Reviewerly/exHarmony huggingface dataset:
Erasure Qubits: Overcoming the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>T</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math> Limit in Superconducting Circuits

The amplitude-damping time T1 has long stood as the major factor limiting quantum fidelity in superconducting circuits, prompting concerted efforts in the material science and design of qubits aimed at increasing T1. In contrast, the dephasing time Tϕ can usually be extended above T1 (via, e.g., dynamical decoupling) to the point where it does not limit fidelity. In this article, we propose a scheme for overcoming the conventional T1 limit on fidelity by designing qubits in a way that amplitude-damping errors can be detected and converted into erasure errors. Compared to standard qubit implementations, our scheme improves the performance of fault-tolerant protocols, as numerically demonstrated by the circuit-noise simulations of the surface code. We describe two simple qubit implementations with superconducting circuits and discuss procedures for detecting amplitude-damping errors, performing entangling gates, and extending Tϕ. Our results suggest that engineering efforts should focus on improving Tϕ and the quality of quantum coherent control, as they effectively become the limiting factor on the performance of fault-tolerant protocols.2 MoreReceived 15 August 2022Revised 2 July 2023Accepted 30 August 2023DOI:https://doi.org/10.1103/PhysRevX.13.041022Published 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 AreasQuantum error correctionQuantum gatesQuantum information architectures & platformsQuantum Information, Science & Technology
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning

Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Graph Completion (KGC) task. Relational patterns which refer to relations with specific semantics exhibiting graph patterns are an important factor in the performance of KGE models. Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern. To address this challenge, we evaluate the performance of 7 KGE models over 4 common relational patterns on 2 benchmarks, then conduct an analysis in theory, entity frequency, and part-to-whole three aspects and get some counterintuitive conclusions. Finally, we introduce a training-free method Score-based Patterns Adaptation (SPA) to enhance KGE models' performance over various relational patterns. This approach is simple yet effective and can be applied to KGE models without additional training. Our experimental results demonstrate that our method generally enhances performance over specific relational patterns. Our source code is available from GitHub at https://github.com/zjukg/Comprehensive-Study-over-Relational-Patterns .
Exploring the potential of ChatGPT in assessing L2 writing accuracy for research purposes

This study investigates ChatGPT's potential for measuring linguistic accuracy in second language writing for research purposes. We processed 100 L2 essays across five proficiency levels with ChatGPT-4 and manually coded for precision and recall with regard to ChatGPT's identification of errors. Our findings indicate a strong correlation (ρ = 0.97 using one method and .94 using another method) between ChatGPT's error detection and human coding, although this correlation diminishes with lower proficiency levels. While ChatGPT infrequently misidentifies errors, it often underestimates the total error count. The study also highlights ChatGPT's limitations, such as the issue of consistency, and provides guidelines for future research applications.
Bedrock mapping based on terrain weighted directed graph convolutional network using stream sediment geochemical samplings

To explore an efficient strategy for intelligent bedrock mapping that can be applied in the areas with coexisting Quaternary coverages and bedrock outcrops, a graph convolutional network (GCN) was implemented for bedrock classification using stream sediment geochemical samplings in the Chahanwusu River area, Qinghai Province, China. The sampling points were organized into a terrain weighted directed graph (TWDG) using Delaunay triangulation to capture the upstream-downstream relationships among the geochemical sampling points. The experimental results indicate that the semi-supervised GCN models, only using 20% of the labeled sampling points, achieved accuracies of 68.20% and 78.31% in ten-type and five-type bedrock discrimination, respectively. In conclusion, it is feasible to map the bedrock type through the concentrations of elements on the stream sediment geochemical sampling points. The proposed data-driven GCN bedrock classification method not only improves the efficiency of bedrock mapping but also may be applied in a large area.
Solving math word problems concerning systems of equations with GPT models

Researchers have been interested in developing AI tools to help students learn various mathematical subjects. One challenging set of tasks for school students is learning to solve math word problems. We explore how recent advances in natural language processing, specifically the rise of powerful transformer based models, can be applied to help math learners with such problems. Concretely, we evaluate the use of GPT-3, GPT-3.5, and GPT-4, all transformer models with billions of parameters recently released by OpenAI, for three related challenges pertaining to math word problems corresponding to systems of two linear equations. The three challenges are classifying word problems, extracting equations from word problems, and generating word problems. For the first challenge, we define a set of problem classes and find that GPT models generally result in classifying word problems with an overall accuracy around 70%. There is one class that all models struggle about, namely the "item and property" class, which significantly lowered the value. For the second challenge, our findings align with researchers' expectation: newer models are better at extracting equations from word problems. The highest accuracy we get from fine-tuning GPT-3 with 1000 examples (78%) is surpassed by GPT-4 given only 20 examples (79%). For the third challenge, we again find that GPT-4 outperforms the other two models. It is able to generate problems with accuracy ranging from 76.7% to 100%, depending on the problem type.
Classification of Metal Handwritten Digits Based on Microwave Diffractive Deep Neural Network

Abstract Recently, optical diffractive deep neural networks (D 2 NNs) have shown unprecedented superiority in terms of processing speed and power consumption. However, in the microwave band, complicated classification based on D 2 NN needs further investigation, which may accelerate the artificial intelligent tasks and simplify systems. Here, a three‐layer D 2 NN is constructed for handwritten digit classification in the microwave frequency. The excited electromagnetic wave, which passes through the metal plane engraved with different digit patterns as the input, will focus on the target plane at designated focal points through the D 2 NN platform. A detector array is deployed to collect the target plane energy for direct digit classification. Each layer of the proposed D 2 NN is composed of 1024 phase modulating meta‐units, and the phase distribution is generated through the stochastic gradient descent algorithm applied on the dataset. The network realizes an accuracy rate of 90% in numeric simulations, together with a 100% accuracy rate on the eighteen fabricated samples on the built‐up platform. The average focal efficiency reaches 18.7% and 11.7% in the simulation and experiment, respectively. The system can be seen as an alternative method for seamless in situ monitoring of security checks and near‐field sensing.
Hardware Implementation of Ultra‐Fast Obstacle Avoidance Based on a Single Photonic Spiking Neuron

Abstract Visual obstacle avoidance is widely applied to unmanned aerial vehicles (UAVs) and mobile robot fields. A simple system architecture, low power consumption, optimized processing, and real‐time performance are extremely needed due to the limited payload of some mini UAVs. To address these issues, an obstacle avoidance system harnessing the rate encoding features of a photonic spiking neuron based on a Fabry–Pérot (FP) laser is proposed, which simulates the monocular vision. Here, time to collision is used to describe the distance of obstacles. The experimental results show that the FP laser excites ultra‐fast spike responses in real time for the following cases, facilitating the generation of control commands by motor neurons to realize accurate decision‐making. Four cases of mobile obstacle avoidance scenarios, including “Constant velocity approach”, “Approach and retreat”, “The motion state involving stays”, and “Approach with different velocities”, and obstacle avoidance problems with multiple stationary obstacles appearing simultaneously are experimentally analyzed. The system exhibits a spike response rate of up to 5 GHz. This work proves the feasibility of applying the ultra‐fast photonic obstacle avoidance system to UAVs and other fields in the future and highlights the potential of photonic neuromorphic processor platforms.
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification

Graph classification is a crucial task in many real-world multimedia applications, where graphs can represent various multimedia data types such as images, videos, and social networks. Previous efforts have applied graph neural networks (GNNs) in balanced situations where the class distribution is balanced. However, real-world data typically exhibit long-tailed class distributions, resulting in a bias towards the head classes when using GNNs and limited generalization ability over the tail classes. Recent approaches mainly focus on re-balancing different classes during model training, which fails to explicitly introduce new knowledge and sacrifices the performance of the head classes. To address these drawbacks, we propose a novel framework called Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature extractor and an unbiased classifier in a decoupled manner. In the feature extractor training stage, we develop a graph retrieval module to search for relevant graphs that directly enrich the intra-class diversity for the tail classes. Moreover, we innovatively optimize a category-centered supervised contrastive loss to obtain discriminative representations, which is more suitable for long-tailed scenarios. In the classifier fine-tuning stage, we balance the classifier weights with two weight regularization techniques, i.e., Max-norm and weight decay. Experiments on various popular benchmarks verify the superiority of the proposed method against state-of-the-art approaches.
Free Fine-tuning: A Plug-and-Play Watermarking Scheme for Deep Neural Networks

Watermarking has been widely adopted for protecting the intellectual property (IP) of Deep Neural Networks (DNN) to defend the unauthorized distribution. Unfortunately, studies have shown that the popular data-poisoning DNN watermarking scheme via tedious model fine-tuning on a poisoned dataset (carefully-crafted sample-label pairs) is not efficient in tackling the tasks on challenging datasets and production-level DNN model protection. To address the aforementioned limitation, in this paper, we propose a plug-and-play watermarking scheme for DNN models by injecting an independent proprietary model into the target model to serve the watermark embedding and ownership verification. In contrast to the prior studies, our proposed method by incorporating a proprietary model is free of target model fine-tuning without involving any parameters update of the target model, thus the fidelity is well preserved and scalable to challenging real tasks. Experimental results on real-world challenging datasets (e.g., ImageNet) and production-level DNN models demonstrated its effectiveness, fidelity w.r.t. the functionality preservation of the target model, robustness against popular watermark removal attacks, and the plug-and-play deployment. The source code and models are available at https://github.com/AntigoneRandy/PTYNet.
DiffBFR: Bootstrapping Diffusion Model for Blind Face Restoration

Blind face restoration (BFR) is important while challenging. Prior works prefer to exploit GAN-based frameworks to tackle this task due to the balance of quality and efficiency. However, these methods suffer from poor stability and adaptability to long-tail distribution, failing to simultaneously retain source identity and restore detail. In this paper, we propose to introduce Diffusion Probabilistic Model (DPM) for BFR to tackle the above problem, given its superiority over GAN in aspects of avoiding training collapse and generating long-tail distribution. We name the proposed framework as DiffBFR. In particular, DiffBFR utilizes a two-step design, that first restores identity information from low-quality images and then enhances texture details according to the distribution of real faces. This design is implemented with two key components: 1) Identity Restoration Module (IRM) for preserving the face details in results. Instead of denoising from pure Gaussian random distribution with LQ images as the condition during the reverse process, we propose a novel truncated sampling method which starts from LQ images with part noise added. We theoretically prove that this change shrinks the evidence lower bound of DPM and then restores more original details. With theoretical proof, two cascade conditional DPMs with different input sizes are introduced to strengthen this sampling effect and reduce training difficulty in the high-resolution image generated directly. 2) Texture Enhancement Module (TEM) for polishing the texture of the image. Here an unconditional DPM, a LQ-free model, is introduced to further force the restorations to appear realistic. We theoretically proved that this unconditional DPM trained on pure HQ images contributes to justifying the correct distribution of inference images output from IRM in pixel-level space. Concretely, truncated sampling with fractional time step is utilized to polish pixel-level textures while preserving identity information. Our experiments demonstrated that the proposed DiffBFR achieves significantly superior results to state-of-the-art methods both quantitatively and qualitatively.
Ensemble reinforcement learning: A survey

Reinforcement Learning (RL) has emerged as a highly effective technique for addressing various scientific and applied problems. Despite its success, certain complex tasks remain challenging to be addressed solely with a single model and algorithm. In response, ensemble reinforcement learning (ERL), a promising approach that combines the benefits of both RL and ensemble learning (EL), has gained widespread popularity. ERL leverages multiple models or training algorithms to comprehensively explore the problem space and possesses strong generalization capabilities. In this study, we present a comprehensive survey on ERL to provide readers with an overview of recent advances and challenges in the field. Firstly, we provide an introduction to the background and motivation for ERL. Secondly, we conduct a detailed analysis of strategies such as model selection and combination that have been successfully implemented in ERL. Subsequently, we explore the application of ERL, summarize the datasets, and analyze the algorithms employed. Finally, we outline several open questions and discuss future research directions of ERL. By offering guidance for future scientific research and engineering applications, this survey significantly contributes to the advancement of ERL.
Neural Video Compression with Spatio-Temporal Cross-Covariance Transformers

Although existing neural video compression~(NVC) methods have achieved significant success, most of them focus on improving either temporal or spatial information separately. They generally use simple operations such as concatenation or subtraction to utilize this information, while such operations only partially exploit spatio-temporal redundancies. This work aims to effectively and jointly leverage robust temporal and spatial information by proposing a new 3D-based transformer module: Spatio-Temporal Cross-Covariance Transformer (ST-XCT). The ST-XCT module combines two individual extracted features into a joint spatio-temporal feature, followed by 3D convolutional operations and a novel spatio-temporal-aware cross-covariance attention mechanism. Unlike conventional transformers, the cross-covariance attention mechanism is applied across the feature channels without breaking down the spatio-temporal features into local tokens. Such design allows for modeling global cross-channel correlations of the spatio-temporal context while lowering the computational requirement. Based on ST-XCT, we introduce a novel transformer-based end-to-end optimized NVC framework. ST-XCT-based modules are integrated into various key coding components of NVC, such as feature extraction, frame reconstruction, and entropy modeling, demonstrating its generalizability. Extensive experiments show that our ST-XCT-based NVC proposal achieves state-of-the-art compression performances on various standard video benchmark datasets.
WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond

Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications. Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice. To balance the visual quality and application, we propose a heuristic normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow. Specifically, we first develop an invertible mapping to achieve the translation between the degraded image and its clear counterpart. Considering the differentiability and interpretability, we incorporate the heuristic prior into the data-driven mapping procedure, where the ambient light and medium transmission coefficient benefit credible generation. Furthermore, we introduce a detection perception module to transmit the implicit semantic guidance into the enhancement procedure, where the enhanced images hold more detection-favorable features and are able to promote the detection performance. Extensive experiments prove the superiority of our WaterFlow, against state-of-the-art methods quantitatively and qualitatively.
Cyberattacks and Security of Cloud Computing: A Complete Guideline

Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-to-service frameworks. However, just like any other technology, cloud computing opens up many forms of security threats and problems. In this work, we focus on discussing different cloud models and cloud services, respectively. Next, we discuss the security trends in the cloud models. Taking these security trends into account, we move to security problems, including data breaches, data confidentiality, data access controllability, authentication, inadequate diligence, phishing, key exposure, auditing, privacy preservability, and cloud-assisted IoT applications. We then propose security attacks and countermeasures specifically for the different cloud models based on the security trends and problems. In the end, we pinpoint some of the futuristic directions and implications relevant to the security of cloud models. The future directions will help researchers in academia and industry work toward cloud computing security.
Study on waste tire pyrolysis product characteristics based on machine learning

Tire pyrolysis is a highly complex thermochemical conversion process that transforms waste tires into high-value products such as pyrolysis oil, pyrolysis gas, and pyrolysis char. This process significantly mitigates the environmental issues caused by waste tires and reduces reliance on fossil resources. The physicochemical properties of tires and pyrolysis operation parameters have a significant impact on the yield of the three-phase products, thus affecting the industrial viability of tire pyrolysis to a large extent. Traditional prediction methods such as computational fluid dynamics and process simulation often fail to provide satisfactory results. However, data-driven machine learning (ML) models have demonstrated their ability to handle complex nonlinear problems and offer more reliable predictions of pyrolysis products yield. This study employed a collected database of tire pyrolysis to develop tire pyrolysis product prediction models based on five ML models. These models were further optimized using Particle Swarm Optimization (PSO), and their prediction performances were quantitatively evaluated to identify the optimal model. Shapley analysis and one-way partial dependence analysis were conducted to explore the impact of input features on the output responses. Furthermore, an industrial-grade software was developed for accurate prediction of tire pyrolysis three-phase products yield. The results revealed that Gaussian process regression (GPR) and random forest regression (RFR), both optimized with PSO, demonstrated impressive prediction performance. Among them, the GPR model achieved the highest prediction accuracy with coefficient of determination (R2) values of 0.964, 0.924, and 0.86 for oil, char, and gas yields respectively, during the testing stage.