Reviewerly/exHarmony huggingface dataset:
SLMSF-Net: A Semantic Localization and Multi-Scale Fusion Network for RGB-D Salient Object Detection

Authors:
raw_author_name: Yanbin Peng
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
raw_author_name: Zhinian Zhai
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
raw_author_name: Mingkun Feng
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China

Abstract: Salient Object Detection (SOD) in RGB-D images plays a crucial role in the field of computer vision, with its central aim being to identify and segment the most visually striking objects within a scene. However, optimizing the fusion of multi-modal and multi-scale features to enhance detection performance remains a challenge. To address this issue, we propose a network model based on semantic localization and multi-scale fusion (SLMSF-Net), specifically designed for RGB-D SOD. Firstly, we designed a Deep Attention Module (DAM), which extracts valuable depth feature information from both channel and spatial perspectives and efficiently merges it with RGB features. Subsequently, a Semantic Localization Module (SLM) is introduced to enhance the top-level modality fusion features, enabling the precise localization of salient objects. Finally, a Multi-Scale Fusion Module (MSF) is employed to perform inverse decoding on the modality fusion features, thus restoring the detailed information of the objects and generating high-precision saliency maps. Our approach has been validated across six RGB-D salient object detection datasets. The experimental results indicate an improvement of 0.20~1.80%, 0.09~1.46%, 0.19~1.05%, and 0.0002~0.0062, respectively in maxF, maxE, S, and MAE metrics, compared to the best competing methods (AFNet, DCMF, and C2DFNet).
Optimizing Performance of Quantum Operations with Non-Markovian Decoherence: The Tortoise or the Hare?

Authors:
raw_author_name: Gerald E. Fux
raw_affiliation_string: Abdus Salam International Center for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy; SUPA, School of Physics and Astronomy, University of St Andrews, St Andrews KY16 9SS, United Kingdom
raw_author_name: Jonathan Keeling
raw_affiliation_string: SUPA, School of Physics and Astronomy, University of St Andrews, St Andrews KY16 9SS, United Kingdom
raw_author_name: Paul R. Eastham
raw_affiliation_string: Trinity Quantum Alliance, Unit 16, Trinity Technology and Enterprise Centre, Pearse Street, Dublin 2, Ireland

Abstract: The interaction between a quantum system and its environment limits our ability to control it and perform quantum operations on it. We present an efficient method to find optimal controls for quantum systems coupled to non-Markovian environments, by using the process tensor to compute the gradient of an objective function. We consider state transfer for a driven two-level system coupled to a bosonic environment, and characterize performance in terms of speed and fidelity. This allows us to determine the best achievable fidelity as a function of process duration. We show there can be a trade-off between speed and fidelity, and that slower processes can have higher fidelity by exploiting non-Markovian effects.
Efficient high-fidelity flying qubit shaping

Authors:
raw_author_name: Guido Burkard
raw_affiliation_string: Department of Physics, University of Konstanz, D-78457 Konstanz, Germany

Abstract: Matter qubit to traveling photonic qubit conversion is the cornerstone of numerous quantum technologies such as distributed quantum computing, as well as several quantum internet and networking protocols. We formulate a theory for stimulated Raman emission which is applicable to a wide range of physical systems, including quantum dots, solid-state defects, and trapped ions, as well as various parameter regimes. We find the upper bound for the photonic pulse emission efficiency of arbitrary matter qubit states for imperfect emitters and show a path forward to optimizing the fidelity. Based on these results, we propose a paradigm shift from optimizing the drive to directly optimizing the temporal mode of the flying qubit using a closed-form expression. Protocols for the production of time-bin encoding and spin-photon entanglement are proposed. Furthermore, the mathematical idea to use input-output theory for pulses to absorb the dominant emission process into the coherent dynamics, followed by a non-Hermitian Schr\"odinger equation approach, has great potential for studying other physical systems.
Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid

Authors:
raw_author_name: Anne Carolina Rodrigues Klaar
raw_affiliation_string: Graduate Program in Education, University of Planalto Catarinense, Lages 88509-900, Brazil
raw_author_name: Laio Oriel Seman
raw_affiliation_string: Department of Automation and Systems Engineering, Federal University of Santa Catarina, Florianópolis 88040-535, Brazil
raw_author_name: Viviana Cocco Mariani
raw_affiliation_string: Department of Electrical Engineering, Federal University of Parana, Curitiba 81530-000, Brazil; Mechanical Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba 80215-901, Brazil
raw_author_name: Leandro dos Santos Coelho
raw_affiliation_string: Department of Electrical Engineering, Federal University of Parana, Curitiba 81530-000, Brazil; Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba 80215-901, Brazil

Abstract: The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from the time series data. This paper proposes a combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods, combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.
Local Disclosure of Quantum Memory in Non-Markovian Dynamics

Authors:
raw_author_name: Konstantin Beyer
raw_affiliation_string: Department of Physics, Stevens Institute of Technology, Hoboken, New Jersey 07030, USA; Institute of Theoretical Physics, TUD Dresden University of Technology, 01062 Dresden, Germany
raw_author_name: Walter T. Strunz
raw_affiliation_string: Institute of Theoretical Physics, TUD Dresden University of Technology, 01062 Dresden, Germany

Abstract: Non-Markovian processes may arise in physics due to memory effects of environmental degrees of freedom. For quantum non-Markovianity, it is an ongoing debate to clarify whether such memory effects have a verifiable quantum origin, or whether they might equally be modeled by a classical memory. In this contribution, we propose a criterion to test locally for a truly quantum memory. The approach is agnostic with respect to the environment, as it solely depends on the local dynamics of the system of interest. Experimental realizations are particularly easy, as only single-time measurements on the system itself have to be performed. We study memory in a variety of physically motivated examples, both for a time-discrete case, and for time-continuous dynamics. For the latter, we are able to provide an interesting class of non-Markovian master equations with classical memory that allows for a physically measurable quantum trajectory representation.
Beyond Legal Constraints: Understanding Barriers to Humanizing Responses in the Aftermath of Hate Speech at Private Universities

Authors:
raw_author_name: Liliana M. Garces
raw_affiliation_string: College of Education, The University of Texas, Austin, TX 78705, USA

Abstract: Supreme Court rulings prevent public institutions from censoring hate speech for content—even if that content has damaging consequences for marginalized students and conflicts with other institutional objectives. This case study examines administrator responses to hate speech and impacts for racial inclusion at a private university unconstrained by First Amendment protections. The findings illustrate that, even in contexts where administrators have both the constitutional leeway to enact stricter speech policies and a deep investment in building an anti-racist community, normative understandings about freedom of expression hamper efforts for racial inclusion and humanizing responses. We propose an “inclusive freedom” approach that leverages norms of academic freedom as a path forward for postsecondary institutions to address harm in the aftermath of hate speech.
An Improved Adaptive Iterative Extended Kalman Filter Based on Variational Bayesian

Authors:
raw_author_name: Qiang Fu
raw_affiliation_string: School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
raw_author_name: Ling Wang
raw_affiliation_string: School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
raw_author_name: Qiyue Xie
raw_affiliation_string: School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
raw_author_name: Yucai Zhou
raw_affiliation_string: School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China

Abstract: The presence of unknown heavy-tailed noise can lead to inaccuracies in measurements and processes, resulting in instability in nonlinear systems. Various estimation methods for heavy-tailed noise exist. However, these methods often trade estimation accuracy for algorithm complexity and parameter sensitivity. To tackle this challenge, we introduced an improved variational Bayesian (VB)-based adaptive iterative extended Kalman filter. In this VB framework, the inverse Wishart distributionis used as the prior for the state prediction covariance matrix. The system state and noise parameter posterior distributions are then iteratively updated for adaptive estimation. Furthermore, we make adaptive adjustments to the IEKF filter parameters to enhance sensitivity and filtering accuracy, thus ensuring robust prediction estimation. A two-dimensional target tracking and nonlinear numerical UNGM simulation validated our algorithm. Compared to existing algorithms RKF-ML and GA-VB, our method showed significant improvements in RMSEpos and RMSEvel, with increases of 21.81% and 22.11% respectively, and a 49.04% faster convergence speed. These results highlight the method’s reliability and adaptability.
Measuring Lexical Diversity in Texts: The Twofold Length Problem

Authors:
raw_author_name: Yves Bestgen
raw_affiliation_string: Université Catholique de Louvain

Abstract: Abstract The impact of text length on the estimation of lexical diversity has captured the attention of the scientific community for more than a century. Numerous indices have been proposed, and many studies have been conducted to evaluate them, but the problem remains. This methodological review provides a critical analysis not only of the most commonly used indices in language learning studies, but also of the length problem itself, as well as of the methodology for evaluating the proposed solutions. Analysis of three data sets of texts produced by English language learners revealed that indices that reduce all texts to the same length using a probabilistic or an algorithmic approach solve the length‐dependency problem; however, all these indices failed to address the second problem, which is their sensitivity to the parameter that determines the length to which the texts are reduced. The paper concludes with recommendations for optimizing lexical diversity analysis.
Leveraging Coverless Image Steganography to Hide Secret Information by Generating Anime Characters Using GAN

Authors:
raw_author_name: Usama Ijaz Bajwa
raw_affiliation_string: Department of Computer Science COMSATS University Islamabad, Lahore Campus, 1.5 KM Defence Road off Raiwind Road, Lahore, 54000, Pakistan
raw_author_name: Rana Hammad Raza
raw_affiliation_string: Electronics and Power Engineering Department Pakistan Navy Engineering College (PNEC), National University of Sciences and Technology (NUST), Habib Ibrahim Rehmatullah Road, Karachi, 75350, Pakistan
raw_author_name: Sultan Alfarhood
raw_affiliation_string: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O.Box 51178, Riyadh 11543, Saudi Arabia
raw_author_name: Mejdl Safran
raw_affiliation_string: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O.Box 51178, Riyadh 11543, Saudi Arabia
raw_author_name: Fan Zhang
raw_affiliation_string: Ocean College, Zhejiang University, Zheda Road Zhoushan Zhejiang, 316000 P.R. China

Abstract: There has been a growing need for secure communication in recent years, particularly in the digital realm. Researchers have developed various text-hiding methods to address this need, but these methods often suffer from limitations such as low embedding capacity or high embedding distortion. However, current methods for information hiding require a large image database or have a low capacity for hiding information, which makes them impractical. To address these issues, we propose a coverless information-hiding method that generates anime characters through generative adversarial networks (GANs). This method converts secret information into an attribute label set for anime characters and uses the label set to generate anime characters directly. The quality of the generated anime characters was improved by the super-resolution GAN (SRGAN) model. The resulting images were used to communicate secret information on the digital channel. Numerous experiments were conducted to evaluate the performance of the proposed framework, including hiding capacity, image clarity, robustness and security. Results showed that the proposed framework outperforms existing text-hiding methods in terms of hiding capacity and robustness while maintaining image clarity and computation time. In conclusion, our proposed framework provides a secure and efficient solution for text encryption and decryption in feasible computation time using anime characters generated by a GAN. The framework has a high embedding capacity and low embedding distortion, making it a promising solution for secure communication in the digital world.
MPFC-Net: A multi-perspective feature compensation network for medical image segmentation

Authors:
raw_author_name: Shucheng Huang
raw_affiliation_string: School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China
raw_author_name: Xin Shu
raw_affiliation_string: School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China
raw_author_name: Chunlong Hu
raw_affiliation_string: School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China
raw_author_name: Xiao-Jun Wu
raw_affiliation_string: School of Artificial Intelligence and Computer Science, Jiangnan University, 214122 China

Abstract: Image segmentation is one of the vital steps in medical image analysis. The segmentation precision and delicacy of targets affect the diagnosis accuracy to a great extent. Deep learning-based technology is currently a hot research direction in the field of medical image analysis. Compared with manual labeling, deep convolutional neural networks (DCNN) can extract valuable deep features that are difficult for humans to identify. However, some existing DCNN methods tend to only focus on extracting deep features and ignore the semantics loss in the process of continuous pooling and convolution. In addition, medical image datasets are characterized by small-scale, mass noise, and imperceptible differences between foreground and background. How to construct sufficient contextual dependency and feature compensation from multiple perspectives to improve the segmentation performance is still faced with great challenges. In this paper, we propose a deep attention network with multi-perspective feature compensation, called Multi-Perspective Feature Compensation Network (MPFC-Net), to supplement semantics loss during continuous pooling and convolution as much as possible and establish strong contextual dependency. Merits of MPFC-Net are as follows: (1) Serializing Multi-scale Aggregation Blocks (SMAB) are constructed to extract features with different scale receptive fields, capture semantics loss in convolutions, and enhance the nonlinear fitting capability of the network. (2) Space and Channel Compound Multidimensional Interaction Attention (SCMI Attention) is designed to consider the weights of feature maps of space and channel and build long-term information interactions between different dimensions. (3) Multi-Perspective Feature Compensation (MPFC) is proposed to establish dense context interaction and compensate for semantics loss in continuous pooling and convolution. (4) Deep Feature Reuse (DFR) module is adopted to strengthen the propagation and advanced features reuse. We conduct extensive experiments on the 2018 Data Science Bowl (2018DSB), the ISIC-2018 Challenge (ISIC2018), the JSUAH-Cerebellum, and the DRIVE dataset, and the results show that MPFC-Net achieves competitive performance with simultaneous increases in various metrics.
Is the Era of Broadband Seismometry Coming to an End?

Authors:
raw_author_name: Horst Rademacher
raw_affiliation_string: 1Seismology Laboratory (retired), University of California, Berkeley, California, U.S.A.; 2Now at, GEO Enterprise Orinda, Orinda, California, U.S.A.

Abstract: Editorial| February 08, 2024 Early Publication Is the Era of Broadband Seismometry Coming to an End? Horst Rademacher Horst Rademacher * 1Seismology Laboratory (retired), University of California, Berkeley, California, U.S.A.2Now at, GEO Enterprise Orinda, Orinda, California, U.S.A. *Corresponding author: horst@berkeley.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Horst Rademacher * 1Seismology Laboratory (retired), University of California, Berkeley, California, U.S.A.2Now at, GEO Enterprise Orinda, Orinda, California, U.S.A. *Corresponding author: horst@berkeley.edu Publisher: Seismological Society of America First Online: 08 Feb 2024 Online ISSN: 1938-2057 Print ISSN: 0895-0695 © Seismological Society of America Seismological Research Letters (2024) https://doi.org/10.1785/0220240007 Article history First Online: 08 Feb 2024 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Horst Rademacher; Is the Era of Broadband Seismometry Coming to an End?. Seismological Research Letters 2024; doi: https://doi.org/10.1785/0220240007 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search It was more than fifty years ago, that seismologists in what was then Czechoslovakia deployed the first experimental broadband feedback seismometers in seismic stations in Poland and in Czechoslovakia (Plešinger and Horalek, 1976). Based on their experience Erhard Wielandt and his student Gunar Streckeisen at the Eidgenössische Technische Hochschule in Zürich, Switzerland, developed the first commercial broadband instrument, dubbed STS‐1 (Wielandt and Streckeisen, 1982). For a detailed overview of the history of the development of broadband seismometers see Rademacher (2023). Several of these new STS‐1 seismometers were first deployed at the Seismologisches Zentralobservatorium Gräfenberg in... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Node-personalized multi-graph convolutional networks for recommendation

Authors:
raw_author_name: Tiantian Zhou
raw_affiliation_string: Department of Applied Mathematics, College of Sciences, China Jiliang University, Hangzhou 310018, China
raw_author_name: Hailiang Ye
raw_affiliation_string: Department of Applied Mathematics, College of Sciences, China Jiliang University, Hangzhou 310018, China
raw_author_name: Feilong Cao
raw_affiliation_string: Department of Applied Mathematics, College of Sciences, China Jiliang University, Hangzhou 310018, China

Abstract: Graph neural networks have revealed powerful potential in ranking recommendation. Existing methods based on bipartite graphs for ranking recommendation mainly focus on homogeneous graphs and usually treat user and item nodes as the same kind of nodes, however, the user-item bipartite graph is always heterogeneous. Additionally, various types of nodes have varying effects on recommendations, and a good node representation can be learned by successfully differentiating the same type of nodes. In this paper, we develop a node-personalized multi-graph convolutional network (NP-MGCN) for ranking recommendation. It consists of a node importance awareness block, a graph construction module, and a node information propagation and aggregation framework. Specifically, a node importance awareness block is proposed to encode nodes using node degree information to highlight the differences between nodes. Subsequently, the Jaccard similarity and co-occurrence matrix fusion graph construction module is devised to acquire user-user and item-item graphs, enriching correlation information between users and between items. Finally, a composite hop node information propagation and aggregation framework, including single-hop and double-hop branches, is designed. The high-order connectivity is used to aggregate heterogeneous information for the single-hop branch, while the multi-hop dependency is utilized to aggregate homogeneous information for the double-hop branch. It makes user and item node embedding more discriminative and integrates the different nodes’ heterogeneity into the model. Experiments on several datasets manifest that NP-MGCN achieves outstanding recommendation performance than existing methods.
ImGame Project

Authors:
raw_author_name: Ieva Gintere
raw_affiliation_string: Vidzeme University of Applied Sciences, Latvia
raw_author_name: Emmanouel Rovithis
raw_affiliation_string: Ionian University, Greece
raw_author_name: Ágnes Karolina Bakk
raw_affiliation_string: Moholy-Nagy University of Art and Design, Hungary

Abstract: The study presents the authors' research for the purpose of designing ImGame, a virtual environment inviting users to playfully learn about the concept of immersion and its historical antecedents. The authors describe ImGame's current pre-production stage and examine the basic characteristics of the feeling of immersion. They intend to deepen the current understanding of the immersive experience in art, taking into account its broad cultural connotations. The article suggests that the aesthetics of immersion can be classified into two psychological modes that have not been explicitly defined in the discourse of immersivity: a calm reflection and one of awe. From the technical standpoint, the project offers a simple handling of triggering animations and events of the game as well as storing of their state in order to create gamification elements, interactions for quizzes and other activities for ImGame or any other game using the a-frame framework to create WebXR experiences.