Reviewerly/exHarmony huggingface dataset:
Scalable and robust quantum computing on qubit arrays with fixed coupling

Abstract We propose a scheme for scalable and robust quantum computing on two-dimensional arrays of qubits with fixed longitudinal coupling. This opens the possibility for bypassing the device complexity associated with tunable couplers required in conventional quantum computing hardware. Our approach is based on driving a subarray of qubits such that the total multi-qubit Hamiltonian can be decomposed into a sum of commuting few-qubit blocks, and efficient optimisation of the unitary evolution within each block. The driving pulses are optimised to implement a target gate on the driven qubits, and at the same time identity gates on the neighbouring undriven qubits, cancelling any unwanted evolution due to the constant qubit-qubit interaction. We show that it is possible to realise a universal set of quantum gates with high fidelity on the basis blocks, and by shifting the driving pattern one can realise an arbitrary quantum circuit on the array. Allowing for imperfect Hamiltonian characterisation, we use robust optimal control to obtain fidelities around 99.99% despite 1% uncertainty in the qubit-qubit and drive-qubit couplings, and a detuning uncertainty at 0.1% of the qubit-qubit coupling strength. This robust feature is crucial for scaling up as parameter uncertainty is significant in large devices.
Dynamic Time Warping Based Adversarial Framework for Time-Series Domain

Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this paper, we propose a novel framework for the time-series domain referred as Dynamic Time Warping for Adversarial Robustness (DTW-AR) using the dynamic time warping measure. Theoretical and empirical evidence is provided to demonstrate the effectiveness of DTW over the standard euclidean distance metric employed in prior methods for the image domain. We develop a principled algorithm justified by theoretical analysis to efficiently create diverse adversarial examples using random alignment paths. Experiments on diverse real-world benchmarks show the effectiveness of DTW-AR to fool DNNs for time-series data and to improve their robustness using adversarial training.
Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence

Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM). The method distributes similarities of candidate matches over a geometric transformation space and evaluates them in a convolutional manner. We cast it into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters. To further improve the efficiency of high-dimensional voting, we also propose to use an efficient kernel decomposition with center-pivot neighbors, which significantly sparsifies the proposed semi-isotropic kernels without performance degradation. To validate the proposed techniques, we develop the neural network with CHM layers that perform convolutional matching in the space of translation and scaling. Our method sets a new state of the art on standard benchmarks for semantic visual correspondence, proving its strong robustness to challenging intra-class variations.
CoqQ: Foundational Verification of Quantum Programs

CoqQ is a framework for reasoning about quantum programs in the Coq proof assistant. Its main components are: a deeply embedded quantum programming language, in which classic quantum algorithms are easily expressed, and an expressive program logic for proving properties of programs. CoqQ is foundational: the program logic is formally proved sound with respect to a denotational semantics based on state-of-art mathematical libraries (MathComp and MathComp Analysis). CoqQ is also practical: assertions can use Dirac expressions, which eases concise specifications, and proofs can exploit local and parallel reasoning, which minimizes verification effort. We illustrate the applicability of CoqQ with many examples from the literature.
Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique

The increase in population has made it possible for better, more cost-effective vehicular services, which warrants good roadways. The sub-base that serves as a stress-transmitting media and distributes vehicle weight to resist shear and radial deformation is a critical component of the pavement structures. Developing novel techniques that can assess the sub-base soil’s geotechnical characteristics and performance is an urgent need. Laterite soil abundantly available in the West Godavari area of India was employed for this research. Roads and highways construction takes a chunk of geotechnical investigation, particularly, California bearing ratio (CBR) of subgrade soils. Therefore, there is a need for intelligent tool to predict or analyze the CBR value without time-consuming and cumbersome laboratory tests. An integrated extreme learning machine-cooperation search optimizer (ELM-CSO) approach is used herein to predict the CBR values. The correlation coefficient is utilized as cost functions of the CSO to identify the optimal activation weights of the ELM. The statistical measures are separately considered, and best solutions are reported in this article. Comparisons are provided with the standard ELM to show the superiorities of the proposed integrated approach to predict the CBR values. Further, the impact of each input variable is studied separately, and reduced models are proposed with limited and inadequate input data without loss of prediction accuracy. When 70% training and 30% testing data are applied, the ELM-CSO outperforms the CSO with Pearson correlation coefficient (R), coefficient of determination (R2), and root mean square error (RMSE) values of 0.98, 0.97, and 0.84, respectively. Therefore, based on the prediction findings, the newly built ELM-CSO can be considered an alternative method for predicting real-time engineering issues, including the lateritic soil properties.
Transforming Complex Problems into K-means Solutions

K-means is a fundamental clustering algorithm widely used in both academic and industrial applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the equivalence of K-means to principal component analysis, non-negative matrix factorization, and spectral clustering. However, these studies focus on standard K-means with squared euclidean distance. In this review paper, we unify the available approaches in generalizing K-means to solve challenging and complex problems. We show that these generalizations can be seen from four aspects: data representation, distance measure, label assignment, and centroid updating. As concrete applications of transforming problems into modified K-means formulation, we review the following applications: iterative subspace projection and clustering, consensus clustering, constrained clustering, domain adaptation, and outlier detection.
Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications

The frequency-hopping communication system has been widely used in anti-jamming communication due to its anti-interception and anti-jamming performance. With the increasingly complex electromagnetic environment, the frequency-hopping communication system needs more flexible frequency-hopping patterns to deal with interferences, which brings great challenges to the communication receiver. In this paper, an intelligent receiving scheme of frequency-hopping sequences is proposed, which combines time–frequency analysis with deep learning to realize an intelligent estimation of frequency-hopping sequences. A hybrid network module is designed by combining a convolutional neural network (CNN) with a gated recurrent unit (GRU). In the proposed network module, the combination of a residual network (ResNet) and squeeze and extraction (SE) improves the feature extraction and expression capabilities of the CNN network. The GRU network is proposed to solve the problem of dealing with signals with variant input lengths. A transfer learning scheme is further proposed to deal with communications systems with different frequency-hopping sets. Simulation results show that the proposed method has strong generalization ability and robustness, and the bit error rate (BER) performance of intelligent receiving is close to the receiving performance under ideal conditions.
EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks

Background and purpose Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of colorectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg). To demonstrate the validity and extensiveness of EBHI-Seg, the experimental results for EBHI-Seg are evaluated using classical machine learning methods and deep learning methods. Results The experimental results showed that deep learning methods had a better image segmentation performance when utilizing EBHI-Seg. The maximum accuracy of the Dice evaluation metric for the classical machine learning method is 0.948, while the Dice evaluation metric for the deep learning method is 0.965. Conclusion This publicly available dataset contained 4,456 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients. EBHI-Seg is publicly available at: https://figshare.com/articles/dataset/EBHI-SEG/21540159/1 .
Entanglement Swapping and Swapped Entanglement

Entanglement swapping is gaining widespread attention due to its application in entanglement distribution among different parts of quantum appliances. We investigate the entanglement swapping for pure and noisy systems, and argue different entanglement quantifiers for quantum states. We explore the relationship between the entanglement of initial states and the average entanglement of final states in terms of concurrence and negativity. We find that if initial quantum states are maximally entangled and we make measurements in the Bell basis, then average concurrence and average negativity of final states give similar results. In this case, we simply obtain the average concurrence (average negativity) of the final states by taking the product of concurrences (negativities) of the initial states. However, the measurement in non-maximally entangled basis during entanglement swapping degrades the average swapped entanglement. Further, the product of the entanglement of the initial mixed states provides an upper bound to the average swapped entanglement of final states obtained after entanglement swapping. The average concurrence of finally obtained states provides an upper bound to the average negativity of these states. We also discuss how successfully the output state can be used as a channel for the teleportation of an unknown qubit.
Joint quantum estimation of loss and nonlinearity in driven-dissipative Kerr resonators

We address multiparameter quantum estimation for coherently driven nonlinear Kerr resonators in the presence of loss. In particular, we consider the realistic situation in which the parameters of interest are the loss rate and the nonlinear coupling, whereas the amplitude of the coherent driving is known and externally tunable. Our results show that this driven-dissipative model is asymptotically classical, i.e. the Uhlmann curvature vanishes, and the two parameters may be jointly estimated without any additional noise of quantum origin. We also find that the ultimate bound to precision, as quantified by the quantum Fisher information (QFI), increases with the interaction time and the driving amplitude for both parameters. Finally, we investigate the performance of quadrature detection, and show that for both parameters the Fisher information oscillates in time, repeatedly approaching the corresponding QFI.
Learning Semantic Representation on Visual Attribute Graph for Person Re-identification and Beyond

Person re-identification (re-ID) aims to match pedestrian pairs captured from different cameras. Recently, various attribute-based models have been proposed to combine the pedestrian attribute as an auxiliary semantic information to learn a more discriminative pedestrian representation. However, these methods usually directly concatenate the visual branch and attribute branch embeddings as the final pedestrian representation, which ignores the semantic relation between the pedestrian revealed by attribute similarity. To capture and explore such semantic relation, we propose a unified pedestrian representation framework, called Visual Attribute Graph Embedding Network (VAGEN), to simultaneously learn attribute and visual representation. We unify the visual embedding and attribute similarity into a Visual Attribute Graph, where pedestrian is considered as a node and attribute similarity as an edge. Then, we learn graph node embedding to generate pedestrian representation through Graph Neural Network. Except for this unified representation for visual and attribute embeddings, VAGEN also conducts implicitly hard example mining for visual similar false-positive results, which has not been explored yet among existing attribute-based methods. We conduct extensive empirical studies on several person re-ID datasets to evaluate our proposed algorithm from different aspects. The results show that our proposed method outperforms state-of-the-art techniques with considerable margins.
XCoS: Explainable Code Search Based on Query Scoping and Knowledge Graph

When searching code, developers may express additional constraints (e.g., functional constraints and nonfunctional constraints) on the implementations of desired functionalities in the queries. Existing code search tools treat the queries as a whole and ignore the different implications of different parts of the queries. Moreover, these tools usually return a ranked list of candidate code snippets without any explanations. Therefore, the developers often find it hard to choose the desired results and build confidence on them. In this article, we conduct a developer survey to better understand and address these issues and induct some insights from the survey results. Based on the insights, we propose XCoS, an explainable code search approach based on query scoping and knowledge graph. XCoS extracts a background knowledge graph from general knowledge bases like Wikidata and Wikipedia. Given a code search query, XCoS identifies different parts (i.e., functionalities, functional constraints, nonfunctional constraints) from it and use the expressions of functionalities and functional constraints to search the codebase. It then links both the query and the candidate code snippets to the concepts in the background knowledge graph and generates explanations based on the association paths between these two parts of concepts together with relevant descriptions. XCoS uses an interactive user interface that allows the user to better understand the associations between candidate code snippets and the query from different aspects and choose the desired results. Our evaluation shows that the quality of the extracted background knowledge and the concept linkings in codebase is generally high. Furthermore, the generated explanations are considered complete, concise, and readable, and the approach can help developers find the desired code snippets more accurately and confidently.
Calibrating segmentation networks with margin-based label smoothing

Despite the undeniable progress in visual recognition tasks fueled by deep neural networks, there exists recent evidence showing that these models are poorly calibrated, resulting in over-confident predictions. The standard practices of minimizing the cross-entropy loss during training promote the predicted softmax probabilities to match the one-hot label assignments. Nevertheless, this yields a pre-softmax activation of the correct class that is significantly larger than the remaining activations, which exacerbates the miscalibration problem. Recent observations from the classification literature suggest that loss functions that embed implicit or explicit maximization of the entropy of predictions yield state-of-the-art calibration performances. Despite these findings, the impact of these losses in the relevant task of calibrating medical image segmentation networks remains unexplored. In this work, we provide a unifying constrained-optimization perspective of current state-of-the-art calibration losses. Specifically, these losses could be viewed as approximations of a linear penalty (or a Lagrangian term) imposing equality constraints on logit distances. This points to an important limitation of such underlying equality constraints, whose ensuing gradients constantly push towards a non-informative solution, which might prevent from reaching the best compromise between the discriminative performance and calibration of the model during gradient-based optimization. Following our observations, we propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances. Comprehensive experiments on a variety of public medical image segmentation benchmarks demonstrate that our method sets novel state-of-the-art results on these tasks in terms of network calibration, whereas the discriminative performance is also improved. The code is available at https://github.com/Bala93/MarginLoss
Modeling Long-range Dependencies and Epipolar Geometry for Multi-view Stereo

This article proposes a network, referred to as Multi-View Stereo TRansformer (MVSTR) for depth estimation from multi-view images. By modeling long-range dependencies and epipolar geometry, the proposed MVSTR is capable of extracting dense features with global context and 3D consistency, which are crucial for reliable matching in multi-view stereo (MVS). Specifically, to tackle the problem of the limited receptive field of existing CNN-based MVS methods, a global-context Transformer module is designed to establish intra-view long-range dependencies so that global contextual features of each view are obtained. In addition, to further enable features of each view to be 3D consistent, a 3D-consistency Transformer module with an epipolar feature sampler is built, where epipolar geometry is modeled to effectively facilitate cross-view interaction. Experimental results show that the proposed MVSTR achieves the best overall performance on the DTU dataset and demonstrates strong generalization on the Tanks &amp; Temples benchmark dataset.
GTMNet: a vision transformer with guided transmission map for single remote sensing image dehazing

Abstract Existing dehazing algorithms are not effective for remote sensing images (RSIs) with dense haze, and dehazed results are prone to over-enhancement, color distortion, and artifacts. To tackle these problems, we propose a model GTMNet based on convolutional neural networks (CNNs) and vision transformers (ViTs), combined with dark channel prior (DCP) to achieve good performance. Specifically, a spatial feature transform (SFT) layer is first used to smoothly introduce the guided transmission map (GTM) into the model, improving the ability of the network to estimate haze thickness. A strengthen-operate-subtract (SOS) boosted module is then added to refine the local features of the restored image. The framework of GTMNet is determined by adjusting the input of the SOS boosted module and the position of the SFT layer. On SateHaze1k dataset, we compare GTMNet with several classical dehazing algorithms. The results show that on sub-datasets of Moderate Fog and Thick Fog, the PSNR and SSIM of GTMNet-B are comparable to that of the state-of-the-art model Dehazeformer-L, with only 0.1 times of parameter quantity. In addition, our method is intuitively effective in improving the clarity and the details of dehazed images, which proves the usefulness and significance of using the prior GTM and the SOS boosted module in a single RSI dehazing.
Ship Detection and Recognition Based on Improved YOLOv7

In this paper, an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks, such as the irregular shapes and varying sizes of ships. The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset. This paper also introduces a novel multi-scale feature fusion module, which comprises Path Aggregation Network (PAN) modules, enabling the efficient capture of ship features across different scales. Furthermore, data preprocessing is enhanced through the application of data augmentation techniques, including random rotation, scaling, and cropping, which serve to bolster data diversity and robustness. The distribution of positive and negative samples in the dataset is balanced using random sampling, ensuring a more accurate representation of real-world scenarios. Comprehensive experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches in terms of both detection accuracy and robustness, highlighting the potential of the improved YOLOv7 model for practical applications in the maritime domain.
CROSS-LANGUAGE TRANSFER LEARNING USING VISUAL INFORMATION FOR AUTOMATIC SIGN GESTURE RECOGNITION

Abstract. Automatic sign gesture recognition (GR) plays a critical role in facilitating communication between hearing-impaired individuals and the rest of society. However, recognizing sign gestures accurately and efficiently remains a challenging task due to the diversity of sign languages (SLs) and their limited availability of labeled data. This scientific paper proposes a new approach to improving the accuracy of automatic sign GR using cross-language transfer learning with visual information. Two large-scale multimodal SL corpora are utilized as the basic SLs for this study: the Ankara University Turkish Sign Language Dataset (AUTSL) and the Thesaurus Russian Sign Language (TheRusLan). Experimental studies were conducted, resulting in an accuracy of 93.33% for 18 different gestures, including the Russian target SL gestures. This result exceeds the previous state-of-the-art accuracy by 2.19%, demonstrating the effectiveness of the proposed approach. The study highlights the potential of the proposed approach to enhance the accuracy and robustness of machine SL translation, improve the naturalness of human-computer interaction, and facilitate the social adaptation of people with hearing impairments. This paper proposes a promising direction for future research to explore the application of the proposed approach to other SLs and to investigate the impact of individual and cultural differences on GR.