Initial Bias Research Articles

Automated RPCs Bias Compensation for KOMPSAT Imagery Using Orthoimage GCP Chips in Korea

High-resolution satellite images, including KOMPSAT, WorldView-3, and Pléiades, are widely used for mapping and environmental monitoring. In particular, satellite images with high spatial resolution should have accurate location information to provide essential spatial information. To this end, most high-resolution satellite images files are provided with rational polynomial coefficients (RPCs) that can be used for the sensor modeling. However, the RPCs have an initial bias. Therefore, most satellite image platforms and researchers match satellite images using ground control points (GCPs) information and perform RPCs bias compensation of images using matching information. In Korea, an image-based GCP chip built using orthographic images is provided for the georegistration of various satellite images. In this manuscript, RPCs bias compensation of KOMPSAT-3A satellite images was performed using GCP chips, and then, the possibility of automation of RPCs bias compensation through GCP chip was analyzed. Image matching such as area-based and edge-based techniques were used, and the results of RPCs bias compensation using GCP chips were analyzed through experiments in various regions. The automated compensation in either area-based or edge-based matching performed well with within 1.5 pixel level of accuracy for test areas with various topographic features. However the forest area was challenging such that new GCP chips with rich feature information were required. In addition, edge-based matching could overcome large seasonal differences including snow cover.

Supplemental Material for Little Evidence for Consistent Initial Elevation Bias in Self-Reported Momentary Affect: A Coordinated Analysis of Ecological Momentary Assessment Studies

Supplemental Material for Little Evidence for Consistent Initial Elevation Bias in Self-Reported Momentary Affect: A Coordinated Analysis of Ecological Momentary Assessment Studies

Industrial IoT-Enabled Prediction Interval Estimation of Mechanical Performances for Hot-Rolling Steel

The prediction of mechanical properties holds the key to ensuring product quality and developing new materials in the steel industry. In this study, an industrial Internet of Things platform is developed to obtain the property-related parameters, and a data-driven approach for estimating the prediction intervals (PIs) of these properties is proposed to address the drawbacks of point prediction methods. By combining an optimized extreme learning machine (ELM) and the delta method, the proposed approach specifically applies the regularization mechanism to improve the generalization and stability of the model; it also uses the artificial bee colony algorithm to optimize the initial weights and bias of the input layer in regularized ELM (RELM). An actual steel coil dataset consisting of 27 input dimensions and 2120 samples was used to validate the proposed method, in comparison with the backpropagation neural network, standard ELM, RELM optimized using the genetic algorithm, and lower upper bound estimation method. Experiment results confirm that the proposed method can obtain PIs with a higher coverage probability, narrower interval width, and smaller calculation error.

Characteristics and GWO–ANN Model Based on Prior Knowledge for Contact Resistance in a Pantograph–Catenary

The current-collection performance of a pantograph–catenary directly influences the safe operation of high-speed electric locomotives. The contact resistance is a primary indicator to evaluate the stability and reliability of the current-collection performance. The effects of sliding speed and electric current on the contact resistance between the carbon strip and the contact wire under a dynamic pressure load were studied using a sliding electrical contact testing machine. To predict the contact resistance under various working conditions, an artificial neural network (ANN) regression model, based on the gray wolf optimizer (GWO) algorithm and prior knowledge, was developed. The GWO algorithm was used to optimize the initial biases and weights of the ANN to avoid becoming trapped in local optima. The performance of the GWO–ANN can be further enhanced by the use of prior knowledge. The results showed that the GWO–ANN model that included prior knowledge can be introduced as a new technique for estimating the contact resistance in pantograph–catenary systems.

Uncertainty and learning in a technologically dynamic industry: Seed density in U.S. maize

AbstractThe large and sustained yield gains achieved since the introduction of maize hybrids in the 1930s (about 1.8 bushels per acre per year) have been accompanied by a remarkably parallel and steady increase in seeding density. This increase occurred in an environment characterized by rapid technological innovation, including genetic engineering, and commercial hybrid varieties with short life cycles. An important question, then, is whether and how breeders and farmers have learned about the optimal planting density. In this paper, we use unique and detailed U.S. farm‐level data, consisting of more than 400,000 planting choices from 1995–2016, to assess the nature of learning about seeding density. Importantly, we control for unobserved confounders through both hybrid and farm‐level fixed effects. We find that the variance in planting rates for a given hybrid has decreased over time, and that farmers tend to plant a given variety at higher rates over time. This is consistent with Bayesian learning in which risk‐neutral farmers possess priors consistently below the true optimal rate. We cast doubt on risk aversion as a credible explanation for this finding by analyzing the contrasting evolution of soybean planting rates (a crop with exogenously different agronomic determinants of seed density). We interpret our results as evidence of inertia: the initial bias in maize farmers' priors is tilted towards the optimal planting rates of varieties planted in the past. One implication of the finding that farmers historically underinvested in seeding rates is that eliminating this tendency could result in productivity gains.

Subseasonal prediction performance for South American land–atmosphere coupling in extended austral summer

AbstractLand–atmosphere feedbacks, through water and energy exchanges, provide subseasonal‐to‐seasonal predictability of the hydrological cycle. We analyse subseasonal land–atmosphere coupling over South America (SA) during extended austral summer for the soil moisture‐to‐precipitation and soil moisture‐to‐air temperature feedback pathways. We evaluate subseasonal hindcasts from global forecasting systems from the UK Met Office, the National Centers for Environmental Prediction (NCEP), the European Centre for Medium Range Weather Forecasts and the Center for Weather Forecast and Climate Studies (CPTEC), for the common period of 1999–2010, against two reanalyses. Biases in land–atmosphere states are established in the first week of hindcasts and increase with lead time. By Week 5, all the models only demonstrate good performance over northern, northeastern and southeastern SA for soil moisture and evapotranspiration and over tropical and subtropical SA for temperature. The hindcasts show stronger coupling at longer lead–lag between variables than reanalyses. Our results highlight possible deficiencies in feedbacks between soil moisture and precipitation in CPTEC and NCEP forecasts over the Amazon due to initial dry soil moisture biases, and in feedbacks between soil moisture and temperature for all four investigated models over southeastern SA due to erroneous representations of evapotranspiration.

The neural computation of human prosocial choices in complex motivational states

Motives motivate human behavior. Most behaviors are driven by more than one motive, yet it is unclear how different motives interact and how such motive combinations affect the neural computation of the behaviors they drive. To answer this question, we induced two prosocial motives simultaneously (multi-motive condition) and separately (single motive conditions). After the different motive inductions, participants performed the same choice task in which they allocated points in favor of the other person (prosocial choice) or in favor of themselves (egoistic choice). We used fMRI to assess prosocial choice-related brain responses and drift diffusion modeling to specify how motive combinations affect individual components of the choice process. Our results showed that the combination of the two motives in the multi-motive condition increased participants’ choice biases prior to the behavior itself. On the neural level, these changes in initial prosocial bias were associated with neural responses in the bilateral dorsal striatum. In contrast, the efficiency of the prosocial decision process was comparable between the multi-motive and the single-motive conditions. These findings provide insights into the computation of prosocial choices in complex motivational states, the motivational setting that drives most human behaviors.

Exchange-bias dependent diffusion rate of hydrogen discovered from evolution of hydrogen-induced noncollinear magnetic anisotropy in FePd thin films

Hydrogenation-induced noncollinear magnetic anisotropy is observed from the evolution of the magnetic domains in FePd alloy thin films using magneto-optic Kerr effect (MOKE) microscopy. MOKE images reveal complicated competitions between different magnetic anisotropies during hydrogen diffusion into the film. An intriguing enhancement of the hydrogen diffusion rate due to the presence of an initial exchange bias induced by a high magnet field is thereby discovered, pointing to an additional scope of controllability of magnetic metal hydrides as potential future hydrogen sensing and storage materials.

The remediating effect of Attention Bias Modification on aggression in young offenders with antisocial tendency: A randomized controlled trial

Background and objectivesAn attention bias toward hostile stimuli is associated with an increased risk of aggressive behavior. Thus, reducing hostile attention bias may help to reduce aggression in young offenders. We evaluated the remediating effects of Attention Bias Modification (ABM) on hostile attention bias and aggressive behavior in Chinese male young offenders with antisocial tendencies. MethodsInstitutionalized male young offenders (ages 16–18) were recruited and randomly assigned to ABM (n = 28), placebo (n = 28), or waiting list (n = 28). The ABM group received four weeks of training using visual search of emotional faces; the placebo group underwent similar training using visual search of neutral objects. Before and after treatment, aggressive behavior, attention bias toward positive stimuli and hostile stimuli were assessed. ResultsLinear mixed models and hierarchical multiple regression analyses showed that the intervention effect of ABM was moderated by participants’ initial attention bias. Young offenders with a high level of initial hostile attention bias, or with a low level of initial positive attention bias, benefitted most from ABM. LimitationsThe small sample size impedes investigation of the mechanism of the intervention's effects. Further research can determine whether the current results can be extrapolated to the population of female young offenders. ConclusionThe results of the present study suggest that ABM is helpful for young offenders who show the greatest bias toward hostile stimuli and away from positive stimuli. These findings highlight the importance of matching the intervention and the participant. Trial registrationosf.io/vj5rk;

Attentional bias modification for chocolate: Sham-n training as a new control group.

Although attentional bias modification has been shown effective in several appetitive domains, results have been mixed. A major contributor seems to be the choice of control condition. The aim of the present study was to compare attentional bias modification for chocolate against a new control condition, sham-n (neutral or no-contingency) training. Using a modified dot probe protocol, participants (N = 192; 17–30 years) were randomly trained to attend to chocolate pictures, avoid chocolate pictures, or received sham-n training. In the attend and avoid conditions, stimulus pairs consisted of one chocolate and one non-chocolate picture, and probes replaced most often (90/10) chocolate or non-chocolate pictures, respectively. In the sham-n training condition, stimulus pairs consisted of two chocolate or two non-chocolate pictures, and probes replaced pictures within pairs with equal frequency (50/50). Attentional bias for chocolate increased following attend training, decreased following avoidance training, and did not change following sham-n training. The findings clearly demonstrate that both attend and avoidance training alter (in opposite direction) attentional bias for chocolate, whereas sham-n training is inert. This makes sham-n training particularly promising for use in clinical samples who tend to show strong initial biases.

Analyst sentiment and earning forecast bias in financial markets

With the natural language processing technique, we quantitatively measure the sentiment expressed in analyst reports of stock markets. Both the analyst sentiment and the earning forecast biases decrease with the fiscal month. The temporal variation of the earning forecast biases displays an asymmetric behavior, i.e., underestimated initial biases may be corrected by the wisdom of crowds, while overestimated ones could not. The Pearson correlation between the analyst sentiment and the earning forecast errors becomes positive, when approaching the end of the fiscal year. In addition, the analyst sentiment is different from the common investor sentiment.

DC model for SiC MOSFETs using artificial neural network optimized by artificial bee colony algorithm

A DC model for silicon carbide (SiC) metal–oxide–semiconductor field effect transistors (MOSFETs) is proposed in this paper using a hybrid modeling method based on the artificial neural network and artificial bee colony (ABC) algorithm. A multi-layer perceptron neural network using the Levenberg–Marquardt (LM) method is applied to model SiC MOSFETs based on the data provided by the datasheet. The search strategy of artificial bees is improved based on the standard ABC, which enhances the search ability of the standard ABC. In view of the sensitivity of the LM method to the initial value, the improved ABC algorithm is adopted to help the neural network find initial weights and biases, which improves the accuracy of the modeling results. Comparing the modeling results with the I–V curves in the datasheet, the accuracy of the DC model is verified under different temperatures. In addition, the small signal parameters gm and gd that are not exposed in the training process also fit well with the datasheet, which fully demonstrates the feasibility of this hybrid modeling method.

Eye movement pattern of attention bias to emotional stimuli in women with high premenstrual symptoms

Background and objectivesPremenstrual Syndrome (PMS) refers to a group of symptoms linked to the menstrual cycle. Women with PMS have cognitive mode of rumination, which leads to their attention bias to emotional stimuli. This study investigated the biases for emotional information in women with high premenstrual symptoms (HPMS) compared with women with low premenstrual symptoms (LPMS). MethodsA total of 38 women with HPMS and 44 women with LPMS completed self-report questionnaires and a free viewing task with eye-tracking technology. ResultsThe questionnaire results indicate that women in the HPMS group had higher levels of rumination than those in the LPMS group. The eye-tracking results show that women in the HPMS group had an orientation bias towards negative emotional stimuli in the early cognitive process. In the late cognitive process, women in the HPMS group had accelerated attentional disengagement to positive emotional stimuli and difficult attentional disengagement to negative emotional stimuli. Further correlation analysis revealed positive relationships between the scores of initial fixation latency bias of positive pictures in premenstrual phase and the scores of symptom rumination in both groups and between the scores of initial fixation latency bias of positive pictures in premenstrual phase and the scores of brooding in HPMS group. LimitationsThis study used a retrospective questionnaire to assess the symptoms of PMS. ConclusionsWomen with HPMS had impaired attentional engagement and disengagement to emotional stimuli compared with women with LPMS, and it may be related to their cognitive mode of rumination.

The Mediating Role of the Social Identity on Agritourism Business

There is a significant relationship between social capital, functional competences and social identity which forms the environment of rural tourism. This complexity was studied using the PLS-SEM approach, applying the initial corrected bias method based on direct questionnaire surveys among rural tourism entrepreneurs in Tunisia. The results of the bias-corrected primer model revealed that the entrepreneur’s social identity mediated the link between social capital and functional competencies. Managerially, social capital supports rural lodge entrepreneurs in the process of defining their marketing strategy and optimizing the different components of their marketing mix, focusing on the differentiation of their products and services. A strong link within the entrepreneur’s social capital network will encourage them to strengthen their social identity, leading to the enhancement of their different functional competencies.

Colour biases in learned foraging preferences in Trinidadian guppies

AbstractLearning allows animals to adaptively adjust their behaviour in response to variable but predictable environments. Stable aspects of the environment may result in evolved or developmental biases in the systems impacting learning, allowing for improved learning performance according to local ecological conditions. Guppies (Poecilia reticulata), like many animals, show striking colour preferences in foraging and mating contexts and guppy artificial selection experiments have found that the form and progress of evolved responses to coloured stimuli differ depending on stimulus colour. Blue colouration is thought to typically be a relatively unimportant food cue in guppies. This raises the possibility that learned foraging associations with blue objects are formed less readily than with other colours. Here, guppies were rewarded for foraging at green or blue objects in two experiments. Guppies readily foraged from these objects, but learning performance differed with rewarded object colour. With equal amounts of training, the preference for green objects became stronger than the preference for blue objects. These differences in performance were not attributable to differences in initial preferences or to foraging more on one colour during training. These findings suggest that associative pairings within a single sensory modality that do not have a historic relevancy can be more difficult for animals to learn even when there is no clear initial bias present.

An approach combining a new weight initialization method and constructive algorithm to configure a single Feedforward Neural Network for multi-class classification

This paper presents a new method for initializing weights in a Feedforward Neural Network (FNN) with a single hidden layer combined with a constructive approach to define the number of hidden units associated with the best classification performance. The strategy consists of defining an initial number of hidden units according to the classification problem, the linearization of the whole network around an equilibrium point and the determination of the initial weights and bias through the maximum approximation of the linearized model to the Optimal Linear Classifier (OLC) whose solution can be obtained analytically. The constructive algorithm comprises a gradual increase in the number of hidden units in such a way that at each training only the weights and bias associated with the new hidden units are initiated randomly while the weights and bias obtained from previous training are used as initial guesses. Additionally, the constructive algorithm seeks to ensure that the loss function of the trained networks decreases with the successive additions of hidden units. The proposed approach (Weight Initialization based on the Linearization of the Whole Neural Network combined with a new Constructive Algorithm, WILWNN-CA) is applied to synthetic and real datasets widely used as benchmark for multi-class classification problems. The comparison with conventional random weight initialization and other approaches involving different network topologies (and initialization strategies) shows that the proposed method is efficient and capable of providing success rates (correct classification rates) higher or similar to those achieved with existing methods.

Development of viscosity model for aluminum alloys using BP neural network

Viscosity is one of the important thermophysical properties of liquid aluminum alloys, which influences the characteristics of mold filling and solidification and thus the quality of castings. In this study, 315 sets of experimental viscosity data collected from the literatures were used to develop the viscosity prediction model. Back-propagation (BP) neural network method was adopted, with the melt temperature and mass contents of Al, Si, Fe, Cu, Mn, Mg and Zn solutes as the model input, and the viscosity value as the model output. To improve the model accuracy, the influence of different training algorithms and the number of hidden neurons was studied. The initial weight and bias values were also optimized using genetic algorithm, which considerably improve the model accuracy. The average relative error between the predicted and experimental data is less than 5%, confirming that the optimal model has high prediction accuracy and reliability. The predictions by our model for temperature- and solute content-dependent viscosity of pure Al and binary Al alloys are in very good agreement with the experimental results in the literature, indicating that the developed model has a good prediction accuracy.

The impact of cooperative participation on income: the case of vegetable production in Vietnam

PurposeThe demand for clean vegetables has rapidly increased, many farmers gradually turn to vegetable cultivation to increase income; therefore, agricultural cooperative mobilized farmers group to facilitate them access to technical training and enhance compliance with the Vietnamese Good Agricultural Practices (VietGAP) standards. The purpose of this paper is to evaluate the impacts of the participation on farmer’s income as well as the major factors that affect the participation in cooperatives by the vegetable farmers in Vietnam.Design/methodology/approachThe study used primary data collected from vegetable farmers in Vietnam. This study utilized propensity score matching to avoid initial selection bias. The differences between participants and nonparticipants will be adjusted by matching each membership individual to a nonmember based on similar observable characteristics by summarizing the conditional probability of a member given pretreatment characteristics.FindingsThis study demonstrates that participation in cooperatives is significantly affected by ethnicity, age of household head, labor involving vegetable production and extension service access. The results of this study also confirm that agricultural cooperatives have positive effects on member farmers to enhance income and that participants – on average – have a higher income than nonparticipants.Research limitations/implicationsThe method cannot rule out the possibility of selection bias due to unobserved differences between participants and even an appropriate comparison group.Originality/valueThis study contributes to an improved understanding about impacts of cooperatives on farmers’ income in developing and emerging economies. Moreover, research also upgrades knowledge regarding the effectiveness of agricultural cooperatives in Vietnam, as well as guides policymakers in supporting the cooperatives in expanding the market and other necessary changes.

Infants' neutral facial expressions elicit the strongest initial attentional bias in adults: Behavioral and electrophysiological evidence.

Recent studies that used adult faces as the baseline have revealed that attentional bias toward infant faces is the strongest for neutral expressions than for happy and sad expressions. However, the time course of the strongest attentional bias toward infant neutral expressions is unclear. To clarify this time course, we combined a behavioral dot-probe task with electrophysiological event-related potentials (ERPs) to measure adults' responses to infant and adult faces with happy, neutral, and sad expressions derived from the same face. The results indicated that compared with the corresponding expressions in adult faces, attentional bias toward infant faces with various expressions resulted in different patterns during rapid and prolonged attention stages. In particular, first, neutral expressions in infant faces elicited greater behavioral attentional bias and P1 responses than happy and sad ones did. Second, sad expressions in infant faces elicited greater N170 responses than neutral and happy ones did; notably, sad expressions elicited greater N170 responses in the left hemisphere in women than in men. Third, late positive potential (LPP) responses were greater for infant faces than for adult faces under each expression condition. Thus, we propose a three-stage model of attentional allocation patterns that reveals the time course of attentional bias toward infant faces with various expressions. This model highlights the prominent role of neutral facial expressions in the attentional bias toward infant faces.

Social Exchange of Motivated Beliefs

Abstract We use laboratory experiments to study whether biases in beliefs grow more severe when people socially exchange these beliefs with one another. We elicit subjects’ (naturally biased) beliefs about their relative performance in an intelligence quotient (IQ) test and allow them to update these beliefs in real time. Part of the way through the task we give each subject access to the beliefs of a counterpart who performed similarly on the test and allow them both to observe the evolution of one another’s beliefs. We find that subjects respond to one another’s beliefs in a highly asymmetric way, causing a severe amplification of subjects’ initial bias. We find no such patterns in response to objective public signals or in control treatments without social exchange or scope for motivated beliefs. We also provide evidence that the pattern is difficult to reconcile with Bayesianism and standard versions of confirmation bias. Overall, our results suggest that bias amplification is likely driven by “motivated assignment of accuracy” to others’ beliefs: subjects selectively attribute higher informational value to social signals that reinforce their motivation.