Effect Of Redundancy Research Articles

Pedestrian Trajectory Prediction Based on Motion Pattern De-Perturbation Strategy

Pedestrian trajectory prediction is extremely challenging due to the complex social attributes of pedestrians. Introducing latent vectors to model trajectory multimodality has become the latest mainstream solution idea. However, previous approaches have overlooked the effects of redundancy that arise from the introduction of latent vectors. Additionally, they often fail to consider the inherent interference of pedestrians with no trajectory history during model training. This results in the model’s inability to fully utilize the training data. Therefore, we propose a two-stage motion pattern de-perturbation strategy, which is a plug-and-play approach that introduces optimization features to model the redundancy effect caused by latent vectors, which helps to eliminate the redundancy effects in the trajectory prediction phase. We also propose loss masks to reduce the interference of invalid data during training to accurately model pedestrian motion patterns with strong physical interpretability. Our comparative experiments on the publicly available ETH and UCY pedestrian trajectory datasets, as well as the Stanford UAV dataset, show that our optimization strategy achieves better pedestrian trajectory prediction accuracies than a range of state-of-the-art baseline models; in particular, our optimization strategy effectively absorbs the training data to assist the baseline models in achieving optimal modeling accuracy.

The Phenotypic Diversity of 232 Germplasm Accessions Identifies the Adverse Effects of Flowering Redundancy on Peanut Yield

Peanut is a leguminous crop with an indeterminate growth habit that will continuously flower during the entire reproductive development stage. Flowering redundancy adversely affects the yield and quality of peanut. In this study, eight flowering and five yield-related traits were collected and comprehensively evaluated from a diverse germplasm panel consisting of 232 peanut accessions, aiming to provide a theoretical basis for improving the flowering habit and yield for future peanut breeding efforts. As a result, large phenotypic diversity was observed in 13 traits. Most of the traits suggested high heritability, except high effective flowering duration (HEFD), days to 100% flower cessation (DTC100), and yield per plant (YPP). Days to 90% flower cessation (DTC90), days to DTC100, flowering duration (FD), and low effective flowering duration (LEFD) showed significant negative correlations with 100-seed weight, 100-pod weight, YPP, average weight per pod, and shelling percentage. Principal component analysis (PCA) suggested that flowering redundancy traits as well as yield-related traits contributed more to the first three PCs when compared to other traits. This study addresses the lack of peanut flowering phenotypic and genetic diversity and lays the foundation for in-depth research on redundancy-related genes in peanut flowering.

Associations between vaccination and protective behaviors against COVID-19: transfer and redundancy effects as potential psychological mechanisms

ABSTRACT This study aimed to investigate mechanisms explaining associations between vaccination and protective health behaviors during the COVID-19 pandemic. The study used a secondary analysis of data from a longitudinal online study at four time points between April 2020 and March 2021. Two hundred and forty participants responded to questionnaires assessing adherence to multiple COVID-19 protection behaviors, COVID-19 vaccination, behavioral specific outcome expectancies and general healthy lifestyle. Statistical analyses included z statistic for differences between correlations and moderation analysis by the SPSS PROCESS macro. The correlation between initial adherence to protective behaviors prior to availability of vaccination and actual vaccination was positive, but when vaccination was available, the concurrent correlation between these behaviors was null. Healthy lifestyle and outcome expectancies moderated the association between vaccination and adherence to protection behaviors. These results were explained by a ‘redundancy effect’, conceptualized as beliefs that engagement in specific health behaviors justifies evading other health behaviors. The ‘redundancy effect’ cancelled the initial positive correlation between vaccination and protective health behaviors, produced by a ‘transfer effect’, based on similarities between the perceived purposes of those behaviors. Theoretical and practical implications of the findings were discussed.

Semantic congruency modulates the speed-up of multisensory responses

Responses to multisensory signals are often faster compared to their unisensory components. This speed-up is typically attributed to target redundancy in that a correct response can be triggered by one or the other signal. In addition, semantic congruency of signals can also modulate multisensory responses; however, the contribution of semantic content is difficult to isolate as its manipulation commonly changes signal redundancy as well. To disentangle the effects of redundancy and semantic congruency, we manipulated semantic content but kept redundancy constant. We presented semantically congruent/incongruent animal pictures and sounds and asked participants to respond with the same response to two target animals (cats and dogs). We find that the speed-up of multisensory responses is larger for congruent (e.g., barking dogs) than incongruent combinations (e.g., barking cats). We then used a computational modelling approach to analyse audio-visual processing interferences that may underlie the effect. Our data is best described by a model that explains the semantic congruency modulation with a parameter that was previously linked to trial sequence effects, which in our experiment occur from the repetition/switching of both sensory modality and animal category. Yet, a systematic analysis of such trial sequence effects shows that the reported congruency effect is an independent phenomenon. Consequently, we discuss potential contributors to the semantic modulation of multisensory responses.

Partial Multilabel Learning Using Noise-Tolerant Broad Learning System With Label Enhancement and Dimensionality Reduction.

Partial multilabel learning (PML) addresses the issue of noisy supervision, which contains an overcomplete set of candidate labels for each instance with only a valid subset of training data. Using label enhancement techniques, researchers have computed the probability of a label being ground truth. However, enhancing labels in the noisy label space makes it impossible for the existing partial multilabel label enhancement methods to achieve satisfactory results. Besides, few methods simultaneously involve the ambiguity problem, the feature space's redundancy, and the model's efficiency in PML. To address these issues, this article presents a novel joint partial multilabel framework using broad learning systems (namely BLS-PML) with three innovative mechanisms: 1) a trustworthy label space is reconstructed through a novel label enhancement method to avoid the bias caused by noisy labels; 2) a low-dimensional feature space is obtained by a confidence-based dimensionality reduction method to reduce the effect of redundancy in the feature space; and 3) a noise-tolerant BLS is proposed by adding a dimensionality reduction layer and a trustworthy label layer to deal with PML problem. We evaluated it on six real-world and seven synthetic datasets, using eight state-of-the-art partial multilabel algorithms as baselines and six evaluation metrics. Out of 144 experimental scenarios, our method significantly outperforms the baselines by about 80%, demonstrating its robustness and effectiveness in handling partial multilabel tasks.

Markov Model-Based Reliability Analysis Considering the Redundancy Effect of Modular Converters

Markov Model-Based Reliability Analysis Considering the Redundancy Effect of Modular Converters

Human adenovirus oncolytic properties and the inhibitory role of E4 orf4 and E4 orf6/7 on endogenously activated NF-κB

Human adenovirus is a promising tool for cancer therapy as an oncolytic virus. To predict which region of the oncolytic adenovirus E4 gene could be deleted, we investigated the relationship between the E4 proteins and NF-κB. Here, we report that TLR2-dependent NF-κB activation in Ad5-infected cells was significantly inhibited 24 h post-infection. Among the six E4 proteins, E4 orf4 and E4 orf6/7 exhibited notable suppressive effects on NF-κB activation. However, only E4 orf4 was co-immunoprecipitated with the RelA protein, also known as p65. It appears likely that E4 orf6/7 represses NF-κB activation via E2F-dependent pathways. Our results suggest that both E4 orf4 and E4 orf6/7 are novel inhibitors of NF-κB activation. The inhibition of endogenous NF-κB activation by E4 proteins during the late phase of infection also appears to elucidate the previously reported suppression of E1A expression in the late phase of infection. These redundant suppressive effects of E4 orf4 and E4 orf6/7 on NF-κB suggest that these proteins may play a major role in the anticancer properties of oncolytic adenovirus.

The effective sample size in Bayesian information criterion for level-specific fixed and random-effect selection in a two-level nested model.

Popular statistical software provides the Bayesian information criterion (BIC) for multi-level models or linear mixed models. However, it has been observed that the combination of statistical literature and software documentation has led to discrepancies in the formulas of the BIC and uncertainties as to the proper use of the BIC in selecting a multi-level model with respect to level-specific fixed and random effects. These discrepancies and uncertainties result from different specifications of sample size in the BIC's penalty term for multi-level models. In this study, we derive the BIC's penalty term for level-specific fixed- and random-effect selection in a two-level nested design. In this new version of BIC, called , this penalty term is decomposed into two parts if the random-effect variance-covariance matrix has full rank: (a) a term with the log of average sample size per cluster and (b) the total number of parameters times the log of the total number of clusters. Furthermore, we derive the new version of BIC, called , in the presence of redundant random effects. We show that the derived formulae, and , adhere to empirical values via numerical demonstration and that ( indicating either or ) is the best global selection criterion, as it performs at least as well as BIC with the total sample size and BIC with the number of clusters across various multi-level conditions through a simulation study. In addition, the use of is illustrated with a textbook example dataset.

Multiple kernel graph clustering with shifted Laplacian reconstruction

Multiple kernel graph clustering methods have gained favor among researchers for their ability to combine the strengths of graph learning and multiple kernel methods. We propose a novel method named multiple kernel graph clustering with shifted Laplacian reconstruction (SLR-MKGC). Specifically, the kernel matrix is regarded as an affinity graph, and then the graph is transformed to a shifted Laplacian matrix. By decomposing the shifted Laplacian matrix, the latent data representation is obtained, simultaneously preserving the main energy and clustering information. As a result, the effects of noise and redundancy are reduced and the quality of the raw data are improved. Then, the obtained representation is used to reconstruct a final affinity graph with a desired block diagonal structure. Further, we conduct extensive experiments on seven benchmark datasets and compare nine state-of-the-art clustering methods. Experimental results show that SLR-MKGC exhibits excellent performance on most datasets. For example, compared to other state-of-the-art methods, SLR-MKGC achieves a performance improvement of 5.13% on the clustering accuracy term on the BBCSport2 dataset, demonstrating the promise of SLR-MKGC. The source code is available at https://github.com/dililidida/SLR-MKGC.

Latent Low-Rank Representation With Weighted Distance Penalty for Clustering.

Latent low-rank representation (LatLRR) is a critical self-representation technique that improves low-rank representation (LRR) by using observed and unobserved samples. It can simultaneously learn the low-dimensional structure embedded in the data space and capture the salient features. However, LatLRR ignores the local geometry structure and can be affected by the noise and redundancy in the original data space. To solve the above problems, we propose a latent LRR with weighted distance penalty (LLRRWD) for clustering in this article. First, a weighted distance is proposed to enhance the original Euclidean distance by enlarging the distance among the unconnected samples, which can enhance the discriminitation of the distance among the samples. By leveraging on the weighted distance, a weighted distance penalty is introduced to the LatLRR model to enable the method to preserve both the local geometric information and global information, improving discrimination of the learned affinity matrix. Moreover, a weight matrix is imposed on the sparse error norm to reduce the effect of noise and redundancy. Experimental results based on several benchmark databases show the effectiveness of our method in clustering.

The redundant target paradigm and its use as a blindsight-test: A meta-analytic study

The redundant target effect (RTE) is the well-known effect whereby a single target is detected faster when a second, redundant target is presented simultaneously. The RTE was shown in different experimental designs and applied in various clinical contexts. However, there are also studies showing non-effects or effects in the opposite direction. Our meta-analysis aims to investigate the replicability of the RTE. Herein, we focused on the clinical context within which the RTE has been applied most often and for which it gained particular prominence: The research on blindsight and other forms of residual vision in patients with damage to the neuronal visual system. The application of the RTE in clinical contexts assumes that whenever vision is present, an RTE will be found. Put differently, the RTE as a tool to uncover residual vision presumes that the RTE is a consistent feature of vision in the healthy population. We found a significant summary effect size of the RTE in healthy participants. The effect size depended on certain experimental features: task type, target configuration in the redundant condition, and how reaction times were computed in the single condition. A specific feature combination is typically used in blindsight research. Analyzing studies with this feature combination revealed a significant summary effect size in healthy participants predicting positive RTEs for future studies. A power-analysis revealed a required sample size of 14 participants to obtain an RTE with high reliability. However, the required sample size is rarely reached in blindsight research. Rather, blindsight research is mostly based on single-case studies. In summary, the RTE is a robust effect on group level but does not occur in every single individual. This means failure to obtain an RTE in a single patient should not be interpreted as evidence for the absence of residual vision in this patient.

Essential function of alveolin PfIMC1g in the Plasmodium falciparum asexual blood stage.

Infection by the Plasmodium falciparum parasite is responsible for the most severe form of human malaria. The asexual blood stage of the parasite, which occurs inside human red blood cells, is responsible for the symptoms of malaria and is the target of most antimalarial drugs. Plasmodium spp. rely on their highly divergent cytoskeletal structures to scaffold their cell division, sustain the mechanical stress of invasion, and survive in both the human bloodstream and the mosquito. We investigate the function of a class of divergent intermediate filament-like proteins called alveolins in the clinically important blood stage. The functional role of individual alveolins in Plasmodium remains poorly understood due to pleiotropic effects of gene knockouts and redundancy among alveolins. We evaluate the localization and essentiality of the four asexual-stage alveolins and find that PfIMC1g and PfIMC1c are essential. Furthermore, we demonstrate that PfIMC1g is critical for survival of the parasite post-invasion.

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

An accurate and efficient estimation of eucalyptus plantation areas is of paramount significance for forestry resource management and ecological environment monitoring. Currently, combining multidimensional optical and SAR images with machine learning has become an important method for eucalyptus plantation classification, but there are still some challenges in feature selection. This study proposes a feature selection method that combines multi-temporal Sentinel-1 and Sentinel-2 data with SLPSO (social learning particle swarm optimization) and RFE (Recursive Feature Elimination), which reduces the impact of information redundancy and improves classification accuracy. Specifically, this paper first fuses multi-temporal Sentinel-1 and Sentinel-2 data, and then carries out feature selection by combining SLPSO and RFE to mitigate the effects of information redundancy. Next, based on features such as the spectrum, red-edge indices, texture characteristics, vegetation indices, and backscatter coefficients, the study employs the Simple Non-Iterative Clustering (SNIC) object-oriented method and three different types of machine-learning models: Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM) for the extraction of eucalyptus plantation areas. Each model uses a supervised-learning method, with labeled training data guiding the classification of eucalyptus plantation regions. Lastly, to validate the efficacy of selecting multi-temporal data and the performance of the SLPSO–RFE model in classification, a comparative analysis is undertaken against the classification results derived from single-temporal data and the ReliefF–RFE feature selection scheme. The findings reveal that employing SLPSO–RFE for feature selection significantly elevates the classification precision of eucalyptus plantations across all three classifiers. The overall accuracy rates were noted at 95.48% for SVM, 96% for CART, and 97.97% for RF. When contrasted with classification outcomes from multi-temporal data and ReliefF–RFE, the overall accuracy for the trio of models saw an increase of 10%, 8%, and 8.54%, respectively. The accuracy enhancement was even more pronounced when juxtaposed with results from single-temporal data and ReliefF-RFE, at increments of 15.25%, 13.58%, and 14.54% respectively. The insights from this research carry profound theoretical implications and practical applications, particularly in identifying and extracting eucalyptus plantations leveraging multi-temporal data and feature selection.

Abnormal Detection Using Two-Stage Method in Combined Power Plant

Complex systems, such as power plants, demand precise and reliable anomaly detection mechanisms. Traditional supervised learning approaches often fall short due to the challenges of imbalanced data and the scarcity of labeled abnormal instances. This paper introduces a two-stage methodology to address these challenges. The first stage emphasizes feature engineering, mitigating redundant sensor effects, and reducing dimensionality through Kmeans clustering and PCA. The second stage employs an LSTM-Autoencoder for abnormal event detection. Validated using data from a combined power plant, our approach demonstrates superior performance over existing techniques in terms of accuracy and computational efficiency. This research not only advances the field of anomaly detection in power plants but also offers insights for other complex systems.

Applications of Cognitive Load Theory in Strength and Conditioning Course: Considerations and Implementations

The Cognitive Load Theory (CLT) aims to foster learning productively under optimal cognitive loads. Students across all ages and stages of learning have limited capacity due to the human brain’s functionality. Therefore, an effective learning design allows for knowledge acquisition that will minimize the loading effect on the working memory and foster the transition of knowledge to unlimited long-term memory. This article introduces the Cognitive Load Theory effects within Kinesiology instructional settings. In specific, the authors share examples of three cognitive load effects: (a) goal-free effect, (b) split attention effect, and (c) redundancy effect; and its implementations within the strength and conditioning for educators’ course in higher education settings.

ABRF: Adaptive BitRate-FEC Joint Control for Real-Time Video Streaming

Adaptive Forward Error Correction (AFEC) algorithms are proposed to achieve efficient Forward Error Correction (FEC) in Real-Time Communication (RTC). However, current AFEC approaches suffer two key limitations. (1) They do not consider the squeezing effect of redundancy on source bitrate while the squeezing usually happens in real RTC applications with limited bandwidth. (2) They estimate the future packet loss simply, ignoring some critical features of packet loss like randomness and multi-pattern. These drawbacks stop them from providing better Quality of Experience (QoE) in RTC services. We propose ABRF, a general QoE-oriented Adaptive BitRate-FEC joint control algorithm. ABRF makes predictions on the network loss pattern in the coming time and jointly calculates the optimal bitrate-FEC decision based on a QoE model for real-time video streaming. Moreover, ABRF is equipped with a fast adaptation method which helps it generalize across diverse network environments. In terms of Video Multi-method Assessment Fusion (VMAF), experimental results tell that ABRF decreases VMAF degradation caused by packet loss by 68%-95% compared with other AFEC algorithms in real-time video streaming in real-world Internet.

System redundancy of steel I‐girder bridges and proposal of a system factor for load rating

Abstract3D nonlinear FE analyses of existing plate‐girder bridges were carried out to investigate system redundancy. A comparison of the ultimate live load factors between a whole bridge system (WBS) model and a single girder (SG) model shows that WBS's ultimate live load factor is larger than SG's factor. This difference in the ultimate load factors is interpreted as the system redundancy effect. A system factor to quantify the system redundancy has been proposed based on the nonlinear FE analysis results. The proposed system factor is defined as a factor to convert the ultimate live load factor and rating factor for load rating based on linear analysis into those based on nonlinear analysis. A case study to calculate the system factor for three existing bridges shows dependency of the system factor on the number of girders, composite/non‐composite girders, simple span/continuous span.

Hierarchical Reliability Modelling and Analysis of Life Support System of Fighter Aircraft

The paper proposes a hierarchical reliability modelling and assessment approach for a life support system (LSS) that provides oxygen to the pilot and is employed in a combat aircraft. The system has the primary function of generating oxygen onboard, and it has a backup gaseous oxygen tank as redundancy. An emergency oxygen bottle is also part of the ejection seat for emergency use. Both backup oxygen and emergency oxygen have a fixed capacity and a fixed duration of oxygen supply. Therefore, it is crucial to assess the reliability of the LSS to ensure its safety and effectiveness of this LSS during a mission by the combat aircraft. The proposed reliability model of LSS is developed as a two-level hierarchical model, that captures the inherent randomness in the operation of the system. At the lowest level of the hierarchy, Markov chains are used to model the events that may lead to the failure of the LSS. The events include the failure of individual components, the depletion of backup oxygen, and the depletion of emergency oxygen. The Markov chains consider the interactions between individual components and events during the mission profile. At the top level of the hierarchy, a fault tree is used to model the interactions between various events during the mission profile. The fault tree considers the interactions between individual events and the effects of redundancy on the reliability of the LSS. The results of the Markov chains at the lower level are exported to the higher level modelled via fault tree to find the overall system reliability. The reliability model is further extended to incorporate the deterministic nature of the LSS due to the fixed capacity of the backup tank and emergency bottle. The work addresses the modelling of six different scenarios of LSS operations. The modelling of these scenarios is achieved using Semi-Markov Processes (SMP), which allow the state holding time to be a general distribution.

Pengaruh Korupsi, Pengeluaran Pemerintah dan Jumlah Uang Beredar (M2) Terhadap Pertumbuhan Ekonomi di ASEAN

The researchers want to analysis about Corruption, government spending, money supply (M2), and economic growth. The method of analysis use panel data. The number of cross-section data is 7 countries and time-series data are nine years. The best model was obtained from redundant fixed effect, Correlated random effects-Hausman test, and technical consideration. The selected model is the fixed-effect model. Based on regression output, government spending has a significant negative effect on economic growth in ASEAN. So, government spending can hamper economic growth in ASEAN. While; other variables such as Corruption and money supply (M2) are not significant. Meaning, that Corruption and money supply (M2) do not affect the economic growth in ASEAN.

Dual Mutual Information Constraints for Discriminative Clustering

Deep clustering is a fundamental task in machine learning and data mining that aims at learning clustering-oriented feature representations. In previous studies, most of deep clustering methods follow the idea of self-supervised representation learning by maximizing the consistency of all similar instance pairs while ignoring the effect of feature redundancy on clustering performance. In this paper, to address the above issue, we design a dual mutual information constrained clustering method named DMICC which is based on deep contrastive clustering architecture, in which the dual mutual information constraints are particularly employed with solid theoretical guarantees and experimental validations. Specifically, at the feature level, we reduce the redundancy among features by minimizing the mutual information across all the dimensionalities to encourage the neural network to extract more discriminative features. At the instance level, we maximize the mutual information of the similar instance pairs to obtain more unbiased and robust representations. The dual mutual information constraints happen simultaneously and thus complement each other to jointly optimize better features that are suitable for the clustering task. We also prove that our adopted mutual information constraints are superior in feature extraction, and the proposed dual mutual information constraints are clearly bounded and thus solvable. Extensive experiments on five benchmark datasets show that our proposed approach outperforms most other clustering algorithms. The code is available at https://github.com/Li-Hyn/DMICC.