Reviewerly/exHarmony huggingface dataset:
Singularity analysis of igneous zircon U-Pb age and Hf isotopic record in the Zhongdian arc, northwest Yunnan, China: Implications for Indosinian magmatic flare-up and the formation of porphyry copper deposits

Situated in the southern part of Yidun arc in the southeastern Tibetan Plateau, the Zhongdian arc records magmatic activity that ranges from approximately 270 to 190 Ma, which is likely attributable to subduction of the Garze-Litang slab and involved steady low-volume magmatic lulls punctuated by short-lived high-volume flare-ups. Many porphyry copper deposits have been found in these magmatic rocks, but the temporal-spatial relationships among magmatism, porphyry copper deposits, and subduction processes around the Zhongdian arc have long been debated. Frequency distributions calculated from igneous zircon U-Pb ages are commonly utilized to interpret the duration of magmatic events. Recently, a new concept of fractal density and a local singularity analysis (LSA) method have been applied to analyze the geometric properties of zircon U-Pb age peak anomalies, which are linked to deeply rooted avalanches associated with short spurts of convection during the formation of supercontinents and continental crust growth. In this paper, the igneous zircon U-Pb dating data collected from the Zhongdian arc are analyzed by the LSA method from the perspective of fractal density to interpret the causational relationship between age peak and subduction process of the Garze-Litang oceanic crust. The results show that the age density around the age peak of 215 Ma can be well fitted and described by power law functions, and the strong singularity (k > 0.8) indicates the occurrence of extreme geological events. Meanwhile, systematic changes in the hafnium isotopic composition of igneous zircons with time reflect the upwelling of asthenospheric mantle materials, characterized by a rapid increase in εHf(t) values that reach the highest around the 215 Ma peak. Moreover, the log–log plot that shows the cumulative number-age distribution reveals another transitional age of 235 Ma. By comparison of the magmatic intensity between the western and eastern belts of the Zhongdian arc, it is indicated that the eastward magmatic migration might have begun at ca. 235 Ma. Combined with the previous work, we propose that the Indosinian magmatic flare-up in the Zhongdian arc might have been caused by superimposition of slab subduction, steepening of slab dip, and slab breakoff. This long-term magmatic hydrothermal evolution system and extreme singularity events also provide indispensable conditions for the formation of large-scale and high-grade porphyry copper deposits in the region. Our findings provide new insights into the subduction process of the Garze-Litang oceanic crust and Indosinian magmatic framework around the Zhongdian arc. It is believed that the LSA method has potential applications in depicting the variation of magmatic activity and predicting mineral resources.
Evaluation of split-and-rephrase output of the knowledge extraction tool in the intelligent tutoring system

Various approaches to text simplification have been proposed in an attempt to increase text readability. The rephrasing of syntactically and semantically complex structures is still challenging. A pedagogically motivated simplified version of the same text can have both positive and negative side effects. On the one hand, it can facilitate reading comprehension because of much shorter sentences and a limited vocabulary, but on the other hand, the simplified text often lacks coherence, unity and style. Therefore, reasonable trade-offs among linguistic simplicity, naturalness and informativeness are highly needed. This is a survey paper that discusses state-of-the-art approaches to sentence/text simplification and evaluation methods, along with an empirical evaluation of our approach. The quality of sentence splitting, using the knowledge extraction tool SAAT was compared to state-of-the-art syntactic simplification systems. The research was carried out on the WikiSplit, the HSplit and MinWikiSplit simplification corpora. Automatic metrics for the HSplit showed that the SAAT outperformed other TS systems in all categories. For the WikiSplit dataset, automatic metrics scores were slightly lower than that of the baseline system DisSim. However, the human evaluation showed that DisSim outperformed the SAAT in terms of simplicity and grammar. The quality of AG18copy output corresponded to that of the SAAT. The inter-annotator agreement was calculated. Research limitations as well as suggestions for future research were also provided.
Deep-NC: A secure image transmission using deep learning and network coding

Visual communications have played an important part in our daily life as a non-verbal way of conveying information using symbols, gestures and images. With the advances of technology, people can visually communicate with each other in a number of forms via digital communications. Recently Image Super-Resolution (ISR) with Deep Learning (DL) has been developed to reproduce the original image from its low-resolution version, which allows us to reduce the image size for saving transmission bandwidth. Although many benefits can be realised, the image transmission over wireless media experiences inevitable loss due to environment noise and inherent hardware issues. Moreover, data privacy is of vital importance, especially when the eavesdropper can easily overhear the communications over the air. To this end, this paper proposes a secure ISR protocol, namely Deep-NC, for the image communications based on the DL and Network Coding (NC). Specifically, two schemes, namely Per-Image Coding (PIC) and Per-Pixel Coding (PPC), are designed so as to protect the sharing of private image from the eavesdropper. Although the PPC scheme achieves a better performance than the PIC scheme for the entire image, it requires a higher computational complexity on every pixel of the image. In the proposed Deep-NC, the intended user can easily recover the original image achieving a much higher performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) than those at the eavesdropper. Simulation results show that an improvement of up to 32 dB in the PSNR can be obtained when the eavesdropper does not have any knowledge of the parameters and the reference image used in the mixing schemes. Furthermore, the original image can be downscaled to a much lower resolution for saving significantly the transmission bandwidth with negligible performance loss.
Improving Deep Metric Learning by Divide and Conquer

Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically similar objects are located nearby and dissimilar objects far from another. The target similarity on the training data is defined by user in form of ground-truth class labels. However, while the embedding space learns to mimic the user-provided similarity on the training data, it should also generalize to novel categories not seen during training. Besides user-provided groundtruth training labels, a lot of additional visual factors (such as viewpoint changes or shape peculiarities) exist and imply different notions of similarity between objects, affecting the generalization on the images unseen during training. However, existing approaches usually directly learn a single embedding space on all available training data, struggling to encode all different types of relationships, and do not generalize well. We propose to build a more expressive representation by jointly splitting the embedding space and the data hierarchically into smaller sub-parts. We successively focus on smaller subsets of the training data, reducing its variance and learning a different embedding subspace for each data subset. Moreover, the subspaces are learned jointly to cover not only the intricacies, but the breadth of the data as well. Only after that, we build the final embedding from the subspaces in the conquering stage. The proposed algorithm acts as a transparent wrapper that can be placed around arbitrary existing DML methods. Our approach significantly improves upon the state-of-the-art on image retrieval, clustering, and re-identification tasks evaluated using CUB200-2011, CARS196, Stanford Online Products, In-shop Clothes, and PKU VehicleID datasets.
A methodology based on Trace-based clustering for patient phenotyping

The current situation of critical progression as regards the resistance of bacteria to antibiotics has led to the use of machine learning techniques in order to provide clinicians with new knowledge for decision making. One of the key aspects is precision medicine, which focuses on finding phenotypes of patients for whom treatments may be more effective or detecting high risk patients whose progress must be closely monitored. The identification of these phenotypes requires the application of a methodology whose results are consistent and interpretable, along with the control of the process by a clinical expert. Studies concerning machine learning phenotyping use conventional clustering or subgroup algorithms that require information to be obtained a priori. We propose a new unsupervised machine learning technique, denominated as Trace-based clustering, and a 5-step methodology in order to support clinicians when identifying patient phenotypes. The steps proposed are: (1) Extraction and transformation of data and analysis of clustering tendency, (2) Selection of clustering algorithm and parameters, (3) Automatic generation of candidate clusters, (4) Visual support for selection of candidate clusters, and (5) Evaluation by clinical experts. We undertake an antimicrobial resistance use case by employing the MIMIC-III open-access database for patients infected with the Methicillin-resistant Staphylococcus Aereus and Enterococcus Faecium treated with Vancomycin. The experiments were carried out using the Hopkins statistic in order to evaluate the clustering tendency of the data, the K-Means algorithm for clustering, and the Dice coefficient to measure the similarity of the clusters. Our experiments computed 370 potential patient sets (clusters) so as to obtain 19 candidate clusters for their final evaluation. We evaluated the final result with a classification model in order to ensure the consistency of the phenotypes obtained and we compared the result with a traditional clustering approach. We found a reduced set of consistent candidate clusters with a common phenotype (resistance and death), which were different from the other candidate clusters. An expert in the domain could add labels with clinical meaning to the reduced number of clusters. We show that the proposed methodology allows physicians to identify consistent patient phenotypes. Our experiments confirm that quality measures, and the visual analysis could help expert clinicians to control the knowledge discovery process and obtain interpretable results. Our approach provides a new perspective: that of finding patient sets using clustering techniques evaluated by overlapping clusters of the previous partitions. The method proposed is general and can be easily adapted to any other problem and any other clinical settings.
FedDNA: Federated Learning with Decoupled Normalization-Layer Aggregation for Non-IID Data

In the federated learning paradigm, multiple mobile clients train their local models independently based on the datasets generated by edge devices, and the server aggregates the model parameters received from multiple clients to form a global model. Conventional methods aggregate gradient parameters and statistical parameters without distinction, which leads to large aggregation bias due to cross-model distribution covariate shift (CDCS), and results in severe performance drop for federated learning under non-IID data. In this paper, we propose a novel decoupled parameter aggregation method called FedDNA to deal with the performance issues caused by CDCS. With the proposed method, the gradient parameters are aggregated using the conventional federated averaging method, and the statistical parameters are aggregated with an importance weighting method to reduce the divergence between the local models and the central model to optimize collaboratively by an adversarial learning algorithm based on variational autoencoder (VAE). Extensive experiments based on various federated learning scenarios with four open datasets show that FedDNA achieves significant performance improvement compared to the state-of-the-art methods.
Robust face recognition based on a new Kernel-PCA using RRQR factorization

In the last ten years, many variants of the principal component analysis were suggested to fight against the curse of dimensionality. Recently, A. Sharma et al. have proposed a stable numerical algorithm based on Householder QR decomposition (HQR) called QR PCA. This approach improves the performance of the PCA algorithm via a singular value decomposition (SVD) in terms of computation complexity. In this paper, we propose a new algorithm called RRQR PCA in order to enhance the QR PCA performance by exploiting the Rank-Revealing QR Factorization (RRQR). We have also improved the recognition rate of RRQR PCA by developing a nonlinear extension of RRQR PCA. In addition, a new robust RBF Lp-norm kernel is proposed in order to reduce the effect of outliers and noises. Extensive experiments on two well-known standard face databases which are ORL and FERET prove that the proposed algorithm is more robust than conventional PCA, 2DPCA, PCA-L1, WTPCA-L1, LDA, and 2DLDA in terms of face recognition accuracy.
Deep Bayesian Sparse Subspace Clustering

Sparse subspace clustering, as one of the most effective subspace clustering method, is widely studied in the data processing realm. However, conventional sparse subspace clustering methods are organized with two separated steps, feature learning and indicator learning. This makes the algorithm suffer the difficulties that: (1) representation and cluster indicator learning cannot affect each other; (2) cluster number and sparse penalty coefficient should be specified a priori; (3) sparse subspace clustering method is designed for the linear subspace data and cannot be exploited in the general clustering task. In this paper, a novel sparse clustering method is proposed, in which we extend the conventional algebraic sparse subspace clustering approach to a Bayesian framework. Then, cluster number estimation and low rank constraint are coupled via a Dirichlet process parameter generation process, in which the rank are no more required to be low but can be generated with a suitable value. Finally, Generative Adversarial Network (GAN) is incorporated into the Bayesian sparse model, which extends the subspace clustering method to a normal clustering model. Experiments on different real world datasets validate our theory analysis and demonstrate the effective of the proposed algorithm.
Modelling the Process of Production of Diesel Fuels by the Use of Generalized Nets

The process of commodity diesel fuel production in a refinery has been modelled by the use of the Generalized Net (GN) apparatus. GNs are extensions of Petri nets and of all their modifications and extensions. The model accounts for the orders of different grades of diesel fuel and the available amounts of the different diesel fuel components. It can be used for the synchronization and optimization of these processes.
Secure data stream transmission method for cell pathological image storage system

Due to the complex structure of cytopathological images, data loss and low transmission efficiency may occur in the transmission of cytopathological images by common data stream transmission methods. To ensure the stable transmission of the data stream of the cytopathology image storage system and maintain the safe operation of the cytopathology image storage system, a safe transmission method of the data stream of the cytopathology image storage system was designed. The security threats faced by the data flow of the cytopathology image storage system were analyzed from the aspects of information network and control network, and the risk indexes of data flow and attack loss were constructed. The security risk indexes were quantified by the general vulnerability scoring system, and the data flow security transmission model of the cytopathology image storage system was constructed. Different transmission nodes and cytopathological image storage system devices were set as attack graph nodes to collect and configure data and optimize data flow transmission path. Deploy the network node equilibrium state, control the time slot window interval equilibrium, output transmission delay allocation, and ensure the confidentiality and integrity of the data stream transmission of the cytopathological image storage system. The simulation results show that the proposed method has a higher transmission efficiency of about 11.76â€‰Mb/s. It is highly practical and can realize the safe transmission of data stream in the cytopathological image storage system.
Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning

Deploying deep learning services for time-sensitive and resource-constrained settings such as IoT using edge computing systems is a challenging task that requires dynamic adjustment of inference time. Multi-exit architectures allow deep neural networks to terminate their execution early in order to adhere to tight deadlines at the cost of accuracy. To mitigate this cost, in this paper we introduce a novel method called Multi-Exit Curriculum Learning that utilizes curriculum learning, a training strategy for neural networks that imitates human learning by sorting the training samples based on their difficulty and gradually introducing them to the network. Experiments on CIFAR-10 and CIFAR-100 datasets and various configurations of multi-exit architectures show that our method consistently improves the accuracy of early exits compared to the standard training approach.
Kernelized distance learning for zero-shot recognition

Zero-Shot Learning (ZSL) has gained growing attention over the past few years mostly because it provides a significant scalability to recognition models for classifying instances from new unobserved classes. This scalability is achieved by providing semantic information about new classes, which could be obtained remarkably easier with lower cost, compared to collecting a new training set. Because seen and unseen classes are completely disjoint, ZSL methods often suffer from domain shift problem that occurs in transferring the knowledge of seen classes to unseen ones. Moreover, hubness problem that usually arises in high-dimensional space is another challenge in most ZSL methods due to applying nearest neighbor search for classification. To address these issues, a kernelized distance function is learned in order to discriminate the classes with a customized large-margin loss function. Furthermore, a simple theoretical-based prototype learning approach is provided by defining a non-linear mapping function to learn the visual prototype of each class from associated semantic information. For classification task, the learned distance function is utilized to measure the distance between instances and class-related prototypes. The evaluation on five benchmarks demonstrates the superiority of the proposed method over the state-of-the-art approaches in both zero-shot and generalized zero-shot learning problems.
Breast Cancer Detection Through Feature Clustering and Deep Learning

In this paper we propose a computerized breast cancer detection and breast masses classification system utilizing mammograms. The motivation of the proposed method is to detect breast cancer tumors in early stages with more accuracy and less negative false cases. Our proposed method utilizes clustering of different features by segmenting the breast mammogram and then extracts deep features using the presented Convolution Neural Network (CNN). The extracted features are then combined with subjective features such as shape, texture and density. The combined features are then utilized by the Extreme Learning Machine Clustering (ELMC) algorithm to combine segments together to identify the breast mass Region of Interest (ROI). We present a detection method utilizing the ELMC clustering technique. Building a multi-feature set, the ELMC classifier is utilized to perform classification of normal, benign and cancer breast masses. Feature fusion is performed on the extracted shape, texture and density features forming a fusion feature set. In the automated detection phase, we utilize the fusion feature sets for classification. Extensive experimentation has been carried out to validate the ability of our proposed method. We utilized a dataset of 600 female mammograms. The experiments measure the accuracy of our proposed detection and classification method. The CNN coupled with the Extreme Learning Machine Clustering algorithm achieves the highest accuracy, sensitivity, specificity and ROC measures when combined with a multi-feature set. The model achieves 98.53% cancer detection accuracy, 95.6% benign detection accuracy and 95% for normal cases.
Dual Head Adversarial Training

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples/attacks, raising concerns about their reliability in safety-critical applications. A number of defense methods have been proposed to train robust DNNs resistant to adversarial attacks, among which adversarial training has so far demonstrated the most promising results. However, recent studies have shown that there exists an inherent tradeoff between accuracy and robustness in adversarially-trained DNNs. In this paper, we propose a novel technique Dual Head Adversarial Training (DH-AT) to further improve the robustness of existing adversarial training methods. Different from existing improved variants of adversarial training, DH-AT modifies both the architecture of the network and the training strategy to seek more robustness. Specifically, DH-AT first attaches a second network head (or branch) to one intermediate layer of the network, then uses a lightweight convolutional neural network (CNN) to aggregate the outputs of the two heads. The training strategy is also adapted to reflect the relative importance of the two heads. We empirically show, on multiple benchmark datasets, that DH-AT can bring notable robustness improvements to existing adversarial training methods. Compared with TRADES, one state-of-the-art adversarial training method, our DH-AT can improve the robustness by 3.4% against PGD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">40</sup> and 2.3% against AutoAttack, and also improve the clean accuracy by 1.8%.
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methods

In value-based deep reinforcement learning methods, approximation of value functions induces overestimation bias and leads to suboptimal policies. We show that in deep actor-critic methods that aim to overcome the overestimation bias, if the reinforcement signals received by the agent have a high variance, a significant underestimation bias arises. To minimize the underestimation, we introduce a parameter-free, novel deep Q-learning variant. Our Q-value update rule combines the notions behind Clipped Double Q-learning and Maxmin Q-learning by computing the critic objective through the nested combination of maximum and minimum operators to bound the approximate value estimates. We evaluate our modification on the suite of several OpenAI Gym continuous control tasks, improving the state-of-the-art in every environment tested.