Reviewerly/exHarmony huggingface dataset:
Perspective Chapter: Experimental Analysis of Black Hole Algorithm with Heuristic Algorithms in Traveling Salesman Problem

Authors:
raw_author_name: Mehmet Fatih Demiral

Abstract: Black hole algorithm (BHA) is a popular metaheuristic algorithm proposed and applied for data clustering in 2013. BHA was applied to continuous and discrete problems; it is also hybridized with some algorithms in the literature. The pure BHA shows better performance than others in discrete optimization, such as traveling salesman problems. However, it requires improving the algorithm with competitive heuristics. Many heuristics have often been used to construct the initial tour of a salesman, such as the nearest neighbor algorithm (NN), nearest insertion algorithm (NI), cheapest insertion algorithm (CI), random insertion algorithm (RI), furthest insertion algorithm (FI), and minimal spanning tree algorithm (MST). In addition, the black hole algorithm is combined with popular heuristics, such as swap/or insert, reverse/or 2-opt swap, and swap-reverse/or 3-opt swap, and tested with proper parameters in this study. In the experimentation, classical datasets are used via TSP-library. The experimental results are given as best, average solutions/or deviations, and CPU time for all datasets. Besides, the hybrid algorithms demonstrate a better performance rate to get optimality. Finally, hybrid algorithms solve the discrete optimization problem in a short computing time for all datasets.
Bayesian multi-proxy reconstruction of early Eocene latitudinal temperature gradients

Authors:
raw_author_name: Kilian Eichenseer
raw_author_name: Lewis A. Jones

Abstract: Abstract. Accurately reconstructing large-scale palaeoclimatic patterns from sparse local records is critical for understanding the evolution of Earth's climate. Particular challenges arise from the patchiness, uneven spatial distribution, and disparate nature of palaeoclimatic proxy records. Geochemical data typically provide temperature estimates via transfer functions derived from experiments. Similarly, transfer functions based on the climatic requirements of modern taxa exist for some fossil groups, such as pollen assemblages. In contrast, most ecological and lithological data (e.g. coral reefs and evaporites) only convey information on broad climatic requirements. Historically, most large-scale proxy-based reconstructions have used either geochemical or ecological data, but few studies have combined multiple proxy types into a single quantitative reconstruction. Large spatial gaps in existing proxy records have often been bridged by simple averaging, without taking into account the spatial distribution of samples, leading to biased temperature reconstructions. Here, we present a Bayesian hierarchical model to integrate ecological data with established geochemical proxies into a unified quantitative framework, bridging gaps in the latitudinal coverage of proxy data. We apply this approach to the early Eocene climatic optimum (EECO), the interval with the warmest sustained temperatures of the Cenozoic. Assuming the conservation of thermal tolerances of modern coral reefs and mangrove taxa, we establish broad sea surface temperature ranges for EECO coral reef and mangrove sites. We integrate these temperature estimates with the EECO geochemical shallow marine proxy record to model the latitudinal sea surface temperature gradient and global average temperatures of the EECO. Our results confirm the presence of a flattened latitudinal temperature gradient and unusually high polar temperatures during the EECO, which is supported by high-latitude ecological data. We show that integrating multiple types of proxy data, and adequate prior information, has the potential to enhance quantitative palaeoclimatic reconstructions, improving temperature estimates from datasets with limited spatial sampling.
Doubly Flexible Estimation under Label Shift

Authors:
raw_author_name: Seong-ho Lee
raw_affiliation_string: Department of Statistics, University of Seoul, Seoul, South Korea
raw_author_name: Yanyuan Ma
raw_affiliation_string: Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA

Abstract: In studies ranging from clinical medicine to policy research, complete data are usually available from a population P, but the quantity of interest is often sought for a related but different population Q which only has partial data. We consider the setting when both outcome Y and covariate X are available from P but only X is available from Q, under the label shift assumption; i.e., the conditional distribution of X given Y is the same in the two populations. To estimate the parameter of interest in Q by leveraging information from P, three ingredients are essential: (a) the common conditional distribution of X given Y, (b) the regression model of Y given X in P, and (c) the density ratio of the outcome Y between the two populations. We propose an estimation procedure that only needs some standard nonparametric technique to approximate the conditional expectations with respect to (a), while by no means needs an estimate or model for (b) or (c); i.e., doubly flexible to the model misspecifications of both (b) and (c). This is conceptually different from the well-known doubly robust estimation in that, double robustness allows at most one model to be misspecified whereas our proposal can allow both (b) and (c) to be misspecified. This is of particular interest in label shift because estimating (c) is difficult, if not impossible, by virtue of the absence of the Y-data from Q. While estimating (b) is occasionally off-the-shelf, it may encounter issues related to the curse of dimensionality or computational challenges. We develop the large sample theory for the proposed estimator, and examine its finite-sample performance through simulation studies as well as an application to the MIMIC-III database.
Taming the Rotating Wave Approximation

Authors:
raw_author_name: Daniel Burgarth
raw_affiliation_string: Department Physik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Staudtstraße 7, 91058 Erlangen, Germany
raw_author_name: Paolo Facchi
raw_affiliation_string: Dipartimento di Fisica, Università di Bari, I-70126 Bari, Italy, and INFN, Sezione di Bari, I-70126 Bari, Italy
raw_author_name: Marilena Ligabò
raw_affiliation_string: Dipartimento di Matematica, Università di Bari, I-70125 Bari, Italy

Abstract: The interaction between light and matter is one of the oldest research areas of quantum mechanics, and a field that just keeps on delivering new insights and applications. With the arrival of cavity and circuit quantum electrodynamics we can now achieve strong light-matter couplings which form the basis of most implementations of quantum technology. But quantum information processing also has high demands requiring total error rates of fractions of percentage in order to be scalable (fault-tolerant) to useful applications. Since errors can also arise from modelling, this has brought into center stage one of the key approximations of quantum theory, the Rotating Wave Approximation (RWA) of the quantum Rabi model, leading to the Jaynes-Cummings Hamiltonian. While the RWA is often very good and incredibly useful to understand light-matter interactions, there is also growing experimental evidence of regimes where it is a bad approximation. Here, we ask and answer a harder question: for which experimental parameters is the RWA, although perhaps qualitatively adequate, already not good enough to match the demands of scalable quantum technology? For example, when is the error at least, and when at most, 1%? To answer this, we develop rigorous non-perturbative bounds taming the RWA. We find that these bounds not only depend, as expected, on the ratio of the coupling strength and the oscillator frequency, but also on the average number of photons in the initial state. This confirms recent experiments on photon-dressed Bloch-Siegert shifts. We argue that with experiments reporting controllable cavity states with hundreds of photons and with quantum error correcting codes exploring more and more of Fock space, this state-dependency of the RWA is increasingly relevant for the field of quantum computation, and our results pave the way towards a better understanding of those experiments.
GA-Net: Accurate and Efficient Object Detection on UAV Images Based on Grid Activations

Authors:
raw_author_name: Ruiyi Zhang
raw_affiliation_string: The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
raw_author_name: Bin Luo
raw_affiliation_string: The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
raw_author_name: Xin Su
raw_affiliation_string: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
raw_author_name: Jun Liu
raw_affiliation_string: The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Abstract: Object detection plays a crucial role in unmanned aerial vehicle (UAV) missions, where captured objects are often small and require high-resolution processing. However, this requirement is always in conflict with limited computing resources, vast fields of view, and low latency requirements. To tackle these issues, we propose GA-Net, a novel approach tailored for UAV images. The key innovation includes the Grid Activation Module (GAM), which efficiently calculates grid activations, the probability of foreground presence at grid scale. With grid activations, the GAM helps filter out patches without objects, minimize redundant computations, and improve inference speeds. Additionally, the Grid-based Dynamic Sample Selection (GDSS) focuses the model on discriminating positive samples and hard negatives, addressing background bias during training. Further enhancements involve GhostFPN, which refines Feature Pyramid Network (FPN) using Ghost module and depth-wise separable convolution. This not only expands the receptive field for improved accuracy, but also reduces computational complexity. We conducted comprehensive evaluations on DGTA-Cattle-v2, a synthetic dataset with added background images, and three public datasets (VisDrone, SeaDronesSee, DOTA) from diverse domains. The results prove the effectiveness and practical applicability of GA-Net. Despite the common accuracy and speed trade-off challenge, our GA-Net successfully achieves a mutually beneficial scenario through the strategic use of grid activations.
IASCAR: Incremental Answer Set Counting by Anytime Refinement

Authors:
raw_author_name: JOHANNES K. FICHTE
raw_author_name: SARAH ALICE GAGGL
raw_author_name: MARKUS HECHER
raw_author_name: DOMINIK RUSOVAC

Abstract: Abstract Answer set programming (ASP) is a popular declarative programming paradigm with various applications. Programs can easily have many answer sets that cannot be enumerated in practice, but counting still allows quantifying solution spaces. If one counts under assumptions on literals, one obtains a tool to comprehend parts of the solution space, so-called answer set navigation . However, navigating through parts of the solution space requires counting many times, which is expensive in theory. Knowledge compilation compiles instances into representations on which counting works in polynomial time. However, these techniques exist only for conjunctive normal form (CNF) formulas, and compiling ASP programs into CNF formulas can introduce an exponential overhead. This paper introduces a technique to iteratively count answer sets under assumptions on knowledge compilations of CNFs that encode supported models. Our anytime technique uses the inclusion–exclusion principle to improve bounds by over- and undercounting systematically. In a preliminary empirical analysis, we demonstrate promising results. After compiling the input (offline phase), our approach quickly (re)counts.
Pistol: Pupil Invisible Supportive Tool in the Wild

Authors:
raw_author_name: Wolfgang Fuhl
raw_affiliation_string: University Tuebingen, Sand, Tuebingen, 72076, Baden Wuerttemberg, Germany
raw_author_name: Daniel Weber
raw_affiliation_string: University Tuebingen, Sand, Tuebingen, 72076, Baden Wuerttemberg, Germany
raw_author_name: Shahram Eivazi
raw_affiliation_string: University Tuebingen, Sand, Tuebingen, 72076, Baden Wuerttemberg, Germany

Abstract: Abstract This paper is an in the wild evaluation of the eye tracking tool Pistol. Pistol supports Pupil Invisible projects and other eye trackers (Dikablis, Emke GmbH, Look, Pupil, and many more) in offline mode. For all eye tracking recordings, Pistol is able to compute pupil and iris ellipse, eye aperture, eye lids, 3D eye ball, optical vector estimation, and eye movement types. For Pupil Invisible projects, it also supports the detection of a special marker and allows the 2D and 3D gaze estimation mapped on the scene camera. In this extended journal paper for Pistol we focus on challenging in the wild situations, such as reflections on glasses, eye tracking during sport, changing lightning conditions, and this for two eye trackers. The tool Pistol can be downloaded here Link
A Study on Robust Finite-Time Visual Servoing with a Gyro-Stabilized Surveillance System

Authors:
raw_author_name: Young-Bok Kim
raw_affiliation_string: Department of Mechanical System Engineering, Division of Energy Transport System Engineering, Pukyong National University, Busan 48513, Republic of Korea

Abstract: This article presents the design and validation of a novel visual servoing scheme for a surveillance system. In this system, a two-axis gimbal mechanism operates the rotation of a camera which is able to provide visual information on the tracked target for the control system. The control objective is to bring the target’s projection to the center of the image plane with the smallest steady-state error and a smooth transient response, even with the unpredictable motion of the target and the influence of external disturbances. To fulfill these tasks, the proposed control scheme is designed consisting of two parts: (1) an observer estimates simultaneously the matched and unmatched disturbances; and (2) a motion control law guarantees the finite-time stability and visual servoing performance. Finally, experiments are conducted for validation and evaluation. The proposed control system shows its consistency and ought to perform better than previous approaches.
Optimizing Camera Motion with MCTS and Target Motion Modeling in Multi-Target Active Object Tracking

Authors:
raw_author_name: Zheng Chen
raw_affiliation_string: University of Science and Technology of China, Hefei, China
raw_author_name: Jian Zhao
raw_affiliation_string: University of Science and Technology of China, Hefei, China
raw_author_name: Mingyu Yang
raw_affiliation_string: University of Science and Technology of China, Hefei, China
raw_author_name: Wengang Zhou
raw_affiliation_string: University of Science and Technology of China, Hefei, China
raw_author_name: Houqiang Li
raw_affiliation_string: University of Science and Technology of China, Hefei, China

Abstract: In this work, we are dedicated to multi-target active object tracking (AOT), where the goal is to achieve continuous tracking of targets through real-time control of camera. This form of active camera control can be applied to unmanned aerial vehicles (UAV), intelligent robots, and sports events. Our work is conducted in an environment featuring multiple cameras and targets, where our goal is to maximize target coverage. Contrasting with previous research, our work introduces additional degrees of freedom for the cameras, allowing them not only to rotate but also to move along boundary lines. In addition, we model the motion of target to predict the future position of the target in environment. With target’s future position, we use Monte Carlo Tree Search (MCTS) method to find the optimal action of camera. Since the action space is large, we propose to leverage the action selection from multi-agent reinforcement learning (MARL) network to prune the search tree of Monte Carlo Tree Search method, so as to find the optimal action more efficiently. We establish a multi-target 2D environment to simulate several sports games, and experimental results demonstrate that our method can effectively improve the target coverage. The code is available at: http://github.com/HopeChanger/ActiveObjectTracking.
Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems

Authors:
raw_author_name: Jongwon Kim
raw_affiliation_string: AI Graduate School, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea

Abstract: Industrial control systems (ICSs) play a crucial role in managing and monitoring critical processes across various industries, such as manufacturing, energy, and water treatment. The connection of equipment from various manufacturers, complex communication methods, and the need for the continuity of operations in a limited environment make it difficult to detect system anomalies. Traditional approaches that rely on supervised machine learning require time and expertise due to the need for labeled datasets. This study suggests an alternative approach to identifying anomalous behavior within ICSs by means of unsupervised machine learning. The approach employs unsupervised machine learning to identify anomalous behavior within ICSs. This study shows that unsupervised learning algorithms can effectively detect and classify anomalous behavior without the need for pre-labeled data using a composite autoencoder model. Based on a dataset that utilizes HIL-augmented ICSs (HAIs), this study shows that the model is capable of accurately identifying important data characteristics and detecting anomalous patterns related to both value and time. Intentional error data injection experiments could potentially be used to validate the model’s robustness in real-time monitoring and industrial process performance optimization. As a result, this approach can improve system reliability and operational efficiency, which can establish a foundation for safe and sustainable ICS operations.
Transfer Reinforcement Learning for Combinatorial Optimization Problems

Authors:
raw_author_name: Samara Oliveira Silva Santos
raw_affiliation_string: Hamilton Institute, Maynooth University, W23VP22 Maynooth, Co. Kildare, Ireland
raw_author_name: André Luiz Carvalho Ottoni
raw_affiliation_string: Technologic and Exact Center, Federal University of Recôncavo da Bahia, R. Rui Barbosa, Cruz das Almas 44380-000, Bahia, Brazil
raw_author_name: Marcos Santos Oliveira
raw_affiliation_string: Department of Mathematics and Statistics, Federal University of São João del-Rei, Praça Frei Orlando, São João del Rei 36309-034, Minas Gerais, Brazil
raw_author_name: Daniela Carine Ramires Oliveira
raw_affiliation_string: Department of Mathematics and Statistics, Federal University of São João del-Rei, Praça Frei Orlando, São João del Rei 36309-034, Minas Gerais, Brazil
raw_author_name: Erivelton Geraldo Nepomuceno
raw_affiliation_string: Centre for Ocean Energy Research, Department of Electronic Engineering, Maynooth University, W23VP22 Maynooth, Co. Kildare, Ireland

Abstract: Reinforcement learning is an important technique in various fields, particularly in automated machine learning for reinforcement learning (AutoRL). The integration of transfer learning (TL) with AutoRL in combinatorial optimization is an area that requires further research. This paper employs both AutoRL and TL to effectively tackle combinatorial optimization challenges, specifically the asymmetric traveling salesman problem (ATSP) and the sequential ordering problem (SOP). A statistical analysis was conducted to assess the impact of TL on the aforementioned problems. Furthermore, the Auto_TL_RL algorithm was introduced as a novel contribution, combining the AutoRL and TL methodologies. Empirical findings strongly support the effectiveness of this integration, resulting in solutions that were significantly more efficient than conventional techniques, with an 85.7% improvement in the preliminary analysis results. Additionally, the computational time was reduced in 13 instances (i.e., in 92.8% of the simulated problems). The TL-integrated model outperformed the optimal benchmarks, demonstrating its superior convergence. The Auto_TL_RL algorithm design allows for smooth transitions between the ATSP and SOP domains. In a comprehensive evaluation, Auto_TL_RL significantly outperformed traditional methodologies in 78% of the instances analyzed.
Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning

Authors:
raw_author_name: Jie Yang
raw_affiliation_string: Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou 310018, China
raw_author_name: Jiafan He
raw_affiliation_string: Science and Technology on Information Systems Engineering Laboratory, Nanjing 210014, China
raw_author_name: Dongliang Peng
raw_affiliation_string: Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou 310018, China

Abstract: Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifier using both labeled and pseudo-labeled samples. However, in data-scarce scenarios, reliance on labeled samples for initial classifier generation can degrade performance. Methods based on consistency regularization have shown promising results by encouraging consistent outputs for different semantic variations of the same sample obtained through diverse augmentation techniques. However, existing methods typically utilize only weak and strong augmentation variants, limiting information extraction. Therefore, a multi-augmentation contrastive semi-supervised learning method (MAC-SSL) is proposed. MAC-SSL introduces moderate augmentation, combining outputs from moderately and weakly augmented unlabeled images to generate pseudo-labels. Cross-entropy loss ensures consistency between strongly augmented image outputs and pseudo-labels. Furthermore, the MixUP is adopted to blend outputs from labeled and unlabeled images, enhancing consistency between re-augmented outputs and new pseudo-labels. The proposed method achieves a state-of-the-art performance (accuracy) through extensive experiments conducted on multiple datasets with varying numbers of labeled samples. Ablation studies further investigate each component’s significance.
Enhancing sparrow search algorithm with OCSSA: Integrating osprey optimization and Cauchy mutation for improved convergence and precision

Authors:
raw_author_name: Chen Peng
raw_affiliation_string: College of Electronic and Electrical Engineering Lanzhou Petrochemical University of Vocational Technology Lanzhou P.R. China

Abstract: Abstract This study enhances the traditional sparrow search algorithm (SSA) to address its weaknesses, such as poor convergence rates and precision due to a tendency to fall into local optima. The improved version, OCSSA, integrates the Osprey optimization algorithm (OOA) and Cauchy mutation. Logistic chaotic mapping is used for initial population generation to increase genetic diversity. OOA boosts global exploration in the producer phase, and Cauchy mutation in the scroungers phase disrupts suboptimal solutions, enhancing the algorithm's ability to avoid local optima. OCSSA's performance, validated through ten benchmark functions and fault diagnosis optimization tasks, significantly improves convergence speed and accuracy, proving its effectiveness in complex optimization challenges.