Consistent Subset Research Articles

Challenge to Reconcile Experimental Micellar Properties of the CnEm Nonionic Surfactant Family

We wished to compile a data set of results from the experimental literature to support the development and validation of accurate computational models (force fields) for an important class of micelle-forming nonionic surfactant compounds, the poly(ethylene oxide) alkyl ethers, usually denoted C nE m. However, careful examination of the experimental literature exposed a striking degree of variation in values reported for critical micelle concentrations (cmc) and mean aggregation numbers ( Nagg). This variation was so large that it masked important trends known to exist within this family of molecules, thereby rendering most of the literature data to be of limited utility for force field development. In this work, we describe some reasons for the wide variability in the experimental literature, and we present a set of cmc and aggregation number data for 12 C nE m compounds that we feel is appropriate to use for the construction of and validation of computational models. The cmc values we selected are from the existing experimental literature and represent a carefully chosen and consistent subset that conveys important trends seen by many of the experimental studies. However, for a corresponding and consistent set of weight-averaged aggregation numbers, we needed to perform new dynamic light scattering (DLS) experiments. The results of these experiments were carefully analyzed to obtain not just mean aggregation numbers but also the underlying micelle size distribution functions. Several trends observed in the cmc and Nagg observables are highlighted and serve as challenges for developers of force field and simulation methodology. The analysis of the DLS experiments accounts for the fact that a broad distribution of micelle sizes exists for many of these compounds and that one must be careful to use the appropriate weighted averages (e.g., mass-weighted vs number-weighted averages) in comparing results from different types of experiments and in comparing results from experiments with those from simulations.

Isolation of Naturally Released Gonococcal Outer Membrane Vesicles as Vaccine Antigens.

The emergence and spread of fully antimicrobial resistant Neisseria gonorrhoeae (GC) highlights a clear need for next-generation antigonococcal therapeutics. A broadly reactive anti-GC vaccine would best address this global public health threat. Polyantigenic outer membrane vesicles (OMVs) derived from GC can overcome the challenges posed by GC's high rate of phase and antigen variation. In fact, GC OMVs have already shown promise as a vaccine antigen; however, all previous studies have utilized vesicles contaminated by RMP, a bacterioprotective antigen known to entirely abrogate vaccine-induced bactericidal activity in vivo. Additionally, these studies primarily utilized vesicles isolated through techniques like membrane disruption with detergents, which are known to increase contamination of cytoplasmic components as compared to naturally released OMVs (nOMVs). This chapter describes the isolation and characterization of naturally released nOMVs through sequential size and weight restrictive filtration. nOMVs are characterized by morphology, proteomics, and bioactivity via various methods. Herein we also describe methods for further evaluation of the innate and induced immunogenicity of rmp-deficient GC nOMVs by cell stimulation and murine vaccination. Per these methods, nOMVs are found to be largely homogenous spherical structures approximately 70nm in diameter containing a consistent subset of GC outer membrane proteins. The rmp-deficient vesicles demonstrate a morphology and, with the exception of RMP, antigenic profile consistent with that of nOMVs derived from wild time N. gonorrhoeae. Additionally, vesicles lacking RMP are able to engage and strongly activate a diverse array of pattern recognition receptors in vitro. These methods lay the groundwork for future experiments examining the in vivo protective efficacy of the anti-GC response induced by these nOMVs as well as studies examining the mechanism of vaccine induced female genital tract immunity.

Strong inconsistency

Minimal inconsistent subsets of knowledge bases play an important role in propositional logic, most notably for diagnosis, axiom pinpointing, and inconsistency measurement. It turns out that for nonmonotonic reasoning a stronger notion is needed. In this paper we develop such a notion, called strong inconsistency. We show that—in an arbitrary logic, monotonic or not—minimal strongly inconsistent subsets play a similar role as minimal inconsistent subsets in propositional logic. In particular, we show that the well-known duality between hitting sets of minimal inconsistent subsets and maximal consistent subsets generalizes to arbitrary logics if the strong notion of inconsistency is used. We investigate the complexity of various related reasoning problems and present a generic algorithm for computing minimal strongly inconsistent subsets of a knowledge base. We also demonstrate the potential of our new notion for applications, focusing on axiom pinpointing and inconsistency measurement.

Facial muscle movements encoding pain-a systematic review.

Facial expressions of pain are not undefined grimaces, but they convey specific information about the internal state of the individual in pain. With this systematic review, we aim to answer the question of which facial movements are displayed most consistently during pain. We searched for studies that used the Facial Action Coding System to analyze facial activity during pain in adults, and that report on distinct facial responses (action units [AUs]). Twenty-seven studies using experimental pain and 10 clinical pain studies were included. We synthesized the data by taking into consideration (1) the criteria used to define whether an AU is pain-related; (2) types of pain; and (3) the cognitive status of the individuals. When AUs were selected as being pain-related based on a "pain > baseline" increase, a consistent subset of pain-related AUs emerged across studies: lowering the brows (AU4), cheek raise/lid tightening (AUs6_7), nose wrinkling/raising the upper lip (AUs9_10), and opening of the mouth (AUs25_26_27). This subset was found independently of the cognitive status of the individuals and was stable across clinical and experimental pain with only one variation, namely that eye closure (AU43) occurred more frequently during clinical pain. This subset of pain-related facial responses seems to encode the essential information about pain available in the face. However, given that these pain-related AUs are most often not displayed all at once, but are differently combined, health care professionals should use a more individualized approach, determining which pain-related facial responses an individual combines and aggregates to express pain, instead of erroneously searching for a uniform expression of pain.

Belief Integration and Source Reliability Assessment

Merging beliefs requires the plausibility of the sources of the information to be merged. They are typically assumed equally reliable when nothing suggests otherwise. A recent line of research has spun from the idea of deriving this information from the revision process itself. In particular, the history of previous revisions and previous merging examples provide information for performing subsequent merging operations.Yet, no examples or previous revisions may be available. In spite of the apparent lack of information, something can still be inferred by a try-and-check approach: a relative reliability ordering is assumed, the sources are integrated according to it and the result is compared with the original information. The final check may contradict the original ordering, like when the result of merging implies the negation of a formula coming from a source initially assumed reliable, or it implies a formula coming from a source assumed unreliable. In such cases, the reliability ordering assumed in the first place can be excluded from consideration.Such a scenario is proved real under the classifications of source reliability and definitions of belief integration considered in this article: sources divided in two, three or multiple reliability classes; integration is mostly by maximal consistent subsets but also weighted distance is considered. Other results mainly concern the integration by maximal consistent subsets and partitions of two and three reliability classes.

Pruning strategies for nearest neighbor competence preservation learners

Abstract In order to alleviate both the spatial and temporal cost of the nearest neighbor classification rule, competence preservation techniques aim at substituting the training set with a selected subset, known as consistent subset. In order to improve generalization and to prevent induction of overly complex models, in this study the application of the Pessimistic Error Estimate (PEE) principle in the context of the nearest neighbor rule is investigated. Generalization is estimated as a trade-off between training set accuracy and model complexity. As major results, it is shown that PEE-like selection strategies guarantee to preserve the accuracy of the consistent subset with a far larger reduction factor and, moreover, that sensible generalization improvements can be obtained by using a reduced subset. Moreover, comparison with state-of-the-art hybrid prototype selection methods highlight that the here introduced FCNN-PAC strategy is able to obtain a model of size comparable to that obtained by the best prototype selection methods, with far smaller time requirements, corresponding to four orders of magnitude on medium-sized datasets.

MVD based information exchange between BIM and building energy performance simulation

Abstract The process of preparing building energy performance simulation (BEPS) models involves repetitive manual operations that often lead to data losses and errors. As a result, BEPS model inputs can vary widely from this time consuming, non-standardised and subjective process. This paper proposes a standardised method of information exchange between Building Information Modelling (BIM) and BEPS tools using the Information Delivery Manual (IDM) and Model View Definition (MVD) methodologies. The methodology leverages a collection of use cases to initiate the identification of exchange requirements needed by BEPS tools. The IDM/MVD framework captures and translates exchange requirements into the Industry Foundation Classes (IFC) schema. The suggested approach aims to facilitate the transfer of information from IFC based BIM to either conventional or advanced BEPS tools (e.g. EnergyPlus and Modelica) through the development of a specific MVD that defines a subset of the IFC data model that deals with building energy performance simulation. By doing so, the potential of BIM-based simulation can be fully unlocked, and a reliable and consistent IFC subset is provided as an input for energy simulation software.

Immune effects after uniportal nonintubated video-thoracoscopic operations

Background: We hypothesized that uniportal video-assisted thoracic surgery (VATS) under nonintubated anesthesia may have a lesser immunological impact than same procedures under general anesthesia. Methods: Between December 2005 and October 2016, a total of consecutive 878 patients underwent VATS operations under nonintubated anesthesia. In a subset of 542 patients we assessed lymphocyte subpopulations at 1, 7 and 14 days postoperatively and matched with a control group of 106 patients who underwent uniportal intubated surgery. Results: Global time spent in the operative room was significantly longer in intubated group (85±41 vs. 118±34 minutes; P=0.03). The total lymphocytes count showed a lesser drop in the nonintubated group at post-operative day 7 (P=0.04) and 14 (P=0.05) with a significant lesser reduction of natural killer lymphocytes at day 7 (P=0.03) and 14 (P=0.04). 30-day mortality (0.9% vs. 4.7%; P=0.04), major morbidity (9.5% vs. 19%; P=0.03) and hospital stay (4.2±2.8 vs. 6.1±2.6 days; P=0.04) were significantly lower in the nonintubated group. Survival rate in the subset of patients operated for malignant effusion was significantly higher in the nonintubated group (P=0.03). Conclusions: Uniportal nonintubated operations demonstrated a significant lower impact on immunological response compared to the traditional procedure in general anesthesia. This impacted postoperative 30-day mortality, major morbidity and hospital stay. We also found a significant influence on long-term survival in a consistent subset of patients operated for malignant pleural effusion.

T e C o R e

The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs ( kg s), maintaining both numerical and non-numerical facts supported by an underlying schema, are a prominent example. Knowledge Graph management is challenging because: (i) most of existing kg s focus on static data, thus impeding the availability of timewise knowledge; (ii) facts in kg s are usually accompanied by a confidence score, which witnesses how likely it is for them to hold. We demonstrate T e C o R e , a system for temporal inference and conflict resolution in uncertain temporal knowledge graphs ( utkg s). At the heart of T e C o R e are two state-of-the-art probabilistic reasoners that are able to deal with temporal constraints efficiently. While one is scalable, the other can cope with more expressive constraints. The demonstration will focus on enabling users and applications to find inconsistencies in utkg s. T e C o R e provides an interface allowing to select utkg s and editing constraints; shows the maximal consistent subset of the utkg , and displays statistics (e.g., number of noisy facts removed) about the debugging process.

GenoCore: A simple and fast algorithm for core subset selection from large genotype datasets.

Selecting core subsets from plant genotype datasets is important for enhancing cost-effectiveness and to shorten the time required for analyses of genome-wide association studies (GWAS), and genomics-assisted breeding of crop species, etc. Recently, a large number of genetic markers (>100,000 single nucleotide polymorphisms) have been identified from high-density single nucleotide polymorphism (SNP) arrays and next-generation sequencing (NGS) data. However, there is no software available for picking out the efficient and consistent core subset from such a huge dataset. It is necessary to develop software that can extract genetically important samples in a population with coherence. We here present a new program, GenoCore, which can find quickly and efficiently the core subset representing the entire population. We introduce simple measures of coverage and diversity scores, which reflect genotype errors and genetic variations, and can help to select a sample rapidly and accurately for crop genotype dataset. Comparison of our method to other core collection software using example datasets are performed to validate the performance according to genetic distance, diversity, coverage, required system resources, and the number of selected samples. GenoCore selects the smallest, most consistent, and most representative core collection from all samples, using less memory with more efficient scores, and shows greater genetic coverage compared to the other software tested. GenoCore was written in R language, and can be accessed online with an example dataset and test results at https://github.com/lovemun/Genocore.

The paradox of verbal autopsy in cause of death assignment: symptom question unreliability but predictive accuracy.

BackgroundWe believe that it is important that governments understand the reliability of the mortality data which they have at their disposable to guide policy debates. In many instances, verbal autopsy (VA) will be the only source of mortality data for populations, yet little is known about how the accuracy of VA diagnoses is affected by the reliability of the symptom responses. We previously described the effect of the duration of time between death and VA administration on VA validity. In this paper, using the same dataset, we assess the relationship between the reliability and completeness of symptom responses and the reliability and accuracy of cause of death (COD) prediction.MethodsThe study was based on VAs in the Population Health Metrics Research Consortium (PHMRC) VA Validation Dataset from study sites in Bohol and Manila, Philippines and Andhra Pradesh, India. The initial interview was repeated within 3–52 months of death. Question responses were assessed for reliability and completeness between the two survey rounds. COD was predicted by Tariff Method.ResultsA sample of 4226 VAs was collected for 2113 decedents, including 1394 adults, 349 children, and 370 neonates. Mean question reliability was unexpectedly low (kappa = 0.447): 42.5 % of responses positive at the first interview were negative at the second, and 47.9 % of responses positive at the second had been negative at the first. Question reliability was greater for the short form of the PHMRC instrument (kappa = 0.497) and when analyzed at the level of the individual decedent (kappa = 0.610). Reliability at the level of the individual decedent was associated with COD predictive reliability and predictive accuracy.ConclusionsFamilies give coherent accounts of events leading to death but the details vary from interview to interview for the same case. Accounts are accurate but inconsistent; different subsets of symptoms are identified on each occasion. However, there are sufficient accurate and consistent subsets of symptoms to enable the Tariff Method to assign a COD.Questions which contributed most to COD prediction were also the most reliable and consistent across repeat interviews; these have been included in the short form VA questionnaire. Accuracy and reliability of diagnosis for an individual death depend on the quality of interview. This has considerable implications for the progressive roll out of VAs into civil registration and vital statistics (CRVS) systems.Electronic supplementary materialThe online version of this article (doi:10.1186/s12963-016-0104-2) contains supplementary material, which is available to authorized users.

A certain version of preservationism

A certain approach to paraconsistency was initiated by works of R. Jennings and P. Schotch. In their “Inference and necessity” [4] they proposed a notion of a level of inconsistency (incoherence) of a given set of premises. This level is a measure that assigns to a given set of premises X, the least number of elements of covers of X that consist of consistent subsets of X. The idea of the level of inconsistency allows to formulate a paraconsistent inference relation called by the authors forcing, while the obtained approach  preservationism. Similarly as classical inference relation is truth-preserving, the obtained inference relation is preserving the level of inconsistency. We will discuss some examples of inferences that are valid in the sense of Jennings-Schotch inference relation and rise some questions on them. Based on that we formulate an inference relation as an answer to the mentioned doubts. As regards forcing inference relation, the set of premises needed to derive a given conclusion can vary when changing covers from one to another. Our proposal is to stipulate to have some common set of relevant premises.

High SNR Consistent Linear Model Order Selection and Subset Selection

High SNR consistency of model order selection criteria in linear regression models has attracted a lot of attention in the signal processing community recently. It is now known that Exponentially Embedded Family, versions of Minimum Description Length etc. are high SNR consistent. However, a general framework for high SNR consistency in linear regression is still missing. This paper fills this gap by deriving necessary and sufficient conditions (NSCs) for model order selection criteria in both known and unknown noise variance situations to be high SNR consistent. Many popular model order selection criteria are proved to be high SNR consistent using these NSCs. We also provide a convergence rate analysis to discriminate between various high SNR consistent model order selection criteria. A direct application of model order selection techniques to the very important subset selection problem is computationally infeasible. This paper establish the high SNR consistency of an existing t-statistics based index ordering scheme that allows the conversion of a subset selection problem into a model order selection problem. Combining this index ordering with high SNR consistent model order selection criteria leads to a novel subset selection procedure (SSP) that is numerically shown to outperform existing high SNR consistent SSPs.

Context-dependent combination of sensor information in Dempster\u2013Shafer theory for BDI

There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work.

The basic principles of uncertain information fusion. An organised review of merging rules in different representation frameworks

We propose and advocate basic principles for the fusion of incomplete or uncertain information items, that should apply regardless of the formalism adopted for representing pieces of information coming from several sources. This formalism can be based on sets, logic, partial orders, possibility theory, belief functions or imprecise probabilities. We propose a general notion of information item representing incomplete or uncertain information about the values of an entity of interest. It is supposed to rank such values in terms of relative plausibility, and explicitly point out impossible values. Basic issues affecting the results of the fusion process, such as relative information content and consistency of information items, as well as their mutual consistency, are discussed. For each representation setting, we present fusion rules that obey our principles, and compare them to postulates specific to the representation proposed in the past. In the crudest (Boolean) representation setting (using a set of possible values), we show that the understanding of the set in terms of most plausible values, or in terms of non-impossible ones matters for choosing a relevant fusion rule. Especially, in the latter case our principles justify the method of maximal consistent subsets, while the former is related to the fusion of logical bases. Then we consider several formal settings for incomplete or uncertain information items, where our postulates are instantiated: plausibility orderings, qualitative and quantitative possibility distributions, belief functions and convex sets of probabilities. The aim of this paper is to provide a unified picture of fusion rules across various uncertainty representation settings.

How historic simulation–observation discrepancy affects future warming projections in a very large model ensemble

Projections of future climate made by model-ensembles have credibility because the historic simulations by these models are consistent with, or near-consistent with, historic observations. However, it is not known how small inconsistencies between the ranges of observed and simulated historic climate change affects the future projections made by a model ensemble. Here, the impact of historical simulation–observation inconsistencies on future warming projections is quantified in a 4-million member Monte Carlo ensemble from a new efficient Earth System Model (ESM). Of the 4-million ensemble members, a subset of 182,500 are consistent with historic ranges of warming, heat uptake and carbon uptake simulated by the Climate Model Intercomparison Project 5 (CMIP5) ensemble. This simulation–consistent subset projects similar future warming ranges to the CMIP5 ensemble for all four RCP scenarios, indicating the new ESM represents an efficient tool to explore parameter space for future warming projections based on historic performance. A second subset of 14,500 ensemble members are consistent with historic observations for warming, heat uptake and carbon uptake. This observation–consistent subset projects a narrower range for future warming, with the lower bounds of projected warming still similar to CMIP5, but the upper warming bounds reduced by 20–35 %. These findings suggest that part of the upper range of twenty-first century CMIP5 warming projections may reflect historical simulation–observation inconsistencies. However, the agreement of lower bounds for projected warming implies that the likelihood of warming exceeding dangerous levels over the twenty-first century is unaffected by small discrepancies between CMIP5 models and observations.

Driver head pose estimation using efficient descriptor fusion

A great interest is focused on driver assistance systems using the head pose as an indicator of the visual focus of attention and the mental state. In fact, the head pose estimation is a technique allowing to deduce head orientation relatively to a view of camera and could be performed by model-based or appearance-based approaches. Model-based approaches use a face geometrical model usually obtained from facial features, whereas appearance-based techniques use the whole face image characterized by a descriptor and generally consider the pose estimation as a classification problem. Appearance-based methods are faster and more adapted to discrete pose estimation. However, their performance depends strongly on the head descriptor, which should be well chosen in order to reduce the information about identity and lighting contained in the face appearance. In this paper, we propose an appearance-based discrete head pose estimation aiming to determine the driver attention level from monocular visible spectrum images, even if the facial features are not visible. Explicitly, we first propose a novel descriptor resulting from the fusion of four most relevant orientation-based head descriptors, namely the steerable filters, the histogram of oriented gradients (HOG), the Haar features, and an adapted version of speeded up robust feature (SURF) descriptor. Second, in order to derive a compact, relevant, and consistent subset of descriptor’s features, a comparative study is conducted on some well-known feature selection algorithms. Finally, the obtained subset is subject to the classification process, performed by the support vector machine (SVM), to learn head pose variations. As we show in experiments with the public database (Pointing’04) as well as with our real-world sequence, our approach describes the head with a high accuracy and provides robust estimation of the head pose, compared to state-of-the-art methods.

On the complexity of sampling query feedback restricted database repair of functional dependency violations

An inconsistent database is a database instance violating integrity constraints. A repair of an inconsistent database is a maximal consistent subset. Sampling from the repair space is an alternative approach meeting the needs of many applications. In this paper, we introduce a new class of repair, query feedback restricted repair, based on the feedback on user's witness query. We first map out a picture of both data and combined complexities of repair existence problems under different cases to identify the intractable cases. Especially, we show that if the query is a projection or a union query, then the decision problem is NP-complete; even worse, if the query is a conjunctive query, the decision problem becomes Σ2P-complete. However, we prove that the combined complexity of the repair existence problem is in LOGSPACE when the witness query is a selection-join query, and this conclusion also implies that the combined complexity of side-effect free deletion propagation problem under group-deletion is in LOGSPACE which is not considered in previous works. Additionally, we provide a polynomial random repair sampling algorithm under combined complexity. At last, we revisit the key preserving condition [1] and show that it will simplify the problem, i.e., some cases become tractable for certain key preserving views, as opposed to their counterparts that are not key preserving.

Consistent subsets: Computationally feasible methods to compute the Houtman–Maks-index

We provide two methods to compute the largest subset of a set of observations that is consistent with the Generalised Axiom of Revealed Preference. The algorithm provided by Houtman and Maks (1985) is not computationally feasible for larger data sets, while our methods are not limited in that respect. The first method is an application of Gross and Kaiser’s (1996) approximate algorithm and is only applicable for two-dimensional data sets, but it is very fast and easy to implement. The second method is a mixed-integer linear programming approach that is slightly more involved but still fast and not limited by the dimension of the data set.

Training Classifiers under Covariate Shift by Constructing the Maximum Consistent Distribution Subset

The assumption that the training and testing samples are drawn from the same distribution is violated under covariate shift setting, and most algorithms for the covariate shift setting try to first estimate distributions and then reweight samples based on the distributions estimated. Due to the difficulty of estimating a correct distribution, previous methods can not get good classification performance. In this paper, we firstly present two types of covariate shift problems. Rather than estimating the distributions, we then desire an effective method to select a maximum subset following the target testing distribution based on feature space split from the auxiliary set or the target training set. Finally, we prove that our subset selection method can consistently deal with both scenarios of covariate shift. Experimental results demonstrate that training a classifier with the selected maximum subset exhibits good generalization ability and running efficiency over those of traditional methods under covariate shift setting.