Reviewerly/exHarmony huggingface dataset:
Enhancing two-stage object detection models via data-driven anchor box optimization in UAV-based maritime SAR

Authors:
raw_author_name: Beigeng Zhao
raw_affiliation_string: College of Public Security Information Technology and Intelligence, Criminal Investigation Police University of China, Shenyang, China
raw_author_name: Rui Song
raw_affiliation_string: College of Public Security Information Technology and Intelligence, Criminal Investigation Police University of China, Shenyang, China

Abstract: Abstract The high-altitude imaging capabilities of Unmanned Aerial Vehicles (UAVs) offer an effective solution for maritime Search and Rescue (SAR) operations. In such missions, the accurate identification of boats, personnel, and objects within images is crucial. While object detection models trained on general image datasets can be directly applied to these tasks, their effectiveness is limited due to the unique challenges posed by the specific characteristics of maritime SAR scenarios. Addressing this challenge, our study leverages the large-scale benchmark dataset SeaDronesSee, specific to UAV-based maritime SAR, to analyze and explore the unique attributes of image data in this scenario. We identify the need for optimization in detecting specific categories of difficult-to-detect objects within this context. Building on this, an anchor box optimization strategy is proposed based on clustering analysis, aimed at enhancing the performance of the renowned two-stage object detection models in this specialized task. Experiments were conducted to validate the proposed anchor box optimization method and to explore the underlying reasons for its effectiveness. The experimental results show our optimization method achieved a 45.8% and a 10% increase in average precision over the default anchor box configurations of torchvision and the SeaDronesSee official sample code configuration respectively. This enhancement was particularly evident in the model’s significantly improved ability to detect swimmers, floaters, and life jackets on boats within the SeaDronesSee dataset’s SAR scenarios. The methods and findings of this study are anticipated to provide the UAV-based maritime SAR research community with valuable insights into data characteristics and model optimization, offering a meaningful reference for future research.
Markovian noise modelling and parameter extraction framework for quantum devices

Authors:
raw_author_name: Ilya Sinayskiy
raw_affiliation_string: School of Chemistry and Physics, University of KwaZulu-Natal, Durban, 4001, South Africa
raw_author_name: Francesco Petruccione
raw_affiliation_string: National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch, 7604, South Africa

Abstract: Abstract In recent years, Noisy Intermediate Scale Quantum (NISQ) computers have been widely used as a test bed for quantum dynamics. This work provides a new hardware-agnostic framework for modelling the Markovian noise and dynamics of quantum systems in benchmark procedures used to evaluate device performance. As an accessible example, the application and performance of this framework is demonstrated on IBM Quantum computers. This framework serves to extract multiple calibration parameters simultaneously through a simplified process which is more reliable than previously studied calibration experiments and tomographic procedures. Additionally, this method allows for real-time calibration of several hardware parameters of a quantum computer within a comprehensive procedure, providing quantitative insight into the performance of each device to be accounted for in future quantum circuits. The framework proposed here has the additional benefit of highlighting the consistency among qubit pairs when extracting parameters, which leads to a less computationally expensive calibration process than evaluating the entire device at once.
Estimating many properties of a quantum state via quantum reservoir processing

Authors:
raw_author_name: Yinfei Li
raw_affiliation_string: Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement of Ministry of Education, School of Physics, Beijing Institute of Technology, Beijing 100081, China
raw_author_name: Sanjib Ghosh
raw_affiliation_string: Beijing Academy of Quantum Information Sciences, Beijing 100193, China
raw_author_name: Jiangwei Shang
raw_affiliation_string: Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement of Ministry of Education, School of Physics, Beijing Institute of Technology, Beijing 100081, China
raw_author_name: Qihua Xiong
raw_affiliation_string: Beijing Academy of Quantum Information Sciences, Beijing 100193, China; State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China
raw_author_name: Xiangdong Zhang
raw_affiliation_string: Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement of Ministry of Education, School of Physics, Beijing Institute of Technology, Beijing 100081, China

Abstract: Estimating properties of a quantum state is an indispensable task in various applications of quantum information processing. To predict properties in the postprocessing stage, it is inherent to first perceive the quantum state with a measurement protocol and store the information acquired. In this paper, we propose a general framework for constructing classical approximations of arbitrary quantum states with quantum reservoirs. A key advantage of our method is that only a single local measurement setting is required for estimating arbitrary properties, while most of the previous methods need an exponentially increasing number of measurement settings. To estimate $M$ properties simultaneously, the size of the classical approximation scales as $lnM$. Moreover, this estimation scheme is extendable to higher-dimensional systems and hybrid systems with nonidentical local dimensions, which makes it exceptionally generic. We support our theoretical findings with extensive numerical simulations.
Character sums over sparse elements of finite fields

Authors:
raw_author_name: László Mérai
raw_affiliation_string: Department of Computer Algebra ELTE Eötvös Loránd University Budapest Hungary; Johann Radon Institute for Computational and Applied Mathematics Austrian Academy of Sciences Linz Austria
raw_author_name: Igor E. Shparlinski
raw_affiliation_string: School of Mathematics and Statistics University of New South Wales Sydney Australia
raw_author_name: Arne Winterhof
raw_affiliation_string: Johann Radon Institute for Computational and Applied Mathematics Austrian Academy of Sciences Linz Austria

Abstract: Abstract We estimate mixed character sums of polynomial values over elements of a finite field with sparse representations in a fixed ordered basis over the subfield . First we use a combination of the inclusion–exclusion principle with bounds on character sums over linear subspaces to get nontrivial bounds for large . Then we focus on the particular case , which is more intricate. The bounds depend on certain natural restrictions. We also provide families of examples for which the conditions of our bounds are fulfilled. In particular, we completely classify all monomials as argument of the additive character for which our bound is applicable. Moreover, we also show that it is applicable for a large family of rational functions, which includes all reciprocal monomials.
Design for performability: a proposal for democratic redesign of the classical guitar

Authors:
raw_author_name: Owain Pedgley
raw_affiliation_string: Department of Industrial Design, Middle East Technical University, Ankara, Turkey

Abstract: Mastery of a musical instrument, including an ability to reach virtuosity in expression and performance, is commonly achieved through a lifelong commitment to practise. The specific instrument played has a crucial role in this journey. This paper reports on design research that aimed to determine the potential for redesigning the classical guitar, whether iteratively or radically, from the perspective of key stakeholder groups: luthiers, composers, and performers. A total of 25 interviews were conducted and analysed to identify shared and unique concerns amongst the stakeholder groups. The findings are used to conceive and develop a new design-related musical term ‘performability’, which builds upon the established term ‘playability’ but has much stronger interactional and experiential dimensions. Furthermore, the paper provides insights into ways in which an industrial designer can contribute to classical guitar design as a fourth stakeholder. Industrial design expertise is proposed to provide a mediating and catalysing role for democratic (i.e. multi-stakeholder, multi-perspective) instrument design or redesign. The paper concludes with a proposal for a ‘design for performability’ framework suited to co-design sessions aiming to achieve a highly performable classical guitar.
Instance-level Adversarial Source-free Domain Adaptive Person Re-identification

Authors:
raw_author_name: Li Liu
raw_affiliation_string: Shandong Normal University, Jinan, China
raw_author_name: Lei Zhu
raw_affiliation_string: Tongji University, Shanghai Shanghai, China
raw_author_name: Liqiang Nie
raw_affiliation_string: Harbin Institute of Technology (Shenzhen), Shenzhen, China
raw_author_name: Huaxiang Zhang
raw_affiliation_string: Shandong Normal University, Jinan, China Aerospace Information Technology University, Jinan, China

Abstract: Domain adaption (DA) for person re-identification (ReID) has attained considerable progress by transferring knowledge from a source domain with labels to a target domain without labels. Nonetheless, most of existing methods require access to source data, which raises privacy concerns. Source-free DA has recently emerged as a response to these privacy challenges, yet its direct application to open-set pedestrian re-identification tasks is hindered by the reliance on a shared category space in existing methods. Current source-free DA approaches for person ReID still encounter several obstacles, particularly the divergence-agnostic problem and the notable domain divergence due to the absent source data. In this paper, we introduce an Instance-level Adversarial Mutual Teaching (IAMT) framework, which utilizes adversarial views to tackle the challenges mentioned above. Technically, we first elaborately develop a variance-based division (VBD) module to segregate the target data into instance-level subsets based on their similarity and dissimilarity to the source using source-trained model, implicitly tackling the divergence-agnostic problem. To mitigate domain divergence, we additionally introduce a dynamic adversarial alignment (DAA) strategy, aiming to enhance the consistence of feature distribution across domains by employing adversarial instances from the target data to confuse the discriminators. Experiments reveal the superiority of the IAMT over state-of-the-art methods for DA person ReID tasks, while preserving the privacy of the source data.
A heuristic for the non‐unicost set covering problem using local branching

Authors:
raw_author_name: J.E. Beasley
raw_affiliation_string: Department of Mathematics Brunel University Uxbridge UB8 3PH UK

Abstract: Abstract In this paper, we present a heuristic for the non‐unicost set covering problem using local branching. Local branching eliminates the need to define a problem specific search neighbourhood for any particular (zero‐one) optimisation problem. It does this by incorporating a generalised Hamming distance neighbourhood into the problem, and this leads naturally to an appropriate neighbourhood search procedure. We apply our approach to the non‐unicost set covering problem. Computational results are presented for 65 test problems that have been widely considered in the literature. Our results indicate that our heuristic is better than six of the eight other heuristics we examined, slightly worse than that of one heuristic, but that there is a single heuristic that outperforms all others. We believe that the work described here illustrates that the potential for using local branching, operating as a stand‐alone matheuristic, has not been fully exploited in the literature.
Characterising Modal Formulas with Examples

Authors:
raw_author_name: Balder ten Cate
raw_affiliation_string: ILLC, University of Amsterdam, Amsterdam, The Netherlands
raw_author_name: Raoul Koudijs
raw_affiliation_string: University of Bergen, Bergen, Norway

Abstract: We study the existence of finite characterisations for modal formulas. A finite characterisation of a modal formula φ is a finite collection of positive and negative examples that distinguishes φ from every other, non-equivalent modal formula, where an example is a finite pointed Kripke structure. This definition can be restricted to specific frame classes and to fragments of the modal language: a modal fragment \(\mathcal {L} \) admits finite characterisations with respect to a frame class \(\mathcal {F} \) if every formula \(\varphi \in \mathcal {L} \) has a finite characterisation with respect to \(\mathcal {L} \) consisting of examples that are based on frames in \(\mathcal {F} \) . Finite characterisations are useful for illustration, interactive specification, and debugging of formal specifications, and their existence is a precondition for exact learnability with membership queries. We show that the full modal language admits finite characterisations with respect to a frame class \(\mathcal {F} \) only when the modal logic of \(\mathcal {F} \) is locally tabular. We then study which modal fragments, freely generated by some set of connectives, admit finite characterisations. Our main result is that the positive modal language without the truth-constants ⊤ and ⊥ admits finite characterisations w.r.t. the class of all frames. This result is essentially optimal: finite characterizability fails when the language is extended with the truth constant ⊤ or ⊥ or with all but very limited forms of negation.
FPGA-Based Acceleration of K-Nearest Neighbor Algorithm on Fully Homomorphic Encrypted Data

Authors:
raw_author_name: Sagarika Behera
raw_affiliation_string: Department of Computer Science and Engineering, CMR Institute of Technology, Visvesveraya Technological University, Belagavi 590018, India
raw_author_name: Jhansi Rani Prathuri
raw_affiliation_string: Department of Computer and Data Sciences, School of Engineering and Computational Sciences, Merrimack College, North Andover, MA 01845, USA

Abstract: The suggested solution in this work makes use of the parallel processing capability of FPGA to enhance the efficiency of the K-Nearest Neighbor (KNN) algorithm on encrypted data. The suggested technique was assessed utilizing the breast cancer datasets and the findings indicate that the FPGA-based acceleration method provides significant performance improvements over software implementation. The Cheon–Kim–Kim–Song (CKKS) homomorphic encryption scheme is used for the computation of ciphertext. After extensive simulation in Python and implementation in FPGA, it was found that the proposed architecture brings down the computational time of KNN on ciphertext to a realistic value in the order of the KNN classification algorithm over plaintext. For the FPGA implementation, we used the Intel Agilex7 FPGA (AGFB014R24B2E2V) development board and validated the speed of computation, latency, throughput, and logic utilization. It was observed that the KNN on encrypted data has a computational time of 41.72 ms which is 80 times slower than the KNN on plaintext whose computational time is of 0.518 ms. The main computation time for CKKS FHE schemes is 41.72 ms. With our architecture, we were able to reduce the calculation time of the CKKS-based KNN to 0.85 ms by using 32 parallel encryption hardware and reaching 300 MHz speed.
Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence

Authors:
raw_author_name: Sheng-Yan Pan
raw_affiliation_string: Department of Geosciences, National Taiwan University, No. 1. Sec. 4. Roosevelt Rd., Taipei, 106, Taiwan
raw_author_name: Chun-Ming Huang
raw_affiliation_string: Department of Geosciences, National Taiwan University, No. 1. Sec. 4. Roosevelt Rd., Taipei, 106, Taiwan
raw_author_name: Chin-Shang Ku
raw_affiliation_string: Institute of Earth Sciences, Academia Sinica, Taipei, 115, Taiwan
raw_author_name: Ching-Chou Fu
raw_affiliation_string: Institute of Earth Sciences, Academia Sinica, Taipei, 115, Taiwan
raw_author_name: Hao Kuo-Chen
raw_affiliation_string: Department of Geosciences, National Taiwan University, No. 1. Sec. 4. Roosevelt Rd., Taipei, 106, Taiwan

Abstract: Abstract On 18 September 2022, the M W 6.9 Chihshang earthquake struck the south half of the Longitudinal Valley, Taiwan, and caused severe damage. A precise and rapid report for the distribution of aftershock sequence after a devastating earthquake provides key information for deciphering the seismogenic structure in the source region. The utilization of deep-learning methodologies for earthquake event detection offers a significant acceleration in data analysis. In this study, we use SeisBlue, a deep-learning platform/package, to extract the whole earthquake sequence from September to October 2022, including the M W 6.5 Guanshan foreshock, the M W 6.9 mainshock, over 14,000 aftershocks, and 866 foal mechanisms from two sets of broadband networks. After applying hypoDD for earthquakes, the distribution of aftershock sequence clearly depicts not only the Central Range Fault and the Longitudinal Valley Fault but also several local, shallow tectonic structures that have not been observed along the southern Longitudinal Valley.
Variability Management in Self-Adaptive Systems through Deep Learning: A Dynamic Software Product Line Approach

Authors:
raw_author_name: Oscar Aguayo
raw_affiliation_string: Departamento de Ciencias de la Computación e Informática, Centro de Estudios en Ingeniería de Software, Universidad de La Frontera, Temuco 4811230, Chile
raw_author_name: Samuel Sepúlveda
raw_affiliation_string: Departamento de Ciencias de la Computación e Informática, Centro de Estudios en Ingeniería de Software, Universidad de La Frontera, Temuco 4811230, Chile
raw_author_name: Raúl Mazo
raw_affiliation_string: Pôle Sciences et Technologies de l’Information et de la Communication (STIC), École Nationale Supérieure de Techniques Avancées Bretagne, 29200 Brest, France

Abstract: Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mapping, we build on the identified gaps in the variability management of self-adaptive systems; we propose a framework that improves the adaptive capability of self-adaptive systems through feature model generation, variation point generation, the selection of a variation point, and runtime variability management using deep learning and the monitor–analysis–plan–execute–knowledge (MAPE-K) control loop. We compute the permutation of domain features and obtain all the possible variation points that a feature model can possess. After identifying variation points, we obtain an adaptation rule for each variation point of the corresponding product line through a two-stage training of an artificial neural network. To evaluate our proposal, we developed a test case in the context of an air quality-based activity recommender system, in which we generated 11 features and 32 possible variations. The results obtained with the proof of concept show that it is possible to manage identifying new variation points at runtime using deep learning. Future research will employ generating and building variation points using artificial intelligence techniques.
Marginal effects for non-linear prediction functions

Authors:
raw_author_name: Christian A. Scholbeck
raw_affiliation_string: Munich Center for Machine Learning (MCML), Ludwig-Maximilians-Universität in Munich, Munich, Germany
raw_author_name: Giuseppe Casalicchio
raw_affiliation_string: Munich Center for Machine Learning (MCML), Ludwig-Maximilians-Universität in Munich, Munich, Germany
raw_author_name: Christoph Molnar
raw_affiliation_string: Ludwig-Maximilians-Universität in Munich, Munich, Germany
raw_author_name: Bernd Bischl
raw_affiliation_string: Munich Center for Machine Learning (MCML), Ludwig-Maximilians-Universität in Munich, Munich, Germany
raw_author_name: Christian Heumann
raw_affiliation_string: Ludwig-Maximilians-Universität in Munich, Munich, Germany

Abstract: Abstract Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models such as generalized linear models, the estimated coefficients cannot be interpreted as a direct feature effect on the predicted outcome. Hence, marginal effects are typically used as approximations for feature effects, either as derivatives of the prediction function or forward differences in prediction due to changes in feature values. While marginal effects are commonly used in many scientific fields, they have not yet been adopted as a general model-agnostic interpretation method for machine learning models. This may stem from the ambiguity surrounding marginal effects and their inability to deal with the non-linearities found in black box models. We introduce a unified definition of forward marginal effects (FMEs) that includes univariate and multivariate, as well as continuous, categorical, and mixed-type features. To account for the non-linearity of prediction functions, we introduce a non-linearity measure for FMEs. Furthermore, we argue against summarizing feature effects of a non-linear prediction function in a single metric such as the average marginal effect. Instead, we propose to average homogeneous FMEs within population subgroups, which serve as conditional feature effect estimates.
Nonlinear erasing of propagating spin-wave pulses in thin-film Ga:YIG

Authors:
raw_author_name: A. Hamadeh
raw_affiliation_string: Fachbereich Physik and Landesforschungszentrum OPTIMAS, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau 1 , D-67663 Kaiserslautern, Germany
raw_author_name: C. Dubs
raw_affiliation_string: INNOVENT e.V. Technologieentwicklung 3 , D-07745 Jena, Germany
raw_author_name: R. Verba
raw_affiliation_string: Institute of Magnetism 5 , Kyiv 03142, Ukraine
raw_author_name: P. Pirro
raw_affiliation_string: Fachbereich Physik and Landesforschungszentrum OPTIMAS, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau 1 , D-67663 Kaiserslautern, Germany

Abstract: Nonlinear phenomena are crucial for magnon-based information processing, but the nonlinear interaction between two spin-wave signals requires their spatiotemporal overlap, which can be challenging for directional processing devices. Our study focuses on a gallium-substituted yttrium iron garnet film, which exhibits an exchange-dominated dispersion relation and, thus, provides a particularly broad range of group velocities compared to pure YIG. Using time- and space-resolved Brillouin light scattering spectroscopy, we demonstrate the excitation of time-separated spin-wave pulses at different frequencies from the same source, where the delayed pulse catches up with the previously excited pulse and outruns it due to its higher group velocity. By varying the excitation power of the faster pulse, the outcome can be finely tuned from a linear superposition to a nonlinear interaction of both pulses, resulting in a full attenuation of the slower pulse. Therefore, our findings demonstrate the all-magnonic erasing process of a propagating magnonic signal, which enables the realization of complex temporal logic operations with potential application, e.g., in inhibitory neuromorphic functionalities.