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Imperfect information and learning are introduced into a production-based asset pricing model. Our model features slow learning about firms’ exposure to aggregate productivity shocks over time. In contrast to a full information case, our framework provides a unified explanation for the stylized empirical features of the cross-section of stocks that differ in capital age: old capital firms (1) have higher capital allocation efficiency; (2) are more exposed to aggregate productivity shocks and, hence, earn higher expected returns, which we refer to as capital age premium; and (3) have shorter cash-flow duration, when compared with young capital firms.

Neuropsychological tests have commonly been used to determine the organization of cognitive functions by identifying latent variables. In contrast, an approach which has seldom been employed is network analysis. We characterize the network structure of a set of representative neuropsychological test scores in cognitively healthy older adults and MCI and dementia patients using network analysis. We employed the neuropsychological battery from the National Alzheimer's Coordinating Center which included healthy controls (n = 7623), mild cognitive impairment patients (n = 5981) and dementia patients (n = 2040), defined according to the Clinical Dementia Rating. The results showed that, according to several network analysis measures, the most central cognitive function is executive function followed by attention, language, and memory. At the test level, the most central test was the Trail Making Test B, which measures cognitive flexibility. Importantly, these results and most other network measures, such as the community organization and graph representation, were similar across the three diagnostic groups. Therefore, network analysis can help to establish a ranking of cognitive functions and tests based on network centrality and suggests that this organization is preserved in dementia. Central nodes might be particularly relevant both from a theoretical and clinical point of view, as they are more associated with other nodes, and their disruption is likely to have a larger effect on the overall network than peripheral nodes. The present analysis may provide a proof of principle for the application of network analysis to cognitive data.

This paper studies second-order properties of the many instruments robust t -ratios based on the limited information maximum likelihood and Fuller estimators for instrumental variable regression models with homoskedastic errors under the many instruments asymptotics, where the number of instruments may increase proportionally with the sample size n , and proposes second-order refinements to the t -ratios to improve the size and power properties. Based on asymptotic expansions of the null and non-null distributions of the t -ratios derived under the many instruments asymptotics, we show that the second-order terms of those expansions may have non-trivial impacts on the size as well as the power properties. Furthermore, we propose adjusted t -ratios whose approximation errors for the null rejection probabilities are of order O ( n − 1 ) in contrast to the ones for the unadjusted t -ratios of order O ( n − 1 / 2 ) , and show that these adjustments induce some desirable power properties in terms of the local maximinity. Although these results are derived under homoskedastic errors, we also establish a stochastic expansion for a heteroskedasticity robust t -ratio, and propose an analogous adjustment under slight deviations from homoskedasticity.

Partial speech input is often understood to trigger rapid and automatic activation of successively higher-level representations of words, from sound to meaning. Here we show evidence from magnetoencephalography that this type of incremental processing is limited when words are heard in isolation as compared to continuous speech. This suggests a less unified and automatic word recognition process than is often assumed. We present evidence from isolated words that neural effects of phoneme probability, quantified by phoneme surprisal, are significantly stronger than (statistically null) effects of phoneme-by-phoneme lexical uncertainty, quantified by cohort entropy. In contrast, we find robust effects of both cohort entropy and phoneme surprisal during perception of connected speech, with a significant interaction between the contexts. This dissociation rules out models of word recognition in which phoneme surprisal and cohort entropy are common indicators of a uniform process, even though these closely related information-theoretic measures both arise from the probability distribution of wordforms consistent with the input. We propose that phoneme surprisal effects reflect automatic access of a lower level of representation of the auditory input (e.g., wordforms) while the occurrence of cohort entropy effects is task sensitive, driven by a competition process or a higher-level representation that is engaged late (or not at all) during the processing of single words.

This paper uses data sampled at hourly and daily frequencies to predict Bitcoin returns. We consider various advanced non-linear models based on a multitude of popular technical indicators that represent market trend, momentum, volume, and sentiment. We run a robust empirical exercise to observe the impact of forecast horizon, model type, time period, and the choice of inputs (predictors) on the forecast performance of the competing models. We find that Bitcoin prices are weakly efficient at the hourly frequency. In contrast, technical analysis combined with non-linear forecasting models becomes statistically significantly dominant relative to the random walk model on a daily horizon. Our comparative analysis identifies the random forest model as the most accurate at predicting Bitcoin. The estimated measures of the relative importance of predictors reveal that the nature of investing in the Bitcoin market evolved from trend-following to excessive momentum and sentiment in the most recent time period.

This article investigates the predictability of dry bulk shipments' physical shipping costs while testing the predictive significance of derivative products. Accordingly, a comprehensive grid search procedure is needed to simulate combinations of model structures subject to a cross-validation process. In this regard, the intelligent model search engine (IMSE) is implemented as the search algorithm. The IMSE algorithm is selected due to its broad model space naturally consisting most of the traditional econometric model structures and advanced features, such as sample size optimization (also detects structural breaks), lag structure optimization (also reflects seasonality), among others. In contrast to the previous studies in the shipping market research, IMSE executes a relaxation of statistical significance criteria to target the out of sample predictive accuracy (instead of the goodness of fit). Sparsification is retained by utilizing a dropout procedure in line with the cross-validation process. The empirical results indicate the fact that predictive accuracy can be achieved or improved with a shorter sample period. More than half of the entire dataset has not been utilized in the final selections for most simulations. One-month and two-month maturity derivative contracts are tested for predictive features, while none of them is selected for the sparse model selections.

Philosophers of well-being have tended to adopt a foundationalist approach to the question of theory and measurement, according to which theories are conceptually prior to measures. By contrast, social scientists have tended to adopt operationalist commitments, according to which they develop and refine well-being measures independently of any philosophical foundation. Unfortunately, neither approach helps us overcome the problem of coordinating between how we characterize well-being and how we measure it. Instead, we should adopt a coherentist approach to well-being science.

In a novel version of the classic dot-pattern prototype-distortion paradigm of category learning, Homa et al. (2019) tested a condition in which individual training instances never repeated, and observed results that they claimed severely challenged exemplar models of classification and recognition. Among the results was a dissociation in which participants classified transfer items with high accuracy in the no-repeat condition, yet in old-new recognition tests showed no ability to discriminate between old and new items of the same level of distortion from the prototype. In addition, speed of classification learning was no faster in a condition in which a small set of training instances was repeated continuously compared with the no-repeat condition. Here we show through computer-simulation modeling that exemplar models naturally capture the classification-recognition dissociation in the no-repeat condition, as well as a wide variety of other qualitative effects reported by Homa et al. (2019). We also conduct new conceptual-replication experiments to investigate their reported null effect of repeated versus nonrepeated training instances on speed of classification learning. In contrast to Homa et al. (2019) we find that speed of learning is substantially faster in the repeat condition than in the no-repeat condition, precisely as exemplar models predict. The exemplar model also captures a wide variety of transfer effects observed following the completion of category learning, including the classification-recognition dissociation observed across the repeat and no-repeat conditions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Feedback is important to improve writing quality; however, to provide timely and personalized feedback is a time-intensive task. Currently, most literature focuses on providing (human or machine) support on product characteristics, especially after a draft is submitted. However, this does not assist students who struggle during the writing process. Therefore, in this study, we investigate the use of keystroke analysis to predict writing quality throughout the writing process. Keystroke data were analyzed from 126 English as a second language learners performing a timed academic summarization task. Writing quality was measured using participants’ final grade. Based on previous literature, 54 keystroke features were extracted. Correlational analyses were conducted to identify the relationship between keystroke features and writing quality. Next, machine learning models (regression and classification) were used to predict final grade and classify students who might need support at several points during the writing process. The results show that, in contrast to previous work, the relationship between writing quality and keystroke data was rather limited. None of the regression models outperformed the baseline, and the classification models were only slightly better than the majority class baseline (highest AUC = 0.57). In addition, the relationship between keystroke features and writing quality changed throughout the course of the writing process. To conclude, the relationship between keystroke data and writing quality might be less clear than previously posited.

This article proposes a novel fixed-time converging forward-backward-forward neurodynamic network (FXFNN) to deal with mixed variational inequalities (MVIs). A distinctive feature of the FXFNN is its fast and fixed-time convergence, in contrast to conventional forward-backward-forward neurodynamic network and projected neurodynamic network. It is shown that the solution of the proposed FXFNN exists uniquely and converges to the unique solution of the corresponding MVIs in fixed time under some mild conditions. It is also shown that the fixed-time convergence result obtained for the FXFNN is independent of initial conditions, unlike most of the existing asymptotical and exponential convergence results. Furthermore, the proposed FXFNN is applied in solving sparse recovery problems, variational inequalities, nonlinear complementarity problems, and min-max problems. Finally, numerical and experimental examples are presented to validate the effectiveness of the proposed neurodynamic network.

Conventional advice discourages controlling for postoutcome variables in regression analysis. By contrast, we show that controlling for commonly available postoutcome (i.e., future) values of the treatment variable can help detect, reduce, and even remove omitted variable bias (unobserved confounding). The premise is that the same unobserved confounder that affects treatment also affects the future value of the treatment. Future treatments thus proxy for the unmeasured confounder, and researchers can exploit these proxy measures productively. We establish several new results: Regarding a commonly assumed data-generating process involving future treatments, we (1) introduce a simple new approach and show that it strictly reduces bias, (2) elaborate on existing approaches and show that they can increase bias, (3) assess the relative merits of alternative approaches, and (4) analyze true state dependence and selection as key challenges. (5) Importantly, we also introduce a new nonparametric test that uses future treatments to detect hidden bias even when future-treatment estimation fails to reduce bias. We illustrate these results empirically with an analysis of the effect of parental income on children’s educational attainment.

Presurgical discrimination of 1p/19q codeletion status may have prognostic and diagnostic value for glioma patients for immediate personalized treatment. Artificial intelligence-based models have been proved as effective method to demonstrate computer aided diagnostic system for glioma cancer. An objective of study is to present an advanced biomedical texture descriptor to perform machine learning-assisted identification of 1p/19q codeletion status of low-grade glioma (LGG) cancer. An aim is to verify efficacy of textures, extracted using local binary pattern and derived from gray level co-occurrence matrix (GLCM). Proposed study used random forest-assisted radiomics model to analyse MRI images of 159 subjects. Four different advanced biomedical texture descriptors are proposed by experimenting different extensions of LBP method. These variants-(as variant I to IV) with 8-bit or 16-bit or 24-bit LBP codes are applied with different orientations in 5 × 5, 7 × 7 square-sized neighbourhood, which are recorded in LBP histograms. These histogram features are concatenated by GLCM-based textures including energy, correlation, contrast and homogeneity. Texture descriptors performed best with classification accuracy of 87.50% (AUC: 0.917, sensitivity: 95%, specificity: 75%, f1-score: 90.48%) using 8-bit LBP variant-I. 10-fold cross-validated accuracy of all four sets range from 65.62% to 87.50% using random forest classifier and mean-AUC range from 0.646 to 0.917.

Recently, the correlation filter (CF) and Siamese network have become the two most popular frameworks in object tracking. Existing CF trackers, however, are limited by feature learning and context usage, making them sensitive to boundary effects. In contrast, Siamese trackers can easily suffer from the interference of semantic distractors. To address the above problems, we propose an end-to-end target-insight correlation network (TICNet) for object tracking, which aims at breaking the above limitations on top of a unified network. TICNet is an asymmetric dual-branch network involving a target-background awareness model (TBAM), a spatial-channel attention network (SCAN), and a distractor-aware filter (DAF) for end-to-end learning. Specifically, TBAM aims to distinguish a target from the background in the pixel level, yielding a target likelihood map based on color statistics to mine distractors for DAF learning. SCAN consists of a basic convolutional network, a channel-attention network, and a spatial-attention network, aiming to generate attentive weights to enhance the representation learning of the tracker. Especially, we formulate a differentiable DAF and employ it as a learnable layer in the network, thus helping suppress distracting regions in the background. During testing, DAF, together with TBAM, yields a response map for the final target estimation. Extensive experiments on seven benchmarks demonstrate that TICNet outperforms the state-of-the-art methods while running at real-time speed.

In this study, we investigated developmental aspects of eye movements during reading of three languages (English, German, and Finnish) that vary widely in their orthographic complexity and predictability. Grapheme-phoneme correspondence rules are rather complex in English and German but relatively simple in Finnish. Despite their differences in complexity, the rules in German and Finnish are highly predictable, whereas English has many exceptions. Comparing eye movement development in these three languages allows us to investigate whether orthographic complexity and predictability have separate effects on eye movement development. Three groups of children, matched on years of reading instruction, along with a group of proficient adult readers in each language were tested. All participants read stimulus materials that were carefully translated and back-translated across all three languages. The length and frequency of 48 target words were manipulated experimentally within the stimulus set. For children, word length effects were stronger in Finnish and German than in English. In addition, in English effects of word frequency were weaker and only present for short words. Generally, English children showed a qualitatively different reading pattern, while German and Finnish children's reading behavior was rather similar. These results indicate that the predictability of an orthographic system is more important than its complexity for children's reading development. Adults' reading behavior, in contrast, was remarkably similar across languages. Our results demonstrate that eye movements are sensitive to language-specific features in children's reading, but become more homogenous as reading skill matures. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

In a market with indivisibilities, Roth and Postlewaite (1977) show that the (weak) core can suffer from instability problems, in the sense that groups of individuals might upset the equilibrium by recontracting among themselves. By contrast, the strong core is stable. Following the seminal contribution of Koray and Yildiz (2018), we re-examine the classical questions of implementation theory under complete information in a setting where coalitions are fundamental behavioral units, and the outcomes of their interactions are predicted by applying the solution concept of the strong core. The planner’s exercise includes designing a code of rights that specifies the collection of coalitions having the right to block one outcome by moving to another.

Most of the recent image segmentation methods have tried to achieve the utmost segmentation results using large-scale pixel-level annotated data sets. However, obtaining these pixel-level annotated training data is usually tedious and expensive. In this work, we address the task of semisupervised semantic segmentation, which reduces the need for large numbers of pixel-level annotated images. We propose a method for semisupervised semantic segmentation by improving the confidence of the predicted class probability map via two parts. First, we build an adversarial framework that regards the segmentation network as the generator and uses a fully convolutional network as the discriminator. The adversarial learning makes the prediction class probability closer to 1. Second, the information entropy of the predicted class probability map is computed to represent the unpredictability of the segmentation prediction. Then, we infer the label-error map of the segmentation prediction and minimize the uncertainty on misclassified regions for unlabeled images. In contrast to existing semisupervised and weakly supervised semantic segmentation methods, the proposed method results in more confident predictions by focusing on the misclassified regions, especially the boundary regions. Our experimental results on the PASCAL VOC 2012 and PASCAL-CONTEXT data sets show that the proposed method achieves competitive segmentation performance.

We introduce a natural temporal analogue of Eulerian circuits and prove that, in contrast with the static case, it is NP-hard to determine whether a given temporal graph is temporally Eulerian even if strong restrictions are placed on the structure of the underlying graph and each edge is active at only three times. However, we do obtain an FPT-algorithm with respect to a new parameter called interval-membership-width which restricts the times assigned to different edges; we believe that this parameter will be of independent interest for other temporal graph problems. Our techniques also allow us to resolve two open question of Akrida, Mertzios and Spirakis [CIAC 2019] concerning a related problem of exploring temporal stars. Furthermore, we introduce a vertex-variant of interval-membership-width (which can be arbitrarily larger than its edge-counterpart) and use it to obtain an FPT-time algorithm for a natural vertex-exploration problem that remains hard even when interval-membership-width is bounded., Extended abstract of this paper appeared in IWOCA 2021: Combinatorial Algorithms pp 107-121 (doi: https://doi.org/10.1007/978-3-030-79987-8_8 )

In established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effectiveness of shortcuts, there remain open questions on the mechanism and characteristics. For example, why are shortcuts powerful? Why do shortcuts generalize well? In this paper, we investigate the expressivity and generalizability of a novel sparse shortcut topology. First, we demonstrate that this topology can empower a one-neuron-wide deep network to approximate any univariate continuous function. Then, we present a novel width-bounded universal approximator in contrast to depth-bounded universal approximators and extend the approximation result to a family of equally competent networks. Furthermore, with generalization bound theory, we show that the proposed shortcut topology enjoys excellent generalizability. Finally, we corroborate our theoretical analyses by comparing the proposed topology with popular architectures, including ResNet and DenseNet, on well-known benchmarks and perform a saliency map analysis to interpret the proposed topology. Our work helps enhance the understanding of the role of shortcuts and suggests further opportunities to innovate neural architectures.

Prior research documents that asset growth is negatively associated with future firm performance. In contrast, we show that growth financed by product market stakeholders (i.e., “operating growth”) is positively associated with future firm performance. Investors and security analysts underestimate the positive effects of operating growth on future performance, resulting in return predictability and overly pessimistic earnings forecasts for firms with high operating growth. Future stock returns largely concentrate around subsequent earnings announcements with declining magnitudes, consistent with the error-in-expectation explanation. Results from cross-sectional tests further support the hypothesis that operating growth signals high future performance but investors underreact to it.

Most previous studies of language and gender have focused on English as well as women’s language. The present study focuses on context dependency and the multiple functions of Japanese men’s language, or “masculine Japanese.” It reports a case study that qualitatively analyzes four conversations between a Japanese male and a female speaker collected in a naturalistic setting. The findings suggest the specific nature of the mixed-gender pair is important in examining these aspects. In mixed pairs where the male speaker is in a superior position to the female speaker, his use of masculine Japanese may be limited in “direct talk” or when he is directly addressing his female interlocutor because of their relatively hierarchical interpersonal relationship. On the other hand, his use of masculine language may be more frequent in direct quotations used to reveal his inner thoughts or simulate male speakers’ speech from prior contexts during his storytelling. In each context, masculine Japanese seems to have different functions. In direct talk, it provides linguistic resources for constructing traditional masculinities, even if they are not necessarily used, for example, when his relative status is a more salient feature than his gender. By contrast, in direct quotation, masculine language may be used as an involvement strategy or to consolidate solidarity, thus constructing different dimensions of interpersonal relationships in the mixed pair, though it may also be used as an entertainment strategy. The paper also discusses the need for a more holistic approach by including interactional features in research on gender and Japanese language.

Previous studies on Chinese dialect variation have mostly focused on the description of dialects, the regions where these dialects are spoken, attitudes towards dialects, and acoustic differences across dialects. The present study draws on experimental evidence concerning a vowel difference in two Taiwan Southern Min (TSM) dialects to provide more understanding on how non-contrastive, dialectal variations may affect speakers’ processing of speech. The variation of interest is a phonemic difference, [ə] and [ɔ], in the vowel inventory in two TSM dialects, in which the difference signals a lexical contrast in one dialect (e.g. [ə-a] ‘oyster’ vs. [ɔ-a] ‘taro’) but not in the other ([ɔ-a] ‘oyster, taro’). A long-term repetition-priming experiment investigating the word recognition involving the two vowels revealed a dialect effect on TSM speakers’ word recognition in accordance with prior exposure, native-ness and variant frequency. Implications of the findings are provided.

In contrast with classic centralized risk sharing, a novel peer-to-peer risk sharing framework is proposed. The presented framework aims to devise a risk allocation mechanism that is structurally decentralized, Pareto optimal, and mathematically fair. An explicit form for the pool allocation ratio matrix is derived, and convex programming techniques are applied to determine the optimal pooling mechanism in a constrained variance reduction setting. A tiered hierarchical generalization is also constructed to improve computational efficiency. As an illustration, these techniques are applied to a flood risk pooling example. It is shown that peer-to-peer risk sharing techniques provide an economically viable alternative to traditional flood policies.

The paper seeks to demonstrate that, first, over the course of interaction in the radio phone-in events, participants display orientation to various aspects of their co-participants´ identities, second, since membership categories emerge and are developed at various sequentially relevant times, membership categorisation processes are closely tied with the event´s sequential organisation, and, third, categorisation bears on politeness aspects of interaction as the participation in the public ´arena´ causes participants´ faces to be constantly at stake. The methodological underpinnings of the paper represent the approaches of Membership Categorisation Analysis and the model of politeness based on the conceptualisation of face. The data are drawn from the corpus of Nočné dialógy (´Night Dialogues´) radio phone-ins broadcast on the Slovak public radio over the period of 1995-2004. The paper further attempts to demonstrate that participants are engaged in category work which sequentially unfolds in the course of the production of phone-in calls. Participants´ progressive involvement in talk is closely linked with the construction of ´layers´ of their categorial identities. The membership category of ´location´ represents the minimum agreed-upon canon of callers´ call-relevant identities. As the category is universally applicable, it bears the least face-threatening potential, for which reason it is used explicitely. In contrast, strategies of non-explicit categorisation, i.e. invoking categories through category-relevant predicates, apply to those topic-relevant categories which carry a significant face-threating ´load´ (e.g. ´family status´, ´political affiliation´, etc.). In summary, sequential organisation and category work are seen as being closely intertwined, with the latter also being employed as a positive and negative politeness strategy.

Quantitative counting of fry is required for multiple reasons, including breeding, transportation, and sales and creating a fish-counting algorithm using image processing technology is proposed as a useful method. The images for fish fry were preprocessed to extract the details of the target fish, using methods including binarization, dilation, and erosion. The thinning and the connected area algorithms were then independently adopted to count the fish fry within the image. The advantages and the disadvantages of the two algorithms under different densities were also compared, revealing that the fish fry pictures with a clear contrast between the foreground and background resulted in a higher accuracy. The main purpose of this research is to solve the problem of fry counting in the case of high density and high overlap. This research provides a reference point for this method based on image technology.

It is infeasible to test many different chemotherapy drugs on actual patients in large clinical trials, which motivates computational methods with the ability to learn and exploit associations between drug effectiveness and patient characteristics. This work proposes a machine learning approach to infer robust predictors of drug responses from patient genomic information. Rather than predicting the exact drug response on a given cell line, we introduce an elastic-net regression methodology to compare a drug-cell line pair against an alternative pair. Using predicted pairwise comparisons we rank the effectiveness of different drugs on the same cell line. A total of 173 cell lines and 100 drug responses were used in various settings for training and testing the proposed models. By comparing our approach against twelve baseline methods, we demonstrate that it outperforms the state-of-the-art methods in the literature. In contrast to most other methods, the algorithm is able to maintain its high performance even when we use a large number of drugs and few cell lines.

In this article, the adaptive finite-time consensus problem is discussed for uncertain nonlinear multiagent systems. In contrast with the correlative literature, the systems permit multiple uncertainties (not only in control coefficients but also in inherent nonlinearities), and typically the consensus protocol is pursued in a fully distributed fashion (independent from any global information of network topology). This essentially challenges the realization of the finite-time consensus. To overcome the challenge, a new continuous fully distributed protocol is proposed by combining adaptive techniques such that the finite-time consensus of the systems under investigation is achieved. Remarkably, a dynamic high gain, rather than multiple ones in the related literature, is adequate to resist two kinds of uncertainties and to guarantee the fully distributed fashion of the consensus protocol. Moreover, the adaptive finite-time consensus protocol is specified on the scenario of leader-following multiagent systems. Simulation results of three interaction topologies are acquired to illustrate the validity and the wider applicability of the proposed protocol.

Recently, deep learning models have been successfully employed in video-based affective computing applications. One key application is automatic depression recognition from facial expressions. State-of-the-art approaches to recognize depression typically explore spatial and temporal information individually, by using convolutional neural networks (CNNs) to analyze appearance information, and then by either mapping feature variations or averaging the depression level over video frames. This approach has limitations to represent dynamic information that can help to discriminate between depression levels. In contrast, 3D CNN-based models can directly encode the spatio-temporal relationships, although these models rely on fixed-range temporal information and single receptive field. This approach limits the ability to capture facial expression variations with diverse ranges, and the exploitation of diverse facial areas. In this paper, a novel 3D CNN architecture the Multiscale Spatiotemporal Network (MSN) is introduced to effectively represent facial information related to depressive behaviours. The basic structure of the model is composed of parallel convolutional layers with different temporal depths and sizes of receptive field, which allows the MSN to explore a wide range of spatio-temporal variations in facial expressions. Experimental results on two benchmark datasets show that our MSN is effective, outperforming state-of-the-art methods in automatic depression recognition.

Judgments are the basis for almost all decisions. They often come from different models and multiple experts. This information is typically aggregated using simple averages, which leads to the well-known shared information problem. A weighted average of the individual judgments based on empirically estimated sophisticated weights is commonly discarded in practice, because the sophisticated weights have large estimation errors. In this paper, I explore mixture weights, which are convex combinations of sophisticated and naive weights. I show analytically that if the data generation process is stable, there always exists a mixture weight which aggregates judgments better than the naive weights. I thus offer a path to alleviate the shared information problem. In contrast to other proposed solutions, it does not require any control over the judgment process. I demonstrate the utility of mixture weights in numerical analyses and in two empirical applications. I also offer heuristic selection algorithms for the correct mixture weight and analyze them in my numerical and empirical settings.

Health indicator (HI) affects the accuracy and reliability of the remaining useful life (RUL) prediction model. The hidden variables of variational autoencoder (VAE) can represent the HI values for a life-cycle dataset with obvious degradation trend. However, for an irregular dataset of a rotary machine, it is still a great challenge to construct the HI that can effectively represent the machinery degradation tendency. Therefore, this work proposes a novel degradation-trend-constrained VAE (DTC-VAE) to construct the HI vector with the distinct degradation trend. Firstly, the multi-dimensional time-domain and frequency-domain characteristics are calculated via the collected vibration samples. Secondly, a new degradation-constraint loss term is proposed and introduced into VAE for constructing DTC-VAE. Thirdly, with the multi-dimensional features and DTC-VAE, various HIs can be generated without supervision. The proposed method is applied to construct the HI vectors of bearing life-cycle datasets and gear fatigue datasets, and then macroscopic-microscopic-attention-based LSTM (MMALSTM) is used to predict the corresponding RULs with the constructed HIs. Via several contrast experiments, the results prove that the proposed unsupervised HI construction approach is superior to other typical methods, and the obtained HI vectors are more suitable for the RUL prediction.

This research investigates the vocational school students’ needs on the implementation of English teaching and learning that is focused on the target needs and learning needs. By adapting Hutchinson and Waters’ model (1987), questionnaire consisting of parts A and B was administered to 32 students. Part A deals with the target needs centred on the learner’s necessities, lacks and wants that are effectively functioned in the target situation. In contrast, part B concerns about learning needs that encompass learning inputs, procedures, teacher’s and learners’ role, and setting. Additionally, interviews were also conducted with the students and an English teacher to gather data in order to examine in-depth results of the practices of English teaching and to uncover possible areas of students’ language difficulties in learning English. The results suggested that the students have different perceptions of their necessities, wants, and lacks. The findings also demonstrated the students’ learning needs including the learning inputs, teaching-learning procedures, the teacher’s and learners’ role, as well as the teachinglearning settings.

We examine how online customer reviews for one generation of a product affect sales of another generation in the same product series. The main intriguing result is that previous generation valence has a positive impact on current generation sales; however, current generation valence has a negative impact on previous generation sales. The positive impact of previous generation valence becomes even stronger (1) as the uncertainty (standard deviation) in reviews for the current generation increases and (2) when the current generation valence is high. In contrast, it becomes weaker (1) as the uncertainty in reviews for the previous generation increases and (2) when the current generation has been on the market for a longer period of time. Other results are discussed. Our data consist of intergenerational pairs of point-and-shoot cameras on the largest online seller of such devices, Amazon.com. We estimate the current and previous generation models jointly, allowing for errors to be clustered at the daily and product levels. In addition, we address endogeneity concerns over the online word of mouth measures by using instrumental variables. This paper was accepted by Juanjuan Zhang, marketing.

Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin’s theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works.

Adults tend to make biased inferences when they are given base-rates (i.e., prior probabilities) that conflict with individuating information (i.e., a personality description), relying heavily on individuating information. Recent work has shown that six-year-olds do the same, whereas four-year-olds rely more on prior probabilities. In the present article, we revisit the argument that producing responses that align closely with base-rates should necessarily be seen as normative. We instead posit that rational inferences should be sensitive to all relevant information and should depend on its strength. In three experiments, we explored four-year-olds', six-year-olds' (N = 200), and adults' (N = 196) information use by manipulating the strength of individuating and base-rate information. Across base-rate manipulations, adults showed a bias for individuating information regardless of its strength. In contrast, six-year-olds appeared to use each type of information flexibly, depending on which was more informative. Four-year-olds' performance was less clear: Although they relied on base-rates when they were informative, they struggled to use the individuating information in their inferences and did not appreciate the manipulation of the strength of individuating information. Thus, six-year-olds appear to more flexibly use multiple sources of information than both younger children and adults, suggesting a period in development where children are able to weigh information before they are too biased toward individuating information. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

When it originated in the late 19th century, psychometrics was a field with both a scientific and a social mission: Psychometrics provided new methods for research into individual differences and at the same time considered these methods a means of creating a new social order. In contrast, contemporary psychometrics—because of its highly technical nature and its limited involvement in substantive psychological research—has created the impression of being a value-free discipline. In this article, we develop a contrasting characterization of contemporary psychometrics as a value-laden discipline. We expose four such values: that individual differences are quantitative (rather than qualitative), that measurement should be objective in a specific sense, that test items should be fair, and that the utility of a model is more important than its truth. Our goal is not to criticize psychometrics for supporting these values but rather to bring them into the open and to show that they are not inevitable and are in need of systematic evaluation.

This essay argues that as a consequence of the projectified society there is a need to consider the individual as inherently different from what rational and administrative decision-making ontologies suggest. ’Homo Projecticus’ is introduced as a new set of ontological assumptions, and the aim of this essay is to outline its characteristics and discuss its implications. In contrast to previous assumptions, we argue that the projectified society produces action seeking individuals who are guided by the notion that life is organized within multiple temporal contexts that both follow each other and exist in parallel. Thus, a key concern is how to create boundaries, or limits, in ways that enable action. Two key mechanisms are used: ‘time bracketing’ to define time limits, and ‘scope bracketing’ to define issues or tasks. Consequently, with an aim of completing tasks through appropriate actions, the rationality guiding decision-making and actions is ‘bounded by brackets’, which means that, in contrast to previous ontologies, the limits for rationality are created by the decision-makers themselves. Through bracketing, sensible segments are created, which enables action to be reached.

Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncertain nature of financial time-series data. In contrast, online learning mode evolving-structured ML models could be promising for financial time-series forecasting. For real-time deployment of such models, low memory demand is a must. Besides, the model's explainability plays a crucial role in forecasting financial time-series. Considering all the requirements, a rule-based autonomous neuro-fuzzy learning algorithm called the parsimonious learning machine (PALM) is proposed here to forecast time-varying stock indices. To provide efficient automation of the proposed algorithm by maintaining the model explainability in terms of limited number linguistic IF-THEN rules, two popular multiobjective evolutionary algorithms (MEAs), such as a real-coded genetic algorithm (GA) and a self-adaptive differential evolution (DE) algorithm are utilized here. In addition, fuzzy type-2 variants of PALMs' are considered here due to better uncertainty handling capacity than their type-1 counterparts. To evaluate the proposed algorithm's performance, the closing stock price of fifteen (15) different stock market indices are predicted here. From the results, it is observed that the MEA-based PALMs are performing better than the state-of-the-art benchmark online ML models and providing a rule-based explainable model to the end-user.

Land surface temperature (LST) is a significant factor in surface energy balance and global climatology studies. Land cover (LC) and elevation are two factors that affect the change of LST, and their effects depend on different geography. This study aims to demonstrate an alternative approach to examine the change of LST during 20 years (2001 to 2020) on Taiwan Island and to investigate the effect of LC change and elevation on a decadal trend of LST using a linear model that adjusting for each determinate factor. MODIS LST and LC data, as well as GMTED2010 elevation product, were downloaded available website. The natural cubic spline function was used to model annual seasonal patterns in LST. Linear regression model was used to estimate decadal change of long-term LST time series. Weighted sum contrasts linear regression was used to assess the effect of LC transformation and elevation on the decadal LST change by comparing adjusting mean of all factors. The adopted analysis method was an appropriate approach to assess categorical factors than those based on treatment contrasts, requiring specifying a control group to compare means and confidence intervals. Results showed that there was an increase in LST for most of the island. The average daytime and nighttime LST trends were 0.12 and 0.31°C/decade, respectively. However, areas in the southern part of the north-south direction mountain range show a statistically significant increase in LST in both daytime and nighttime. The major landslides caused this noticeable change of surface temperature due to the catastrophic damage of typhoon Morakot in 2009. The results also revealed that the different pattern of LC change has a significant effect on daytime LST, but not on nighttime LST trends. The elevation above 600 m had affected both daytime and nighttime LSTs.

Grammars can serve as producers for structured test inputs that are syntactically correct by construction. A probabilistic grammar assigns probabilities to individual productions, thus controlling the distribution of input elements. Using the grammars as input parsers, we show how to learn input distributions from input samples, allowing to create inputs that are similar to the sample; by inverting the probabilities, we can create inputs that are dissimilar to the sample. This allows for three test generation strategies: 1) Common inputs by learning from common inputs, we can create inputs that are similar to the sample; this is useful for regression testing. 2) Uncommon inputs learning from common inputs and inverting probabilities yields inputs that are strongly dissimilar to the sample; this is useful for completing a test suite with inputs from hell that test uncommon features, yet are syntactically valid. 3) Failure-inducing inputs learning from inputs that caused failures in the past gives us inputs that share similar features and thus also have a high chance of triggering bugs; this is useful for testing the completeness of fixes. Our evaluation on three common input formats (JSON, JavaScript, CSS) shows the effectiveness of these approaches. Results show that common inputs reproduced 96% of the methods induced by the samples. In contrast, for almost all subjects (95%), the uncommon inputs covered significantly different methods from the samples. Learning from failure-inducing samples reproduced all exceptions (100%) triggered by the failure-inducing samples and discovered new exceptions not found in any of the samples learned from.

Depression is a serious psychiatric disorder that restricts an individual's ability to work properly in both their daily and professional lives. Usually, the diagnosis of depression often needs a thorough assessment by an expert professional. Recently, significant consideration has been given to automatic depression prediction for more reliable and efficient depression investigation. In this paper, we propose a novel framework to estimate the depression level from video data by employing a two-stream deep spatiotemporal network. Our approach extracts spatial information using the Inception-ResNet-v2 network. In contrast, we introduce a volume local directional number (VLDN) based dynamic feature descriptor to capture facial motions. Then, the feature map obtained from the VLDN is fed into a convolutional neural network (CNN) to obtain more discriminative features. Additionally, we designed a multilayer bidirectional long short-term memory (Bi-LSTM) model to obtain temporal information by integrating the temporal median pooling (TMP) approach into the model. The TMP approach is employed on the temporal fragments of spatial and temporal features. Finally, extensive experimental analysis of two challenging datasets, AVEC2013 and AVEC2014, demonstrates that the proposed approach shows promising performance compared to the existing approaches for depression level prediction.

Purpose The Irritable Bowel Syndrome Quality of Life (IBS-QoL) questionnaire is a commonly used and validated IBS-specific QoL instrument. However, this questionnaire is in contrast to the EQ-5D-5L, not preference-based and as such does not allow calculation of QALYs. The objective of this study was to describe the convergent- and known-group validity of both questionnaires and to develop a mapping algorithm from EQ-5D-5L which enable IBS-QoL scores to be transformed into utility scores for use in economic evaluations. Methods We used data from two multicenter randomized clinical trials, which represented the estimation and external validation dataset. The convergent validity was investigated by examining correlations between the EQ-5D-5L and IBS-QoL and the known-group validity by calculating effect sizes. Ordinary least squares (OLS), censored least absolute deviations (CLAD), and mixture models were used in this mapping approach. Results 283 IBS patients were included (n = 189 vs. n = 84). Mean IBS-QoL score was 71.13 (SD 15.66) and mean EQ-5D-5L utility score was 0.73 (SD 0.19). The overall sensitivity of the IBS-QoL and EQ-5D-5L to discriminate between patient and disease characteristics was similar. CLAD model 4, containing the total IBS-QoL score and squared IBS-SSS (IBS severity scoring system), was chosen as the most appropriate model to transform IBS-QoL scores into EQ-5D-5L utility scores. Conclusion This study reports the development of an algorithm where the condition-specific questionnaire IBS-QoL can be used to calculate utility values for use in economic evaluations. Including a clinical measure, IBS-SSS, in the model improved the performance of the algorithm.

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools. To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (< 6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal FWERs. Those rejected clusters were outlying values in the distribution of cluster size but cannot be distinguished from true positive findings without further analyses, including assessing whether fMRI signal in those regions correlates with other clinical, behavioral, or cognitive measures. Rejecting the large clusters, however, significantly reduced the statistical power of nonparametric methods in detecting true findings compared with parametric methods, which would have detected most true findings that are essential for making valid biological inferences in MRI data. Parametric analyses, in contrast, detected most true findings while generating relatively few false positives: on average, less than one of those very large clusters would be deemed a true finding in each brain-wide analysis. We therefore recommend the continued use of parametric methods that model nonstationary smoothness for cluster-level, familywise control of false positives, particularly when using a cluster defining threshold of 2.5 or higher, and subsequently assessing rigorously the biological plausibility of the findings, even for large clusters. Finally, because nonparametric methods yielded a large reduction in statistical power to detect true positive findings, we conclude that the modest reduction in false positive findings that nonparametric analyses afford does not warrant a re-analysis of previously published fMRI studies using nonparametric techniques.

In object counting, objects often exhibit different sizes at different scales, even if they have similar physical sizes in reality. This is particularly true when targeting crowd counting and vehicle counting in intelligent transportation. Failing to model such variations leads to the mismatch between the object size and image scale. To address this problem, existing methods often extract multi-scale features, but they either still generate the single-scale prediction or lack an explicit suppression mechanism to eliminate predictions engendered by inappropriate scales. Our scale analysis manifests that, the single-scale estimation only works well for objects of certain sizes, and a suppression operator is required to isolate the estimation of a specific scale. In this work, we propose a scale-aware counting network termed NSSNet. NSSNet has two key features: it not only i) generates multi-scale predictions but also ii) applies a novel non-scale suppression (NSS) operator to suppress scale-mismatched estimations. NSS is inspired by the widely-used non-maximum suppression (NMS). In contrast to NMS that only reserves the maximum response, NSS filters out those clearly wrong predictions (the remaining predictions may still be from multiple scales). We evaluate NSSNet on four standard crowd and vehicle counting benchmarks and report state-of-the-art performance. We also show the scale adaptability of NSSNet through a controlled multi-scale experiment. Code and pretrained models are available at https://git.io/nssnet.

Rationale and Objectives A critical performance metric for any quantitative imaging biomarker is its ability to reliably generate similar values on repeat testing. This is known as the repeatability of the biomarker, and it is used to determine the minimum detectable change needed in order to show that a change over time is real change and not just due to measurement error. Test-retest studies are the classic approach for estimating repeatability; however, these studies can be infeasible when the imaging is expensive, time-consuming, invasive, or requires contrast agents. The objective of this study was to develop and test a method for estimating repeatability without a test-retest study. Materials and Methods We present a statistical method for estimating repeatability and testing whether an imaging method meets a specified criterion for repeatability in the absence of a test-retest study. The new method is applicable for the particular situation where a reference standard is available. A Monte Carlo simulation study was conducted to evaluate the performance of the new method. Results The proposed estimator is unbiased, and hypothesis tests with the new estimator have nominal type I error rate and power similar to a test-retest study. We considered the situation where the reference standard provides the true value, as well as when the reference standard itself has various magnitudes of measurement error. An example from CT imaging biomarkers of atherosclerosis illustrates the new method. Conclusion Precision of a QIB can be measured without a test-retest study in the situation where a reference standard is available.

We describe and contrast two distinct problem areas for statistical causality: studying the likely effects of an intervention (effects of causes) and studying whether there is a causal link between the observed exposure and outcome in an individual case (causes of effects). For each of these, we introduce and compare various formal frameworks that have been proposed for that purpose, including the decision-theoretic approach, structural equations, structural and stochastic causal models, and potential outcomes. We argue that counterfactual concepts are unnecessary for studying effects of causes but are needed for analyzing causes of effects. They are, however, subject to a degree of arbitrariness, which can be reduced, though not in general eliminated, by taking account of additional structure in the problem.

The success of a new drug is assessed within a clinical trial using a primary endpoint, which is typically the true outcome of interest—for example, overall survival. However, regulators sometimes approve drugs using a surrogate outcome—an intermediate indicator that is faster or easier to measure than the true outcome of interest—for example, progression-free survival—as the primary endpoint when there is demonstrable medical need. Although using a surrogate outcome (instead of the true outcome) as the primary endpoint can substantially speed up clinical trials and lower costs, it can also result in poor drug-approval decisions because the surrogate is not a perfect predictor of the true outcome. In this paper, we propose combining data from both surrogate and true outcomes to improve decision making within a late-phase clinical trial. In contrast to broadly used clinical trial designs that rely on a single primary endpoint, we propose a Bayesian adaptive clinical trial design that simultaneously leverages both observed outcomes to inform trial decisions. We perform comparative statics on the relative benefit of our approach, illustrating the types of diseases and surrogates for which our proposed design is particularly advantageous. Finally, we illustrate our proposed design on metastatic breast cancer. We use a large-scale clinical trial database to construct a Bayesian prior and simulate our design on a subset of clinical trials. We estimate that our design would yield a 16% decrease in trial costs relative to existing clinical trial designs, while maintaining the same Type I/II error rates. This paper was accepted by J. George Shanthikumar, Management Science Special Section on Data-Driven Prescriptive Analytics.

Consumers frequently compare themselves with others and find themselves to be inferior or superior to the comparison targets. This article examines the effect of social comparisons on the relative focus of self-efficacy and relationship needs and the subsequent impact on consumers’ preference for competence- versus warmth-oriented products. Across six studies, we show that upward comparisons (i.e., comparisons with superior targets) result in feelings of self-threat, which heighten the need for self-efficacy and increase the preference for products that convey competence. In contrast, downward comparisons (i.e., comparisons with inferior targets) elicit feelings of social distress, which heighten the need for social relationships and increase the preference for products that convey warmth. These effects are mitigated when the comparison is made in a social skill-related domain and when the comparison target is an out-group member.

We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features. This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts. We present extensive experiments with a focus on 3D neuroimaging, showing that this strategy enables robust and accurate registration of arbitrary MRI contrasts even if the target contrast is not seen by the networks during training. We demonstrate registration accuracy surpassing the state of the art both within and across contrasts, using a single model. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images. Our code is available at https://w3id.org/synthmorph., 16 pages, 15 figures, 3 tables, deformable image registration, data independence, deep learning, MRI-contrast invariance, anatomy agnosticism, final published version

Predicting the location of parking is a long-lasting problem that has ultimate importance in our daily life. In this paper, artificial neural networks are used to predict the parking location that will be helpful for drivers to settle on a reasonable area for stopping. This approach eventually adds to the familiarity and wellbeing of traffic on the roads which results in a decrease in turbulence. By using the approach of Deep Extreme Learning Machine (DELM), reliability can be achieved with a marginal error rate thus reducing the skeptical inclination. In this article, the Proposed Car Parking Space Prediction (CPSP) to elaborate on the dilemma of parking space for vehicles, we have used deep learning neural networks in contrast with feedforward and backward propagation. When the results were taken into consideration, it was unveiled that extreme deep machine learning neural network bears the highest accuracy rate with 60% of training (21431 samples), 40% of test and validation (14287 examples). It has been observed that the proposed DELM has the highest precision rate of 91.25%. Simulation results validate the prediction effectiveness of the proposed DELM strategy.

Robust scientific evidence shows that human performance predictions are more valid when information is combined mechanically (with a decision rule) rather than holistically (in the decision-maker's mind). Yet, information is often combined holistically in practice. One reason is that decision makers lack the knowledge of evidence-based decision making. In a performance prediction task, we tested whether watching an educational video on evidence-based decision making increased decision-makers' use of a decision rule and their prediction accuracy immediately after the manipulation and a month later. Furthermore, we manipulated whether participants earned incentives for accurate predictions. Existing research showed that incentives decrease decision-rule use and prediction accuracy. We hypothesized that this is the case for decision makers who did not receive educational information about evidence-based decision making, but that incentives increase decision-rule use and prediction accuracy for participants who received educational information. Our results showed that educational information increased decision-rule use. This resulted in increased prediction accuracy, but only immediately after receiving the educational information. In contrast to the existing literature, incentives slightly increased decision-rule use. We did not find evidence that this effect was larger for educated participants. Providing decision makers with educational information may be effective to increase decision-rule use in practice. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

In this paper, a novel projection neurodynamic network with fixed-time convergence is proposed for solving absolute value equations. In contrast to most existing projection neurodynamic networks, a conservative convergence time of the proposed projection neurodynamic network is presented. It is shown that the solution of the proposed approach converges to the solution of the corresponding absolute value equation in fixed-time under some mild conditions. Finally, a numerical example is presented to validate the main results.