Concise Representation Research Articles

Efficient mining of concise and informative representations of frequent high utility itemsets

The discovery of frequent closed high utility itemsets (FCHUIs) and frequent generators of high utility itemsets (FGHUIs) is significant because they serve as important and concise representations of frequent high utility itemsets (FHUIs), offering a brief but essential summary that can be considerably smaller. Besides, they facilitate the generation of nonredundant high utility association rules that are crucial for decision-makers. However, the challenge lies in the difficulty of mining these representations due to scalability issues, high memory usage, and long runtimes, particularly when dealing with dense and large datasets. To address this issue, this paper proposes a novel approach for efficiently mining FCHUIs and FGHUIs using a novel weak lower bound named wlbu on the utility. The approach includes effective pruning strategies for early eliminating non-closed and/or non-generator high utility branches in the prefix search tree based on wlbu. These pruning strategies allow faster execution with lower memory usage. In addition, the paper presents two novel algorithms, FCGHUI-Miner and FGHUI-Miner, which can simultaneously discover both FGHUIs and FCHUIs or solely mine FGHUIs, respectively. The experimental results demonstrate that the proposed algorithms outperform state-of-the-art algorithms in terms of efficiency and effectiveness.

Backstepping Boundary Control for a Class of Gantry Crane Systems.

In this article, two boundary feedback controllers are designed via the backstepping approach for a class of gantry crane systems. To provide an accurate and concise representation of the dynamic behavior, the gantry crane with a flexible cable is described by a hybrid system. The hybrid system is formed by an ordinary differential equation coupled with a partial differential equation. In the first control strategy, a backstepping-based boundary state-feedback controller is proposed for the gantry crane to transport a payload to an expected position with less shaking. In the second control strategy, a boundary output-feedback controller is explored with an observer estimating the inaccessible states. By using the backstepping technique and kernel functions, the original systems with different control strategies are transformed into target systems. By using the operator semigroup and Lyapunov stability theories, the target system is proven to be well-posed and exponentially stable, respectively. Finally, numerical simulations and comparisons are provided to illustrate the efficiency and the advantages of the proposed methods.

Deeppipe: An intelligent framework for predicting mixed oil concentration in multi-product pipeline

Accurately predicting mixed oil concentration distribution exerts a core effect on the optimization of pipelines and the quality of oil. Due to the neglect of mechanism features, high-dimensional complex feature correlations, and insufficient feature information on small batch data, the current methods cannot predict mixed oil concentration accurately. This work proposes a hybrid intelligent framework to provide an accurate and effective monitoring tool for mixed oil concentration of multi-product pipelines. In the proposed framework, the development mechanism of mixed oil is analyzed to select and reconstruct holistic features to explore the influencing mechanism of mixed oil concentration. Then, a parameterization and nonlinear transformation module is designed to acquire the accurate and concise representation of mixed oil concentration, thus decreasing the complexity of feature space and promoting the approximating ability of the prediction model. Eventually, a novel virtual samples generation module is established to obtain high-quality samples of mixed oil concentration, aiming to extract more comprehensive correlations of feature variables and improve the prediction performance. Cases from real-world multi-product pipelines suggest more accurate prediction results of mixed oil concentration compared to other advanced methods, with RMSE and R2 being 0.0500 and 0.9688. Furthermore, it is also proved that acquiring more holistic and accurate feature variables of mixed oil development and fully exploring comprehensive correlations between feature variables are crucial for the performance enhancement of the prediction model.

Crystal structure search with principal invariants

In this study, we propose a general way of finding a new crystal structure starting from a database. We also introduce a concise representation of the data based on principal invariants derived from the spatial arrangement of atoms in the crystal structure. Since the selection of the present representation does not depend on the size and selection method of the unit-cell defining the crystal structure, it is ideal for measuring similarity between two arbitrarily different crystal structures. For a more general search for crystal structures, the known crystal structure space is better defined using a variational autoencoder. Furthermore, we used either Pearson's correlation or the square root of the Jensen-Shannon divergence to directly search similar crystal structures for a wide variety of space groups. Our novel method uses extended database information, the similarity measure, and first-principles electronic structure calculation to predict several new metastable crystal structures of SnSe, P, and SnS.

Course map learning with graph convolutional network based on AuCM

Concept map provides a concise structured representation of knowledge in the educational scenario. It consists of various concepts connected by prerequisite dependencies. With the abundance of educational resources available through MOOCs, encyclopedias, and electronic textbooks, extracting prerequisite dependencies and building concept maps becomes feasible. However, publicly accessible taxonomies or learning object information that can help identify prerequisites are rare. To address this, we have constructed a comprehensive dataset called the Australian Course Map data (AuCM), specifically tailored for training concept maps in the IT/CS field. The dataset comprises course descriptions from 14 different Australian universities. To identify prerequisite relationships between course concepts, we have employed an embedding-based approach that combines the Graph Convolutional Network (GCN) with pairwise features of concepts. We have evaluated the performance of our model with non-neural classifiers and neural networks for extracting these prerequisite relations.

Operation characteristics based on a novel performance model based on capacity utilization rate of a variable refrigerant flow air conditioning system

In the context of a large market share, enhancing the actual performance of variable refrigerant flow (VRF) air-conditioning systems is of considerable importance. The operating states of indoor units (IUs) significantly influence the energy efficiency of a VRF system with multiple indoor terminals. Therefore, it is challenging to address accurate and concise representation of the operational characteristics of IUs for analyzing their impact on system performance. This study explored a novel performance index called capacity utilization rate (CUR) to illustrate the effect of IUs on system performance and developed a performance model based on this index. Simulation and experimental results demonstrated that energy efficiency and evaporating temperature decrease with a reduction in CUR under specific ambient temperatures and part-load rates. The proposed performance model was applied to real buildings in field tests to examine the operational characteristics of the VRF system. This research offers valuable insights for system design, performance simulation, and performance evaluation of VRF systems.

Discovery of crime event sequences with constricted spatio-temporal sequential patterns

In this article, we introduce a novel type of spatio-temporal sequential patterns called Constricted Spatio-Temporal Sequential (CSTS) patterns and thoroughly analyze their properties. We demonstrate that the set of CSTS patterns is a concise representation of all spatio-temporal sequential patterns that can be discovered in a given dataset. To measure significance of the discovered CSTS patterns we adapt the participation index measure. We also provide CSTS-Miner: an algorithm that discovers all participation index strong CSTS patterns in event data. We experimentally evaluate the proposed algorithms using two crime-related datasets: Pittsburgh Police Incident Blotter Dataset and Boston Crime Incident Reports Dataset. In the experiments, the CSTS-Miner algorithm is compared with the other four state-of-the-art algorithms: STS-Miner, CSTPM, STBFM and CST-SPMiner. As the results of the experiments suggest, the proposed algorithm discovers much fewer patterns than the other selected algorithms. Finally, we provide the examples of interesting crime-related patterns discovered by the proposed CSTS-Miner algorithm.

A visual notation for succinct program traces

Program traces are a widely used representation for explaining the dynamic behavior of programs. They help to make sense of computations and also support the location and elimination of bugs. Unfortunately, program traces can grow quite big very quickly, even for small programs, which compromises their usefulness. In this paper we present a visual notation for program traces that supports their concise representation. We explain the design decisions of the notation and compare it in detail with several alternatives. An important part of the trace representation is its flexibility and adaptability, which allows users to transform traces by applying filters that capture common abstractions for trace representations. We also present an evaluation of the trace notation and filters on a set of standard examples. The results show that our representation can reduce the overall size of traces by at least 79%, which suggests that our notation is an effective improvement over the use of plain traces in the explanation of dynamic program behavior.

TOM-Odometry: A Generalized Localization Framework Based on Topological Map and Odometry

The localization of autonomous vehicles mainly depends on GPS-based methods in large-scale outdoor scenarios. Unfortunately, GPS is vulnerable and not reliable in some regions, such as urban canyons. Odometry is taken as a complementary method for positioning in GPS-limited areas, but it suffers from accumulated errors, which limits its application. Topological map is a concise and low-cost representation of real world environment, which can be deemed as one of the information sources for localization. To realize accurate localization in the GPS-limited and large-scale scenes for autonomous vehicles, a generalized localization framework based on the topological map and odometry is proposed in this paper. The odometry trajectory can be automatically aligned with the correct paths of the map through matching without the initial global position. Under this framework, odometry is deemed as the kinematic model while topological map is regarded as a global sensor that is utilized to correct the drift errors of odometry. As an example, the integration of visual odometry and topological map is used to validate the effectiveness of the proposed method on KITTI benchmark. Experimental results demonstrate that the proposed method can effectively constrain the drift errors of odometry, and it achieves state-of-the-art localization performance compared with existing map-aided methods.

Forest GUMP: a tool for verification and explanation

AbstractIn this paper, we present Forest GUMP (for Generalized, Unifying Merge Process) a tool for verification and precise explanation of Random forests. Besides pre/post-condition-based verification and equivalence checking, Forest GUMP also supports three concepts of explanation, the well-known model explanation and outcome explanation, as well as class characterization, i.e., the precise characterization of all samples that are equally classified. Key technology to achieve these results is algebraic aggregation, i.e., the transformation of a Random Forest into a semantically equivalent, concise white-box representation in terms of Algebraic Decision Diagrams (ADDs). The paper sketches the method and demonstrates the use of Forest GUMP along illustrative examples. This way readers should acquire an intuition about the tool, and the way how it should be used to increase the understanding not only of the considered dataset, but also of the character of Random Forests and the ADD technology, here enriched to comprise infeasible path elimination. As Forest GUMP is publicly available all experiments can be reproduced, modified, and complemented using any dataset that is available in the ARFF format.

A characterization of the existence of succinct linear representation of subset-valuations

Decisions that involve bundling or unbundling a large number of objects, such as deciding on the bundle structure or optimizing bundle prices, are based on underlying valuation function over the set of all possible bundles. Given that the number of possible bundles (i.e., subsets of the given set of objects) is exponential in the number of objects, it is important for the decision-maker to be able to represent this valuation function succinctly. Identifying all structural sources of synergy in subset valuations might point to simple and concise representation of the valuation function. We characterize additive and multiplicative representations of synergies in subset valuations and subset utility, which in turn points to necessary and sufficient conditions for a succinct representation of subset valuations to exist.

Qualitative inverse problems: mapping data to the features of trajectories and parameter values of an ODE model

Our recent work on linear and affine dynamical systems has laid out a general framework for inferring the parameters of a differential equation model from a discrete set of data points collected from a system being modeled. It introduced a new class of inverse problems where qualitative information about the parameters and the associated dynamics of the system is determined for regions of the data space, rather than just for isolated experiments. Rigorous mathematical results have justified this approach and have identified common features that arise for certain classes of integrable models. In this work we present a thorough numerical investigation that shows that several of these core features extend to a paradigmatic linear-in-parameters model, the Lotka–Volterra (LV) system, which we consider in the conservative case as well as under the addition of terms that perturb the system away from this regime. A central construct for this analysis is a concise representation of parameter and dynamical features in the data space that we call the P n -diagram, which is particularly useful for visualization of the qualitative dependence of the system dynamics on data for low-dimensional (small n) systems. Our work also exposes some new properties related to non-uniqueness that arise for these LV systems, with non-uniqueness manifesting as a multi-layered structure in the associated P 2-diagrams.

A BIM-based framework for automatic numerical modelling and geotechnical analysis of a large-scale deep excavation for transportation infrastructures

Abstract Employing emerging information and communication technologies such as building information modelling (BIM) to streamline engineering design and analysis has been mainstream over the past several years. Although BIM models are well established for aboveground infrastructures, underground BIM is still in its infancy. On one hand, valuable subsurface ground models are missing in current BIM applications as site-specific geotechnical data are often sparse and limited. On the other hand, BIM has been mainly used as a visualization tool for concise representation of design and construction data, and it has not been integrated and utilized to assess geotechnical risks associated with underground infrastructures. This paper proposes a BIM-based approach for automatic numerical modelling and geotechnical analysis. Structural information (e.g. geometries) of different project BIMs are grouped, exported, and saved, which provides a unified interface for automatic information extraction using computer codes (e.g. Python). Subsequently, subsurface ground models generated from sparse data are integrated with extracted basic geometric properties for automatic geotechnical model setup and finite element analysis. The performance of the proposed framework is illustrated using a real deep excavation project. It is revealed that BIM, as a data repository, enables timely and accurate information exchange between structural and geotechnical models in an automatic manner, which emphasizes the need of a BIM-based approach for assessing and managing geotechnical risks associated with deep excavations for underground transportation infrastructures, especially in the urban context.

The power of typed affine decision structures: a case study

AbstractTADS are a novel, concise white-box representation of neural networks. In this paper, we apply TADS to the problem of neural network verification, using them to generate either proofs or concise error characterizations for desirable neural network properties. In a case study, we consider the robustness of neural networks to adversarial attacks, i.e., small changes to an input that drastically change a neural networks perception, and show that TADS can be used to provide precise diagnostics on how and where robustness errors a occur. We achieve these results by introducing Precondition Projection, a technique that yields a TADS describing network behavior precisely on a given subset of its input space, and combining it with PCA, a traditional, well-understood dimensionality reduction technique. We show that PCA is easily compatible with TADS. All analyses can be implemented in a straightforward fashion using the rich algebraic properties of TADS, demonstrating the utility of the TADS framework for neural network explainability and verification. While TADS do not yet scale as efficiently as state-of-the-art neural network verifiers, we show that, using PCA-based simplifications, they can still scale to medium-sized problems and yield concise explanations for potential errors that can be used for other purposes such as debugging a network or generating new training samples.

Evaluation of the indirect impact of the Sars-CoV-2 epidemic on primary and ambulatory care processes using synthetic quality measures and graphic representations.

A concise representation of different primary and ambulatory care quality indicators can be very useful for quickly understanding the data and defining appropriate intervention strategies. The objectives of this study are to implement a graphical representation based on the TreeMap, a tool capable of summarizing results from heterogeneous indicators, with different measurement scales and thresholds, and take advantage of TreeMap's potential to measure the indirect impact of the Sars-CoV-2 epidemic on primary and ambulatory care processes. Seven healthcare areas were considered each defined by a set of different indicators representative of the area. A discrete score ranging from 1 (very high quality) to 5 (very low quality) was assigned to the value of each indicator, based on the level of adherence to evidence-based recommendations. Finally, the score of each healthcare area is obtained as the weighted average of the scores of the representative indicators. The TreeMap is calculated for each Local health authority (Lha) of the Lazio Region. In order to assess the impact of the epidemic, a comparison was made between the results observed in 2019 and those observed in 2020. As an example, the results of one of the 10 Lhas of the Lazio Region have been reported. Compared to 2019, in 2020 there was an improvement in primary and ambulatory healthcare regarding all of the evaluated areas, with the exception of the metabolic area which remained stable. "Avoidable" hospitalizations have decreased, such as those for heart failure, Copd and diabetes. The incidence of cardio-cerebrovascular events following myocardial infarction or ischemic stroke has decreased, and inappropriate visits to emergency room have reduced. Furthermore, after decades of overprescribing, the use of drugs with a high risk of inappropriateness, such as antibiotics and aerosolized corticosteroids, has significantly decreased. The TreeMap has proven to be a valid tool for evaluating the quality of primary care, summarizing evidence from different and heterogeneous indicators. The improvements in quality levels observed in 2020, compared to 2019, should be interpreted with extreme caution because they could represent a paradox generated by the indirect effects of the Sars-CoV-2 epidemic. If, in the case of the epidemic, the distorting factors can be easily identified, in different and more ordinary evaluative analyses the "research for the causes" could be much more complex.

Skeleton Clustering: Dimension-Free Density-Aided Clustering

ABSTRACT We introduce a density-aided clustering method called Skeleton Clustering that can detect clusters in multivariate and even high-dimensional data with irregular shapes. To bypass the curse of dimensionality, we propose surrogate density measures that are less dependent on the dimension but have intuitive geometric interpretations. The clustering framework constructs a concise representation of the given data as an intermediate step and can be thought of as a combination of prototype methods, density-based clustering, and hierarchical clustering. We show by theoretical analysis and empirical studies that the skeleton clustering leads to reliable clusters in multivariate and high-dimensional scenarios. Supplementary materials for this article are available online.

Accurate and efficient protein sequence design through learning concise local environment of residues.

Computational protein sequence design has been widely applied in rational protein engineering and increasing the design accuracy and efficiency is highly desired. Here, we present ProDESIGN-LE, an accurate and efficient approach to protein sequence design. ProDESIGN-LE adopts a concise but informative representation of the residue's local environment and trains a transformer to learn the correlation between local environment of residues and their amino acid types. For a target backbone structure, ProDESIGN-LE uses the transformer to assign an appropriate residue type for each position based on its local environment within this structure, eventually acquiring a designed sequence with all residues fitting well with their local environments. We applied ProDESIGN-LE to design sequences for 68 naturally occurring and 129 hallucinated proteins within 20 s per protein on average. The designed proteins have their predicted structures perfectly resembling the target structures with a state-of-the-art average TM-score exceeding 0.80. We further experimentally validated ProDESIGN-LE by designing five sequences for an enzyme, chloramphenicol O-acetyltransferase type III (CAT III), and recombinantly expressing the proteins in Escherichia coli. Of these proteins, three exhibited excellent solubility, and one yielded monomeric species with circular dichroism spectra consistent with the natural CAT III protein. The source code of ProDESIGN-LE is available at https://github.com/bigict/ProDESIGN-LE.

‘Some residue of prejudice against atomic power’: Oscar Newman’s underground city and peaceful nuclear explosions

Beginning in 1957, the United States Government pursued an unimaginable enterprise. For eighteen years, and as part of President Dwight D. Eisenhower’s ‘Atoms for Peace’ initiative, the controversial Project Plowshare employed hundreds of scientists, engineers, and policymakers, and facilitated 27 tests and 31 nuclear detonations for an outrageous goal: the use of nuclear weapons for constructive means. The experiments were designed to explore a range of energy-related and infrastructural projects such as gas and isotope extraction or excavations for the purposes of constructing new harbours, canals, and transportation ways. This article examines a speculative architectural project inspired directly by Project Plowshare: a proposal for an underground city to be built beneath Manhattan in a spherical cavity created by a nuclear bomb, suggested by the Canadian architect and planner Oscar Newman (1935–2004), and published in Esquire magazine in 1969. Rather than examining this project for its architectural qualities or plausibility, this article explores Newman’s underground city as a document that testifies to the conditions of its making and situates it as a concise, literal, and radical representation of the conditions under which architecture was imagined in the Cold War decades in the United States. In Newman’s underground city, architecture is embedded within a constellation of geopolitics, warfare, and propaganda, and makes visible the inherent dependability between technological progress, construction and development, and environmental destruction.

Union Models for Model Families: Efficient Reasoning over Space and Time

A model family is a set of related models in a given language, with commonalities and variabilities that result from evolution of models over time and/or variation over intended usage (the spatial dimension). As the family size increases, it becomes cumbersome to analyze models individually. One solution is to represent a family using one global model that supports analysis. In this paper, we propose the concept of union model as a complete and concise representation of all members of a model family. We use graph theory to formalize a model family as a set of attributed typed graphs in which all models are typed over the same metamodel. The union model is formalized as the union of all graph elements in the family. These graph elements are annotated with their corresponding model versions and configurations. This formalization is independent from the modeling language used. We also demonstrate how union models can be used to perform reasoning tasks on model families, e.g., trend analysis and property checking. Empirical results suggest potential time-saving benefits when using union models for analysis and reasoning over a set of models all at once as opposed to separately analyzing single models one at a time.

Techno-economic-environmental evaluation of aircraft propulsion electrification: Surrogate-based multi-mission optimal design approach

Driven by the sustainability initiatives in the aviation sector, the emerging technologies of aircraft propulsion electrification have been identified as the promising approach to realize sustainable and decarbonized aviation. This study proposes a surrogate-based multi-mission optimal design approach for aircraft propulsion electrification, which innovatively incorporates realistic aviation operations into the electric aircraft design, with the aim of improving the overall aircraft fuel economy over multiple flight missions and conditions in practical scenarios. The proposed optimal design approach starts with the flight route data analysis to cluster the flight operational data using gaussian mixture model, so that a concise representation of flight mission profiles can be achieved. Then, an optimal orthogonal array-based Latin hypercubes are employed to generate sampling points of design variables for electrified aircraft propulsion. The mission analysis is performed with coupled propulsion-airframe integration in order to propose energy management strategy for mission-dependent aircraft performance. Consequently, fuel economy surrogate model is established via support vector machines to obtain the optimal design points of electrified aircraft propulsion. For assessing the viability of novel propulsion technologies, techno-economic evaluation is conducted using sensitivity analysis and breakeven electricity prices under a series of environmental regulatory policy scenarios.