Inference Error Research Articles

Surface of rapidly-rotating neutron stars: Implications to neutron star parameter estimation

The Neutron star Interior Composition Explorer (NICER) is currently observing the x-ray pulse profiles emitted by hot spots on the surface of rotating neutron stars allowing for an inference of their radii with unprecedented precision. A critical ingredient in the pulse profile model is an analytical formula for the oblate shape of the star. These formulas require a fitting over a large ensemble of neutron star solutions, which cover a wide set of equations of state, stellar compactnesses and rotational frequencies. However, this procedure introduces a source of systematic error, as (i) the fits do not describe perfectly the surface of all stars in the ensemble and (ii) neutron stars are described by a single equation of state, whose influence on the surface shape is averaged out during the fitting procedure. Here we perform a first study of this systematic error, finding evidence that it is subdominant relative to the statistical error in the radius inference by NICER. We also find evidence that the formula currently used by NICER can be used in the inference of the radii of rapidly rotating stars, outside of the formula's domain of validity. Moreover, we employ an accurate enthalpy-based method to locate the surface of numerical solutions of rapidly rotating neutron stars and a new highly accurate formula to describe their surfaces. These results can be used in applications that require an accurate description of oblate surfaces of rapidly rotating neutron stars.

Keeping Cell Selection Model Up-to-Date to Adapt to Time-Dependent Environment in Sparse Mobile Crowdsensing

Sparse mobile crowdsensing (MCS) requires participants to collect data from partial cells and then intelligently infer the data of the rest cells. Since collecting data from different cells will probably result in different data inference quality, cell selection (i.e., which cells need to be selected to collect data) is a critical issue in Sparse MCS. Currently, state-of-the-art cell selection algorithms are implemented based on reinforcement learning. These algorithms ignore the problem that the urban environment is usually time dependent, and the cell selection model needs to be kept up-to-date to adapt to the time-dependent environment. However, Sparse MCS applications require participants to collect data only in a few cells, which makes it hard to obtain suitable training data for continuous cell selection model learning. To solve this problem, we model the spatiotemporal correlations in the collected sparse data, and then design various methods to update training data based on it. Particularly, these methods make full use of the gradual changes of data in time and space, and reasonably transform and splice sparse data at different moments. Finally, updated training data is fed to the cell selection model to keep it up-to-date. We conduct experimental evaluations by performing several sensing tasks in air quality monitoring. The results show that our proposed methods can effectively update training data as well as the cell selection model. Compared with several baselines, our best method can reduce inference error by more than 10% on average.

A Cost-Quality Beneficial Cell Selection Approach for Sparse Mobile Crowdsensing With Diverse Sensing Costs

The Internet of Things (IoT) and mobile techniques enable real-time sensing for urban computing systems. By recruiting only a small number of users to sense data from selected subareas (namely, cells), sparse mobile crowdsensing (MCS) emerges as an effective paradigm to reduce sensing costs for monitoring the overall status of a large-scale area. The current sparse MCS solutions reduce the sensing subareas (by selecting the most informative cells) based on the assumption that each sample has the same cost, which is not always realistic in the real world, as the cost of sensing in a subarea can be diverse due to many factors, e.g., the condition of the device, location, and routing distance. To address this issue, we proposed a new cell selection approach consisting of three steps (information modeling, cost estimation, and cost-quality beneficial cell selection) to further reduce the total costs and improve the task quality. Specifically, we discussed the properties of the optimization goals and modeled the cell selection problem as a solvable biobjective optimization problem under certain assumptions and approximations. Then, we presented two selection strategies, i.e., the Pareto optimization selection (POS) and generalized cost-benefit greedy (GCB-GREEDY) selection along with our proposed cell selection algorithm. Finally, the superiority of our cell selection approach is assessed through four real-life urban monitoring data sets (Parking, Flow, Traffic, and Humidity) and three cost maps (independent identically distributed with dynamic cost map, monotonic with dynamic cost map, and spatial-correlated cost map). Results show that our proposed selection strategies POS and GCB-GREEDY can save up to 15.2% and 15.02% sample costs and reduce the inference errors to a maximum of 16.8% (15.5%) compared to the baseline-query by committee (QBC) in a sensing cycle. The findings show important implications in sparse MCS for urban context properties.

Characterizing the Sample Selection for Supernova Cosmology

Type Ia supernovae (SNe Ia) are used as distance indicators to infer the cosmological parameters that specify the expansion history of the universe. Parameter inference depends on the criteria by which the analysis SN sample is selected. Only for the simplest selection criteria and population models can the likelihood be calculated analytically, otherwise it needs to be determined numerically, a process that inherently has error. Numerical errors in the likelihood lead to errors in parameter inference. This article presents toy examples where the distance modulus is inferred given a set of SNe at a single redshift. Parameter estimators and their uncertainties are calculated using Monte Carlo techniques. The relationship between the number of Monte Carlo realizations and numerical errors is presented. The procedure can be applied to more realistic models and used to determine the computational and data management requirements of the transient analysis pipeline.

Gene Tree Discord, Simplex Plots, and Statistical Tests under the Coalescent.

A simple graphical device, the simplex plot of quartet concordance factors, is introduced to aid in the exploration of a collection of gene trees on a common set of taxa. A single plot summarizes all gene tree discord and allows for visual comparison to the expected discord from the multispecies coalescent model (MSC) of incomplete lineage sorting on a species tree. A formal statistical procedure is described that can quantify the deviation from expectation for each subset of four taxa, suggesting when the data are not in accord with the MSC, and thus that either gene tree inference error is substantial or a more complex model such as that on a network may be required. If the collection of gene trees is in accord with the MSC, the plots reveal when substantial incomplete lineage sorting is present. Applications to both simulated and empirical multilocus data sets illustrate the insights provided. [Gene tree discordance; hypothesis test; multispecies coalescent model; quartet concordance factor; simplex plot; species tree].

Systematic errors in orthology inference and their effects on evolutionary analyses

SummaryThe availability of complete sets of genes from many organisms makes it possible to identify genes unique to (or lost from) certain clades. This information is used to reconstruct phylogenetic trees; identify genes involved in the evolution of clade specific novelties; and for phylostratigraphy—identifying ages of genes in a given species. These investigations rely on accurately predicted orthologs. Here we use simulation to produce sets of orthologs that experience no gains or losses. We show that errors in identifying orthologs increase with higher rates of evolution. We use the predicted sets of orthologs, with errors, to reconstruct phylogenetic trees; to count gains and losses; and for phylostratigraphy. Our simulated data, containing information only from errors in orthology prediction, closely recapitulate findings from empirical data. We suggest published downstream analyses must be informed to a large extent by errors in orthology prediction that mimic expected patterns of gene evolution.

DDNN based data allocation method for IIoT

Abstract With the complete application of artificial intelligence in the field of industrial production and manufacturing and the rapid development of edge computing, industrial processing sites often need to deploy machine learning tasks at edges and terminals. We propose a data allocation method based on Distributed Deep Neural Networks (DDNN) framework, which allocates data to edge servers or stays locally for processing. DDNN divides deep learning tasks and deploys pre-trained shallow neural networks and deep neural networks at local or edges, respectively. However, all data is processed locally, and the failure is sent to the edge server or the cloud. It will lead to excessive pressure on local terminal equipment and long-term idle edge servers, which cannot meet industrial production’s real-time requirements on user privacy and time-sensitive tasks. In this paper, the complexity and inference error rate of machine learning model, the data processing speed of local equipment and edge server, and the transmission time are comprehensively considered to establish the system model. A joint optimization problem is proposed to minimize the total data processing delay. The optimal solution is derived analytically, and the optimal data allocation methhod is given. Simulation experiments are designed to verify the method’s effectiveness and study the influence of key parameters on the allocation method.

Exploitation of image statistics with sparse coding in the case of stereo vision

The sparse coding algorithm has served as a model for early processing in mammalian vision. It has been assumed that the brain uses sparse coding to exploit statistical properties of the sensory stream. We hypothesize that sparse coding discovers patterns from the data set, which can be used to estimate a set of stimulus parameters by simple readout. In this study, we chose a model of stereo vision to test our hypothesis. We used the Locally Competitive Algorithm (LCA), followed by a naïve Bayes classifier, to infer stereo disparity. From the results we report three observations. First, disparity inference was successful with this naturalistic processing pipeline. Second, an expanded, highly redundant representation is required to robustly identify the input patterns. Third, the inference error can be predicted from the number of active coefficients in the LCA representation. We conclude that sparse coding can generate a suitable general representation for subsequent inference tasks.

Data-driven inference modeling based on an on-line Wang-Mendel fuzzy approach

To address the modeling of continuous production process with dynamic and nonlinear characteristics, an on-line Wang-Mendel fuzzy inference model is proposed in this paper, which extracts the fuzzy rules from the raw data without prior knowledge. Considering the state of the industrial system changes dynamically online, typical data samples with support degrees are used to describe the sample distribution characteristics in each fuzzy region. With respect to online dynamic learning process, a self-evolutionary strategy with adaptive memory factors is proposed, and the fuzzy rule structures are updated gradually by a stochastic gradient descent method. For on-line updating, a loss function is constructed by considering the inference errors and the previous rule structure, in order to achieve continuous learning without forgetting the knowledge learned before. In the reasoning process, a sparse fuzzy reasoning approach is designed for extrapolating the knowledge of the fuzzy regions without sample data. To verify the effectiveness of the proposed method, chaotic time series data with noises and industrial practical data coming from a steel plant are employed for experimental analyses. The experimental results show that, the proposed method is capable of describing a variety of dynamic features and exhibiting high accuracy for the industrial data.

Quantifying the impact of an inference model in Bayesian phylogenetics

Abstract Phylogenetic trees are currently routinely reconstructed from an alignment of character sequences (usually nucleotide sequences). Bayesian tools, such as MrBayes, RevBayes and BEAST2, have gained much popularity over the last decade, as they allow joint estimation of the posterior distribution of the phylogenetic trees and the parameters of the underlying inference model. An important ingredient of these Bayesian approaches is the species tree prior. In principle, the Bayesian framework allows for comparing different tree priors, which may elucidate the macroevolutionary processes underlying the species tree. In practice, however, only macroevolutionary models that allow for fast computation of the prior probability are used. The question is how accurate the tree estimation is when the real macroevolutionary processes are substantially different from those assumed in the tree prior. Here we present pirouette, a free and open‐source r package that assesses the inference error made by Bayesian phylogenetics for a given macroevolutionary diversification model. pirouette makes use of BEAST2, but its philosophy applies to any Bayesian phylogenetic inference tool. We describe pirouette’s usage providing full examples in which we interrogate a model for its power to describe another. Last, we discuss the results obtained by the examples and their interpretation.

Improving entrepreneurial competencies in the classroom: an extension and in-study replication

PurposeA recent paper by Morris et al. (2013b) presented evidence that students can develop entrepreneurial competencies through international fieldwork. This paper explores whether the same results can be developed in a traditional classroom setting.Design/methodology/approachThe study is a systematic replication of the Morris study with the addition of a matched pair, quasi-experimental design, with a self-replication. Data were collected on 13 self-reported competencies at the start of a semester from two groups using the Morris instrument. The treatment group was exposed to a curriculum designed to teach entrepreneurial competencies, and both groups were re-surveyed at the end of the semester. The process was then repeated with a different cohort, one year later, to replicate the initial study.FindingsFive competencies saw significant increases in the first treatment group. However, only three of these competencies increased more in the treatment group than the control group. In the replication study, only one competency was significantly higher in the treatment group, and that competency was not one of the original three.Practical implicationsEducators and policymakers should select a curriculum that is valid and reliable. Entrepreneurship educators and policymaker should devote more time to evaluating the effectiveness of different pedagogical techniques for improving entrepreneurial competencies.Originality/valueTo the best of our knowledge, this is one of the first studies in entrepreneurship education to undertake a matched pair, quasi-experimental design with an in-study replication. The results indicate that serious inferential errors arise if simpler designs are used, even though such designs are the norm in entrepreneurship research.

Adaptive and Robust Partition Learning for Person Retrieval With Policy Gradient

Person retrieval aims at effectively matching the pedestrian images over an extensive database given a specified identity. As extracting effective features is crucial in a high-performance retrieval system, recent significant progress was achieved by part-based models that have constructed robust local representations on top of vertically striped part features. However, this kind of models use predefined partitioning strategies, making the number and size of each partition identical even when input images vary a lot. This unchangeable setting usually leads to less flexibility and robustness in capturing visual variance. The primary reason for such a negative effect is that a fixed partitioning strategy is unable to deal with (a) the significant variance from pose, illumination and viewpoint which is common in a pedestrian image dataset, and (b) also the inference error and misalignment of human bodies introduced by the prepositive pedestrian detection module or human pose estimation module. In this paper, we tackle this problem via introducing the novel Adaptive Partition Network (APN). The APN utilizes deep reinforcement learning and applies an agent to generate optimal partitioning strategies dynamically for different input images. The agent inside the APN is optimized with the policy gradient algorithm and maximizes the reward of choosing the best partition setting. By leveraging the supervision cues from the objective partitioning strategies that are generated on a set of held-out training images, the agent is trained jointly with other parts of APN, which ensures the APN's robustness and generalization ability. Extensive experimental results on multiple datasets, including CUHK03, DukeMTMC and Market-1501, demonstrate the superiority of APN over the state-of-the-art models.

Streamlined solutions to multilevel sparse matrix problems

We define and solve classes of sparse matrix problems that arise in multilevel modelling and data analysis. The classes are indexed by the number of nested units, with two-level problems corresponding to the common situation, in which data on level-1 units are grouped within a two-level structure. We provide full solutions for two-level and three-level problems, and their derivations provide blueprints for the challenging, albeit rarer in applications, higher-level versions of the problem. While our linear system solutions are a concise recasting of existing results, our matrix inverse sub-block results are novel and facilitate streamlined computation of standard errors in frequentist inference as well as allowing streamlined mean field variational Bayesian inference for models containing higher-level random effects. doi: 10.1017/S1446181120000061

Consumers’ Abductive Inference Error as Cognitive Impairment

Consumers’ Abductive Inference Error as Cognitive Impairment

Consumers’ Abductive Inference Error as Cognitive Impairment

Consumers’ Abductive Inference Error as Cognitive Impairment

How Can Randomized Routing Protocols Hide Flow Information in Wireless Networks?

Preventing the source-destination network flow information from being disclosed is pivotal for anonymous wireless network applications. However, the advance of network inference, which is able to obtain the flow information without directly measuring it, poses severe challenges towards this goal. Randomized routing is capable of hiding the flow information by injecting substantial errors to the network inference process. In this paper, we systematically study the behavior of randomized routing protocols, and categorize them into three templates, k -random-relay, k -random-neighbor and k -random-path based on their routing behaviors. We propose technical models to characterize these templates in terms of their induced inference errors and their delay costs. We also use simulations to validate the theoretical results. Our work provides the first systematic study on understanding both the benefit and the cost of using randomized routing to hide the flow information in wireless networks.

Statistical Learning and Inference Is Impaired in the Nonclinical Continuum of Psychosis.

Our perceptions result from the brain's ability to make inferences, or predictive models, of sensory information. Recently, it has been proposed that psychotic traits may be linked to impaired predictive processes. Here, we examine the brain dynamics underlying statistical learning and inference in stable and volatile environments, in a population of healthy human individuals (N = 75; 36 males, 39 females) with a range of psychotic-like experiences. We measured prediction error responses to sound sequences with electroencephalography, gauged sensory inference explicitly by behaviorally recording sensory statistical learning errors, and used dynamic causal modeling to tap into the underlying neural circuitry. We discuss the findings that were robust to replication across the two experiments (Discovery dataset, N = 31; Validation dataset, N = 44). First, we found that during stable conditions, participants demonstrated greater precision in their predictive model, reflected in a larger prediction error response to unexpected sounds, and decreased statistical learning errors. Moreover, individuals with attenuated prediction errors in stable conditions were found to make greater incorrect predictions about sensory information. Critically, we show that greater errors in statistical learning and inference are related to increased psychotic-like experiences. These findings link neurophysiology to behavior during statistical learning and prediction formation, as well as providing further evidence for the idea of a continuum of psychosis in the healthy, nonclinical population.SIGNIFICANCE STATEMENT While perceiving the world, we make inferences by learning the statistics present in the sensory environment. It has been argued that psychosis may emerge because of a failure to learn sensory statistics, resulting in an impaired representation of the world. Recently, it has been proposed that psychosis exists on a continuum; however, there is conflicting evidence on whether sensory learning deficits align on the nonclinical end of the psychosis continuum. We found that statistical learning of sensory events is associated with the magnitude of mismatch negativity and, critically, is impaired in healthy people who report more psychotic-like experiences. We replicated these findings in an independent sample, demonstrating strengthened credibility to support the continuum of psychosis that extends into the nonclinical population.

Affective experience in the predictive mind: a review and new integrative account

This paper aims to offer an account of affective experiences within Predictive Processing, a novel framework that considers the brain to be a dynamical, hierarchical, Bayesian hypothesis-testing mechanism. We begin by outlining a set of common features of affective experiences (or feelings) that a PP-theory should aim to explain: feelings are conscious, they have valence, they motivate behaviour, and they are intentional states with particular and formal objects. We then review existing theories of affective experiences within Predictive Processing and delineate two families of theories: Interoceptive Inference Theories (which state that feelings are determined by interoceptive predictions) and Error Dynamics Theories (which state that feelings are determined by properties of error dynamics). We highlight the strengths and shortcomings of each family of theories and develop a synthesis: the Affective Inference Theory. Affective Inference Theory claims that valence corresponds to the expected rate of prediction error reduction. In turn, the particular object of a feeling is the object predicted to be the most likely cause of expected changes in prediction error rate, and the formal object of a feeling is a predictive model of the expected changes in prediction error rate caused by a given particular object. Finally, our theory shows how affective experiences bias action selection, directing the organism towards allostasis and towards optimal levels of uncertainty in order to minimise prediction error over time.

Statistical approaches for spatial sample survey: Persistent misconceptions and new developments

AbstractSeveral misconceptions about the design‐based approach for sampling and statistical inference, based on classical sampling theory, seem to be quite persistent. These misconceptions are the result of confusion about basic statistical concepts such as independence, expectation, and bias and variance of estimators or predictors. These concepts have a different meaning in the design‐based and model‐based approach, because they consider different sources of randomness. Also, a population mean is still often confused with a model mean, and a population variance with a model‐variance, leading to invalid formulas for the variance of an estimator of the population mean. In this paper the fundamental differences between these two approaches are illustrated with simulations, so that hopefully more pedometricians get a better understanding of this subject. An overview is presented of how in the design‐based approach we can make use of knowledge of the spatial structure of the study variable. In the second part, new developments in both the design‐based and model‐based approach are described that try to combine the strengths of the two approaches.Highlights Ignorance of fundamental differences between design‐based and model‐based approaches still cause errors in statistical inference. Basic statistical concepts such as independence, variance and bias of an estimator have a different meaning in the two approaches. In estimating and testing it is important to distinguish population parameters from model parameters. Hybrid methods that combine the strengths of the two approaches are reviewed.

Disentangling Sources of Gene Tree Discordance in Phylogenomic Data Sets: Testing Ancient Hybridizations in Amaranthaceae s.l.

Gene tree discordance in large genomic data sets can be caused by evolutionary processes such as incomplete lineage sorting and hybridization, as well as model violation, and errors in data processing, orthology inference, and gene tree estimation. Species tree methods that identify and accommodate all sources of conflict are not available, but a combination of multiple approaches can help tease apart alternative sources of conflict. Here, using a phylotranscriptomic analysis in combination with reference genomes, we test a hypothesis of ancient hybridization events within the plant family Amaranthaceae s.l. that was previously supported by morphological, ecological, and Sanger-based molecular data. The data set included seven genomes and 88 transcriptomes, 17 generated for this study. We examined gene-tree discordance using coalescent-based species trees and network inference, gene tree discordance analyses, site pattern tests of introgression, topology tests, synteny analyses, and simulations. We found that a combination of processes might have generated the high levels of gene tree discordance in the backbone of Amaranthaceae s.l. Furthermore, we found evidence that three consecutive short internal branches produce anomalous trees contributing to the discordance. Overall, our results suggest that Amaranthaceae s.l. might be a product of an ancient and rapid lineage diversification, and remains, and probably will remain, unresolved. This work highlights the potential problems of identifiability associated with the sources of gene tree discordance including, in particular, phylogenetic network methods. Our results also demonstrate the importance of thoroughly testing for multiple sources of conflict in phylogenomic analyses, especially in the context of ancient, rapid radiations. We provide several recommendations for exploring conflicting signals in such situations. [Amaranthaceae; gene tree discordance; hybridization; incomplete lineage sorting; phylogenomics; species network; species tree; transcriptomics.]