Reviewerly/exHarmony huggingface dataset:
Perfect intrinsic squeezing at the superradiant phase transition critical point

Abstract Some of the most exotic properties of the quantum vacuum are predicted in ultrastrongly coupled photon–atom systems; one such property is quantum squeezing leading to suppressed quantum fluctuations of photons and atoms. This squeezing is unique because (1) it is realized in the ground state of the system and does not require external driving, and (2) the squeezing can be perfect in the sense that quantum fluctuations of certain observables are completely suppressed. Specifically, we investigate the ground state of the Dicke model, which describes atoms collectively coupled to a single photonic mode, and we found that the photon–atom fluctuation vanishes at the onset of the superradiant phase transition in the thermodynamic limit of an infinite number of atoms. Moreover, when a finite number of atoms is considered, the variance of the fluctuation around the critical point asymptotically converges to zero, as the number of atoms is increased. In contrast to the squeezed states of flying photons obtained using standard generation protocols with external driving, the squeezing obtained in the ground state of the ultrastrongly coupled photon–atom systems is resilient against unpredictable noise.
Multi-patch Learning: Looking More Pixels in the Training Phase

Due to the limitations of computation capability and memory of GPUs, most image restoration tasks are trained with cropped patches instead of full-size images. Existing extensive experiments show that the model trained with a larger patch size could achieve better performance since a larger patch size typically means larger receptive fields. However, it comes at the cost of extremely long training times and significant memory consumption. To alleviate the dilemma mentioned above, we propose a multi-patch method to expand the receptive field with negligible memory and computation increase (less than $$1\%$$ ). In addition, we collect 100K high-quality images of 1K categories, following ImageNet, from flickr.com for low-level image tasks. Our method improves the quantitative performance by 0.3412dB on the validation set of the "Compressed Input Super-Resolution Challenge - Image Track".
Dynamic texture classification based on bag-of-models with mixture of student’s t-hidden Markov models

Hidden Markov model (HMM) is a popular statistical approach for modeling sequential data comprising continuous attributes, and it has been applied in finance, machine vision and other fields. Due to higher tolerance to outliers than Gaussian distribution, the multivariate student's t-distribution is used to serve as the observation emission distribution in continuous HMM (SHMM), which has been exploited to improve the performance for modeling sequential data. In this paper, a bag-of-models method based on the mixture of SHMMs is proposed to describe dynamic texture for dynamic texture classification, in which codebook is constructed with SHMMs. Specifically, a novel mixture model, mixture of SHMMs, is proposed to model and cluster the observation sequences of the dynamic texture. The parameter learning method is derived by using the expectation maximization (EM) algorithm. Then all components of the mixture of SHMMs are assembled to obtain the codewords and the bag-of-models based features are extracted for describing the dynamic texture. Finally, we demonstrate the effectiveness of the bag-of-models based on the mixture of SHMMs for dynamic texture classification on different benchmark datasets. The experimental results show the potential performance of the proposed approach by comparing with some existing dynamic texture classification methods.
Machine Learning Models to Predict the Risk of Rapidly Progressive Kidney Disease and the Need for Nephrology Referral in Adult Patients with Type 2 Diabetes

Early detection of rapidly progressive kidney disease is key to improving the renal outcome and reducing complications in adult patients with type 2 diabetes mellitus (T2DM). We aimed to construct a 6-month machine learning (ML) predictive model for the risk of rapidly progressive kidney disease and the need for nephrology referral in adult patients with T2DM and an initial estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m2. We extracted patients and medical features from the electronic medical records (EMR), and the cohort was divided into a training/validation and testing data set to develop and validate the models on the basis of three algorithms: logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost). We also applied an ensemble approach using soft voting classifier to classify the referral group. We used the area under the receiver operating characteristic curve (AUROC), precision, recall, and accuracy as the metrics to evaluate the performance. Shapley additive explanations (SHAP) values were used to evaluate the feature importance. The XGB model had higher accuracy and relatively higher precision in the referral group as compared with the LR and RF models, but LR and RF models had higher recall in the referral group. In general, the ensemble voting classifier had relatively higher accuracy, higher AUROC, and higher recall in the referral group as compared with the other three models. In addition, we found a more specific definition of the target improved the model performance in our study. In conclusion, we built a 6-month ML predictive model for the risk of rapidly progressive kidney disease. Early detection and then nephrology referral may facilitate appropriate management.
Dynamic Model in Estimating the Impact of Competition on Banking Efficiency: Evidence form MENA Countries

This paper investigates the role of competition in the Middle East and North Africa countries for the period of 2012-2021.The bank efficiency is measured using Stochastic Frontier Analysis.Using a sample of 177 commercial banks and a dynamic panel data approach in GMM technique, the empirical results revealed that the higher the competition the higher bank efficiency.This finding suggests that banks in countries with low high competition tend to perform more efficiently.Furthermore, a negative but insignificant relationship between total assets and bank efficiency reflects the fact that larger banks in developing countries suffer from diseconomies of scale.
The Effectiveness of Hybrid Learning-Based Adaptive Media to Empower Student’s Critical Thinking Skills: Is It Really for VARK Learning Style?

Students needed to understand more complex knowledge through critical thinking.The discovery of a good understanding could be directed with high critical thinking skills.This study intended to propose a new learning approach, namely a hybrid learning model that adapts the VARK learning style to empower students' critical thinking skills.The research used the Research and Development method which includes the stages of exploration, development, testing, and dissemination.Adaptive learning media had been designed to implement the proposed new learning model.Testing the research product used 98 elementary school students in Surakarta City who were taken through the cluster random sampling technique.Product effectiveness analysis used the Wilcoxon Signed Rank Test and the Mann-Whitney U test after knowing the results of the normality and homogeneity prerequisite tests.The score for increasing critical thinking skills in the aspect of interpretation was 94.18%, the inference was 77.74%, the explanation was 97.33%, the analysis was 98.87%, evaluation was 65.01%, and self-regulation was 97.13 %.The test results prove that adaptive media based on hybrid learning models has a very significant impact on improving students' critical thinking aspects.This implied that the application of the hybrid learning model by adjusting the VARK learning style is able to effectively improve students' critical thinking skills.
Visualized Multiple Image Selection Encryption Based on Log Chaos System and Multilayer Cellular Automata Saliency Detection

For the security of multiple image regions of interest, this paper designs a multi-image visual encryption algorithm based on region of interest (ROI). Firstly, a new one-dimensional chaotic system (1-DLCP) is proposed. The performance analysis shows that the system has better dynamic behavior. Then, the salient parts of multiple images are extracted using multilayer cellular automata saliency detection. The measured matrix generated by the proposed chaotic system compresses the extracted plaintext images respectively. The compressed image and the extracted salient parts are recombined into one image, and a scrambling-diffusion synchronization operation is performed on the recombined image to obtain a secret image. The final embedding stage uses integer wavelet transform (IWT) to embed the secret image into a color carrier image. It is worth mentioning that this design scheme not only reconstructs the significant part without damage, but also reduces the storage space.
iU-Net: a hybrid structured network with a novel feature fusion approach for medical image segmentation

Abstract In recent years, convolutional neural networks (CNNs) have made great achievements in the field of medical image segmentation, especially full convolutional neural networks based on U-shaped structures and skip connections. However, limited by the inherent limitations of convolution, CNNs-based methods usually exhibit limitations in modeling long-range dependencies and are unable to extract large amounts of global contextual information, which deprives neural networks of the ability to adapt to different visual modalities. In this paper, we propose our own model, which is called iU-Net bacause its structure closely resembles the combination of i and U. iU-Net is a multiple encoder-decoder structure combining Swin Transformer and CNN. We use a hierarchical Swin Transformer structure with shifted windows as the primary encoder and convolution as the secondary encoder to complement the context information extracted by the primary encoder. To sufficiently fuse the feature information extracted from multiple encoders, we design a feature fusion module (W-FFM) based on wave function representation. Besides, a three branch up sampling method(Tri-Upsample) has developed to replace the patch expand in the Swin Transformer, which can effectively avoid the Checkerboard Artifacts caused by the patch expand. On the skin lesion region segmentation task, the segmentation performance of iU-Net is optimal, with Dice and Iou reaching 90.12% and 83.06%, respectively. To verify the generalization of iU-Net, we used the model trained on ISIC2018 dataset to test on PH2 dataset, and achieved 93.80% Dice and 88.74% IoU. On the lung feild segmentation task, the iU-Net achieved optimal results on IoU and Precision, reaching 98.54% and 94.35% respectively. Extensive experiments demonstrate the segmentation performance and generalization ability of iU-Net.
A Quality Assessment of the ARM-Net Registry Design and Data Collection

Background: Registries are important in rare disease research.The Anorectal Malformation Network (ARM-Net) registry is a well-established European patient registry collecting demographic, clinical, and functional outcome data.We assessed the quality of this registry through review of the structure, data elements, collected data, and user experience.Material and methods: Design and data elements were assessed for completeness, consistency, usefulness, accuracy, validity, and comparability.An intra-and inter-user variability study was conducted through monitoring and re-registration of patients.User experience was assessed via a questionnaire on registration, design of registry, and satisfaction.Results: We evaluated 119 data elements, of which 107 were utilized and comprised 42 string and 65 numeric elements.A minority (37.0%) of the 2278 included records had complete data, though this improved to 83.5% when follow-up elements were excluded.Intra-observer variability demonstrated 11.7% incongruence, while inter-observer variability was 14.7%.Users were predominantly pediatric surgeons and typically registered patients within 11e30 min.Users did not experience any significant difficulties with data entry and were generally satisfied with the registry, but preferred more longitudinal data and patient-reported outcomes.Conclusions: The ARM-Net registry presents one of the largest ARM cohorts.Although its collected data are valuable, they are susceptible to error and user variability.Continuous evaluations are required to maintain relevant and high-quality data and to achieve long-term sustainability.With the recommendations resulting from this study, we call for rare disease patient registries to take example and aim to continuously improve their data quality to enhance the small, but impactful, field of rare disease research.Level of Evidence: V.
Masked Vision-language Transformer in Fashion

Abstract We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation. Technically, we simply utilize the vision transformer architecture for replacing the bidirectional encoder representations from Transformers (BERT) in the pre-training model, making MVLT the first end-to-end framework for the fashion domain. Besides, we designed masked image reconstruction (MIR) for a fine-grained understanding of fashion. MVLT is an extensible and convenient architecture that admits raw multi-modal inputs without extra pre-processing models (e.g., ResNet), implicitly modeling the vision-language alignments. More importantly, MVLT can easily generalize to various matching and generative tasks. Experimental results show obvious improvements in retrieval (rank@5: 17%) and recognition (accuracy: 3%) tasks over the Fashion-Gen 2018 winner, Kaleido-BERT. The code is available at https://github.com/GewelsJI/MVLT .
‘Quantum supremacy’ revisited: low-complexity, deterministic solutions of the original Deutsch–Jozsa problem in classical physical systems

The original Deutsch-Jozsa (oDJ) problem is for an oracle (realized here as a database) of size N, where, according to their claim, the deterministic solution of the problem on a classical Turing computer requires O(N) computational complexity. They produced the famous Deutsch-Jozsa quantum algorithm that offered an exponential speed-up over the classical computer, namely O[log(N)] complexity for the solution in a quantum computer. In this paper, the problem is implemented on an instantaneous noise-based logic processor. It is shown that, similarly to the quantum algorithm, the oDJ problem can deterministically be solved with O[log(N)] complexity. The implication is that by adding a truly random coin to a classical Turing machine and using this classical-physical algorithm can also speed up the deterministic solution of the Deutsch-Jozsa problem exponentially, similarly to the quantum algorithm. Then it is realized that the same database and the solution of the Deutsch-Jozsa problem can also be realized by using an identical algorithmic structure in a simpler way, even without noise/random coin. The only lost function in this new system, as compared with noise-based logic, is the ability to do generic parallel logic operations over the whole database. As the latter feature is not needed for the oDJ problem, it is concluded that the problem can be solved on a classical computer with O[log(N)] complexity even without a random coin. Therefore, while the oDJ algorithm is a historically important step in the developments of quantum computers, it is insufficient to prove quantum supremacy. Note, there is also a simplified Deutsch-Jozsa problem proposed later, which is more popular in the field; however, it is irrelevant for the present paper.
Secure Data Transfer Based on a Multi-Level Blockchain for Internet of Vehicles

Because of the decentralized trait of the blockchain and the Internet of vehicles, both are very suitable for the architecture of the other. This study proposes a multi-level blockchain framework to secure information security on the Internet of vehicles. The main motivation of this study is to propose a new transaction block and ensure the identity of traders and the non-repudiation of transactions through the elliptic curve digital signature algorithm ECDSA. The designed multi-level blockchain architecture distributes the operations within the intra_cluster blockchain and the inter_cluster blockchain to improve the efficiency of the entire block. On the cloud computing platform, we exploit the threshold key management protocol, and the system can recover the system key as long as the threshold partial key is collected. This avoids the occurrence of PKI single-point failure. Thus, the proposed architecture ensures the security of OBU-RSU-BS-VM. The proposed multi-level blockchain framework consists of a block, intra-cluster blockchain and inter-cluster blockchain. The roadside unit RSU is responsible for the communication of vehicles in the vicinity, similar to a cluster head on the Internet of vehicles. This study exploits RSU to manage the block, and the base station is responsible for managing the intra-cluster blockchain named intra_clusterBC, and the cloud server at the back end is responsible for the entire system blockchain named inter_clusterBC. Finally, RSU, base stations and cloud servers cooperatively construct the multi-level blockchain framework and improve the security and the efficiency of the operation of the blockchain. Overall, in order to protect the security of the transaction data of the blockchain, we propose a new transaction block structure and adopt the elliptic curve cryptographic signature ECDSA to ensure that the Merkle tree root value is not changed and also make sure the transaction identity and non-repudiation of transaction data. Finally, this study considers information security in a cloud environment, and therefore we propose a secret-sharing and secure-map-reducing architecture based on the identity confirmation scheme. The proposed scheme with decentralization is very suitable for distributed connected vehicles and can also improve the execution efficiency of the blockchain.
Facilitating innovation and knowledge transfer between homogeneous and heterogeneous datasets: Generic incremental transfer learning approach and multidisciplinary studies

Open datasets serve as facilitators for researchers to conduct research with ground truth data. Generally, datasets contain innovation and knowledge in the domains that could be transferred between homogeneous datasets and have become feasible using machine learning models with the advent of transfer learning algorithms. Research initiatives are drawn to the heterogeneous datasets if these could extract useful innovation and knowledge across datasets of different domains. A breakthrough can be achieved without the restriction requiring the similarities between datasets. A multiple incremental transfer learning is proposed to yield optimal results in the target model. A multiple rounds multiple incremental transfer learning with a negative transfer avoidance algorithm are proposed as a generic approach to transfer innovation and knowledge from the source domain to the target domain. Incremental learning has played an important role in lowering the risk of transferring unrelated information which reduces the performance of machine learning models. To evaluate the effectiveness of the proposed algorithm, multidisciplinary studies are carried out in 5 disciplines with 15 benchmark datasets. Each discipline comprises 3 datasets as studies with homogeneous datasets whereas heterogeneous datasets are formed between disciplines. The results reveal that the proposed algorithm enhances the average accuracy by 4.35% compared with existing works. Ablation studies are also conducted to analyse the contributions of the individual techniques of the proposed algorithm, namely, the multiple rounds strategy, incremental learning, and negative transfer avoidance algorithms. These techniques enhance the average accuracy of the machine learning model by 3.44%, 0.849%, and 4.26%, respectively.
Utilization of Crowdsourced Felt Reports to Distinguish High-Impact from Low-Impact Earthquakes Globally within Minutes of an Event

Abstract Rapid assessment of an earthquake’s impact on the affected society is a crucial step in the early phase of disaster management, navigating the need for further emergency response measures. We demonstrate that felt reports collected via the LastQuake service of the European Mediterranean Seismological Center can be utilized to rapidly estimate the probability of a felt earthquake being high impact rather than low impact on a global scale. Our data-driven, transparent, and reproducible method utilizing Bayes’ theorem and kernel density estimation provides results within 10 min for 393 felt events in 2021. Although a separation of high- and low-impact events remains challenging, the correct and unambiguous assessment of a large portion of low-impact events is a key strength of our approach. We consider our method as an inexpensive addition to the pool of earthquake impact assessment tools, one that is fully independent of seismic data and can be utilized in many populated areas on the planet. Although practical deployment of our method remains an open task, we demonstrate the potential to improve disaster management in regions that currently lack expensive seismic instrumentation.
Focal and Global Spatial-Temporal Transformer for Skeleton-Based Action Recognition

Despite great progress achieved by transformer in various vision tasks, it is still underexplored for skeleton-based action recognition with only a few attempts. Besides, these methods directly calculate the pair-wise global self-attention equally for all the joints in both the spatial and temporal dimensions, undervaluing the effect of discriminative local joints and the short-range temporal dynamics. In this work, we propose a novel Focal and Global Spatial-Temporal Transformer network (FG-STFormer), that is equipped with two key components: (1) FG-SFormer: focal joints and global parts coupling spatial transformer. It forces the network to focus on modelling correlations for both the learned discriminative spatial joints and human body parts respectively. The selective focal joints eliminate the negative effect of non-informative ones during accumulating the correlations. Meanwhile, the interactions between the focal joints and body parts are incorporated to enhance the spatial dependencies via mutual cross-attention. (2) FG-TFormer: focal and global temporal transformer. Dilated temporal convolution is integrated into the global self-attention mechanism to explicitly capture the local temporal motion patterns of joints or body parts, which is found to be vital important to make temporal transformer work. Extensive experimental results on three benchmarks, namely NTU-60, NTU-120 and NW-UCLA, show our FG-STFormer surpasses all existing transformer-based methods, and compares favourably with state-of-the-art GCN-based methods.