Reviewerly/exHarmony huggingface dataset:
ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

Due to the various reasons, such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between the spectral bands of satellite images collected from different geographic locations. The large shift between the spectral distributions of training and test data causes the current state-of-the-art supervised learning approaches to output unsatisfactory maps. We present a novel semantic segmentation framework that is robust to such a shift. The key component of the proposed framework is color mapping generative adversarial networks (ColorMapGANs) that can generate fake training images that are semantically exactly the same as training images, but whose spectral distribution is similar to the distribution of the test images. We then use the fake images and the ground truth for the training images to fine-tune the already trained classifier. Contrary to the existing generative adversarial networks (GANs), the generator in ColorMapGAN does not have any convolutional or pooling layers. It learns to transform the colors of the training data to the colors of the test data by performing only one elementwise matrix multiplication and one matrix-addition operation. Due to the architecturally simple but powerful design of ColorMapGAN, the proposed framework outperforms the existing approaches with a large margin in terms of both accuracy and computational complexity.
An Improved Reversible Data Hiding in Encrypted Images Using Parametric Binary Tree Labeling

This work proposes an improved reversible data hiding scheme in encrypted images using parametric binary tree labeling(IPBTL-RDHEI), which takes advantage of the spatial correlation in the entire original image but not in small image blocks to reserve room for hiding data. Then the original image is encrypted with an encryption key and the parametric binary tree is used to label encrypted pixels into two different categories. Finally, one of the two categories of encrypted pixels can embed secret information by bit replacement. According to the experimental results, compared with several state-of-the-art methods, the proposed IPBTL-RDHEI method achieves higher embedding rate and outperforms the competitors. Due to the reversibility of IPBTL-RDHEI, the original plaintext image and the secret information can be restored and extracted losslessly and separately.
Blockchain-Empowered Multi-Robot Collaboration to Fight COVID-19 and Future Pandemics

This conceptual paper overviews how blockchain technology is involving the operation of multi-robot collaboration for combating COVID-19 and future pandemics.Robots are a promising technology for providing many tasks such as spraying, disinfection, cleaning, treating, detecting high body temperature/mask absence, and delivering goods and medical supplies experiencing an epidemic COVID-19.For combating COVID-19, many heterogeneous and homogenous robots are required to perform different tasks for supporting different purposes in the quarantine area.Managmnt and decentralizing multi-robot play a vital role in combating COVID-19 by reducing human interaction, monitoring, delivering goods.Blockchain technology can manage multi-robot collaboration in a decentralized fashion, improve the interaction among them to exchange information, share representation, share goals, and trust.We highlight the challenges and provide the tactical solutions enabled by integrating blockchain and multi-robot collaboration to combat the COVID-19 pandemic.The proposed conceptual framework can increase the intelligence, decentralization, and autonomous operations of connected multi-robot collaboration in the blockchain network.We overview blockchain potential benefits to defining a framework of multi-robot collaboration applications to combat COVID-19 epidemics such as monitoring and outdoor and hospital End to End (E2E) delivery systems.Furthermore, we discuss the challenges and opportunities of integrated blockchain, multi-robot collaboration, and the Internet of Things (IoT) for combating COVID-19 and future pandemics.
Adversarially Robust Distillation

Knowledge distillation is effective for producing small, high-performance neural networks for classification, but these small networks are vulnerable to adversarial attacks. This paper studies how adversarial robustness transfers from teacher to student during knowledge distillation. We find that a large amount of robustness may be inherited by the student even when distilled on only clean images. Second, we introduce Adversarially Robust Distillation (ARD) for distilling robustness onto student networks. In addition to producing small models with high test accuracy like conventional distillation, ARD also passes the superior robustness of large networks onto the student. In our experiments, we find that ARD student models decisively outperform adversarially trained networks of identical architecture in terms of robust accuracy, surpassing state-of-the-art methods on standard robustness benchmarks. Finally, we adapt recent fast adversarial training methods to ARD for accelerated robust distillation.
JÂA-Net: Joint Facial Action Unit Detection and Face Alignment Via Adaptive Attention

Facial action unit (AU) detection and face alignment are two highly correlated tasks, since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. However, most existing AU detection works handle the two tasks independently by treating face alignment as a preprocessing, and often use landmarks to predefine a fixed region or attention for each AU. In this paper, we propose a novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before. In particular, multi-scale shared feature is learned firstly, and high-level feature of face alignment is fed into AU detection. Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively. Finally, the assembled local features are integrated with face alignment feature and global feature for AU detection. Extensive experiments demonstrate that our framework (i) significantly outperforms the state-of-the-art AU detection methods on the challenging BP4D, DISFA, GFT and BP4D+ benchmarks, (ii) can adaptively capture the irregular region of each AU, (iii) achieves competitive performance for face alignment, and (iv) also works well under partial occlusions and non-frontal poses. The code for our method is available at https://github.com/ZhiwenShao/PyTorch-JAANet .
Quantum Computation of Finite-Temperature Static and Dynamical Properties of Spin Systems Using Quantum Imaginary Time Evolution

Simulations of the finite-temperature quantum dynamics of spin systems are performed on digital quantum computers, enhancing many capabilities that are critical to study realistic many-body physics on near-term quantum hardware.
Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction

An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm (MI), AE, and ELM. First, the importance of practical variables is computed by the MI algorithm, and the mechanism is analyzed to determine the variables related to the NOx emission concentration. Then, the time delay correlations between the selected variables and NOx emission concentration are further analyzed to reconstruct the modeling data. Subsequently, the AE is applied to extract hidden features within the input variables. Finally, an ELM algorithm establishes the relationship between the NOx emission concentration and deep features. The experimental results on practical data indicate that the proposed model shows promising performance compared to state-of-art models.
Semi-supervised Learning for Neural Machine Translation

While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi-supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the sourceto-target and target-to-source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the ChineseEnglish dataset show that our approach achieves significant improvements over state-of-the-art SMT and NMT systems.
Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Survey

With the availability of low-cost and compact 2.5/3D visual sensing devices, computer vision community is experiencing a growing interest in visual scene understanding of indoor environments. This survey paper provides a comprehensive background to this research topic. We begin with a historical perspective, followed by a popular 3D data representation and a comparative analysis of available datasets. Before delving into the application specific details, this survey provides a succinct introduction to the core technologies that are the underlying methods extensively used in this paper. Afterwards, we review the developed techniques according to a taxonomy based on the scene understanding tasks. This covers holistic indoor scene understanding as well as subtasks, such as scene classification, object detection, pose estimation, semantic segmentation, 3D reconstruction, saliency detection, physics-based reasoning, and affordance prediction. Later on, we summarize the performance metrics used for evaluation in different tasks and a quantitative comparison among the recent state-of-the-art techniques. We conclude this review with the current challenges and an outlook toward the open research problems requiring further investigation.
Multi-View Linear Discriminant Analysis Network

In many real-world applications, an object can be described from multiple views or styles, leading to the emerging multi-view analysis. To eliminate the complicated (usually highly nonlinear) view discrepancy for favorable cross-view recognition and retrieval, we propose a Multi-view Linear Discriminant Analysis Network (MvLDAN) by seeking a nonlinear discriminant and view-invariant representation shared among multiple views. Unlike existing multi-view methods which directly learn a common space to reduce the view gap, our MvLDAN employs multiple feedforward neural networks (one for each view) and a novel eigenvalue-based multi-view objective function to encapsulate as much discriminative variance as possible into all the available common feature dimensions. With the proposed objective function, the MvLDAN could produce representations possessing: 1) low variance within the same class regardless of view discrepancy, 2) high variance between different classes regardless of view discrepancy, and 3) high covariance between any two views. In brief, in the learned multi-view space, the obtained deep features can be projected into a latent common space in which the samples from the same class are as close to each other as possible (even though they are from different views), and the samples from different classes are as far from each other as possible (even though they are from the same view). The effectiveness of the proposed method is verified by extensive experiments carried out on five databases, in comparison with the 19 state-of-the-art approaches.
Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.
Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning

With quantum resources a precious commodity, their efficient use is highly desirable. Quantum autoencoders have been proposed as a way to reduce quantum memory requirements. Generally, an autoencoder is a device that uses machine learning to compress inputs, that is, to represent the input data in a lower-dimensional space. Here, we experimentally realize a quantum autoencoder, which learns how to compress quantum data using a classical optimization routine. We demonstrate that when the inherent structure of the data set allows lossless compression, our autoencoder reduces qutrits to qubits with low error levels. We also show that the device is able to perform with minimal prior information about the quantum data or physical system and is robust to perturbations during its optimization routine.
A Comparison of Immersive Realities and Interaction Methods: Cultural Learning in Virtual Heritage

In recent years, Augmented Reality (AR), Virtual Reality (VR), Augmented Virtuality (AV), and Mixed Reality (MxR) have become popular immersive reality technologies for cultural knowledge dissemination in Virtual Heritage (VH). These technologies have been utilised for enriching museums with a personalised visiting experience and digital content tailored to the historical and cultural context of the museums and heritage sites. Various interaction methods, such as sensor-based, device-based, tangible, collaborative, multimodal, and hybrid interaction methods, have also been employed by these immersive reality technologies to enable interaction with the virtual environments. However, the utilisation of these technologies and interaction methods isn’t often supported by a guideline that can assist Cultural Heritage Professionals (CHP) to predetermine their relevance to attain the intended objectives of the VH applications. In this regard, our paper attempts to compare the existing immersive reality technologies and interaction methods against their potential to enhance cultural learning in VH applications. To objectify the comparison, three factors have been borrowed from existing scholarly arguments in the Cultural Heritage (CH) domain. These factors are the technology’s or the interaction method’s potential and/or demonstrated capability to: (1) establish a contextual relationship between users, virtual content, and cultural context, (2) allow collaboration between users, and (3) enable engagement with the cultural context in the virtual environments and the virtual environment itself. Following the comparison, we have also proposed a specific integration of collaborative and multimodal interaction methods into a Mixed Reality (MxR) scenario that can be applied to VH applications that aim at enhancing cultural learning in situ.
Self-Configuring and Reconfigurable Silicon Photonic Signal Processor

Photonic signal processing is essential in the optical communication and optical computing. Numerous photonic signal processors have been proposed, but most of them exhibit limited reconfigurability and automaticity. A feature of fully automatic implementation and intelligent response is highly desirable for the multipurpose photonic signal processors. Here, a self-configuring and fully reconfigurable silicon photonic signal processor is proposed and experimentally demonstrated. The proposed photonic signal processor is capable of performing various functions, including multichannel optical switching, optical multiple-input-multiple-output descrambler, and tunable optical filter. All the functions are achieved by complete self-configuration without knowing the inner structure. Our demonstration suggests great potential for chip-scale fully programmable optical signal processing with the self-configuring ability.
Reduced Reference Perceptual Quality Model With Application to Rate Control for Video-Based Point Cloud Compression

In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with the perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and videos, no such one exists for point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization step sizes and whose coefficients can easily be computed from two features extracted from the original point cloud. Subjective quality tests with 400 compressed point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearson linear correlation coefficient. Moreover, we show that for the same target bitrate, rate-distortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric. Our datasets are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qdushl/Waterloo-Point-Cloud-Database-2.0</uri> .
Privacy-Preserving Deep Learning via Weight Transmission

This paper considers the scenario that multiple data owners wish to apply a machine learning method over the combined dataset of all owners to obtain the best possible learning output but do not want to share the local datasets owing to privacy concerns. We design systems for the scenario that the stochastic gradient descent (SGD) algorithm is used as the machine learning method because SGD (or its variants) is at the heart of recent deep learning techniques over neural networks. Our systems differ from existing systems in the following features: {\bf (1)} any activation function can be used, meaning that no privacy-preserving-friendly approximation is required; {\bf (2)} gradients computed by SGD are not shared but the weight parameters are shared instead; and {\bf (3)} robustness against colluding parties even in the extreme case that only one honest party exists. We prove that our systems, while privacy-preserving, achieve the same learning accuracy as SGD and hence retain the merit of deep learning with respect to accuracy. Finally, we conduct several experiments using benchmark datasets, and show that our systems outperform previous system in terms of learning accuracies.
Quantifying the magic of quantum channels

Abstract To achieve universal quantum computation via general fault-tolerant schemes, stabilizer operations must be supplemented with other non-stabilizer quantum resources. Motivated by this necessity, we develop a resource theory for magic quantum channels to characterize and quantify the quantum ‘magic’ or non-stabilizerness of noisy quantum circuits. For qudit quantum computing with odd dimension d , it is known that quantum states with non-negative Wigner function can be efficiently simulated classically. First, inspired by this observation, we introduce a resource theory based on completely positive-Wigner-preserving quantum operations as free operations, and we show that they can be efficiently simulated via a classical algorithm. Second, we introduce two efficiently computable magic measures for quantum channels, called the mana and thauma of a quantum channel. As applications, we show that these measures not only provide fundamental limits on the distillable magic of quantum channels, but they also lead to lower bounds for the task of synthesizing non-Clifford gates. Third, we propose a classical algorithm for simulating noisy quantum circuits, whose sample complexity can be quantified by the mana of a quantum channel. We further show that this algorithm can outperform another approach for simulating noisy quantum circuits, based on channel robustness. Finally, we explore the threshold of non-stabilizerness for basic quantum circuits under depolarizing noise.
Programmable linear quantum networks with a multimode fibre

Reconfigurable quantum circuits are fundamental building blocks for the implementation of scalable quantum technologies. Their implementation has been pursued in linear optics through the engineering of sophisticated interferometers. While such optical networks have been successful in demonstrating the control of small-scale quantum circuits, scaling up to larger dimensions poses significant challenges. Here, we demonstrate a potentially scalable route towards reconfigurable optical networks based on the use of a multimode fibre and advanced wavefront-shaping techniques. We program networks involving spatial and polarisation modes of the fibre and experimentally validate the accuracy and robustness of our approach using two-photon quantum states. In particular, we illustrate the reconfigurability of our platform by emulating a tunable coherent absorption experiment. By demonstrating reliable reprogrammable linear transformations, with the prospect to scale, our results highlight the potential of complex media driven by wavefront shaping for quantum information processing.
Single-Loop and Composite-Loop Realization of Nonadiabatic Holonomic Quantum Gates in a Decoherence-Free Subspace

High-fidelity quantum gates are essential for large-scale quantum computation, which can naturally be realized in a noise-resilient way. Geometric manipulation and decoherence-free subspace encoding are promising ways toward robust quantum computation. Here, by combining the advantages of both strategies, we propose and experimentally realize universal holonomic quantum gates in both a singleloop scheme and a composite scheme, based on nonadiabatic and non-Abelian geometric phases, in a decoherence-free subspace with nuclear magnetic resonance. Our experiment uses only two-body resonant spin-spin interactions and thus is experimental friendly. In particular, we also experimentally verify that the composite scheme is more robust against the pulse errors than the single-loop scheme. Therefore, our experiment provides a promising way toward faithful and robust geometric quantum manipulation.