Robust Analysis Research Articles

Exploiting Neighbor Effect: Conv-Agnostic GNN Framework for Graphs With Heterophily.

Due to the homophily assumption in graph convolution networks (GCNs), a common consensus in the graph node classification task is that graph neural networks (GNNs) perform well on homophilic graphs but may fail on heterophilic graphs with many interclass edges. However, the previous interclass edges' perspective and related homo-ratio metrics cannot well explain the GNNs' performance under some heterophilic datasets, which implies that not all the interclass edges are harmful to GNNs. In this work, we propose a new metric based on the von Neumann entropy to reexamine the heterophily problem of GNNs and investigate the feature aggregation of interclass edges from an entire neighbor identifiable perspective. Moreover, we propose a simple yet effective Conv-Agnostic GNN framework (CAGNNs) to enhance the performance of most GNNs on the heterophily datasets by learning the neighbor effect for each node. Specifically, we first decouple the feature of each node into the discriminative feature for downstream tasks and the aggregation feature for graph convolution (GC). Then, we propose a shared mixer module to adaptively evaluate the neighbor effect of each node to incorporate the neighbor information. The proposed framework can be regarded as a plug-in component and is compatible with most GNNs. The experimental results over nine well-known benchmark datasets indicate that our framework can significantly improve performance, especially for the heterophily graphs. The average performance gain is 9.81%, 25.81%, and 20.61% compared with graph isomorphism network (GIN), graph attention network (GAT), and GCN, respectively. Extensive ablation studies and robustness analysis further verify the effectiveness, robustness, and interpretability of our framework. Code is available at https://github.com/JC-202/CAGNN.

Robustness Analysis of a Distributed Adaptive Model Predictive Control for Connected and Automated Vehicles Against Delays

Robustness Analysis of a Distributed Adaptive Model Predictive Control for Connected and Automated Vehicles Against Delays

Bayesian Dictionary Learning on Robust Tubal Transformed Tensor Factorization.

The recent study on tensor singular value decomposition (t-SVD) that performs the Fourier transform on the tubes of a third-order tensor has gained promising performance on multidimensional data recovery problems. However, such a fixed transformation, e.g., discrete Fourier transform and discrete cosine transform, lacks being self-adapted to the change of different datasets, and thus, it is not flexible enough to exploit the low-rank and sparse property of the variety of multidimensional datasets. In this article, we consider a tube as an atom of a third-order tensor and construct a data-driven learning dictionary from the observed noisy data along the tubes of the given tensor. Then, a Bayesian dictionary learning (DL) model with tensor tubal transformed factorization, aiming to identify the underlying low-tubal-rank structure of the tensor effectively via the data-adaptive dictionary, is developed to solve the tensor robust principal component analysis (TRPCA) problem. With the defined pagewise tensor operators, a variational Bayesian DL algorithm is established and updates the posterior distributions instantaneously along the third dimension to solve the TPRCA. Extensive experiments on real-world applications, such as color image and hyperspectral image denoising and background/foreground separation problems, demonstrate both effectiveness and efficiency of the proposed approach in terms of various standard metrics.

Robust Principal Component Analysis via Joint Reconstruction and Projection.

Principal component analysis (PCA) is one of the most widely used unsupervised dimensionality reduction algorithms, but it is very sensitive to outliers because the squared l2 -norm is used as distance metric. Recently, many scholars have devoted themselves to solving this difficulty. They learn the projection matrix from minimum reconstruction error or maximum projection variance as the starting point, which leads them to ignore a serious problem, that is, the original PCA learns the projection matrix by minimizing the reconstruction error and maximizing the projection variance simultaneously, but they only consider one of them, which imposes various limitations on the performance of model. To solve this problem, we propose a novel robust principal component analysis via joint reconstruction and projection, namely, RPCA-RP, which combines reconstruction error and projection variance to fully mine the potential information of data. Furthermore, we carefully design a discrete weight for model to implicitly distinguish between normal data and outliers, so as to easily remove outliers and improve the robustness of method. In addition, we also unexpectedly discovered that our method has anomaly detection capabilities. Subsequently, an effective iterative algorithm is explored to solve this problem and perform related theoretical analysis. Extensive experimental results on several real-world datasets and RGB large-scale dataset demonstrate the superiority of our method.

Swift-Eye: Towards Anti-blink Pupil Tracking for Precise and Robust High-Frequency Near-Eye Movement Analysis with Event Cameras.

Eye tracking has shown great promise in many scientific fields and daily applications, ranging from the early detection of mental health disorders to foveated rendering in virtual reality (VR). These applications all call for a robust system for high-frequency near-eye movement sensing and analysis in high precision, which cannot be guaranteed by the existing eye tracking solutions with CCD/CMOS cameras. To bridge the gap, in this paper, we propose Swift-Eye, an offline precise and robust pupil estimation and tracking framework to support high-frequency near-eye movement analysis, especially when the pupil region is partially occluded. Swift-Eye is built upon the emerging event cameras to capture the high-speed movement of eyes in high temporal resolution. Then, a series of bespoke components are designed to generate high-quality near-eye movement video at a high frame rate over kilohertz and deal with the occlusion over the pupil caused by involuntary eye blinks. According to our extensive evaluations on EV-Eye, a large-scale public dataset for eye tracking using event cameras, Swift-Eye shows high robustness against significant occlusion. It can improve the IoU and F1-score of the pupil estimation by 20% and 12.5% respectively, compared with the second-best competing approach, when over 80% of the pupil region is occluded by the eyelid. Lastly, it provides continuous and smooth traces of pupils in extremely high temporal resolution and can support high-frequency eye movement analysis and a number of potential applications, such as mental health diagnosis, behaviour-brain association, etc. The implementation details and source codes can be found at https://github.com/ztysdu/Swift-Eye.

Multidimensional Data Processing With Bayesian Inference via Structural Block Decomposition.

How to handle large multidimensional datasets, such as hyperspectral images and video information, efficiently and effectively plays a critical role in big-data processing. The characteristics of low-rank tensor decomposition in recent years demonstrate the essentials in describing the tensor rank, which often leads to promising approaches. However, most current tensor decomposition models consider the rank-1 component simply to be the vector outer product, which may not fully capture the correlated spatial information effectively for large-scale and high-order multidimensional datasets. In this article, we develop a new novel tensor decomposition model by extending it to the matrix outer product or called Bhattacharya-Mesner product, to form an effective dataset decomposition. The fundamental idea is to decompose tensors structurally in a compact manner as much as possible while retaining data spatial characteristics in a tractable way. By incorporating the framework of the Bayesian inference, a new tensor decomposition model on the subtle matrix unfolding outer product is established for both tensor completion and robust principal component analysis problems, including hyperspectral image completion and denoising, traffic data imputation, and video background subtraction. Numerical experiments on real-world datasets demonstrate the highly desirable effectiveness of the proposed approach.

On the Robustness of Average Losses for Partial-Label Learning.

Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL). In PLL, identification-based strategy (IBS) purifies each PL on the fly to select the (most likely) TL for training; average-based strategy (ABS) treats all candidate labels equally for training and let trained models be able to predict TL. Although PLL research has focused on IBS for better performance, ABS is also worthy of study since modern IBS behaves like ABS in the beginning of training to prepare for PL purification and TL selection. In this paper, we analyze why ABS was unsatisfactory and propose how to improve it. Theoretically, we propose two problem settings of PLL and prove that average PL losses (APLLs) with bounded multi-class losses are always robust, while APLLs with unbounded losses may be non-robust, which is the first robustness analysis for PLL. Experimentally, we have two promising findings: ABS using bounded losses can match/exceed state-of-the-art performance of IBS using unbounded losses; after using robust APLLs to warm start, IBS can further improve upon itself. Our work draws attention to ABS research, which can in turn boost IBS and push forward the whole PLL.

Retaining Black Women in Computing: A Comparative Analysis of Interventions for Computing Persistence

Black women remain severely underrepresented in computing despite ongoing efforts to diversify the field. Given that Black women exist at the intersection of both racial and gendered identities, tailored approaches are necessary to address the unique barriers Black women face in computing. However, it is difficult to quantitatively evaluate the efficacy of interventions designed to retain Black women in computing, since samples of computing students typically contain too few Black women for robust statistical analysis. Using about a decade of student survey responses from an National Science Foundation–funded Broadening Participation in Computing alliance, we use regression analyses to quantitatively examine the connection between different types of interventions and Black women’s intentions to persist in computing and how this compares to other students (specifically, Black men, white women, and white men). This comparison allows us to quantitatively explore how Black women’s needs are both distinct from—and similar to—other students. We find that career awareness and faculty mentorship are the two interventions that have a statistically significant, positive correlation with Black women’s computing persistence intentions. No evidence was found that increasing confidence or developing skills/knowledge was correlated with Black women’s computing persistence intentions, which we posit is because Black women must be highly committed and confident to pursue computing in college. Last, our results suggest that many efforts to increase the number of women in computing are focused on meeting the needs of white women. While further analyses are needed to fully understand the impact of complex intersectional identities in computing, this large-scale quantitative analysis contributes to our understanding of the nuances of Black women’s needs in computing.

Agile fusion: developing 'Eat at Right Place' sentiment analysis tool

This study presents the development and validation of the "Eat at Right Place Initiative," a sentiment analysis tool for restaurant reviews. Combining a user-centric approach with the Scrum framework, the mHealth agile development and evaluation framework was implemented, deviating from the initially considered Scrum framework. A multidisciplinary team navigated three phases, aligning sprints, goals, and backlogs. Phase 1 focused on product identification through interviews and surveys. Phase 2 involved development and alpha testing using a bidirectional encoder representation from transformers (BERT) rule-based sentiment analysis model. The final phase, beta testing, incorporated user feedback for usability enhancements. Ethical considerations were prioritized, ensuring participant consent and confidentiality. The study culminated in a robust aspect-based sentiment analysis model, effective in capturing nuanced insights from diverse restaurant aspects. Beta testing revealed actionable insights, marking the tool as fit for release. This sentiment analysis tool addresses consumer and owner needs, with iterative development and real-world testing laying the groundwork for future enhancements.

Fostering students’ mathematical reasoning through a cooperative learning model

This study sought to ascertain whether the student teams-achievement division (STAD) model of cooperative learning is effective in fostering students’ mathematical reasoning. Using the cluster random sampling technique, 301 eleventh-grade students between the ages of 14 and 20 were chosen from six public secondary schools within one district in Zambia. Students were given tasks on quadratic equations and functions both before and after the intervention. A robust analysis of the covariance test revealed that students’ mathematical reasoning abilities were significantly higher for the group that received instruction using the STAD approach than for the group that was taught using conventional methods of instruction at each of the five design points where regression slopes were comparable. A Chi-square test of independence further revealed that the STAD learning approach was associated with a greater proportion of students who demonstrated an appropriate degree of mathematical reasoning ability for each of the three indicators (conjecturing, justifying, and mathematizing). These results demonstrate that enhancing students’ mathematical reasoning abilities through the integration of classroom activities that engage students intellectually, physically, and socially is beneficial.

Multimodal Contrastive Transformer for Explainable Recommendation

Explanations play an essential role in helping users evaluate results from recommender systems. Various natural language generation methods have been proposed to generate explanations for the recommendation. However, they usually suffer from two problems. First, since user-provided review text contains noisy data, the generated explanations may be irrelevant to the recommended items. Second, as lacking some supervision signals, most of the generated sentences are similar, which cannot meet the diversity and personalized needs of users. To tackle these problems, we propose a multimodal contrastive transformer (MMCT) model for an explainable recommendation, which incorporates multimodal information into the learning process, including sentiment features, item features, item images, and refined user reviews. Meanwhile, we propose a dynamic fusion mechanism during the decoding stage, which generates supervision signals to guide the explanation generation. Additionally, we develop a contrastive objective to generate diverse explainable texts. Comprehensive experiments on two real-world datasets show that the proposed model outperforms comparable explainable recommendation baselines in terms of explanation performance and recommendation performance. Efficiency analysis and robustness analysis verify the advantages of the proposed model. While ablation analysis establishes the relative contributions of the respective components and various modalities, the case study shows the working of our model from an intuitive sense.

Abstract 4373: Molecular interactions of human receptor tyrosine kinase oncogenic fusions

Abstract Oncogenic fusions (OFs) play a critical role in cancer development. Despite the exponential increase in identifications, only a small number have been extensively characterized functionally. In this study, we are focused on receptor tyrosine kinase OFs (RTK-OFs) prevalent in aggressive cancers. Our primary objective is to elucidate the underlying molecular mechanisms of 182 in-frame RTK-OFs identified in The Cancer Genome Atlas. An automated high-throughput PCR technique will be used to clone individual gene fusions using the sequences obtained from RNA-Seq data sourced from the TCGA dataset. These clones will be used to establish an RTK-OF-expressing isogenic cell line library. Multiple Approaches Combined (MAC)-tag workflow combining Affinity Purification (AP) and proximity biotin-dependent (BioID) purification within a single construct will be used. Subsequent high-throughput EvoSep One Liquid Chromatography coupled to ultra-fast TimsTOF Pro 2 Mass Spectrometry sample processing enables sensitive and robust analysis. These methods enable the capture of stable protein complexes and transient interactors associated with RTK-OFs. In our proof of concept study, we have identified four in-frame variants of FGFR3::TACC3 oncogenic fusions.FGFR3, part of the fibroblast growth factor (FGF) RTK family, activates cellular proliferation and growth through FGF binding and receptor dimerization. In FGFR3-TACC3 OFs, diverse isoforms are observed, with prevalent rearrangements linking FGFR3 exon 17 to various TACC3 exons. TACC3 is vital for mitotic microtubule organization and stabilizes kinetochore fibers and the mitotic spindle through its conserved C-terminal coiled-coil domain (TACC domain). TACC3 contributes to the fusion with varying sizes of the C-terminal coiled-coil domain which all constitutively activate the FGFR3 tyrosine kinase. In our studies, we show that FGFR3::TACC3 variants have a different localization to their wild-type counterparts. While FGFR3 and TACC3 are found in the cytoplasm all the OF variants strongly localize around the nucleus. Our identified high-confidence interactors represent a comprehensive FGFR3::TACC3 interaction network and reveal central pathways and key interactors related to microtubule assembly and possible deregulation of the cell cycle. The resultant data from these advanced technologies enable the construction of comprehensive interaction networks, shedding light on critical signaling pathways and subnetworks relevant to RTK-OFs. Further in-depth analysis of 182 RTK-OFs may uncover actionable pathways and druggable interactors within these networks. Our multi-faceted research strategy holds significant promise for advancing targeted drug discovery, ultimately contributing to improved cancer treatment strategies. Citation Format: Giovanna Dashi, Markku Varjosalo. Molecular interactions of human receptor tyrosine kinase oncogenic fusions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4373.

The evolution and robustness analysis of global soybean trade network

In recent years, the frequent occurrence of extreme weather and national conflicts has highlighted the necessity of enhancing the robustness of the grain trade network. In this paper, we study the evolution of major trading countries, and the network robustness under random and different attack strategies. The results show that the heterogeneity of the Global Soybean Trade Network (GSTN) and trade connections among countries are increasing. The soybean trade network has strong resistance to random attacks, while subjected to targeted attacks, the stability of the network is significantly affected and damaged.

Toward One‐Way Smoke: Synthesis of Copper‐Based Microclubs with Asymmetric Scattering and Absorption

AbstractThe ultimate goal of this work is to create an engineered aerosol that acts as one‐way smoke, i.e. it creates an asymmetric vision environment in which the ability to image objects depends on the viewing direction. To this end, a rapid, one‐pot synthesis of copper‐based microclubs is developed that consists of a Cu2O octahedron attached to a Cu2O@Cu shaft. Millions of synthesized particles are analyzed in minutes with a FlowCam to provide a robust statistical analysis of their geometry, and rapidly elucidate the roles of the reaction constituents on the particle shape and yield. The combination of asymmetry in both shape and composition introduces a 30% difference in scattering of light propagating parallel to the microclub axis from opposing directions. This work represents a first step toward the creation of an asymmetric imaging environment with an aerosol consisting of acoustically aligned microclubs.

The heterogeneous impacts of environmental technologies and research and development spending on green growth in emerging economies: the moderating role of financial globalization

Introduction: Understanding the heterogeneous impacts of environmental technologies (ETs), research and development (RD) spending and financial globalization (FG) on green economic growth (GEG) is worthwhile to promote progress toward GEG. Besides, exploring the moderating role of FG is essential to uncover the nuanced dynamics that shape the relationship between ET, RD, GEG, and the influence of global financial integration. Thus, this study examines the effects of ET, RD and FD on GEG in emerging market economies (EMEs). In addition, we investigate the moderating role of FG on the effects of ETs and RD on GEG.Methods: The method of moments quantile regression (MMQR) is applied using a fixed effects model that can capture distributional heterogeneity and nonnormality concerns for the panel of 25 EMEs from 2000 to 2019. In addition, other alternative models are applied to conduct robustness analysis. We use green total factor productivity (GP) to proxy for GEG using the Malmquist–Luenberger Productivity Index (MLPI) strategy based on the directional distance function (DDF).Results and discussion: The findings imply that ETs significantly impact GEG, revealing evidence that promoting environmental innovation positively contributes to GEG progress in EMEs. Likewise, RD promotes GEG progression in EMEs. Additionally, FG positively impacts GEG. FG also positively moderates the effects of ETs and RD on GEG, implying that countries open to FG can better harness the positive roles of investment in ETs and RD on GEG in EMEs. Therefore, policymakers should develop prudent policies to encourage ETs and RD to promote GEG in EMEs, which aligns with the goals of controlling climate variation (SDG-13) and fostering innovation (SDG-9) to promote GEG in EMEs.

Improved frequency control performance of restructured multi-area power system through optimized controller design

This paper presents an optimal cascaded three degree of freedom proportional integral (3DOF PI) controller and compares its performance with conventional proportional integral (PI) controller for load frequency control of two area deregulated power system. The 3DOF PI controller enhances the degree of freedom and gives better dynamic response. Genetic algorithm (GA) is used to get the optimal gains of the proposed controllers. In the considered two area thermal-gas-fuel cell system, the thermal system consists of a reheat turbine with appropriate generation rate constraint non linearity. Battery storage and unified power flow controller (UPFC) are also assumed to be present. The performance of the proposed controllers is evaluated under different operating conditions that take place in deregulated power market. To tackle any contract violation this article emphasizes on the ancillary services provided by electric vehicles (EVs) and distributed generations. A robustness analysis is also presented by varying parameters from their nominal values.

Coherent multi-band MIMO radar: robustness analysis to SSMF-based RF signal delivery.

A numerical evaluation is conducted to assess the impact of distributing radio frequency (RF) signals through optical fiber links on the performance of a coherent multi-band multiple-input multiple-output (MIMO) radar system. The analysis focuses on scenarios where the antennas are widely separated in comparison to the employed signal wavelengths. The development of a model to quantify the phase noise (PN) induced on each RF band due to the signal transmission through optical fiber links between the centralized base station and each radar peripheral is described. Monte Carlo simulation results are collected to estimate the key performance indicators (KPIs) for varying standard single-mode fiber (SSMF) length and different PN contributions. The main contributors to the PN are revealed to be chromatic dispersion (CD), double Rayleigh scattering (DRS), and mechanical vibrations. In a shipborne scenario, a significant performance degradation occurs only when the length of the fiber links reaches approximately 20 km. Further, the PN impact has also been studied in a shipborne scenario to analyze the robustness of the system for worse phase noise level assumptions. The results reveal excellent robustness of the proposed centralized acquisition and processing approach in the presence of both very long fiber links and economically employed RF oscillators.

My Data Are Ready, How Do I Analyze Them: Navigating Data Analysis in Social Science Research

This paper offers a practical guide for researchers to effectively analyze their data in social science research, addressing the challenges and pitfalls commonly encountered in data analysis. By exploring various data analysis techniques, highlighting key challenges such as data quality issues and statistical assumptions violations, and providing practical tips and guidelines, this study fills a gap in the existing literature by offering a comprehensive approach to navigating data analysis in social science research. The significance of this study lies in its potential to improve the quality and reliability of research findings in the social sciences, equipping researchers with the necessary knowledge and skills to conduct robust data analysis. This study is a valuable resource for researchers seeking to enhance their analytical skills, avoid common pitfalls, and advance knowledge in their field of study.

Digital Payments and Consumption: Evidence from the 2016 Demonetization in India

Abstract We study how consumer spending responds to digital payments, using the differential switch to digital payments across consumers induced by the sudden 2016 Indian Demonetization for identification. Digital payment use rose by 2.94 percentage points and monthly spending increased by 2.38% for an additional 10 percentage points in prior cash dependence. Spending remained elevated even when cash availability recovered. Robustness analyses show that the spending response is not driven by purchase substitution, income shocks, credit supply, or price changes. We provide causal evidence that digital payments increase consumer spending due to subdued endowment effects.

Transdiagnostic psychopathology in the light of robust single-trial event-related potentials.

Recent evidence indicates that event-related potentials (ERPs) as measured on the electroencephalogram (EEG) are more closely related to transdiagnostic, dimensional measures of psychopathology (TDP) than to diagnostic categories. A comprehensive examination of correlations between well-studied ERPs and measures of TDP is called for. In this study, we recruited 50 patients with emotional disorders undergoing 14 weeks of transdiagnostic group psychotherapy as well as 37 healthy comparison subjects (HC) matched in age and sex. HCs were assessed once and patients three times throughout treatment (N = 172 data sets) with a battery of well-studied ERPs and psychopathology measures consistent with the TDP framework The Hierarchical Taxonomy of Psychopathology (HiTOP). ERPs were quantified using robust single-trial analysis (RSTA) methods and TDP correlations with linear regression models as implemented in the EEGLAB toolbox LIMO EEG. We found correlations at several levels of the HiTOP hierarchy. Among these, a reduced P3b was associated with the general p-factor. A reduced error-related negativity correlated strongly with worse symptomatology across the Internalizing spectrum. Increases in the correct-related negativity correlated with symptoms loading unto the Distress subfactor in the HiTOP. The Flanker N2 was related to specific symptoms of Intrusive Cognitions and Traumatic Re-experiencing and the mismatch negativity to maladaptive personality traits at the lowest levels of the HiTOP hierarchy. Our study highlights the advantages of RSTA methods and of using validated TDP constructs within a consistent framework. Future studies could utilize machine learning methods to predict TDP from a set of ERP features at the subject level.