Reconciliation Problem Research Articles

Isometric gene tree reconciliation revisited

BackgroundIsometric gene tree reconciliation is a gene tree/species tree reconciliation problem where both the gene tree and the species tree include branch lengths, and these branch lengths must be respected by the reconciliation. The problem was introduced by Ma et al. in 2008 in the context of reconstructing evolutionary histories of genomes in the infinite sites model.ResultsIn this paper, we show that the original algorithm by Ma et al. is incorrect, and we propose a modified algorithm that addresses the problems that we discovered. We have also improved the running time from O(N^2) to O(Nlog N), where N is the total number of nodes in the two input trees. Finally, we examine two new variants of the problem: reconciliation of two unrooted trees and scaling of branch lengths of the gene tree during reconciliation of two rooted trees.ConclusionsWe provide several new algorithms for isometric reconciliation of trees. Some questions in this area remain open; most importantly extensions of the problem allowing for imprecise estimates of branch lengths.

Exact Algorithms for Duplication-Transfer-Loss Reconciliation with Non-Binary Gene Trees.

Duplication-Transfer-Loss (DTL) reconciliation is a powerful method for studying gene family evolution in the presence of horizontal gene transfer. DTL reconciliation seeks to reconcile gene trees with species trees by postulating speciation, duplication, transfer, and loss events. Efficient algorithms exist for finding optimal DTL reconciliations when the gene tree is binary. In practice, however, gene trees are often non-binary due to uncertainty in the gene tree topologies, and DTL reconciliation with non-binary gene trees is known to be NP-hard. In this paper, we present the first exact algorithms for DTL reconciliation with non-binary gene trees. Specifically, we (i) show that the DTL reconciliation problem for non-binary gene trees is fixed-parameter tractable in the maximum degree of the gene tree, (ii) present an exponential-time, but in-practice efficient, algorithm to track and enumerate all optimal binary resolutions of a non-binary input gene tree, and (iii) apply our algorithms to a large empirical data set of over 4,700 gene trees from 100 species to study the impact of gene tree uncertainty on DTL-reconciliation and to demonstrate the applicability and utility of our algorithms. The new techniques and algorithms introduced in this paper will help biologists avoid incorrect evolutionary inferences caused by gene tree uncertainty.

Robust and reliable estimation via recursive nonlinear dynamic data reconciliation based on cubature Kalman filter

Since measurements of process variables are subject to measurements errors as well as process variability, data reconciliation is the procedure of optimally adjusting measured date so that the adjusted values obey the conservation laws and constraints. Thus, data reconciliation for dynamic systems is fundamental and important for control, fault detection, and system optimization. Attempts to successfully implement estimators are often hindered by serve process nonlinearities, complicated state constraints, and un-measurable perturbations. As a constrained minimization problem, the dynamic data reconciliation is dynamically carried out to product smoothed estimates with variances from the original data. Many algorithms are proposed to solve such state estimation such as the extended Kalman filter (EKF), the unscented Kalman filter, and the cubature Kalman filter (CKF). In this paper, we investigate the use of CKF algorithm in comparative with the EKF to solve the nonlinear dynamic data reconciliation problem. First we give a broad overview of the recursive nonlinear data dynamic reconciliation (RNDDR) scheme, then present an extension to the CKF algorithm, and finally address the issue of how to solve the constraints in the CKF approach. The CCRNDDR method is proposed by applying the RNDDR in the CKF algorithm to handle nonlinearity and algebraic constraints and bounds. As the sampling idea is incorporated into the RNDDR framework, more accurate estimates can obtained via the recursive nature of the estimation procedure. The performance of the CKF approach is compared with EKF and RNDDR on nonlinear process systems with constraints. The conclusion is that with an error optimization solution of the correction step, the reformulated CKF shows high performance on the selection of nonlinear constrained process systems. Simulation results show the CCRNDDR is an efficient, accurate and stable method for real-time state estimation for nonlinear dynamic processes.

On the computational complexity of the maximum parsimony reconciliation problem in the duplication-loss-coalescence model

BackgroundPhylogenetic tree reconciliation is a widely-used method for inferring the evolutionary histories of genes and species. In the duplication-loss-coalescence (DLC) model, we seek a reconciliation that explains the incongruence between a gene and species tree using gene duplication, loss, and deep coalescence events. In the maximum parsimony framework, costs are associated with these event types and a reconciliation is sought that minimizes the total cost of the events required to map the gene tree onto the species tree.ResultsWe show that this problem is NP-hard even for the special case of minimizing the number of duplications. We then show that the problem is APX-hard when both duplications and losses are considered, implying that no polynomial-time approximation scheme can exist for the problem unless P = NP.ConclusionsThese intractability results are likely to guide future research on algorithmic aspects of the DLC-reconciliation problem.

Entity reconciliation in big data sources: A systematic mapping study

The entity reconciliation (ER) problem aroused much interest as a research topic in today's Big Data era, full of big and open heterogeneous data sources. This problem poses when relevant information on a topic needs to be obtained using methods based on: (i) identifying records that represent the same real world entity, and (ii) identifying those records that are similar but do not correspond to the same real-world entity. ER is an operational intelligence process, whereby organizations can unify different and heterogeneous data sources in order to relate possible matches of non-obvious entities. Besides, the complexity that the heterogeneity of data sources involves, the large number of records and differences among languages, for instance, must be added. This paper describes a Systematic Mapping Study (SMS) of journal articles, conferences and workshops published from 2010 to 2017 to solve the problem described before, first trying to understand the state-of-the-art, and then identifying any gaps in current research. Eleven digital libraries were analyzed following a systematic, semiautomatic and rigorous process that has resulted in 61 primary studies. They represent a great variety of intelligent proposals that aim to solve ER. The conclusion obtained is that most of the research is based on the operational phase as opposed to the design phase, and most studies have been tested on real-world data sources, where a lot of them are heterogeneous, but just a few apply to industry. There is a clear trend in research techniques based on clustering/blocking and graphs, although the level of automation of the proposals is hardly ever mentioned in the research work.

Using clustering based logical equation set to decompose large scale chemical processes for parallel solving data reconciliation and parameter estimation problem

Data reconciliation and parameter estimation (DRPE) are important to the performance improvement of real time optimization and process control. As the computational difficulty of nonlinear DRPE optimization problems increases significantly with the number of variables and the equations in large scale processes, solving several smaller DRPE problems iteratively can be more efficient than solving a single large one. A novel parallel processing strategy is proposed to address the optimal distributed DRPE sub-problems. The clustering based logical equation set decomposition (CLESD) is developed to decrease the size of DRPE sub-problems and minimize the information loss of the large-scale DRPE problem. The decomposition of a large DRPE problem requires a two-stage approach, including the equation clustering decomposition and the variable clustering decomposition. CLESD-DRPE is compared with the traditional DRPE method via two industrial applications. The results show that CLESD-DRPE outperforms the traditional DRPE in terms of the solution time and convergence efficiency.

Identifying the same person across two similar social networks in a unified way: Globally and locally

With the increasing popularity of online social networks, people today typically own several such accounts (e.g., Facebook, Twitter, and Flickr). In this scenario, it arises an interesting and challenging problem: how to identify the same person in different social networks, which is known as the network reconciliation problem. Prior work on this problem first assumes that the relationships between users are homogeneous, and then makes use of the local features (degree, common mapped neighbors) to achieve high precision. However, this assumption does not hold in reality since users usually have different tie strengths between each other. In this paper, we remodel the reconciliation problem by considering the users’ heterogeneous relationships and propose a unified framework called UniRank for incorporating the local features and global features together. Based on UniRank, we design an efficient two-stage network reconciliation algorithm. First, we design a global matching algorithm to explore more seeds with fast speed. Second, for each explored seed, we design a breadth-first strategy based local matching algorithm to match more seeds. Extensive simulations on real-world and synthetic social network datasets show that our algorithm significantly improves the state-of-art algorithm by up to 9X in terms of F1 score even under very rough conditions.

Mass Balance Reconciliation for Bilinear Systems: A Case Study of a Raw Mill Separator in a Typical Moroccan Cement Plant

Stream flow rates and their several compositions are measured in a typical cement raw mill separator. In order to simultaneously reconcile flow and composition measurements in this circuit, the component mass balances was included as constraints which contain the products of flow rate and composition variables in the data reconciliation problem. In this paper, the effectiveness of simultaneous procedures for bilinear data reconciliation is established, the numerical problem constraints were coded in MATLAB and a mass balance model is built. Moreover, based on the difference between the measured and reconciled data it was found that it performs optimally.

Towards sub-quadratic time and space complexity solutions for the dated tree reconciliation problem.

BackgroundRecent coevolutionary analysis has considered tree topology as a means to reduce the asymptotic complexity associated with inferring the complex coevolutionary interrelationships that arise between phylogenetic trees. Targeted algorithmic design for specific tree topologies has to date been highly successful, with one recent formulation providing a logarithmic space complexity reduction for the dated tree reconciliation problem.MethodsIn this work we build on this prior analysis providing a further asymptotic space reduction, by providing a new formulation for the dynamic programming table used by a number of popular coevolutionary analysis techniques. This model gives rise to a sub quadratic running time solution for the dated tree reconciliation problem for selected tree topologies, and is shown to be, in practice, the fastest method for solving the dated tree reconciliation problem for expected evolutionary trees. This result is achieved through the analysis of not only the topology of the trees considered for coevolutionary analysis, but also the underlying structure of the dynamic programming algorithms that are traditionally applied to such analysis.ConclusionThe newly inferred theoretical complexity bounds introduced herein are then validated using a combination of synthetic and biological data sets, where the proposed model is shown to provide an O(sqrt{n}) space saving, while it is observed to run in half the time compared to the fastest known algorithm for solving the dated tree reconciliation problem. What is even more significant is that the algorithm derived herein is able to guarantee the optimality of its inferred solution, something that algorithms of comparable speed have to date been unable to achieve.

Using Behavioral Economics to Understand Premium Tax Credit Reconciliation

The Affordable Care Act provides advanced premium tax credits to millions of Americans to help with the cost of purchasing private health insurance on the new health insurance marketplaces. The amount of subsidy a family qualifies for is based on their projected income for the year ahead. However, since income is fairly unpredictable, some families end up qualifying for a larger tax credit when they do their taxes, while others end up with a smaller credit and must repay what they received throughout the year. In the first year of this reconciliation process, half of those who received advanced premium tax credits had to pay at least a part back. This outcome is consistent with recent literature in behavioral economics, which explores psychological, social, and cognitive influences on decision-making. This paper explores the reconciliation problem and possible approaches to reducing or eliminating it.

<b>Evaluation of robust functions for data reconciliation in thermal systems

Process variables regularly control and evaluate industrial processes. Information with gross errors may in some cases not be attenuated by function reconciliation and change the calculation of process balance, leading optimization results towards non-feasible regions or to optimal sites. A promising alternative for reconciling functions is the use of robust functions. Current paper considers the above scenario and evaluates the fitness of some robust functions in solving in steady state chemical processes data reconciliation problems represented by linear and nonlinear systems in the presence of gross errors. Traditional Cauchy, Fair, Contaminated Normal and Logistic robust functions are used in the reconciliation problem where their estimates are compared to those obtained with the use of the latest features, such as New Target and Alarm. Rates for gross errors in tests were limited between 4 and 10σ of the measured current and elaborated a region of outliers. Results showed that New Target and Alarm functions are different from the others as the magnitude of the gross error increases, tending towards true rates specified by set point.

Data reconciliation for the overall thermal system of a steam turbine power plant

Accuracy of online measurement data is usually not satisfactory for coal-fired power plants due to constraints of measurement techniques. Data reconciliation can help to improve the accuracy of online measurement with no extra requirements on equipment upgrading. Traditionally, data reconciliation is applied to a sub-system of a coal-fired power plant, for instance, mass balance of a steam turbine system. In this work, we present a systematic approach where data reconciliation is applied to the overall thermal system of a real-life steam turbine power plant. Improvement of data accuracy is obtained via the proposed approach compared with sub-system studies. Optimization of system selection and configuration of a data reconciliation problem is analyzed. Results show that uncertainty of on-line measured data can be reduced by up to 50% in a 1000MW ultra-supercritical coal-fired steam turbine power plant compared with previous studies.

Dynamic Data Reconciliation and Model Validation of a MEA-Based CO2 Capture System using Pilot Plant Data

This work focuses on development of a “gold standard” process model for a MEA-based post-combustion CO2 capture process. The steady-state model includes a comprehensive thermodynamic framework in conjunction with the chemistry model. Parameters for all thermodynamic, transport, and physical properties models are regressed using extensive data available in the literature. An integrated mass transfer model is developed and validated using experimental data. The steady-state model is validated using data collected from the U.S. DOE’s National Carbon Capture Center (NCCC) in Wilsonville, Alabama. In addition to the steady-state runs, dynamic test runs were conducted at NCCC by introducing carefully-designed step changes and recording the transients of all key variables. Due to measurement noise and missing measurements for a number of key variables, a dynamic data reconciliation problem was solved to ensure material and energy balance of the collected data. Both the steady-state and dynamic models were validated against plant data for a wide range of operating conditions.

On the Complexity of Duplication-Transfer-Loss Reconciliation with Non-Binary Gene Trees.

Duplication-Transfer-Loss (DTL) reconciliation has emerged as a powerful technique for studying gene family evolution in the presence of horizontal gene transfer. DTL reconciliation takes as input a gene family phylogeny and the corresponding species phylogeny, and reconciles the two by postulating speciation, gene duplication, horizontal gene transfer, and gene loss events. Efficient algorithms exist for finding optimal DTL reconciliations when the gene tree is binary. However, gene trees are frequently non-binary. With such non-binary gene trees, the reconciliation problem seeks to find a binary resolution of the gene tree that minimizes the reconciliation cost. Given the prevalence of non-binary gene trees, many efficient algorithms have been developed for this problem in the context of the simpler Duplication-Loss (DL) reconciliation model. Yet, no efficient algorithms exist for DTL reconciliation with non-binary gene trees and the complexity of the problem remains unknown. In this work, we resolve this open question by showing that the problem is, in fact, NP-hard. Our reduction applies to both the dated and undated formulations of DTL reconciliation. By resolving this long-standing open problem, this work will spur the development of both exact and heuristic algorithms for this important problem.

Reconciling normative and behavioural economics

The aim of this thesis is to address from a methodological, philosophical and theoretical perspective the problem of how to reconcile normative and behavioural economics — the “reconciliation problem”. The first part develops a methodological assessment of behavioural welfare economics (and more specifically of libertarian paternalism), which currently constitutes the most accepted approach to deal with the reconciliation problem. We argue that behavioural welfare economics is in the direct continuation of neoclassical welfare economics, and that its conception of individual behaviour is tightly connected to Pareto’s reductionist model of the Homo economicus. It is assumed that an individual can be defined by “true” preferences — distinct from her revealed preferences — and that those preferences are consistent and context-independent. The satisfaction of those true preferences is taken as the normative criterion. The existence of true preferences requires accepting that people have access to a latent mode of reasoning able to generate consistent and context-independent preferences, and to treat individual decision-making as if it was the result of an optimisation problem faced by an inner rational agent trapped in an outer psychological shell. We argue on the contrary that we cannot define unambiguously such true preferences. We then argue that the normative challenge raised by behavioural economics is that individuals may lack of autonomy: rather than focusing on the satisfaction of one’s preferences, we argue that what matters is the ability to choose one’s own preferences. Since we suggest that the difficulties of behavioural welfare economics are related to its commitment to an implausible model of individual preferences, we provide in the second part of the thesis a model of preferences compatible with our methodological analysis: we take Bacharach’s variable frame theory as the primitive of our analysis, and build a model of endogenous preferences, in which individuals can choose to some extent their own preferences. The main contributions of this model are that (i) it does not require the existence of latent true preferences, and (ii) the individuals are allowed to team reason. We assume that collective preferences are strategically chosen, and show that team reasoners tend to be aggressive (cooperative) with other teams in submodular (supermodular) games. We finally show that team reasoning can be empirically justified as an ecologically rational heuristic.

Concurrent reconciliation of chemical and mineral assays for mineral processing circuits

The steady-state data reconciliation of mineral processing plants has traditionally depended on chemical assays of samples collected from various streams in the plant. In some cases, additional information in the form of size assays and size-wise chemical assays have been employed to improve the reconciliation problem. Reconciliation algorithms, in addition to making the measured data consistent with the basic conservation principles, also help in predicting the material flow rates in different streams. This information helps the process engineer to identify and troubleshoot problem areas, and further use it to optimize the plant's performance. If all the minerals present in the ore system are known (established through X-Ray Diffraction), the chemical formulae of the minerals provide the relationship between the chemical assays and mineral composition. In this communication, first we propose an approach to estimate the mineral composition from the measured chemical assays for a given sample or stream. It is then extended to a plant-wide concurrent data reconciliation methodology, involving both the chemical assays and mineral composition. This concurrent data reconciliation procedure provides estimates of mineral composition around the plant even when mineral composition measurements are not available. This additional information will help the plant engineers to track and improve their plant's behavior more intuitively. When mineral compositions are measured (for example, through QEMSCAN) they can be reconciled along with chemical assays. The methodology is demonstrated with data from batch and semi-batch laboratory studies on selective dispersion-flocculation of iron ore slimes.

Inferring gene duplications, transfers and losses can be done in a discrete framework.

In the field of phylogenetics, the evolutionary history of a set of organisms is commonly depicted by a species tree-whose internal nodes represent speciation events-while the evolutionary history of a gene family is depicted by a gene tree-whose internal nodes can also represent macro-evolutionary events such as gene duplications and transfers. As speciation events are only part of the events shaping a gene history, the topology of a gene tree can show incongruences with that of the corresponding species tree. These incongruences can be used to infer the macro-evolutionary events undergone by the gene family. This is done by embedding the gene tree inside the species tree and hence providing a reconciliation of those trees. In the past decade, several parsimony-based methods have been developed to infer such reconciliations, accounting for gene duplications ([Formula: see text]), transfers ([Formula: see text]) and losses ([Formula: see text]). The main contribution of this paper is to formally prove an important assumption implicitly made by previous works on these reconciliations, namely that solving the (maximum) parsimony [Formula: see text] reconciliation problem in the discrete framework is equivalent to finding a most parsimonious [Formula: see text] scenario in the continuous framework. In the process, we also prove several intermediate results that are useful on their own and constitute a theoretical toolbox that will likely facilitate future theoretical contributions in the field.

Validation of reverse circulation drilling rig for reconciliation purposes

Author Summary: Representative samples of ore containing precious metals is a difficult task. The lower the grade and the higher the nugget and/or cluster effect, the more complex and difficult extracting samples that are both accurate and precise. Reconciliation practices can be used as an effective tool to evaluate sampling accuracy throughout grade control processes. However, a proper reconciliation system must be based on reliable data and, therefore, optimisation of sampling techniques is a must for development of a reliable reconciliation system. This paper is a result of an extensive reconciliation study carried out at a copper and gold mine in Brazil, where a significant reconciliation problem took place while using manual sampling for grade control and short-term modeling. After analysing several sampling equipment/sample selection techniques, the authors suggested to use of a reverse circulation drilling rig with an automatic sampling system for grade control sampling. The samples generated by this automatic system were compared with the manual samples collected from the piles generated by the previous percussion rotary air blast drilling rig. Also, three pairs of twin holes were drilled in order to validate the new reverse circulation approach. Results allowed estimation of the bias related to the Increment Weighting Error (IWE) generated by manual sampling. Results show that the reverse circulation rig eliminates significant sampling biases, thus improving the general sample representativeness by increasing both sample accuracy and precision.

Bayesian method for simultaneous gross error detection and data reconciliation

Process measurements collected from daily industrial plant operations are essential for process monitoring, control, and optimization. However, those measurements are generally corrupted by errors, which include gross errors and random errors. Conventionally, those two types of errors were addressed separately by gross error detection and data reconciliation. Solving the simultaneous gross error detection and data reconciliation problem using the hierarchical Bayesian inference technique is focused. The proposed approach solves the following problems in a unified framework. First, it detects which measurements contain gross errors. Second, the magnitudes of the gross errors are estimated. Third, the covariance matrix of the random errors is estimated. Finally, data reconciliation is performed using the maximum a posteriori estimation. The proposed algorithm is applicable to both linear and nonlinear systems. For nonlinear case, the algorithm does not involve any linearization or approximation steps. Numerical case studies are provided to demonstrate the effectiveness of the proposed method. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3232–3248, 2015

Gross error management in data reconciliation

This paper reviews the problems associated to the presence of gross errors in data reconciliation problems as well as the main approaches to avoid their undesirable effects: detection and removal of the faulty measurements and minimization of their effect on the reconciled results by using robust estimators that modify the cost function. In the first case, Principal Component Analysis is used to detect the gross errors while, in the second, the Fair function is used. A combined approach is also presented that improves the PCA results. The methods are evaluated in a realistic large-scale problem with plant data corresponding to the hydrogen network of a petrol refinery.