Reviewerly/exHarmony huggingface dataset:
Non-radiative configurations of a few quantum emitters ensembles: Evolutionary optimization approach

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

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

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

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

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

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

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

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.
How do AI markings on screening mammograms correspond to cancer location? An informed review of 270 breast cancer cases in BreastScreen Norway

To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative.In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative.All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative.AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting.In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score.• All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. • Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. • Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting.
Multi-scale V-net architecture with deep feature CRF layers for brain extraction

Abstract Background Brain extraction is a computational necessity for researchers using brain imaging data. However, the complex structure of the interfaces between the brain, meninges and human skull have not allowed a highly robust solution to emerge. While previous methods have used machine learning with structural and geometric priors in mind, with the development of Deep Learning (DL), there has been an increase in Neural Network based methods. Most proposed DL models focus on improving the training data despite the clear gap between groups in the amount and quality of accessible training data between. Methods We propose an architecture we call Efficient V-net with Additional Conditional Random Field Layers (EVAC+). EVAC+ has 3 major characteristics: (1) a smart augmentation strategy that improves training efficiency, (2) a unique way of using a Conditional Random Fields Recurrent Layer that improves accuracy and (3) an additional loss function that fine-tunes the segmentation output. We compare our model to state-of-the-art non-DL and DL methods. Results Results show that even with limited training resources, EVAC+ outperforms in most cases, achieving a high and stable Dice Coefficient and Jaccard Index along with a desirable lower Surface (Hausdorff) Distance. More importantly, our approach accurately segmented clinical and pediatric data, despite the fact that the training dataset only contains healthy adults. Conclusions Ultimately, our model provides a reliable way of accurately reducing segmentation errors in complex multi-tissue interfacing areas of the brain. We expect our method, which is publicly available and open-source, to be beneficial to a wide range of researchers.
Enhanced Identification of Fracture Smectites and Other Alteration Minerals Via Short-Wave Infrared Reflectance at Two Finnish Crystalline Sites, Olkiluoto and Hyrkkölä

Abstract Fracture filling minerals and the smectite-group clay minerals in particular play a significant role in the geotechnical stability of the fractured rock mass. Smectites have multiple uses as geomaterials in waste management solutions, of which the most technically demanding is the geological disposal of radioactive waste. Here, the stability and occurrence of smectites as fracture filling minerals were studied with two main objectives: 1) to test the capability of short-wave infrared spectroscopy as a tool to streamline the site assessment where clay mineralogy needs to be characterised in detail, and 2) to provide a first assessment of naturally occurring smectites in various sample types collected from two Finnish sites (Olkiluoto and Hyrkkölä) and their potential as natural analogues of smectite stability for bentonite used in the engineered barrier systems of geological disposal repositories. The short-wave infrared (SWIR) method has been validated to be a useful tool in fracture mineral characterisation, specifically due to its fast acquisition and interpretation time compared to the conventional methods. The study confirms the in situ occurrence of smectites at both sites studied showing that, especially in Hyrkkölä, the smectite-group member is close to montmorillonite, the main constituent of bentonite. At Olkiluoto, the smectites studied show more interstratified features. The differences in the mode of occurrence are related to the history of their formation, especially hydrothermal alteration, and parent rock composition.
SFL-MDrone: Synchronous federated learning enabled multi drones

UAVs or Drones can be used to support wireless communication by acting as flying or mobile Base Stations for the accumulation of the different types of data to train the models. However, in traditional or DL-based UAVs, the raw data is sent from the devices to the centralized server, which causes problems in terms of the privacy of the devices and the UAVs’ communication resources or limited processing. Therefore, the issue with DL-based UAVs is that sending the original data to the centralized body raises questions about security and privacy. The transmission of distributed, unprocessed data from the drones to the cloud, including interactive media information data types, requires a significant amount of network bandwidth and more energy, which has an enormous effect on several trade-offs, including communication rates and computation latencies. Data packet loss caused by asynchronous transmission, which doesn’t prevent peer-to-peer communication, is a concern with AFL-based UAVs. Therefore, in order to address the aforementioned issues, we have introduced SFL-based UAVs that focus on creating algorithms in which the models simultaneously update the server as they wait for all of the chosen devices to communicate. The proposed framework enables a variety of devices, including mobile and UAV devices, to train or learn their algorithms for machine learning before updating the models and parameters simultaneously to servers or manned aerial data centers for model buildup without transferring their original private information. This decreases packet loss and privacy threats while also enhancing round effectiveness as well as model accuracy. The comparative analysis of AFL and SFL techniques in terms of accuracy, global rounds, and communication rounds are offered. Simulation findings suggest that the proposed methodology improves in terms of global rounds and accuracy.
Influence of Digital Skills on The Academic Performance of University Students: A Socioeconomic Approach

Purpose: Analyzing the influence of digital competencies on the academic performance of university students through a socioeconomic approach.&#x0D; &#x0D; Theoretical framework: Theories associated with digital competence, academic performance and the influence of socioeconomic factors were addressed.&#x0D; &#x0D; Method: A mixed Ditriac-type approach was applied, with a non-experimental and cross-sectional design, where the total population was 1215 university students and the probabilistic sample for the survey was 292 students and the non-probabilistic sample for the interview was 29 students who served to collect quantitative and qualitative data respectively. Quantitative data were obtained through the COMPDIG-PED instrument of digital competence in pedagogy, whose reliability was assessed by Cronbach's Alpha coefficient α = 0.911 and academic performance was evaluated through documentary analysis of the institution's academic records, which were then processed in SPSS, and qualitative data were analyzed with Atlas.ti software considering 15 codes, 10 sub categories and 6 categories, also taking into account the socioeconomic aspect throughout the process, from the selection of participants to the interpretation of the results.&#x0D; &#x0D; Results and conclusions: The results support a moderate degree of positive correlation of digital competencies and academic performance with an rs = 0.546 and with a significance level p = 0.000, where with respect to the coefficient of determination it is shown that digital competency influences 30.5% on the academic performance of university students, while the remaining 69.5% is affected by other socioeconomic factors. Concluding that there is positive correlation and 30.5% of influence of digital competencies with respect to academic performance, where the remaining 69.5% is due to socioeconomic factors such as educational level in parents, economic level, work, sentimental relationship and the management of time dedicated to study.&#x0D; &#x0D; Originality/value: To provide insight into students' perceptions on the management of digital competencies and their influence on academic performance, integrating socioeconomic aspects.