Reviewerly/exHarmony huggingface dataset:
Embedding Overhead Scaling of Optimization Problems in Quantum Annealing

In order to treat all-to-all-connected quadratic binary optimization problems (QUBOs) with hardware quantum annealers, an embedding of the original problem is required due to the sparsity of the topology of the hardware. The embedding of fully connected graphs—typically found in industrial applications—incurs a quadratic space overhead and thus a significant overhead in the time to solution. Here, we investigate this embedding penalty of established planar embedding schemes such as square-lattice embedding, embedding on a chimera lattice, and the Lechner-Hauke-Zoller scheme, using simulated quantum annealing on classical hardware. Large-scale quantum Monte Carlo simulation suggests a polynomial time-to-solution overhead. Our results demonstrate that standard analog quantum annealing hardware is at a disadvantage in comparison to classical digital annealers, as well as gate-model quantum annealers, and could also serve as a benchmark for improvements of the standard quantum annealing protocol.3 MoreReceived 29 March 2021Revised 13 August 2021Accepted 8 September 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040322Published 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 AreasAdiabatic quantum optimizationOptimization problemsTechniquesQuantum Monte CarloQuantum InformationCondensed Matter, Materials & Applied Physics
Blockchain-Enabled Telehealth Services Using Smart Contracts

Telehealth has gained a huge traction during the Coronavirus (COVID-19) pandemic. Telehealth enables physicians and medical care providers to remotely care for patients and monitor their symptoms. Today’s telehealth systems fall short in providing transparent, immutable, traceable, auditable, secure, and trustworthy services. In addition, they are centralized and subject to the single point of control and failure. In this paper, we propose a private blockchain-based solution to overcome the aforementioned challenges. We demonstrate how specifically three important telehealth services; namely, teleconsultation, drug administration, and medical testing can be enhanced using blockchain technology. Our proposed solution also ensures integrity, immutability, accountability, and non-repudiation for telehealth transactions initiated by multiple actors. For storing and keeping track of large-size digital content, such as images and audio and video recordings of telehealth service sessions, our proposed solution is integrated with off-chain storage systems including cloud storage or a decentralized storage system as that of the Interplanetary File System (IPFS). The registered participants are provided with access privileges based on their roles to ensure that restrictions are enforced on-chain. Smart contracts are developed to maintain data provenance and generate reliable alerts and notifications. The implementation and testing details of the algorithms are presented. We discuss, compare, and analyze the security features of our solution.
Progressive Self-Training with Discriminator for Aspect Term Extraction

Aspect term extraction aims to extract aspect terms from a review sentence that users have expressed opinions on.One of the remaining challenges for aspect term extraction resides in the lack of sufficient annotated data.While self-training is potentially an effective method to address this issue, the pseudo-labels it yields on unlabeled data could induce noise.In this paper, we use two means to alleviate the noise in the pseudo-labels.One is that inspired by the curriculum learning, we refine the conventional self-training to progressive self-training.Specifically, the base model infers pseudo-labels on a progressive subset at each iteration, where samples in the subset become harder and more numerous as the iteration proceeds.The other is that we use a discriminator to filter the noisy pseudo-labels.Experimental results on four SemEval datasets show that our model significantly outperforms the previous baselines and achieves state-ofthe-art performance.
Beyond the standard quantum limit for parametric amplification of broadband signals

Abstract The low-noise amplification of weak microwave signals is crucial for countless protocols in quantum information processing. Quantum mechanics sets an ultimate lower limit of half a photon to the added input noise for phase-preserving amplification of narrowband signals, also known as the standard quantum limit (SQL). This limit, which is equivalent to a maximum quantum efficiency of 0.5, can be overcome by employing nondegenerate parametric amplification of broadband signals. We show that, in principle, a maximum quantum efficiency of unity can be reached. Experimentally, we find a quantum efficiency of 0.69 ± 0.02, well beyond the SQL, by employing a flux-driven Josephson parametric amplifier and broadband thermal signals. We expect that our results allow for fundamental improvements in the detection of ultraweak microwave signals.
Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos

Moving Object Detection (MOD) is a fundamental step for many computer vision applications. MOD becomes very challenging when a video sequence captured from a static or moving camera suffers from the challenges: camouflage, shadow, dynamic backgrounds, and lighting variations, to name a few. Deep learning methods have been successfully applied to address MOD with competitive performance. However, in order to handle the overfitting problem, deep learning methods require a large amount of labeled data which is a laborious task as exhaustive annotations are always not available. Moreover, some MOD deep learning methods show performance degradation in the presence of unseen video sequences because the testing and training splits of the same sequences are involved during the network learning process. In this work, we pose the problem of MOD as a node classification problem using Graph Convolutional Neural Networks (GCNNs). Our algorithm, dubbed as GraphMOD-Net, encompasses instance segmentation, background initialization, feature extraction, and graph construction. GraphMOD-Net is tested on unseen videos and outperforms state-of-the-art methods in unsupervised, semi-supervised, and supervised learning in several challenges of the Change Detection 2014 (CDNet2014) and UCSD background subtraction datasets.
Augmented reality visualization in brain lesions: a prospective randomized controlled evaluation of its potential and current limitations in navigated microneurosurgery

Augmented reality (AR) has the potential to support complex neurosurgical interventions by including visual information seamlessly. This study examines intraoperative visualization parameters and clinical impact of AR in brain tumor surgery.Fifty-five intracranial lesions, operated either with AR-navigated microscope (n = 39) or conventional neuronavigation (n = 16) after randomization, have been included prospectively. Surgical resection time, duration/type/mode of AR, displayed objects (n, type), pointer-based navigation checks (n), usability of control, quality indicators, and overall surgical usefulness of AR have been assessed.AR display has been used in 44.4% of resection time. Predominant AR type was navigation view (75.7%), followed by target volumes (20.1%). Predominant AR mode was picture-in-picture (PiP) (72.5%), followed by 23.3% overlay display. In 43.6% of cases, vision of important anatomical structures has been partially or entirely blocked by AR information. A total of 7.7% of cases used MRI navigation only, 30.8% used one, 23.1% used two, and 38.5% used three or more object segmentations in AR navigation. A total of 66.7% of surgeons found AR visualization helpful in the individual surgical case. AR depth information and accuracy have been rated acceptable (median 3.0 vs. median 5.0 in conventional neuronavigation). The mean utilization of the navigation pointer was 2.6 × /resection hour (AR) vs. 9.7 × /resection hour (neuronavigation); navigation effort was significantly reduced in AR (P < 0.001).The main benefit of HUD-based AR visualization in brain tumor surgery is the integrated continuous display allowing for pointer-less navigation. Navigation view (PiP) provides the highest usability while blocking the operative field less frequently. Visualization quality will benefit from improvements in registration accuracy and depth impression.DRKS00016955.
Microscopic 3D reconstruction based on point cloud data generated using defocused images

Most of the 3D reconstruction requirements of microscopic scenes exist in industrial detection, and this scene requires real-time object reconstruction and can get object surface information quickly. However, this demand is challenging to obtain for micro scenarios. The reason is that the microscope’s depth of field is shallow, and it is easy to blur the image because the object’s surface is not in the focus plane. Under the video microscope, the images taken frame by frame are mostly defocused images. In the process of 3D reconstruction, a single sheet or a few 2D images are used for geometric-optical calculation, and the affine transformation is used to obtain the 3D information of the object and complete the 3D reconstruction. The feature of defocus image is that its complete information needs to be restored by a whole set of single view defocus image sequences. The defocused image cannot complete the task of affine transformation due to the lack of information. Therefore, using defocus image sequence to restore 3D information has higher processing difficulty than ordinary scenes, and the real-time performance is more difficult to guarantee. In this paper, the surface reconstruction process based on point-cloud data is studied. A Delaunay triangulation method based on plane projection and synthesis algorithm is used to complete surface fitting. Finally, the 3D reconstruction experiment of the collected image sequence is completed. The experimental results show that the reconstructed surface conforms to the surface contour information of the selected object.
Artificial Electric Field Algorithm with Greedy State Transition Strategy for Spherical Multiple Traveling Salesmen Problem

Abstract The multiple traveling salesman problem (MTSP) is an extension of the traveling salesman problem (TSP). It is found that the MTSP problem on a three-dimensional sphere has more research value. In a spherical space, each city is located on the surface of the Earth. To solve this problem, an integer-serialized coding and decoding scheme was adopted, and artificial electric field algorithm (AEFA) was mixed with greedy strategy and state transition strategy, and an artificial electric field algorithm based on greedy state transition strategy (GSTAEFA) was proposed. Greedy state transition strategy provides state transition interference for AEFA, increases the diversity of population, and effectively improves the accuracy of the algorithm. Finally, we test the performance of GSTAEFA by optimizing examples with different numbers of cities. Experimental results show that GSTAEFA has better performance in solving SMTSP problems than other swarm intelligence algorithms.
Explanation-Based Human Debugging of NLP Models: A Survey

Abstract Debugging a machine learning model is hard since the bug usually involves the training data and the learning process. This becomes even harder for an opaque deep learning model if we have no clue about how the model actually works. In this survey, we review papers that exploit explanations to enable humans to give feedback and debug NLP models. We call this problem explanation-based human debugging (EBHD). In particular, we categorize and discuss existing work along three dimensions of EBHD (the bug context, the workflow, and the experimental setting), compile findings on how EBHD components affect the feedback providers, and highlight open problems that could be future research directions.
Paraphrase Generation: A Survey of the State of the Art

This paper focuses on paraphrase generation, which is a widely studied natural language generation task in NLP.With the development of neural models, paraphrase generation research has exhibited a gradual shift to neural methods in the recent years.This has provided architectures for contextualized representation of an input text and generating fluent, diverse and human-like paraphrases.This paper surveys various approaches to paraphrase generation with a main focus on neural methods.
An intelligent and privacy-enhanced data sharing strategy for blockchain-empowered Internet of Things

With the development of the Internet of Things (IoT), the massive data sharing between IoT devices improves the Quality of Service (QoS) and user experience in various IoT applications. However, data sharing may cause serious privacy leakages to data providers. To address this problem, in this study, data sharing is realized through model sharing, based on which a secure data sharing mechanism, called BP2P-FL, is proposed using peer-to-peer federated learning with the privacy protection of data providers. In addition, by introducing the blockchain to the data sharing, every training process is recorded to ensure that data providers offer high-quality data. For further privacy protection, the differential privacy technology is used to disturb the global data sharing model. The experimental results show that BP2P-FL has high accuracy and feasibility in the data sharing of various IoT applications.
Active readout-error mitigation

Mitigating errors is a significant challenge for near-term quantum computers. One of the most important sources of errors is related to the readout of the quantum state into a classical bit stream. A variety of techniques have been proposed to mitigate these errors with post-hoc corrections. We propose a complementary scheme to actively reduce readout errors on a shot-by-shot basis by encoding single qubits, immediately prior to readout, into multiqubit states. The computational resources of our technique are independent of the circuit depth and are compatible with the error rates and connectivity of many current devices. We analyze the potential of our approach using two types of error-correcting codes, and, as a proof of principle, we demonstrate an 80% improvement in readout error on the IBMQ Mumbai quantum computer.
Rank-2 toric code in two dimensions

We study a two-dimensional spin model obtained by "Higgsing" the rank-2 U(1) lattice gauge theory (LGT) with scalar or vector charges on the L_x * L_y square lattice under the periodic boundary condition (PBC). There are p degrees of freedom per orbital and three orbitals per unit cell in the spin model. The resulting spin model is a stabilizer code consisting of three mutually commuting projectors that are, in turn, obtained by Higgsing the mutually commuting Gauss's law operators and the magnetic field operators in the underlying LGT. The spin model thus obtained is exactly solvable, with the ground state degeneracy (GSD) D given by log_p D=2+(1+delta_{L_x mod p,0})(1+delta_{L_y mod p,0}) when p is a prime number. Two types of dipole excitations, pristine and emergent, are identified. Both the monopoles and the dipoles are free to move, with restrictions on monopoles to hop only by p lattice spacing along with certain directions. The monopole-monopole braiding phase depends on the separation of the x or y coordinates of the initial monopole positions, making it distinct from the ordinary anyon braiding statistics. The monopole-dipole braiding obeys the usual anyonic statistics. Despite the oddity, the monopole-monopole braiding phase can be understood as the Aharonov-Bohm phase of some emergent vector potentials.
Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems

Inspection of mining assets is a crucial part of the maintenance process and is of interest to several stakeholders (e.g., OEMs, owners, users, and inspectors). Inspections require an inspector to verify several characteristics of the assets onsite, typically using legacy and poorly digitized procedures. Thus, many research opportunities arise from the adoption of digital technologies to make these procedures more efficient, reliable, and straightforward. In addition to cloud computing, the ubiquitous presence of modern mobile devices, new measurement tools with embedded connectivity capabilities, and blockchain technologies could greatly improve trust and transparency between the stakeholders interested in the inspection. However, there has been little discussion on integrating these technologies into the mining domain. This paper presents and evaluates an end-to-end system to conduct inspections using mobile devices that directly interact with constrained IoT sensor devices. Furthermore, our proposal provides a method to integrate constrained IoT devices as smart measuring tools that directly interact with a blockchain system, guaranteeing data integrity and increasing the trustworthiness of the data. Finally, we highlight the benefits of our proposed architecture by evaluating a real case study in a mining inspection scenario.
A Novel Chaos-Based Image Encryption Using Magic Square Scrambling and Octree Diffusing

Digital chaotic maps have been widely used in the fields of cryptography owing to their dynamic characteristics, however, some unfavorable security properties arise when they operate on devices with limited precision. Thus, enhancing the properties of chaotic maps are beneficial to the improvement of chaos-based encryption algorithms. In this paper, a scheme to integrate a one-dimensional Logistic map by perturbation parameters with a delayed coupling method and feedback control is proposed and further deepens the randomness by selectively shifting the position of the chaotic sequence. Then, through a number of simulation experiments, the results demonstrate that the two-dimensional chaotic map treated by this mode exhibits better chaotic characteristics, including a larger chaos range and higher complexity. In addition, a new image encryption algorithm is designed based on these modified chaotic sequences, in which magic square theorem is incorporated to exchange pixel positions, and the octree principle is invoked to achieve pixel bit shifting. Several simulation experiments present findings that the image encryption algorithm contains a high level of security, and can compete with other encryption algorithms.
A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction

Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosis, and therapy of cancers. By integrating these heterogeneous but complementary data, many multi-modal methods are proposed to study the complex mechanisms of cancers, and most of them achieve comparable or better results from previous single-modal methods. However, these multi-modal methods are restricted to a single task (e.g., survival analysis or grade classification), and thus neglect the correlation between different tasks. In this study, we present a multi-modal fusion framework based on multi-task correlation learning (MultiCoFusion) for survival analysis and cancer grade classification, which combines the power of multiple modalities and multiple tasks. Specifically, a pre-trained ResNet-152 and a sparse graph convolutional network (SGCN) are used to learn the representations of histopathological images and mRNA expression data respectively. Then these representations are fused by a fully connected neural network (FCNN), which is also a multi-task shared network. Finally, the results of survival analysis and cancer grade classification output simultaneously. The framework is trained by an alternate scheme. We systematically evaluate our framework using glioma datasets from The Cancer Genome Atlas (TCGA). Results demonstrate that MultiCoFusion learns better representations than traditional feature extraction methods. With the help of multi-task alternating learning, even simple multi-modal concatenation can achieve better performance than other deep learning and traditional methods. Multi-task learning can improve the performance of multiple tasks not just one of them, and it is effective in both single-modal and multi-modal data.
LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining

Generally, seafarers face a higher risk of illnesses and accidents than land workers. In most cases, there are no medical professionals on board seagoing vessels, which makes disease diagnosis even more difficult. When this occurs, onshore doctors may be able to provide medical advice through telemedicine by receiving better symptomatic and clinical details in the health abstracts of seafarers. The adoption of text mining techniques can assist in extracting diagnostic information from clinical texts. We applied lexicon sentimental analysis to explore the automatic labeling of positive and negative healthcare terms to seafarers' text healthcare documents. This was due to the lack of experimental evaluations using computational techniques. In order to classify diseases and their associated symptoms, the LASSO regression algorithm is applied to analyze these text documents. A visualization of symptomatic data frequency for each disease can be achieved by analyzing TF-IDF values. The proposed approach allows for the classification of text documents with 93.8% accuracy by using a machine learning model called LASSO regression. It is possible to classify text documents effectively with tidy text mining libraries. In addition to delivering health assistance, this method can be used to classify diseases and establish health observatories. Knowledge developed in the present work will be applied to establish an Epidemiological Observatory of Seafarers' Pathologies and Injuries. This Observatory will be a collaborative initiative of the Italian Ministry of Health, University of Camerino, and International Radio Medical Centre (C.I.R.M.), the Italian TMAS.
An approach for extractive text summarization using fuzzy evolutionary and clustering algorithms

Automatic text summarization schemes are indeed helpful for glancing briefly at the text document. With this motivation, we introduce here a two-stage hybrid model for text summarization task by utilizing the strength of various approaches. In the first step, we cluster the sentences of a document according to their similarity using a partitional clustering algorithm. We then use a linear combination of the normalized Google distance and word mover’s distance to differentiate two sentences. The notion of gap statistics is exploited to approximate the number of partitions for the given document needed in the partitional clustering algorithm. We extract the significant sentences from each cluster (partition), which are recognized by their adjusted text feature scores, in the second step. The teaching–learning based optimization approach is used to find the optimal weights for the text features whereas a fuzzy inference system with a full-fledged knowledge base generated by humans is employed to determine the final score of the sentences. Moreover, we have also proposed an exact method to give a solution for the summarization problem by modeling it as an Integer Linear Programming (ILP) problem. We evaluate the proposed methods on three different datasets: DUC 2001, DUC 2002, and CNN. The observed results on these standard datasets manifest the efficacy of the proposed methods. We further show that partitioning a document in an optimal number of clusters plays a major role in content coverage in summaries. The performance of the proposed hybrid method shows that the combination of fuzzy, evolutionary, and clustering algorithms produces good summaries of the documents.