Training Strategies Research Articles

Pre-service teachers’ pedagogical knowledge and attitudes towards slow learner students

The pedagogical knowledge and acceptance of pre-service teachers towards slow learner students contribute to the implementation of inclusive education in regular schools. This study investigates the level of knowledge and acceptance pre-service teachers have in applying effective teaching strategies for students with slower learning needs. The participants of this study were 187 students at a private university in. This research is ex-post facto research. The research instrument used was a pedagogical knowledge test and attitude questionnaire. The data obtained were analyzed using correlation analysis, and t-test, to determine the relationship between pedagogical knowledge and attitudes towards slow learner students. The finding reveals that there was a positive relationship between pedagogical knowledge and attitudes towards slow learners, but the correlation between the two is not strong, in the medium category. Pre-service teachers gain pedagogical knowledge from inclusive education courses. Pre-service teachers also form a positive attitude towards slow learner students because of their experience interacting with slow learner students. A strong correlation between the variables in this study suggests that pre-service teachers' pedagogical knowledge has a limited impact on their attitudes toward slow learners. This highlights the need for improved training in effective teaching strategies for slow learners.

Accentuated Eccentric Loading and Alternative Set Structures: A Narrative Review for Potential Synergies in Resistance Training.

Chae, S, McDowell, KW, Baur, ML, Long, SA, Tufano, JJ, and Stone, MH. Accentuated eccentric loading and alternative set structures: A narrative review for potential synergies in resistance training. J Strength Cond Res 38(11): 1987-2000, 2024-As athletes become adapted to training over time, it becomes more difficult to develop their strength and power. In a conventional resistance training strategy, volume or load may be increased to provide novel stimuli to break through a plateau. However, physiological stress markers increase with increased volume or load, which is an innate shortcoming. In that case, practitioners strive to develop unconventional strategies that could increase training stimuli while adjusting fatigue. Two programming tactics, accentuated eccentric loading (AEL) using eccentric overload and alternative set structures (AS) using intraset rests, have been reported to increase training stimuli and alleviate fatigue, respectively. Importantly, when merging AEL and AS in various contexts, the 2 benefits could be accomplished together. Because AEL and AS cause different outcomes, it is important to deal with when and how they may be integrated into periodization. Moreover, prescribing eccentric overload and intraset rests requires logistical considerations that need to be addressed. This review discusses the scientific and practical aspects of AEL and AS to further optimize strength and power adaptations. This review discusses (a) scientific evidence as to which tactic is effective for a certain block, (b) potential practical applications, and (c) related discussions and future research directions.

The Effects of Cognitive Remediation on Negative Symptoms in Schizophrenia-Spectrum Illness: A Meta-analytic Investigation of Efficacy.

The number of clinical efficacy trials of Cognitive Remediation (CR), a behavioral intervention consisting of cognitive task practice and/or strategy training to improve cognitive skills in schizophrenia, has increased substantially over the past 25 years. While recent reviews have highlighted the effects of CR on cognition and function, CR effects on negative symptoms remain understudied. Given the overlap between negative symptoms and cognition, CR effects might be expected. Electronic databases were evaluated up to September 2023 using a broad range of search terms. Sixty-nine unique, controlled trials that used negative symptoms as an outcome were meta-analyzed. Data were independently extracted with excellent (>98%) reliability. Random effects models assessed the effects of CR on summary and expressive vs. experiential negative symptoms. Moderator analyses evaluated a broad array of treatment and participant factors. The meta-analysis (5319 participants) revealed that CR produced a small effect size improvement on summary negative symptoms (Hedge's g = 0.179). Sample differences in age, duration of illness, symptoms, and antipsychotic dosage did not serve as a barrier to treatment benefit. CR also produced small-to-moderate improvements in alogia (Hedge's g = 0.312) but not experiential negative symptoms. Programs of CR that utilized bridging activities that relate training of cognitive skills to activities of daily living produced greater improvement in negative symptoms (g = 0.281 vs 0.055). Longer CR programs also produce larger effects on negative symptoms. CR produces small, consistent reductions in negative symptoms in people with schizophrenia. Variations in CR effects may be linked to different treatment ingredients.

Effects of MOOC and video conferencing deliveries on vocabulary usage and learning strategies

While vocabulary learning strategies (VLSs) have primarily been taught in physical classroom settings, the potential for applying strategy training in an online environment remains under-explored. With the rising acceptance of remote education, there is a need to investigate the impacts of applying strategy training in online contexts. Hence, this study explores the effects of online deliveries on VLSs and vocabulary usage in writing among adult English as second language (ESL) learners via massive open online course (MOOC) and video conferencing platforms. A total of 50 pre-intermediate ESL learners completed a 5-week VLSs course on a MOOC while 44 students learnt from the instructor synchronously using Microsoft Teams. The data comprised questionnaires and learners’ compositions, which were collected before and after treatment. The data analysis procedures involved Lextutor’s version of lexical frequency profiling (LFP), descriptive and inferential statistics. The results revealed that there was a significant increase of usage in all categories of the VLSs after the course for both groups. Additionally, the effects of VLSs instruction on vocabulary usage could be reflected in both groups’ writing though the choice of words might have been task-influenced. The findings from this study could yield useful insights on online vocabulary learning for ESL instructors and learners.

A Heatmap-Supplemented R-CNN Trained Using an Inflated IoU for Small Object Detection

Object detection architectures struggle to detect small objects across applications including remote sensing and autonomous vehicles. Specifically, for unmanned aerial vehicles, poor detection of small objects directly limits this technology’s applicability. Objects both appear smaller than they are in large-scale images captured in aerial imagery and are represented by reduced information in high-altitude imagery. This paper presents a new architecture, CR-CNN, which predicts independent regions of interest from two unique prediction branches within the first stage of the network: a conventional R-CNN convolutional backbone and an hourglass backbone. Utilizing two independent sources within the first stage, our approach leads to an increase in successful predictions of regions that contain smaller objects. Anchor-based methods such as R-CNNs also utilize less than half the number of small objects compared to larger ones during training due to the poor intersection over union (IoU) scores between the generated anchors and the groundtruth—further reducing their performance on small objects. Therefore, we also propose artificially inflating the IoU of smaller objects during training using a simple, size-based Gaussian multiplier—leading to an increase in the quantity of small objects seen per training cycle based on an increase in the number of anchor–object pairs during training. This architecture and training strategy led to improved detection overall on two challenging aerial-based datasets heavily composed of small objects while predicting fewer false positives compared to Mask R-CNN. These results suggest that while new and unique architectures will continue to play a part in advancing the field of object detection, the training methodologies and strategies used will also play a valuable role.

A Comprehensive Survey on Brain Tumor Detection and Classification Techniques Using Machine Learning and Deep Learning Models

Abstract: Brain tumor detection and classification have become critical areas of research due to the complexities and challenges involved in accurate diagnosis. With the advancement of machine learning and deep learning techniques, numerous intelligent systems have been proposed to automate and enhance the process of detecting and classifying brain tumors. This survey paper reviews recent approaches and techniques used for brain tumor detection, focusing primarily on deep learning architectures like Convolutional Neural Networks (CNNs) and pre-trained models such as VGG, ResNet, and EfficientNet. In particular, methods using MRI images to classify tumors into categories such as glioma, meningioma, pituitary tumors, and healthy tissue are explored. A detailed analysis of various models, their architectures, training strategies, and evaluation metrics is provided. Moreover, the paper identifies the key challenges faced in the field, including imbalanced datasets, generalization issues, and computational efficiency. By comparing different approaches, we aim to highlight the strengths and limitations of each method and provide insights into how future research can address these challenges. The paper concludes with a discussion on promising future directions in the application of intelligent techniques for brain tumor detection, including the integration of multimodal data and the development of more interpretable models. This survey serves as a comprehensive resource for researchers interested in understanding the current landscape of brain tumor detection using advanced deep learning techniques.

THE EVOLUTION OF PERFORMANCES ON A NATIONAL LEVEL IN AEROBIC GYMNASTICS

Background. Performance aerobic gymnastics is, indeed, a form of art in motion, harmoniously blending elements from gymnastics, dance, and aerobics. Focusing on aspects such as coordination, mobility, strength, and endurance, this discipline challenges both the physical and mental abilities of athletes. The hallmark of performance aerobic gymnastics is the ability to execute quick and precise movements without compromising technique and accuracy. This requires a unique combination of strength, flexibility, and grace, as well as a good understanding of rhythm and music. Through this combination of traits, performance aerobic gymnastics becomes not just a form of training but also a captivating spectacle, attracting both athletes eager to test their limits and the audience with its dynamism and complexity. It is a discipline that manages to blend technical perfection with artistic expressiveness, thus creating an impressive and memorable spectacle. Purpose: The research aimed to assess the level of preparedness of aerobic gymnastics practitioners from the second category in Romania in relation to the specific requirements of technical, artistic, and execution components imposed by the scoring code. Additionally, the study aimed to identify any errors that athletes may have presented within these components. Methods: The research utilized bibliographic study to obtain a comprehensive understanding of the field of aerobic gymnastics and relevant points for evaluating athletes' performance. Statistical method was applied to analyze and interpret the results obtained by female athletes at the National Aerobic Gymnastics Championships, allowing us to highlight their evolution and identify performance trends. Additionally, graphical method was employed to visually represent the data of our analysis, facilitating their comprehension and interpretation. Subjects: The research period covers the evolution of athletes between the years 2019 and 2021, within the national championship. Our objective is to highlight and analyze the progress, stagnation, or regression of athletes participating in competitions over the course of the three years of study. Results: It is interesting to observe the evolution of the UNEFS sports club over these years and the fluctuations in their positions in the competition's ranking. Although there have been changes in the ranking from year to year, with a variation in the positions occupied, it is remarkable that UNEFS has generally remained in the upper part of the ranking. In 2019, the UNEFS club occupied positions 1, 4, and 6 in the ranking, and the following year, they managed to rise to positions 1 and 2, signifying an improvement in their performance. However, in 2020, they experienced a decline, dropping to 7th place. In 2021, the sports club once again managed to rank in the top two positions, but also found themselves in 6th place in the ranking. Conclusions: These fluctuations in the ranking can be influenced by a variety of factors, such as changes in athletes' training, training strategies, the level of competition, or even external conditions, such as changes in regulations or unexpected events. A detailed examination of these fluctuations can help identify patterns or trends that may affect the performance of the UNEFS club in the future. Based on these analyses, adjustments can be made in training and competition strategies to improve performance and ensure that the club remains competitive in the national championship and other future competitions.

Developing Generalizable Scoring Functions for Molecular Docking: Challenges and Perspectives.

Structure-based drug discovery methods, such as molecular docking and virtual screening, have become invaluable tools in developing novel drugs. At the core of these methods are Scoring Functions (SFs), which predict the binding affinity between ligands and protein targets. This study aims to review and contextualize the challenges and best practices in training novel scoring functions to improve their accuracy and generalizability in predicting protein-ligand binding affinities. Effective training of scoring functions requires careful attention to the quality of training data and methodologies. We emphasize the need for robust training strategies to produce consistent and generalizable SFs. Key considerations include addressing hidden biases and overfitting in machine-learning models, as well as ensuring the use of high-quality, unbiased datasets for both training and evaluation of SFs. Innovative hybrid methods, combining the advantages of empirical and machine-learning approaches, hold promise for outperforming current scoring functions while displaying greater generalizability and versatility.

The suitability of skilled talent training in higher vocational colleges based on AI and 5G network connection in the context of educational transformation and upgrading

The development of artificial intelligence (AI) and 5G network technology has changed the production and lifestyle of people. AI also has promoted the transformation of talent training mode under the integration of college industry and education. In the context of the current transformation of education, AI and 5G networks are increasingly used in the education industry. This paper optimizes and upgrades the training mode of skilled talents in higher vocational colleges by using its advanced methods and technologies of information display. This means is helpful to analyze and solve a series of objective problems such as the single training form of the current talent training mode. This paper utilizes the principles and laws of industry university research (IUR) collaboration for reference to construct and optimize the talent training mode based on the analysis of the requirements of talent training and the role of each subject in talent training. Then, the ecological talent training environment can be realized. In the analysis of talent training mode under the cooperation of production and education, the correlation coefficients of network construction, environment construction, scientific research funds, scientific research level, and policy support were 0.618, 0.576, 0.493, 0.785, and 0.451, respectively. This showed that the scientific research level had the greatest impact on talent training in the talent training mode of IUR collaboration, while policy support had less impact on talent training compared with other factors. The combination of AI and 5G network technology with the talent training mode of IUR cooperation can effectively analyze the influencing factors and problems of the talent training mode. The hybrid method is of great significance to the talent training strategy and fitting degree.

A sales force effectiveness framework for enhancing healthcare access through pharmaceutical sales and training programs

This paper introduces a conceptual framework aimed at enhancing sales force effectiveness within the pharmaceutical industry, specifically focusing on improving healthcare access through optimized sales training programs and customer relationship management (CRM) systems. The proposed framework recognizes the critical role that pharmaceutical sales teams play in facilitating healthcare delivery, particularly during public health crises. It emphasizes the integration of effective training strategies and advanced CRM tools to enhance engagement with healthcare professionals, thereby improving product distribution and patient outcomes. The framework comprises three essential components Sales Force Training Enhancement, which focuses on developing targeted training programs that equip sales representatives with the necessary skills and knowledge to effectively communicate the value of pharmaceutical products to healthcare providers. This includes understanding clinical data, addressing healthcare providers’ concerns, and fostering a consultative selling approach. Secondly, CRM System Implementation, which underscores the significance of adopting robust CRM systems that streamline interactions between sales teams and healthcare professionals. By leveraging data analytics and customer insights, pharmaceutical companies can tailor their approach to meet the specific needs of healthcare providers, thereby enhancing relationship-building and communication. Thirdly, engagement and Outcome Measurement, which highlights the importance of measuring engagement levels and patient outcomes resulting from effective sales strategies. This component advocates for the use of key performance indicators (KPIs) to assess the impact of training and CRM initiatives on product distribution and healthcare access. The framework ultimately aims to foster a more collaborative environment between pharmaceutical sales teams and healthcare professionals, ensuring that patients receive timely access to essential medications and services. By focusing on the strategic integration of training and CRM systems, this framework presents a comprehensive approach to improving sales force effectiveness in the pharmaceutical sector. Future research and practical applications of this framework can further explore its implications for enhancing healthcare access, particularly in times of public health challenges.

Climate-sensitive training for sustainable business in Dickson County with global applications

Purpose This paper aims to examine the implementation and impact of climate-sensitive training programs tailored to the specific climate risks and business needs of Dickson County, Tennessee. It aims to emphasize the importance of integrating climate adaptation strategies into business training and to propose a replicable model that can be adapted to regions facing similar challenges, contributing to global sustainability efforts. Design/methodology/approach The research uses Dickson County’s climate profile as a case study, analyzing its impact on local businesses in sectors such as agriculture, manufacturing and tourism. This paper integrates theoretical frameworks, including organizational learning and adaptive capacity, to provide practical strategies for climate-sensitive training. It further incorporates insights from local institutions, industry partnerships and public data, using a qualitative approach to analyze key themes related to training effectiveness and sustainability. Findings The findings demonstrate that climate-sensitive training significantly enhances the resilience and sustainability of businesses by preparing them to address local climate challenges. Critical factors for successful implementation include stakeholder engagement, industry-specific customization and the continuous evaluation of training outcomes. The model presented shows promise for broader applicability in other regions, especially when localized climate data and collaborative efforts are integrated. Originality/value This paper provides a novel, region-specific approach to climate-sensitive training, distinguishing itself by combining theoretical insights with practical strategies for businesses. By focusing on localized adaptation and stakeholder collaboration, the model addresses an existing gap in the literature on climate adaptation training for smaller, climate-vulnerable regions. This work offers a replicable and scalable framework that contributes to the global discourse on sustainable development and climate resilience in business practices.

Spare Parts Management Strategy of High-Speed Railway Running Department Based on Performance Prediction

Spare parts management is a critical aspect of high-speed train health management, playing a vital role in maximizing in-service time and minimizing maintenance costs. However, traditional spare parts management methods, which rely solely on historical experience and suggest spare parts quantities or ratios in equipment manuals, often lack practicality and fail to meet real-world demands. To address these limitations, this paper proposes a performance prediction-based spare parts management strategy for high-speed trains. The strategy comprises three main components. First, a performance degradation model is developed using performance evaluation results to define a performance degradation envelope. Next, the required quantity or ratio of spare parts for multiple devices in different performance states is determined using the expected performance score method. Finally, the timing of spare parts orders is scientifically optimized by accounting for production and transportation lead times. To demonstrate the effectiveness of the proposed strategy, we conducted experiments using the spare parts management of a specific high-speed train running gear as a case study and compared it with existing spare parts management methods.

Dopamine in Sports: A Narrative Review on the Genetic and Epigenetic Factors Shaping Personality and Athletic Performance

This narrative review examines the relationship between dopamine-related genetic polymorphisms, personality traits, and athletic success. Advances in sports genetics have identified specific single nucleotide polymorphisms (SNPs) in dopamine-related genes linked to personality traits crucial for athletic performance, such as motivation, cognitive function, and emotional resilience. This review clarifies how genetic variations can influence athletic predisposition through dopaminergic pathways and environmental interactions. Key findings reveal associations between specific SNPs and enhanced performance in various sports. For example, polymorphisms such as COMT Val158Met rs4680 and BDNF Val66Met rs6265 are associated with traits that could benefit performance, such as increased focus, stress resilience and conscientiousness, especially in martial arts. DRD3 rs167771 is associated with higher agreeableness, benefiting teamwork in sports like football. This synthesis underscores the multidimensional role of genetics in shaping athletic ability and advocates for integrating genetic profiling into personalized training to optimize performance and well-being. However, research gaps remain, including the need for standardized training protocols and exploring gene–environment interactions in diverse populations. Future studies should focus on how genetic and epigenetic factors can inform tailored interventions to enhance both physical and psychological aspects of athletic performance. By bridging genetics, personality psychology, and exercise science, this review paves the way for innovative training and performance optimization strategies.

Few-Shot Face Sketch-to-Photo Synthesis via Global-Local Asymmetric Image-to-Image Translation

Face sketch-to-photo synthesis is widely used in law enforcement and digital entertainment, which can be achieved by Image-to-Image (I2I) translation. Traditional I2I translation algorithms usually regard the bidirectional translation of two image domains as two symmetric processes, so the two translation networks adopt the same structure. However, due to the scarcity of face sketches and the abundance of face photos, the sketch-to-photo and photo-to-sketch processes are asymmetric. Considering this issue, we propose a few-shot face sketch-to-photo synthesis model based on asymmetric I2I translation, where the sketch-to-photo process uses a feature-embedded generating network, while the photo-to-sketch process uses a style transfer network. On this basis, a three-stage asymmetric training strategy with style transfer as the trigger is proposed to optimize the proposed model by utilizing the advantage that the style transfer network only needs few-shot face sketches for training. Additionally, we discover that stylistic differences between the global and local sketch faces lead to inconsistencies between the global and local sketch-to-photo processes. Thus, a dual branch of the global face and local face is adopted in the sketch-to-photo synthesis model to learn the specific transformation processes for global structure and local details. Finally, the high-quality synthetic face photo can be generated through the global-local face fusion sub-network. Extensive experimental results demonstrate that the proposed Global-Local Asymmetric (GLAS) I2I translation algorithm compared to SOTA methods, at least improves FSIM by 0.0126, and reduces LPIPS (alex), LPIPS (squeeze), and LPIPS (vgg) by 0.0610, 0.0883, and 0.0719, respectively.

Organization of Vaccination Sites in Quebec: Perception of Users, Employees and managers

Abstract Since late 2020, vaccination sites have emerged rapidly in order to protect the population against COVID-19. Health services in Montérégie (Québec) are provided by three organizations. Each adopted a different approach to immunization. The Regional Public Health Department took this opportunity to describe, compare, and explain these three approaches. A mixed methods design through a multiple case study was conducted. Users, employees and managers were interviewed at the end of 2021 on several performance-related topics. Individual interviews with employees and managers in mid-2022 further explored these topics. 38 managers, 360 employees, and 946 users participated in the survey. 9 managers and 10 employees were interviewed. Most participants were satisfied with vaccination organization regardless of the model used, mainly because of high geographic and temporal accessibility of the sites. Further signage to indicate directions could optimize accessibility. High adaptability was essential for managers and employees. Training strategies such as people to refer to and pairing allowed to better deal with frequent changes and staff rotation. The addition of ongoing training could be beneficial. Rapid changes in employee schedules were identified as a challenge. The use of technology-based scheduling tools, accessible to all, brought added flexibility, which was appreciated. Several ideas were used to communicate updates on a daily basis. However, timely implementation of new recommendations remained an ongoing challenge. Dedicated clinics for specific clienteles were successful. Community partnerships were generally beneficial, although establishing more of them could potentially reach other vulnerable communities. To improve accessibility and minimize health social inequities, a greater investment in local partnerships might reach more vulnerable clienteles. Regular communication and innovative employee support are key elements to improve fluidity of operations. Key messages • Reaching out for vulnerable clienteles is essential to minimize health social inequities. • Rapid changes in services needs underlign fluidity communication importance.

Deafness and Inclusion: Perceived Communicative Barriers in the Area of Rehabilitation in Italy

Abstract Introduction Deafness can create a communication deficit/barrier between the Deaf or Hard of Hearing patient and the Healthcare Workers (HWs), with negative outcomes for diagnostic and therapeutic success. As underlined by the COVID-19 pandemic, there is a need to train HWs in effective communication strategies in order to avoid marginalising or excluding the Deaf Community by denying them access to healthcare services. This study highlights the urgent need and desire of Healthcare staff for training in effective communication strategies, such as sign language, to help the Deaf community gain equal access to Healthcare. Materials and methods A cross-sectional survey was carried out between May and July 2022 using the Google Forms platform. An anonymous questionnaire was given to Rehabilitation HWs (RHWs) at a national level to collect information from the HWs of the various areas of rehabilitation, identifying their knowledge, training, and experience with regard to deafness. Data analysis was then carried out using the Google Forms program, SPSS program Windows7 Excel Spreadsheet. Results 252 RHWs belonging to 8 different profiles answered this survey. 89% were women. 46% were younger than 30. 57% of these 252 had deaf patients, but 54.6% of these had no prior training. 33% of the RHWs had followed courses or seminars on deafness at least once. When asked, 94% were interested in courses or training in communication with deaf people. Conclusions HWs recognize the gap in communication and are willing to learn more in the matter for effective communication with their patients to gain a better therapeutic success. They also consider a program that imparts such knowledge and training to be highly important. Key messages • Therapeutic success is increased if there are effective communication strategies, such as sign language, between the Deaf and Healthcare provider. • This study shows the importance of training all health workers in effective communication strategies with deaf people to allow an all-inclusive healthcare.

MW‐SAM:Mangrove wetland remote sensing image segmentation network based on segment anything model

AbstractMangrove wetlands are important ecosystems in tropical and subtropical coastal areas, providing wind and wave attenuation and embankment protection functions. However, mangrove wetlands worldwide are facing severe loss and degradation. Accurate identification of mangrove wetland extent is crucial for their protection, but traditional methods struggle to meet the requirements of large‐scale, high‐precision identification. Furthermore, field data collection and annotation in wetlands in complex intertidal zones pose challenges, and the lack of high‐quality training samples limits the application of deep learning methods in this field. This paper proposes a novel semantic segmentation framework for mangrove wetlands called mangrove wetland remote sensing image segmentation network based on segment anything model (MW‐SAM) to address these issues. MW‐SAM is based on the pre‐trained SAM and achieves cross‐domain adaptation through parameter‐efficient fine‐tuning techniques. It introduces wetland‐specific prompts, auxiliary branches, semi‐supervised training, and iterative optimization strategies to improve accuracy and tackle the problem of sample scarcity. Moreover, on the mangrove wetland dataset constructed in this paper, MW‐SAM significantly outperforms SAM and other traditional methods. MW‐SAM provides a new technology for monitoring and protecting mangrove wetlands, and the constructed dataset will be made publicly available, which is expected to promote the development of mangrove wetland conservation efforts.

Quantifying Tennis Players’ Performance and Analyzing Momentum Fluctuations Based on Weighted Logistic Regression Model

This paper proposes a dual-model approach to analyze tennis players’ performance and predict match outcomes, which combines a weighted logistic regression model and a decision tree model. By examining key factors such as scoring rate and serving status, the models calculate the scoring probabilities at different time points, quantifying the performance of players throughout the match. Furthermore, the paper constructs a decision tree model to predict shifts in the match situation based on momentum fluctuations. The research findings underscore the importance of momentum in determining match outcomes and provide valuable insights for training and match strategies. This dual-model approach offers a fresh perspective on the impact of momentum in tennis matches, allowing players to better understand and adapt to the dynamics of momentum fluctuations throughout a match. Additionally, the study suggests targeted training, simulated match conditions, psychological training, real-time data tracking, and physical preparation as strategies to enhance performance and adapt to momentum changes. This comprehensive approach provides players with valuable insights to optimize their performance and strategies in tennis matches.

PASCL: supervised contrastive learning with perturbative augmentation for particle decay reconstruction

Abstract In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. In particular, we use a compact matrix representation termed as lowest common ancestor generations matrix, to encode the particle decay tree structure. Then, we introduce a perturbative augmentation technique applied to node features, aiming to mimic experimental uncertainties and increase data diversity. We further propose a supervised graph contrastive learning algorithm to utilize the information of inter-particle relations from multiple decay processes. Extensive experiments show that our proposed supervised graph contrastive learning with perturbative augmentation method outperforms state-of-the-art baseline models on an existing physics-based dataset, significantly improving the reconstruction accuracy. This method provides a more effective training strategy for models with the same parameters and makes way for more accurate and efficient high-energy particle physics data analysis.

Lifelong QoT prediction: an adaptation to real-world optical networks

Predicting the quality of transmission (QoT) is a critical task in the management and optimization of modern fiber-optic networks. Traditional machine learning (ML) QoT prediction models, typically trained on pre-collected datasets, are designed to make long-term predictions once deployed. However, this static training strategy often falls short in the face of time-dependent network evolution and variations. We identify the root cause of these shortcomings as shifts in data distribution, which are not accounted for in conventional static models. In response to these challenges, we propose an online continual learning pipeline that is specifically designed for stable QoT prediction in optical networks. This pipeline directly addresses the problem of distribution shifts by continuously updating the prediction model in response to real-time network data. We explore and compare various strategies within this framework and demonstrate that the integration of the adaptive retraining strategy and the regularized online continual learning algorithm (OCL-REG) significantly enhances the QoT prediction stability while optimizing the resource efficiency. OCL-REG demonstrates superior adaptability and stability, achieving an average cumulative mean squared error (C-MSE) of 0.19 on a testbench with a data distribution shift sequence containing 1000 batches. Moreover, the OCL-REG model requires fewer samples for adaptation, averaging around 107 samples, compared to the conventional retraining strategy, which requires an average of 253 samples. Our approach presents a paradigm shift in QoT prediction, moving from a static to a dynamic, lifelong learning model that is more attuned to the evolving realities of real fiber-optic networks.