Reviewerly/exHarmony huggingface dataset:
Unsuitability of cubic phase gates for non-Clifford operations on Gottesman-Kitaev-Preskill states

With the Gottesman-Kitaev-Preskill (GKP) encoding, Clifford gates and error correction can be carried out using simple Gaussian operations. Still, non-Clifford gates, required for universality, require non-Gaussian elements. In their original proposal, GKP suggested a particularly simple method of using a single application of the cubic phase gate to perform the logical non-Clifford T-gate. Here we show that this cubic phase gate approach performs extraordinarily poorly, even for arbitrarily large amounts of squeezing in the GKP state. Thus, contrary to common belief, the cubic phase gate is not suitable for achieving universal fault-tolerant quantum computation with GKP states.
Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study

The automatic recognition of human activities with sensors available in off-the-shelf mobile devices has been the subject of different research studies in recent years. It may be useful for the monitoring of elderly people to present warning situations, monitoring the activity of sports people, and other possibilities. However, the acquisition of the data from different sensors may fail for different reasons, and the human activities are recognized with better accuracy if the different datasets are fulfilled. This paper focused on two stages of a system for the recognition of human activities: data imputation and data classification. Regarding the data imputation, a methodology for extrapolating the missing samples of a dataset to better recognize the human activities was proposed. The K-Nearest Neighbors (KNN) imputation technique was used to extrapolate the missing samples in dataset captures. Regarding the data classification, the accuracy of the previously implemented method, i.e., Deep Neural Networks (DNN) with normalized and non-normalized data, was improved in relation to the previous results without data imputation.
Parts2Whole: Self-supervised Contrastive Learning via Reconstruction

Contrastive representation learning is the state of the art in computer vision, but requires huge mini-batch sizes, special network design, or memory banks, making it unappealing for 3D medical imaging, while in 3D medical imaging, reconstruction-based self-supervised learning reaches a new height in performance, but lacks mechanisms to learn contrastive representation; therefore, this paper proposes a new framework for self-supervised contrastive learning via reconstruction, called Parts2Whole, because it exploits the universal and intrinsic part-whole relationship to learn contrastive representation without using contrastive loss: Reconstructing an image (whole) from its own parts compels the model to learn similar latent features for all its own partsin the latent space, while reconstructing different images (wholes) from their respective parts forces the model to simultaneously push those parts belonging to different wholes farther apart from each other in the latent space; thereby the trained model is capable of distinguishing images. We have evaluated our Parts2Whole on five distinct imaging tasks covering both classification and segmentation, and compared it with four competing publicly available 3D pretrained models, showing that Parts2Whole significantly outperforms in two out of five tasks while achieves competitive performance on the rest three. This superior performance is attributable to the contrastive representations learned with Parts2Whole. Codes and pretrained models are available at github.com/JLiangLab/Parts2Whole.
RENT—Repeated Elastic Net Technique for Feature Selection

Feature selection is an essential step in data science pipelines to reduce the complexity associated with large datasets. While much research on this topic focuses on optimizing predictive performance, few studies investigate stability in the context of the feature selection process. In this study, we present the Repeated Elastic Net Technique (RENT) for Feature Selection. RENT uses an ensemble of generalized linear models with elastic net regularization, each trained on distinct subsets of the training data. The feature selection is based on three criteria evaluating the weight distributions of features across all elementary models. This fact leads to the selection of features with high stability that improve the robustness of the final model. Furthermore, unlike established feature selectors, RENT provides valuable information for model interpretation concerning the identification of objects in the data that are difficult to predict during training. In our experiments, we benchmark RENT against six established feature selectors on eight multivariate datasets for binary classification and regression. In the experimental comparison, RENT shows a well-balanced trade-off between predictive performance and stability. Finally, we underline the additional interpretational value of RENT with an exploratory post-hoc analysis of a healthcare dataset.
Private Blockchain in Industrial IoT

The three articles in this special section focus are devoted to sharing state-of-the-art research on private blockchain in industrial IoT (Internet of Things). A blockchain is a decentralized and distributed ledger with a chain network of blocks recording historical transactions. A transaction is a record of action. A transaction inside the block will be of: sensing/saving data, assembling parts, depositing money, processing a business deal, ordering/ delivering a product part, notarizing a paperwork, and so on. Blockchains are categorized into two types, public blockchain and private blockchain. Private blockchains have been more in the spotlight in industry recently because they are much faster, cheaper, and privacy-oriented compared to the public blockchain. We expect that private blockchains will be much more widely implemented soon.
GEP-based classifier for mining imbalanced data

The paper proposes an incremental Gene Expression Programming classifier for mining imbalanced datasets. Imbalanced datasets are commonly encountered in real-life applications. There exist numerous algorithms, techniques, and tools which are proposed as suitable for dealing with imbalanced class distribution. Yet, none of them seems to be able to outperform all others in all possible applications. We believe that our approach can extend the available range of learners that have proven good performance in mining imbalanced data and imbalanced streams. The idea is to adapt the GEP classifier to requirements of the imbalanced data environment with reuse of the minority class instances, and application of the incremental learning paradigm. The paper offers an overview of the related work and a detailed description of the proposed incremental learner. An extensive computational experiment, based on data from the KEEL dataset repository, proves that in numerous cases the approach is competitive to other state-of-the-art learners.
Covid-19 Outbreak and New Normal Teaching in Higher Education: Empirical Resolve from Islamic Universities in Indonesia

Mid of March 2020, the coronavirus 19 pandemic outbreaks caused many offices, markets, schools, and universities closed to maintain its dead spreading. It makes all the offline activities should be changed into online interaction including at higher education. Lawrence &amp; Tar (2018) reflected a study about factors that influence teachers’ adoption and integration of ICT in teaching/learning process. This study is about the activity of new normal in conducting online education relating to the use of six strategies applied which are applied by teachers in the Islamic University of Antasari Banjarmasin Indonesia. Within the new normal period, this study reveals the six strategies on conducting online education namely; a) the strategy of contextualization, b) the strategy of proper delivery, c) the strategy of high adaptation on using technology, d) the strategy of adequate support, e) the strategy of quality participation, f) the strategy of sustainable processes.
Solving Jigsaw Puzzles by the Graph Connection Laplacian

We propose a novel mathematical framework to address the problem of automatically solving large jigsaw puzzles.This problem assumes a large image, which is cut into equal square pieces that are arbitrarily rotated and shuffled, and asks to recover the original image given the transformed pieces.The main contribution of this work is a method for recovering the rotations of the pieces when both shuffles and rotations are unknown.A major challenge of this procedure is estimating the graph connection Laplacian without the knowledge of shuffles.A careful combination of our proposed method for estimating rotations with any existing method for estimating shuffles results in a practical solution for the jigsaw puzzle problem.Our theory guarantees, in a clean setting, that our basic idea of recovering rotations is robust to some corruption of the connection graph.Numerical experiments demonstrate the competitive accuracy of this solution, its robustness to corruption and its computational advantage for large puzzles.
A Deep Learning Technique for Web Phishing Detection Combined URL Features and Visual Similarity

The most popular way to deceive online users nowadays is phishing.Consequently, to increase cybersecurity, more efficient web page phishing detection mechanisms are needed.In this paper, we propose an approach that rely on websites image and URL to deals with the issue of phishing website recognition as a classification challenge.Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into benign and phishing pages.The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack.
Adoption of hybrid time series neural network in the underwater acoustic signal modulation identification

The deep learning methods powerfully enhance the identification performance by retrieving the deep data features in many fields, which can be used in automatic modulation classification (AMC) work for the better results in the acoustic underwater communication. A novel hybrid time series network structure is scheduled for AMC in this paper. It can accommodate the variable-length signal datas to match the fixed-length input request in the common neural network, and there is the ability to suitably deal with the zero data in the signal sequence to alleviate the effect losses. The proposed network has the mix of two time series network styles to enrich the extracted signal modulation classification features, and dramatically improves the recognition capability and owns the low computation complexity. In the meanwhile, the internal network structure is optimized by the well-designed cascading order, which acquires more hidden signal data representations to increase the accuracy. The simulation experiments shows that the proposed network is more effective and robust than the conventional deep learning methods to identify ten modulation modes in the serious interference communication environment.
Thinking Fast and Slow in AI

This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities in an AI system by embedding these causal components. We hope that the high-level description of our vision included in this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks, and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence.
A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

In order to achieve a highly autonomous and reliable navigation system for aerial vehicles that involves the spectral redshift navigation system (SRS), the inertial navigation (INS)/spectral redshift navigation (SRS)/celestial navigation (CNS) integrated system is designed and the spectral-redshift-based velocity measurement equation in the INS/SRS/CNS system is derived. Furthermore, a new chi-square test-based robust Kalman filter (CSTRKF) is also proposed in order to improve the robustness of the INS/SRS/CNS navigation system. In the CSTRKF, the chi-square test (CST) not only detects measurements with outliers and in non-Gaussian distributions, but also estimates the statistical characteristics of measurement noise. Finally, the results of our simulations indicate that the INS/SRS/CNS integrated navigation system with the CSTRKF possesses strong robustness and high reliability.
Detection and Classification of Multi-Magnetic Targets Using Mask-RCNN

To detect the shape of a small magnetic target in the shallow underground layer, this article proposes a recognition method based on Mask-RCNN.Firstly, using COMSOL software and MATLAB software to establish the database of magnetic targets model under different shapes and orientations, which greatly enriched the diversity of the training data set.Then, the G zz component of the magnetic gradient tensor matrix is selected to highlight the shape features of the magnetic target, and the contour image is generated.The experimental data set is created by using the deep learning annotation tool Labelme.Finally, Resnet101 is used as the backbone network and feature pyramid network (FPN) structure is used to extract features.The regional recommendation network (RPN) is trained end-to-end to create regional recommendations for each feature map.The detection results of 200 test images show that the average detection accuracy of the method is 97%, and the recall rate is 94%.The simulation results show that the recognition accuracy and robustness of the method are improved.
Distilling Multiple Domains for Neural Machine Translation

Neural machine translation achieves impressive results in high-resource conditions, but performance often suffers when the input domain is low-resource.The standard practice of adapting a separate model for each domain of interest does not scale well in practice from both a quality perspective (brittleness under domain shift) as well as a cost perspective (added maintenance and inference complexity).In this paper, we propose a framework for training a single multi-domain neural machine translation model that is able to translate several domains without increasing inference time or memory usage.We show that this model can improve translation on both highand low-resource domains over strong multidomain baselines.In addition, our proposed model is effective when domain labels are unknown during training, as well as robust under noisy data conditions.
Astability <i>versus</i> Bistability in van der Waals Tunnel Diode for Voltage Controlled Oscillator and Memory Applications

van der Waals (vdW) tunnel junctions are attractive because of their atomically sharp interface, gate tunability, and robustness against lattice mismatch between the successive layers. However, the negative differential resistance (NDR) demonstrated in this class of tunnel diodes often exhibits noisy behavior with low peak current density and lacks robustness and repeatability, limiting their practical circuit applications. Here, we propose a strategy of using a 1L-WS2 as an optimum tunnel barrier sandwiched in a broken gap tunnel junction of highly doped black phosphorus (BP) and SnSe2. We achieve high yield tunnel diodes exhibiting highly repeatable, ultraclean, and gate-tunable NDR characteristics with a signature of intrinsic oscillation, and a large peak-to-valley current ratio (PVCR) of 3.6 at 300 K (4.6 at 7 K), making them suitable for practical applications. We show that the thermodynamic stability of the vdW tunnel diode circuit can be tuned from astability to bistability by altering the constraint through choosing a voltage or a current bias, respectively. In the astable mode under voltage bias, we demonstrate a compact, voltage-controlled oscillator without the need for an external tank circuit. In the bistable mode under current bias, we demonstrate a highly scalable, single-element, one-bit memory cell that is promising for dense random access memory applications in memory intensive computation architectures.
Optimizing practice scheduling requires quantitative tracking of individual item performance

Abstract Decades of research has shown that spacing practice trials over time can improve later memory, but there are few concrete recommendations concerning how to optimally space practice. We show that existing recommendations are inherently suboptimal due to their insensitivity to time costs and individual- and item-level differences. We introduce an alternative approach that optimally schedules practice with a computational model of spacing in tandem with microeconomic principles. We simulated conventional spacing schedules and our adaptive model-based approach. Simulations indicated that practicing according to microeconomic principles of efficiency resulted in substantially better memory retention than alternatives. The simulation results provided quantitative estimates of optimal difficulty that differed markedly from prior recommendations but still supported a desirable difficulty framework. Experimental results supported simulation predictions, with up to 40% more items recalled in conditions where practice was scheduled optimally according to the model of practice. Our approach can be readily implemented in online educational systems that adaptively schedule practice and has significant implications for millions of students currently learning with educational technology.
Criticality-enhanced quantum sensing in ferromagnetic Bose-Einstein condensates: Role of readout measurement and detection noise

We theoretically investigate estimation of the control parameter in a ferromagnetic Bose-Einstein condensate near second-order quantum phase transitions. We quantify sensitivity by quantum and classical Fisher information and use the error-propagation formula. For these different metrics, we find the same beyond standard quantum limit (SQL) scaling with atom number near critical points and SQL scaling away from critical points. We find that both depletion of the ${m}_{f}=0$ Zeeman sublevel and transverse magnetization provide signals of sufficient quality to saturate the sensitivity scaling. To explore the effect of experimental imperfections, we study the scaling around criticality at nonzero temperature and with nonzero detection noise. Our results suggest the feasibility of sub-SQL sensing in ferromagnetic condensates with current experimental capabilities.
Separation of geochemical signals in fluvial sediments: New approaches to grain-size control and anthropogenic contamination

A compositional data analysis (CoDA) in fluvial sediments is performed to achieve separation of the geochemical signals (SGS) of grain size, anthropogenic contamination, and possible post-depositional alteration. The SGS is demonstrated and developed in the study of the sediments from the Skalka Reservoir (Czechia) and the floodplain of its tributary rivers, which have been impacted by pollution from the Chemical Factory Marktredwitz (Bavaria, Germany) brought through temporary sinks in the channels and floodplains to the reservoir. This paper compares CoDA tools with standard empirical approaches based on using deeper strata as uncontaminated or pre-industrial (examination of element concentration depth profiles), scatterplots with risk elements (mainly Zn in this study) as dependent variables and lithogenic reference elements as independent variables to construct background functions and to calculate local enrichment factors (LEF), and a principal component analysis performed on raw and geochemically normalised elemental concentrations. The utilised CoDA tools include classical and robust methods using the log-ratio approach that fully respects the mathematical specificity of the compositional data (data closure, or more generally scale invariance, and further related aspects like non-Gaussian distribution, and commonly polymodality) like the robust PCA with centred log-ratio (clr) transformation of concentrations; consequently, histograms of the raw and normalised concentrations and contamination scores were compared. The multivariate CoDA was considerably facilitated by a novel tool for understanding the grain-size control of sediment composition, i.e. a functional data analysis of particle size distributions (densities) based on Bayes spaces. Also, the robust correlation analysis was efficient using a (log-)ratio methodology. Several elements can be used for the geochemical normalisation and LEF calculations, of which Al, Fe, and Ti can definitely be recommended, while Cr, Mg, and even Si also produced comparable results. A more critical factor is a proper selection of the background functions. We demonstrated the limits of using some popular tools for the compositional data mining: the ordinary PCA failed or performed worse than LEF in the separation of grain-size and contamination signals. Some log-ratio methods performed well, in particular robust regression with selected (lithogenic elements at explaining side) and robust PCA with clr transformation. Even for apparently simple tasks, such as the separation of anthropogenic contamination signals, knowledgeable decisions during data preparation for the CoDA are still indispensable.
A Source Code Similarity Based on Siamese Neural Network

Finding similar code snippets is a fundamental task in the field of software engineering. Several approaches have been proposed for this task by using statistical language model which focuses on syntax and structure of codes rather than deep semantic information underlying codes. In this paper, a Siamese Neural Network is proposed that maps codes into continuous space vectors and try to capture their semantic meaning. Firstly, an unsupervised pre-trained method that models code snippets as a weighted series of word vectors. The weights of the series are fitted by the Term Frequency-Inverse Document Frequency (TF-IDF). Then, a Siamese Neural Network trained model is constructed to learn semantic vector representation of code snippets. Finally, the cosine similarity is provided to measure the similarity score between pairs of code snippets. Moreover, we have implemented our approach on a dataset of functionally similar code. The experimental results show that our method improves some performance over single word embedding method.