Reviewerly/exHarmony huggingface dataset:
Universal Parity Quantum Computing

We propose a universal gate set for quantum computing with all-to-all connectivity and intrinsic robustness to bit-flip errors based on parity encoding. We show that logical controlled phase gate and R_{z} rotations can be implemented in parity encoding with single-qubit operations. Together with logical R_{x} rotations, implemented via nearest-neighbor controlled-NOT gates and an R_{x} rotation, these form a universal gate set. As the controlled phase gate requires only single-qubit rotations, the proposed scheme has advantages for several cornerstone quantum algorithms, e.g., the quantum Fourier transform. We present a method to switch between different encoding variants via partial on-the-fly encoding and decoding.
Improving Quantum Measurements by Introducing “Ghost” Pauli Products

Reducing the number of measurements required to estimate the expectation value of an observable is crucial for the variational quantum eigensolver to become competitive with state-of-the-art classical algorithms. To measure complicated observables such as a molecular electronic Hamiltonian, one of the common strategies is to partition the observable into linear combinations (fragments) of mutually commutative Pauli products. The total number of measurements for obtaining the expectation value is then proportional to the sum of variances of individual fragments. We propose a method that lowers individual fragment variances by modifying the fragments without changing the total observable expectation value. Our approach is based on adding Pauli products (“ghosts”) that are compatible with members of multiple fragments. The total expectation value does not change because a sum of coefficients for each “ghost” Pauli product introduced to several fragments is zero. Yet, these additions change individual fragment variances because of the non-vanishing contributions of “ghost” Pauli products within each fragment. The proposed algorithm minimizes individual fragment variances using a classically efficient approximation of the quantum wavefunction for variance estimations. Numerical tests on a few molecular electronic Hamiltonian expectation values show several-fold reductions in the number of measurements in the “ghost” Pauli algorithm compared to those in the other recently developed techniques.
Deep Complex-Valued Convolutional Neural Network for Drone Recognition Based on RF Fingerprinting

Drone-aided ubiquitous applications play important roles in our daily lives. Accurate recognition of drones is required in aviation management due to their potential risks and disasters. Radiofrequency (RF) fingerprinting-based recognition technology based on deep learning (DL) is considered an effective approach to extracting hidden abstract features from the RF data of drones. Existing deep learning-based methods are either high computational burdens or have low accuracy. In this paper, we propose a deep complex-valued convolutional neural network (DC-CNN) method based on RF fingerprinting for recognizing different drones. Compared with existing recognition methods, the DC-CNN method has a high recognition accuracy, fast running time, and small network complexity. Nine algorithm models and two datasets are used to represent the superior performance of our system. Experimental results show that our proposed DC-CNN can achieve recognition accuracies of 99.5% and 74.1%, respectively, on four and eight classes of RF drone datasets.
Computer-aided diagnosis through medical image retrieval in radiology

Currently, radiologists face an excessive workload, which leads to high levels of fatigue, and consequently, to undesired diagnosis mistakes. Decision support systems can be used to prioritize and help radiologists making quicker decisions. In this sense, medical content-based image retrieval systems can be of extreme utility by providing well-curated similar examples. Nonetheless, most medical content-based image retrieval systems work by finding the most similar image, which is not equivalent to finding the most similar image in terms of disease and its severity. Here, we propose an interpretability-driven and an attention-driven medical image retrieval system. We conducted experiments in a large and publicly available dataset of chest radiographs with structured labels derived from free-text radiology reports (MIMIC-CXR-JPG). We evaluated the methods on two common conditions: pleural effusion and (potential) pneumonia. As ground-truth to perform the evaluation, query/test and catalogue images were classified and ordered by an experienced board-certified radiologist. For a profound and complete evaluation, additional radiologists also provided their rankings, which allowed us to infer inter-rater variability, and yield qualitative performance levels. Based on our ground-truth ranking, we also quantitatively evaluated the proposed approaches by computing the normalized Discounted Cumulative Gain (nDCG). We found that the Interpretability-guided approach outperforms the other state-of-the-art approaches and shows the best agreement with the most experienced radiologist. Furthermore, its performance lies within the observed inter-rater variability.
TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation

Recently, deep networks have shown impressive performance for the segmentation of cardiac Magnetic Resonance Imaging (MRI) images. However, their achievement is proving slow to transition to widespread use in medical clinics because of robustness issues leading to low trust of clinicians to their results. Predicting run-time quality of segmentation masks can be useful to warn clinicians against poor results. Despite its importance, there are few studies on this problem. To address this gap, we propose a quality control method based on the agreement across decoders of a multi-view network, TMS-Net, measured by the cosine similarity. The network takes three view inputs resliced from the same 3D image along different axes. Different from previous multi-view networks, TMS-Net has a single encoder and three decoders, leading to better noise robustness, segmentation performance and run-time quality estimation in our experiments on the segmentation of the left atrium on STACOM 2013 and STACOM 2018 challenge datasets. We also present a way to generate poor segmentation masks by using noisy images generated with engineered noise and Rician noise to simulate undertraining, high anisotropy and poor imaging settings problems. Our run-time quality estimation method show a good classification of poor and good quality segmentation masks with an AUC reaching to 0.97 on STACOM 2018. We believe that TMS-Net and our run-time quality estimation method has a high potential to increase the thrust of clinicians to automatic image analysis tools.
Chasing Metaverses: Reflecting on Existing Literature to Understand the Business Value of Metaverses

Abstract Metaverses refer to immersive virtual worlds in which people, places, and things of the physical world are represented by their digital representations. The wide adoption of metaverses is expected to widely disrupt the way we interact in the virtual world by elevating our online interactive experiences and bringing a plethora of implications for businesses. Following a structured literature review of related research published in the last decade, we shed light on our current understanding of metaverses and reflect on the potentially transformative value of metaverses for businesses in the near future. We draw on an established research framework to organize the insights of existing literature across different levels of analysis and activities’ purpose. Through this analysis, we reveal eight propositions on the changes brought by the use of metaverses and identify a number of open questions which could serve as future research avenues.
PHTI: Pashto Handwritten Text Imagebase for Deep Learning Applications

Document Image Analysis (DIA) is one of the research areas of Artificial Intelligence (AI) that converts document images into machine-readable codes. In DIA systems, Optical Character Recognition (OCR) plays a key role in digitizing document images. The output of an OCR system is further used in many applications including, Natural Language Processing (NLP), Sentiment Analysis, Speech Recognition, and Translation Services. However, standard datasets are an essential requirement for the development, evaluation and comparison of different text recognition techniques. Pashto is one of such low resource languages that lacks availability regarding standard dataset of handwritten text. This paper therefore, addresses the unavailability of standard dataset for the Pashto handwritten text by developing a dataset named Pashto Handwritten Text Imagebase (PHTI). The PHTI is created by collecting handwritten samples from diverse genre of the Pashto language including poetry, religion, short stories, articles, novels, sports, culture and news. The dataset consists of 4,000 scanned images, written by 400 writers including 200 males and 200 females. These 4,000 images are further segmented into 36,082 text-line images. Each text-line image is annotated/ transcribed with UTF-8 codecs. The dataset can be used for many deep learning-based applications including, text recognition, skew detection, gender classification and age-groups classification.
OMCBIR: Offline mobile content-based image retrieval with lightweight CNN optimization

Convolutional Neural Networks (CNNs) have achieved great success in computer vision applications. However, due to the high requirements for computation power and memory usage, most state-of-the-art CNNs are difficult to deploy on resource-constrained mobile devices. Although many typical lightweight neural networks have been proposed in the industry, such as MobileNetV2, which reduce the amount of parameters and calculations, they still have a lot of redundancy. Furthermore, few papers consider the use of deep learning models to implement image retrieval on terminals, so we propose a new offline retrieval framework based on lightweight neural network models, called Offline Mobile Content-Based Image Retrieval (OMCBIR). In this framework, we focus on the feature extraction of the model, by introducing pointwise group convolution and channel shuffle into the bottleneck block, reconstructing the network structure, and introducing the convolutional attention module, we propose an extremely lightweight small network-Attention-based Lightweight Network (ALNet). Compared to MobileNetV2, ALNet obtains a higher mAP on each dataset in OMCBIR when the model parameters are reduced by more than 62% and the model size is reduced by more than 63%. Extensive experiments conducted on five public datasets provide a trade-off between retrieval performance and model size of different algorithms, which proves the efficiency of the proposed OMCBIR.
Federated Learning with Privacy-preserving and Model IP-right-protection

Abstract In the past decades, artificial intelligence (AI) has achieved unprecedented success, where statistical models become the central entity in AI. However, the centralized training and inference paradigm for building and using these models is facing more and more privacy and legal challenges. To bridge the gap between data privacy and the need for data fusion, an emerging AI paradigm federated learning (FL) has emerged as an approach for solving data silos and data privacy problems. Based on secure distributed AI, federated learning emphasizes data security throughout the lifecycle, which includes the following steps: data preprocessing, training, evaluation, and deployments. FL keeps data security by using methods, such as secure multi-party computation (MPC), differential privacy, and hardware solutions, to build and use distributed multiple-party machine-learning systems and statistical models over different data sources. Besides data privacy concerns, we argue that the concept of “model” matters, when developing and deploying federated models, they are easy to expose to various kinds of risks including plagiarism, illegal copy, and misuse. To address these issues, we introduce FedIPR, a novel ownership verification scheme, by embedding watermarks into FL models to verify the ownership of FL models and protect model intellectual property rights (IPR or IP-right for short). While security is at the core of FL, there are still many articles referred to distributed machine learning with no security guarantee as “federated learning”, which are not satisfied with the FL definition supposed to be. To this end, in this paper, we reiterate the concept of federated learning and propose secure federated learning (SFL), where the ultimate goal is to build trustworthy and safe AI with strong privacy-preserving and IP-right-preserving. We provide a comprehensive overview of existing works, including threats, attacks, and defenses in each phase of SFL from the lifecycle perspective.
Recovering the Tight Security Proof of SPHINCS$$^+$$

In 2020, Kudinov, Kiktenko, and Fedorov pointed out a flaw in the tight security proof of the SPHINCS $$^+$$ construction. This work gives a new tight security proof for SPHINCS $$^+$$ . The flaw can be traced back to the security proof for the Winternitz one-time signature scheme (WOTS) used within SPHINCS $$^+$$ . In this work, we give a stand-alone description of the WOTS variant used in SPHINCS $$^+$$ that we call WOTS-TW. We provide a security proof for WOTS-TW and multi-instance WOTS-TW against non-adaptive chosen message attacks where the adversary only learns the public key after it made its signature query. Afterwards, we show that this is sufficient to give a tight security proof for SPHINCS $$^+$$ . We recover almost the same bound for the security of SPHINCS $$^+$$ , with only a factor w loss compared to the previously claimed bound, where w is the Winternitz parameter that is commonly set to 16. On a more technical level, we introduce new lower bounds on the quantum query complexity for generic attacks against properties of cryptographic hash functions and analyse the constructions of tweakable hash functions used in SPHINCS $$^+$$ with regard to further security properties.
Tbps parallel random number generation based on a single quarter-wavelength-shifted DFB laser

We propose and experimentally demonstrate a parallel physical random number generator (Ph-RNG) based on a single quarter-wavelength-shifted distributed feedback laser (QWS-DFB LD). Though simply connecting the two output ports of the QWS-DFB LD to form a cross-coupled perturbation, we simultaneously produce two paths of complex optical chaotic signals with a bandwidth above 10 GHz. Furthermore, a dual-channel Ph-RNG with a bit rate of order terabit per second (Tbps) is implemented by combining a multi-bit extraction method. Final results show that our generated physical random numbers can successfully pass standard benchmark tests for randomness.
Engineering Dynamically Decoupled Quantum Simulations with Trapped Ions

An external drive can improve the coherence of a quantum many-body system by averaging out noise sources. It can also be used to realize models that are inaccessible in the static limit, through Floquet Hamiltonian engineering. The full possibilities for combining these tools remain unexplored. We develop the requirements needed for a pulse sequence to decouple a quantum many-body system from an external field without altering the intended dynamics. Demonstrating this technique experimentally in an ion-trap platform, we show that it can provide a large improvement to coherence in real-world applications. Finally, we engineer an approximate quantum simulation of the Haldane-Shastry model, an exactly solvable paradigm for long-range interacting spins. Our results expand and unify the quantum simulation toolbox.2 MoreReceived 12 September 2022Revised 15 February 2023Accepted 6 March 2023DOI:https://doi.org/10.1103/PRXQuantum.4.010334Published 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 AreasOpen quantum systems & decoherenceQuantum controlQuantum engineeringQuantum information architectures & platformsQuantum simulationQuantum statistical mechanicsPhysical SystemsQuantum many-body systemsTrapped ionsAtomic, Molecular & OpticalQuantum InformationStatistical Physics
AI and data science for smart emergency, crisis and disaster resilience

The uncertain world has seen increasing emergencies, crises and disasters (ECDs), such as the COVID-19 pandemic, hurricane Ian, global financial inflation and recession, misinformation disaster, and cyberattacks. AI for smart disaster resilience (AISDR) transforms classic reactive and scripted disaster management to digital proactive and intelligent resilience across ECD ecosystems. A systematic overview of diverse ECDs, classic ECD management, ECD data complexities, and an AISDR research landscape are presented in this article. Translational disaster AI is essential to enable smart disaster resilience.
Hertz-rate metropolitan quantum teleportation

Quantum teleportation can transfer an unknown quantum state between distant quantum nodes, which holds great promise in enabling large-scale quantum networks. To advance the full potential of quantum teleportation, quantum states must be faithfully transferred at a high rate over long distance. Despite recent impressive advances, a high-rate quantum teleportation system across metropolitan fiber networks is extremely desired. Here, we demonstrate a quantum teleportation system which transfers quantum states carried by independent photons at a rate of 7.1$\pm$0.4 Hz over 64-km-long fiber channel. An average single-photon fidelity of $\geqslant$ 90.6$\pm$2.6% is achieved, which exceeds the maximum fidelity of 2/3 in classical regime. Our result marks an important milestone towards quantum networks and opens the door to exploring quantum entanglement based informatic applications for the future quantum internet.
Super-resolution reconstruction of turbulent flows with a transformer-based deep learning framework

Details of flow field are highly relevant to understand the mechanism of turbulence, but obtaining high-resolution turbulence often requires enormous computing resources. Although the super-resolution reconstruction of turbulent flow fields is an efficient way to obtain the details, the traditional interpolation methods are difficult to reconstruct small-scale structures, and the results are too smooth. In this paper, based on the transformer backbone architecture, we present a super-resolution transformer for turbulence to reconstruct turbulent flow fields with high quality. It is supervised and has a broader perceptual field for better extraction of deep-level features. The model is applied to forced isotropic turbulence and turbulent channel flow dataset, and the reconstructed instantaneous flow fields are comprehensively compared and analyzed. The results show that SRTT can recover the turbulent flow fields with high spatial resolution and capture small-scale details. It can obtain either the isotropic or the anisotropic turbulent properties even in complex flow configurations.
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement sub-network (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://rmcong.github.io/proj PUGAN.html.
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities

Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation. As an essential component of the architecture, graph pooling is indispensable for obtaining a holistic graph-level representation of the whole graph. Although a great variety of methods have been proposed in this promising and fast-developing research field, to the best of our knowledge, little effort has been made to systematically summarize these works. To set the stage for the development of future works, in this paper, we attempt to fill this gap by providing a broad review of recent methods for graph pooling. Specifically, 1) we first propose a taxonomy of existing graph pooling methods with a mathematical summary for each category; 2) then, we provide an overview of the libraries related to graph pooling, including the commonly used datasets, model architectures for downstream tasks, and open-source implementations; 3) next, we further outline the applications that incorporate the idea of graph pooling in a variety of domains; 4) finally, we discuss certain critical challenges facing current studies and share our insights on future potential directions for research on the improvement of graph pooling.
Exciting Avocados and Dull Pears Combining Behavioural and Argumentative Theory for Producing Effective Advice

To produce effective advice several sources of knowledge are needed. Knowledge about the application domain the advice is concerned with is of course necessary, but not sufficient. If the aim of the intervention is inducing people to modify their habits, we also need specific theories of how and why people change behaviour to guide the advising process. In some cases, however, it still does not suffice: when suggesting a change in a well established habit, several factors have to be taken into account, and a good adviser might also need argumentative capabilities, in order to overcome possible personal and environmental barriers to the change. This paper presents a model of advice giving that integrates Artificial Intelligence with concepts and methods coming from different disciplines. The model has been implemented in Daphne, an advice giving system that operates in the nutrition education domain.