Effect Of Set Size Research Articles

Introducing ART: A new method for testing auditory memory with circular reproduction tasks.

Theories of visual working memory have seen significant progress through the use of continuous reproduction tasks. However, these tasks have mainly focused on studying visual features, with limited examples existing in the auditory domain. Therefore, it is unknown to what extent newly developed memory models reflect domain-general limitations or are specific to the visual domain. To address this gap, we developed a novel methodology: the Auditory Reproduction Task (ART). This task utilizes Shepard tones, which create an infinite rising or falling tone illusion by dissecting pitch chroma and height, to create a 1-360° auditory circular space. In Experiment 1, we validated the perceptual circularity and uniformity of this auditory stimulus space. In Experiment 2, we demonstrated that auditory working memory shows similar set size effects to visual working memory-report error increased at a set size of 2 relative to 1, caused by swap errors. In Experiment 3, we tested the validity of ART by correlating reproduction errors with commonly used auditory and visual working memory tasks. Analyses revealed that ART errors were significantly correlated with performance in both auditory and visual working memory tasks, albeit with a stronger correlation observed with auditory working memory. While these experiments have only scratched the surface of the theoretical and computational constraints on auditory working memory, they provide a valuable proof of concept for ART. Further research with ART has the potential to deepen our understanding of auditory working memory, as well as to explore the extent to which existing models are tapping into domain-general constraints.

Dissecting the components of error in analogue report tasks

Over the last two decades, the analogue report task has become a standard method for measuring the fidelity of visual representations across research domains including perception, attention, and memory. Despite its widespread use, there has been no methodical investigation of the different task parameters that might contribute to response variability. To address this gap, we conducted two experiments manipulating components of a typical analogue report test of memory for colour hue. We found that human response errors were independently affected by changes in storage and maintenance requirements of the task, demonstrated by a strong effect of set size even in the absence of a memory delay. In contrast, response variability remained unaffected by physical size of the colour wheel, implying negligible contribution of motor noise to task performance, or by its chroma radius, highlighting non-uniformity of the standard colour space. Comparing analogue report to a matched forced-choice task, we found variation in adjustment criterion made a limited contribution to analogue report variability, becoming meaningful only with low representational noise. Our findings validate the analogue report task as a robust measure of representational fidelity for most purposes, while also quantifying non-representational sources of noise that would limit its reliability in specialized settings.

The effects of types of touch and haptic cues on choice overload

AbstractAlthough consumers are commonly exposed to numerous choices and the opportunity to touch products when they shop, the literature remains unclear on how both factors simultaneously affect choice. With the growth of online shopping, touch literature has studied the effects of mere touch (i.e., nondiagnostic tactile contact) or vicarious touch (e.g., observing a hand in physical contact with a product or imagining touching a product) to demonstrate that these nondiagnostic haptic inputs can comparably influence consumer attitude, just like diagnostic touch. By investigating the moderating effects of different haptic inputs on choice overload, this research reveals certain conditions in which nondiagnostic haptic inputs operate differently than those of diagnostic touch. We find that both mere touch (Study 1) and vicarious touch (Study 2) help reduce choice overload effects by eliminating the effect of choice set size on choice uncertainty, which is typically observed in the no touch (i.e., control) condition. As haptic cues have been increasingly employed to aid the online shopping experience for consumers, we also find that the choice overload effect is eliminated when an autotelic haptic cue is presented but amplified when an instrumental haptic cue is provided (Study 3). Besides, this research demonstrates that, while the moderating effects of the nondiagnostic haptic inputs are driven by increased personal control, instrumental touch can amplify choice overload effects due to the increased choice difficulty when choosing from large (vs. small) choice sets. These findings help retailers know what type of haptic cues to apply or avoid, as consumers have already been overloaded with abundant choices when they shop online.

THE EFFECT OF INVESTMENT OPPORTUNITY SET (IOS), EARNINGS PERSISTENCE, AND FIRM SIZE ON EARNINGS QUALITY WITH INFORMATION ASYMMETRY AS A MODERATING VARIABLE

This study examines the effect of investment opportunity set (IOS), earnings persistence, and firm size on earnings quality with information asymmetry as a moderating variable. The primary impetus for this study was the number of instances of financial statement manipulation, which indicated that the firm's earnings quality was low and earnings information was subject to change. In 2020-2022, the sample consisted of as many as 15 infrastructure firms listed on the IDX, selected using purposive sampling. This study employed multiple linear regression analysis, and moderated regression techniques to analyze the data. The results of this study showed that earnings quality was unaffected by the investment opportunity set (IOS) and earnings persistence. Meanwhile, the firm size did affect the earnings quality. According to the results of moderated regression analysis (MRA), information asymmetry did not reduce the effect of investment opportunity set (IOS), earnings persistence, and firm size on earnings quality.

Effects of set size and planting date on yield, quality and shelf life of kharif onion under low hill region of himachal pradesh

The experiment was conducted at an experimental farm to find out the optimum set size and suitable planting date of onion during kharif season for obtaining the maximum yield. The trials had three replications and 16 treatment combinations on Agrifound Dark Red. The treatments included four set sizes (small 1-1.5cm, medium 1.5-2.0cm, large 2.0-2.5cm, and extra large >2.5cm) and four planting dates (July 10, July 25, August 10, and August 25). The treatment combination of 1.5-2.0cm set size and 10th August planting was advantageous for several characteristics. Small sets (1-1.5 cm) produced small bulbs and also the yield was reduced. Large and exceptionally large size sets (2.0-2.5cm and >2.5cm) produced more twins, triplets, split bulbs, and bolting, which were undesirable features. Considering net profit and economic yields, medium set size and planting on August 10th was found to be more profitable (1: 3.84). Bangladesh J. Bot. 53(1): 17-23, 2024 (March)

Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.

Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure-property relationship models are generally trained on large data sets that include diverse chemical series (global models). In the pharmaceutical industry, these ML global models are available across discovery projects as an "out-of-the-box" solution to assist in drug design, synthesis prioritization, and experiment selection. However, drug discovery projects typically focus on confined parts of the chemical space (e.g., chemical series), where global models might not be applicable. Local ML models are sometimes generated to focus on specific projects or series. Herein, ML-based global models, local models, and hybrid global-local strategies were benchmarked. Analyses were done for more than 300 drug discovery projects at Novartis and ten absorption, distribution, metabolism, and excretion (ADME) assays. In this work, hybrid global-local strategies based on transfer learning approaches were proposed to leverage both historical ADME data (global) and project-specific data (local) to adapt model predictions. Fine-tuning a pretrained global ML model (used for weights' initialization, WI) was the top-performing method. Average improvements of mean absolute errors across all assays were 16% and 27% compared with global and local models, respectively. Interestingly, when the effect of training set size was analyzed, WI fine-tuning was found to be successful even in low-data scenarios (e.g., ∼10 molecules per project). Taken together, this work highlights the potential of domain adaptation in the field of molecular property predictions to refine existing pretrained models on a new compound data distribution.

Emergent human-like covert attention in feedforward convolutional neural networks

Covert attention allows the selection of locations or features of the visual scene without moving the eyes. Cues and contexts predictive of a target's location orient covert attention and improve perceptual performance. The performance benefits are widely attributed to theories of covert attention as a limited resource, zoom, spotlight, or weighting of visual information. However, such concepts are difficult to map to neuronal populations. We show that a feedforward convolutional neural network (CNN) trained on images to optimize target detection accuracy and with no explicit incorporation of an attention mechanism, a limited resource, or feedback connections learns to utilize cues and contexts in the three most prominent covert attention tasks (Posner cueing, set size effects in search, and contextual cueing) and predicts the cue/context influences on human accuracy. The CNN's cueing/context effects generalize across network training schemes, to peripheral and central pre-cues, discrimination tasks, and reaction time measures, and critically do not vary with reductions in network resources (size). The CNN shows comparable cueing/context effects to a model that optimally uses image information to make decisions (Bayesian ideal observer) but generalizes these effects to cue instances unseen during training. Together, the findings suggest that human-like behavioral signatures of covert attention in the three landmark paradigms might be an emergent property of task accuracy optimization in neuronal populations without positing limited attentional resources. The findings might explain recent behavioral results showing cueing and context effects across a variety of simple organisms with no neocortex, from archerfish to fruit flies.

Application Analysis of English Personalized Learning Based on Large-scale Open Network Courses

In the context of Big data, large-scale open online courses increase learning paths for learners, but in the face of countless high-quality curriculum resources, it is easy for derivative learners to face the dilemma of rich curriculum resources but difficult to choose resources, which leads to information maze for learners. How to help learners quickly and accurately find their own learning resources in the explosive growth of MOOC resources is an urgent problem in the field of education Big data. However, the traditional Collaborative filtering recommendation technology does not perform well when dealing with sparse data and cold start. The recommendation content is repeated and can not effectively deal with high-dimensional and nonlinear data of online learning users, resulting in low efficiency of resource recommendation. Therefore, the study adopts a deep belief network (DBN) to construct a personalized resource recommendation model. The model combines the learner behavior characteristics with the curriculum resource content attribute characteristics to form the learner feature vector. The parameters of the model are adjusted according to the characteristics of learners. Through experiments, the proposed model has shown good performance. The experiment explored the effects of training set size, learner characteristics, and GPU on model performance. The experimental results show that when the training set proportion is 100%, the RMSE, Accuracy, Recall, and F1 values of the model are 0.76, 0.946, 0.957, and 0.951, respectively. When the model is trained using a training set containing learner features, the RMSE, Accuracy, Recall, and F1 values of the model are 0.75, 0.962, 0.908, and 0.958, respectively. After using GPU to accelerate the model, the running time of the model decreased from 360 minutes to 90 minutes. The results indicate that the model cannot effectively mine data information when the degree of correlation between sample information is low. The richer the relationships between samples, the better the performance of the model. Simultaneously learning hunger feature vectors and learner behavior feature vectors for training can significantly improve the recommendation accuracy of the model. The main contribution of this study is to propose a recommendation method based on DBN classification to replace traditional similarity calculation methods, using DBN's efficient feature abstraction and feature extraction capabilities to fully explore learners' interest and preference for course resources. In addition, in view of the common problems of cold start and data sparsity in traditional Collaborative filtering recommendation methods, the research deeply mines the characteristics of learners' Demographics and curriculum resources' content attributes, and constructs a learner interest model based on DBN combined with learners' behavior characteristics, which effectively solves the problems of cold start and data sparsity, as well as the inaccurate expression of learners' interest preferences for curriculum resources.

Scaling models of visual working memory to natural images

Over the last few decades, psychologists have developed precise quantitative models of human recall performance in visual working memory (VWM) tasks. However, these models are tailored to a particular class of artificial stimulus displays and simple feature reports from participants (e.g., the color or orientation of a simple object). Our work has two aims. The first is to build models that explain people’s memory errors in continuous report tasks with natural images. Here, we use image generation algorithms to generate continuously varying response alternatives that differ from the stimulus image in natural and complex ways, in order to capture the richness of people’s stored representations. The second aim is to determine whether models that do a good job of explaining memory errors with natural images also explain errors in the more heavily studied domain of artificial displays with simple items. We find that: (i) features taken from state-of-the-art deep encoders predict trial-level difficulty in natural images better than several reasonable baselines; and (ii) the same visual encoders can reproduce set-size effects and response bias curves in the artificial stimulus domains of orientation and color. Moving forward, our approach offers a scalable way to build a more generalized understanding of VWM representations by combining recent advances in both AI and cognitive modeling.

A population response model of ensemble perception.

Ensemble representations have been considered as one of the strategies that the visual system adopts to cope with its limited capacity. Thus, they include various statistical summaries such as mean, variance, and distributional properties and are formed over many stages of visual processing. The present study proposes a population-coding model of ensemble perception to provide a theoretical and computational framework for these various facets of ensemble perception. The proposed model consists of a simple feature layer and a pooling layer. We assumed ensemble representations as population responses in the pooling layer and decoded various statistical properties from population responses. Our model successfully predicted averaging performance in orientation, size, color, and motion direction across different tasks. Furthermore, it predicted variance discrimination performance and the priming effects of feature distributions. Finally, it explained the well-known variance and set-size effects and has a potential for explaining the adaptation and clustering effects. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Assessing the agronomic potential of sorghum B‐lines using genomic prediction

AbstractIn hybrid sorghum breeding, basic agronomic traits, such as days to flowering and plant height, of sorghum seed parents must be within a specific range for hybrid seed production. Because many sorghum programs select new B‐lines outside of commercial seed production environments, the purpose of this study was to determine if genomic prediction is effective to evaluate new lines for their agronomic potential for seed production. Two B‐line RIL populations were evaluated across several environments for days to mid‐anthesis (DY), plant height (PH), panicle length (PL), and seed yield (SY). Across environments and populations, average prediction accuracies were between 0.47 and 0.61 for DY, 0.24 and 0.60 for PH, 0.16 and 0.37 for SY, and 0.37 and 0.57 for PL. The effect of training set size was assessed by subsampling various amounts of data into the training set, ranging from 5% to 65%. Prediction accuracies generally improved as the proportion of the total data in the training set increased for both inter‐ and intrapopulation predictions, but a relatively small portion (15%) of the total data still produced modest prediction accuracies. The effect of the marker density on genomic prediction accuracies was assessed by subsampling various numbers of single nucleotide polymorphisms. It was observed that as few as 500 markers were able to produce prediction accuracies similar to when all markers were used. The results of this study indicated that genomic prediction can be a useful and cost‐effective tool that sorghum breeding programs should incorporate into their pipeline.

Geographical origin identification of ginseng using near-infrared spectroscopy coupled with subspace-based ensemble classifiers

Ginseng is a well-known traditional herbal medicine and the ginseng available on the market may not actually be produced in a certain place as claimed. Traditional methods of identifying the geographical origin of Ginseng are subjective, time-consuming or destructive. A more efficient approach is desirable. The feasibility of combining near-infrared (NIR) spectroscopy with ensemble learning for discriminating ginseng producing area was explored. A total of 270 samples were collected and evenly partitioned into the training and test sets. Random subspace ensemble (RSE) that uses linear discriminant classifier (LDA) as weak learner (abbreviated RSE-LDA) was used to construct predictive models. Two parameters including the size of subspace and the number of learners in ensemble were optimized. Classic partial least algorithm (PLS) was applied to build the reference model. The sensitivity, specificity, and total accuracy of final RSE-LDA and PLS models were 97.8 %, 100 %, 99.3 %, and 93.3 %, 96.7 %, 95.6 %, respectively. In order to study the impact of training set composition on the results, the samples were randomly divided 200 times and the algorithm was run repeatedly to statistically analyze the sensitivity and specificity on the test set. Similar results were obtained. The effect of training set size was also investigated. It indicates that the combination of NIR spectroscopy with the RSE algorithm is a potential tool of discriminating the origin of Ginseng.

Determinants of Company Value with Asset Revaluation as an Intervening

The study aims to determine the effect of Investment Opportunity Set, Liquidity, and Company Size both directly and indirectly on Company Value in companies that revalued assets listed on the Indonesia Stock Exchange in the 2016-2019 period. The research method used is a hypothesis testing method with a quantitative approach. The statistical test used is multiple linear regression with the use of partialsignificance test for hypothesis testing. The number of samples studied was 136 using panel data. The results showed that the direct effect of investment opportunity set and liquiditypartially had no effect on firm value, but firm size partially had an effect on firm value. While the indirect effect of investment opportunity set, and company size partially affect the value of the company. Liquidity partially has no effect on firm value through revaluation of fixed assets as an intervening variable.

Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2

Lakes and ponds are extensive features throughout the circumpolar region, spanning a broad range of environmental conditions which controls their hydro-ecological processes and spatiotemporal distribution. The physical characteristics of these freshwater ecosystems, including their extent and depth, are particularly responsive to climatic conditions. Thus, having the ability to efficiently map and monitor these elements is crucial as the climate continues to warm, especially over large spatial extents. In this study, satellite derived bathymetry (SDB) methods were implemented to model regional inland Arctic water depths within the open-source and cloud-based Google Earth Engine (GEE) platform. High-resolution (10 m) spectral reflectance data from Sentinel-2 was used as covariates in non-parametric and non-linear machine learning (ML) models, namely random forest (RF), support vector regression (SVR), and classification and regression trees (CART). These three ML models were also compared to a more classical and parametric multiple linear regression (MLR) model. All models were calibrated using in situ bathymetric data collected near the Toolik Field Station, in the Alaskan Arctic tundra. With such a large sample repository available, the effects of training set size, noise, and multicollinearity on model performance was comprehensively investigated. Results clearly demonstrated the superior efficacy and robustness of the RF algorithm for predicting water depths, achieving a best coefficient of determination (R2) of 0.74, mean absolute error (MAE) of 2.12 m, and root mean square error (RMSE) of 3.04 m. Overall, this study highlights the potential of advanced and flexible ML algorithms within the GEE, and demonstrates the capabilities of the polar-orbiting Copernicus Sentinel-2 satellite for this Arctic application. In the future, this ML-driven methodology can be applied using GEE’s cloud infrastructure to produce updated, cost-effective, and accurate bathymetry maps over large expanses of the Arctic tundra.

Prospective search time estimates reveal the strengths and limits of internal models of visual search.

Having an internal model of one's attention can be useful for effectively managing limited perceptual and cognitive resources. While previous work has hinted at the existence of an internal model of attention, it is still unknown how rich and flexible this model is, whether it corresponds to one's own attention or to a generic person-invariant schema, and whether it is specified as a list of facts and rules or alternatively as a probabilistic simulation model. To this end, we tested participants' ability to estimate their own behavior in a visual search task with novel displays. In six online experiments (four pre-registered), prospective search time estimates reflected accurate metacognitive knowledge of key findings in the visual search literature, including the set-size effect, higher efficiency of color over conjunction search, and the asymmetric contributions of target and distractor identities to search difficulty. In contrast, estimates were biased to assume serial search, and demonstrated little to no insight into sizeable effects of search asymmetries for basic visual features, and of target-distractor similarity. Together, our findings reveal a complex picture, where internal models of visual search are sensitive to some, but not all, of the factors that make some searches more difficult than others. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Comparative Study on the Set-size Effects in Polygon and Words in Visual Search Task

Collection size has a very important effect on visual working memory. In this paper, we use an interview and repeated measure design to empirically investigate the relationship between larger ensemble sizes (more than four distractors) and reaction times for participant responses. We set up a scene for daily observation of polygons and words. Participants were required to identify a correctly written Chinese character and the target item, and the pentagon in the hexagon (distractor). The study found that there was no significant difference in the impact trend of large and small groups on people, and there was a high degree of consistency. At the same time, this experiment also found that the effect of ensemble size may be different for males and females. And the reaction time has a great relationship with the location of the target and the reading habits of the subjects. This requires further exploration.

CNN-based in situ tool wear detection: A study on model training and data augmentation in turning inserts

We present an automated turning insert wear detection system developed for aeronautical Low Pressure Turbines (LPT) casing machining process based on a binary classifier using Convolutional Neural Networks (CNN). This method involves acquiring the image on the machine itself. During this process, removing the insert from the tool holder is not necessary, and the wear assessment is performed before the next workpiece is mechanized. Since datasets in tool wear prediction are often imbalanced, a multi perspective camera technology as well as data augmentation and class weighting are utilized to address both the number of worn parts considered and the cost of image acquisition. In this study four different insert types and two CNN architectures (specific and universal models) are considered and evaluated. The effects of data augmentation and training set size are discussed. While the models trained perform well on round inserts, they fail on rhombic insert types. An accuracy up to 97.8% (Matthew’s correlation coefficient of 0.955) is achieved by the machine learning model. Additionally, it can detect defects on a variety of insert types.

Which processes dominate visual search: Bottom-up feature contrast, top-down tuning or trial history?

Previous research has identified three mechanisms that guide visual attention: bottom-up feature contrasts, top-down tuning, and the trial history (e.g., priming effects). However, only few studies have simultaneously examined all three mechanisms. Hence, it is currently unclear how they interact or which mechanisms dominate over others. With respect to local feature contrasts, it has been claimed that a pop-out target can only be selected immediately in dense displays when the target has a high local feature contrast, but not when the displays are sparse, which leads to an inverse set-size effect. The present study critically evaluated this view by systematically varying local feature contrasts (i.e., set size), top-down knowledge, and the trial history in pop-out search. We used eye tracking to distinguish between early selection and later identification-related processes. The results revealed that early visual selection was mainly dominated by top-down knowledge and the trial history: When attention was biased to the target feature, either by valid pre-cueing (top-down) or automatic priming, the target could be localised immediately, regardless of display density. Bottom-up feature contrasts only modulated selection when the target was unknown and attention was biased to the non-targets. We also replicated the often-reported finding of reliable feature contrast effects in the mean RTs, but showed that these were due to later, target identification processes (e.g., in the target dwell times). Thus, contrary to the prevalent view, bottom-up feature contrasts in dense displays do not seem to directly guide attention, but only facilitate nontarget rejection, probably by facilitating nontarget grouping.

Performance of Density Functionals and Semiempirical 3c Methods for Small Gold–Thiolate Clusters

In light of the recent surge in computational studies of gold thiolate clusters, we present a comparison of popular density functionals (DFAs) and three-part corrected methods (3c-methods) on their performance by taking a data set named as AuSR18 consisting of 18 isomers of Aun(SCH3)m (m ≤ n = 1-3). We have compared the efficiency and accuracy of the DFAs and 3c-methods in geometry optimization with RI-SCS-MP2 as the reference method. Similarly, the performance for accurate and efficient energy evaluation was compared with DLPNO-CCSD(T) as the reference method. The lowest energy structure among the isomers of the largest stoichiometry from our data set, AuSR18, i.e., Au3(SCH3)3, is considered to evaluate the computational time for SCF and gradient evaluations. Alongside this, the numbers of optimization steps to locate the most stable minima of Au3(SCH3)3 are compared to assess the efficiency of the methods. A comparison of relevant bond lengths with the reference geometries was made to estimate the accuracy in geometry optimization. Some methods, such as LC-BLYP, ωB97M-D3BJ, M06-2X, and PBEh-3c, could not locate many of the minima found by most of the other methods; thus, the versatility in locating various minima is also an important criterion in choosing a method for the given project. To determine the accuracy of the methods, we compared the relative energies of the isomers in each stoichiometry and the interaction energy of the gold core with the ligands. The dependence of basis set size and relativistic effects on energies are also compared. The following are some of the highlights. TPSS has shown accuracy, while mPWPW shows comparable speed and accuracy. For the relative energies of the clusters, the hybrid range-separated DFAs are the best option. CAM-B3LYP excels, whereas B3LYP performs poorly. Overall, LC-BLYP is a balanced performer considering both the geometry and relative stability of the structures, but it lacks diversity. The 3c-methods, although fast, are less impressive in relative stability.

A Comparative Study of Time Series Anomaly Detection Models for Industrial Control Systems.

Anomaly detection has been known as an effective technique to detect faults or cyber-attacks in industrial control systems (ICS). Therefore, many anomaly detection models have been proposed for ICS. However, most models have been implemented and evaluated under specific circumstances, which leads to confusion about choosing the best model in a real-world situation. In other words, there still needs to be a comprehensive comparison of state-of-the-art anomaly detection models with common experimental configurations. To address this problem, we conduct a comparative study of five representative time series anomaly detection models: InterFusion, RANSynCoder, GDN, LSTM-ED, and USAD. We specifically compare the performance analysis of the models in detection accuracy, training, and testing times with two publicly available datasets: SWaT and HAI. The experimental results show that the best model results are inconsistent with the datasets. For SWaT, InterFusion achieves the highest F1-score of 90.7% while RANSynCoder achieves the highest F1-score of 82.9% for HAI. We also investigate the effects of the training set size on the performance of anomaly detection models. We found that about 40% of the entire training set would be sufficient to build a model producing a similar performance compared to using the entire training set.