Reviewerly/exHarmony huggingface dataset:
Hyperspectral Image Restoration via Global <i>L<sub>1-2</sub> </i> Spatial–Spectral Total Variation Regularized Local Low-Rank Tensor Recovery

Hyperspectral images (HSIs) are usually corrupted by various noises, e.g., Gaussian noise, impulse noise, stripes, dead lines, and many others. In this article, motivated by the good performance of the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> nonconvex metric in image sparse structure exploitation, we first develop a 3-D L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> spatial-spectral total variation ( L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> SSTV) regularization to globally represent the sparse prior in the gradient domain of HSIs. Then, we divide HSIs into local overlapping 3-D patches, and low-rank tensor recovery (LTR) is locally used to effectively separate the low-rank clean HSI patches from complex noise. The patchwise LTR can not only adapt to the local low-rank property of HSIs well but also significantly reduce the information loss caused by the global LTR. Finally, integrating the advantages of both the global L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> SSTV regularization and local LTR model, we propose a L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> SSTV regularized local LTR model for hyperspectral restoration. In the framework of the alternating direction method of multipliers, the difference of convex algorithm, the split Bregman iteration method, and tensor singular value decomposition method are adopted to solve the proposed model efficiently. Simulated and real HSI experiments show that the proposed model can reduce the dependence on noise independent and identical distribution hypotheses, and simultaneously remove various types of noise, even structure-related noise.
Orbital Angular Momentum Multiplexed Quantum Dense Coding

To beat the channel capacity limit of conventional quantum dense coding (QDC) with fixed quantum resources, we experimentally implement the orbital angular momentum (OAM) multiplexed QDC (MQDC) in a continuous variable system based on a four-wave mixing process. First, we experimentally demonstrate that the Einstein-Podolsky-Rosen entanglement source coded on OAM modes can be used in a single channel to realize the QDC scheme. Then, we implement the OAM MQDC scheme by using the Einstein-Podolsky-Rosen entanglement source coded on OAM superposition modes. In the end, we make an explicit comparison of channel capacities for four different schemes and find that the channel capacity of the OAM MQDC scheme is substantially enhanced compared to the conventional QDC scheme without multiplexing. The channel capacity of our OAM MQDC scheme can be further improved by increasing the squeezing parameter and the number of multiplexed OAM modes in the channel. Our results open an avenue to construct high-capacity quantum communication networks.
Facial attributes: Accuracy and adversarial robustness

Facial attributes, emerging soft biometrics, must be automatically and reliably extracted from images in order to be usable in stand-alone systems. While recent methods extract facial attributes using deep neural networks (DNNs) trained on labeled facial attribute data, the robustness of deep attribute representations has not been evaluated. In this paper, we examine the representational stability of several approaches that recently advanced the state of the art on the CelebA benchmark by generating adversarial examples formed by adding small, non-random perturbations to inputs yielding altered classifications. We show that our fast flipping attribute (FFA) technique generates more adversarial examples than traditional algorithms, and that the adversarial robustness of DNNs varies highly between facial attributes. We also test the correlation of facial attributes and find that only for related attributes do the formed adversarial perturbations change the classification of others. Finally, we introduce the concept of natural adversarial samples, i.e., misclassified images where predictions can be corrected via small perturbations. We demonstrate that natural adversarial samples commonly occur and show that many of these images remain misclassified even with additional training epochs, even though their correct classification may require only a small adjustment to network parameters.
Saliency for fine-grained object recognition in domains with scarce training data

This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate a large dataset. The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network’s performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline.
Query-oriented text summarization based on hypergraph transversals

Existing graph- and hypergraph-based algorithms for document summarization represent the sentences of a corpus as the nodes of a graph or a hypergraph in which the edges represent relationships of lexical similarities between sentences. Each sentence of the corpus is then scored individually, using popular node ranking algorithms, and a summary is produced by extracting highly scored sentences. This approach fails to select a subset of jointly relevant sentences and it may produce redundant summaries that are missing important topics of the corpus. To alleviate this issue, a new hypergraph-based summarizer is proposed in this paper, in which each node is a sentence and each hyperedge is a theme, namely a group of sentences sharing a topic. Themes are weighted in terms of their prominence in the corpus and their relevance to a user-defined query. It is further shown that the problem of identifying a subset of sentences covering the relevant themes of the corpus is equivalent to that of finding a hypergraph transversal in our theme-based hypergraph. Two extensions of the notion of hypergraph transversal are proposed for the purpose of summarization, and polynomial time algorithms building on the theory of submodular functions are proposed for solving the associated discrete optimization problems. The worst-case time complexity of the proposed algorithms is squared in the number of terms, which makes it cheaper than the existing hypergraph-based methods. A thorough comparative analysis with related models on DUC benchmark datasets demonstrates the effectiveness of our approach, which outperforms existing graph- or hypergraph-based methods by at least 6% of ROUGE-SU4 score.
Asymmetric sending or not sending twin-field quantum key distribution in practice

Quantum key distribution (QKD) offers a secret way to share keys between legitimate users which is guaranteed by the law of quantum mechanics. Most recently, the limitation of transmission distance without quantum repeaters was broken through by twin-field QKD [M. Lucamarini et al., Nature (London) 557, 400 (2018)]. Based on its main idea, sending or not-sending (SNS) QKD protocol was proposed [X. B. Wang et al. Phys. Rev. A 98, 062323 (2018)], which filled the remaining security loopholes and can tolerate large misalignment errors. In this paper, we give a more general model for SNS QKD, where two legitimate users, Alice and Bob, can possess asymmetric quantum channels. By applying the method present in the paper, the legitimate users can achieve dramatically increased key generation rates and transmission distances compared with compensating the channel asymmetry and utilizing the original symmetric protocol. Therefore, our present paper represents a further step along the progress of practical QKD.
Deep Coupled ISTA Network for Multi-Modal Image Super-Resolution

Given a low-resolution (LR) image, multi-modal image super-resolution (MISR) aims to find the high-resolution (HR) version of this image with the guidance of an HR image from another modality. In this paper, we use a model-based approach to design a new deep network architecture for MISR. We first introduce a novel joint multi-modal dictionary learning (JMDL) algorithm to model cross-modality dependency. In JMDL, we simultaneously learn three dictionaries and two transform matrices to combine the modalities. Then, by unfolding the iterative shrinkage and thresholding algorithm (ISTA), we turn the JMDL model into a deep neural network, called deep coupled ISTA network. Since the network initialization plays an important role in deep network training, we further propose a layer-wise optimization algorithm (LOA) to initialize the parameters of the network before running back-propagation strategy. Specifically, we model the network initialization as a multi-layer dictionary learning problem, and solve it through convex optimization. The proposed LOA is demonstrated to effectively decrease the training loss and increase the reconstruction accuracy. Finally, we compare our method with other state-of-the-art methods in the MISR task. The numerical results show that our method consistently outperforms others both quantitatively and qualitatively at different upscaling factors for various multi-modal scenarios.
A New Image Encryption Algorithm Based on Composite Chaos and Hyperchaos Combined with DNA Coding

In order to obtain chaos with a wider chaotic scope and better chaotic behavior, this paper combines the several existing one-dimensional chaos and forms a new one-dimensional chaotic map by using a modular operation which is named by LLS system and abbreviated as LLSS. To get a better encryption effect, a new image encryption method based on double chaos and DNA coding technology is proposed in this paper. A new one-dimensional chaotic map is combined with a hyperchaotic Qi system to encrypt by using DNA coding. The first stage involves three rounds of scrambling; a diffusion algorithm is applied to the plaintext image, and then the intermediate ciphertext image is partitioned. The final encrypted image is formed by using DNA operation. Experimental simulation and security analysis show that this algorithm increases the key space, has high sensitivity, and can resist several common attacks. At the same time, the algorithm in this paper can reduce the correlation between adjacent pixels, making it close to 0, and increase the information entropy, making it close to the ideal value and achieving a good encryption effect.
Toward Enhanced Teleoperation Through Embodiment

Telerobotics aims to transfer human manipulation skills and dexterity over an arbitrary distance and at an arbitrary scale to a remote workplace. A telerobotic system that is transparent enables a natural and intuitive interaction. We postulate that embodiment (with three sub-components: sense of ownership, agency, and self-location) of the robotic system leads to optimal perceptual transparency and increases task performance. However, this has not yet been investigated directly. We reason along four premises and present findings from the literature that substantiate each of them: 1) the brain can embody non-bodily objects (e.g., robotic hands), 2) embodiment can be elicited with mediated sensorimotor interaction, 3) embodiment is robust against inconsistencies between the robotic system and the operator’s body, and 4) embodiment positively correlates to dexterous task performance. We use the predictive encoding theory as a framework to interpret and discuss the results reported in the literature. Numerous previous studies have shown that it is possible to induce embodiment over a wide range of virtual and real extracorporeal objects (including artificial limbs, avatars, and android robots) through mediated sensorimotor interaction. Also, embodiment can occur for non-human morphologies including for elongated arms and a tail. In accordance with the predictive encoding theory, none of the sensory modalities is critical in establishing ownership, and discrepancies in multisensory signals do not necessarily lead to loss of embodiment. However, large discrepancies in terms of multisensory synchrony or visual likeness can prohibit embodiment from occurring. The literature provides less extensive support for the link between embodiment and (dexterous) task performance. However, data gathered with prosthetic hands do indicate a positive correlation. We conclude that all four premises are supported by direct or indirect evidence in the literature, suggesting that embodiment of a remote manipulator may improve dexterous performance in telerobotics. This warrants further implementation testing of embodiment in telerobotics. We formulate a first set of guidelines to apply embodiment in telerobotics and identify some important research topics.
Deep Neural Networks for Survival Analysis Using Pseudo Values

There has been increasing interest in modelling survival data using deep learning methods in medical research. Current approaches have focused on designing special cost functions to handle censored survival data. We propose a very different method with two simple steps. In the first step, we transform each subject's survival time into a series of jackknife pseudo conditional survival probabilities and then use these pseudo probabilities as a quantitative response variable in the deep neural network model. By using the pseudo values, we reduce a complex survival analysis to a standard regression problem, which greatly simplifies the neural network construction. Our two-step approach is simple, yet very flexible in making risk predictions for survival data, which is very appealing from the practice point of view. The source code is freely available at http://github.com/lilizhaoUM/DNNSurv.
A Deep Learning Inspired Belief Rule-Based Expert System

Recent technological advancements in the area of the Internet of Things (IoT) and cloud services, enable the generation of large amounts of raw data. However, the accurate prediction by using this data is considered as challenging for machine learning methods. Deep Learning (DL) methods are widely used to process large amounts of data because they need less preprocessing than traditional machine learning methods. Various types of uncertainty associated with large amounts of raw data hinder the prediction accuracy. Belief Rule-Based Expert Systems (BRBES) are widely used to handle uncertain data. However, due to their incapability of integrating associative memory within the inference procedures, they demonstrate poor accuracy of prediction when large amounts of data is considered. Therefore, we propose the integration of an associative memory based DL method within the BRBES inference procedures, allowing to discover accurate data patterns and hence, the improvement of prediction under uncertainty. To demonstrate the applicability of the proposed method, which is named BRB-DL, it has been fine tuned against two datasets, one in the area of air pollution and the other in the area of power generation. The reliability of the proposed BRB-DL method, has also been compared with other DL methods such as Long-Short Term Memory and Deep Neural Network, and BRBES by taking into account of the air quality dataset from Beijing city and the power generation dataset of a combined cycle power plant. BRB-DL outperforms the above-mentioned methods in terms of prediction accuracy. For example, the Mean Square Error value of BRB-DL is 4.12 whereas for Long-Short Term Memory, Deep Neural Network, Fuzzy Deep Neural Network, Adaptive Neuro Fuzzy Inference System and BRBES it is 18.66, 28.49, 17.05, 16.37 and 38.15 for combined cycle power plant respectively, which are significantly higher.
Fully reconfigurable coherent optical vector–matrix multiplication

Optics is a promising platform in which to help realize the next generation of fast, parallel, and energy-efficient computation. We demonstrate a reconfigurable free-space optical multiplier that is capable of over 3000 computations in parallel, using spatial light modulators with a pixel resolution of only 340×340. This enables vector-matrix multiplication and parallel vector-vector multiplication with vector size of up to 56. Our design is, to the best of our knowledge, the first to simultaneously support optical implementation of reconfigurable, large-sized, and real-valued linear algebraic operations. Such an optical multiplier can serve as a building block of special-purpose optical processors such as optical neural networks and optical Ising machines.
Probing quantum processor performance with pyGSTi

PyGSTi is a Python software package for assessing and characterizing the performance of quantum computing processors. It can be used as a standalone application, or as a library, to perform a wide variety of quantum characterization, verification, and validation (QCVV) protocols on as-built quantum processors. We outline pyGSTi's structure, and what it can do, using multiple examples. We cover its main characterization protocols with end-to-end implementations. These include gate set tomography, randomized benchmarking on one or many qubits, and several specialized techniques. We also discuss and demonstrate how power users can customize pyGSTi and leverage its components to create specialized QCVV protocols and solve user-specific problems.
Safe Robot Navigation Via Multi-Modal Anomaly Detection

Navigation in natural outdoor environments requires a robust and reliable traversability classification method to handle the plethora of situations a robot can encounter. Binary classification algorithms perform well in their native domain but tend to provide overconfident predictions when presented with out-of-distribution samples, which can lead to catastrophic failure when navigating unknown environments. We propose to overcome this issue by using anomaly detection on multi-modal images for traversability classification, which is easily scalable by training in a self-supervised fashion from robot experience. In this work, we evaluate multiple anomaly detection methods with a combination of uni- and multi-modal images in their performance on data from different environmental conditions. Our results show that an approach using a feature extractor and normalizing flow with an input of RGB, depth and surface normals performs best. It achieves over 95% area under the ROC curve and is robust to out-of-distribution samples.
A Hardware-Aware Heuristic for the Qubit Mapping Problem in the NISQ Era

Due to several physical limitations in the realisation of quantum hardware, today's quantum computers are qualified as Noisy Intermediate-Scale Quantum (NISQ) hardware. NISQ hardware is characterized by a small number of qubits (50 to a few hundred) and noisy operations. Moreover, current realisations of superconducting quantum chips do not have the ideal all-to-all connectivity between qubits but rather at most a nearest-neighbour connectivity. All these hardware restrictions add supplementary low-level requirements. They need to be addressed before submitting the quantum circuit to an actual chip. Satisfying these requirements is a tedious task for the programmer. Instead, the task of adapting the quantum circuit to a given hardware is left to the compiler. In this paper, we propose a Hardware-Aware mapping transition algorithm (HA) that takes the calibration data into account with the aim to improve the overall fidelity of the circuit. Evaluation results on IBM quantum hardware show that our HA approach can outperform the state of the art both in terms of the number of additional gates and circuit fidelity.
Straggler Root-Cause and Impact Analysis for Massive-scale Virtualized Cloud Datacenters

Increased complexity and scale of virtualized distributed systems has resulted in the manifestation of emergent phenomena substantially affecting overall system performance. This phenomena is known as “Long Tail”, whereby a small proportion of task stragglers significantly impede job completion time. While work focuses on straggler detection and mitigation, there is limited work that empirically studies straggler root-cause and quantifies its impact upon system operation. Such analysis is critical to ascertain in-depth knowledge of straggler occurrence for focusing developmental and research efforts towards solving the Long Tail challenge. This paper provides an empirical analysis of straggler root-cause within virtualized Cloud datacenters; we analyze two large-scale production systems to quantify the frequency and impact stragglers impose, and propose a method for conducting root-cause analysis. Results demonstrate approximately 5 percent of task stragglers impact 50 percent of total jobs for batch processes, and 53 percent of stragglers occur due to high server resource utilization. We leverage these findings to propose a method for extreme straggler detection through a combination of offline execution patterns modeling and online analytic agents to monitor tasks at runtime. Experiments show the approach is capable of detecting stragglers less than 11 percent into their execution lifecycle with 95 percent accuracy for short duration jobs.
A superconducting thermal switch with ultrahigh impedance for interfacing superconductors to semiconductors

A number of current approaches to quantum and neuromorphic computing use superconductors as the basis of their platform or as a measurement component, and will need to operate at cryogenic temperatures. Semiconductor systems are typically proposed as a top-level control in these architectures, with low-temperature passive components and intermediary superconducting electronics acting as the direct interface to the lowest-temperature stages. The architectures, therefore, require a low-power superconductor/semiconductor interface, which is not currently available. Here we report a superconducting switch that is capable of translating low-voltage superconducting inputs directly into semiconductor-compatible (above 1,000 mV) outputs at kelvin-scale temperatures (1 K or 4 K). To illustrate the capabilities in interfacing superconductors and semiconductors, we use it to drive a light-emitting diode in a photonic integrated circuit, generating photons at 1 K from a low-voltage input and detecting them with an on-chip superconducting single-photon detector. We also characterize our device’s timing response (less than 300 ps turn-on, 15 ns turn-off), output impedance (greater than 1 MΩ) and energy requirements (0.18 fJ m−2, 3.24 mV nW−1). A superconducting switch that is capable of translating low-voltage superconducting inputs directly into semiconductor-compatible outputs at kelvin-scale temperatures could provide a superconductor-to-semiconductor logical interface for future quantum and neuromorphic computing architectures.