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Deep reinforcement learning is confronted with problems of sampling inefficiency and poor task migration capability. Meta-reinforcement learning (meta-RL) enables meta-learners to utilize the task-solving skills trained on similar tasks and quickly adapt to new tasks. However, meta-RL methods lack enough queries toward the relationship between task-agnostic exploitation of data and task-related knowledge introduced by latent context, limiting their effectiveness and generalization ability. In this article, we develop an algorithm for off-policy meta-RL that can provide the meta-learners with self-oriented cognition toward how they adapt to the family of tasks. In our approach, we perform dynamic task-adaptiveness distillation to describe how the meta-learners adjust the exploration strategy in the meta-training process. Our approach also enables the meta-learners to balance the influence of task-agnostic self-oriented adaption and task-related information through latent context reorganization. In our experiments, our method achieves 10%-20% higher asymptotic reward than probabilistic embeddings for actor-critic RL (PEARL).

Today’s intelligent system based on cloud computing platform can realize “unattended”, real-time monitoring observation and forecast by remote sensing. In order to import the development and efficiency of groundwater potential assessment(GPA) by remote sensing, the cloud computing platform was tried to use in the computing GPA. In this study, the Pearl River Estuary islands region(China) was selected as the study area. The slope, aspect, water-density(WD), land surface temperature(LST), NDVI and NDWI were used as the GPA indexes, which have been used before. Considering the similar geological and geomorphological conditions of the islands area, the analytic hierarchy process (AHP) method and these indexes can be used to assess GPA in the remote sensing cloud computing platform efficiently and conveniently. The results of the assessment were in good agreement with the actual hydrogeological map. Besides, the other intelligent algorithms can also be applied in this platform. Finally, this study realized the rapid “unattended” and “real-time monitoring” groundwater potential assessment, and carried out a multi-level GPA. It will be of certain reference significance to the exploitation of groundwater in the island area, which has realized convenient and efficient processing and analysis of data anytime and anywhere. At the same time, attention must be paid to the security of data and the maintenance of the system.

Compilers are difficult to write, and difficult to get right. Bahr and Hutton recently developed a new technique for calculating compilers directly from specifications of their correctness, which ensures that the resulting compilers are correct-by-construction. To date, however, this technique has only been applicable to source languages that are untyped. In this article, we show that moving to a dependently-typed setting allows us to naturally support typed source languages, ensure that all compilation components are type-safe, and make the resulting calculations easier to mechanically check using a proof assistant.

This study aims to assess the potential of multi-temporal Sentinel-1 and RADARSAT-2 data for pearl millet discrimination using various classifiers—machine learning and knowledge-based decision tree...

In recent times, the concept of sustainability has become a focus of attention for many institutions and communities. The reason for the spread of this concept and great interest is the pursuit of sustainable development of societies which aims at balancing the three factors: environmental, economic, and social. This paper aims to develop a system to implement sustainability ideas in school building projects based on the international guidelines and practices for sustainability. For example, (Building Research Establishment Environmental Assessment Method (BREEAM)-Leadership in Energy and Environmental Design (LEED)-The Pearl Building Rating System (PBRS)) and the current practices in Iraq. The purpose of the collected and reported information was for making decisions during various stages of preconstruction, construction, and post-construction. The system employed suitable criteria, subcriteria, and standard weights for school buildings assessment, which was set according to a field survey. The survey included interviewing a group of specialists in school buildings and conducting a closed questionnaire. A computer program was prepared to assess school buildings in the 3rd General Directorate of Education in Baghdad-Karkh. These buildings were executed during (2008–2018). The School buildings were classified into four types (traditional, precast concrete units, steel structures, and structural concrete construction). The field survey has shown that the highest standard of sustainability is the environmental design (20.79), and the lowest sustainability criteria are water treatment system (5.81). However, the study aims to develop a sustainability assessment system for school buildings. A comparison was made between the current buildings with ones that having ideal sustainability criteria. Similarly, a monitoring was done with the perfect situation to observe the deviation within the project progress. Finally, recommendations were given for the purpose of employing the sustainability concepts in school buildings.

Rubin and Pearl offered approaches to causal effect estimation and Lewis and Pearl offered theories of counterfactual conditionals. Arguments offered by Pearl and his collaborators support a weak form of equivalence such that notation from the rival theory can be re-purposed to express Pearl’s theory in a way that is equivalent to Pearl’s theory expressed in its native notation. Nonetheless, the many fundamental differences between the theories rule out any stronger form of equivalence. A renewed emphasis on comparative research can help to guide applications, further develop each theory, and better understand their relative strengths and weaknesses.

This study aimed at focussing the translation of movement verbs in The Good Earth written by Pearl S. Buck. The Natural Semantic Metalanguage (NSM) is applied in translation process. The novel The Good Earth was chosen as data source because it has two texts which have different language types. This study has the purpose to describe the application of translation procedures that is proposed by Vinay and Darlbener and it is applied to examine the mapping of Action Verbs by the NSM (Natural Semantic Metalanguage) theory. The analysis was shown as the mapping between Indonesian and English action verbs which was presented into the subtype of the verbs based on the classification. This study attempts to apply that theory in solving the problem of the different language. Semantic mapping (SM) is a method in statistics for dimensionality reduction that can be used in a set of multidimensional vectors of features to extract a few new features that preserves the main data characteristics. Translation is very important in various areas which are used to translate from one language into another language. However, it is not all the references written in Indonesian as they are coming from different countries and use different languages.

In the context of China’s fast urbanization, the background of urban landmarks is constantly changing, which provides useful examples for the study of visual preference evaluation of urban landmark...

This paper describes an implementation of Harper's continuation-based regular-expression matcher as a strong functional program in Cedille; i.e., Cedille statically confirms termination of the program on all inputs. The approach uses neither dependent types nor termination proofs. Instead, a particular interface dubbed a recursion universe is provided by Cedille, and the language ensures that all programs written against this interface terminate. Standard polymorphic typing is all that is needed to check the code against the interface. This answers a challenge posed by Bove, Krauss, and Sozeau.

Parsing with Derivatives (PwD) is an elegant approach to parsing context-free grammars (CFGs). It takes the equational theory behind Brzozowski's derivative for regular expressions and augments that theory with laziness, memoization, and fixed points. The result is a simple parser for arbitrary CFGs. Although recent work improved the performance of PwD, it remains inefficient due to the algorithm repeatedly traversing some parts of the grammar. In this functional pearl, we show how to avoid this inefficiency by suspending the state of the traversal in a zipper. When subsequent derivatives are taken, we can resume the traversal from where we left off without retraversing already traversed parts of the grammar. However, the original zipper is designed for use with trees, and we want to parse CFGs. CFGs can include shared regions, cycles, and choices between alternates, which makes them incompatible with the traditional tree model for zippers. This paper develops a generalization of zippers to properly handle these additional features. Just as PwD generalized Brzozowski's derivatives from regular expressions to CFGs, we generalize Huet's zippers from trees to CFGs. Abstract The resulting parsing algorithm is concise and efficient: it takes only 31 lines of OCaml code to implement the derivative function but performs 6,500 times faster than the original PwD and 3.24 times faster than the optimized implementation of PwD.

This paper presents a simple mechanized formalization of Separation Logic for sequential programs. This formalization is aimed for teaching the ideas of Separation Logic, including its soundness proof and its recent enhancements. The formalization serves as support for a course that follows the style of the successful Software Foundations series, with all the statement and proofs formalized in Coq. This course only assumes basic knowledge of lambda-calculus, semantics and logics, and therefore should be accessible to a broad audience.

This study's purpose is to increase interior designers' sustainable attitude through suggesting a strategy for an Interior Design Rating Tool designed for that purpose. Pearl Rating Tool of Estidama of Abu Dhabi is the first tailored to the Middle East region. Its aim is to create more sustainable communities, cities and global enterprises and to balance the four pillars of sustainability: environmental, economic, cultural and social. As sustainability in designing interior spaces is the same in importance as in designing and constructing buildings and designing communities; this study suggested a strategy for an Interior Design Pearl Rating Tool for Estidama. Ten sustainable interior design criteria are included with the most important strategies to be met so that the interior designer should be aware of the four pillars of sustainability it has to balance between. In addition, designers have to learn about the maximum credit score they can achieve through fulfilling human needs, wants and values in the built environment. This strategy is designed to be presented to designers before starting their project in order for their project to be rated.

As a shining pearl in traditional Tibetan culture, historical Tibetan documents have received extensive attention from historians, linguists and Buddhist scholars. These documents are converted into digital form using Tibetan document segmentation and recognition methods. The document digitization is of great significance for the research, protection and inheritance of Tibetan history. This paper proposes an overall segmentation and recognition framework for historical Tibetan document images. Firstly, the historical Tibetan document image is preprocessed to correct imbalanced illumination, tilt and noises, and is further transformed into the binarized image. Secondly, we propose a layout segmentation method based on block projection to segment Tibetan document images into texts, lines and frames. Thirdly, in order to solve the problems of touching strokes between text-lines and curvilinear text-lines, we present a text-line segmentation method based on graph model for historical Tibetan text-line segmentation. Lastly, we present a touching segmentation method to segment touching Tibetan character string, and then recognize Tibetan characters. Experimental results show our proposed methods on layout segmentation, text-line segmentation and touching character string segmentation, achieve the satisfactory performance. The proposed methods can also be applied to other fonts in Tibetan font family.

Critical infrastructures have to withstand advanced and persistent threats, which can be addressed using Byzantine fault tolerant state-machine replication (BFT-SMR). In practice, unattended cyberdefense systems rely on threat level detectors that synchronously inform them of changing threat levels. How- ever, to have a BFT-SMR protocol operate unattended, the state- of-the-art is still to configure them to withstand the highest possible number of faulty replicas f they might encounter, which limits their performance, or to make the strong assumption that a trusted external reconfiguration service is available, which introduces a single point of failure. In this work, we present ThreatAdaptive the first BFT-SMR protocol that is automatically strengthened or optimized by its replicas in reaction to threat level changes. We first determine under which conditions replicas can safely reconfigure a BFT-SMR system, i.e., adapt the number of replicas n and the fault threshold f, so as to outpace an adversary. Since replicas typically communicate with each other using an asynchronous network they cannot rely on consensus to decide how the system should be reconfigured. ThreatAdaptive avoids this pitfall by proactively preparing the reconfiguration that may be triggered by an increasing threat when it optimizes its performance. Our evaluation shows that ThreatAdaptive can meet the latency and throughput of BFT baselines configured statically for a particular level of threat, and adapt 30% faster than previous methods, which make stronger assumptions to provide safety.

Ancient buildings have various geometric and material changes caused by the historical and natural factors, and their comprehensive detection has also been a more important challenge. This way, in this paper, a flexible, scientific approach from terrestrial laser scanning and hyperspectral imaging is provided for this issue. It is possible to flexibly and accurately detect some potential crisis, which cannot be found in some surface phenomena of historical buildings. Furthermore, one of the main characteristic of this method is that the time and place of the two data acquisition need not be limited, but they can be accurately fused. Another one of the main features is that the fusion data can synthetically detect geometric and material changes of historical buildings. This method was applied to the case study of the Beijing Tianningsi Tower, an extremely dazzling pearl of the Chinese Buddhist pagoda, on which the signs of deformation and restoration were found in the tower shape and in the tower-body sculpture. It was possible to assess the typical physical, chemical, and biological changes of historical buildings, to provide scientific basis for comprehensive research. The results demonstrate that this method is feasible and applicable for detecting changes of ancient buildings and is applied to similar research using more analytical methods for multisource data.

Toxicology in the 21st Century has seen a shift from chemical risk assessment based on traditional animal tests, identifying apical endpoints and doses that are “safe”, to the prospect of Next Generation Risk Assessment based on non-animal methods. Increasingly, large and high throughput in vitro datasets are being generated and exploited to develop computational models. This is accompanied by an increased use of machine learning approaches in the model building process. A potential problem, however, is that such models, while robust and predictive, may still lack credibility from the perspective of the end-user. In this commentary, we argue that the science of causal inference and reasoning, as proposed by Judea Pearl, will facilitate the development, use and acceptance of quantitative AOP models. Our hope is that by importing established concepts of causality from outside the field of toxicology, we can be “constructively disruptive” to the current toxicological paradigm, using the “Causal Revolution” to bring about a “Toxicological Revolution” more rapidly.

Parser combinators are a popular and elegant approach for parsing in functional languages. The design and implementation of such libraries are well discussed, but having a well-designed library is only one-half of the story. In this paper we explore several reusable approaches to writing parsers in combinator style, focusing on easy to apply patterns to keep parsing code simple, separated, and maintainable.

Tensor kernels in machine learning (ML) often correspond to pure mathematical expressions, making term rewriting an attractive strategy for optimization and mapping to specialized hardware accelerators. However, existing ML intermediate representations (IRs) tend to either be \textit{pure but high-level}, making low-level rewrites to hardware targets inexpressible, or \textit{low-level but impure}, hampering the use of term rewriting altogether. This paper introduces Glenside, a pure IR whose core abstraction -- the \textit{access pattern} -- enables low-level, layout-aware, hardware-centric program rewrites. We demonstrate how term rewriting in Glenside can be used to map program fragments to hardware accelerator invocations and automatically discover classic data layout transformations like \texttt{im2col}. Glenside establishes a new foundation for exploring further term rewriting techniques in optimizing low-level tensor programs., To be published at MAPS 2021

Ultra-high-definition (UHD) videos are enjoying increased popularity in people’s daily usage because of the good visual experience. However, the data size of UHD videos is 416 times larger of HD videos. This will bring many challenges to existing video delivery systems, such as the shortage of network bandwidth resources and longer network transmission latency. Super resolution (SR) algorithms are widely used in video delivery applications to tackle these challenges. However, applying the super resolution model on UHD videos requires much more GPU memory, as compared with HD videos, which brings a significant challenge to existing systems.In this paper, we propose a deep compression framework named Pearl, which utilizes the power of deep learning to compress UHD videos. New channel-based super resolution models are developed to overcome the GPU memory shortage problem. In pearl, instead of applying the traditional RGB-based super resolution model, three separate super resolution models are trained based on the Y, U, and V channels of UHD videos. These super resolution models are used to reconstruct a UHD video from a low-resolution video. With Pearl, super resolution algorithms can be successfully applied to UHD videos. As a result, the data size of UHD videos can be significantly reduced during network transmission. At the same time, the efficiency of video encoding and decoding can also be improved with Pearl. To the best of our knowledge, Pearl is the first deep learning driven compression framework on UHD videos. We evaluate the performance of Pearl with extensive experiments. In all considered scenarios, Pearl can compress up to 95% of video data size during the video transmission and achieve 2.4 times faster, as compared with existing systems1.

Team semantics is a highly general framework for logics which describe dependencies and independencies among variables. Typically, the (in)dependencies considered in this context are properties of sets of configurations or data records. We show how team semantics can be further generalized to support languages for the discussion of interventionist counterfactuals and causal dependencies, such as those that arise in manipulationist theories of causation (Pearl, Hitchcock, Woodward, among others). We show that the “causal teams” we introduce in the present paper can be used for modelling some classical counterfactual scenarios which are not captured by the usual causal models. We then analyse the basic properties of our counterfactual languages and discuss extensively the differences with respect to the Lewisian tradition.

Navigational information is of great significance to the safety of maritime navigation. To better guarantee navigator safety and improve navigation efficiency, an applied model of geographic information services (GI services) that consists of an operational architecture, several subsystems and multiple GI services is presented. This work is an e-navigation testbed that follows e-navigation technical architecture and integrates a large amount of navigation-related resources. Real-time, location-based and on-demand digital navigational information can be exchanged and applied in a standardised way. An experiment conducted in the Pearl River Estuary area of the South China Sea showed that application of GI services in e-navigation can supplement the existing methods of exchanging navigational information and better assist navigators in decision making. Furthermore, the proposed model is adaptable and could be easily applied in other areas.

This paper focuses on automatic pearl classification by adopting deep learning method, using multiview pearl images. Traditionally, in order to get a satisfying classification result, we need to collect a huge number of labeled pearl images, which however is expensive in industry. Fortunately, generative adversarial network (GAN) was proposed recently to effectively expand training set, so as to improve the performance of deep learning models. We thus propose a multiview GAN (MV-GAN) to automatically expand our labeled multiview pearl images, and the expanded data set is then used to train the multistream convolutional neural network (MS-CNN). The experiments show that the utilization of images generated by the MV-GAN can indeed significantly reduce the classification error of the basic MS-CNN (up to 26.71%, relatively), obtaining the state-of-the-art results. More interestingly, it can also help the MS-CNN resist the brightness disturbance, leading to more robust classification.

Compiler correctness is an old problem, with results stretching back beyond the last half-century. Founding the field, John McCarthy and James Painter set out to build a "completely trustworthy compiler". And yet, until quite recently, even despite truly impressive verification efforts, the theorems being proved were only about the compilation of whole programs, a theoretically quite appealing but practically unrealistic simplification. For a compiler correctness theorem to assure complete trust, the theorem must reflect the reality of how the compiler will be used. There has been much recent work on more realistic "compositional" compiler correctness aimed at proving correct compilation of components while supporting linking with components compiled from different languages using different compilers. However, the variety of theorems, stated in remarkably different ways, raises questions about what researchers even mean by a "compiler is correct." In this pearl, we develop a new framework with which to understand compiler correctness theorems in the presence of linking, and apply it to understanding and comparing this diversity of results. In doing so, not only are we better able to assess their relative strengths and weaknesses, but gain insight into what we as a community should expect from compiler correctness theorems of the future.

Causal knowledge is sought after throughout data-driven fields due to its explanatory power and potential value to inform decision-making. If the targeted system is well-understood in terms of its causal components, one is able to design more precise and surgical interventions so as to bring certain desired outcomes about. The idea of leveraging the causal understanding of a system to improve decision-making has been studied in the literature under the rubric of structural causal bandits (Lee and Bareinboim, 2018). In this setting, (1) pulling an arm corresponds to performing a causal intervention on a set of variables, while (2) the associated rewards are governed by the underlying causal mechanisms. One key assumption of this work is that any observed variable (X) in the system is manipulable, which means that intervening and making do(X = x) is always realizable. In many real-world scenarios, however, this is a too stringent requirement. For instance, while scientific evidence may support that obesity shortens life, it’s not feasible to manipulate obesity directly, but, for example, by decreasing the amount of soda consumption (Pearl, 2018). In this paper, we study a relaxed version of the structural causal bandit problem when not all variables are manipulable. Specifically, we develop a procedure that takes as argument partially specified causal knowledge and identifies the possibly-optimal arms in structural bandits with non-manipulable variables. We further introduce an algorithm that uncovers non-trivial dependence structure among the possibly-optimal arms. Finally, we corroborate our findings with simulations, which shows that MAB solvers enhanced with causal knowledge and leveraging the newly discovered dependence structure among arms consistently outperform causal-insensitive solvers.

Edelkamp et al. (2005) proved that A*, given an admissible heuristic, is guaranteed to return an optimal solution in any cost algebra, not just in the traditional shortest path setting. In this paper, we investigate cost-algebraic A*’s optimal efficiency: in the cost-algebraic setting, under what conditions is A* guaranteed to expand the fewest possible states? In the traditional setting, this question was examined in detail by Dechter & Pearl (1985). They identified five different situations in which A* was optimally efficient. We show that three of them continue to hold in the cost-algebraic setting, but that one does not. We also show that one of them is false, it does not hold even in the traditional setting. We introduce an alternative that does hold in the cost-algebraic setting. Finally, we show that a well-known result due to Nilsson does not hold in the general cost-algebraic setting but does hold in a slightly less general setting.

CO2-enhanced oil recovery (CO2-EOR) and storage is currently the most effective and economic technology for reducing CO2 emissions from burning fossil fuels in large scale. This paper is the first effort of proposing a modelling assessment of CO2-EOR and storage in the HZ2-1 oilfield in the Pearl River Mouth Basin in northern South China Sea offshore Guangdong Province. We attempt to couple the multi-parameter dimensionless quick screening model and reservoir compositional simulation for optimization of site screen and injection simulation. Through the quick screening, the reservoirs are ranked by EOR dimensionless recovery RD, and by CO2 storage in pore volume SCO2. Our results indicate that SCO2 is highly pressure dependent and not directly related to RD. Of these reservoirs, CO2-EOR and storage potential of the M10 was estimated through a compositional simulation as a case study based on a 3D geological model. Nine scenarios of CO2 injection operations have been simulated for 20 years with different well patterns and injection pressures. The simulation results represent an obvious improvement in oil production by CO2 flooding over No−CO2 production. The best operation for M10 is miscible CO2 flooding, which led to the higher recovery factors of 52%˜58% and CO2 stored masses of 8.1×106˜10.8×106t. The optimum operation for CO2 injection should be set well pattern in region of injector I1 and high injection pressure for miscible flooding. In a whole, the HZ21-1 field can be used as a candidate geological site for GDCCUS project. We are fully aware of the limitation in the primary modelling including reservoir and fluid properties and production history matching, and regard this study as a general and hypothetic proposal.

In this commentary I respond to Lewis (2019) and Lanier (2019), building on their critiques and ideas, offering some additional thoughts about the dissemination of the Campbell, Rubin, and Pearl causal frameworks and their potential emergent value to the future of family violence research. I clarify that the central issue to credibility is the plausibility of assumptions, that some widely utilized methods often require researchers to make implausible assumptions, and that there is value to knowing and using all three frameworks.

Obtaining optimal operation rules for cascade reservoirs is crucial for making the most of the comprehensive benefits of reservoirs. However, the operation of cascade reservoirs is generally complex, which involves with challenges including various decision variables, multiple conflicting objectives and constraints. In this paper, a new algorithm named multi-objective tangent algorithm (MOTA) is proposed for optimizing operation rules of cascade reservoirs with the objectives of hydropower generation, ecology and navigation. The performance of MOTA is firstly validated through several well-known benchmark problems. Then it is applied to a case study of cascade reservoirs optimization in China's Pearl River. Using observed inflow data (1956-2015) of cascade reservoirs, a uniform Pareto front is obtained eventually via MOTA after 1000 generations. The optimal operation rules fully considers the comprehensive benefits of hydropower generation, ecology and navigation in China's Pearl River. The optimal operation rules can be used as a guidance tool for decision makers, through the objectives' tradeoff without having to embed a priori preferences in the decision process. Finally, this paper use observed inflow data (2016) of cascade reservoirs for examining the operational rule to comprehend the analysis under different optimal operation rules. The obtained results show that MOTA can be a viable alternative for generating optimal operation rules for cascade reservoirs planning and management.

This article points out what is important to consider when planning to perform and analyze a commonly seen study involving a comparison of an innovation with something established. A hands-on guide on how to perform this type of research trial and how to choose proper statistical methods is given.

Background: T ransformer-based language models have delivered clear improvements in a wide range of natural language processing (NLP) tasks. However, those models have a significant limitation; specifically, they cannot infer causality, a prerequisite for deployment in pharmacovigilance, and health care. Therefore, these transformer-based language models should be developed to infer causality to address the key question of the cause of a clinical outcome.Results: In this study, we propose an innovative causal inference model–InferBERT, by integrating the A Lite Bidirectional Encoder Representations from Transformers (ALBERT) and Judea Pearl’s Do-calculus to establish potential causality in pharmacovigilance. Two FDA Adverse Event Reporting System case studies, including Analgesics-related acute liver failure and Tramadol-related mortalities, were employed to evaluate the proposed InferBERT model. The InferBERT model yielded accuracies of 0.78 and 0.95 for identifying Analgesics-related acute liver failure and Tramadol-related death cases, respectively. Meanwhile, the inferred causes of the two clinical outcomes, (i.e. acute liver failure and death) were highly consistent with clinical knowledge. Furthermore, inferred causes were organized into a causal tree using the proposed recursive do-calculus algorithm to improve the model’s understanding of causality. Moreover, the high reproducibility of the proposed InferBERT model was demonstrated by a robustness assessment.Conclusion: The empirical results demonstrated that the proposed InferBERT approach is able to both predict clinical events and to infer their causes. Overall, the proposed InferBERT model is a promising approach to establish causal effects behind text-based observational data to enhance our understanding of intrinsic causality.Availability and implementation: The InferBERT model and preprocessed FAERS data sets are available on GitHub at https://github.com/XingqiaoWang/DeepCausalPV-master.

Multiple sustainability standards and rating systems have been developed to draw attention to constructing sustainable buildings. The Pearl Rating System (PRS) is a mandate for all new construction projects in Abu Dhabi. Hence, it is important to understand the main components, advantages, and limitations of the PRS. The feasibility and the practical relevance of the PRS are still being studied. This paper addresses this gap and critically evaluates the PRS against some of the well-established rating systems like LEED and BREEAM. The analysis suggests that the PRS considers the cultural aspect of sustainability, in addition to the environmental, societal, and economic aspects. It was also found that most rating systems, including the PRS, have a very superficial inclusion of life cycle assessment (LCA). The paper finally concludes with other observations and outlook for a more robust implementation of the PRS.

This paper shows how a small verified bootstrapped compiler can be developed inside an interactive theorem prover (ITP). Throughout, emphasis is put on clarity and minimalism.

In the causal modelling literature, it is well known that “ill-defined” variables may give rise to “ambiguous manipulations” (Spirtes and Scheines, 2004). Here, we illustrate how ill-defined variables may also induce mistakes in causal inference when standard causal search methods are applied (Spirtes et al., 2000; Pearl, 2009). To address the problem, we introduce a representation framework, which exploits an independent component representation of the data, and demonstrate its potential for detecting ill-defined variables and avoiding mistaken causal inferences.

Smart Factories are designed by Universities [e.g. 7, 1] and Research Institutes (e.g. IPA, IFF, IOSB, IFF, DFKI). Also, automotive companies are developing their ‘own’ Smart Factories [e.g. 3, 6]. The focus is on manufacturing processes, technical equipment, and control of technical processes but not on business and order processes [4, 5]. To run a Smart Factory also a concept for a “Digital Order Twin” (DOT) is needed to ensure that for each final product the right components are available in the right quantity at the right time and at the right place, especially if the product is configurated by a customer or dealer. But existing IT-Systems for Enterprise-Resource-Planning (ERPS), Material-Requirement-Planning (MRP) and Manufacturing Execution (MES) are not able to exploit the huge amount of acquired data ‘in-time’, because they are separated IT-Modules which are connected by batch-oriented interfaces only, the concept and algorithm base on the “Water-Fall-Model” without recursion to a preceding IT-System or IT-Module. In an autonomously controlled Smart Factory the right components must be referenced to each single final product instantly and in a more flexible way than the concept of pearl chain can do, instead of a unique DOT uses instantly data of RFID, QR-Code, Smart Devices and Cyber-Physical Objects and can interact in time with Systems and Modules.

The non-deterministic algorithmic procedure PEARL (acronym for ‘Propositional variables Elimination Algorithm for Relevance Logic’) has been recently developed for computing first-order equivalents of formulas of the language of relevance logics \(\mathcal {L}_R\) in terms of the standard Routley-Meyer relational semantics. It succeeds on a large class of axioms of relevance logics, including all so called inductive formulas. In the present work we re-interpret PEARL from an algebraic perspective, with its rewrite rules seen as manipulating quasi-inequalities interpreted over Urquhart’s relevant algebras, and report on its recent Python implementation. We also show that all formulae on which PEARL succeeds are canonical, i.e., preserved under canonical extensions of relevant algebras. This generalizes the “canonicity via correspondence” result in [37]. We also indicate that with minor modifications PEARL can also be applied to bunched implication algebras and relation algebras.

With the development of digital and intelligent technology in industrial control, the safety and reliability of control and protection system is becoming increasingly prominent. As the Pearl of modern industry, heavy duty gas turbine is very important. The ongoing independent R&D work of UGTC (China United Gas Turbine Technology Co., Ltd.) has been highly concerned by the industry. As the nerve center system of gas turbine, whether the control and protection system can complete the expected function safely and reliably is also an important part of the independent R&D work of heavy-duty gas turbine. In this paper, combined with the independent research and development experience of nuclear power control and protection system in China, based on the functional safety certification requirements of IEC 61508 standard, the technical scheme of heavy-duty gas turbine control and protection system software V&V is discussed, and the relevant technical key points are put forward, which has a certain guiding significance for the design and commissioning of control and protection system of heavy-duty gas turbine.

This chapter tells the story of the three cooperative robots—or cobots—Ro-Bert, Pearl, and Te-Ma and their lives in the year 2030. COVID-27 and COVID-28 have once again led to a complete shutdown of public life. The cobots are hard at work trying to ease the lives of their human families, so they can safely remain in their homes. Ro-Bert and Pearl run errands, perform household duties, and assist their owners in planning their daily lives in and outside of pandemics; Te-Ma is a telemedical assistance robot that can monitor the health status of its owners and automatically send reports to their doctor. Massive technological developments like 5G networks, conversational AI, and quantum computing as well as research in the areas of social robotics and human–robot collaboration have made all this possible in the year 2030. Read on to find out what makes these three cobots such a special team.

The chapter introduces knowledge representation for expressing causal calculus in Internet of Things (IoT). This research discusses how IoT devices and sensors harness causal inference. Causal calculi are the mathematical foundations for expressing and computing causation. Causation describes how one event may cause another. IoT devices and systems may collect, parse, process, calculate, and send causal implications. There are several approaches to causal calculi. Independently Pearl and Shafer give causal systems. In addition, Halpern and Pearl give a causal system. Causal systems are founded in logic and probability theories—Pearl’s method uses Bayesian networks on acyclic directed graphs. Shafer’s method works on the dynamics of probability trees. And, the Halpern–Pearl model builds on Pearl’s model yielding two causal views. One of these views attribute causes of past while the other view focuses on causal reasoning giving events.

Pearl opened the door to formally definingactual causationusing causal models. His approach rests on two strategies: first, capturing the widespread intuition thatX=xcausesY=yiffX=xis aNecessary Element of a Sufficient SetforY=y, and second, showing that his definition gives intuitive answers on a wide set of problem cases. This inspired dozens of variations of his definition of actual causation, the most prominent of which are due to Halpern & Pearl. Yet all of them ignore Pearl’s first strategy, and the second strategy taken by itself is unable to deliver a consensus. This paper offers a way out by going back to the first strategy: it offers six formal definitions ofcausal sufficiencyand two interpretations of necessity. Combining the two gives twelve new definitions of actual causation. Several interesting results about these definitions and their relation to the various Halpern & Pearl definitions are presented. Afterwards the second strategy is evaluated as well. In order to maximize neutrality, the paper relies mostly on the examples and intuitions of Halpern & Pearl. One definition comes out as being superior to all others, and is therefore suggested as a new definition of actual causation.

We recently renamed our “education” track’s papers from Education Pearl to Education Matters. This would be a good time to explain our vision for the track as a whole, as well as this specific renaming.

Recently, the use of Reinforcement Learning with Neural Networks in Abstractive Summarization is getting more popular, but still currently restricted. In this paper, we propose PEARL as a novel framework to expand the proficiency of Reinforcement Learning approach in Abstractive Text Summarization. PEARL consists of two out-of-the-box Reinforcement Learning algorithms: \(F_{Rouge}\) and \(D_{Threshold}\), where \(F_{Rouge}\) reconstructs the training objective, and \(D_{Threshold}\) helps to improve the flexibility for the arbitrary data. We evaluate PEARL in the large-scale CNN/DailyMail and the medium-scale VNTC-Abs datasets. Results show that our PEARL produces significantly greater Rouge scores than baselines as well as achieves the new state-of-the-art model without either pre-trained models or extra training data. This research provides proof of validity based on data analysis.

While previous works have discussed the network delay upper bound that guarantees the consistency of Nakamoto consensus, measuring the actual network latencies and evaluating their impact on miners/pools in Bitcoin remain open questions. This paper fills this gap by: (1) defining metrics that quantify the impact of network latency on the mining network; (2) developing a tool, named miner entanglement (ME), to experimentally evaluate these metrics with a focus on the network latency of the top mining pools; and (3) quantifying the impact of the current network delays on Bitcoin’s mining network. For example, we evaluated that Poolin, a Bitcoin mining pool, was able to gain between 0.5% and 1.9% of blocks in addition (i.e., from 36.27 BTC to 137.83 BTC) per week thanks to its low network latency. Moreover, as pools are rational in Bitcoin, we model the strategy a pool would follow to improve its network latency (e.g., by leveraging our ME tool) as a two party game. We show that a Bitcoin mining pool could improve its effective hash rate by up to 4.5%. For a multi-party game, we use a state-of-the-art Bitcoin mining simulator to study the situation where all pools attempt to improve their network latency and show that the largest mining pools would improve their revenue and reach a Nash equilibrium while the smaller mining pools would suffer from a decreased access to the network, and therefore a decreased revenue. These conclusions further incentivize the centralisation of the mining network in Bitcoin, and provide an empirical explanation for the observed tendency of pools to design and rely on low latency private networks.

The whole point throughout this work is a publicly available and annotated images dataset for vehicle license plate (LP) detection and recognition. This contribution is driving by the fact that no Tunisian LP dataset has been provided up to this point in time. Pearl Guard Tunisian LP (PGTLP) dataset is fully annotated database that contains presently up to 3, 000 images captured by high definition quality camera mounted on a security mobile robot, called Pearl Guard and made accessible to the Academia. For benchmarking and ranking supply, we propose a unified, real-time (LP) detection module based on YOLOv4-tiny detector. We also provide baseline results for vehicle LP detection using the PGTLP dataset.

As part of technological forecasting the electrical vehicle (EV) ecosystem, there are specific technologies that need to be developed to reach the desirable extreme fast charging technology (XFC) to propel commercial use and passenger markets. There are economic, environmental, and industry efficiencies of EV charging station technologies necessary for success. From what was gathered during the research, results have shown that XFC stations will be able to recharge battery electric vehicles (BEVs) in less than 10 min and provide roughly 200 miles of driving time. Data collected do support and identify the critical component technologies for the enablement and widespread adoption of XFC technology and infrastructure, to evaluate current and projected progress of critical component technologies, and to detect the potential gating technologies and recommend areas that will most benefit from accelerated development. The authors of this chapter used an evaluative bibliometric approach and literature review to gather data sets and industry findings to lead their results. The data sets are represented in four S-curves elaborated using the Pearl method. The authors’ goal is to identify which technology of the EV charging ecosystem component engineering, specifically XFC batteries to forecast the evolution of this new technology.

Community detection is a significant research problem in Network Science since it identifies groups of nodes that may have certain functional importance - termed communities. Our goal is to further study this problem from a different perspective related to the questions of the cause of belongingness to a community. To this end, we apply the framework of causality and responsibility developed by Halpern and Pearl [11]. We provide an algorithm-semi-agnostic framework for computing causes and responsibility of belongingness to a community. To the best of the authors’ knowledge, this is the first work that examines causality in community detection. Furthermore, the proposed framework is easily adaptable to be also used in other network processing operations apart from community detection.

Programmers are told "depend on interfaces, not implementations." But, given a program, is it possible even to assess objectively whether such advice has been followed? Programmers frequently talk in ways like this about dependence, but the very term, like many used in software engineering, has hitherto eluded precise definition. In this work, we resolve a variety of confusions about dependence, and present a formal definition unifying multiple varieties of software dependence, grounded in Halpern and Pearl's theory of actual causation. This definition is parameterized by the formal system characterizing the property of interest, and by constraints on "reasonable changes" to the program. By picking different choices of formal system, one can specialize our definition to characterize several notions of dependence, including build, correctness, and performance dependences. Overall, our work provides a path to making conversations about software dependence fully objective, and might serve as a basis for future work that automatically checks forms of dependence that were previously too abstract or high-level to be candidates for tools.

In this study, under the environmental conditions of factory circulating aquaculture, by measuring various parameters affecting the growth and development of pearl gentian grouper, a Radial Basis Function (RBF) neural network model for its growth was established, and the fit of the model was analyzed which provides a reference for the breeding of pearl gentian grouper. Aiming at the problems of RBF neural network, the Principal Component Analysis (PCA) method was used to screen out the main growth factors as the input of RBF, and the iterative fitting effect of RBF neural network was improved. The model in this paper decreases rapidly after the start of training, about 30 iterations, and the error decline rate gradually slows down. After 281 iterations, the error starts to converge, the value tends to stabilize, and the stable value error is about 0.05. The relative error of the prediction result of the trained model on the test set gradually decreases as the true value becomes larger. When the true value is larger and stable, the relative error is smaller and the overall fitting effect is better. The growth model of pearl gentian grouper was established by using RBF neural network and traditional mathematical method stepwise regression analysis, and the established growth model was used to predict the weight of pearl gentian grouper. By comparing the results, we can find that the RBF neural network prediction algorithm is closer to the real result in the prediction result, with a minimum error of 0.24209.

Causality is an important part of science and our daily life, e.g., to determine responsibility in legal arguments and causal explanations for observations. This paper presents how actual causality can be modeled by contextual abduction in logic programming. We refer to causal model of Halpern and Pearl, and translate it into a causal abductive framework. Using this framework and by treating the given actual world as the context of abduction, actual causes are computed by abducing explanations consistent to that context. We provide a procedure for computing actual causes by means of contextual abduction in Prolog and experiment with examples from the literature. The result coincides with that computed by the clausal causal theory of Bochman.

The Water Margin is the first novel written in vernacular in Chinese history, which is also a work with full epic features in Chinese literature. There are many translation versions of Water Margin, but it is not difficult to find that the depth and breadth of current English translation studies have great difference. The current research on English translation is limited to the level of detail evaluation, which has not formed a system and is scattered. Based on this background, the author thinks that the English translation of Water Margin should be studied in a more all-around angle through multiple translation versions. At the same time, translation comparison and commentary can only be based on specific research data In order to obtain more objective, recommendable and evident concluslons[1]. Therefore, the study of four English versions of “Water Margin” based on corpus (i.e. Pearl Sai-Sai' Version, Jackson-Jackson's Version, Shaboli-Sha's Version, Dent Yang-Yang's Version) has great significance and helps to open up a new way for the translation of Water Margin. This thesis discusses the translator's style of corpus based on the four English versions of Water Margin.

We present a new type-oriented framework for writing shared memory multithreaded programs that the Haskell type system guarantees are deadlock-free. The implementation wraps all concurrent computation inside a graded monad and assumes a total order is defined between locks. The grades within the type of such a computation specify which locks it acquires and releases. This information is drawn from an algebra that ensures that types can, in principle, be inferred in polynomial time.