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The use of data mining to address the issue of people who consume tobacco and other harmful substances for their health has led to a significant dependence among smokers, which over time causes illnesses that may result in the addict's death. As a result, the research's goal is to apply a data mining study whose findings showed that the confidence intervals are less than 0.355. However, the lift and conviction in the last three rules are also lower, making it unlikely that these rules will be followed. On the other hand, the knowledge discovery in data bases method was used. It consists of the following stages: data selection, preparation, data mining, and evaluation and interpretation of the results. To that end, comparisons of agile data mining methodologies like crisp-dm, knowledge discovery in data, and Semma are also done. As a result, using specific criteria, dimensions are segmented to allow for the differentiation of these methodologies. As a result, a comparison graph of models such as naive Bayes, decision trees, and rule induction is used. To sum up, it can be said that the rules of association apply to men, the number of admissions, and the cancers that can be brought on by smoking. Also, the percentage of male patients admitted with cancers that can be brought on by smoking Last but not least, the number of admissions and cancers that can be brought on by smoking. [ABSTRACT FROM AUTHOR]

Association Rule Mining (ARM) relies on concept lattices as an effective knowledge representation structure. However, classical ARM methods face significant limitations, including the generation of misleading rules during data-to-formal-context mapping and poor handling of heterogeneous data types such as linguistic, continuous, and imprecise data. This study aims to address these limitations by introducing a novel fuzzy data structure called the "fuzzy iceberg lattice" and its corresponding construction algorithm. The primary objectives of this study are to enhance the efficiency of extracting and visualizing frequent fuzzy closed item sets and to optimize both execution time and storage requirements. The necessity of this research stems from the high computational cost and redundancy associated with traditional fuzzy approaches, which, while capable of managing quantitative and imprecise data, are often impractical for large-scale applications in real scenarios. The proposed approach incorporates a 'fuzzy min-max basis algorithm' to derive exact and approximate rule bases from the extracted fuzzy closed item sets, eliminating redundancy while preserving valuable insights. Experimental results on benchmark datasets demonstrate that the proposed fuzzy iceberg lattice outperforms traditional fuzzy concept lattices, achieving an average reduction of 74.75% in execution time and 70.53% in memory usage. This efficiency gain, coupled with the lattice's ability to handle crisp, quantitative, fuzzy, and heterogeneous data types, underscores its potential to advance ARM by yielding a manageable number of high-quality fuzzy concepts and rules. [ABSTRACT FROM AUTHOR]

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Trigger–action programming (TAP) enables users to automate Internet of Things (IoT) devices by creating rules such as "IF Device1.TriggerState is triggered, THEN Device2.ActionState is executed". As the number of IoT devices grows, the combination space between the functions provided by devices expands, making manual rule creation time-consuming for end-users. Existing TAP recommendation systems enhance the efficiency of rule discovery but face two primary issues: they ignore the association of rules between users and fail to model collaborative information among users. To address these issues, this article proposes a graph contrastive learning-based recommendation system for TAP rules, named GCL4TAP. In GCL4TAP, we first devise a data partitioning method called DATA2DIV, which establishes cross-user rule relationships and is represented by a user–rule bipartite graph. Then, we design a user–user graph to model the similarities among users based on the categories and quantities of devices that they own. Finally, these graphs are converted into low-dimensional vector representations of users and rules using graph contrastive learning techniques. Extensive experiments conducted on a real-world smart home dataset demonstrate the superior performance of GCL4TAP compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Classification rule mining represents a significant field of machine learning, facilitating informed decision-making through the extraction of meaningful rules from complex data. Many classification methods cannot simultaneously optimize both explainability and different performance metrics at the same time. Metaheuristic optimization-based solutions, inspired by natural phenomena, offer a potential paradigm shift in this field, enabling the development of interpretable and scalable classifiers. In contrast to classical methods, such rule extraction-based solutions are capable of classification by taking multiple purposes into consideration simultaneously. To the best of our knowledge, although there are limited studies on metaheuristic based classification, there is not any method that optimize more than three objectives while increasing the explainability and interpretability for classification task. In this study, data sets are treated as the search space and metaheuristics as the many-objective rule discovery strategy and study proposes a metaheuristic many-objective optimization-based rule extraction approach for the first time in the literature. Chaos theory is also integrated to the optimization method for performance increment and the proposed chaotic rule-based SPEA2 algorithm enables the simultaneous optimization of four different success metrics and automatic rule extraction. Another distinctive feature of the proposed algorithm is that, in contrast to classical random search methods, it can mitigate issues such as correlation and poor uniformity between candidate solutions through the use of a chaotic random search mechanism in the exploration and exploitation phases. The efficacy of the proposed method is evaluated using three distinct data sets, and its performance is demonstrated in comparison with other classical machine learning results. [ABSTRACT FROM AUTHOR]

The editorial "Biology: The Open Road to a Theory of Life" discusses the vastness and complexity of the field of Biology, emphasizing the study of organisms at different scales of organization. It highlights the integration of information from various biological disciplines to deepen our understanding of life, evolution, and cellular processes. The journal Biology aims to advance scientific knowledge by exploring regularities and laws within different levels of life organization, with a focus on developing a theory of cellular life. The editorial encourages contributions that contribute to the theoretical advancement of Biology and the discovery of generalities within the study of life. [Extracted from the article]

In the present study, the topic of educational motivation among students majoring in “Tourism” at “St. Cyril and St. Methodius” University of Veliko Tarnovo. For this purpose, a survey was conducted among participants in the "Get to know Bulgaria with us" educational project. The article aims to establish the educational motivation of tourism students through their participation in the mentioned project. A survey method and the method for the discovery of association rules to search for regularities based on received data were used. The results of the research conducted among the participants show that students who share the purpose of the project find a way to get involved in it and work to achieve its main goal. As an important part of their academic performance, they share in improving their communication, teamwork and adaptability. The basis of their motivation is the acquisition of knowledge, skills and competences, and on a personal level, they share the positive impact of the project on their self-evaluation. Enhancing the institution's image also scores high among the reasons. In summary, we can determine that educational motivation among tourism students is closely dependent on their attitudes towards future development. The development presents specific conclusions based on the data obtained. [ABSTRACT FROM AUTHOR]

This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost, a machine learning algorithm renowned for its efficiency and performance. The framework proposed herein utilizes the fusion of diversified feature formats, specifically, metadata, textual, and pattern features. The goal is to enhance the system's ability to discern and generalize transformation rules from source to destination formats in varied contexts. Firstly, the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model. Subsequent sections expound on the mechanism of feature optimization using Recursive Feature Elimination (RFE) with linear regression, aiming to retain the most contributive features and eliminate redundant or less significant ones. The core of the research revolves around the deployment of the XGBoost model for training, using the prepared and optimized feature sets. The article presents a detailed overview of the mathematical model and algorithmic steps behind this procedure. Finally, the process of rule discovery (prediction phase) by the trained XGBoost model is explained, underscoring its role in real-time, automated data transformations. By employing machine learning and particularly, the XGBoost model in the context of Business Rule Engine (BRE) data transformation, the article underscores a paradigm shift towards more scalable, efficient, and less human-dependent data transformation systems. This research opens doors for further exploration into automated rule discovery systems and their applications in various sectors. [ABSTRACT FROM AUTHOR]

A three-roll rotary piercing process (TRPP) with severe plastic deformation was conducted to fabricate stainless steel seamless tubes with high mechanical properties. To address production issues such as front stuck (FS) where the front end of the tube blank cannot be accessed, back stuck (BS) where the back end of the tube blank cannot be penetrated and steel-heaping, the process parameters were optimized through simulation and testing in this study. Additionally, a novel structure for the three-roll piercing machine was designed and developed. The changes in roll feed angle and plug advance were systematically analyzed for defects in the tubular billet. Numerical simulations of the TRPP were performed using commercially available Finite Element Method (FEM) software (Simufact Forming) to analyze the stress, strain, temperature, friction stress and contact pressure distributions in the pierced tubes. Experimental analysis was conducted on seamless tubes with different diameter defects, leading to the discovery of optimal rules. Based on the experimental and numerical investigations, it was found that optimizing plug advance and roll feed angle can enhance the forming quality of the tubes. [ABSTRACT FROM AUTHOR]

American society highly values protecting intellectual property from infringement. The Copyright Act of 1976 (the “Copyright Act”) protects a wide range of expressive and creative works. The Copyright Act grants owners of qualifying works a bundle of rights and provides for damages when those rights are infringed. However, Congress implemented a three-year statute of limitations to restrict an owner’s ability to bring an infringement lawsuit. But Congress failed to specify when the statute of limitations begins, which is known as the “accrual” date. As a result, lower federal courts created two accrual rules-the injury rule and the discovery rule. The two conflicting accrual rules generate widespread confusion and uncertainty for copyright owners and courts. Because the lower courts employ different rules, copyright infringement case outcomes lack uniformity. As a result, many copyright owners engage in forum shopping to benefit their cases. Despite renouncing forum shopping, the Supreme Court denied previous opportunities to choose a rule. In November 2023, Hearst Newspapers petitioned the Supreme Court to hear their case, Hearst Newspapers v. Martinelli, but their petition was denied. This Comment argues the Supreme Court should have granted Hearst Newspapers’ petition. By granting the petition and choosing an accrual rule, the Supreme Court would have created a uniform accrual period across the country and prevented plaintiffs from forum shopping. However, unlike the position of Hearst in Hearst Newspapers, this Comment recommends the Supreme Court choose the discovery rule because this rule provides courts with an objective standard and promotes the overarching goal of the Copyright Act, which is to promote the timely prosecution of claims while not unnecessarily punishing diligent copyright owners. [ABSTRACT FROM AUTHOR]

This paper examines the potential profit of bull flag trading rules in the Shanghai Stock Exchange Composite Index (SSE) using a template matching technique based on price pattern recognition. This paper fills a gap in the literature by applying a template matching technique for the recognition of bull flag patterns in the Shanghai Stock Exchange Composite Index (SSE) during the period of 1991–2021. To the best of our knowledge, no previous study has applied bull flag trading rules to the Chinese stock market. Our results indicate that bull flag trading rules can correctly predict the price movement direction of the index most of the time, achieving significantly positive excess profits. Moreover, shorter fitting windows and better quality of price fit values for lower holding periods are associated with better performance. This research may have relevant practical implications for investors who opt for this indicator in their asset allocation decisions. [ABSTRACT FROM AUTHOR]

This study focuses on the examination of the isnad (chain of transmission) of hadith as a fundamental source and the second authority in determining Islamic law. Unlike the Qur'an, the authenticity of hadiths, especially legal hadiths, remains a subject of extensive debate among hadith scholars, both Muslim thinkers and orientalists. Goldziher and Schacht were the primary pioneers in the critique of isnad studies. While Goldziher centered his research on hadith studies in general, Schacht focused specifically on the isnad of legal hadiths. This study's findings demonstrate that the 'conclusions' of Goldziher and Schacht are incorrect. Both scholars concluded that the isnad method only emerged in the second half of the second century Hijri. In reality, the isnad method existed much earlier than what Goldziher and Schacht had concluded. The second finding of this study indicates that the isnad of hadith significantly influences the determination of Islamic law among jurists (fuqaha'). [ABSTRACT FROM AUTHOR]

This paper explores the application of inferring software architecture rules from examples using Machine Learning (ML). We investigate different methods from Inductive Rule Learning and utilize Large Language Models (LLMs). Traditional manual rule specification approaches are time-consuming and error-prone, motivating the need for automated rule discovery. Leveraging a dataset of software architecture instances and a meta-model capturing implementation facts, we used inductive learning algorithms and LLMs to extract meaningful rules. The induced rules are evaluated against a predefined hypothesis and their generalizability across different system subsets is investigated. The research highlights the capabilities and limitations of ML-based rule learning in the area of software architecture, aiming to inspire further innovation in data-driven rule discovery for more intelligent software architecture practices. [ABSTRACT FROM AUTHOR]

The discovery of the law of the wall, the log-law including the von Kármán constant, is seen to be one of the biggest accomplishments of fluid mechanics. However, after more than ninety years, there is still a controversial debate about the validity and universality of the law of the wall. In particular, evidence in favor of a universal log-law was recently questioned by data analyses of the majority of existing direct numerical simulation (DNS) and experimental results, arguing in favor of nonuniversality of the law of the wall. Future progress requires it to resolve this discrepancy: in absence of alternatives, a reliable and universal theory involving the law of the wall is needed to provide essential guideline for the validation of theory, computational methods, and experimental studies of very high Reynolds number flows. This paper presents an analysis of concepts used to derive controversial conclusions. Similar to the analysis of observed variations of the Kolmogorov constant, it is shown that nonuniversality is a consequence of simplified modeling concepts, leading to unrealizable models. Realizability implies universality: there is no need to adjust simplified models to different flows. [ABSTRACT FROM AUTHOR]

For over eighty years, Rule 11 of the Federal Rules of Civil Procedure has authorized sanctions to combat frivolous legal arguments and factual allegations in some paper presentations. Since 1993, however, the rule has not applied to discovery. This article demonstrates how Rule 11 should be used to deter advocacy based on discovery that has "gone bad" since its initial disclosure. Disciplinary actions, malpractice claims, the discovery rules, and even the inherent sanctioning authority of the court are insufficient to deter lawyers from relying on now-bad discovery papers. Rule 11 would hold lawyers to a duty of reasonable inquiry before relying on prior discovery, including discovery conducted or presented by a prior lawyer or firm. This article proceeds in six parts. First, it generally demonstrates why it matters that Rule 11 apply to now-rotten discovery papers. Second, it employs a 2001 Fourth Circuit opinion to show how a Rule 11 application to gone-bad discovery would help deter frivolity in civil litigation. Third, it reviews the current rule's exclusion of "discovery process" violations, arguing that it does not encompass presentations of discovery once good, but now gone bad. Fourth, it briefly reviews how federal case practices on pleadings gone bad can apply to discovery gone bad. Fifth, it reviews comparable state laws and precedents on presentations of civil litigation papers gone bad. Finally, it concludes with suggestions on how Rule 11 sanctions should be assessed for advocacy of now-bad discovery papers. [ABSTRACT FROM AUTHOR]

When dealing with highly variable processes, the models may not accurately reflect the changes in the rules between different decision points in the process operation, by using the existing automatic process mining techniques. From the perspective of declarative process discovery, this paper proposed a decision table Petri net mining method with visual decision rules, to realize the mapping from real event log to a declarative process decision table Petri net model. Firstly, this method formalized both the decision table Petri net model and its rule analysis decision table, and designed the static and dynamic semantics of the formalized decision Petri net model. Secondly, through adding extended attributes, it analyzed whether the internal attributes or event attributes of the process would affect the decision. Furthermore, it generated outlier attributes with their deviation degrees of the rule analysis decision table to determine the degree of exception of the rule. Finally, it conducted experimental simulation on the basis of a set of artificial logs and practical event logs, and at the same time, it analyzed the experimental results by comparing with the extant data mining technology by Petri nets. The experimental results show that the proposed method has certain advantages in representing the change of rules during the operation of the process, which can provide quantitative and interpretable analysis for data flow anomaly detection. At the same time, the proposed decision table Petri net mining method can integrate the decision information with the model structure together, providing a formal basis for the variability modeling of the process model. [ABSTRACT FROM AUTHOR]

Léon Werth, a Jewish writer living in Vichy France during World War II, kept a diary titled "Deposition" to document daily life in the countryside. The diary captures a variety of perspectives on the war, Vichy, and the occupation, ranging from collaboration to resistance. Werth's observations also shed light on French antisemitism and collaboration with the Nazis, providing valuable insights into this dark period of French history. The abridged edition of Werth's diary, translated and edited by David Ball, offers a compelling narrative for readers seeking to understand the complexities of World War II France through the eyes of an extraordinary witness. [Extracted from the article]

Based upon the demarcation between Elementalism and Atomism Chemistry from the perspective of the long-term history of chemistry, the authors re-examine the Berthollet-Proust controversy on the three types of chemical compounds, pointing out that Berthollet proposed the law of indefinite proportions by deduction, while Proust proposed the law of definite proportions by induction. The controversy is beyond the framework of affinity chemistry and entail a synthesis of meta-chemical thinking and experiments. Proust's discovery of the law of definite proportions not only function as Bacon's "instances of lamp" to invoke Dalton and other atomism chemists to envision atomism, but also served as a bridge linking the two meta-chemistries. John Dalton, the third choice, envisioned his atomism by abduction. The case study on "the Berthollet-Proust controversy and Dalton's resolution" mandates a reinvestigation of the crucial role of the system of experiments and the evolution of chemistry according to the demarcation between the established branches of Elementalism and Atomism Chemistry. [ABSTRACT FROM AUTHOR]

The simulation hypothesis is a philosophical theory, in which the entire universe and our objective reality are just simulated constructs. Despite the lack of evidence, this idea is gaining traction in scientific circles as well as in the entertainment industry. Recent scientific developments in the field of information physics, such as the publication of the mass-energy-information equivalence principle, appear to support this possibility. In particular, the 2022 discovery of the second law of information dynamics (infodynamics) facilitates new and interesting research tools at the intersection between physics and information. In this article, we re-examine the second law of infodynamics and its applicability to digital information, genetic information, atomic physics, mathematical symmetries, and cosmology, and we provide scientific evidence that appears to underpin the simulated universe hypothesis. [ABSTRACT FROM AUTHOR]

An introduction is presented in which editor discusses various articles within the issue on topics including treatment of income from real estate capital in the Income Tax; crime of confiscation of assets and discovery of "indications" of crime in inspection actions.

In the 1823 decision of Johnson v. M'Intosh, Chief Justice John Marshall formulated the international law of colonialism. Known as the Doctrine of Discovery, Marshall's opinion drew on the practices of European nations during the Age of Exploration to legitimize European acquisition of territory owned and occupied by Indigenous peoples. Two centuries later, Johnson--and the international law of colonialism--remains good law throughout the world. In this Article we examine how the Doctrine of Discovery was adapted and applied in Australia and the United States. As Indigenous peoples continue to press for a re-examination of their relationships with governments, we also consider whether and how the international law of colonialism has been mitigated or unraveled in these two countries. While we find that the Doctrine lingers, close examination provides several important lessons for all Indigenous nations and governments burdened by colonization. [ABSTRACT FROM AUTHOR]

This paper examines the polysemy of οὐ̑λος ("deadly," "repeated," "whole/healthy") in Nicander's Theriaca as a window onto the poet's interest in the process of discovery. The core of this article treats the use of the term to describe a hound's howl in the aetiological narrative of Alcibius' discovery of a curative herb. I argue that Nicander deploys οὐ̑λος to call attention to the polysemy of the adjective whose meaning shifts and overlaps as one reads the narrative of Alcibius' discovery. The challenge of formulating a contextually appropriate meaning of οὐ̑λος reinforces the theme of aetiological discovery and is consonant with Nicander's use of the term elsewhere in the Theriaca as well as his broader interest in rare words and philological research. [ABSTRACT FROM AUTHOR]

This article traces references to nature and naturalism (understood as an attempt to ground and legitimize art in natural phenomena) in Ligeti's writings, but also in selected aspects of his oeuvre. The references to nature recur here in various manifestations, with romantic depictions of nature appearing alongside more recent, modernist approaches. The concept of nature is associated with the romantic setting for human emotions, with the discovery of scientific laws, with listening to soundscape, with phenomena of auditory perception and with the spectral explorations of sound. Although nature is not a central, strategic concept for Ligeti, it remains a constant, even if hidden, context for his work, a point of reference. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the implementation of discovery learning in teaching English. This study was qualitative research, and it applied a case study. The data source was from three lecturers. The researchers used interview guidelines to gather data. The findings release that there were six stages of discovery learning implementation. The stages of this implementation were stimulation, problem identification, data collection, data processing, verification, and generalization. The highest frequency of using marked the selection of each category discovery learning in teaching English. The frequency of implementation of discovery learning can be described into different categories, namely: High Implemented Discovery Learning (HIDL), Moderate Implemented Discovery Learning (MIDL), and Law Implemented Discovery Learning (LIDL). All lecturers are the High Implemented Discovery Learning is marked by the highest frequency of using discovery learning in teaching English education students. [ABSTRACT FROM AUTHOR]

The article discusses the focus of the European Centre of Tort and Insurance Law (ECTIL) and the Institute for European Tort Law on liability for defective products. While the 1985 Product Liability Directive provided a uniform regime in Member States, a new proposal for a revised directive was published by the European Commission in 2022. The article highlights a Special Session of the 23rd Annual Conference on European Tort Law dedicated to product liability and the papers presented at the session. These papers provide an overview and analysis of the changes in the new regime, focus on rules regarding burden of proof and presumptions, offer a law and economics analysis, and assess the new directive's ability to address challenges in a changing world. [Extracted from the article]

Insight problems are particularly interesting, because problems which require restructuring allow researchers to investigate the underpinnings of the Aha- experience, creativity and out of the box thinking. There is a need for new insight tasks to probe and extend the limits of existing theories and cognitive frameworks. To shed more light on this fascinating issue, we addressed the question: Is it possible to convey a well-known card sorting game into an insight task? We introduced different conditions and tested them via two online experiments (N = 546). Between the conditions we systematically varied the available perceptual features, and the existence of non-obvious rules. We found that our card sorting game elicited insight experience. In the first experiment, our data revealed that solution strategies and insight experience varied by the availability and saliency of perceptual features. The discovery of a non-obvious rule, which is not hinted at by perceptual features, was most difficult. With our new paradigm, we were able to construe ambiguous problems which allowed participants to find more than one solution strategy. Interestingly, we realized interindividual preferences for different strategies. The same problem drove strategies which either relied on feature integration or on more deliberate strategies. The second experiment varied the degree of independence of a sorting rule from the standard rules which were in accordance with prior knowledge. It was shown that the more independent the hidden rule was, the more difficult the task became. In sum, we demonstrated a new insight task which extended the available task domains and shed light on sequential and multi-step rule learning problems. Finally, we provided a first sketch of a cognitive model that should help to integrate the data within the existing literature on cognitive models and speculated about the generalizability of the interplay of prior knowledge modification and variation for problem solving. [ABSTRACT FROM AUTHOR]

Our research deals with intelligent decision support systems based on rule-based knowledge bases. Decision support systems use rules "If a condition, then a decision" as a form of knowledge representation. In the process of inference, which mirrors the process of human reasoning, we look for rules that confirm the facts and thus generate new knowledge. Such rule-based knowledge bases can (and often do) contain outlier rules. Our goal is to find such unusual rules. Thanks to this, we can influence the completeness of the knowledge base by finding unusual rules and asking domain experts to supplement knowledge in a rare area. To enhance the effectiveness of decision support systems, we conducted separate investigations into two distinct methods. The first method involved the utilisation of the Local Outlier Factor (LOF) algorithm in detecting rule outliers, while the second method employed the Self-Organizing Maps (SOM) algorithm for the same purpose. Our experiments not only confirmed the effectiveness of both the LOF and SOM algorithms but also involved comparing the results obtained from both methods. The discovery of outlier rules can aid knowledge engineers and domain experts in knowledge exploration and enhance the completeness of the knowledge base, which is crucial for decision support systems. [ABSTRACT FROM AUTHOR]

With reference to Wason's 2-4-6 rule discovery task, this study investigated the effects of a simple training session that prompted participants to "think in opposites". The results showed a significant improvement in performance under the training condition when compared to the control condition, both in terms of the proportion of participants who discovered the correct rule and how quickly it was discovered. An analysis of whether or not participant submitted test triples formed of descending numbers showed that fewer participants under the control condition considered ascending/descending to represent a critical dimension and, in any case, this occurred later (that is, after more test triples) than in the training condition. These results are discussed in relation to previous literature showing improvements in performance that were prompted by strategies involving "contrast" as a critical factor. The limitations of the study are discussed, as well as the benefits of a training program like this, which is non-content related. [ABSTRACT FROM AUTHOR]

The capacity to generate recursive sequences is a marker of rich, algorithmic cognition, and perhaps unique to humans. Yet, the precise processes driving recursive sequence generation remain mysterious. We investigated three potential cognitive mechanisms underlying recursive pattern processing: hierarchical reasoning, ordinal reasoning, and associative chaining. We developed a Bayesian mixture model to quantify the extent to which these three cognitive mechanisms contribute to adult humans' performance in a sequence generation task. We further tested whether recursive rule discovery depends upon relational information, either perceptual or semantic. We found that the presence of relational information facilitates hierarchical reasoning and drives the generation of recursive sequences across novel depths of center embedding. In the absence of relational information, the use of ordinal reasoning predominates. Our results suggest that hierarchical reasoning is an important cognitive mechanism underlying recursive pattern processing and can be deployed across embedding depths and relational domains. [ABSTRACT FROM AUTHOR]

Discovery disputes are prevalent in pretrial practice but are largely absent from law-school casebooks and the decisions of the Supreme Court. The lack of formal appellate decisions contributes to the view that discovery functions like civil litigation's Wild West, without meaningful law development or error correction. But, by looking at every reference to "discovery" in the Roberts Court's jurisprudence and hundreds of district courts' review of magistrate judges' discovery orders, this Article identifies how this story leaves out a few critical developments. First, focusing on the lack of formal appellate decisions misses how discovery is actually extensively featured in the Roberts Court's decisions. For example, Twombly and Iqbal changed the pleading standard in federal court because of concerns about the ostensible cost of discovery. And there are dozens of other examples in which fears about discovery are used to justify decisions about jurisdiction, interbranch conflicts, First Amendment challenges, the reach of certain statutes, and other issues. In this way, discovery acts like "dark matter," which is most easily identified by its effect on other areas of law. Second, the assumption that discovery is beset by a lack of error correction and law clarification because of the absence of formal appellate guidance fails to recognize the normative guidance provided by the Court's dark-matter discovery and the quasi-appellate review following from the rise of magistrate judges as the frontline managers of discovery. To the former, this Article canvases almost fifty trial-level courts' decisions that reference the discovery dicta in Twombly and Iqbal to decide discovery disputes. To the latter, district judges routinely review magistrate judges' discovery decisions, and this Article highlights several examples in which they correct unjust applications of the law or clarify important doctrinal questions. In this way, one sees how the functions of appellate review have been channeled in ways consistent with institutional expertise instead of formal judicial hierarchy. The trial-level judges are given primary responsibility for the management of discovery disputes, including error correction and law clarification, by having district judges review the decisions of magistrate judges. At the other end of the spectrum, the Roberts Court has used its bully pulpit to make pronouncements about the normative tradeoffs implicated by common discovery disputes, which has some--but not necessarily a great deal of--traction. While this jury-rigged system largely seems to be working, it has not been explicitly identified, and several factors are poised to disrupt the existing equilibrium. [ABSTRACT FROM AUTHOR]

Increasing the milk production of small dairy producers is necessary to cover the increase in milk demand in Tanzania. Currently, the population of people in both Tanzania and the world has increased and is predicted to increase more in the year 2050. The use of multilevel association rule mining methods to mine strong patterns among smallholder dairy farmers could help in identifying the best dairy farming practices and increase their milk production by adopting them. This study employed multi-level association rule mining to discover strong rules in three clusters, resulting in three levels of rules in each cluster. These three clusters were high, medium, and low milk producers. Rules were obtained for feeding practices, milk production, and breeding and health practices. These rules represent strong patterns among smallholder dairy farmers that could help them improve their dairy farming practices and have a gradual increase in milk production, from low to medium and from medium to higher milk production. Smallholder dairy producers would be provided with recommendations on their dairy farming practices, using rules based on the cluster to which they belong that could help them achieve higher milk production. [ABSTRACT FROM AUTHOR]

LiteBIRD , the Lite (Light) satellite for the study of B -mode polarization and Inflation from cosmic background Radiation Detection, is a space mission for primordial cosmology and fundamental physics. The Japan Aerospace Exploration Agency (JAXA) selected LiteBIRD in May 2019 as a strategic large-class (L-class) mission, with an expected launch in the late 2020s using JAXA's H3 rocket. LiteBIRD is planned to orbit the Sun–Earth Lagrangian point L2, where it will map the cosmic microwave background polarization over the entire sky for three years, with three telescopes in 15 frequency bands between 34 and 448 GHz, to achieve an unprecedented total sensitivity of |$2.2\, \mu$| K-arcmin, with a typical angular resolution of 0.5○ at 100 GHz. The primary scientific objective of LiteBIRD is to search for the signal from cosmic inflation, either making a discovery or ruling out well-motivated inflationary models. The measurements of LiteBIRD will also provide us with insight into the quantum nature of gravity and other new physics beyond the standard models of particle physics and cosmology. We provide an overview of the LiteBIRD project, including scientific objectives, mission and system requirements, operation concept, spacecraft and payload module design, expected scientific outcomes, potential design extensions, and synergies with other projects. [ABSTRACT FROM AUTHOR]

We develop a new class of distribution-free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening, and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data-driven threshold along the ranking to control the FDR. The SDA filter substantially outperforms the Knockoff method in power under moderate to strong dependence, and is more robust than existing methods based on asymptotic p-values. We first develop finite-sample theories to provide an upper bound for the actual FDR under general dependence, and then establish the asymptotic validity of SDA for both the FDR and false discovery proportion control under mild regularity conditions. The procedure is implemented in the R package sdafilter. Numerical results confirm the effectiveness and robustness of SDA in FDR control and show that it achieves substantial power gain over existing methods in many settings. [ABSTRACT FROM AUTHOR]

Background/Objectives: Early referral and treatment of infantile hemangioma (IH) is a major challenge for treatment success. However, there is a lack of data supporting a specific threshold for initiating treatment with oral propranolol. The aim of this analysis was to find factors, such as age at treatment initiation, leading to a higher success rate with oral propranolol treatment. Methods: Based on data from the pivotal phase 2–3 clinical trial of oral propranolol in IH, we used Generalized Additive Model (GAM) charts with Generalized Linear Models (GLM), then a rule discovery algorithm, to identify sub‐groups presenting a high probability of occurrence of the predefined outcome (i.e., success [complete or nearly complete resolution of the target hemangioma] at 6 months of treatment). Results: Our analyses identified that patients who started oral propranolol 3 mg/kg/day before the age of 10 weeks had a success rate of 86%, higher than the 60% success rate for all patients that received the same regimen commencing after 10 weeks of age. Conclusions: Treatment initiation before 10 weeks of age was associated with a significantly higher rate of treatment success with oral propranolol 3 mg/kg/day. Infants with IH requiring treatment should be referred to an expert center and treated as soon as possible. [ABSTRACT FROM AUTHOR]

Knowledge data has been widely applied to artificial intelligence applications for interpretable and complex reasoning. Modern knowledge bases are constructed via automatic knowledge extraction from open‐accessible sources. Thus the sizes of KBs are continuously growing, heavily burdening the maintenance and application of the knowledge data. Besides the grammatical redundancies, semantically repeated information also frequently appears in knowledge bases but is still under‐explored. Existing semantic compressors fail to efficiently discover expressive patterns and thus perform unsatisfyingly on knowledge data. This article proposes SInC, a semantic inductive compressor, to efficiently induce first‐order Horn rules and semantically compress knowledge bases. SInC improves the scalability of top‐down rule mining by batching correlated records in the cache and further optimizes the pruning of duplication and specialization via an identifier structure of Horn rules. SInC was evaluated on real‐world and synthetic datasets and compared against the state‐of‐the‐art. The results show that the batched caching speed up the rule mining procedure by more than two orders while consuming fewer than three times memory space. The identifier technique speeds up the duplication and specialization pruning by orders of magnitude with less than 5‰ and 15% error rates, respectively. SInC outperforms the state‐of‐the‐art from the perspective of overall compression on both scalability and compression effect. [ABSTRACT FROM AUTHOR]

Systematic pattern recognition as well as the corresponding description of determined patterns entail numerous challenges in the application context of high-dimensional communication data. These can cause increased effort, especially with regard to machine-based processing concerning the determination of regularities in underlying datasets. Due to the increased expansion of dimensions in multidimensional data spaces, determined patterns are no longer interpretable by humans. Taking these challenges into account, this paper investigates to what extent pre-defined communication patterns can be interpreted for the application area of high-dimensional business communication data. An analytical perspective is considered by taking into account a holistic research approach and by subsequently applying selected Machine Learning methods from Association Rule Discovery, Topic Modelling and Decision Trees with regard to the overall goal of semi-automated pattern labelling. The results show that meaningful descriptions can be derived for the interpretation of pre-defined patterns. [ABSTRACT FROM AUTHOR]

As the 100th anniversary approaches of Albert Einstein being awarded a Nobel Prize, questions remain about the motivation for the prize and about the absence of any specific mention of his theory of relativity. By revisiting and supplementing earlier scholarly studies, it will be shown that "Einstein did not receive, as often claimed, a prize for his theory of the photoelectric effect and that committee member Allvar Gullstrand's error in comprehending relativity was not the cause for rejecting this strongly nominated achievement." Rather, in their evaluations of relativity, "Svante Arrhenius (1920) and Gullstrand (1921 & 1922) brought to the task bias, if not prejudice; they incorporated arguments from the German ultranationalist experimental physicists' politically and racially motivated opposition to Einstein and his theories of relativity and gravitation. Only when Carl Wilhelm Oseen joined the committee in 1922, he nominated, evaluated, and proposed a prize for the discovery of the law of the photoelectric effect." The precise wording and deliberate silence about Einstein's quantum theoretical derivation of the law owes to Oseen's insightful understanding of the challenges facing any effort to award a Nobel Prize to both Einstein and Niels Bohr. As the 100th anniversary of Albert Einstein's Nobel Prize approaches, some confusion remains as to what actually transpired and what it means. A widely‐cited account claiming Einstein received a prize for his theory of the photoelectric effect and attributing relativity's absence to an error in evaluation carries misunderstanding and oversimplification of a much more complex and troubling history. [ABSTRACT FROM AUTHOR]