Journal articles on the topic 'Discovery'
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Cook, D. J., and L. B. Holder. "Substructure Discovery Using Minimum Description Length and Background Knowledge." Journal of Artificial Intelligence Research 1 (February 1, 1994): 231–55. http://dx.doi.org/10.1613/jair.43.
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The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE substructure discovery system based on the minimum description length principle. The SUBDUE system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimum description length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find substructures capable of compressing the original data and to discover structural concepts important to the domain. Description of Online Appendix: This is a compressed tar file containing the SUBDUE discovery system, written in C. The program accepts as input databases represented in graph form, and will output discovered substructures with their corresponding value.
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Zambrano, Diego. "Discovery as Regulation." Michigan Law Review, no. 119.1 (2020): 71. http://dx.doi.org/10.36644/mlr.119.1.discovery.
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This article develops an approach to discovery that is grounded in regulatory theory and administrative subpoena power. The conventional judicial and scholarly view about discovery is that it promotes fair and accurate outcomes and nudges the parties toward settlement. While commonly held, however, this belief is increasingly outdated and suffers from limitations. Among them, it has generated endless controversy about the problem of discovery costs. Indeed, a growing chorus of scholars and courts has offered an avalanche of reforms, from cost shifting and bespoke discovery contracts to outright elimination. Recently, Judge Thomas Hardiman quipped that if he had absolute power, he would abolish discovery for cases involving less than $500,000. These debates, however, are at a standstill, and existing scholarship offers incomplete treatment of discovery theory that might move debates forward. The core insight of the project is that in the private-enforcement context—where Congress deliberately employs private litigants as the main method of statutory enforcement—there is a surprisingly strong case that our current discovery system should be understood in part as serving regulatory goals analogous to administrative subpoena power. That is, discovery here can be seen as an extension of the subpoena power that agencies like the SEC, FTC, and EPA possess and is the lynchpin of a system that depends on private litigants to enforce our most important statutes. By forcing parties to disclose large amounts of information, discovery deters harm and, most importantly, shapes industry-wide practices and the primary behavior of regulated entities. This approach has a vast array of implications for the scope of discovery as well as the debate over costs. Scholars and courts should thus grapple with the consequences of what I call “regulatory discovery” for the entire legal system.
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Kamel, Mohammed B. M., Yuping Yan, Peter Ligeti, and Christoph Reich. "Attred: Attribute Based Resource Discovery for IoT." Sensors 21, no. 14 (July 10, 2021): 4721. http://dx.doi.org/10.3390/s21144721.
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While the number of devices connected together as the Internet of Things (IoT) is growing, the demand for an efficient and secure model of resource discovery in IoT is increasing. An efficient resource discovery model distributes the registration and discovery workload among many nodes and allow the resources to be discovered based on their attributes. In most cases this discovery ability should be restricted to a number of clients based on their attributes, otherwise, any client in the system can discover any registered resource. In a binary discovery policy, any client with the shared secret key can discover and decrypt the address data of a registered resource regardless of the attributes of the client. In this paper we propose Attred, a decentralized resource discovery model using the Region-based Distributed Hash Table (RDHT) that allows secure and location-aware discovery of the resources in IoT network. Using Attribute Based Encryption (ABE) and based on predefined discovery policies by the resources, Attred allows clients only by their inherent attributes, to discover the resources in the network. Attred distributes the workload of key generations and resource registration and reduces the risk of central authority management. In addition, some of the heavy computations in our proposed model can be securely distributed using secret sharing that allows a more efficient resource registration, without affecting the required security properties. The performance analysis results showed that the distributed computation can significantly reduce the computation cost while maintaining the functionality. The performance and security analysis results also showed that our model can efficiently provide the required security properties of discovery correctness, soundness, resource privacy and client privacy.
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Gagliardi, Francesco. "A Cognitive Approach to Scientific Data Mining for Syndrome Discovery." International Journal of Software Science and Computational Intelligence 4, no. 1 (January 2012): 1–33. http://dx.doi.org/10.4018/jssci.2012010101.
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The author introduces a machine learning system for cluster analysis to take on the problem of syndrome discovery in the clinical domain. A syndrome is a set of typical clinical features (a prototype) that appear together often enough to suggest they may represent a single, unknown, disease. The discovery of syndromes and relative taxonomy formation is therefore the critical early phase of the process of scientific discovery in the medical domain. The system proposed discovers syndromes following Eleanor Rosch’s prototype theory on how the human mind categorizes and forms taxonomies, and thereby to understand how humans perform these activities and to automate or assist the process of scientific discovery. The system implemented can be considered a scientific discovery support system as it can discover unknown syndromes to the advantage of subsequent clinical practices and research activities.
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Ramadhan, Haider Ali. "DISCOVER: AN INTELLIGENT DISCOVERY PROGRAMMING SYSTEM." Cybernetics and Systems 31, no. 1 (January 2000): 87–114. http://dx.doi.org/10.1080/019697200124937.
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Das, Shubhomoy, Md Rakibul Islam, Nitthilan Kannappan Jayakodi, and Janardhan Rao Doppa. "Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning." Journal of Artificial Intelligence Research 80 (May 23, 2024): 127–70. http://dx.doi.org/10.1613/jair.1.14741.
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Anomaly detection (AD) task corresponds to identifying the true anomalies among a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies. The ranked list of anomalies is then analyzed by a human to discover the true anomalies. Ensemble of tree-based anomaly detectors trained in an unsupervised manner and scoring based on uniform weights for ensembles are shown to work well in practice. However, the manual process of analysis can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensembles based on label feedback allows us to quickly discover true anomalies. This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. First, we provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. We also show empirical results on real-world data to support our insights and theoretical analysis to support active learning. Second, we develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. Third, we develop a novel active learning algorithm to handle streaming data setting. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner. Fourth, we present extensive experiments to evaluate our insights and our tree-based active anomaly discovery algorithms in both batch and streaming data settings. Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup.
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KANWAL, ATTIYA, SAHAR FAZAL, SOHAIL ASGHAR, and Muhammad Naeem. "SUBGROUP DISCOVERY OF THE MODY GENES;." Professional Medical Journal 20, no. 05 (October 15, 2013): 644–52. http://dx.doi.org/10.29309/tpmj/2013.20.05.1207.
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Background: The pandemic of metabolic disorders is accelerating in the urbanized world posing huge burden to healthand economy. The key pioneer to most of the metabolic disorders is Diabetes Mellitus. A newly discovered form of diabetes is MaturityOnset Diabetes of the Young (MODY). MODY is a monogenic form of diabetes. It is inherited as autosomal dominant disorder. Till to date11 different MODY genes have been reported. Objective: This study aims to discover subgroups from the biological text documentsrelated to these genes in public domain database. Data Source: The data set was obtained from PubMed. Period: September-December,2011. Materials and Methodology: APRIORI-SD subgroup discovery algorithm is used for the task of discovering subgroups. A wellknown association rule learning algorithm APRIORI is first modified into classification rule learning algorithm APRIORI-C. APRIORI-Calgorithm generates the rule from the discretized dataset with the minimum support set to 0.42% with no confidence threshold. Total 580rules are generated at the given support. APRIOIR-C is further modified by making adaptation into APRIORI-SD. Results: Experimentalresults demonstrate that APRIORI discovers the substantially smaller rule sets; each rule has higher support and significance. The rulesthat are obtained by APRIORI-C are ordered by weighted relative accuracy. Conclusion: Only first 66 rules are ordered as they cover therelation between all the 11 MODY genes with each other. These 66 rules are further organized into 11 different subgroups. The evaluationof obtained results from literature shows that APRIORI-SD is a competitive subgroup discovery algorithm. All the association amonggenes proved to be true.
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Romanova, V. V. "DISCOVERY AND DEVELOPMENT OF IULTINSKOYE TIN-TUNGSTEN DEPOSIT." Proceedings of higher educational establishments. Geology and Exploration, no. 3 (June 25, 2018): 65–73. http://dx.doi.org/10.32454/0016-7762-2018-3-65-73.
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The Iultinskoye deposit was discovered 80 years ago in the remote and unexplored region of Chukotka. Discovery and development of the deposit were accompanied by serious difficulties. A brief biographic data about the discoverer of the deposit V.N. Milyaev has been presented here. The Iultinsky mining processing plant, which was opened in 1959, was closed in 1994 due to the changing in the economic situation. The deposit was conserved, Iultinsky village was liquidated. Vernadsky State Geological Museum RAS has samples of cassiterite and wolframite, collected by geologists of the Second Chukotka Glavsevmorput expedition in the year of the deposit discovery.
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Cai, Borui, Guangyan Huang, Yong Xiang, Maia Angelova, Limin Guo, and Chi-Hung Chi. "Multi-Scale Shapelets Discovery for Time-Series Classification." International Journal of Information Technology & Decision Making 19, no. 03 (May 2020): 721–39. http://dx.doi.org/10.1142/s0219622020500133.
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Shapelets are subsequences of time-series that represent local patterns and can improve the accuracy and the interpretability of time-series classification. The major task of time-series classification using shapelets is to discover high quality shapelets. However, this is challenging since local patterns may have various scales/lengths rather than a unified scale. In this paper, we resolve this problem by discovering shapelets with multiple scales. We propose a novel Multi-Scale Shapelet Discovery (MSSD) algorithm to discover expressive multi-scale shapelets by extending initial single-scale shapelets (i.e., shapelets with a unified scale). MSSD adopts a bi-directional extension process and is robust to extend single-shapelets obtained by different methods. A supervised shapelet quality measurement is further developed to qualify the extension of shapelets. Comprehensive experiments conducted on 25 UCR time-series datasets show that multi-scale shapelets discovered by MSSD improve classification accuracy by around 10% (in average), compared with single-scale shapelets discovered by counterpart methods.
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Sit, Masganti, Putri Lestari, and Yusnaili Budianti. "Improving The Understanding of Science Concept Through Guided Discovery Learning Model in Azzahra Preschool Kindergarten." Unnes Science Education Journal 9, no. 3 (November 21, 2020): 128–36. http://dx.doi.org/10.15294/usej.v9i3.39590.
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Understanding simple concepts (scientific concepts) was one of important understanding in aspects of cognitive development for early childhood. Children were trained to think actively and critically in order to understand they activities, namely by applying a constructivist approach or guided discovey learning. However in Azzahra Preschool Kindergarten has not applied a guided discovey learning. It considered to be a factor in children’s low understanding of science concepts. Therefore, this research aimed to improve the children's understanding of science concepts by applying guided discovery learning. This research used a classroom action research with two cycles. Subject of this research is all of children group B in Azzahra Preschool Kindergarten. And the results show that the guided discovery learning can improved children’s understanding of science concept. This is based on percentage score of children’s understanding of science concept which is increasing in each cycle. Other findings of this research showed that children were eager to learn, curiosity was increasing, and active to conduct experiments to discover various simple concepts. So, this research was recommended that apply the guided discovery learning model to develop various aspects of the child.
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Huang, Ying, Liyun Zhong, and Yan Chen. "Filtering Infrequent Behavior in Business Process Discovery by Using the Minimum Expectation." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (April 2020): 1–15. http://dx.doi.org/10.4018/ijcini.2020040101.
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The aim of process discovery is to discover process models from the process execution data stored in event logs. In the era of “Big Data,” one of the key challenges is to analyze the large amounts of collected data in meaningful and scalable ways. Most process discovery algorithms assume that all the data in an event log fully comply with the process execution specification, and the process event logs are no exception. However, real event logs contain large amounts of noise and data from irrelevant infrequent behavior. The infrequent behavior or noise has a negative influence on the process discovery procedure. This article presents a technique to remove infrequent behavior from event logs by calculating the minimum expectation of the process event log. The method was evaluated in detail, and the results showed that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.
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Murugesan, Dr G., Dr S. Maheswaran, S. Selvapriya, V. Suganthan, S. Vinith, B. Vivek, and S. Sathesh. "Discovery of Asteroids." Journal of Advanced Research in Dynamical and Control Systems 12, no. 1 (January 31, 2020): 301–7. http://dx.doi.org/10.5373/jardcs/v12i1/20201157.
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Zymbler, Mikhail, and Yana Kraeva. "High-Performance Time Series Anomaly Discovery on Graphics Processors." Mathematics 11, no. 14 (July 20, 2023): 3193. http://dx.doi.org/10.3390/math11143193.
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Currently, discovering subsequence anomalies in time series remains one of the most topical research problems. A subsequence anomaly refers to successive points in time that are collectively abnormal, although each point is not necessarily an outlier. Among numerous approaches to discovering subsequence anomalies, the discord concept is considered one of the best. A time series discord is intuitively defined as a subsequence of a given length that is maximally far away from its non-overlapping nearest neighbor. Recently introduced, the MERLIN algorithm discovers time series discords of every possible length in a specified range, thereby eliminating the need to set even that sole parameter to discover discords in a time series. However, MERLIN is serial, and its parallelization could increase the performance of discord discovery. In this article, we introduce a novel parallelization scheme for GPUs called PALMAD, parallel arbitrary length MERLIN-based anomaly discovery. As opposed to its serial predecessor, PALMAD employs recurrent formulas we have derived to avoid redundant calculations, and advanced data structures for the efficient implementation of parallel processing. Experimental evaluation over real-world and synthetic time series shows that our algorithm outperforms parallel analogs. We also apply PALMAD to discover anomalies in a real-world time series, employing our proposed discord heatmap technique to illustrate the results.
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Hayat, O., R. Ngah, and Yasser Zahedi. "Cooperative GPS and Neighbors Awareness Based Device Discovery for D2D Communication in in-Band Cellular Networks." International Journal of Engineering & Technology 7, no. 2.29 (May 22, 2018): 700. http://dx.doi.org/10.14419/ijet.v7i2.29.14001.
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Device to Device (D2D) communication is a new paradigm for next-generation wireless systems to offload data traffic. A device needs to discover neighbor devices on the certain channel to initiate the D2D communication within the minimum period. A device discovery technique based on Global Positioning System (GPS) and neighbor awareness base are proposed for in-band cellular networks. This method is called network-centric approach, and it improves the device discovery efficiency, accuracy, and channel capacity. The differential code is applied to measure the signal to noise ratio of each discovered device. In the case that the signal to noise ratio (SNR) of two devices is above a specified threshold value, then these two devices are qualified for D2D communication. Two procedures are explored for device discovery; discovery by CN (core network) and eNB (evolved node B) cooperation with the help of GPS and neighbor awareness. Using ‘Haversine’ formula, SNR base distance is calculated. Results show an increment in the channel capacity relative to SNR obtained for each device.
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Muslimah, R. H., T. Mahatmanto, J. Kusnadi, and U. Murdiyatmo. "General methods to isolate, characterize, select, and identify fructophilic lactic acid bacteria from fructose-rich environments – A mini-review." IOP Conference Series: Earth and Environmental Science 924, no. 1 (November 1, 2021): 012079. http://dx.doi.org/10.1088/1755-1315/924/1/012079.
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Abstract Fructophilic lactic acid bacteria are a group of newly discovered lactic acid bacteria. Despite their potential application as probiotics in the food industry, exploration of ecological niches to discover new fructophilic lactic acid bacteria is scarce, and information that concisely describes the practical aspects of their discovery process is limited. In this mini-review, we focus on methods that have been developed to discover fructophilic lactic acid bacteria from fructose-rich environments such as flowers and bee products. First, we briefly introduce the definition, classification, diversity, and ecological niches of fructophilic lactic acid bacteria. Next, we discuss the unique characteristics that distinguish fructophilic lactic acid bacteria from other microorganisms. Finally, we outline the principles and steps to isolate, characterize, select, and identify fructophilic lactic acid bacteria. The discovery of fructophilic lactic acid bacteria with unique characteristics could provide an impetus for the development of probiotics from fructophilic lactic acid bacteria.
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Page, Katharine, Matthew Tucker, and Patrick Woodward. "DISCOVER: ORNL's total scattering diffractometer for materials discovery." Acta Crystallographica Section A Foundations and Advances 73, a1 (May 26, 2017): a204. http://dx.doi.org/10.1107/s0108767317097999.
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Fairbairn, Donald M. "Activities for Students: What Did One Angle Say to the Other Angles?" Mathematics Teacher 102, no. 1 (August 2008): 62–68. http://dx.doi.org/10.5951/mt.102.1.0062.
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Robert Oppenheimer (1904–67), the famous theoretical physicist known as the father of the atomic bomb, was once asked how he became such a great scientist. He replied that early on he had teachers who afforded him, as he put it, the joy of discovery. It truly is a joy to discover something for yourself, even if it were previously discovered by someone else. Discovery learning should be a significant part of the mathematics curriculum. Activities that lead students to discover some interesting and perhaps unexpected results are fun for both students and teachers. This article presents an inductive angle-sequence activity that uses paper folding to arrive at a surprising conjecture. Two versions of a second activity leading to deductive verification of the conjecture are also provided. Although these activities are suitable for individual effort, I find that having students work in small groups of three or four students is more enjoyable for them and thus have formatted the activities for group effort.
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Fairbairn, Donald M. "Activities for Students: What Did One Angle Say to the Other Angles?" Mathematics Teacher 102, no. 1 (August 2008): 62–68. http://dx.doi.org/10.5951/mt.102.1.0062.
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Robert Oppenheimer (1904–67), the famous theoretical physicist known as the father of the atomic bomb, was once asked how he became such a great scientist. He replied that early on he had teachers who afforded him, as he put it, the joy of discovery. It truly is a joy to discover something for yourself, even if it were previously discovered by someone else. Discovery learning should be a significant part of the mathematics curriculum. Activities that lead students to discover some interesting and perhaps unexpected results are fun for both students and teachers. This article presents an inductive angle-sequence activity that uses paper folding to arrive at a surprising conjecture. Two versions of a second activity leading to deductive verification of the conjecture are also provided. Although these activities are suitable for individual effort, I find that having students work in small groups of three or four students is more enjoyable for them and thus have formatted the activities for group effort.
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Derungs, A., C. Schuster-Amft, O. Amft, G. Tröster, and J. Seiter. "Daily Life Activity Routine Discovery in Hemiparetic Rehabilitation Patients Using Topic Models." Methods of Information in Medicine 54, no. 03 (2015): 248–55. http://dx.doi.org/10.3414/me14-01-0082.
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Summary Background: Monitoring natural behavior and activity routines of hemiparetic rehabilitation patients across the day can provide valuable progress information for therapists and patients and contribute to an optimized rehabilitation process. In particular, continuous patient monitoring could add type, frequency and duration of daily life activity routines and hence complement standard clinical scores that are assessed for particular tasks only. Machine learning methods have been applied to infer activity routines from sensor data. However, supervised methods require activity annotations to build recognition models and thus require extensive patient supervision. Discovery methods, including topic models could provide patient routine information and deal with variability in activity and movement performance across patients. Topic models have been used to discover characteristic activity routine patterns of healthy individuals using activity primitives recognized from supervised sensor data. Yet, the applicability of topic models for hemiparetic rehabilitation patients and techniques to derive activity primitives without supervision needs to be addressed. Objectives: We investigate, 1) whether a topic model-based activity routine discovery framework can infer activity routines of rehabilitation patients from wearable motion sensor data. 2) We compare the performance of our topic model-based activity routine discovery using rule-based and clustering-based activity vocabulary. Methods: We analyze the activity routine discovery in a dataset recorded with 11 hemiparetic rehabilitation patients during up to ten full recording days per individual in an ambulatory daycare rehabilitation center using wearable motion sensors attached to both wrists and the non-affected thigh. We introduce and compare rule-based and clustering-based activity vocabulary to process statistical and frequency acceleration features to activity words. Activity words were used for activity routine pattern discovery using topic models based on Latent Dirichlet Allocation. Discovered activity routine patterns were then mapped to six categorized activity routines. Results: Using the rule-based approach, activity routines could be discovered with an average accuracy of 76% across all patients. The rule-based approach outperformed clustering by 10% and showed less confusions for predicted activity routines. Conclusion: Topic models are suitable to discover daily life activity routines in hemiparetic rehabilitation patients without trained classifiers and activity annotations. Activity routines show characteristic patterns regarding activity primitives including body and extremity postures and movement. A patient-independent rule set can be derived. Including expert knowledge supports successful activity routine discovery over completely data-driven clustering.
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Abe, Akinori. "Relation between Chance Discovery and Black Swan Awareness." International Journal of Knowledge and Systems Science 4, no. 1 (January 2013): 62–76. http://dx.doi.org/10.4018/jkss.2013010105.
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Researchers have been researching for chance discovery more than 10 years. The aim of chance discovery is discover something, but chance discovery is rather different from data mining. In 2007 Taleb introduced a concept ``Black Swan.’’ Taleb uses this rare black swan metaphor to explain how general persons tend to ignore rare or novel events and the importance of being aware of such rare or novel events. The main concept of Black Swan seems to coincide with that of chance discovery. Thus, in this paper, the author compares a chance with Black Swan and presents the future feather of chance discovery. In addition, the author will discuss how to discover the better chance from the viewpoint of abduction and affordance, and give formalization.
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Kaur, Navneet, Mymoona Akhter, and Chhavi Singla. "Drug designing: Lifeline for the drug discovery and development process." Research Journal of Chemistry and Environment 26, no. 8 (July 25, 2022): 173–79. http://dx.doi.org/10.25303/2608rjce1730179.
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Drug discovery and development field has entered into a revolutionary phase with the introduction of Computer Aided Drug Designing (CADD) tools in the designing and development of new drugs. Traditional drug discovery and designing is a tedious, expensive and time-consuming process. Pharmaceutical industries spend billions of dollars to launch a potential drug candidate into the drug market. It takes 15-20 years of research to discover a new drug candidate. The advancements in the Computer Aided Drug Designing techniques have significantly contributed towards lowering the cost and time involved in new drug discovery. Different types of approaches are used to find out the potential drug candidates. Numerous compounds have been successfully discovered and launched into the market using computational tools. Various novel software-based methods like Structure- Based Drug Designing (SBDD), Ligand-Based Drug Designing (LBDD), Pharmacophore Mapping and Fragment-Based Drug Designing (FBDD) are considered as powerful tools for determining the pharmacokinetics, pharmacodynamics and structure activity relationship between target protein and its ligand. These tools provide valuable information about experimental findings and the mechanism of action of drug molecules. This has greatly expedited the discovery of promising drug candidates by sidestepping the lengthy steps involved in the synthesis of unnecessary compounds.
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Luo, Qian, Lin Zhang, Zhiwei Xing, Huan Xia, and Zhao-Xin Chen. "Causal Discovery of Flight Service Process Based on Event Sequence." Journal of Advanced Transportation 2021 (September 17, 2021): 1–17. http://dx.doi.org/10.1155/2021/2869521.
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The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains support from the ground support log to study the causal relationship between service nodes and flight delays. Most ground support studies mainly use machine learning methods to predict flight delays, and the flight support model they are based on is an ideal model. The study did not conduct an in-depth study of the causal mechanism behind the ground support link and did not reveal the true cause of flight delays. Therefore, there is a certain deviation in the prediction of flight delays by machine learning, and there is a certain deviation between the ideal model based on the research and the actual service process. Therefore, it is of practical significance to obtain the process model from the guarantee log and analyze its causality. However, the existing process causal factor discovery methods only do certain research when the assumption of causal sufficiency is established and does not consider the existence of latent variables. Therefore, this article proposes a framework to realize the discovery of process causal factors without assuming causal sufficiency. The optimized fuzzy mining process model is used as the service benchmark model, and the local causal discovery algorithm is used to discover the causal factors. Under this framework, this paper proposes a new Markov blanket discovery algorithm that does not assume causal sufficiency to discover causal factors and uses benchmark data sets for testing. Finally, the actual flight service data are used for causal discovery among flight service nodes. The local causal discovery algorithm proposed in this paper has a certain competitive advantage in accuracy, F1, and other aspects of the existing causal discovery algorithm. It avoids the occurrence of its dimensional disaster. Through the in-depth analysis of the flight safety reason node discovered by this method, it is found that the unreasonable scheduling of flight support personnel is an important reason for frequent flight delays at the airport.
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Parkhill, Susannah L., and Eachan O. Johnson. "Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery." Biochemical Journal 481, no. 13 (July 3, 2024): 839–64. http://dx.doi.org/10.1042/bcj20220062.
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The application of dyes to understanding the aetiology of infection inspired antimicrobial chemotherapy and the first wave of antibacterial drugs. The second wave of antibacterial drug discovery was driven by rapid discovery of natural products, now making up 69% of current antibacterial drugs. But now with the most prevalent natural products already discovered, ∼107 new soil-dwelling bacterial species must be screened to discover one new class of natural product. Therefore, instead of a third wave of antibacterial drug discovery, there is now a discovery bottleneck. Unlike natural products which are curated by billions of years of microbial antagonism, the vast synthetic chemical space still requires artificial curation through the therapeutics science of antibacterial drugs — a systematic understanding of how small molecules interact with bacterial physiology, effect desired phenotypes, and benefit the host. Bacterial molecular genetics can elucidate pathogen biology relevant to therapeutics development, but it can also be applied directly to understanding mechanisms and liabilities of new chemical agents with new mechanisms of action. Therefore, the next phase of antibacterial drug discovery could be enabled by integrating chemical expertise with systematic dissection of bacterial infection biology. Facing the ambitious endeavour to find new molecules from nature or new-to-nature which cure bacterial infections, the capabilities furnished by modern chemical biology and molecular genetics can be applied to prospecting for chemical modulators of new targets which circumvent prevalent resistance mechanisms.
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Cheung, David W., Vincent T. Ng, and Benjamin W. Tam. "Incremental Updates of Discovered Multi-Level Association Rules." International Journal on Artificial Intelligence Tools 06, no. 02 (June 1997): 273–90. http://dx.doi.org/10.1142/s0218213097000153.
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Update of the single- and multi-level association rules discovered in large databases is inherently costly. The straight forward approach of re-running the discovery algorithm on the entire updated database to re-discover the association rules is not cost-effective. An incremental algorithm FUP have been proposed for the update of discovered single-level association rules. In this study, we have shown that the incremental technique in FUP can be generalized to other data mining systems. An efficient algorithm MLUp has been proposed for the updating of discovered multi-level association rules. Our performance study shows that MLUp has a superior performance over the representative mining algorithm such as ML-T2 in updating discovered multi-level association rules.
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Zhang, Kai, Fei Zhao, Shoushan Luo, Yang Xin, Hongliang Zhu, and Yuling Chen. "Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)." Applied Sciences 10, no. 7 (April 10, 2020): 2596. http://dx.doi.org/10.3390/app10072596.
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With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not applicable for continuous online learning and analyzing. In this paper, a new online intrusion scenario discovery framework is proposed and the intelligence algorithm HTM (Hierarchical Temporal Memory) is employed to improve the performance of the online learning ability of the system. The proposed framework can discover and model intrusion scenarios, and the constructed model keeps evolving with the variance of the data. Additionally, a series of data preprocessing methods are introduced to enhance its adaptability to the noisy and twisted data. The experimental results show that the framework is effective in intrusion scenario discovery, and the discovered scenario is more concise and accurate than our previous work.
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Lin, F., and Y. Chen. "Discovering Classes of Strongly Equivalent Logic Programs." Journal of Artificial Intelligence Research 28 (April 10, 2007): 431–51. http://dx.doi.org/10.1613/jair.2131.
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In this paper we apply computer-aided theorem discovery technique to discover theorems about strongly equivalent logic programs under the answer set semantics. Our discovered theorems capture new classes of strongly equivalent logic programs that can lead to new program simplification rules that preserve strong equivalence. Specifically, with the help of computers, we discovered exact conditions that capture the strong equivalence between a rule and the empty set, between two rules, between two rules and one of the two rules, between two rules and another rule, and between three rules and two of the three rules.
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Villamor, D. E. V., T. Ho, M. Al Rwahnih, R. R. Martin, and I. E. Tzanetakis. "High Throughput Sequencing For Plant Virus Detection and Discovery." Phytopathology® 109, no. 5 (May 2019): 716–25. http://dx.doi.org/10.1094/phyto-07-18-0257-rvw.
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Over the last decade, virologists have discovered an unprecedented number of viruses using high throughput sequencing (HTS), which led to the advancement of our knowledge on the diversity of viruses in nature, particularly unraveling the virome of many agricultural crops. However, these new virus discoveries have often widened the gaps in our understanding of virus biology; the forefront of which is the actual role of a new virus in disease, if any. Yet, when used critically in etiological studies, HTS is a powerful tool to establish disease causality between the virus and its host. Conversely, with globalization, movement of plant material is increasingly more common and often a point of dispute between countries. HTS could potentially resolve these issues given its capacity to detect and discover. Although many pipelines are available for plant virus discovery, all share a common backbone. A description of the process of plant virus detection and discovery from HTS data are presented, providing a summary of the different pipelines available for scientists’ utility in their research.
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Alehaideb, Zeyad, Nimer Mehyar, Mai Al Ajaji, Mohammed Alassiri, Manal Alaamery, Bader Al Debasi, Bandar Alghanem, et al. "KAIMRC’S Second Therapeutics Discovery Conference." Proceedings 43, no. 1 (April 29, 2020): 6. http://dx.doi.org/10.3390/proceedings2020043006.
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Following the success of our first therapeutic discovery conference in 2017 and the selection of King Abdullah International Medical Research Centre (KAIMRC) as the first Phase 1 clinical site in the Kingdom of Saudi Arabia, we organized our second conference in partnership with leading institutions in academic drug discovery, which included the Structural Genomic Constorium (Oxford, UK), Fraunhofer (Germany) and Institute Material Medica (China); the participation of members of the American Drug Discovery Consterium; European Biotech companies; and local pharma companies, SIPMACO and SudairPharma. In addition, we had European and Northern American venture capital experts attending and presenting at the conference. The purpose of the conference was to bridge the gap between biotech, pharma and academia regarding drug discovery and development. Its aim primarily was to: (a) bring together world experts on academic drug discovery to discuss and propose new approaches to discover and develop new therapies; (b) establish a permanent platform for scientific exchange between academia and the biotech and pharmaceutical industries; (c) entice national and international investors to consider funding drugs discovered in academia; (d) educate the population about the causes of diseases, approaches to prevent them from happening and their cure; (e) attract talent to consider the drug discovery track for their studies and career. During the conference, we discussed the unique academic drug discovery disrupting business models, which can make their discoveries easily accessible in an open source mode. This unique model accelerates the dissemination of knowledge to all world scientists to guide them in their research. This model is aimed at bringing effective and affordable medicine to all mankind in a very short time. Moreover, the program discussed rare disease targets, orphan drug discovery, immunotherapy discovery and process, the role of bioinformatics in drug discovery, anti-infective drug discovery in the era of bad bugs, natural products as a source of novel drugs and innovative drug formulation and delivery. Additionally, as the conference was organized during the surge of the epidemic, we dedicated the first day (25 February) to coronavirus science, detection and therapy. The day was co-organized with the King Saud bin Abdulaziz University for Health Sciences, Kingdom of Saudi Arabia(KSA) Ministry of Education to announce the grant winner for infectious diseases. Simultaneously, intensive courses were delivered to junior scientists on the principle of drug discovery, immunology and clinical trials, as well as rare diseases. The second therapeutics discovery forum provided a platform for interactive knowledge sharing and the convergence of researchers, governments, pharmaceuticals, biopharmaceuticals, hospitals and non-profit organizations on the topic of academic drug discovery. The event presented showcases on global drug discovery initiatives and demonstrated how collaborations are leading to successful new therapies. In line with the KSA 2030 vision on becoming world leaders with an innovative economy and healthy population, therapeutic discovery is becoming an area of interest to science leaders in the kingdom, and our conference gave us the opportunity to identity key areas of interest as well as potential future collaborations.
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Stokowy, Tomasz, Danuta Gawel, and Bartosz Wojtas. "Differences in miRNA and mRNA Profile of Papillary Thyroid Cancer Variants." International Journal of Endocrinology 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/1427042.
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Papillary thyroid cancer (PTC) can be divided into classical variant of PTC (cPTC), follicular variant of PTC (fvPTC), and tall cell variant (tcPTC). These variants differ in their histopathology and cytology; however, their molecular background is not clearly understood. Our results shed some new light on papillary thyroid cancer biology as new direct miRNA-gene regulations are discovered. The Cancer Genome Atlas (TCGA) 466 thyroid cancer samples were studied in parallel datasets to discover potential miRNA-mRNA regulations. Additionally, miRNAs and genes differentiating PTC variants (cPTC, fvPTC, and tcPTC) were indicated. Putative miRNA regulatory pairs were discovered: hsa-miR-146b-5p with PHKB and IRAK1, hsa-miR-874-3p with ITGB4 characteristic for classic PTC samples, and hsa-miR-152-3p with TGFA characteristic for follicular variant PTC samples. MiRNA-mRNA regulations discovery opens a new perspective in understanding of PTC biology. Furthermore, our successful pipeline of miRNA-mRNA regulatory pathways discovery could serve as a universal tool to find new miRNA-mRNA regulations, also in different datasets.
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Mu, Lin, Xiao Dong Qiao, Chun Yun Hao, and Yong Xiang Mu. "Automatic Discovery of Non-Ranking Functions with DISCOVERER." Applied Mechanics and Materials 336-338 (July 2013): 2119–23. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2119.
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This paper proposes a definition of non-ranking functions of loop programs and investigates how to apply the techniques on synthesize Non-ranking functions of loop programs. It is shown that this new non-ranking function works well to determine the termination of loop programs.
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Kovács, Zoltán. "Discovering geometry via the Discover command in GeoGebra Discovery." REMATEC 16, no. 37 (January 14, 2021): 14–25. http://dx.doi.org/10.37084/rematec.1980-3141.2021.n37.p14-25.id313.
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We present a new way to discover statements in a planar geometric figure by using GeoGebra Discovery, an experimental version of GeoGebra, the free dynamic mathematics software package. A new command "Discover" (which is also available as a tool) requires an input point of the figure---as output several properties of the figure are communicated by the program. That is, "Discover" reports a list of the observed geometric properties, including point equality, equal long segments, collinearity, concyclicity, parallelism and perpendicularity. All of the obtained statements are checked symbolically: this means that the verification is done with computer algebra means. The obtained properties are also highlighted with colors or dashed lines in the original figure. The discovery process can always be continued by creating new objects and selecting a new target point to discover. We focus on possible uses in a classroom: two basic examples are shown from an Austrian textbook first. Then some more difficult topics are introduced that are usually covered by the secondary school curriculum. As a final example, we consider the discovery of a more advanced theorem, namely, a proposition according to Napoleon. We learn that discovery can lead to unexpected results, but this is an important characteristic of mathematics. In the paper we give some references to related software systems and the applied mathematical background as well.
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Sheldon, Mark A., Andrzej Duda, Ron Weiss, and David K. Gifford. "Discover: a resource discovery system based on content routing." Computer Networks and ISDN Systems 27, no. 6 (April 1995): 953–72. http://dx.doi.org/10.1016/0169-7552(95)00044-8.
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Nasim, Md, Xinghang Zhang, Anter El-Azab, and Yexiang Xue. "End-to-End Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23005–11. http://dx.doi.org/10.1609/aaai.v38i21.30342.
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The availability of tera-byte scale experiment data calls for AI driven approaches which automatically discover scientific models from data. Nonetheless, significant challenges present in AI-driven scientific discovery: (i) The annotation of large scale datasets requires fundamental re-thinking in developing scalable crowdsourcing tools. (ii) The learning of scientific models from data calls for innovations beyond black-box neural nets. (iii) Novel visualization & diagnosis tools are needed for the collaboration of experimental and theoretical physicists, and computer scientists. We present Phase-Field-Lab platform for end-to-end phase field model discovery, which automatically discovers phase field physics models from experiment data, integrating experimentation, crowdsourcing, simulation and learning. Phase-Field-Lab combines (i) a streamlined annotation tool which reduces the annotation time (by ~50-75%), while increasing annotation accuracy compared to baseline; (ii) an end-to-end neural model which automatically learns phase field models from data by embedding phase field simulation and existing domain knowledge into learning; and (iii) novel interfaces and visualizations to integrate our platform into the scientific discovery cycle of domain scientists. Our platform is deployed in the analysis of nano-structure evolution in materials under extreme conditions (high temperature and irradiation). Our approach reveals new properties of nano-void defects, which otherwise cannot be detected via manual analysis.
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Sawicki, Jerzy. "Kleist vs. Musschenbroek – trudna droga do prawdy." Studia Historiae Scientiarum 17 (December 12, 2018): 275–340. http://dx.doi.org/10.4467/2543702xshs.18.011.9331.
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On October 11, 1745, a German scientist Ewald Georg (Jürgen) Kleist in Cammin in Pommern (today Kamień Pomorski) discovered both the phenomenon of storing electricity in a glass vessel with water, and a new device – an electric capacitor. Kleist quickly and correctly announced his discovery to the scientific community. The greatest help in confirming the discovery and its publication was received by Kleist from Daniel Gralath who was active in the first Polish Society for Experimental Physics Societas Physicae Experimentalis in Gdańsk. At the beginning of 1746, in the Dutch Leiden, in the workshop of the famous professor Pieter Musschenbroek, an experiment was conducted similar to the one in Cammin. The information about the Leiden experiment quickly reached Paris, the centre of European science of that time, and which lead to a proclamation of a new, very important physical discovery. The experiment gained wide publicity in Europe thanks to numerous public repetitions. The French promoter of the Leiden experiment was physicist Jean-Antoine Nollet. The discoverer’s fame was unjustly attributed to Musschenbroek and Leiden, although Daniel Gralath reported Nollet’s letter about Kleist’s priority. From the moment of discovery to modern times, scientific publications in the field of physics and history of science often misrepresent the person of the discoverer, the place of discovery and its name. The aim of the article is to present a broad overview of the reports, descriptions and opinions contained in scientific publications about the discovery. In the review presented in the article, 117 books are divided by country of issue, language and time of publication. The most frequent errors were classified and assigned to the analyzed publications. The result turned out to be surprising, as only 6 items were free of errors, and in the remaining, 254 errors were found. Unfortunately, in both former and contemporary publications, Kleist is sometimes ignored, and even if noticed, his discovery is usually depreciated in various ways. It may come as a surprise that the first two works on the history of electrical research written in the eighteenth century by Daniel Gralath and Joseph Priestley correctly and profoundly convey the course of events and the priority of Kleist’s discovery. It turns out that the French untrue version of the history of this finding is still alive, especially in European countries, so that pupils, students and physics enthusiasts receive a false message about this important discovery. In the circle of reliable researchers in the history of science, the priority of Kleist’s discovery is widely recognized, but even they have a problem with naming the electric capacitor discovered by the Cammin physicist differently than the Leiden jar. One of the reasons for the poor knowledge of Kleist and his experiment is scant scientific literature on the subject and the ignorance of the source texts written by the Cammin explorer. This gap is bridged by a scientific monograph written by the author of the present article. The text of this paper complements the information presented in the author’s book entitled Ewald Georg Kleist – Wielki odkrywca z małego miasta (A great discoverer from a small town): Kamień Pomorski 1745 (Warszawa: Instytut Historii Nauki PAN, Stowarzyszenie Elektryków Polskich, Zachodniopomorski Uniwersytet Technologiczny w Szczecinie, 2018).
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Ouyang, Wei Min, and Qin Hua Huang. "Mining Indirect Temporal Sequential Patterns in Large Transaction Databases." Applied Mechanics and Materials 385-386 (August 2013): 1362–65. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1362.
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Sequential pattern is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns focus on the frequent sequences, which do not consider the infrequent sequences and lifespan of each sequence. On the one hand, some infrequent patterns can provide very useful insight view into the data set, on the other hand, without taking lifespan of each sequence into account, not only some discovered patterns may be invalid, but also some useful patterns may not be discovered. So, we extend the sequential patterns to the indirect temporal sequential patterns, and put forward an algorithm to discover indirect temporal sequential patterns in this paper.
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Hammer, David. "Discovery Learning and Discovery Teaching." Cognition and Instruction 15, no. 4 (December 1997): 485–529. http://dx.doi.org/10.1207/s1532690xci1504_2.
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Gajdardziska-Josifovska, Marija, Richard G. McClean, Marvin A. Schofield, Cynthia V. Sommer, and William F. Kean. "Discovery of nanocrystalline botanical magnetite." European Journal of Mineralogy 13, no. 5 (September 27, 2001): 863–70. http://dx.doi.org/10.1127/0935-1221/2001/0013/0863.
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Jadhav, Mr Gahininath Thansing, and Mr Rahul Bhavlal Jadhav. "Drug Discovery and Development Process." International Journal of Research Publication and Reviews 5, no. 1 (January 8, 2024): 1891–95. http://dx.doi.org/10.55248/gengpi.5.0124.0225.
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Objantoro, Enggar, John Mardin, C. K. Hrang Tiam, and Gregory Ajima Onah. "The Process of Discovering the Joy of the Kingdom of Heaven: An Analysis of Matthew 13:44-46." SAINT PAUL'S REVIEW 3, no. 1 (June 5, 2023): 52–62. http://dx.doi.org/10.56194/spr.v3i1.37.
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Matthew is a book full of richness in it. Matthew 13:44-46 is one of the parts of the book that has a central theme in Christian life, namely the kingdom of God. This article is written to discuss the process of discovering the joy of the kingdom of heaven. We conducted a biblical analysis of this book, with a focus on interpretation to understand the text and context of Matthew 13:44-46. The results show that there are three stages in the process of discovering the joy of the kingdom of heaven. The first stage is the discovery stage. In the discovery stage believers find a life value that brings believers to the joy of the kingdom of heaven. The second stage is the awareness stage. The awareness stage is when the believer realizes the value of life that has been discovered in the discovery stage. In this stage the believer realizes that the value of the kingdom of heaven is very precious, thus leading to the next stage. The third stage is the decision stage. The decision stage is the final stage after believers discover and realize the value of the kingdom of heaven. At this stage believers take action on the discovery by denying themselves as a form of giving their best for the sake of something more valuable, namely the kingdom of heaven.
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Minkina, Waldemar. "How Infrared Radiation Was Discovered—Range of This Discovery and Detailed, Unknown Information." Applied Sciences 11, no. 21 (October 21, 2021): 9824. http://dx.doi.org/10.3390/app11219824.
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The reason for writing this article is that the details and mainly the scope of the fundamental discovery of infrared radiation are not widely known, and different accounts of this story are found in the literature. For example, not everyone knows that the discoverer of infrared radiation, F. W. Herschel, simultaneously studied its properties, which he, then, described in detail in his publications. It can be concluded that the history of the discovery of infrared radiation is treated marginally in the literature. This is not fair, considering the fact that infrared radiation is of fundamental importance to modern man. On the other hand, the history of the discovery of, for example, X-rays or Maxwell’s electromagnetic radiation is well known—this information is passed on to students of electrical faculties during lectures on “Fundamentals of Physics” or “Fundamentals of Electrical Engineering”. Although it is currently believed that the significance of infrared radiation for modern man is comparable to that of X-rays, when I ask the students during lectures who discovered infrared radiation and how, there is usually a deafening silence.
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Fukunaga, Alex S. "Automated Discovery of Local Search Heuristics for Satisfiability Testing." Evolutionary Computation 16, no. 1 (March 2008): 31–61. http://dx.doi.org/10.1162/evco.2008.16.1.31.
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The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.
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YANG, SERENA. "John Cage and George Herbert Mead: The Unknown Influence of Van Meter Ames." Journal of the Society for American Music 11, no. 3 (August 2017): 354–69. http://dx.doi.org/10.1017/s1752196317000244.
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AbstractAs John Cage wrote in his bookA Year from Monday, the “current use for art [is] giving instances of society suitable for social imitation—suitable because they show ways . . . people can do things without being told or telling others what to do.” Cage's ideal anarchic music emphasizes not only renouncing compositional control, but also the process of self-discovery happening to everyone, a process that leads participants to discover their creative abilities. This paper argues that Cage's penchant for self-discovery came from his understanding of George Herbert Mead's theories of the process of individuation (the “me” and the “I”). Cage discovered Mead through readingZen and American Thought(1962) by his friend Van Meter Ames, a professor of philosophy at the University of Cincinnati, who saw the compatibility between Zen and Mead's concept of self in the capacity of the “I,” a phase of self whose unpredictable steps contribute to human innovation. Cage found the possibility of overthrowing the thought of the world through triggering a self-discovery of the “I” in everyone. He realized this idea in his happenings, such as0’00”, by requiring performers to respond to the simple descriptions without specifying sound or duration.
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Beauchamp, Guy, and Graeme D. Ruxton. "Frequency-dependent conspecific attraction to food patches." Biology Letters 10, no. 8 (August 2014): 20140522. http://dx.doi.org/10.1098/rsbl.2014.0522.
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In many ecological situations, resources are difficult to find but become more apparent to nearby searchers after one of their numbers discovers and begins to exploit them. If the discoverer cannot monopolize the resources, then others may benefit from joining the discoverer and sharing their discovery. Existing theories for this type of conspecific attraction have often used very simple rules for how the decision to join a discovered resource patch should be influenced by the number of individuals already exploiting that patch. We use a mechanistic, spatially explicit model to demonstrate that individuals should not necessarily simply join patches more often as the number of individuals exploiting the patch increases, because those patches are likely to be exhausted soon or joining them will intensify future local competition. Furthermore, we show that this decision should be sensitive to the nature of the resource patches, with individuals being more responsive to discoveries in general and more tolerant of larger numbers of existing exploiters on a patch when patches are resource-rich and challenging to locate alone. As such, we argue that this greater focus on underlying joining mechanisms suggests that conspecific attraction is a more sophisticated and flexible tactic than currently appreciated.
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Xing, Hengrui, Ansaf Salleb-Aouissi, and Nakul Verma. "Automated Symbolic Law Discovery: A Computer Vision Approach." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 660–68. http://dx.doi.org/10.1609/aaai.v35i1.16146.
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One of the most exciting applications of modern artificial intelligence is to automatically discover scientific laws from experimental data. This is not a trivial problem as it involves searching for a complex mathematical relationship over a large set of explanatory variables and operators that can be combined in an infinite number of ways. Inspired by the incredible success of deep learning in computer vision, we tackle this problem by adapting various successful network architectures into the symbolic law discovery pipeline. The novelty of our approach is in (1) encoding the input data as an image with super-resolution, (2) developing an appropriate deep network pipeline, and (3) predicting the importance of each mathematical operator from the relationship image. This allows us to prior the exponentially large search with the predicted importance of the symbolic operators, which can significantly accelerate the discovery process. We apply our model to a variety of plausible relationships---both simulated and from physics and mathematics domains---involving different dimensions and constituents. We show that our model is able to identify the underlying operators from data, achieving a high accuracy and AUC (91% and 0.96 on average resp.) for systems with as many as ten independent variables. Our method significantly outperforms the current state of the art in terms of data fitting (R^2), discovery rate (recovering the true relationship), and succinctness (output formula complexity). The discovered equations can be seen as first drafts of scientific laws that can be helpful to the scientists for (1) hypothesis building, and (2) understanding the complex underlying structure of the studied phenomena. Our approach holds a real promise to help speed up the rate of scientific discovery.
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Pan, Guohui, Zhengren Xu, Zhikai Guo, Hindra, Ming Ma, Dong Yang, Hao Zhou, et al. "Discovery of the leinamycin family of natural products by mining actinobacterial genomes." Proceedings of the National Academy of Sciences 114, no. 52 (December 11, 2017): E11131—E11140. http://dx.doi.org/10.1073/pnas.1716245115.
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Nature’s ability to generate diverse natural products from simple building blocks has inspired combinatorial biosynthesis. The knowledge-based approach to combinatorial biosynthesis has allowed the production of designer analogs by rational metabolic pathway engineering. While successful, structural alterations are limited, with designer analogs often produced in compromised titers. The discovery-based approach to combinatorial biosynthesis complements the knowledge-based approach by exploring the vast combinatorial biosynthesis repertoire found in Nature. Here we showcase the discovery-based approach to combinatorial biosynthesis by targeting the domain of unknown function and cysteine lyase domain (DUF–SH) didomain, specific for sulfur incorporation from the leinamycin (LNM) biosynthetic machinery, to discover the LNM family of natural products. By mining bacterial genomes from public databases and the actinomycetes strain collection at The Scripps Research Institute, we discovered 49 potential producers that could be grouped into 18 distinct clades based on phylogenetic analysis of the DUF–SH didomains. Further analysis of the representative genomes from each of the clades identified 28 lnm-type gene clusters. Structural diversities encoded by the LNM-type biosynthetic machineries were predicted based on bioinformatics and confirmed by in vitro characterization of selected adenylation proteins and isolation and structural elucidation of the guangnanmycins and weishanmycins. These findings demonstrate the power of the discovery-based approach to combinatorial biosynthesis for natural product discovery and structural diversity and highlight Nature’s rich biosynthetic repertoire. Comparative analysis of the LNM-type biosynthetic machineries provides outstanding opportunities to dissect Nature’s biosynthetic strategies and apply these findings to combinatorial biosynthesis for natural product discovery and structural diversity.
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Vieira, Sheila de Souza, Maria Cecília Bevilacqua, Noeli Marchioro Liston Andrade Ferreira, and Giselle Dupas. "Discovery of hearing impairment by the family: seeing an idealized future collapse." Acta Paulista de Enfermagem 25, spe2 (2012): 82–88. http://dx.doi.org/10.1590/s0103-21002012000900013.
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OBJECTIVE: To portray the family experience when the discovery of hearing impairment in their child. METHODS: Qualitative research with Symbolic Interactionism and Grounded Theory as theoretical and methodological frameworks. Data collection instrument: semi-structured interview. The study included nine families (32 participants). RESULTS: The theme, "Seeing an idealized future collapse", shows that for the family, discovered the possibility of having a child with hearing loss is a moment that involves many negative feelings. CONCLUSION: Discover the hearing loss has a meaning of the expected loss of the perfect child, frustrated expectations and uncertain future. The family has been inadequately approached and the diagnosis has been made late, which requires immediate changes to the practices of professionals.
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Rall, Jack A. "Generation of life in a test tube: Albert Szent-Gyorgyi, Bruno Straub, and the discovery of actin." Advances in Physiology Education 42, no. 2 (June 1, 2018): 277–88. http://dx.doi.org/10.1152/advan.00189.2017.
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This is a story about a great scientist, luck, great discovery that changed the future direction of muscle research, war, a clandestine war mission, postwar politics, and an attempt to rewrite scientific history. Albert Szent-Gyorgyi, at 44 yr of age, won the Nobel Prize in 1937 for his work on vitamin C and the establishment of the groundwork of the citric acid cycle. He now wanted to investigate one of the fundamental aspects of life and settled on the study of muscle contraction. The Szent-Gyorgyi laboratory in Hungary during World War II demonstrated that contraction could be reproduced in vitro by threads consisting of just two proteins, myosin and the newly discovered protein by Bruno Straub that they called actin. Szent-Gyorgyi called seeing the contraction of these threads, which occurred in the presence of ATP and ions, “the most thrilling moment” of his scientific life. This major discovery of the generation of “life” in a test tube was totally unknown for years by the rest of the world because of the war. When the discovery was finally communicated to the world, it was not immediately accepted by all as being relevant to the physiology of muscle contraction. Nonetheless, this discovery opened up the modern phase of muscle research. Serendipity played an important role in the great discovery, and much later politics would lead to a shocking controversy around the true discoverer of actin.
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Zhang, Yuanyuan, Liangxiong Wei, Min Guo, Wei Wang, Yufang Sun, Junfeng Wang, and Liangyin Chen. "VN-NDP: A Neighbor Discovery Protocol Based on Virtual Nodes in Mobile WSNs." Sensors 19, no. 21 (October 31, 2019): 4739. http://dx.doi.org/10.3390/s19214739.
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As an indispensable part of Internet of Things (IoT), wireless sensor networks (WSNs) are more and more widely used with the rapid development of IoT. The neighbor discovery protocols are the premise of communication between nodes and networking in energy-limited self-organizing wireless networks, and play an important role in WSNs. Because the node energy is limited, neighbor discovery must operate in an energy-efficient manner, that is, under the condition of a given energy budget, the neighbor discovery performance should be as good as possible, such that the discovery latency would be as small as possible and the discovered neighbor percentage as large as possible. The indirect neighbor discovery mainly uses the information of the neighbors that have been found by a pairwise discovery method to more efficiently make a re-planning of the discovery wake-up schedules of the original pairwise neighbor discovery, thereby improving the discovery energy efficiency. The current indirect neighbor discovery methods are mainly divided into two categories: one involves removing the inefficient active slots in the original discovery wake-up schedules, and the other involves adding some efficient active slots. However, the two categories of methods have their own limitations. The former does not consider that this removal operation destroys the integrity of the original discovery wake-up schedules and hence the possibility of discovering new neighbors is reduced, which adversely affects the discovered neighbor percentage. For the latter category, there are still inefficient active slots that were not removed in the re-planned wake-up schedules. The motivation of this paper is to combine the advantages of these two types of indirect neighbor discovery methods, that is, to combine the addition of efficient active slots and the removal of inefficient active slots. To achieve this goal, this paper proposes, for the first time, the concept of virtual nodes in neighbor discovery to maximize the integrity of the original wake-up schedules and achieve the goals of adding efficient active slots and removing inefficient active slots. Specifically, a virtual node is a collaborative group that is formed by nodes within a small range. The nodes in a collaborative group share responsibility for the activating task of one member node, and the combination of these nodes’ wake-up schedules forms the full wake-up schedule of a node that only uses a pairwise method. In addition, this paper proposes a set of efficient group management mechanisms, and the key steps affecting energy efficiency are analyzed theoretically to obtain the energy-optimal parameters. The extended simulation experiments in multiple scenarios show that, compared with other methods, our neighbor discovery protocol based on virtual nodes (VN-NDP) has a significant improvement in average discovery delay and discovered neighbor percentage performance at a given energy budget. Compared with the typical indirect neighbor discovery algorithm EQS, a neighbor discovery with extended quorum system, our proposed VN-NDP method reduces the average discovery delay by up to 10 . 03 % and increases the discovered neighbor percentage by up to 18 . 35 % .
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Hu, Ruo, and Zan Fu Xie. "Classification of Knowledge Discovery Methods." Applied Mechanics and Materials 63-64 (June 2011): 859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.859.
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Knowledge Discovery, the science and technology of exploring knowledge in order to discover previously unknown patterns, is a part of the overall process of getting information in databases. In today’s computer-driven world, these databases contain a lot of information. The significant value of this information makes knowledge discovery a matter of considerable importance and necessity. A decision tree is a predictive model which can be used to represent both classifiers and regression models. When a decision tree is used for classification tasks, it is more appropriately referred to as a classification tree.in this paper, Classification Trees Method of Knowledge Discovery In Internet is given.
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HO, TUBAO, TRONGDUNG NGUYEN, DUCDUNG NGUYEN, and SAORI KAWASAKI. "VISUALIZATION SUPPORT FOR USER-CENTERED MODEL SELECTION IN KNOWLEDGE DISCOVERY AND DATA MINING." International Journal on Artificial Intelligence Tools 10, no. 04 (December 2001): 691–713. http://dx.doi.org/10.1142/s0218213001000726.
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The problem of model selection in knowledge discovery and data mining—the selection of appropriate discovered patterns/models or algorithms to achieve such patterns/models—is generally a difficult task for the user as it requires meta-knowledge on algorithms/models and model performance metrics. Viewing knowledge discovery as a human-centered process that requires an effective collaboration between the user and the discovery system, our work aims to make model selection in knowledge discovery easier and more effective. For such a collaboration, our solution is to give the user the ability to try easily various alternatives and to compare competing models quantitatively and qualitatively. The basic idea of our solution is to integrate data and knowledge visualization with the knowledge discovery process in order to the support the participation of the user. We introduce the knowledge discovery system D2MS in which several visualization techniques of data and knowledge are developed and integrated into the steps of the knowledge discovery process. The visualizers in D2MS greatly help the user gain better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge in the whole process.