this critical problem.First, we demonstrate that data augmentation is unable to fix the generalization problem.Second, we argue a more robust passage encoder helps facilitate better question adaptation using specialized question encoders.We hope our work can shed light on the challenges in creating a robust, universal dense retriever that works well across different input distributions.
A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

Human Pose Estimation (HPE) has received considerable attention during the past years, improving its performance thanks to the use of Deep Learning, and introducing new interesting uses, such as its application in Sport and Physical Exercise (SPE). The aim of this systematic review is to analyze the literature related to the application of HPE in SPE, the available data, methods, performance, opportunities, and challenges. One reviewer applied different inclusion and exclusion criteria, as well as quality metrics, to perform the paper filtering through the paper databases. The Association for Computing Machinery Digital Library, Web of Science, and dblp included more than 500 related papers after the initial filtering, finally resulting in 20. In addition, research was carried out regarding the publicly available data related to this topic. It can be concluded that even if related public data can be found, much more data is needed to be able to obtain good performance in different contexts. In relation with the methods of the authors, the use of general purpose systems as base, such as Openpose, combined with other methods and adaptations to the specific use case can be found. Finally, the limitations, opportunities, and challenges are presented.
A Bagging Strategy-Based Kernel Extreme Learning Machine for Complex Network Intrusion Detection

Network intrusion can enter the network through informal channels.Some illegal users utilize Trojans and selfprogrammed attack to change the network security system, so that the system loses the defense and alarm function and the Hacker can steal the internal information.Network intrusion seriously harms the security of network information and the legitimate rights of users.Therefore, a bagging strategy-based kernel extreme learning machine for complex network intrusion detection is presented in this paper.This method adopts a bagging strategy to train several sub-kernel extreme learning machines independently.Then the integrated gain of above machines is measured based on the margin distance minimization (MDM) criterion.Selected machines with high gain degree are selected for selective integration to obtain selective integrated learners with strong generalization ability and high efficiency.Then an improved universal gravitation search algorithm is used to optimize the kernel parameters.Meanwhile, a sub-kernel extreme learning machine online update strategy based on incremental learning of batch samples is introduced to realize the online update of intrusion detection model, so that the proposed detection method can effectively be adapted to the changes of complex network environment.Finally, experiments illustrate that the proposed method has better effect on network intrusion detection in terms of detection accuracy and speed, especially for unknown network intrusion connection events, the response speed is fast, the false alarm rate is low.
Device-Independent Quantum Key Distribution with Random Postselection

Device-independent quantum key distribution (QKD) can permit the superior security even with unknown devices. In practice, however, the realization of device-independent QKD is technically challenging because of its low noise tolerance. In the photonic setup, due to the limited detection efficiency, a large amount of the data generates from no-detection events which contain few correlations but contribute high errors. Here, we propose the device-independent QKD protocol with random postselection, where the secret keys are extracted only from the postselected subset of outcomes. This could not open the detection loophole as long as the entropy of the postselected subset is evaluated from the information of the entire set of data, including both detection and no-detection events. This postselection has the advantage to significantly reduce the error events, thus relaxing the threshold of required detection efficiency. In the model of collective attacks, our protocol can tolerate detector efficiency as low as 68.5%, which goes beyond standard security proofs. The results make a concrete step for the implementation of device-independent QKD in practice.
Introduction to NFTs: The Future of Digital Collectibles

This paper commences by introducing the essentials of blockchain technology and then goes into how Ethereum blockchain revolutionized blockchain.Smart contracts are presented in the context of showing how they play an important role in implementing rules regarding the Ethereum blockchain, allowing the user to regulate digital assets.The standards used in the Ethereum blockchain to build Non-Fungible Tokens (NFTs) are discussed.The paper concludes by presenting the benefits of NFTs as well as the use of Ethereum blockchain for future applications.
Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes

Over the past few years, deep convolutional neural network-based methods have made great progress in semantic segmentation of street scenes. Some recent methods align feature maps to alleviate the semantic gap between them and achieve high segmentation accuracy. However, they usually adopt the feature alignment modules with the same network configuration in the decoder and thus ignore the different roles of stages of the decoder during feature aggregation, leading to a complex decoder structure. Such a manner greatly affects the inference speed. In this paper, we present a novel Stage-aware Feature Alignment Network (SFANet) based on the encoder-decoder structure for real-time semantic segmentation of street scenes. Specifically, a Stage-aware Feature Alignment module (SFA) is proposed to align and aggregate two adjacent levels of feature maps effectively. In the SFA, by taking into account the unique role of each stage in the decoder, a novel stage-aware Feature Enhancement Block (FEB) is designed to enhance spatial details and contextual information of feature maps from the encoder. In this way, we are able to address the misalignment problem with a very simple and efficient multi-branch decoder structure. Moreover, an auxiliary training strategy is developed to explicitly alleviate the multi-scale object problem without bringing additional computational costs during the inference phase. Experimental results show that the proposed SFANet exhibits a good balance between accuracy and speed for real-time semantic segmentation of street scenes. In particular, based on ResNet-18, SFANet respectively obtains 78.1% and 74.7% mean of class-wise Intersection-over-Union (mIoU) at inference speeds of 37 FPS and 96 FPS on the challenging Cityscapes and CamVid test datasets by using only a single GTX 1080Ti GPU.
AUV Obstacle Avoidance Planning Based on Deep Reinforcement Learning

In a complex underwater environment, finding a viable, collision-free path for an autonomous underwater vehicle (AUV) is a challenging task. The purpose of this paper is to establish a safe, real-time, and robust method of collision avoidance that improves the autonomy of AUVs. We propose a method based on active sonar, which utilizes a deep reinforcement learning algorithm to learn the processed sonar information to navigate the AUV in an uncertain environment. We compare the performance of double deep Q-network algorithms with that of a genetic algorithm and deep learning. We propose a line-of-sight guidance method to mitigate abrupt changes in the yaw direction and smooth the heading changes when the AUV switches trajectory. The different experimental results show that the double deep Q-network algorithms ensure excellent collision avoidance performance. The effectiveness of the algorithm proposed in this paper was verified in three environments: random static, mixed static, and complex dynamic. The results show that the proposed algorithm has significant advantages over other algorithms in terms of success rate, collision avoidance performance, and generalization ability. The double deep Q-network algorithm proposed in this paper is superior to the genetic algorithm and deep learning in terms of the running time, total path, performance in avoiding collisions with moving obstacles, and planning time for each step. After the algorithm is trained in a simulated environment, it can still perform online learning according to the information of the environment after deployment and adjust the weight of the network in real-time. These results demonstrate that the proposed approach has significant potential for practical applications.
AESOP: Paraphrase Generation with Adaptive Syntactic Control

We propose to control paraphrase generation with carefully chosen target syntactic structures to generate more proper and higher quality paraphrases.Our model, AESOP, leverages a pretrained language model and purposefully selected syntactical control via a retrieval-based selection module to generate fluent paraphrases.Experiments show that AE-SOP achieves state-of-the-art performances on semantic preservation and syntactic conformation on two benchmark datasets with groundtruth syntactic control from human-annotated exemplars.Moreover, with the retrieval-based target syntax selection module, AESOP generates paraphrases with even better qualities than the current best model using human-annotated target syntactic parses according to human evaluation.We further demonstrate the effectiveness of AESOP to improve classification models' robustness to syntactic perturbation by data augmentation on two GLUE tasks. AESOP What to say?How to guide paraphrasing?source parse (ROOT (S (NP ) (VP ) (. ) ) (ROOT (SBAR (IN ) (S ) (. )))with the gold everything changes .the gold will change everything .
Computer-assisted quantification of tumor-associated collagen signatures to improve the prognosis prediction of breast cancer

Collagen fibers play an important role in tumor initiation, progression, and invasion. Our previous research has already shown that large-scale tumor-associated collagen signatures (TACS) are powerful prognostic biomarkers independent of clinicopathological factors in invasive breast cancer. However, they are observed on a macroscale and are more suitable for identifying high-risk patients. It is necessary to investigate the effect of the corresponding microscopic features of TACS so as to more accurately and comprehensively predict the prognosis of breast cancer patients.In this retrospective and multicenter study, we included 942 invasive breast cancer patients in both a training cohort (n = 355) and an internal validation cohort (n = 334) from one clinical center and in an external validation cohort (n = 253) from a different clinical center. TACS corresponding microscopic features (TCMFs) were firstly extracted from multiphoton images for each patient, and then least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust features to build a TCMF-score. Finally, the Cox proportional hazard regression analysis was used to evaluate the association of TCMF-score with disease-free survival (DFS).TCMF-score is significantly associated with DFS in univariate Cox proportional hazard regression analysis. After adjusting for clinical variables by multivariate Cox regression analysis, the TCMF-score remains an independent prognostic indicator. Remarkably, the TCMF model performs better than the clinical (CLI) model in the three cohorts and is particularly outstanding in the ER-positive and lower-risk subgroups. By contrast, the TACS model is more suitable for the ER-negative and higher-risk subgroups. When the TACS and TCMF are combined, they could complement each other and perform well in all patients. As expected, the full model (CLI+TCMF+TACS) achieves the best performance (AUC 0.905, [0.873-0.938]; 0.896, [0.860-0.931]; 0.882, [0.840-0.925] in the three cohorts).These results demonstrate that the TCMF-score is an independent prognostic factor for breast cancer, and the increased prognostic performance (TCMF+TACS-score) may help us develop more appropriate treatment protocols.
Technology acceptance drivers for AR smart glasses in the middle east: A quantitative study

This study aims to establish Middle East users' perspectives on the major factors that impact their decision to adopt Augmented Reality AR smart glasses (ARSG). Thus, an online questionnaire was designed and sent directly to the respondents, and 584 valid data points were collected from individuals living in the Middle East. The data were analyzed using Pearson correlations and Exploratory Factor Analysis (EFA) techniques using SPSS. Eleven hypotheses were tested using Multiple Regression analysis, where seven independent variables out of eleven were confirmed to have a significant impact on the perceived adoption of ARSG. The results indicate that four of the independent variables including Pre-Market Knowledge, Image, Own privacy and Technology innovativeness show the significant impact on ARSG adoption at the 1% significant level. In addition, the results indicate that three of the social and technological factors include Perceived Ease of use, Perceived usefulness and Other's privacy show the significant effect on ARSG adoption at the 5% significant level. Among the 7 social and technological factors, the results suggest that technology innovation expresses the strongest effect on ARSG adoption with the highest coefficient value of 0.413 (b = 0.413, t = 12.881, ρ &lt; 0.01). Moreover, user intention is significantly impacted by gender and place of living but not by education or age. The research also provides pre-market insights on users' personal types that represent who will most likely adopt the new smart glasses and that differentiate them based on their priorities. To the best of our knowledge, this is among the first works to investigate technology acceptance drivers of AR smart glasses in the Middle East.
Deep Attention Diffusion Graph Neural Networks for Text Classification

Text classification is a fundamental task with broad applications in natural language processing.Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability.However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods and low-frequency information within texts, which cannot fully utilize the rich context information of documents.Moreover, these models suffer from over-smoothing issues if many graph layers are stacked.In this paper, a Deep Attention Diffusion Graph Neural Network (DADGNN) model is proposed to learn text representations, bridging the chasm of interaction difficulties between a word and its distant neighbors.Experimental results on various standard benchmark datasets demonstrate the superior performance of the present approach.