Reviewerly/exHarmony huggingface dataset:
Preliminary report of field reconnaissance on the 6 February 2023 Kahramanmaras Earthquakes in Türkiye

Authors:
raw_author_name: Halil Kumsar
raw_affiliation_string: Earthquake Science and Engineering Institute, Department of Geological Engineering, Pamukkale University, Denizli, Turkey
raw_author_name: Ömer Aydan
raw_affiliation_string: Department of Civil Engineering and Architecture, Ryukyu University, Okinawa, Japan
raw_author_name: Reşat Ulusay
raw_affiliation_string: Department of Geological Engineering, Hacettepe University, Ankara, Turkey
raw_author_name: Ye Chen
raw_affiliation_string: Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, China
raw_author_name: Fawu Wang
raw_affiliation_string: Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, China

Abstract: Abstract On February 6, a successive rupture of major faults in the Eastern Anatolian Fault Zone and Cardak-Surgu fault triggered a strong mainshock (M w 7.7) and a major aftershock (M w 7.6) in Kahramanmaras. The successive earthquake sequence hit southern provinces in Türkiye and northern regions in Syria, causing severe fatality and economic loss. After the earthquakes, the International Consortium on Geo-disaster Reduction (ICGdR) organized an investigation team, involving specialists from China, Japan and Türkiye, to conduct a primary field reconnaissance on seismic damage of infrastructure and ground failures. The 10-day reconnaissance, including a mini-symposium at the Istanbul Technical University (ITU), was conducted from 25 March to 3 April and specifically focused on fault ruptures, liquefaction, landslide, rockfall and lateral spreading along the major ruptured faults from Antakya in Hatay to Goksun in Kahramanmaras, passing through provinces of Gaziantep, Adıyaman and Malatya. By this reconnaissance, a large amount of original seismic data was collected and a primary understanding was established for further steps on mitigation and reduction of seismic damages and its secondary geohazards.
TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models

Authors:
raw_author_name: Nicholas Tatonetti
raw_affiliation_string: Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA

Abstract: <h2>Summary</h2> In cancer research, pathology report text is a largely untapped data source. Pathology reports are routinely generated, more nuanced than structured data, and contain added insight from pathologists. However, there are no publicly available datasets for benchmarking report-based models. Two recent advances suggest the urgent need for a benchmark dataset. First, improved optical character recognition (OCR) techniques will make it possible to access older pathology reports in an automated way, increasing the data available for analysis. Second, recent improvements in natural language processing (NLP) techniques using artificial intelligence (AI) allow more accurate prediction of clinical targets from text. We apply state-of-the-art OCR and customized post-processing to report PDFs from The Cancer Genome Atlas, generating a machine-readable corpus of 9,523 reports. Finally, we perform a proof-of-principle cancer-type classification across 32 tissues, achieving 0.992 average AU-ROC. This dataset will be useful to researchers across specialties, including research clinicians, clinical trial investigators, and clinical NLP researchers.
Data encoding for healthcare data democratization and information leakage prevention

Authors:
raw_author_name: Anshul Thakur
raw_affiliation_string: Department of Engineering Science, University of Oxford, OX3 7DQ, Oxfordshire, UK
raw_author_name: Tingting Zhu
raw_affiliation_string: Department of Engineering Science, University of Oxford, OX3 7DQ, Oxfordshire, UK
raw_author_name: Vinayak Abrol
raw_affiliation_string: Infosys Centre for AI, IIIT Delhi, Delhi, India
raw_author_name: Jacob Armstrong
raw_affiliation_string: Department of Engineering Science, University of Oxford, OX3 7DQ, Oxfordshire, UK
raw_author_name: Yujiang Wang
raw_affiliation_string: Department of Engineering Science, University of Oxford, OX3 7DQ, Oxfordshire, UK
raw_author_name: David A. Clifton
raw_affiliation_string: Oxford Suzhou Centre for Advanced Research, Suzhou, China

Abstract: Abstract The lack of data democratization and information leakage from trained models hinder the development and acceptance of robust deep learning-based healthcare solutions. This paper argues that irreversible data encoding can provide an effective solution to achieve data democratization without violating the privacy constraints imposed on healthcare data and clinical models. An ideal encoding framework transforms the data into a new space where it is imperceptible to a manual or computational inspection. However, encoded data should preserve the semantics of the original data such that deep learning models can be trained effectively. This paper hypothesizes the characteristics of the desired encoding framework and then exploits random projections and random quantum encoding to realize this framework for dense and longitudinal or time-series data. Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models.
Non-radiative configurations of a few quantum emitters ensembles: Evolutionary optimization approach

Authors:
raw_author_name: Danil Kornovan
raw_affiliation_string: School of Physics and Engineering, ITMO University 1 , St. Petersburg 197101, Russia
raw_author_name: Alexandra Sheremet
raw_affiliation_string: School of Physics and Engineering, ITMO University 1 , St. Petersburg 197101, Russia
raw_author_name: Roman Savelev
raw_affiliation_string: School of Physics and Engineering, ITMO University 1 , St. Petersburg 197101, Russia
raw_author_name: Mihail Petrov
raw_affiliation_string: School of Physics and Engineering, ITMO University 1 , St. Petersburg 197101, Russia

Abstract: Suppressing the spontaneous emission in quantum emitters ensembles (atoms) is one of the topical problems in quantum optics and quantum technology. While many approaches are based on utilizing the subradiance effect in ordered quantum emitters arrays, the ensemble configurations providing the minimal spontaneous emission rate are yet unknown. In this work, we employ the differential evolution algorithm to identify the optimal configurations of a few atomic ensembles that support quantum states with maximal radiative lifetime. We demonstrate that atoms tend to assemble mostly in quasi-regular structures with specific geometry, which strongly depends on the minimally allowed interatomic distance rmin. While the discovered specific non-radiative realizations of small ensembles cannot be immediately predicted, there is particular correspondence to the non-radiative states in the atomic lattices. In particular, we have found that states inheriting their properties either from the bound states in the continuum or band edge states of infinite lattices dominate across a wide range of rmin values. Additionally, we show that for small interatomic distances, the linear arrays with modulated spacing have the smallest radiative losses exponentially decreasing as the size of the ensemble increases.
Experimental Demonstration of Inequivalent Mutually Unbiased Bases

Authors:
raw_author_name: Wen-Zhe Yan
raw_affiliation_string: CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, People's Republic of China.
raw_author_name: Yunting Li
raw_affiliation_string: State Key Laboratory of Surface Physics and Department of Physics, Fudan University, Shanghai 200433, China.
raw_author_name: Zhibo Hou
raw_affiliation_string: CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, People's Republic of China.
raw_author_name: Huangjun Zhu
raw_affiliation_string: State Key Laboratory of Surface Physics and Department of Physics, Fudan University, Shanghai 200433, China.
raw_author_name: Guo-Yong Xiang
raw_affiliation_string: CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, People's Republic of China.
raw_author_name: Chuan-Feng Li
raw_affiliation_string: CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, People's Republic of China.
raw_author_name: Guang-Can Guo
raw_affiliation_string: CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, People's Republic of China.

Abstract: Quantum measurements based on mutually unbiased bases (MUBs) play crucial roles in foundational studies and quantum information processing. It is known that there exist inequivalent MUBs, but little is known about their operational distinctions, not to say experimental demonstration. In this Letter, by virtue of a simple estimation problem, we experimentally demonstrate the operational distinctions between inequivalent triples of MUBs in dimension 4 based on high-precision photonic systems. The experimental estimation fidelities coincide well with the theoretical predictions with only 0.16% average deviation, which is 25 times less than the difference (4.1%) between the maximum estimation fidelity and the minimum estimation fidelity. Our experiments clearly demonstrate that inequivalent MUBs have different information extraction capabilities and different merits for quantum information processing.
Learning within fiber-crafted algorithms: Posthumanist perspectives for capturing human-material collaboration

Authors:
raw_author_name: Anna Keune

Abstract: Abstract A key commitment of computer-supported collaborative learning research is to study how people learn in collaborative settings to guide development of methods for capture and design for learning. Computer-supported collaborative learning research has a tradition of studying how the physical world plays a part in collaborative learning. Within the field, a material turn is emerging that considers how digital and tangible technologies actively contribute to collaborative learning processes. Studying how tangible materials produce collaborative learning visibly and algorithmically is particularly important at a time when advanced algorithms are integrated into educational contexts in ways that are not always transparent. However, the needed methodologies for capturing how non-human agents take part in collaborative learning remains underdeveloped. The present study builds on current CSCL research that investigates materials in collaborative learning and introduces posthumanist perspectives with the aim to decenter humans methodologically and to probe empirically whether and how these perspectives contribute to empirical understanding of collaborative learning processes. Taking fiber crafts (e.g., weaving and fabric manipulation) as a context for computational learning, the present study conducted a posthumanist analysis of differences among human and non-human participants in collaboration using video data to investigate how middle school youths and fiber craft components performed algorithms over time. The findings show how both youths and craft materials actively contributed to the performance of algorithms. In weaving, algorithms became repeated youth-material movements one dimension at a time. In fabric manipulation, algorithms became a repeated confluence of component parts. Decentering humans through an analysis of differences among human and non-human introduced human-material collaboration as a productive unit of analysis for understanding how materials and people together contribute to producing what can be recognized as computational performance. The findings of this research contribute to ongoing conversations in CSCL research on how computational materials can be considered in collaborative learning and present a new approach to capture collaborative learning as physical expansion over time. The study has implications for future research on capturing collaborative computational learning and designing physical computational learning opportunities that show technology as evolving.
Multigene and Improved Anti-Collision RRT* Algorithms for Unmanned Aerial Vehicle Task Allocation and Route Planning in an Urban Air Mobility Scenario

Authors:
raw_author_name: Qiang Zhou
raw_affiliation_string: School of Electronic and Information Engineering, Beihang University, 37 XueYuan Road, Haidian District, Beijing 100191, China

Abstract: Compared to terrestrial transportation systems, the expansion of urban traffic into airspace can not only mitigate traffic congestion, but also foster establish eco-friendly transportation networks. Additionally, unmanned aerial vehicle (UAV) task allocation and trajectory planning are essential research topics for an Urban Air Mobility (UAM) scenario. However, heterogeneous tasks, temporary flight restriction zones, physical buildings, and environment prerequisites put forward challenges for the research. In this paper, multigene and improved anti-collision RRT* (IAC-RRT*) algorithms are proposed to address the challenge of task allocation and path planning problems in UAM scenarios by tailoring the chance of crossover and mutation. It is proved that multigene and IAC-RRT* algorithms can effectively minimize energy consumption and tasks’ completion duration of UAVs. Simulation results demonstrate that the strategy of this work surpasses traditional optimization algorithms, i.e., RRT algorithm and gene algorithm, in terms of numerical stability and convergence speed.
Motion-guided and occlusion-aware multi-object tracking with hierarchical matching

Authors:
raw_author_name: Lei Li
raw_affiliation_string: Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing, 100074, China; The Beijing Electro-Mechanical Engineering Institute, Beijing, 100074, China
raw_author_name: Shan Li
raw_affiliation_string: The Beijing Electro-Mechanical Engineering Institute, Beijing, 100074, China
raw_author_name: Chu He
raw_affiliation_string: School of Electronic Information, Wuhan University, Wuhan, 430000, Hubei, China
raw_author_name: Dingwen Wang
raw_affiliation_string: School of Computer Science, Wuhan University, Wuhan, 430000, Hubei, China

Abstract: In the field of multi-target tracking, the widely embraced tracking-by-detection paradigm has rapidly progressed with the refinement of detectors and matching techniques. However, the paradigm of joint detection and tracking is relatively limited, and it is difficult to model complex scenes, such as the complexities introduced by camera motion and occlusion. In this work, a hierarchical joint detection and tracking framework is proposed, namely MSPNet. From a temporal concern, a motion-guided feature aggregation module is proposed to address the complexities of multi-frame variations. From a spatial concern, an occlusion-aware head and hierarchical spatial association are proposed to handle the challenges of occlusion. Extensive experiments on MOT challenging benchmarks demonstrate that the MSPNet can effectively reduce false negatives and improve the accuracy of tracking while outperforming a wide range of existing methods.
Improved moth-flame algorithm based on cat chaotic and dynamic cosine factor

Authors:
raw_author_name: Zhicheng Tu
raw_affiliation_string: School of Automation, Guangdong Polytechnic Normal University 1 , Guangzhou 510665, China
raw_author_name: Dan Liu
raw_affiliation_string: School of Automation, Guangdong Polytechnic Normal University 1 , Guangzhou 510665, China
raw_author_name: Jian Cen
raw_affiliation_string: Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory 2 , Guangzhou 510665, China; School of Automation, Guangdong Polytechnic Normal University 1 , Guangzhou 510665, China
raw_author_name: Haiying Song
raw_affiliation_string: School of Automation, Guangdong Polytechnic Normal University 1 , Guangzhou 510665, China

Abstract: The moth-flame algorithm shows some shortcomings in solving the complex problem of optimization, such as insufficient population diversity and unbalanced search ability. In this paper, an IMFO (Improved Moth-Flame Optimization) algorithm is proposed to be applied in solving the optimization problem of function. First, cat chaotic mapping is used to generate the initial position of moth to improve the population diversity. Second, cosine inertia weight is introduced to balance the global and local search abilities of the algorithm. Third, the memory information in the particle swarm algorithm is introduced into the iterative process of the algorithm to speed up the convergence of the population. Finally, Gaussian mutation strategy is used in the current optimal solution to avoid the algorithm from falling into the local optimum. Simulation experiments are conducted on 11 benchmark test functions, compared with other improved MFO (Moth-Flame Optimization) algorithms and classical optimization algorithms. The results show that the IMFO has higher accuracy and stability in solving the above-mentioned test functions. The proposed algorithm is experimented and verified by optimizing the KELM (Kernel Extreme Learning Machine) in an engineering example and exhibits a better optimization performance.
Enhanced Robot Motion Block of A-Star Algorithm for Robotic Path Planning

Authors:
raw_author_name: Raihan Kabir
raw_affiliation_string: Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan
raw_author_name: Yutaka Watanobe
raw_affiliation_string: Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan
raw_author_name: Md Rashedul Islam
raw_affiliation_string: Division of Computer Vision and AI, Department of R&D, Chowagiken Corp., Sapporo 001-0021, Japan
raw_author_name: Keitaro Naruse
raw_affiliation_string: Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan

Abstract: An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.
A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

Authors:
raw_author_name: Kiran Jabeen
raw_affiliation_string: Department of Computer Science, HITEC University, Pakistan
raw_author_name: Muhammad Attique Khan
raw_affiliation_string: Department of Computer Science, HITEC University, Pakistan
raw_author_name: Mohamed Abdel Hameed
raw_affiliation_string: Department of Computer Science, Faculty of Computers and Information, Luxor University, Egypt
raw_author_name: M. Turki-Hadj Alouane
raw_affiliation_string: College of Computer Science, King Khalid University, Saudi Arabia
raw_author_name: Anum Masood
raw_affiliation_string: Department of Physics, Norwegian University of Science and Technology, Norway

Abstract: With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease’s influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue’s nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework’s higher accuracy and precision rate.