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The Multimodal Named Entity Recognition (MNER) task enhances the text representations and improves the accuracy and robustness of named entity recognition by leveraging visual information from images. However, previous methods have two limitations: (i) the semantic mismatch between text and image modalities makes it challenging to establish accurate internal connections between words and visual representations. Besides, the limited number of characters in social media posts leads to semantic and contextual ambiguity, further exacerbating the semantic mismatch between modalities. (ii) Existing methods employ cross-modal attention mechanisms to facilitate interaction and fusion between different modalities, overlooking fine-grained correspondences between semantic units of text and images. To alleviate these issues, we propose a graph neural network approach for MNER (GNN-MNER), which promotes fine-grained alignment and interaction between semantic units of different modalities. Specifically, to mitigate the issue of semantic mismatch between modalities, we construct corresponding graph structures for text and images, and leverage graph convolutional networks to augment text and visual representations. For the second issue, we propose a multimodal interaction graph to explicitly represent the fine-grained semantic correspondences between text and visual objects. Based on this graph, we implement deep-level feature fusion between modalities utilizing graph attention networks. Compared with existing methods, our approach is the first to extend graph deep learning throughout the MNER task. Extensive experiments on the Twitter multimodal datasets validate the effectiveness of our GNN-MNER. [ABSTRACT FROM AUTHOR]

Most existing cross-domain recommendation (CDR) systems apply the embedding and mapping idea to tackle the cold-start user problem and, to this end, they learn a common bridge function to transfer the user preferences from the source domain into the target domain. However, sharing a bridge function for all users inevitably leads to biased recommendations. This paper proposes a novel method, named CDR to cold-start users via categorized preference transfer (CDRCPT), to overcome the shortcomings of existing approaches. First, the embeddings of users and items in both the source and target domain are learned through pretraining and we utilize preference encoder to obtain the preference embeddings of users in the source domain. Second, mini-batch clustering is applied in the source domain to group users according to their preferences; here, each cluster identifies a specific class of users, and each cluster is represented by its center. Finally, the general representation is fed into a meta network to learn a bridge function for each available class of users. Experiments on two real data sets show that our CDRCPT method is effective in improving the accuracy and robustness of recommendations. [ABSTRACT FROM AUTHOR]

Random forest is a powerful ensemble learning technique celebrated for its heightened predictive performance and robustness in handling complex datasets; nevertheless, it is criticized for its computational expense, particularly with a large number of trees in the ensemble. Moreover, the model's interpretability diminishes as the ensemble's complexity increases, presenting challenges in understanding the decision-making process. Although various pruning techniques have been proposed by researchers to tackle these issues, achieving a consensus on the optimal strategy across diverse datasets remains elusive. In response to these challenges, this paper introduces an innovative machine learning algorithm that integrates random forest with Naïve Bayes to predict student performance. The proposed method employs the Naïve Bayes formula to evaluate random forest branches, classifying data by prioritizing branches based on importance and assigning each example to a single branch for classification. The algorithm is utilized on two sets of student data and is evaluated against seven alternative machine-learning algorithms. The results confirm its strong performance, characterized by a minimal number of branches. [ABSTRACT FROM AUTHOR]

Closeness is one of the most studied characteristics of networks. Residual closeness is a very sensitive measure of graphs robustness. Additional closeness is a measure of growth potentials of networks. In this article, we calculate the closeness, vertex residual closeness, link residual closeness and additional closeness of lollipop graphs. [ABSTRACT FROM AUTHOR]

We investigate how culture affects gender differences in willingness to compete in a large pre-registered experiment using an epidemiological approach. Our sample of 1,943 Norwegians with parents born in 59 different countries shows a smaller gender gap in willingness to compete among individuals of more gender-equal ancestries. The difference is driven by women with parents from more gender-equal countries wanting to compete more and men with the same ancestry wanting to compete less. The results are robust to controlling for a large set of factors at the individual, parental and ancestral country levels, indicating that gendered culture shapes competitive preferences. [ABSTRACT FROM AUTHOR]

The temperature difference of fruit itself will affect its near infrared spectrum and the accuracy of its soluble solids content (SSC) prediction model. To eliminate the influence of apple temperature difference on the SSC model, a diffuse transmission dynamic online detection device was used to collect the spectral data of apples at different temperatures, and four methods were used to establish partial least squares correction models: global correction, orthogonal signal processing, generalized least squares weighting and external parameter orthogonal (EPO). The results show that the temperature has a strong influence on the diffuse transmission spectrum of apples. The 20 ºC model can get a satisfactory prediction result when the temperature is constant, and there will be great errors when detecting samples at other temperatures. The effect of temperature must be corrected to establish a more general model. These methods all improve the accuracy of the model, with the EPO method giving the best results; the prediction set correlation coefficient is 0.947, the root mean square error of prediction is 0.489%, and the prediction bias is 0.009%. The research results are of great significance to the practical application of SSC prediction of fruits in sorting workshops or orchards. [ABSTRACT FROM AUTHOR]

We investigate the relationship between investor attention and financial market anomalies. We find that anomaly returns tend to be higher following high-attention days. The result is robust after controlling for the effect of news and in a natural experiment setting in which a stock market regulation and rounding errors generate exogenous variations in attention. An analysis of order imbalances suggests that large traders trade on anomaly signals more aggressively upon observing higher attention. We discuss the extent to which the findings are driven by inattention-driven underreaction, bias amplification, or coordinated arbitrage mechanisms, thereby providing insight into the understanding of anomalies. [ABSTRACT FROM AUTHOR]

This paper proposes a new Multi-Objective Equilibrium Optimizer (MOEO) to handle complex optimization problems, including real-world engineering design optimization problems. The Equilibrium Optimizer (EO) is a recently reported physics-based metaheuristic algorithm, and it has been inspired by the models used to predict equilibrium state and dynamic state. A similar procedure is utilized in MOEO by combining models in a different target search space. The crowding distance mechanism is employed in the MOEO algorithm to balance exploitation and exploration phases as the search progresses. In addition, a non-dominated sorting strategy is also merged with the MOEO algorithm to preserve the population diversity and it has been considered as a crucial problem in multi-objective metaheuristic algorithms. An archive with an update function is used to uphold and improve the coverage of Pareto with optimal solutions. The performance of MOEO is validated for 33 contextual problems with 6 constrained, 12 unconstrained, and 15 practical constrained engineering design problems, including non-linear problems. The result obtained by the proposed MOEO algorithm is compared with other state-of-the-art multi-objective optimization algorithms. The quantitative and qualitative results indicate that the proposed MOEO provides more competitive outcomes than the different algorithms. From the results obtained for all 33 benchmark optimization problems, the efficiency, robustness, and exploration ability to solve multi-objective problems of the MOEO algorithm are well defined and clarified. The paper is further supported with extra online service and guideline at https://premkumarmanoharan.wixsite.com/mysite. [ABSTRACT FROM AUTHOR]

It is sometimes thought that regulation often creates opportunities for public officials to extract bribes. If this is true, deregulation offers a simple way of combating corruption. However, empirical evidence on the corruption and regulation nexus is limited. Further, the corruption indices used are based on experts' opinions, which may suffer from perception bias. The present paper attempts to address these shortcomings using firm-level survey data for 131 mostly developing countries on the actual experience of the firms with bribery and regulatory burden. The study examines the level of overall corruption and petty corruption. Exploiting variation in regulatory burden, both within-country and industry-level, a large positive effect of regulatory burden on corruption is found. For the baseline results, the bribery rate is higher by about 0.03 percentage points for each percentage point increase in the regulatory burden. The finding is robust to several controls and endogeneity checks. [ABSTRACT FROM AUTHOR]

Background: We evaluated deficit accumulation and how deficits affected cognition and physical activity among breast cancer survivors and non-cancer controls.Methods: Newly diagnosed nonmetastatic survivors (n = 353) and matched non-cancer controls (n = 355) ages 60-98 years without neurological impairments were assessed presystemic therapy (or at enrollment for controls) from August 2010 to December 2016 and followed for 36 months. Scores on a 42-item index were analyzed in growth-mixture models to determine deficit accumulation trajectories separately and combined for survivors and controls. Multilevel models tested associations between trajectory and cognition (FACT-Cog and neuropsychological tests) and physical activity (IPAQ-SF) for survivors and controls.Results: Deficit accumulation scores were in the robust range, but survivors had higher scores (95% confidence intervals [CI]) than controls at 36 months (0.18, 95% CI = 0.16 to 0.19, vs 0.16, 95% CI = 0.14 to 0.17; P = .001), and averages included diverse deficit trajectories. Survivors who were robust but became frailer (8.8%) had similar baseline characteristics to those remaining robust (76.2%) but experienced a 9.6-point decline self-reported cognition (decline of 9.6 vs 3.2 points; P = .04) and a 769 MET minutes per week decline in physical activity (P < .001). Survivors who started and remained prefrail (15.0%) had self-reported and objective cognitive problems. At baseline, frail controls (9.5%) differed from robust controls (83.7%) on deficits and self-reported cognition (P < .001). Within combined trajectories, frail survivors had more sleep disturbances than frail controls (48.6% [SD = 17.4%] vs 25.0% [SD = 8.2%]; P = .05).Conclusions: Most survivors and controls remained robust, and there were similar proportions on a frail trajectory. However, there were differences in deficit patterns between survivors and controls. Survivor deficit accumulation trajectory was associated with patient-reported outcomes. Additional research is needed to understand how breast cancer and its treatments affect deficit accumulation. [ABSTRACT FROM AUTHOR]

Aims Leaf nutrient resorption is sensitive to changes in soil nutrients. However, the effects of N deposition on nutrient resorption efficiency (NuRE) in plant macro-nutrients remain unclear. Poplar (Populus deltoids) is one of the most extensively cultivated hardwood species worldwide. We explored general patterns and dominant drivers of NuRE and stoichiometry of poplar plantations in response to N addition. Methods We conducted a 4-year N-addition experiment to explore NuRE and stoichiometric responses to N addition in two poplar (P. deltoids) plantations (8- and 12-year-old stands) in a coastal region of eastern China. We measured soil and foliar (green and senesced leaves) concentrations of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg) for a series of N addition treatments including N0 (0 kg N ha−1 yr−1), N1 (50 kg N ha−1 yr−1), N2 (100 kg N ha−1 yr−1), N3 (150 kg N ha−1 yr−1) and N4 (300 kg N ha−1 yr−1). Important Findings Consistent for (both) 8- and 12-year-old stands, N addition did not affect the NuRE and stoichiometry (with the exception of CaRE and CaRE:MgRE ratio). N resorption efficiency–P resorption efficiency (NRE–PRE) scaling slopes were consistently less than 1.0 under N addition. These results suggest that NRE generally decouples from PRE within each N treatment. Moreover, these results point to robust control of green leaf nutritional status on nutrient resorption processes as indicated by the positive relationships between NuRE and green leaf nutrient concentrations. Our findings provided a direct evidence that growth in 12-year-old poplar plantations was N-limited in the coastal region of eastern China. [ABSTRACT FROM AUTHOR]

The preliminary design stage ensures to evaluate machine tool performances according to the simulation of reduced models. Performance criteria are defined regarding the attempted machining process requirements. In our case, we study the problem of machine tool design for hard metal cutting, where a high level of stiffness is required. In this context, this paper's aim is to introduce a new methodology of machine tool architectures modeling, optimization, and selection with regards to their stiffness and dynamic performances at the preliminary design stage. However, this type of study requires a quantitative evaluation of performance indicators. Studied machine tool structures are modelled with simplified shape parts. The dimensions of these parts are defined as design variables. Afterward, for each considered architecture, parametric design optimization is performed to minimize its mass under the constraint of a minimal attempted stiffness all over the workspace. This approach allowed restricting the total number of machine tool architectures to be detailed further and analyzed more accurately. In a first time, the paper includes an illustration of the developed methodology through an example of machine tool architecture evaluation and optimization. In a second time, the method is used to compare different kinds of machine tool architectures regarding their ability to be light for an attempted stiffness. [ABSTRACT FROM AUTHOR]

Recent progress in Device-Free Wi-Fi Sensing (DFWS) has established the use of wireless signals like Wi-Fi not only to communicate but also as a tool to enable device-free sensing. As an emerging technique, DFWS has many capable applications such as sensing activity and gesture and fall detection, monitoring elderly, surveillance, and many more applications while waiving out the necessity to mount devices on the object. A wide range of applications can use the channel state information (CSI) from commercial Wi-Fi devices pervasively for ubiquitous sensing. Existing CSI tools, such as the Intel 5300 network interface controller tool or the Atheros 9390 tool, have limitations when deployed in large-scale systems due to their high deployment costs and limitations in the resolution of CSI measurements. Due to these shortcomings, DFWS applications need an alternative CSI tool in order to perform efficiently. In this paper, we present ESP32-based Wi-ESP as a CSI gathering tool that can report detailed CSI measurements based on 802.11n standards. The proposed Wi-ESP tool works as a complete device by collecting the CSI measurements as well as processing further for DFWS applications. Wi-ESP can work as standalone device, unlike other CSI tools, and can offer large-scale deployment to many DFWS applications. In this paper, we have explored the options of Wi-ESP as a tool for CSI measurements and processing and propose it as a tool for DFWS. [ABSTRACT FROM AUTHOR]

This paper describes an approach to extracting a predicate-argument structure (PAS) in building design rule sentences using natural language processing (NLP) and deep learning models. For the computer to reason about the compliance of building design, design rules represented by natural language must be converted into a computer-readable format. The rule interpretation and translation processes are challenging tasks because of the vagueness and ambiguity of natural language. Many studies have proposed approaches to address this problem, but most of these are dependent on manual tasks, which is the bottleneck to expanding the scope of design rule checking to design requirements from various documents. In this paper, we apply deep learning-based NLP techniques for translating design rule sentences into a computer-readable data structure. To apply deep learning-based NLP techniques to the rule interpretation process, we identified the semantic role elements of building design requirements and defined a PAS for design rule checking. Using a bidirectional long short-term memory model with a conditional random field layer, the computer can intelligently analyze constituents of building design rule sentences and automatically extract the logical elements. The proposed approach contributes to broadening the scope of building information modeling-enabled rule checking to any natural language-based design requirements. [ABSTRACT FROM AUTHOR]

One of the most actual and consistent drivers for the industry is sustainability, which includes three main pillars: environment, economics, and society. While numerous methods for environmental and economic sustainability assessment have been proposed, social sustainability assessment is still lacking in structured methods and tools, although human has always played a key role. Moreover, technological development is pushing the industrial world toward a new paradigm, the "Industry 4.0," which embeds topics such as data digitalization, cyber-physical systems, and machine learning. It entails significant changes in human resources management, without reducing their importance. Humans were part of the manufacturing system from the first industrial revolution, and no automation or digitalization can be possible without humans. The industry can no longer underestimate the reasonable application of human factors and ergonomics principles to the workplace. For this purpose, the paper provides a novel transdisciplinary engineering method to measure and promote social sustainability on production sites. It exploits Internet of Things technology to support the (re)design of manufacturing processes and plants toward human-centered connected factories. To improve the workers' well-being has positive effects on their health, satisfaction, and performance. The method has been implemented in a real industrial case study within the footwear industry. The sole finishing process has been analyzed from different perspectives to solve ergonomics-related problems and implement effective improvement strategies. [ABSTRACT FROM AUTHOR]

In this study, we propose an electroencephalogram (EEG)-based long short-term memory networks model for recognizing user preferences toward architectural design images. An EEG is an approach that records the electrical activity in the brain, and EEG-based affection recognition is a technique used for quantitatively recognizing human emotion by analysing the recorded signals. Decision-makers' subjective reactions toward architectural design alternatives may play a key role in the architectural planning and design stage. In this regard, the proposed model enables the quantitative recognition of their preferences and supports architects in the planning and design stages. The suggested model classifies the recorded data using a deep-learning technique. To build the model, an EEG recording experiment was conducted with 18 subjects, who were asked to select their most/least preferred images among eight images of small-housing design. Post recording, a positive and negative affect schedule questionnaire was distributed to the subjects to rate their affection. Google TensorFlow and Keras were used to structure the model. After training, precision, recall, and f1 score metrics were used to evaluate and validate the model. This model can help designers to evaluate design alternatives in terms of decision-making. Moreover, as this model uses biosignal data, which is universal to humans, architectural design processes for children, the elderly, etc., may be supported. Furthermore, a data-driven design database may be proposed in a future research for cross-validating with previous methods such as interviews and observations. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage procedure for density-based optimization towards a fail-safe design. Existing approaches either are computationally extremely expensive or do not explicitly consider fail-safe requirements in the optimization. The current approach trades off both aspects by employing two sequential optimizations to deliver redundant designs that offer robustness to partial failure. In the first stage, a common topology optimization or a topology optimization with local volume constraints is performed. The second stage is referred to as "density-based shape optimization" since it only alters the outline of the structure while still acting on a fixed voxel-type finite element mesh with pseudo-densities assigned to each element. The performance gain and computational efficiency of the current approach are demonstrated by application to various 2D and 3D examples. The results show that, in contrast to explicitly enforcing fail-safety in topology optimization, the current approach can be carried out with reasonable computational cost. Compared to the local volume constraint approach, the suggested procedure further increases the fail-safe performance by 47% for the example considered. [ABSTRACT FROM AUTHOR]

We study the distribution of public services by local politicians when political support spreads through social networks. We sketch a model showing that incumbents target goods and services to individuals who would lead to the largest aggregate loss of support if they stopped supporting the incumbent. Those individuals have high betweenness centrality. Using data on 3.6 million households from the Philippines, we show that households with high betweenness centrality receive a greater number of public services from their local government. This result is robust to the inclusion of controls for program eligibility, detailed measure of family wealth and elite status, family ties with politicians, and other measures of centrality. [ABSTRACT FROM AUTHOR]

Infrasound array data are commonly used to detect and characterize infrasonic signals from a variety of natural and anthropogenic sources. Here we examine the effectiveness of robust regression estimators (L1-norm regression, M-estimators and least trimmed squares) for infrasound array processing, and compare them against standard array processing algorithms (least-squares estimation, frequency–wavenumber analysis and progressive multi-channel correlation) using a combination of real and synthetic data. Of particular interest is how each algorithm performs when one of the array elements produces data outliers. Synthetic tests on elements containing a clock error, constant values or only pink noise are performed, and we analyse the relative ability of the estimators to recover plane wave parameters. The L1-norm regression, M-estimate, frequency–wavenumber analysis and least trimmed squares estimates provided superior results than conventional least-squares estimation. Evaluation of least trimmed squares weights consistently identified the element with the simulated error, providing additional information on array performance. Least trimmed squares processing consistently identified an element with reversed polarity for Alaska Volcano Observatory array ADKI. International Monitoring System stations IS57 and IS55 were likewise processed. Data from an element of IS57, which had lower cross-correlation values than the remaining elements, were consistently identified as having outliers in array processing. An element with a timing error was identified in the analysis of IS55 data. These results suggest robust regression methods, in particular least trimmed squares, improve upon standard methods and should be used more widely, as they can provide robust array processing results and insight into array performance. Further, robust regression methods are not limited to infrasound array processing applications, and it is likely that they would also be effective for seismic array data. [ABSTRACT FROM AUTHOR]

As the parameters of a map are varied an attractor may vary continuously in the Hausdorff metric. The purpose of this paper is to explore the continuation of chaotic attractors. We argue that this is not a helpful concept for smooth unimodal maps for which periodic windows fill parameter space densely, but that for many families of piecewise-smooth maps it provides a way to think about changing structures within parameter regions of robust chaos and form a stronger notion of robustness. We obtain conditions for the continuity of an attractor and demonstrate the results with coupled skew tent maps, the Lozi map and the border-collision normal form. [ABSTRACT FROM AUTHOR]

Cold stress profoundly affects plant growth and development and is a key factor affecting the geographic distribution and evolution of plants. Plants have evolved adaptive mechanisms to cope with cold stress. Here, through the genomic analysis of Arabidopsis, three Brassica species and 17 other representative species, we found that both cold-related genes (CRGs) and their collinearity were preferentially retained after polyploidization followed by genome instability, while genome-wide gene sets exhibited a variety of other expansion mechanisms. The cold-related regulatory network was increased in Brassicaceae genomes, which were recursively affected by polyploidization. By combining our findings regarding the selective retention of CRGs from this ecological genomics study with the available knowledge of cold-induced chromosome doubling, we hypothesize that cold stress may have contributed to the success of polyploid plants through both increasing polyploidization and selectively maintaining CRGs during evolution. This hypothesis requires further biological and ecological exploration to obtain solid supporting evidence, which will potentially contribute to understanding the generation of polyploids and to the field of ecological genomics. [ABSTRACT FROM AUTHOR]

Consider an economy with |$d$| stochastic factors that have an ambiguous variance–covariance matrix. An ambiguity- and risk-averse agent seeks to determine the optimal investment and consumption strategy that is robust to the uncertainty in the covariances. We formulate the robust decision rule as an expected utility maximization over the worst-case scenario with respect to all possible covariances. As this variance–covariance ambiguity leads to robust optimal decisions over a set of non-equivalent probability measures, the |$G$| -expectation framework is adopted to characterize the problem as a maximin optimization. Our problem formulation can be applied to finite and infinite horizon investment–consumption problems with or without a subsistence consumption constraint. We demonstrate our models using two examples including the defined contribution pension problem and lifetime optimal investment–consumption problems. [ABSTRACT FROM AUTHOR]

Neo-Schumpeterian streams of research emphasize the close relationship between changes in economic structure in favor of high-skill and high-tech branches and rapid economic growth. They identify the emergence of a new technological paradigm in the 1970s, strongly based on the application of information and communication technologies (ICTs), arguing that in such periods of transition and emergence of new techno-economic paradigms, the intermediate development countries and the countries which are not at the technological frontier have higher opportunities to catch-up. Although this debate is theoretically well documented, the empirics seem to lag behind the theory. In this article, we contribute to this literature by adding enlightening evidence on the issue. More precisely, we relate the growth experiences of countries which had relatively similar economic structures in the late 1970s, with changes occurring in these countries’ structures between 1979 and 2003. The results reveal a robust relationship between structure and (labor) productivity growth, and lend support to the view that producing (though not user) ICT-related industries are strategic branches of economic activity. [ABSTRACT FROM PUBLISHER]

Diverse theories of industry dynamics predict heterogeneity in production efficiency to be the driver of firms’ growth, survival, and industrial change, either through a direct link between efficiency and growth, or through an indirect effect via profitabilities, as more productive firms can enjoy higher profit margins which, under imperfect capital markets, allow them to invest and grow more. Does the empirical evidence bear out such predictions? This article explores the dynamics of selection and reallocation through an investigation of the relations linking productivity, profitability and growth at the firm level. Exploiting large panels of Italian and French industrial firms, we find that heterogeneity in efficiencies primarily yields persistent profitability differentials, whereas the relationships of corporate growth with either productivity or profitability appear much weaker, if at all existent. This suggests that selection forces are much less strong than usually assumed. The results robustly apply across different industrial sectors and across the two countries. [ABSTRACT FROM PUBLISHER]

We hypothesize that several characteristics of a country’s institutional environment encourage firms to agglomerate. To test our hypotheses, we examine the collocation pattern of 931 plant investments made by 266 firms across 29 countries in the global semiconductor industry from 1975 to 2004. The results show that the decision to agglomerate is strongly dependent on a country’s institutional context. Specifically, firms prefer to agglomerate when investing within countries with collectivist cultures, and those characterized by political and economic uncertainty. We assess the robustness of the results across multiple indicators of these institutional conditions. [ABSTRACT FROM PUBLISHER]

The bookbuilding IPO procedure has captured significant market share from auction alternatives recently, despite the significantly lower costs related to the auction mechanism. In France, where both mechanisms were used in the 1990s, the ostensible advantages of bookbuilding were advertising-related benefits. Book-built issues were more likely to be followed and positively recommended by lead underwriters. Even nonunderwriters' analysts promote book-built issues more in order to curry favor with the IPO underwriter for allocations of future deals. Yet we do not observe valuation or post-IPO return differentials that suggest these types of promotion have any value to the issuing firm. [ABSTRACT FROM AUTHOR]

The article shows that two measures of the amount of private information in stock price--price nonsynchronicity and probability of informed trading (PIN) have a strong positive effect on the sensitivity of corporate investment to stock price. Moreover, the effect is robust to the inclusion of controls for managerial information and for other information-related variables. The results suggest that firm managers learn from the private information in stock price about their own firms' fundamentals and incorporate this information in the corporate investment decisions. We relate our findings to an alternative explanation for the investment-to-price sensitivity, namely that it is generated by capital constraints, and show that both the learning channel and the alternative channel contribute to this sensitivity. [ABSTRACT FROM AUTHOR]

Background: Probe4Cronobacter test kit is based on the use of a fluorescence-labeled peptide nucleic acid probe (PNA) allied to fluorescence microscopy. A sample is taken after a 24 h enrichment of rehydrated 30 g portions of powdered infant formula (PIF). The method uses ready to use dropper solutions applied directly in the sample. This simple process takes less than 2 h to provide a result. In the presence of Cronobacter species, bright red rodshaped cells will be visible under a fluorescence microscope. Objective: Probe4Cronobacter validation as a new method for the detection Cronobacter species in Powdered Infant Formula (PIF) under the AOAC Performance Tested MethodsSM (No. 081702). Methods: The validation study encompassed matrix comparison study, inclusivity and exclusivity testing and robustness studies (stability, kit variation, and ruggedness). Results: The inclusivity and exclusivity testing (50 and 35 strains, respectively) yielded no false negative or false positive results. Probe4Cronobacter was compared to the ISO/TS 22964:2006 in 30 g of PIF samples within method comparison in an unpaired study. A total of 30 samples with both low and high level of inoculation were analyzed by Probe4Cronobacter and compared to the same number of samples screened by ISO/TS 22964:2006. No statistically significant differences between presumptive and confirmed results or between candidate and reference method results were observed. Robustness studies showed a high level of consistency and integrity of the kit when different parameters were varied. The deviation conditions tested did not affect the performance of the kit. Conclusions: Probe4Cronobacter test kit has shown to be a accurate, highly sensitive and robust methods for the detection of Cronobacter spp. in PIF samples. [ABSTRACT FROM AUTHOR]

Motivation Flow cytometry and mass cytometry are widely used to diagnose diseases and to predict clinical outcomes. When associating clinical features with cytometry data, traditional analysis methods require cell gating as an intermediate step, leading to information loss and susceptibility to batch effects. Here, we wish to explore an alternative approach that predicts clinical features from cytometry data without the cell-gating step. We also wish to test if such a gating-free approach increases the accuracy and robustness of the prediction. Results We propose a novel strategy (CytoDx) to predict clinical outcomes using cytometry data without cell gating. Applying CytoDx on real-world datasets allow us to predict multiple types of clinical features. In particular, CytoDx is able to predict the response to influenza vaccine using highly heterogeneous datasets, demonstrating that it is not only accurate but also robust to batch effects and cytometry platforms. Availability and implementation CytoDx is available as an R package on Bioconductor (bioconductor.org/packages/CytoDx). Data and scripts for reproducing the results are available on bitbucket.org/zichenghu_ucsf/cytodx_study_code/downloads. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: We propose semi‐parametric methods to model cohort data where repeated outcomes may be missing due to death and non‐ignorable dropout. Our focus is to obtain inference about the cohort composed of those who are still alive at any time point (partly conditional inference). We propose: i) an inverse probability weighted method that upweights observed subjects to represent subjects who are still alive but are not observed; ii) an outcome regression method that replaces missing outcomes of subjects who are alive with their conditional mean outcomes given past observed data; and iii) an augmented inverse probability method that combines the previous two methods and is double robust against model misspecification. These methods are described for both monotone and non‐monotone missing data patterns, and are applied to a cohort of elderly adults from the Health and Retirement Study. Sensitivity analysis to departures from the assumption that missingness at some visit t is independent of the outcome at visit t given past observed data and time of death is used in the data application. [ABSTRACT FROM AUTHOR]

Robust policy design is an activity that is ‘done’ by policymakers; in this paper we look further into how exactly robust policy design is done. We use the literature on strategizing to distinguish various theoretical perspectives on how policy design is done in practice. Moreover, we conducted a survey-feedback study with a group of senior level policymakers to see what they do when ‘doing’ robust policy design, how they think it should be done, and what they think are challenges for doing robust policy design in the context of a public organization. The study shows that although the language of strategy and policy design is rooted in the rational-analytical ‘planning’ approach, other perspectives are in fact more suitable for designing robust policies for deeply uncertain and volatile conditions. Moreover, our study shows that practitioners are well-aware of this bias; they have developed practical ways to combine the rational-analytical approach to design that is expected from them with other approaches for doing design. [ABSTRACT FROM AUTHOR]

Policy often has to perform amidst uncertain and rapidly changing conditions. Robustness is the ability to perform and achieve intended effects under unknown conditions. Robust design can benefit from the capacity to anticipate developments early. A system of early warning is a method to anticipate change early and help policy design to be more robust. Early warning can enable moment of ‘consecration’, moments where reflection on assumptions and biased of policy design can be challenged and changed; this can be an important addition to the robustness of policy design. However, early warning is not unproblematic; it is an organizational practice that inherently invokes issues of habituation, organizational bias, as it challenges the status quo. In this paper, we study how policy makers who ‘do’ early warning in the context of robust policy engage with these issues and how they managed to improve the robustness of policy design. We do so by studying an empirical case of an early warning system in The Netherlands. We analyze how the system was build, how it worked, how it was further professionalized, and what the system did to the capacity of the organization to design and deliver robust policy. [ABSTRACT FROM AUTHOR]

Faced with growing policy complexity and environmental uncertainty, policymakers are increasingly concerned with ensuring that policy processes retain functionality amidst shock and uncertainty. In this paper, we seek to address the ways in which robustness - or the capability of policies to maintain functionality and effectiveness in policy goal attainment - can be designed into policies, institutions or systems. We suggest that robust policy designs can be characterized by diversity, modularity and redundancy, whereas robust policy design processes require the presence of polycentric decisional process, political capacity and technical capacity. In identifying these design elements of policy robustness, we argue that robustness is a property that can be designed to ensure that policies continue to deliver, over time, its intended functions, purposes and objectives, even under negative circumstances. [ABSTRACT FROM AUTHOR]

How best to deal with uncertainty and surprise in policy-making is an issue which has troubled policy studies for some time. Studies of policy uncertainty and policy failure have emphasized the need to create policies able to be improvised upon in the face of an uncertain future, meaning there is a need to design and adopt policies featuring agility, and flexibility in their components and processes. Such policies require redundant resources and capabilities and this need is in strong opposition to ideas about design which equate better designs with efficiency, implying the allocation of only the minimum amount of resources possible, and which also often emphasize routinization and the replication of standard operating procedures and programme elements in order to ensure consistency in programme delivery. While these latter designs may be appropriate in stable circumstances or where competition can provide a degree of system-level resilience, this is not true for many public sector activities where government is the sole provider of particular goods and where services and future scenarios are unknown, contested or unpredictable. As studies of crisis management and other similar situations have emphasized, in these instances robustness is needed and can be planned for. This article examines the concepts of surprise, agility and improvisation and their linkages to robustness in order to both clarify terminology and outline the organizational and managerial features of policies and policy-making which prevent, and facilitate, flexible adaptation in both policy content and processes. [ABSTRACT FROM AUTHOR]

While several studies have examined how the specific wording (i.e., semantic cue) used to communicate a price offer affects consumers' perceptions of value, this area of research has not produced a set of consistent findings. To resolve the apparent inconsistencies, the current article builds on past research and explains why a consumer's response to a semantic cue depends on the situation (or decision context) and the discount size. The results of two studies are reported. The first experiment provides evidence that the relative effectiveness of two widely used types of semantic cues depends on both consumers' decision context and the level of processing evoked by the discount size. The second experiment replicates the semantic cue by situation interaction and demonstrates the robustness of this effect across store familiarity. [ABSTRACT FROM AUTHOR]

To quantify polygenic effects, i.e. undetected genetic effects, in large-scale association studies, we propose a generalized estimating equation (GEE) based estimation framework. We develop a marginal model for single-variant association test statistics of complex diseases that generalizes existing approaches such as LD Score regression and that is applicable to population-based designs, to family-based designs or to arbitrary combinations of both. We extend the standard GEE approach so that the parameters of the proposed marginal model can be estimated based on working-correlation/linkage-disequilibrium (LD) matrices from external reference panels. Our method achieves substantial efficiency gains over standard approaches, while it is robust against misspecification of the LD structure, i.e. the LD structure of the reference panel can differ substantially from the true LD structure in the study population. In simulation studies and in applications to population-based and family-based studies, we illustrate the features of the proposed GEE framework. Our results suggest that our approach can be up to 100% more efficient than existing methodology. [ABSTRACT FROM AUTHOR]

The merging of two divergent genomes in a hybrid is believed to trigger a "genomic shock", disrupting gene regulation and transposable element (TE) silencing. Here, we tested this expectation by comparing the pattern of expression of transposable elements in their native and hybrid genomic context. For this, we sequenced the transcriptome of the Arabidopsis thaliana genotype Col-0, the A. lyrata genotype MN47 and their F1 hybrid. Contrary to expectations, we observe that the level of TE expression in the hybrid is strongly correlated to levels in the parental species. We detect that at most 1.1% of expressed transposable elements belonging to two specific subfamilies change their expression level upon hybridization. Most of these changes, however, are of small magnitude. We observe that the few hybrid-specific modifications in TE expression are more likely to occur when TE insertions are close to genes. In addition, changes in epigenetic histone marks H3K9me2 and H3K27me3 following hybridization do not coincide with TEs with changed expression. Finally, we further examined TE expression in parents and hybrids exposed to severe dehydration stress. Despite the major reorganization of gene and TE expression by stress, we observe that hybridization does not lead to increased disorganization of TE expression in the hybrid. Although our study did not examine TE transposition activity in hybrids, the examination of the transcriptome shows that TE expression is globally robust to hybridization. The term "genomic shock" is perhaps not appropriate to describe transcriptional modification in a viable hybrid merging divergent genomes. [ABSTRACT FROM AUTHOR]

We present a new method of DEEM, the direct energy encircling method, for characterizing the performance of fibres in most astronomical spectroscopic applications. It is a versatile platform to measure focal ratio degradation (FRD), throughput, and point spread function. The principle of DEEM and the relation between the encircled energy and the spot size were derived and simulated based on the power distributionmodel (PDM).We analysed the errors of DEEM and pointed out the major error source for better understanding and optimization. The validation of DEEM has been confirmed by comparing the results with conventional method which shows that DEEM has good robustness with high accuracy in both stable and complex experiment environments. Applications on the integral field unit (IFU) show that the FRD of 50 μm core fibre is substandard for the requirement which requires the output focal ratio to be slower than 4.5. The homogeneity of throughput is acceptable and higher than 85 per cent. The prototype IFU of the first generation helps to find out the imperfections to optimize the new design of the next generation based on the staggered structure with 35 μm core fibres of N.A. = 0.12, which can improve the FRD performance. The FRD dependence on wavelength and core size is revealed that higher output focal ratio occurs at shorter wavelengths for large core fibres, which is in agreement with the prediction of PDM. But the dependence of the observed data is weaker than the prediction. [ABSTRACT FROM AUTHOR]

In this study, we investigate some counterintuitive but frequent performance issues that arise when doing high-speed networking (or I/O in general) with Virtual Machines (VMs). VMs use one or more single-producer/single-consumer systems to exchange I/O data (e.g. network packets) with their hypervisor. We show that when the producer and the consumer process packets at different rates, the high cost required for synchronization (interrupts and 'kicks') may reduce throughput of the system well below the slowest of the two parties; moreover, accelerating the faster party may cause the throughput to decrease. Our work provides a model for throughput, efficiency and latency of producer/consumer systems when notifications or sleeping are used as a synchronization mechanism; identifies different operating regimes depending on the operating parameters; validates the accuracy of our model against a VirtIO-based prototype, taking into account most of the details of real-world deployments; provides practical and robust strategies to maximize throughput and minimize energy while keeping the latency under control, without depending on precise timing measurements nor unreasonable assumptions on the system's behavior. The study is particularly interesting for Network Function Virtualization deployments, where high-rate producer/consumer systems in virtualized environments are the core components. [ABSTRACT FROM AUTHOR]

Summary: Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. Under the standard instrumental variable model, however, the average treatment effect is only partially identifiable. To address this, we propose novel assumptions that enable identification of the average treatment effect. Our identification assumptions are clearly separated from model assumptions that are needed for estimation, so researchers are not required to commit to a specific observed data model in establishing identification. We then construct multiple estimators that are consistent under three different observed data models, and multiply robust estimators that are consistent in the union of these observed data models. We pay special attention to the case of binary outcomes, for which we obtain bounded estimators of the average treatment effect that are guaranteed to lie between −1 and 1. Our approaches are illustrated with simulations and a data analysis evaluating the causal effect of education on earnings. [ABSTRACT FROM AUTHOR]

Legislators, designers of legal information systems, as well as citizens face often problems due to the interdependence of the laws and the growing number of references needed to interpret them. In this article, we introduce the 'Legislation Network' (LN) as a novel approach to address several quite challenging issues for identifying and quantifying the complexity inside the Legal Domain. We have collected an extensive data set of a more than 60-year old legislation corpus, as published in the Official Journal of the European Union, and we further analysed it as a complex network, thus gaining insight into its topological structure. Among other issues, we have performed a temporal analysis of the evolution of the LN, as well as a robust resilience test to assess its vulnerability under specific cases that may lead to possible breakdowns. Results are quite promising, showing that our approach can lead towards an enhanced explanation in respect to the structure and evolution of legislation properties. [ABSTRACT FROM AUTHOR]

The pale grass blue butterfly, Zizeeria maha, has been used to evaluate biological impacts of the Fukushima nuclear accident in March 2011. Here, we examined the possibility that butterflies have adapted to be robust in the contaminated environment. Larvae (n = 2432) were obtained from adult butterflies (n = 20) collected from 7 localities with various contamination levels in May 2012, corresponding to the 7th generation after the accident. When the larvae were reared on noncontaminated host plant leaves from Okinawa, the normality rates of natural exposure without artificial irradiation (as an indication of robustness) were high not only in the least contaminated locality but also in the most contaminated localities. The normality rates were similarly obtained when the larvae were reared on non-contaminated leaves with external irradiation or on contaminated leaves from Fukushima to deliver internal irradiation. The normality rate of natural exposure and that of external or internal exposure were correlated, suggesting that radiation resistance (or susceptibility) likely reflects general state of health. The normality rate of external or internal exposure was divided by the relative normality rate of natural exposure, being defined as the resistance value. The resistance value was the highest in the populations of heavily contaminated localities and was inversely correlated with the distance from the Fukushima Dai-ichi nuclear power plant. These results suggest that the butterfly population might have adapted to the contaminated environment within approximately 1 year after the accident. The present study may partly explain the decrease in mortality and abnormality rates later observed in the contaminated areas. [ABSTRACT FROM AUTHOR]

Background: In cross-population comparisons of disease occurrence (prevalence, incidence), a common public health question is the extent to which variations in the distribution of risk factors for the disease explain observed differences. Limited work has been done on formalizing this problem, which is conceptually tantamount to quantifying the degree of confounding for the 'population effect' induced by different factors. A common approach is to compare 'unadjusted' and 'adjusted' regression-based estimates of that parameter, but the interpretation of the resulting 'contribution' measures may be hindered by other confounding sources and non-collapsibility issues. Interactions also raise interpretational challenges.Methods: We formalized this problem using directed acyclic graphs and the potential outcomes framework, on the basis of which we defined a series of estimands that address specific questions and are identifiable under certain causal assumptions. We subsequently determined possible estimators. A study of regional differences in egg allergy prevalence in 1-year-olds was used for illustration.Results: The main estimands defined were: the change in the prevalence or incidence difference induced by compositional variations in measured risk factors, all at once and individually, relative to a reference population; and the proportion of the crude difference that remains unexplained by measured factors. Standardization (g-computation), inverse probability weighted (IPW) and doubly robust IPW estimators of these estimands were considered.Conclusions: This work provides a causal theoretical basis for studying disease occurrence differences between populations. The proposed measures can be used to answer the questions that arise in this context under a set of clearly stated assumptions. [ABSTRACT FROM AUTHOR]

Network coding is a routing technique that differs from traditional ‘store-and-forward’ mechanisms. It allows intermediate nodes to modify packets in transit. It is well known that network coding can increase throughput and improve robustness in network. However, it is the messages mixing feature that makes network coding susceptive to pollution attacks. To address this problem, homomorphic message authentication codes (MACs) have been proposed. The existing homomorphic MAC schemes adopt inner product to authenticate a message with a tag over a field Fq. In practical instantiations, the size of the field Fq is normally chosen (or desired) to be small (typically set as 28) to limit computational and communication overheads. In these settings, an adversary will break the schemes with probability at least 1/q (typically 1/28). The security is not guaranteed in this case. To waver the limitations and enhance the security, multiple tags are adopted for each message, that certainly incurs large key size overhead and is not preferred in applications. A scheme of homomorphic MAC with preferring security and shorter keys is much expected, and till now, to our knowledge, is not successfully constructed. This work solves this problem by presenting a new homomorphic MAC scheme for authentication in network coding. The proposed scheme allows us to authenticate a message in a linear space over a field of moderate size and at the same time, achieves a reliable security with a short key. The construction is based on a recently invented somewhat public-key notion: algebraic one-way function, by Catalano et al. (TCC 2013). Compared to the existing schemes, our scheme possesses the advantages that it achieves stronger security with much shorter keys, and is practical in applications. Hence resolve the longstanding problem. [ABSTRACT FROM AUTHOR]

Motivation: In genetic association studies, linear mixed models (LMMs) are used to test for associations between phenotypes and candidate single nucleotide polymorphisms (SNPs). These same models are also used to estimate heritability, which is central not only to evolutionary biology but also to the prediction of the response to selection in plant and animal breeding, as well as the prediction of disease risk in humans. However, when one or more of the underlying assumptions are violated, the estimation of variance components may be compromised and therefore so may the estimates of heritability and any other functions of these. Considering that datasets obtained from real life experiments are prone to several sources of contamination, which usually induce the violation of the assumption of the normality of the errors, a robust derivative-free restricted-maximum likelihood framework (DF-REML) together with a robust coefficient of determination are proposed for the LMM in the context of genetic studies of continuous traits. Results: The proposed approach, in addition to the robust estimation of variance components and robust computation of the coefficient of determination, allows in particular for the robust estimation of SNP-based heritability by reducing the bias and increasing the precision of its estimates. The performance of both classical and robust DF-REML approaches is compared via a Monte Carlo simulation study. Additionally, three examples of application of the methodologies to real datasets are given in order to validate the usefulness of the proposed robust approach. Although the main focus of this article is on plant breeding applications, the proposed methodology is applicable to both human and animal genetic studies. [ABSTRACT FROM AUTHOR]

Previous research has established that the predictions of game theory are quite sensitive to the assumptions made about the players' beliefs. We evaluate the severity of this robustness problem by characterizing conditions on the primitives of the model-the players' beliefs and higher-order beliefs about the payoff-relevant parameters-for the behaviour of a given Harsanyi type to be approximated by the behaviour of (a sequence of) perturbed types. This amounts to providing belief-based characterizations of the strategic topologies of Dekel et al. (2006).We apply our characterizations to a variety of questions concerning robustness to perturbations of higher-order beliefs, including genericity of types that are consistent with a common prior, and we investigate the connections between our notions of robustness and the notion of common p-belief of Monderer and Samet (1989). [ABSTRACT FROM AUTHOR]

In many epidemiology studies, family data with survival endpoints are collected to investigate the association between risk factors and disease incidence. Sometimes the risk of the disease may change when a certain risk factor exceeds a certain threshold. Finding this threshold value could be important for disease risk prediction and diseases prevention. In this work, we propose a change-point proportional hazards model for clustered event data. The model incorporates the unknown threshold of a continuous variable as a change point in the regression. The marginal pseudo-partial likelihood functions are maximized for estimating the regression coefficients and the unknown change point. We develop a supremum test based on robust score statistics to test the existence of the change point. The inference for the change point is based on the m out of n bootstrap. We establish the consistency and asymptotic distributions of the proposed estimators. The finite-sample performance of the proposed method is demonstrated via extensive simulation studies. Finally, the Strong Heart Family Study dataset is analyzed to illustrate the methods. [ABSTRACT FROM AUTHOR]

Networks are a popular way to visually represent and analyse interactions between species in ecological communities, and there have been several recent calls for interaction network measures to be targets of conservation and management efforts. However, it is challenging to deliberately manipulate empirical network properties. Our objective was to determine whether the network property of connectance could be manipulated in a planned and deliberate fashion, without altering the size of the community. Connectance (proportion of realized interactions out of total number of possible interactions) is often used as a proxy for community complexity, and theory also suggests it is related to community robustness and stability. We designed a field experiment to increase the connectance of a mutualistic community by manipulating the attractiveness of plant species in a bee-plant interaction network. Specifically, we applied a chemical fertilizer to enhance the floral display and resource quality provided by experimental plants, and assessed the effect on bee community structure. We found the connectance was significantly higher in fertilized plots relative to controls. This manipulation was associated with a significant increase in both the species richness (+41%) and abundance (+77%) of pollinators; there were more pollinator species in treatment plots, and these pollinators visited more plant species with a higher frequency. This study shows that a network property can indeed be altered experimentally; here we were able to deliberately increase the connectance of a bee-plant network while keeping constant both the number of plant species and the background community of potential floral visitors. Deliberate manipulation of experimental community structure therefore could be a valuable avenue for future research. Integration of network theory with empirical research also has the potential to inform the design of network control approaches. [ABSTRACT FROM AUTHOR]

We propose a procedure based on a high-breakdown mean function estimator to detect outliers in functional data. The robust estimator is obtained from a clean subset of observations, excluding potential outliers, by minimizing the least-trimmed-squares projection coefficients after functional principal component analysis. A threshold rule based on the asymptotic distribution of the functional score-based distance robustly controls the false positive rate and detects outliers effectively. Further improvement in power can be achieved by adding a one-step reweighting procedure. The finite-sample performance of our method demonstrates satisfactory false positive and false negative rates compared with existing outlier detection methods for functional data. [ABSTRACT FROM AUTHOR]