Статті в журналах: "Learning tracks"

1

Goncharov, Pavel, Egor Shchavelev, Gennady Ososkov, and Dmitriy Baranov. "BM@N Tracking with Novel Deep Learning Methods." EPJ Web of Conferences 226 (2020): 03009. http://dx.doi.org/10.1051/epjconf/202022603009.

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Three deep tracking methods are presented for the BM@N experiment GEM track detector, which differ in their concepts. The first is a two-stage method with data preprocessing by a directional search in the k-d tree to find all possible candidates for tracks, and then use a deep recurrent neural network to classify them by true and ghost tracks. The second end-to-end method used a deep recurrent neural network to extrapolate the initial tracks, similar to the Kalman filter, which learns necessary parameters from the data. The third method implements our new attempt to adapt the neural graph network approach developed in the HEP.TrkX project at CERN to GEM-specific data. The results of applying these three methods to simulated events are presented.

2

Franceschini, Giordano, Mara Terzini, and Elisabetta M. Zanetti. "Learning curves of elite car racers." International Journal of Sports Science & Coaching 12, no. 2 (February 21, 2017): 245–51. http://dx.doi.org/10.1177/1747954117694929.

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This work is focused on racing cars driver’s training. Nine different tracks are considered and six drivers. Each driver drives on every track and performs consecutive trial sessions on each track; each session is made of various laps, and lap times are fitted using an exponential model, yielding an estimate of the initial performance, the learning constant, and the asymptotic performance. According to results, the learning curve varies significantly among pilots and among tracks; all pilots reach their session asymptotic performance in less than nine laps. The asymptotic performance in consecutive trial sessions improves significantly, and it is strongly correlated to the initial session performance (r2 > 0.99). As a conclusion, it is more profitable to perform separated sessions made of few laps (less than 10) rather than performing a smaller number of longer sessions. Whenever the initial lap time stops decreasing systematically, trial sessions should end because the asymptotic performance is not likely to improve further.

3

Latkar, Charu. "Railway Track Monitoring System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1403–10. http://dx.doi.org/10.22214/ijraset.2021.36134.

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For the protection and proximity of railway networks it is substantial to Promptly detect and identify faults in the railway tracks. In this paper, railway track fault diagnosis is approximated from the vertical and lateral acceleration using a MPU6050. MPU6050 consisting of three sensors namely gyroscope, magnetometer and accelerometer are used to distinguish line and level as symetricities in a railway track. A GSM module is used to notify the location of faults on tracks. Arduino Microcontroller is interfaced using Arduino UNO IDE. The results show that the condition of railway track irregularity and railway track striation can be approximated constructively. The processed data is uploaded to the open source cloud provider thingspeak.com. The use of various Machine Learning Algorithms are proposed to accomplish the above tasks based on the commonly available measured signals. By considering the signals from multiple railway tracks in a geographic location, faults are diagnosed from their spatial and temporal dependencies. The irregularities in the railway tracks are detected using the Inertial Monitoring Unit, providing the necessary data about future deformities using Machine Learning. Using Python 3.0, a generative model is developed to show that the AdaBoost network can learn these dependencies directly from the data. Seven different classification algorithms used for this project are Logistic regression,Naive Bayes Algorithm,Support Vector Machine, Ensemble Machine (Average) learning Algorithm, XGBoost Classifier, Extreme Machine Learning and AdaBoost Classifier. Among the above 7 classification algorithms, AdaBoost Learning has given the highest accuracy,i.e of 93.93 %. The AdaBoost Machine Learning Model is used throughout the model.

4

Togelius, Julian, Noor Shaker, Sergey Karakovskiy, and Georgios N. Yannakakis. "The Mario AI Championship 2009-2012." AI Magazine 34, no. 3 (September 15, 2013): 89–92. http://dx.doi.org/10.1609/aimag.v34i3.2492.

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We give a brief overview of the Mario AI Championship, a series of competitions based on an open source clone of the seminal platform game Super Mario Bros. The competition has four tracks. The gameplay and learning tracks resemble traditional reinforcement learning competitions, the Level generation track focuses on the generation of entertaining game levels, and the Turing Test track focuses on humanlike game-playing behavior. We also outline some lessons learned from the competition and its future. The article is written by the four organizers of the competition.

5

Daher, Wajeeh. "Wiki Interaction Tracks in Geometry Learning." International Journal of E-Adoption 2, no. 4 (October 2010): 15–34. http://dx.doi.org/10.4018/jea.2010100102.

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The constant comparative method (Lincoln & Guba, 1985) was used to analyze preservice teachers’ discussions and interactions in wiki discussion sections regarding geometric lessons that were written by other preservice teachers in the year before. The data was compared for the following interaction aspects of knowledge building: dialogical actions, participants’ roles, and discussion tracks. Research shows that building their content and pedagogic content knowledge, the preservice teachers together with the lecturer used mainly proposing, asking, requesting, arguing, presenting, and moving the discussion forward as dialogical actions. Proposing and asking were used for various goals such as proposing various ideas and actions, and asking about different issues concerned with geometric content and pedagogic content knowledge. The lecturer asked questions more than the preservice teachers, while the preservice teachers proposed more than the lecturer. The knowledge building was collaborative in nature, and one important aspect which enabled the collaboration is the topology of the wiki discussion section. This topology enables presenting the content of the messages; not just the titles, where the contents are presented as having the same level and thus the same importance.

6

Zhang, Yao, Ye Yuan, and Qiumei Ma. "BESIII Drift Chamber Tracking with Machine Learning." EPJ Web of Conferences 245 (2020): 02033. http://dx.doi.org/10.1051/epjconf/202024502033.

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The tracking efficiency and the quality for the drift chamber of the BESIII experiment is essential to the physics analysis. The tracking efficiency of the drift chamber of BESIII is high for the high momentum tracks but still have room to improve for the low momentum tracks, especially for the tracks with multiple turn. A novel way to use a convolutional network called U-Net network is represented to solve the identification of the first turn’s hits for the multiple-turn tracks.

7

Jin, Cong, Tao Wang, Shouxun Liu, Yun Tie, Jianguang Li, Xiaobing Li, and Simon Lui. "A Transformer-Based Model for Multi-Track Music Generation." International Journal of Multimedia Data Engineering and Management 11, no. 3 (July 2020): 36–54. http://dx.doi.org/10.4018/ijmdem.2020070103.

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Most of the current works are still limited to dealing with the melody generation containing pitch, rhythm, duration of each note, and pause between notes. This paper proposes a transformer-based model to generate multi-track music including tracks of piano, guitar, and drum, which is abbreviated as MTMG model. The proposed MTMG model is mainly innovated and improved on the basis of transformer. Firstly, the model obtains three target sequences after pairwise learning through learning network. Then, according to these three target sequences, GPT is applied to predict and generate three closely related sequences of instrument tracks. Finally, the three generated instrument tracks are fused to obtain multi-track music pieces containing piano, guitar, and drum. To verify the effectiveness of the proposed model, related experiments are conducted on a pair of comparative subjective and objective evaluation. The encouraging performance of the proposed model over other state-of-the-art models demonstrates its superiority in musical representation.

8

Schmidt, James R., and Jan De Houwer. "Contingency Learning Tracks With Stimulus-Response Proportion." Experimental Psychology 63, no. 2 (March 2016): 79–88. http://dx.doi.org/10.1027/1618-3169/a000313.

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Abstract. We investigate the processes involved in human contingency learning using the color-word contingency learning paradigm. In this task, participants respond to the print color of neutral words. Each word is frequently presented in one color. Results show that participants respond faster and more accurately to words presented in their expected color. In Experiment 1, we observed better performance for high- relative to medium-frequency word-color pairs, and for medium- relative to low-frequency pairs. Within the medium-frequency condition, it did not matter whether the word was predictive of a currently-unpresented color, or the color was predictive of a currently-unpresented word. We conclude that a given word facilitates each potential response proportional to how often they co-occurred. In contrast, there was no evidence for costs associated with violations of high-frequency expectancies. Experiment 2 further introduced a novel word baseline condition, which also provided no evidence for competition between retrieved responses.

9

Gadson, Mark, Deanne Repetto, and Sherri L. Wallace. "2008 APSA Teaching and Learning Track Summaries—Track Six: Simulations and Role Play II." PS: Political Science & Politics 41, no. 03 (June 18, 2008): 618–20. http://dx.doi.org/10.1017/s1049096508270897.

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The 2008 Simulations and Role Play (S&RP) II track focused on issues in American politics and theory. Building on conversations in previous tracks (see S&RP track summaries from 2006 and 2007 inPS), the 25 discussants focused the dialogue mainly on best practices with some discussion on faculty research.

10

Kapoor, Rajiv, Rohini Goel, and Avinash Sharma. "Deep Learning Based Object and Railway Track Recognition Using Train Mounted Thermal Imaging System." Journal of Computational and Theoretical Nanoscience 17, no. 11 (November 1, 2020): 5062–71. http://dx.doi.org/10.1166/jctn.2020.9342.

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An intelligent railways safety system is very essential to avoid the accidents. The motivation behind the problem is the large number of collisions between trains and various obstacles, resulting in reduced safety and high costs. Continuous research is being carried out by distinct researchers to ensure railway safety and to reduce accident rates. In this paper, a novel method is proposed for identifying objects (obstacles) on the railway tracks in front of a moving train using a thermal camera. This approach presents a novel way of detecting the railway tracks as well as a deep network based method to recognize obstacles on the track. A pre-trained network is used that provides the model understanding of real world objects and enables deep learning classifiers for obstacle identification. The validation data is acquired by thermal imaging using night vision IR camera. In this work, the Faster R-CNN is used that efficiently recognize obstacles on the railway tracks. This process can be a great help for railways to reduce accidents and financial burdens. The result shows that the proposed method has good accuracy of approximately 83% which helps to enhance the railway safety.

11

Everitt, Regina. "All change: switching tracks to Learning Zone 2.0." Art Libraries Journal 36, no. 1 (2011): 17–21. http://dx.doi.org/10.1017/s0307472200016746.

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Nearly four years ago, the University of the Arts London launched its first flexible learning space for students, called the Learning Zone. The service model for the space was underpinned by an ethos of peer support. When London’s Crossrail Development project necessitated relocation, the Learning Zone team had to build the service all over again. Learning Zone 2.0 was a chance to reflect on the use and design of the space . . . and to improve on the first version. Despite personnel changes, building delays and lost furniture and equipment, the new space opened in May 2010.

12

Iksal, Sébastien. "Tracks Analysis in Learning Systems: A Prescriptive Approach." International Journal for e-Learning Security 1, no. 1 (March 1, 2011): 3–9. http://dx.doi.org/10.20533/ijels.2046.4568.2011.0001.

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13

Chen, Chong, Yuki Omiya, and Si Yang. "Dissociating contributions of ventral and dorsal striatum to reward learning." Journal of Neurophysiology 114, no. 3 (September 2015): 1364–66. http://dx.doi.org/10.1152/jn.00873.2014.

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Reward learning plays a central role in decision making and adaptation. Accumulating evidence suggests that the striatum contributes enormously to reward learning but that its subregions may have distinct functions. A recent article by Tricomi and Lempert ( J Neurophysiol. First published 22 October 2014, doi:10.1152/jn.00086.2014.) found that ventral striatum tracks reward value, whereas dorsolateral and dorsomedial striatum track the trialwise reward probability. In this Neuro Forum we reinterpret their findings and provide additional insights.

14

Chen, Yingyan, Hongze Wang, Yi Wu, and Haowei Wang. "Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method." Materials 13, no. 22 (November 10, 2020): 5063. http://dx.doi.org/10.3390/ma13225063.

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Though selective laser melting (SLM) has a rapidly increasing market these years, the quality of the SLM-fabricated part is extremely dependent on the process parameters. However, the current metallographic examination method to find the parameter window is time-consuming and involves subjective assessments of the experimenters. Here, we proposed a supervised machine learning (ML) method to detect the track defect and predict the printability of material in SLM intelligently. The printed tracks were classified into five types based on the measured surface morphologies and characteristics. The classification results were used as the target output of the ML model. Four indicators had been calculated to evaluate the quality of the tracks quantitatively, serving as input variables of the model. The data-driven model can determine the defect-free process parameter combination, which significantly improves the efficiency in searching the process parameter window and has great potential for the application in the unmanned factory in the future.

15

Murray, R. Charles, and Hans W. Guesgen. "Report on the Twenty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-23)." AI Magazine 31, no. 3 (July 28, 2010): 125. http://dx.doi.org/10.1609/aimag.v31i3.2301.

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The 23rd International Florida Artificial Intelligence Research Society Conference (FLAIRS-23) was held May 19-21, 2010 at The Shores Resort & Spa in Daytona Beach Shores, Florida, USA. The conference featured an exciting lineup of invited speakers, a general conference track on artificial intelligence research, and numerous special tracks. The conference chair was David Wilson from the University of North Carolina at Charlotte. The program co-chairs were R. Charles Murray from Carnegie Learning and Hans W. Guesgen from Massey University in New Zealand. The special tracks coordinator was Philip McCarthy from the University of Memphis.

16

Firlik, Bartosz, and Maciej Tabaszewski. "Monitoring of the technical condition of tracks based on machine learning." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 7 (August 6, 2019): 702–8. http://dx.doi.org/10.1177/0954409719866368.

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This paper presents the concept of a simple system for the identification of the technical condition of tracks based on a trained learning system in the form of three independent neural networks. The studies conducted showed that basic measurements based on the root mean square of vibration acceleration allow for monitoring the track condition provided that the rail type has been included in the information system. Also, it is necessary to select data based on the threshold value of the vehicle velocity. In higher velocity ranges (above 40 km/h), it is possible to distinguish technical conditions with a permissible error of 5%. Such selection also enables to ignore the impact of rides through switches and crossings. Technical condition monitoring is also possible at lower ride velocities; however, this comes at the cost of reduced accuracy of the analysis.

17

Mahboubi, Zouhair, and Mykel J. Kochenderfer. "Learning Traffic Patterns at Small Airports From Flight Tracks." IEEE Transactions on Intelligent Transportation Systems 18, no. 4 (April 2017): 917–26. http://dx.doi.org/10.1109/tits.2016.2598064.

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18

Stimpfl-Abele, Georg. "NEURAL NETS FOR KINK FINDING." International Journal of Neural Systems 03, supp01 (January 1992): 297–302. http://dx.doi.org/10.1142/s0129065792000644.

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Neural network learning techniques for the recognition of decays of charged tracks are improved by adding the track momentum to the input. This allows the use of one single network for a wide range of energies. The efficiency of this method is compared with previous results and conventional methods and the behaviour of the nets is discussed in detail.

19

Lengyel, Péter. "Practical experiences in Moodle LMS." Acta Agraria Debreceniensis, no. 29 (July 28, 2008): 129–33. http://dx.doi.org/10.34101/actaagrar/29/2977.

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We use Moodle at the University of Debrecen, Business- and Agricultural Department since January 2007. Moodle is an open source Learning Management System.Learning Management System (or LMS) is a software package that enables the management and delivery of learning content and resources to students. Most LMS systems are web-based to facilitate "anytime, anywhere" access to learning content and administration. LMS tracks student progress in a course and indicates completions. At the least, learning management systems track individual student progress, record scores of quizzes and tests within an online learning program, and track course completions.Moodle has more and more function at our Department in education. At present, we work to introduce Moodle in our Faculty. Therefore, we took lessons for the tutors about the usage of the Moodle. Our aim to develop such a learning system, which is an integral part of educational process.

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De Jesus-Ojeda, Lizbelle, Maria T. San Martin, Barbara Segarra, Karen G. Martinez, and Ruth Rios-Motta. "3364 Implementation of Instructional Design through a Learning Management System to Engage Scholars in Novel Methodologies Health Disparities Research Tracks." Journal of Clinical and Translational Science 3, s1 (March 2019): 71. http://dx.doi.org/10.1017/cts.2019.168.

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OBJECTIVES/SPECIFIC AIMS: This project presents the implementation of research tracks instructional design using a learning management system (LMS). METHODS/STUDY POPULATION: On January 2018, a Novel Methodologies in Health Disparities Research Symposium was held, with participation of local and national collaborators. The purpose was to identify the most important areas of knowledge, essential skills, available online resources and conferences associated with each research track. The recommendations provided contributed to the instructional design of novel methodologies research tracks aiming to improve health disparity research. The LMS includes general documents, instructional materials and assessment instruments, among others. Scholars are required to comply with 30 contact hours. The content and strategies utilized will be evaluated. RESULTS/ANTICIPATED RESULTS: Active scholar participation through the LMS is expected. Evaluation results will reflect the strengths and challenges of the implementation of instructional design. DISCUSSION/SIGNIFICANCE OF IMPACT: This strategy will engage scholars in an active learning experience enhancing their career development as independent researchers to eliminate health disparities.

21

Chen, Ying, Yuanning Liu, Xiaodong Zhu, Huiling Chen, Fei He, and Yutong Pang. "Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion." Scientific World Journal 2014 (2014): 1–21. http://dx.doi.org/10.1155/2014/670934.

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For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks’ information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples’ own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

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Wake, Laura M., Derek B. Allison, Alisha D. Ware, Jody E. Hooper, Alex S. Baras, Evan M. Bloch, William Clarke, et al. "Pathology Residency Program Special Expertise Tracks Meet the Needs of an Evolving Field." Academic Pathology 8 (January 1, 2021): 237428952110370. http://dx.doi.org/10.1177/23742895211037034.

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Pathologists who enter the workforce must have a diverse skill set beyond that of clinical diagnostics alone. Anticipating this need, the Johns Hopkins Pathology Residency Program developed Special Expertise Tracks to enhance training in relevant subspecialty domains. Using a combination of discussions and surveys, we assessed: (1) our current resident curriculum; (2) perceived curricular strengths and needs; (3) resident career preferences and ultimate career paths; (4) perceived barriers to implementing an advanced elective curriculum; and (5) available departmental/institutional resources. Additionally, we utilized the Accreditation Council for Graduate Medical Education Pathology Milestones as a curricular guide. Six professional residency training Special Expertise Tracks were established: Education, Physician-Scientist Research, Informatics, Quality Improvement/Quality Assurance/Value-Based Care, Health Policy/Hospital Management and Global Health. After implementation in 2017, the Education track has had 4 residents complete the curriculum successfully; the Physician-Scientist Research track has had 2 residents and the Informatics and Global Health tracks have each had one resident successfully complete their respective curricula. Currently, 5 residents are pursuing the Education track, one is pursuing the Physician-Scientist Research track, one is pursuing the Informatics track, and 2 residents are pursuing the Global Health track. Five residents have completed long-term projects including developing several e-learning modules, an online free digital cytopathology atlas, peer-reviewed articles, book chapters, and books. The Johns Hopkins Pathology Resident Special Expertise Track program provides pathology residents an opportunity to gain meaningful experience and additional skills tailored to their individual career interests.

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Shafique, Rahman, Hafeez-Ur-Rehman Siddiqui, Furqan Rustam, Saleem Ullah, Muhammad Abubakar Siddique, Ernesto Lee, Imran Ashraf, and Sandra Dudley. "A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis." Sensors 21, no. 18 (September 16, 2021): 6221. http://dx.doi.org/10.3390/s21186221.

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Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.

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Hirashima, Yoichi. "A New Reinforcement Learning for Train Marshaling with Generalization Capability." Advanced Materials Research 974 (June 2014): 269–73. http://dx.doi.org/10.4028/www.scientific.net/amr.974.269.

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This paper proposes a new marshaling method for assembling an outgoing train. In the proposed method, each set of freight cars that have the same destination make a group, and the desirable group layout constitutes the best outgoing train. The incoming freight cars are classified into several ``sub-tracks'' searching better assignment in order to reduce the transfer distance of locomotive. Classifications and marshaling plans based on the transfer distance of a locomotive are obtained autonomously by a reinforcement learning system. Then, the number of sub-tracks utilized in the classification is determined by the learning system in order to yield generalization capability.

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Coles, Amanda, Andrew Coles, Angel García Olaya, Sergio Jiménez, Carlos Linares López, Scott Sanner, and Sungwook Yoon. "A Survey of the Seventh International Planning Competition." AI Magazine 33, no. 1 (March 15, 2012): 83–88. http://dx.doi.org/10.1609/aimag.v33i1.2392.

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In this article we review the 2011 International Planning Competition. We give an overview of the history of the competition, discussing how it has developed since its first edition in 1998. The 2011 competition was run in three main separate tracks: the deterministic (classical) track; the learning track; and the uncertainty track. Each track proposed its own distinct set of new challenges and the participants rose to these admirably, the results of each track showing promising progress in each area. The competition attracted a record number of participants this year, showing its continued and strong position as a major central pillar of the international planning research community.

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Lengyel, Péter. "Using of e-learning in agricultural training programs." Acta Agraria Debreceniensis, no. 34 (September 2, 2009): 117–24. http://dx.doi.org/10.34101/actaagrar/34/2833.

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We use the Moodle at the University of Debrecen, Businessand Agricultural Department since January 2007. The Moodle is an open source Learning Management System. Learning Management System (or LMS) which is a software package, which enables the management and delivery of learning content and resources to students. Most LMS systems are web-based to facilitate „anytime, anywhere” access to learning content and administration. LMS tracks student progress in a course and indicates completions. At the least, learning management systemstrack individual student progress, record scores of quizzes and tests within an online learning program, and track course completions. The Moodle has more and more function at our Department in education. In January we worked to introduce Moodle in our Faculty. Therefore, we took lessons for the tutors about the usage of the Moodle. Our aim was to develop such a learning system, which is an integral part of educational process,to improve the efficiency of agricultural higher education.

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Spiers, Hugo J., Bradley C. Love, Mike E. Le Pelley, Charlotte E. Gibb, and Robin A. Murphy. "Anterior Temporal Lobe Tracks the Formation of Prejudice." Journal of Cognitive Neuroscience 29, no. 3 (March 2017): 530–44. http://dx.doi.org/10.1162/jocn_a_01056.

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Despite advances in understanding the brain structures involved in the expression of stereotypes and prejudice, little is known about the brain structures involved in their acquisition. Here, we combined fMRI, a task involving learning the valence of different social groups, and modeling of the learning process involved in the development of biases in thinking about social groups that support prejudice. Participants read descriptions of valenced behaviors performed by members of novel social groups, with majority groups being more frequently encountered during learning than minority groups. A model-based fMRI analysis revealed that the anterior temporal lobe tracked the trial-by-trial changes in the valence associated with each group encountered in the task. Descriptions of behavior by group members that deviated from the group average (i.e., prediction errors) were associated with activity in the left lateral PFC, dorsomedial PFC, and lateral anterior temporal cortex. Minority social groups were associated with slower acquisition rates and more activity in the ventral striatum and ACC/dorsomedial PFC compared with majority groups. These findings provide new insights into the brain regions that (a) support the acquisition of prejudice and (b) detect situations in which an individual's behavior deviates from the prejudicial attitude held toward their group.

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Sonderen, Peter, and Jur Koksma. "Tracing transforming honors tracks - Arts and sciences beyond borders." Journal of the European Honors Council 1, no. 1 (May 12, 2017): 1–10. http://dx.doi.org/10.31378/jehc.61.

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In this paper we describe how honors students of an art university and a classical research university in the east of the Netherlands, came together to work on the development of new ecologies of art and science. We narrate a yearlong learning journey by highlighting two main projects, first a local Wunderkammer project and subsequently a joint research trip to New York, Boston and Cambridge for investigating similar initiatives across the Atlantic. While going beyond the borders of disciplines and institutions, in search of new terrain, students reframe their own field as well. By experimenting with the form of our honors tracks we hope to widen the horizon of young people and help them unleash their potential. Our meandering story describes how the honors track kept on changing form, by allowing students to claim radical ownership, and how this has taught us that such experiments can not only be carried out in a responsible manner, but may also create more powerful environments for learning across borders.

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Sonwane, Kalyani A. "Simulating Self Driving Car Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (July 15, 2021): 738–43. http://dx.doi.org/10.22214/ijraset.2021.36454.

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Self-driving cars became a trending subject with a big improvement in technologies within the last decade. The project aims to coach a neural network to drive associate degree autonomous automobile agent on Udacity’s automobile Simulator's tracks. Udacity has discharged the machine as ASCII text file computer code and enthusiasts have hosted a contest (challenge) to show an automobile the way to drive victimisation solely camera pictures and deep learning. Autonomously driving an automobile needs learning to regulate steering angle, throttle and brakes. The activity biological research technique is employed to mimic human driving behaviour within the coaching model on the track. which means a dataset is generated within the machine by a user-driven automobile in coaching mode, and therefore the deep neural network model then drives the automobile in autonomous mode. 3 architectures area unit compared regarding their performance. Though the models performed well for the track it had been trained with, the important challenge was to generalize this behaviour on a second track out there on the machine. The dataset for Track_1, that was straightforward with favourable road conditions to drive, was used because the coaching set to drive the automobile autonomously on Track_2, consisting of sharp turns, barriers, elevations, and shadows. Image process and completely different augmentation techniques were accustomed tackle this downside, that allowed extracting the maximum amount data and options within the knowledge as doable. Ultimately, the automobile was ready to run on Track_2 generalizing well. The project aims at reaching an equivalent accuracy on period of time knowledge within the future.

30

Nasrallah, Fatima A., Xuan Vinh To, Der-Yow Chen, Aryeh Routtenberg, and Kai-Hsiang Chuang. "Functional connectivity MRI tracks memory networks after maze learning in rodents." NeuroImage 127 (February 2016): 196–202. http://dx.doi.org/10.1016/j.neuroimage.2015.08.013.

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31

Reynolds, Robert G. "Networks Do Matter." International Journal of Swarm Intelligence Research 1, no. 1 (January 2010): 17–41. http://dx.doi.org/10.4018/jsir.2010010102.

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This article describes a socially motivated evolutionary algorithm, Cultural Algorithms, to design a controller for a 3D racing game for use in a competitive event held at the 2008 IEEE World Congress. The controller was modeled as a state machine and a set of utility functions were associated with actions performed in each state. Cultural Algorithms are used to optimize these functions. Cultural Algorithms consist of a Population Space, a collection of knowledge sources in the Belief Space, and a communication protocol connecting the components together. The knowledge sources in the belief space vie to control individuals in the population through the social fabric influence function. Here the population is a network of chromosomes connected by the LBest topology. This LBest configuration was employed to train the system on an example oval track prior to the contest, but it did not generalize to other tracks. The authors investigated how other topologies performed when learning on each of the contest tracks. The square network (a type of small world network) worked best at distributing the influence of the knowledge sources, and reduced the likelihood of premature convergence for complex tracks.

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Banaie Boroujeni, Kianoush, Mariann Oemisch, Seyed Alireza Hassani, and Thilo Womelsdorf. "Fast spiking interneuron activity in primate striatum tracks learning of attention cues." Proceedings of the National Academy of Sciences 117, no. 30 (July 13, 2020): 18049–58. http://dx.doi.org/10.1073/pnas.2001348117.

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Cognitive flexibility depends on a fast neural learning mechanism for enhancing momentary relevant over irrelevant information. A possible neural mechanism realizing this enhancement uses fast spiking interneurons (FSIs) in the striatum to train striatal projection neurons to gate relevant and suppress distracting cortical inputs. We found support for such a mechanism in nonhuman primates during the flexible adjustment of visual attention in a reversal learning task. FSI activity was modulated by visual attention cues during feature-based learning. One FSI subpopulation showed stronger activation during learning, while another FSI subpopulation showed response suppression after learning, which could indicate a disinhibitory effect on the local circuit. Additionally, FSIs that showed response suppression to learned attention cues were activated by salient distractor events, suggesting they contribute to suppressing bottom-up distraction. These findings suggest that striatal fast spiking interneurons play an important role when cues are learned that redirect attention away from previously relevant to newly relevant visual information. This cue-specific activity was independent of motor-related activity and thus tracked specifically the learning of reward predictive visual features.

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Buchanan, George R. "Academic promotion and tenure: a user’s guide for junior faculty members." Hematology 2009, no. 1 (January 1, 2009): 736–41. http://dx.doi.org/10.1182/asheducation-2009.1.736.

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Abstract Securing a junior faculty position is an important early step in an academic career in hematology. Shortly thereafter one should begin to plan for eventual promotion and possible tenure. The process is not straightforward, as the “rules of the road” regarding academic positions, academic tracks, assessment and evaluation metrics, and timelines vary immensely from one institution to another. It is critically important, therefore, for the new junior faculty member to become knowledgeable about the institutional policies and “culture” regarding this process. This understanding includes the definition of and criteria for achieving tenure, the academic tracks and the policies for advancement on each track, and the process by which the institutional committee responsible for promotion and tenure conducts its activities. Learning the rules and successfully navigating the academic pathway will help ensure success by achieving the desired promotion and the self-satisfaction, prestige, and financial awards that may accompany it.

34

Kuttan, Manjunath Omana, Jan Steinheimer, Kai Zhou, Andreas Redelbach, and Horst Stoecker. "Deep Learning Based Impact Parameter Determination for the CBM Experiment." Particles 4, no. 1 (February 2, 2021): 47–52. http://dx.doi.org/10.3390/particles4010006.

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In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm.

35

Navarro Gonzalez, Jairo, Ann S. Zweig, Matthew L. Speir, Daniel Schmelter, Kate R. Rosenbloom, Brian J. Raney, Conner C. Powell, et al. "The UCSC Genome Browser database: 2021 update." Nucleic Acids Research 49, no. D1 (November 22, 2020): D1046—D1057. http://dx.doi.org/10.1093/nar/gkaa1070.

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Abstract For more than two decades, the UCSC Genome Browser database (https://genome.ucsc.edu) has provided high-quality genomics data visualization and genome annotations to the research community. As the field of genomics grows and more data become available, new modes of display are required to accommodate new technologies. New features released this past year include a Hi-C heatmap display, a phased family trio display for VCF files, and various track visualization improvements. Striving to keep data up-to-date, new updates to gene annotations include GENCODE Genes, NCBI RefSeq Genes, and Ensembl Genes. New data tracks added for human and mouse genomes include the ENCODE registry of candidate cis-regulatory elements, promoters from the Eukaryotic Promoter Database, and NCBI RefSeq Select and Matched Annotation from NCBI and EMBL-EBI (MANE). Within weeks of learning about the outbreak of coronavirus, UCSC released a genome browser, with detailed annotation tracks, for the SARS-CoV-2 RNA reference assembly.

36

Flora, Montgomery L., Corey K. Potvin, Patrick S. Skinner, Shawn Handler, and Amy McGovern. "Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System." Monthly Weather Review 149, no. 5 (May 2021): 1535–57. http://dx.doi.org/10.1175/mwr-d-20-0194.1.

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AbstractA primary goal of the National Oceanic and Atmospheric Administration Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0–3 h) severe weather forecasts. Postprocessing is required to maximize the usefulness of probabilistic guidance from an ensemble of convection-allowing model forecasts. Machine learning (ML) models have become popular methods for postprocessing severe weather guidance since they can leverage numerous variables to discover useful patterns in complex datasets. In this study, we develop and evaluate a series of ML models to produce calibrated, probabilistic severe weather guidance from WoF System (WoFS) output. Our dataset includes WoFS ensemble forecasts available every 5 min out to 150 min of lead time from the 2017–19 NOAA Hazardous Weather Testbed Spring Forecasting Experiments (81 dates). Using a novel ensemble storm-track identification method, we extracted three sets of predictors from the WoFS forecasts: intrastorm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. We then trained random forests, gradient-boosted trees, and logistic regression algorithms to predict which WoFS 30-min ensemble storm tracks will overlap a tornado, severe hail, and/or severe wind report. To provide rigorous baselines against which to evaluate the skill of the ML models, we extracted the ensemble probabilities of hazard-relevant WoFS variables exceeding tuned thresholds from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced more reliable probabilities than the baseline predictions. Overall, the results suggest that ML-based postprocessing of dynamical ensemble output can improve short-term, storm-scale severe weather probabilistic guidance.

37

Nahhas, Safia, Omaimah Bamasag, Maher Khemakhem, and Nada Bajnaid. "Added Values of Linked Data in Education: A Survey and Roadmap." Computers 7, no. 3 (September 1, 2018): 45. http://dx.doi.org/10.3390/computers7030045.

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Education values such as knowledge sharing, and the linked data (LD) abilities such as interoperability are in perfect harmony. Much research has exploited that and provided important contributions and improvements in education through LD. International universities, large open education repositories, OpenCourseWare (OCW) and Massive Open Online Courses (MOOCs) initiatives, educational search engines, blending and adaptive learning, learning analysis and other various areas were the targets of many works on leveraging LD. However, this research exists in a scattered way without any type of categorization or organization. In this paper, we present a survey on the current works in educational linked data (ELD) to provide a starting point and a comprehensive roadmap to help researchers in recognizing the main tracks in ELD area. In addition, the paper extracted the common life cycle, outcome datasets and vocabularies from the overall presented works. The paper also provides samples of applications that exhibit the practical benefit of adopting LD in the various tracks and highlights the challenges that each track faced during the utilization of LD. Pioneer ELD’s projects, other existing overviews and landscapes and the most used tools based on the stages and prevalent are presented. Lastly, discussion and recommendations were provided based on the overall study.

38

Suwasono, Suwasono, Dwi Prihanto, Irawan Dwi Wahyono, and Andrew Nafalski. "Virtual Laboratory for Line Follower Robot Competition." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (August 1, 2017): 2253. http://dx.doi.org/10.11591/ijece.v7i4.pp2253-2260.

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<p>Laboratory serves as an important facility for experiment and research activity. The limitation of time, equipment, and capacity in the experiment and research undertaking impede both students and college students in undertaking research for competition preparation, particularly dealing with line follower robot competition which requires a wide space of the room with various track types. Unsettled competition track influences PID control setting of line follower robot. This study aims at developing Virtual Laboratory (V-Lab) for students or college students who are preparing for line follower robot competition with unsettled and changeable tracks. This study concluded that the trial data score reached 98.5%, the material expert score obtained 89.7%, learning model expert score obtained 97.9%, and the average score of small group learning model and field of 82.4%, which the average score of the entire aspects obtained 90.8%.</p>

39

Finley, James M., Amy J. Bastian, and Jinger S. Gottschall. "Learning to be economical: the energy cost of walking tracks motor adaptation." Journal of Physiology 591, no. 4 (February 2013): 1081–95. http://dx.doi.org/10.1113/jphysiol.2012.245506.

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40

Bojarczak, Piotr, and Waldemar Nowakowski. "Application of Deep Learning Networks to Segmentation of Surface of Railway Tracks." Sensors 21, no. 12 (June 12, 2021): 4065. http://dx.doi.org/10.3390/s21124065.

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The article presents a vision system for detecting elements of railway track. Four types of fasteners, wooden and concrete sleepers, rails, and turnouts can be recognized by our system. In addition, it is possible to determine the degree of sleeper ballast coverage. Our system is also able to work when the track is moderately covered by snow. We used a Fully Convolutional Neural Network with 8 times upsampling (FCN-8) to detect railway track elements. In order to speed up training and improve performance of the model, a pre-trained deep convolutional neural network developed by Oxford’s Visual Geometry Group (VGG16) was used as a framework for our system. We also verified the invariance of our system to changes in brightness. To do this, we artificially varied the brightness of images. We performed two types of tests. In the first test, we changed the brightness by a constant value for the whole analyzed image. In the second test, we changed the brightness according to a predefined distribution corresponding to Gaussian function.

41

Akar, Simon, Gowtham Atluri, Thomas Boettcher, Michael Peters, Henry Schreiner, Michael Sokoloff, Marian Stahl, William Tepe, Constantin Weisser, and Mike Williams. "Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices." EPJ Web of Conferences 251 (2021): 04012. http://dx.doi.org/10.1051/epjconf/202125104012.

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The locations of proton-proton collision points in LHC experiments are called primary vertices (PVs). Preliminary results of a hybrid deep learning algorithm for identifying and locating these, targeting the Run 3 incarnation of LHCb, have been described at conferences in 2019 and 2020. In the past year we have made significant progress in a variety of related areas. Using two newer Kernel Density Estimators (KDEs) as input feature sets improves the fidelity of the models, as does using full LHCb simulation rather than the “toy Monte Carlo” originally (and still) used to develop models. We have also built a deep learning model to calculate the KDEs from track information. Connecting a tracks-to-KDE model to a KDE-to-hists model used to find PVs provides a proof-of-concept that a single deep learning model can use track information to find PVs with high efficiency and high fidelity. We have studied a variety of models systematically to understand how variations in their architectures affect performance. While the studies reported here are specific to the LHCb geometry and operating conditions, the results suggest that the same approach could be used by the ATLAS and CMS experiments.

42

Orgován, László, Tamás Bécsi, and Szilárd Aradi. "Autonomous Drifting Using Reinforcement Learning." Periodica Polytechnica Transportation Engineering 49, no. 3 (September 1, 2021): 292–300. http://dx.doi.org/10.3311/pptr.18581.

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Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA.

43

Meinke, Timothy S. "2008 APSA Teaching and Learning Track Summaries—Track Eight: Civic Engagement I." PS: Political Science & Politics 41, no. 03 (June 18, 2008): 622–23. http://dx.doi.org/10.1017/s104909650829089x.

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Political science has always pondered questions of civic engagement. Socrates described and defended his intimate engagement with Athens in theApologyand Aristotle argued in thePoliticsthat it was only through engagement with the polis that humans could set forth and discuss notions of justice. Stephen Leonard (1999) and Hindy Schachter (1998) pointed out in earlier volumes of this journal that at the end of the nineteenth century the “founding fathers” of modern academic political science were motivated by ideas of improving citizens through civic education. And this has continued to be a focus for the American Political Science Association (APSA) through collaborative efforts such as the 1996 Task Force on Civic Education for the Next Century or, more recently, tracks during the association's Teaching and Learning Conference.

44

Santur, Yunus, Mehmet Karaköse, and Erhan Akın. "Learning Based Experimental Approach For Condition Monitoring Using Laser Cameras In Railway Tracks." International Journal of Applied Mathematics, Electronics and Computers 4, Special Issue-1 (December 2, 2016): 1. http://dx.doi.org/10.18100/ijamec.270656.

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45

Pan, Yafeng, Giacomo Novembre, Bei Song, Xianchun Li, and Yi Hu. "Interpersonal synchronization of inferior frontal cortices tracks social interactive learning of a song." NeuroImage 183 (December 2018): 280–90. http://dx.doi.org/10.1016/j.neuroimage.2018.08.005.

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46

Tsunashima, Hitoshi. "Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique." Applied Sciences 9, no. 13 (July 5, 2019): 2734. http://dx.doi.org/10.3390/app9132734.

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A track condition monitoring system that uses a compact on-board sensing device has been developed and applied for track condition monitoring of regional railway lines in Japan. Monitoring examples show that the system is effective for regional railway operators. A classifier for track faults has been developed to detect track fault automatically. Simulation studies using SIMPACK and field tests were carried out to detect and isolate the track faults from car-body vibration. The results show that the feature of track faults is extracted from car-body vibration and classified from proposed feature space using machine learning techniques.

47

Solovyov, V. I., O. V. Rybalskiy, V. V. Zhuravel, and V. K. Zheleznyak. "Application of neuron networks of deep learning for exposures editing of digital phonograms." Proceedings of the National Academy of Sciences of Belarus, Physical-Technical Series 65, no. 4 (December 31, 2020): 506–12. http://dx.doi.org/10.29235/1561-8358-2020-65-4-506-512.

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Possibility of creation of effective system, which is intended for exposure of tracks of editing in digital phonograms and is built on the basis of neuron networks of the deep learning, is experimentally proven. Sense of experiment consisted in research of ability of the systems on the basis of such networks to expose pauses with tracks of editing. The experimental array of data is created in a voice editor from phonograms written on the different apparatus of the digital audio recording (at frequency of discretisation 44,1 kHz). A preselection of pauses was produced from it, having duration from 100 мs to a few seconds. From 1000 selected pauses the array of fragments of pauses is formed in the automatic (computer) mode, from which the arrays of fragments of pauses of different duration are generated by a dimension about 100 000. For forming of array of fragments of pauses with editing, the chosen pauses were divided into casual character parts in arbitrary correlation. Afterwards, the new pauses were created from it with the fixed place of editing. The general array of all fragments of pauses was broken into training and test arrays. The maximum efficiency, achieved on a test array in the process of educating, was determined. In general case this efficiency is determined by the maximum size of probability of correct classification of fragments with editing and fragments without editing. Scientifically reasonable methodology of exposure of signs of editing in digital phonograms is offered on the basis of neuron networks of the deep learning. The conducted experiments showed that the construction of the effective system is possible for the exposure of such tracks. Further development of methodology must be directed to find the ways to increase the probability of correct binary classification of investigated pauses.

48

Solovyov, V. I., O. V. Rybalskiy, V. V. Zhuravel, and V. K. Zheleznyak. "Application of neuron networks of deep learning for exposures editing of digital phonograms." Proceedings of the National Academy of Sciences of Belarus, Physical-Technical Series 65, no. 4 (December 31, 2020): 506–12. http://dx.doi.org/10.29235/1561-8358-2020-65-4-506-512.

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Possibility of creation of effective system, which is intended for exposure of tracks of editing in digital phonograms and is built on the basis of neuron networks of the deep learning, is experimentally proven. Sense of experiment consisted in research of ability of the systems on the basis of such networks to expose pauses with tracks of editing. The experimental array of data is created in a voice editor from phonograms written on the different apparatus of the digital audio recording (at frequency of discretisation 44,1 kHz). A preselection of pauses was produced from it, having duration from 100 мs to a few seconds. From 1000 selected pauses the array of fragments of pauses is formed in the automatic (computer) mode, from which the arrays of fragments of pauses of different duration are generated by a dimension about 100 000. For forming of array of fragments of pauses with editing, the chosen pauses were divided into casual character parts in arbitrary correlation. Afterwards, the new pauses were created from it with the fixed place of editing. The general array of all fragments of pauses was broken into training and test arrays. The maximum efficiency, achieved on a test array in the process of educating, was determined. In general case this efficiency is determined by the maximum size of probability of correct classification of fragments with editing and fragments without editing. Scientifically reasonable methodology of exposure of signs of editing in digital phonograms is offered on the basis of neuron networks of the deep learning. The conducted experiments showed that the construction of the effective system is possible for the exposure of such tracks. Further development of methodology must be directed to find the ways to increase the probability of correct binary classification of investigated pauses.

49

Orpella, Joan, Ernest Mas-Herrero, Pablo Ripollés, Josep Marco-Pallarés, and Ruth de Diego-Balaguer. "Language statistical learning responds to reinforcement learning principles rooted in the striatum." PLOS Biology 19, no. 9 (September 7, 2021): e3001119. http://dx.doi.org/10.1371/journal.pbio.3001119.

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Statistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks the online SL of simple syntactic structures combined with computational modeling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate—on 2 different cohorts—that a temporal difference model, which relies on prediction errors, accounts for participants’ online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the long-standing gap between language learning and reinforcement learning phenomena.

50

Singer, Yoram. "Adaptive Mixtures of Probabilistic Transducers." Neural Computation 9, no. 8 (November 1, 1997): 1711–33. http://dx.doi.org/10.1162/neco.1997.9.8.1711.

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We describe and analyze a mixture model for supervised learning of probabilistic transducers. We devise an online learning algorithm that efficiently infers the structure and estimates the parameters of each probabilistic transducer in the mixture. Theoretical analysis and comparative simulations indicate that the learning algorithm tracks the best transducer from an arbitrarily large (possibly infinite) pool of models. We also present an application of the model for inducing a noun phrase recognizer.

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