Reviewerly/exHarmony huggingface dataset:
Dynamical subset sampling of quantum error-correcting protocols

Authors:
raw_author_name: Sascha Heußen
raw_affiliation_string: Institute for Quantum Information, RWTH Aachen University, 52056 Aachen, Germany and Institute for Theoretical Nanoelectronics (PGI-2), Forschungszentrum Jülich, 52425 Jülich, Germany
raw_author_name: Manuel Rispler
raw_affiliation_string: Institute for Quantum Information, RWTH Aachen University, 52056 Aachen, Germany and Institute for Theoretical Nanoelectronics (PGI-2), Forschungszentrum Jülich, 52425 Jülich, Germany
raw_author_name: Markus Müller
raw_affiliation_string: Institute for Quantum Information, RWTH Aachen University, 52056 Aachen, Germany and Institute for Theoretical Nanoelectronics (PGI-2), Forschungszentrum Jülich, 52425 Jülich, Germany

Abstract: Quantum error-correcting (QEC) stabilizer codes enable protection of quantum information against errors during storage and processing. Simulation of noisy QEC codes is used to identify the noise parameters necessary for advantageous operation of logical qubits in realistic quantum computing architectures. Typical quantum error-correction techniques contain intermediate measurements and classical feedback that determine the actual noisy circuit sequence in an instance of performing the protocol. Dynamical subset sampling enables efficient simulation of such nondeterministic quantum error-correcting protocols for any type of quantum circuit and incoherent noise of low strength. As an importance sampling technique, dynamical subset sampling allows one to effectively make use of computational resources to only sample the most relevant sequences of quantum circuits in order to estimate a protocol's logical failure rate with well-defined error bars. We demonstrate the capabilities of dynamical subset sampling with examples from fault-tolerant (FT) QEC. We show that, in a typical stabilizer simulation with incoherent Pauli noise of strength $p={10}^{\ensuremath{-}3}$, our method can reach a required sampling accuracy on the logical failure rate with two orders of magnitude fewer samples than direct Monte Carlo simulation. Furthermore, dynamical subset sampling naturally allows for efficient simulation of realistic multi-parameter noise models describing faulty quantum processors. It can be applied not only for QEC in the circuit model but any noisy quantum computing framework with incoherent fault operators including measurement-based quantum computation and quantum networks.
Revealing spoofing of classical radar using quantum noise

Authors:
raw_author_name: Jonathan N. Blakely
raw_affiliation_string: U. S. Army DEVCOM Aviation & Missile Center, Redstone Arsenal, Alabama 35898, USA
raw_author_name: Shawn D. Pethel
raw_affiliation_string: U. S. Army DEVCOM Aviation & Missile Center, Redstone Arsenal, Alabama 35898, USA
raw_author_name: Kurt Jacobs
raw_affiliation_string: U. S. Army DEVCOM Army Research Laboratory, Adelphi, Maryland 20783, USA

Abstract: Electromagnetic remote sensing technologies such as radar can be misled by targets that generate spoof pulses. Typically, a would-be spoofer must make measurements to characterize a received pulse in order to design a convincing spoof pulse. The precision of such measurements is ultimately limited by quantum noise. Here we introduce a model of electromagnetic spoofing that includes effects of practical importance that were neglected in prior theoretical studies. In particular, the model includes thermal background noise and digital quantization noise, as well as loss in transmission, propagation, and reception. We derive the optimal probability of detecting a spoofer allowed by quantum physics. We show that heterodyne reception and thresholding closely approaches this optimal performance. Finally, we show that a high degree of certainty in spoof detection can be reached by Bayesian inference from a sequence of received pulses. Together these results suggest that a practically realizable receiver could plausibly detect a radar spoofer by observing errors in the spoof pulses due to quantum noise.
Relating the Glauber-Sudarshan, Wigner, and Husimi quasiprobability distributions operationally through the quantum-limited amplifier and attenuator channels

Authors:
raw_author_name: Tomasz Linowski
raw_affiliation_string: International Centre for Theory of Quantum Technologies, University of Gdansk, 80-308 Gdańsk, Poland
raw_author_name: Łukasz Rudnicki
raw_affiliation_string: Center for Theoretical Physics, Polish Academy of Sciences, 02-668 Warszawa, Poland; International Centre for Theory of Quantum Technologies, University of Gdansk, 80-308 Gdańsk, Poland

Abstract: The Glauber-Sudarshan, Wigner and Husimi quasiprobability distributions are indispensable tools in quantum optics. However, although mathematical relations between them are well established, not much is known about their operational connection. In this paper, we prove that a single composition of finite-strength quantum limited amplifier and attenuator channels, known for their noise-adding properties, turns the Glauber-Sudarshan distribution of any input operator into its Wigner distribution, and its Wigner distribution into its Husimi distribution. As we dissect, the considered process, which can be performed in a quantum optical laboratory with relative ease, may be interpreted as realizing a quantum-to-classical transition.
Elastic Deep Sparse Self-Representation Subspace Clustering Network

Authors:
raw_author_name: Qiaoping Wang
raw_affiliation_string: School of Mathematics and Statistics, Fuzhou University, Fuzhou, 350116, Fujian, China
raw_author_name: Xiaoyun Chen
raw_affiliation_string: School of Mathematics and Statistics, Fuzhou University, Fuzhou, 350116, Fujian, China
raw_author_name: Yan Li
raw_affiliation_string: School of Mathematics and Statistics, Fuzhou University, Fuzhou, 350116, Fujian, China
raw_author_name: Yanming Lin
raw_affiliation_string: School of Mathematics and Statistics, Fuzhou University, Fuzhou, 350116, Fujian, China

Abstract: Abstract Subspace clustering model based on self-representation learning often use $$\ell _1, \ell _2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> or kernel norm to constrain self-representation matrix of the dataset. In theory, $$\ell _1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> norm can constrain the independence of subspaces, but which may lead to under-connection because the sparsity of the self-representation matrix. $$\ell _2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> and nuclear norm regularization can improve the connectivity between clusters, but which may lead to over-connection of the self-representation matrix. Because a single regularization term may cause subspaces to be over or insufficiently divided, this paper proposes an elastic deep sparse self-representation subspace clustering network (EDS-SC), which imposes sparse constraints on deep features, and introduces the elastic network regularization mixed $$\ell _1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> and $$\ell _2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> norm to constraint self-representation matrix. The network can extract deep sparse features and provide a balance between subspace independence and connectivity. Experiments on human faces, objects, and medical imaging datasets prove the effectiveness of EDS-SC network.
Formal Software Architecture Rule Learning: A Comparative Investigation between Large Language Models and Inductive Techniques

Authors:
raw_author_name: Andreas Rausch
raw_affiliation_string: Institute for Software and Systems Engineering, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germany

Abstract: This paper explores the application of inferring software architecture rules from examples using Machine Learning (ML). We investigate different methods from Inductive Rule Learning and utilize Large Language Models (LLMs). Traditional manual rule specification approaches are time-consuming and error-prone, motivating the need for automated rule discovery. Leveraging a dataset of software architecture instances and a meta-model capturing implementation facts, we used inductive learning algorithms and LLMs to extract meaningful rules. The induced rules are evaluated against a predefined hypothesis and their generalizability across different system subsets is investigated. The research highlights the capabilities and limitations of ML-based rule learning in the area of software architecture, aiming to inspire further innovation in data-driven rule discovery for more intelligent software architecture practices.
Life Cycle Assessment and Forecasting for 30kW Solar Power Plant using Machine Learning Algorithms

Authors:
raw_author_name: Satyabrata Behera
raw_affiliation_string: C.V. Raman Global University, Bhubaneswar, India

Abstract: Highly competitiveness of solar power plants in the energy market requires addressing the active research problem of solar energy forecasting. To make precise forecasts, however, historical meteorological, production, or irradiance data is insufficient. As the conservation of these Renewable Energy Sources (RES) is that much essential, the use of Photovoltaic (PV) panels have subsequently increased. The output of these PV panels completely depends on the climate. As the PV panels convert the solar energy to electrical energy, therefore, it produces the most when there is enough sunlight throughout the summer and the least when there is rain. Accurate forecasting of energy generation from solar power plants is crucial in terms of economics due to this uncertainty in the output in different seasons and the change in meteorological conditions. The objective of this paper is to investigate machine learning algorithms can accurately anticipate solar power for the upcoming hour and hourly days in advance. The Naïve Bayes Algorithm, Multilayer Perceptron Theorem (MLP), and Long Short-Term Memory networks (LSTM) are the machine learning algorithms investigated. Both historical weather data and generation data are used in the study. This study also includes the payback period (PB) and life cycle assessment calculation of the roof top solar power plant located in Bhubaneswar India.
Securing IoT Systems in a Post-Quantum Environment: Vulnerabilities, Attacks, and Possible Solutions

Authors:
raw_author_name: Ahmad Alomari
raw_affiliation_string: Department of Computer Science, Cleveland State University, Cleveland, OH USA, 44115
raw_author_name: Sathish A.P. Kumar
raw_affiliation_string: Department of Computer Science, Cleveland State University, Cleveland, OH USA, 44115

Abstract: The Internet of Things (IoT) refers to the distributed systems environment connecting billions of devices to the Internet, and quantum computing is an emerging technology that has a positive impact on IoT security by speeding up data processing while also having a negative impact due to post-quantum security attacks. In this survey, we provide detailed information on the possible post-quantum security attacks that threaten the security of the layers of IoT systems in terms of their vulnerabilities. Also, we provide detailed information on the existing solutions against post-quantum security attacks and show how limitations in these solutions decrease the security performance. Furthermore, we develop classification models to allow the readers to choose the best security approach against post-quantum security attacks in terms of IoT layers. Finally, we show the open challenges of the surveyed quantum security solutions and propose a framework based on quantum machine learning that takes advantage of optical pulses of secure communication as a future solution for detecting post-quantum security attacks.
E-voting System using Cloud-based Hybrid Blockchain Technology

Authors:
raw_author_name: Jani Anbarasi
raw_affiliation_string: School of Computer Science and Engineering Vellore Institute of Technology Chennai, India
raw_author_name: Vinayakumar Ravi
raw_affiliation_string: Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
raw_author_name: Malathy Jawahar
raw_affiliation_string: Leather Process Technology Division CSIR-Central Leather Research Institute, Chennai, India

Abstract: With the invention of Internet-enabled devices, cloud and blockchain-based technologies, an online voting system can smoothly carry out election processes. During pandemic situations, citizens tend to develop panic about mass gatherings, which may influence the decrease in the number of votes. This urges a reliable, flexible, transparent, secure, and cost-effective voting system. The proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system, and it is carried out in three phases: the registration phase, vote casting phase and vote counting phase. A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting phases. Using smart contracts, third-party interventions are eliminated, and the transactions are secured in the blockchain network. Finally, to provide accurate voting results, the practical Byzantine fault tolerance (PBFT) consensus mechanism is adopted to ensure that the vote has not been modified or corrupted. Hence, the overall performance of the proposed system is significantly better than that of the existing system. Further performance was analyzed based on authentication delay, vote alteration, response time, and latency.
A comprehensive evaluation of large Language models on benchmark biomedical text processing tasks

Authors:
raw_author_name: Israt Jahan
raw_affiliation_string: Information Retrieval and Knowledge Management Research Lab, York University, Canada
raw_author_name: Md Tahmid Rahman Laskar
raw_affiliation_string: Dialpad Inc., Canada; School of Information Technology, York University, Canada
raw_author_name: Jimmy Xiangji Huang
raw_affiliation_string: School of Information Technology, York University, Canada

Abstract: Recently, Large Language Models (LLMs) have demonstrated impressive capability to solve a wide range of tasks. However, despite their success across various tasks, no prior work has investigated their capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of LLMs on benchmark biomedical tasks. For this purpose, a comprehensive evaluation of 4 popular LLMs in 6 diverse biomedical tasks across 26 datasets has been conducted. To the best of our knowledge, this is the first work that conducts an extensive evaluation and comparison of various LLMs in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot LLMs even outperform the current state-of-the-art models when they were fine-tuned only on the training set of these datasets. This suggests that pre-training on large text corpora makes LLMs quite specialized even in the biomedical domain. We also find that not a single LLM can outperform other LLMs in all tasks, with the performance of different LLMs may vary depending on the task. While their performance is still quite poor in comparison to the biomedical models that were fine-tuned on large training sets, our findings demonstrate that LLMs have the potential to be a valuable tool for various biomedical tasks that lack large annotated data.
Routes to relevance: Philosophies of relevant logics

Authors:
raw_author_name: Shawn Standefer
raw_affiliation_string: National Taiwan University  Taipei Taiwan

Abstract: Abstract Relevant logics are a family of non‐classical logics characterized by the behavior of their implication connectives. Unlike some other non‐classical logics, such as intuitionistic logic, there are multiple philosophical views motivating relevant logics. Further, different views seem to motivate different logics. In this article, we survey five major views motivating the adoption of relevant logics: Use Criterion, sufficiency, meaning containment, theory construction, and truthmaking. We highlight the philosophical differences as well as the different logics they support. We end with some questions for future research.
Nonlinear causal network learning via Granger causality based on extreme support vector regression

Authors:
raw_author_name: Weiwei Hu
raw_affiliation_string: School of Electrical and Information Engineering, Jiangsu University 1 , Zhenjiang 212013, China
raw_author_name: Lidong He
raw_affiliation_string: School of Automation, Nanjing University of Science and Technology 2 , Nanjing 210094, China

Abstract: For complex networked systems, based on the consideration of nonlinearity and causality, a novel general method of nonlinear causal network learning, termed extreme support vector regression Granger causality (ESVRGC), is proposed. The nonuniform time-delayed influence of the driving nodes on the target node is particularly considered. Then, the restricted model and the unrestricted model of Granger causality are, respectively, formulated based on extreme support vector regression, which uses the selected time-delayed components of system variables as the inputs of kernel functions. The nonlinear conditional Granger causality index is finally calculated to confirm the strength of a causal interaction. Generally, based on the simulation of a nonlinear vector autoregressive model and nonlinear discrete time-delayed dynamic systems, ESVRGC demonstrates better performance than other popular methods. Also, the validity and robustness of ESVRGC are also verified by the different cases of network types, sample sizes, noise intensities, and coupling strengths. Finally, the superiority of ESVRGC is successful verified by the experimental study on real benchmark datasets.
Exploiting negative correlation for unsupervised anomaly detection in contaminated time series

Authors:
raw_author_name: Xiaohui Lin
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
raw_author_name: Zuoyong Li
raw_affiliation_string: Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China
raw_author_name: Haoyi Fan
raw_affiliation_string: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
raw_author_name: Yanggeng Fu
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China

Abstract: Anomaly detection in time series data is crucial for many fields such as healthcare, meteorology, and industrial fault detection. However, traditional unsupervised time series anomaly detection methods suffer from biased anomaly measurement under contaminated training data. Most of existing methods employ hard strategies for contamination calibration by assigning pseudo-label to training data. These hard strategies rely on threshold selection and result in suboptimal performance. To address this problem, in this paper, we propose a novel unsupervised anomaly detection framework for contaminated time series (NegCo), which builds an effective soft contamination calibration strategy by exploiting the observed negative correlation between semantic representation and anomaly detection inherent within the autoencoder framework. We innovatively redefine anomaly detection in data contamination scenarios as an optimization problem rooted in this negative correlation. To model this negative correlation, we introduce a dual construct: morphological similarity captures semantic distinctions relevant to normality, while reconstruction consistency quantifies deviations indicative of anomalies. Firstly, the morphological similarity is effectively measured based on the representative normal samples generated from the center of the learned Gaussian distribution. Then, an anomaly measurement calibration loss function is designed based on negative correlation between morphological similarity and reconstruction consistency, to calibrate the biased anomaly measurement caused by contaminated samples. Extensive experiments on various time series datasets show that the proposed NegCo outperforms state-of-the-art baselines, achieving an improvement of 6.2% to 26.8% in Area Under the Receiver Operating Characteristics (AUROC) scores, particularly in scenarios with heavily contaminated training data.
Diffusion model-based text-guided enhancement network for medical image segmentation

Authors:
raw_author_name: Genji Yuan
raw_affiliation_string: School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
raw_author_name: Zhen Hua
raw_affiliation_string: School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
raw_author_name: Jinjiang Li
raw_affiliation_string: School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, China

Abstract: In recent years, denoising diffusion models have achieved remarkable success in generating pixel-level representations with semantic values for image generation modeling. In this study, we propose a novel end-to-end framework, called TGEDiff, focusing on medical image segmentation. TGEDiff fuses a textual attention mechanism with the diffusion model by introducing an additional auxiliary categorization task to guide the diffusion model with textual information to generate excellent pixel-level representations. To overcome the limitation of limited perceptual fields for independent feature encoders within the diffusion model, we introduce a multi-kernel excitation module to extend the model’s perceptual capability. Meanwhile, a guided feature enhancement module is introduced in Denoising-UNet to focus the model’s attention on important regions and attenuate the influence of noise and irrelevant background in medical images. We critically evaluated TGEDiff on three datasets (Kvasir-SEG, Kvasir-Sessile, and GLaS), and TGEDiff achieved significant improvements over the state-of-the-art approach on all three datasets, with F1 scores and mIoU improving by 0.88% and 1.09%, 3.21% and 3.43%, respectively, 1.29% and 2.34%. These data validate that TGEDiff has excellent performance in medical image segmentation. TGEDiff is expected to facilitate accurate diagnosis and treatment of medical diseases through more precise deconvolutional structural segmentation.