Reviewerly/exHarmony huggingface dataset:
Persistence of topological phases in non-Hermitian quantum walks

Abstract Discrete-time quantum walks are known to exhibit exotic topological states and phases. Physical realization of quantum walks in a lossy environment may destroy these phases. We investigate the behaviour of topological states in quantum walks in the presence of a lossy environment. The environmental effects in the quantum walk dynamics are addressed using the non-Hermitian Hamiltonian approach. We show that the topological phases of the quantum walks are robust against moderate losses. The topological order in one-dimensional split-step quantum walk persists as long as the Hamiltonian respects exact $${{\mathcal {P}}}{{\mathcal {T}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:mi>T</mml:mi> </mml:mrow> </mml:math> -symmetry. Although the topological nature persists in two-dimensional quantum walks as well, the $${{\mathcal {P}}}{{\mathcal {T}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:mi>T</mml:mi> </mml:mrow> </mml:math> -symmetry has no role to play there. Furthermore, we observe topological phase transition in two-dimensional quantum walks that is induced by losses in the system.
Parameter Identification of Motors by Cuckoo Search Using Steady-State Relations

The direct current (DC) motors are widely used; therefore, they are subject to multiple studies, different control techniques or analyses require a dynamic DC motor model. The parameters are needed to complete the model, which can be challenging to obtain. Therefore, multiple parametric estimation techniques have been developed. This paper presents a metaheuristic cuckoo search algorithm modified for motors as a parametric estimation tool. A cost function is based on the current and velocity error obtained when an input voltage step is applied to the motor. The main difference with similar works is that we used the steady-state equations to determine the parameters. The algorithm proposed is compared with the Steiglitz-McBride and the original cuckoo search algorithms to evaluate its performance objectively. Simulated and experimental results show that the algorithm proposed can calculate the parameters with better accuracy than the original cuckoo search and Steiglitz-McBride. The modifications made to the original algorithm of the cuckoo search allowed finding the values of the parameters motor with a root mean square error of less than 0.1% for signals obtained with simulation and less than 1% for real signals sampled at 0.001 s.
RFC: A feature selection algorithm for software defect prediction

Software defect prediction (SDP) is used to perform the statistical analysis of historical defect data to find out the distribution rule of historical defects, so as to effectively predictdefects in the new software. However, there are redundant and irrelevant features in the software defect datasets affecting the performance of defect predictors. In order to identify and remove the redundant and irrelevant features in software defectdatasets, we propose Relief F-based clustering (RFC), a cluster-based feature selection algorithm. Then, the correlation between features is calculated based on the symmetric uncertainty. According to the correlation degree, RFC partitions features into kclusters based on the k-medoids algorithm, and finally selects the representative features from each cluster to form the final feature subset. In the experiments, we compare the proposed RFC with classical feature selection algorithms on nine National Aeronautics and Space Administration (NASA) software defectprediction datasets in terms of area under curve (AUC) and F-value. The experimental results show that RFC can effectively improve the performance of SDP.
Robust Adaptive Steganography Based on Dither Modulation and Modification With Re-Compression

Traditional adaptive steganography is a technique used for covert communication with high security, but it is invalid in the case of stego images are sent to legal receivers over networks which is lossy, such as JPEG compression of channels. To deal with such problem, robust adaptive steganography is proposed to enable the receiver to extract secret messages from the damaged stego images. Previous works utilize reverse engineering and compression-resistant domain constructing to implement robust adaptive steganography. In this paper, we adopt modification with re-compression scheme to improve the robustness of stego sequences in stego images. To balance security and robustness, we move the embedding domain to the low frequency region of DCT (Discrete Cosine Transform) coefficients to improve the security of robust adaptive steganography. In addition, we add additional check codes to further reduce the average extraction error rate based on the framework of E-DMAS (Enhancing Dither Modulation based robust Adaptive Steganography). Compared with GMAS (Generalized dither Modulation based robust Adaptive Steganography) and E-DMAS, experiment results show that our scheme can achieve strong robustness and improve the security of robust adaptive steganography greatly when the channel quality factor is known.
SCTN: Sparse Convolution-Transformer Network for Scene Flow Estimation

We propose a novel scene flow estimation approach to capture and infer 3D motions from point clouds. Estimating 3D motions for point clouds is challenging, since a point cloud is unordered and its density is significantly non-uniform. Such unstructured data poses difficulties in matching corresponding points between point clouds, leading to inaccurate flow estimation. We propose a novel architecture named Sparse Convolution-Transformer Network (SCTN) that equips the sparse convolution with the transformer. Specifically, by leveraging the sparse convolution, SCTN transfers irregular point cloud into locally consistent flow features for estimating spatially consistent motions within an object/local object part. We further propose to explicitly learn point relations using a point transformer module, different from exiting methods. We show that the learned relation-based contextual information is rich and helpful for matching corresponding points, benefiting scene flow estimation. In addition, a novel loss function is proposed to adaptively encourage flow consistency according to feature similarity. Extensive experiments demonstrate that our proposed approach achieves a new state of the art in scene flow estimation. Our approach achieves an error of 0.038 and 0.037 (EPE3D) on FlyingThings3D and KITTI Scene Flow respectively, which significantly outperforms previous methods by large margins.
Deep HDR Hallucination for Inverse Tone Mapping

Inverse Tone Mapping (ITM) methods attempt to reconstruct High Dynamic Range (HDR) information from Low Dynamic Range (LDR) image content. The dynamic range of well-exposed areas must be expanded and any missing information due to over/under-exposure must be recovered (hallucinated). The majority of methods focus on the former and are relatively successful, while most attempts on the latter are not of sufficient quality, even ones based on Convolutional Neural Networks (CNNs). A major factor for the reduced inpainting quality in some works is the choice of loss function. Work based on Generative Adversarial Networks (GANs) shows promising results for image synthesis and LDR inpainting, suggesting that GAN losses can improve inverse tone mapping results. This work presents a GAN-based method that hallucinates missing information from badly exposed areas in LDR images and compares its efficacy with alternative variations. The proposed method is quantitatively competitive with state-of-the-art inverse tone mapping methods, providing good dynamic range expansion for well-exposed areas and plausible hallucinations for saturated and under-exposed areas. A density-based normalisation method, targeted for HDR content, is also proposed, as well as an HDR data augmentation method targeted for HDR hallucination.
Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering

The current state-of-the-art generative models for open-domain question answering (ODQA) have focused on generating direct answers from unstructured textual information.However, a large amount of world's knowledge is stored in structured databases, and need to be accessed using query languages such as SQL.Furthermore, query languages can answer questions that require complex reasoning, as well as offering full explainability.In this paper, we propose a hybrid framework that takes both textual and tabular evidence as input and generates either direct answers or SQL queries depending on which form could better answer the question.The generated SQL queries can then be executed on the associated databases to obtain the final answers.To the best of our knowledge, this is the first paper that applies Text2SQL to ODQA tasks.Empirically, we demonstrate that on several ODQA datasets, the hybrid methods consistently outperforms the baseline models that only take homogeneous input by a large margin.Specifically we achieve state-of-theart performance on OpenSQuAD dataset using a T5-base model.In a detailed analysis, we demonstrate that the being able to generate structural SQL queries can always bring gains, especially for those questions that requires complex reasoning.
Alignment Rationale for Natural Language Inference

Deep learning models have achieved great success on the task of Natural Language Inference (NLI), though only a few attempts try to explain their behaviors.Existing explanation methods usually pick prominent features such as words or phrases from the input text.However, for NLI, alignments among words or phrases are more enlightening clues to explain the model.To this end, this paper presents AREC, a post-hoc approach to generate alignment rationale explanations for co-attention based models in NLI.The explanation is based on feature selection, which keeps few but sufficient alignments while maintaining the same prediction of the target model.Experimental results show that our method is more faithful and readable compared with many existing approaches.We further study and reevaluate three typical models through our explanation beyond accuracy, and propose a simple method that greatly improves the model robustness. 1
Predicting quantum dynamical cost landscapes with deep learning

State-of-the-art quantum algorithms routinely tune dynamically parametrized cost functionals for combinatorics, machine learning, equation solving, and energy minimization. However, large search complexity often demands many (noisy) quantum measurements, leading to the increasing use of classical probability models to estimate which areas in the cost functional landscape are of highest interest. Introducing deep learning based modeling of the landscape, we demonstrate order-of-magnitude increases in accuracy and speed over state-of-the-art Bayesian methods with respect to control of a many-body spin chain. Moreover, once trained, the deep neural network enables the extraction of information at a much faster rate than conventional numerical simulation. This allows for on-the-fly experimental optimizations of circuits with large depth and moderate width and detailed classification of problem complexity and navigability throughout the phase diagram of the landscape.
The SmARtphone Controller

Abstract Current interaction modalities for mobile Augmented Reality (AR) are tedious and lack expressiveness. To overcome these prevalent limitations, we developed and evaluated a multimodal interaction concept by pairing a smartphone as an input and output modality for mobile AR. In a user study (n = 24), we investigated the effects on interaction speed, accuracy, and task load for (1) virtual object manipulation as well as (2) interaction with established graphical user interfaces (GUIs). Our findings show that a smartphone-based AR controller results in significantly faster and more accurate object manipulation with reduced task load than state-of-art mid-air gestures. Our results also indicate a significant enhancement for using the physical touchscreen as a modality compared to mid-air gestures for GUI interaction. We conclude that interaction in mobile AR environments can be improved by utilizing a smartphone as an omnipresent controller. Additionally, we discuss how future AR systems can benefit from tangible touchscreens as an additional and complementary interaction modality.
An improved K‐means algorithm for big data

An improved version of K-means clustering algorithm that can be applied to big data through lower processing loads with acceptable precision rates is presented here. In this method, the distances from one point to its two nearest centroids were used along with their variations in the last two iterations. Points with an equidistance threshold greater than the equidistance index were eliminated from the distance calculations and were stabilised in the cluster. Although these points are compared with the research index —cluster radius—again in the algorithm iteration, the excluded points are again included in the calculations if their distances from the stabilised cluster centroid are longer than the cluster radius. This can improve the clustering quality. Computerised tests as well as synthetic and real samples show that this method is able to improve the clustering quality by up to 41.85% in the best-case scenario. According to the findings, the proposed method is very beneficial to big data.
An Information-Theoretic Perspective on Proper Quaternion Variational Autoencoders

Variational autoencoders are deep generative models that have recently received a great deal of attention due to their ability to model the latent distribution of any kind of input such as images and audio signals, among others. A novel variational autoncoder in the quaternion domain H, namely the QVAE, has been recently proposed, leveraging the augmented second order statics of H-proper signals. In this paper, we analyze the QVAE under an information-theoretic perspective, studying the ability of the H-proper model to approximate improper distributions as well as the built-in H-proper ones and the loss of entropy due to the improperness of the input signal. We conduct experiments on a substantial set of quaternion signals, for each of which the QVAE shows the ability of modelling the input distribution, while learning the improperness and increasing the entropy of the latent space. The proposed analysis will prove that proper QVAEs can be employed with a good approximation even when the quaternion input data are improper.
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning

Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. Image feature learning can be considered as a multitask learning problem because different tasks may have a similar feature space. Genetic programming (GP) has been successfully applied to image feature learning for classification. However, most of the existing GP methods solve one task, independently, using sufficient training data. No multitask GP method has been developed for image feature learning. Therefore, this article develops a multitask GP approach to image feature learning for classification with limited training data. Owing to the flexible representation of GP, a new knowledge sharing mechanism based on a new individual representation is developed to allow GP to automatically learn what to share across two tasks and to improve its learning performance. The shared knowledge is encoded as a common tree, which can represent the common/general features of two tasks. With the new individual representation, each task is solved using the features extracted from a common tree and a task-specific tree representing task-specific features. To find the best common and task-specific trees, a new evolutionary search process and fitness functions are developed. The performance of the new approach is examined on six multitask learning problems of 12 image classification datasets with limited training data and compared with 17 competitive methods. The experimental results show that the new approach outperforms these comparison methods in almost all the comparisons. Further analysis reveals that the new approach learns simple yet effective common trees with high effectiveness and transferability.
Gesture Recognition Algorithm of Human Motion Target Based on Deep Neural Network

There are some problems in the current human motion target gesture recognition algorithms, such as classification accuracy, overlap ratio, low recognition accuracy and recall, and long recognition time. A gesture recognition algorithm of human motion based on deep neural network was proposed. First, Kinect interface equipment was used to collect the coordinate information of human skeleton joints, extract the characteristics of motion gesture nodes, and construct the whole structure of key node network by using deep neural network. Second, the local recognition region was introduced to generate high-dimensional feature map, and the sampling kernel function was defined. The minimum space-time domain of node structure map was located by sampling in the space-time domain. Finally, the deep neural network classifier was constructed to integrate and classify the human motion target gesture data features to realize the recognition of human motion target. The results show that the proposed algorithm has high classification accuracy and overlap ratio of human motion target gesture, the recognition accuracy is as high as 93%, the recall rate is as high as 88%, and the recognition time is 17.8 s, which can effectively improve the human motion target attitude recognition effect.
Cyber risk management in SMEs: insights from industry surveys

Purpose This article aims to gain insights on the current state of small- and medium-sized enterprises’ (SMEs’) cyber risk management process and to derive future research directions. Design/methodology/approach This is done by collecting market insights from 37 recent industry surveys and structuring them based on the steps of the risk management process. From this analysis, major challenges are derived and future fields of research identified. Findings The results indicate that deficiencies in risk culture as well as the strained market for IT experts are the major obstacles with respect to the implementation of cyber risk management in SMEs, and that these challenges are similar across countries. The findings suggest that especially the relationship between cyber security culture and cyber risk management should be investigated further, and that a stronger link between the research streams on enterprise risk management and cyber risk management would be desirable. Originality/value This paper contributes to the literature by providing a systematic overview on the current state of SMEs' cyber risk management from a market perspective. The findings provide support for the existing academic literature by emphasizing the central role of cyber security culture (perception, knowledge, attitude) for a successful cyber risk management, which however should be addressed in more depth in future (empirical) research.
Hardware-Efficient Leakage-Reduction Scheme for Quantum Error Correction with Superconducting Transmon Qubits

Leakage outside of the qubit computational subspace poses a threatening challenge to quantum error correction (QEC). We propose a scheme using two leakage-reduction units (LRUs) that mitigate these issues for a transmon-based surface code, without requiring an overhead in terms of hardware or QEC-cycle time as in previous proposals. For data qubits, we consider a microwave drive to transfer leakage to the readout resonator, where it quickly decays, ensuring that this negligibly disturbs the computational states for realistic system parameters. For ancilla qubits, we apply a |1⟩↔|2⟩π pulse conditioned on the measurement outcome. Using density-matrix simulations of the distance-3 surface code, we show that the average leakage lifetime is reduced to almost one QEC cycle, even when the LRUs are implemented with limited fidelity. Furthermore, we show that this leads to a significant reduction of the logical error rate. This LRU scheme opens the prospect for near-term scalable QEC demonstrations.3 MoreReceived 2 March 2021Revised 16 June 2021Accepted 2 July 2021DOI:https://doi.org/10.1103/PRXQuantum.2.030314Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionQuantum information with solid state qubitsSurface code quantum computingQuantum Information
Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images

Histomorphologic types of gastric cancer (GC) have significant prognostic values that should be considered during treatment planning. Because the thorough quantitative review of a tissue slide is a laborious task for pathologists, deep learning (DL) can be a useful tool to support pathologic workflow. In the present study, a fully automated approach was applied to distinguish differentiated/undifferentiated and non-mucinous/mucinous tumor types in GC tissue whole-slide images from The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset (TCGA-STAD). By classifying small patches of tissue images into differentiated/undifferentiated and non-mucinous/mucinous tumor tissues, the relative proportion of GC tissue subtypes can be easily quantified. Furthermore, the distribution of different tissue subtypes can be clearly visualized. The patch-level areas under the curves for the receiver operating characteristic curves for the differentiated/undifferentiated and non-mucinous/mucinous classifiers were 0.932 and 0.979, respectively. We also validated the classifiers on our own GC datasets and confirmed that the generalizability of the classifiers is excellent. The results indicate that the DL-based tissue classifier could be a useful tool for the quantitative analysis of cancer tissue slides. By combining DL-based classifiers for various molecular and morphologic variations in tissue slides, the heterogeneity of tumor tissues can be unveiled more efficiently.
Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil's case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms.