Journal articles: 'Multi-modal statistics'

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Tai, J. J., and A. J. Gross. "Complex Segregation Analysis on Multi-Modal Distributions." Biometrical Journal 31, no. 1 (1989): 123–29. http://dx.doi.org/10.1002/bimj.4710310116.

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Sharma, Pulkit, Achut Manandhar, Patrick Thomson, Jacob Katuva, Robert Hope, and David A. Clifton. "Combining Multi-Modal Statistics for Welfare Prediction Using Deep Learning." Sustainability 11, no. 22 (November 11, 2019): 6312. http://dx.doi.org/10.3390/su11226312.

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In the context of developing countries, effective groundwater resource management is often hindered by a lack of data integration between resource availability, water demand, and the welfare of water users. As a consequence, drinking water-related policies and investments, while broadly beneficial, are unlikely to be able to target the most in need. To find the households in need, we need to estimate their welfare status first. However, the current practices for estimating welfare need a detailed questionnaire in the form of a survey which is time-consuming and resource-intensive. In this work, we propose an alternate solution to this problem by performing a small set of cost-effective household surveys, which can be collected over a short amount of time. We try to compensate for the loss of information by using other modalities of data. By combining different modalities of data, this work aims to characterize the welfare status of people with respect to their local drinking water resource. This work employs deep learning-based methods to model welfare using multi-modal data from household surveys, community handpump abstraction, and groundwater levels. We employ a multi-input multi-output deep learning framework, where different types of deep learning models are used for different modalities of data. Experimental results in this work have demonstrated that the multi-modal data in the form of a small set of survey questions, handpump abstraction data, and groundwater level can be used to estimate the welfare status of households. In addition, the results show that different modalities of data have complementary information, which, when combined, improves the overall performance of our ability to predict welfare.

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Arefi, Ahmad, and Reza Pourtaheri. "Multi-modal tempered stable distributions and prosses with applications to finance." Communications in Statistics - Theory and Methods 49, no. 17 (February 3, 2020): 4133–49. http://dx.doi.org/10.1080/03610926.2019.1594304.

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Sun, Yan, Maoxiang Lang, and Danzhu Wang. "Optimization Models and Solution Algorithms for Freight Routing Planning Problem in the Multi-Modal Transportation Networks: A Review of the State-of-the-Art." Open Civil Engineering Journal 9, no. 1 (September 17, 2015): 714–23. http://dx.doi.org/10.2174/1874149501509010714.

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With the remarkable development of international trade, global commodity circulation has grown significantly. To accomplish commodity circulation among various regions and countries, multi-modal transportation scheme has been widely adopted by a large number of companies. Meanwhile, according to the relevant statistics, the international logistics costs reach up to approximate 30-50% of the total production cost of the companies. Lowering the transportation costs has become one of the most important sources for a company to raise profits and maintain competitiveness in the global market. Thus, how to optimize freight routes selection to move commodities through the multi-modal transportation network has gained great concern of both the decision makers of the companies and the multi-modal transport operators. In this study, we present a systematical review on the multi-modal transportation freight routing planning problem from the aspects of model formulation and algorithm design. Following contents are covered in this review: (1) distinguishing the formulation characteristics of various optimization models; (2) identifying the optimization models in recent studies according to the formulation characteristics; and (3) discussing the solution approaches that are developed to solve the optimization models, especially the heuristic algorithms.

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Whittle, P. "A construction for multi-modal processes, and a potential memory device." Journal of Applied Probability 27, no. 1 (March 1990): 146–55. http://dx.doi.org/10.2307/3214602.

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It is shown that if parameters occurring linearly in the transition intensity of a Markov process are replaced by their respective ‘Bayesian estimates' then the new process thus generated has an equilibrium distribution which is a mixture (over parameter values) of the original parametrised equilibrium distribution.One effectively then has an extra state dependence in that one selects from a given class of transition rules those rules which are most consistent with the value of current state. The effect of this is thus to preserve the status quo, in that unlikely transitions are made even less likely. By this means one can construct processes which show several distinct and metastable modes of behaviour, and which can serve as models for memory devices.

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Whittle, P. "A construction for multi-modal processes, and a potential memory device." Journal of Applied Probability 27, no. 01 (March 1990): 146–55. http://dx.doi.org/10.1017/s0021900200038493.

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It is shown that if parameters occurring linearly in the transition intensity of a Markov process are replaced by their respective ‘Bayesian estimates' then the new process thus generated has an equilibrium distribution which is a mixture (over parameter values) of the original parametrised equilibrium distribution. One effectively then has an extra state dependence in that one selects from a given class of transition rules those rules which are most consistent with the value of current state. The effect of this is thus to preserve the status quo, in that unlikely transitions are made even less likely. By this means one can construct processes which show several distinct and metastable modes of behaviour, and which can serve as models for memory devices.

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Sun, Hong Tao, Yong Shou Dai, Fang Wang, and Xing Peng. "Seismic Wavelet Estimation Using High-Order Statistics and Chaos-Genetic Algorithm." Advanced Materials Research 433-440 (January 2012): 4241–47. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4241.

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Accurate and effective seismic wavelet estimation has an extreme significance in the seismic data processing of high resolution, high signal-to-noise ratio and high fidelity. The emerging non-liner optimization methods enhance the applied potential for the statistical method of seismic wavelet extraction. Because non-liner optimization algorithms in the seismic wavelet estimation have the defects of low computational efficiency and low precision, Chaos-Genetic Algorithm (CGA) based on the cat mapping is proposed which is applied in the multi-dimensional and multi-modal non-linear optimization. The performance of CGA is firstly verified by four test functions, and then applied to the seismic wavelet estimation. Theoretical analysis and numerical simulation demonstrate that CGA has better convergence speed and convergence performance.

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Rivas, Alberto, Alfonso González-Briones, Juan J. Cea-Morán, Arnau Prat-Pérez, and Juan M. Corchado. "My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles." Electronics 10, no. 11 (May 25, 2021): 1263. http://dx.doi.org/10.3390/electronics10111263.

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New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during their journey through the data, statistics and trends extracted from their previous uses of the application. The My-Trac application is dedicated to the research and development of these user-centered services to improve the multi-modal experience using various techniques. Among these techniques are preference extraction systems, which extract user information from social networks, such as Twitter. In this article, we present a system that allows to develop a profile of the preferences of each user, on the basis of the tweets published on their Twitter account. The system extracts the tweets from the profile and analyzes them using the proposed algorithms and returns the result in a document containing the categories and the degree of affinity that the user has with each category. In this way, the My-Trac application includes a recommender system where the user receives preference-based suggestions about activities or services on the route to be taken.

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Park, Chanseok, and W. J. Padgett. "Analysis of strength distributions of multi-modal failures using the EM algorithm." Journal of Statistical Computation and Simulation 76, no. 7 (July 2006): 619–36. http://dx.doi.org/10.1080/10629360500108970.

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Yu, Yuan Chih, Shing Chern D. You, and Dwen Ren Tsai. "Hill Climbing Algorithm for License Plate Recognition." Advanced Materials Research 267 (June 2011): 995–1000. http://dx.doi.org/10.4028/www.scientific.net/amr.267.995.

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Histogram thresholding has been widely used for image processing—it is simple, fast, and computationally inexpensive. In this paper, we develop a creative approach based on histogram’s distributions to segment interest regions from background. Unlike the existing threshold detection methods which measure the statistics of histogram in the multi-modal images, our approach analyses the shape representation of multi-modal which has several hill-climbing curves. The behavior of algorithm works like human vision which focuses on the high contrast areas and scans the shape variation first. Moreover, such an algorithm presents a new type of histogram analysis that depends on the particular shape of certain distribution in histogram. Experimental results reveal that the proposed algorithm performs distinct effects especially on the recognition of artificial signs such as road sign, vehicle plate, and signboard.

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Maeda, Iwao, David deGraw, Michiharu Kitano, Hiroyasu Matsushima, Kiyoshi Izumi, Hiroki Sakaji, and Atsuo Kato. "Latent Segmentation of Stock Trading Strategies Using Multi-Modal Imitation Learning." Journal of Risk and Financial Management 13, no. 11 (October 23, 2020): 250. http://dx.doi.org/10.3390/jrfm13110250.

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While exchanges and regulators are able to observe and analyze the individual behavior of financial market participants through access to labeled data, this information is not accessible by other market participants nor by the general public. A key question, then, is whether it is possible to model individual market participants’ behaviors through observation of publicly available unlabeled market data alone. Several methods have been suggested in the literature using classification methods based on summary trading statistics, as well as using inverse reinforcement learning methods to infer the reward function underlying trader behavior. Our primary contribution is to propose an alternative neural network based multi-modal imitation learning model which performs latent segmentation of stock trading strategies. As a result that the segmentation in the latent space is optimized according to individual reward functions underlying the order submission behaviors across each segment, our results provide interpretable classifications and accurate predictions that outperform other methods in major classification indicators as verified on historical orderbook data from January 2018 to August 2019 obtained from the Tokyo Stock Exchange. By further analyzing the behavior of various trader segments, we confirmed that our proposed segments behaves in line with real-market investor sentiments.

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Guan, Yongtao, and Stephen M. Krone. "Small-world MCMC and convergence to multi-modal distributions: From slow mixing to fast mixing." Annals of Applied Probability 17, no. 1 (February 2007): 284–304. http://dx.doi.org/10.1214/105051606000000772.

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Kim, Hani Jieun, Yingxin Lin, Thomas A. Geddes, Jean Yee Hwa Yang, and Pengyi Yang. "CiteFuse enables multi-modal analysis of CITE-seq data." Bioinformatics 36, no. 14 (April 30, 2020): 4137–43. http://dx.doi.org/10.1093/bioinformatics/btaa282.

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Abstract Motivation Multi-modal profiling of single cells represents one of the latest technological advancements in molecular biology. Among various single-cell multi-modal strategies, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) allows simultaneous quantification of two distinct species: RNA and cell-surface proteins. Here, we introduce CiteFuse, a streamlined package consisting of a suite of tools for doublet detection, modality integration, clustering, differential RNA and protein expression analysis, antibody-derived tag evaluation, ligand–receptor interaction analysis and interactive web-based visualization of CITE-seq data. Results We demonstrate the capacity of CiteFuse to integrate the two data modalities and its relative advantage against data generated from single-modality profiling using both simulations and real-world CITE-seq data. Furthermore, we illustrate a novel doublet detection method based on a combined index of cell hashing and transcriptome data. Finally, we demonstrate CiteFuse for predicting ligand–receptor interactions by using multi-modal CITE-seq data. Collectively, we demonstrate the utility and effectiveness of CiteFuse for the integrative analysis of transcriptome and epitope profiles from CITE-seq data. Availability and implementation CiteFuse is freely available at http://shiny.maths.usyd.edu.au/CiteFuse/ as an online web service and at https://github.com/SydneyBioX/CiteFuse/ as an R package. Contact pengyi.yang@sydney.edu.au Supplementary information Supplementary data are available at Bioinformatics online.

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Sarkar, Dhrubasish, Piyush Kumar, Poulomi Samanta, Suchandra Dutta, and Moumita Chatterjee. "A Two-Level Multi-Modal Analysis for Depression Detection From Online Social Media." International Journal of Software Innovation 10, no. 1 (January 1, 2022): 1–22. http://dx.doi.org/10.4018/ijsi.309114.

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According to World Health Organization statistics, depression is a prominent cause of concern worldwide, leading to suicide in the majority of these cases if left untreated. Nowadays, social media is a great place for users to express themselves through text, emoticons, images, etc., which reflect their thoughts and moods. This has opened up the possibility of studying social networks in order to better comprehend the mental states of their participants. The primary goal of the research is to examine Twitter user personas and tweets in order to uncover traits that may signal depressive symptoms among online users. A two-level depression detection method is proposed in which suspected depressed individuals are identified using social media features, personality traits, temporal and sentiment analysis of user biographies. Using the support vector machine classifier, these qualities are integrated with additional linguistic and topic features to achieve an accuracy of 89%. According to the research, effective feature selection and their combinations aid in enhancing performance.

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Kasahara, Kota, Neetha Mohan, Ikuo Fukuda, and Haruki Nakamura. "mDCC_tools: characterizing multi-modal atomic motions in molecular dynamics trajectories." Bioinformatics 32, no. 16 (April 7, 2016): 2531–33. http://dx.doi.org/10.1093/bioinformatics/btw129.

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Teretenkov, A. E. "Irreversible quantum evolution with quadratic generator: Review." Infinite Dimensional Analysis, Quantum Probability and Related Topics 22, no. 04 (December 2019): 1930001. http://dx.doi.org/10.1142/s0219025719300019.

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We review results on GKSL-type equations with multi-modal generators which are quadratic in bosonic or fermionic creation and annihilation operators. General forms of such equations are presented. The Gaussian solutions are obtained in terms of equations for the first and the second moments. Different approaches for their solutions are discussed.

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Hua, ChunJian, MingChun Sun, Yu Zhu, Yi Jiang, JianFeng Yu, and Ying Chen. "Pedestrian detection network with multi-modal cross-guided learning." Digital Signal Processing 122 (April 2022): 103370. http://dx.doi.org/10.1016/j.dsp.2021.103370.

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Pacchiano, Aldo, Heinrich Jiang, and Michael I. Jordan. "Robustness Guarantees for Mode Estimation with an Application to Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9277–84. http://dx.doi.org/10.1609/aaai.v35i10.17119.

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Mode estimation is a classical problem in statistics with a wide range of applications in machine learning. Despite this, there is little understanding in its robustness properties under possibly adversarial data contamination. In this paper, we give precise robustness guarantees as well as privacy guarantees under simple randomization. We then introduce a theory for multi-armed bandits where the values are the modes of the reward distributions instead of the mean. We prove regret guarantees for the problems of top arm identification, top m-arms identification, contextual modal bandits, and infinite continuous arms top arm recovery. We show in simulations that our algorithms are robust to perturbation of the arms by adversarial noise sequences, thus rendering modal bandits an attractive choice in situations where the rewards may have outliers or adversarial corruptions.

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K G, Srinivasa. "An Intelligent Network Intrusion Detection System Based on Multi-Modal Support Vector Machines." International Journal of Information Security and Privacy 7, no. 4 (October 2013): 37–52. http://dx.doi.org/10.4018/ijisp.2013100104.

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Increase in the number of network based transactions for both personal and professional use has made network security gain a significant and indispensable status. The possible attacks that an Intrusion Detection System (IDS) has to tackle can be of an existing type or of an entirely new type. The challenge for researchers is to develop an intelligent IDS which can detect new attacks as efficiently as they detect known ones. Intrusion Detection Systems are rendered intelligent by employing machine learning techniques. In this paper we present a statistical machine learning approach to the IDS using the Support Vector Machine (SVM). Unike conventional SVMs this paper describes a milti model approach which makes use of an extra layer over the existing SVM. The network traffic is modeled into connections based on protocols at various network layers. These connection statistics are given as input to SVM which in turn plots each input vector. The new attacks are identified by plotting them with respect to the trained system. The experimental results demonstrate the lower execution time of the proposed system with high detection rate and low false positive number. The 1999 DARPA IDS dataset is used as the evaluation dataset for both training and testing. The proposed system, SVM NIDS is bench marked with SNORT (Roesch, M. 1999), an open source IDS.

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Man, Kaiwen, Jeffrey R. Harring, and Yang Liu. "Methods of Integrating Multi-Modal Data for Assessing Aberrant Test-Taking Behaviors." Multivariate Behavioral Research 55, no. 1 (December 20, 2019): 155–56. http://dx.doi.org/10.1080/00273171.2019.1699010.

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Caramia, Massimiliano, and Giovanni Storchi. "Evaluating the effects of parking price and location in multi-modal transportation networks." Networks & Heterogeneous Media 1, no. 3 (2006): 441–65. http://dx.doi.org/10.3934/nhm.2006.1.441.

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Kıbış, Eyyüb Y., and İ. Esra Büyüktahtakın. "Optimizing multi-modal cancer treatment under 3D spatio-temporal tumor growth." Mathematical Biosciences 307 (January 2019): 53–69. http://dx.doi.org/10.1016/j.mbs.2018.10.010.

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Nokelainen, P., T. Silander, P. Ruohotie, and H. Tirri. "Investigating the Number of Non-linear and Multi-modal Relationships Between Observed Variables Measuring Growth-oriented Atmosphere." Quality & Quantity 41, no. 6 (September 14, 2006): 869–90. http://dx.doi.org/10.1007/s11135-006-9030-x.

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Deng, Lei, Yibiao Huang, Xuejun Liu, and Hui Liu. "Graph2MDA: a multi-modal variational graph embedding model for predicting microbe–drug associations." Bioinformatics 38, no. 4 (November 23, 2021): 1118–25. http://dx.doi.org/10.1093/bioinformatics/btab792.

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Abstract Motivation Accumulated clinical studies show that microbes living in humans interact closely with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes have become novel targets for the development of antibacterial agents. Therefore, screening of microbe–drug associations can benefit greatly drug research and development. With the increase of microbial genomic and pharmacological datasets, we are greatly motivated to develop an effective computational method to identify new microbe–drug associations. Results In this article, we proposed a novel method, Graph2MDA, to predict microbe–drug associations by using variational graph autoencoder (VGAE). We constructed multi-modal attributed graphs based on multiple features of microbes and drugs, such as molecular structures, microbe genetic sequences and function annotations. Taking as input the multi-modal attribute graphs, VGAE was trained to learn the informative and interpretable latent representations of each node and the whole graph, and then a deep neural network classifier was used to predict microbe–drug associations. The hyperparameter analysis and model ablation studies showed the sensitivity and robustness of our model. We evaluated our method on three independent datasets and the experimental results showed that our proposed method outperformed six existing state-of-the-art methods. We also explored the meaning of the learned latent representations of drugs and found that the drugs show obvious clustering patterns that are significantly consistent with drug ATC classification. Moreover, we conducted case studies on two microbes and two drugs and found 75–95% predicted associations have been reported in PubMed literature. Our extensive performance evaluations validated the effectiveness of our proposed method. Availability and implementation Source codes and preprocessed data are available at https://github.com/moen-hyb/Graph2MDA. Supplementary information Supplementary data are available at Bioinformatics online.

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Liu, Changrui, and Chaozhu Zhang. "Remove Artifacts from a Single-Channel EEG Based on VMD and SOBI." Sensors 22, no. 17 (September 4, 2022): 6698. http://dx.doi.org/10.3390/s22176698.

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With the development of portable EEG acquisition systems, the collected EEG has gradually changed from being multi-channel to few-channel or single-channel, thus the removal of single-channel EEG signal artifacts is extremely significant. For the artifact removal of single-channel EEG signals, the current mainstream method is generally a combination of the decomposition method and the blind source separation (BSS) method. Between them, a combination of empirical mode decomposition (EMD) and its derivative methods and ICA has been used in single-channel EEG artifact removal. However, EMD is prone to modal mixing and it has no relevant theoretical basis, thus it is not as good as variational modal decomposition (VMD) in terms of the decomposition effect. In the ICA algorithm, the implementation method based on high-order statistics is widely used, but it is not as effective as the implementation method based on second order statistics in processing EMG artifacts. Therefore, aiming at the main artifacts in single-channel EEG signals, including EOG and EMG artifacts, this paper proposed a method of artifact removal combining variational mode decomposition (VMD) and second order blind identification (SOBI). Semi-simulation experiments show that, compared with the existing EEMD-SOBI method, this method has a better removal effect on EOG and EMG artifacts, and can preserve useful information to the greatest extent.

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Scott, Rachel Elizabeth. "Ongoing and Multifaceted Assessment of Academic Library Professional Development Programs Enhances Their Efficacy." Evidence Based Library and Information Practice 13, no. 2 (June 5, 2018): 115–17. http://dx.doi.org/10.18438/eblip29413.

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A Review of: Harker, K. R., O'Toole, E., & Sassen, C. (2018). Assessing an academic library professional development program. portal: Libraries and the Academy, 18(1), 199-223. https://doi.org/10.1353/pla.2018.0010 Abstract Objective – To analyze various measures of need, participation, satisfaction, and impact of an academic library professional development program. Design – Multi-modal; surveys, curriculum vitae (CV) analysis, and attendance statistics. Setting – Academic library in the United States. Subjects – Library faculty of all ranks. Methods – Assessment of the Career Development Program began with an interest survey conducted at the beginning of the fiscal year in which participants ranked their interest in professional development topics. Attendance statistics were collected at all program sessions and participants were emailed post-event surveys comprised of three Likert-scale questions and an open-ended question. Participants in the peer-review service were emailed a survey with two Likert-scale questions and an open-ended question. All programs and surveys were voluntary. An “activities survey” attempted to document counts of scholarly publications and presentations according to geographic scope, format, and peer-review. However, due to low response rates, the activities survey was replaced after two years with an analysis of library faculty member CVs on a publicly-accessible university website. The final assessment was a narrative annual report that drew on and summarized all of the previously conducted assessments. Main Results – Multi-modal assessment of the professional development program improved its relevance and quality while also documenting its impact. Conclusion – Continuous and multi-faceted assessment of professional development programs not only leads to improved efficacy, but also provides accountability and details the value of the program to stakeholders. Professional development programs promote scholarly productivity, which has implications for the career satisfaction of academic librarians. Further research should investigate the validity of professional development program assessment instruments and identify which assessment methods are most effective for evaluating professional development programs and measuring the impact of this programming on scholarship.

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Dhillon, Anamika, and Gyanendra K. Verma. "Multi-modal feature fusion for object detection using neighbourhood component analysis and bounding box regression." International Journal of Business Intelligence and Data Mining 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijbidm.2022.10047465.

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Su, Nan, Zhishuo Lin, Wenlong You, Nan Zheng, and Kun Ma. "RMGCS: Real-time multimodal garbage classification system for recyclability." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 3963–73. http://dx.doi.org/10.3233/jifs-212225.

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Management of garbage classification is a general term for a series of activities to sort, store and transport garbage into public resources according to certain regulations or standards. Current garbage classification systems have several drawbacks, such as inability to identify multiple garbage categories, and high dependence on the surrounding environment. To address these issues, this paper has proposed the Real Time Multi-Modal Garbage classification System (abbreviated as RMGCS). It consists of two sub systems: an indoor garbage classification applet (abbreviated as IGCA) and an outdoor garbage classification system (abbreviated as OGCS). IGCA provides users with three methods of garbage classification, and OGCS provides users with outdoor real-time multi-target garbage classification and can dynamically update the recognition model. RMGCS achieves real-time, accurate, and multimodal classification. Finally, the experiments with RMGCS show that our approaches are effective and efficient.

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Villalobos-Cid, Manuel, César Rivera, Eduardo I. Kessi-Pérez, and Mario Inostroza-Ponta. "A multi-modal algorithm based on an NSGA-II scheme for phylogenetic tree inference." Biosystems 213 (March 2022): 104606. http://dx.doi.org/10.1016/j.biosystems.2022.104606.

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Matarirano, Obert, Onke Gqokonqana, and Abor Yeboah. "Students’ Responses to Multi-Modal Emergency Remote Learning During COVID-19 in a South African Higher Institution." Research in Social Sciences and Technology 6, no. 2 (September 15, 2021): 199–218. http://dx.doi.org/10.46303/ressat.2021.19.

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COVID-19 pandemic forced several higher education institutions (HEI) to operate remotely. Emergency remote teaching, using synchronous and asynchronous instruction, was adopted by several HEIs. The experiences of students with remote teaching and learning in certain situations are not fully understood, thus need to be explored. This study explored the experiences of students with the emergency remote teaching and learning practices adopted at a selected HEI in South Africa. A cross-sectional and self-administered survey was used to gather data from 243 conveniently sampled returning students within the Department of Accounting and Finance. Descriptive statistics were used to make sense of the collected data. The study found that students preferred a face-to-face approach to learning to remote learning. The respondents underscored insufficient data, unstable network connection, unconducive home environments and loneliness as deterrents to effective remote learning. Despite these negative experiences, students appreciated the flexibility and convenience of recorded video lectures and acknowledged the compassion and support of lecturers during remote learning. An understanding of the experiences of students during remote learning provides a basis for future teaching plans, which would improve students' learning experiences. In its current format and students living in their home environments, remote learning greatly diminishes the chances of success for most students. Lecturers need to be compassionate and considerate of student’s struggles in their plans for remote teaching and learning as well as online learning.

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Iliopoulos, Aggelos C., Nikolaos S. Nikolaidis, and Elias C. Aifantis. "Analysis of serrations and shear bands fractality in UFGs." Journal of the Mechanical Behavior of Materials 24, no. 1-2 (May 1, 2015): 1–9. http://dx.doi.org/10.1515/jmbm-2015-0001.

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AbstractTsallis nonextensive statistics is employed to characterize serrated flow, as well as multiple shear band formation in ultrafine grain (UFG) size materials. Two such UFG materials, a bi-modal Al-Mg alloy and a Fe-Cu alloy, were chosen. In the first case, at low strain rates serrated flow emerges as recorded in the stress-strain graphs, whereas at high strain rates, extensive shear banding occurs. In the second case, multiple shear banding is the only mechanism for plastic deformation, but serrations in the stress-strain graph are not recorded. The analysis aims at the estimation of Tsallis entropic index qstat (stat denotes stationary state), as well as the estimation of fractal dimension. The results reveal that the distributions of serrations and shear bands do not follow Gaussian statistics as implied by Boltzmann-Gibbs extensive thermodynamics, but are approximated instead by Tsallis q-Gaussian distributions, as suggested by nonextensive thermodynamics. In addition, fractal analysis of multiple shear band images reveals a (multi)fractal and hierarchical profile of the spatial arrangement of shear bands.

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Matarirano, Obert, Abor Yeboah, and Onke Gqokonqana. "Readiness of Students for Multi-Modal Emergency Remote Teaching at A Selected South African Higher Education Institution." International Journal of Higher Education 10, no. 6 (June 21, 2021): 135. http://dx.doi.org/10.5430/ijhe.v10n6p135.

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The closures of Higher Education Institutions (HEIs) due to the Covid-19 pandemic meant that face to face classes had to be put on hold. However, the growth in information and communication technologies (ICT) made it possible for HEIs to continue with their core activities remotely, primarily using learning management systems (LMSs). The overuse of LMS at the selected HEI resulted in the former’s collapse. The consequence was that management of the institution advised lecturers to use multi-modal emergency remote teaching (ERT) to save the academic year. Lecturers adopted a variety of platforms and approaches, largely depending on their preferences. This study identified the ICT platforms and approaches used by lecturers during remote teaching as well as estimating the readiness of students for emergency remote learning. Readiness was established with the use of the Technology Readiness Index 2.0 (TRI2.0) of the Technology Readiness Model. In addition, the effects of age, gender and level of study on technology readiness were estimated. A self-administered questionnaire was shared with senior students within the accounting department of the selected HEI. Descriptive and inferential statistics were used to analyse the data collected from 243 respondents. The study found that Microsoft teams was the commonly used platform whilst pre-recorded lectures and live classes were the popular approaches used. In terms of technology readiness, the study found that students were not ready as indicated by a low TRI 2.0 of 2.8. Age and study level had a positive effect on technology readiness. To provide the best possible learning experiences to students, lecturers need to understand what worked, what did not and why. The results of this study provide invaluable information and lay a foundation for successful future e-learning projects.

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Chu, P. C., and L. M. Ivanov. "Statistical characteristics of irreversible predictability time in regional ocean models." Nonlinear Processes in Geophysics 12, no. 1 (January 28, 2005): 129–38. http://dx.doi.org/10.5194/npg-12-129-2005.

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Abstract. Probabilistic aspects of regional ocean model predictability is analyzed using the probability density function (PDF) of the irreversible predictability time (IPT) (called τ-PDF) computed from an unconstrained ensemble of stochastic perturbations in initial conditions, winds, and open boundary conditions. Two-attractors (a chaotic attractor and a small-amplitude stable limit cycle) are found in the wind-driven circulation. Relationship between attractor's residence time and IPT determines the τ-PDF for the short (up to several weeks) and intermediate (up to two months) predictions. The τ-PDF is usually non-Gaussian but not multi-modal for red-noise perturbations in initial conditions and perturbations in the wind and open boundary conditions. Bifurcation of τ-PDF occurs as the tolerance level varies. Generally, extremely successful predictions (corresponding to the τ-PDF's tail toward large IPT domain) are not outliers and share the same statistics as a whole ensemble of predictions.

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Taylor, M., S. Kazadzis, and E. Gerasopoulos. "Multi-modal analysis of aerosol robotic network size distributions for remote sensing applications: dominant aerosol type cases." Atmospheric Measurement Techniques 7, no. 3 (March 31, 2014): 839–58. http://dx.doi.org/10.5194/amt-7-839-2014.

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Abstract. To date, size distributions obtained from the aerosol robotic network (AERONET) have been fit with bi-lognormals defined by six secondary microphysical parameters: the volume concentration, effective radius, and the variance of fine and coarse particle modes. However, since the total integrated volume concentration is easily calculated and can be used as an accurate constraint, the problem of fitting the size distribution can be reduced to that of deducing a single free parameter – the mode separation point. We present a method for determining the mode separation point for equivalent-volume bi-lognormal distributions based on optimization of the root mean squared error and the coefficient of determination. The extracted secondary parameters are compared with those provided by AERONET's Level 2.0 Version 2 inversion algorithm for a set of benchmark dominant aerosol types, including desert dust, biomass burning aerosol, urban sulphate and sea salt. The total volume concentration constraint is then also lifted by performing multi-modal fits to the size distribution using nested Gaussian mixture models, and a method is presented for automating the selection of the optimal number of modes using a stopping condition based on Fisher statistics and via the application of statistical hypothesis testing. It is found that the method for optimizing the location of the mode separation point is independent of the shape of the aerosol volume size distribution (AVSD), does not require the existence of a local minimum in the size interval 0.439 μm ≤ r ≤ 0.992 μm, and shows some potential for optimizing the bi-lognormal fitting procedure used by AERONET particularly in the case of desert dust aerosol. The AVSD of impure marine aerosol is found to require three modes. In this particular case, bi-lognormals fail to recover key features of the AVSD. Fitting the AVSD more generally with multi-modal models allows automatic detection of a statistically significant number of aerosol modes, is applicable to a very diverse range of aerosol types, and gives access to the secondary microphysical parameters of additional modes currently not available from bi-lognormal fitting methods.

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Taylor, M., S. Kazadzis, and E. Gerasopoulos. "Multi-modal analysis of aerosol robotic network size distributions for remote sensing applications: dominant aerosol type cases." Atmospheric Measurement Techniques Discussions 6, no. 6 (December 9, 2013): 10571–615. http://dx.doi.org/10.5194/amtd-6-10571-2013.

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Abstract. To date, size distributions obtained from the aerosol robotic network have been fit with bi-lognormals defined by six secondary microphysical parameters: the volume concentration, effective radius, and the variance of fine and coarse particle modes. However, since the total integrated volume concentration is easily calculated and can be used as an accurate constraint, the problem of fitting the size distribution can be reduced to that of deducing a single free parameter – the mode separation point. We present a method for determining the mode separation point for equivalent-volume bi-lognormal distributions based on optimisation of the root mean squared error and the coefficient of determination. The extracted secondary parameters are compared with those provided by AERONET's Level 2.0 Version 2 inversion algorithm for a set of benchmark dominant aerosol types including: desert dust, biomass burning aerosol, urban sulphate and sea salt. The total volume concentration constraint is then also lifted by performing multi-modal fits to the size distribution using nested Gaussian mixture models and a method is presented for automating the selection of the optimal number of modes using a stopping condition based on Fisher statistics and via the application of statistical hypothesis testing. It is found that the method for optimizing the location of the mode separation point is independent of the shape of the AVSD, does not require the existence of a local minimum in the size interval 0.439 μm ≤ r ≤ 0.992 μm, and shows some potential for optimizing the bi-lognormal fitting procedure used by AERONET particularly in the case of desert dust aerosol. The AVSD of impure marine aerosol is found to require 3 modes. In this particular case, bi-lognormals fail to recover key features of the AVSD. Fitting the AVSD more generally with multi-modal models allows automatic detection of a statistically-significant number of aerosol modes, is applicable to a very diverse range of aerosol types, and gives access to the secondary microphysical parameters of additional modes currently not available from bi-lognormal fitting methods.

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Jia, Heming, and Chunbo Lang. "Salp swarm algorithm with crossover scheme and Lévy flight for global optimization." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 9277–88. http://dx.doi.org/10.3233/jifs-201737.

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Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

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Adisasmita, Sakti Adji, and Lucky Caroles. "Multi-Airport System Development Model: Case Study of Airports in Indonesia." Civil Engineering Journal 7 (August 22, 2022): 182–93. http://dx.doi.org/10.28991/cej-sp2021-07-013.

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Indonesia's air transportation business has grown substantially to suit the community's transportation needs. An increasing number of people travel by airline each year. By way of secondary and tertiary multiplier effects, the intensifying competition between airport services provided in neighboring regions is likely to have a multiplier influence on a territory. Simultaneously, airport services that are more competitive are focused on areas with rising economic growth in sectors like tourism. MAS is an airport system consisting of at least two airports within a metropolitan area that support civil aviation. MAS includes both big and small airports. MAS is the development of an air transportation system to suit the growing demand for air transportation services. As an example of an integrated multi-model airport design in Indonesia, this research will examine Juanda International Airport, Surabaya, as the major airport and Abdul Rachman Saleh Airport as the secondary airport. In order to establish an integrated multi-modal airport in Indonesia, it is necessary to adopt a multi-airport system. This study's airport location is in East Java Province and includes two airports: Juanda International Airport and Abdul Rachman Saleh Airport. The Juanda International Airport is situated in Sedati, Sidoarjo, whilst the Abdul Rachman Saleh Airport is in Pakis, Malang Regency. Using modelling findings and final passenger statistics, airport capacity in 2045 is determined. The results demonstrated the necessity for more comprehensive points in the MADAM simulation used in this research study, which can estimate a number of crucial parameters. Doi: 10.28991/CEJ-SP2021-07-013 Full Text: PDF

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He, Haoxiang, Wei Wang, and Xiaofu Zhang. "Frequency modification of continuous beam bridge based on co-integration analysis considering the effect of temperature and humidity." Structural Health Monitoring 18, no. 2 (February 19, 2018): 376–89. http://dx.doi.org/10.1177/1475921718755573.

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The variation of the temperature and humidity significantly affects the modal parameters of the structure, and the effect and the quantitative statistics of single environmental factors such as temperature on the frequency of bridge are intensively studied, but the study on the mechanism and the effective model considering the comprehensive effect of multi-environmental factors is relatively rare. The principle and analysis method of co-integration is introduced, and the frequency-modified method based on co-integration is presented. Through the monitoring of a three-span concrete bridge model in the natural environment, the effective monitoring data are analyzed to establish a long-term equilibrium model about “frequency–temperature–humidity” based on co-integration theory. The experimental results indicate that the model has a better fitting ability and accuracy, and it is can be used to predict the variation trend of frequency. Based on the mathematical model of co-integration analysis, the modified frequency model considering multi-environmental factors is proposed, which can eliminate the comprehensive effect of temperature and humidity on frequency, and the variation of dynamic characteristics due to the internal causes of structure is revealed. The extracted sequence can provide effective information for further safety assessment and damage detection of bridge.

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De Bortoli, Marta, and Farzin Zareian. "Performance Prediction Equations for Linear Planar Structural Systems: Concept, Formulation, and Validation." Earthquake Spectra 34, no. 2 (May 2018): 697–718. http://dx.doi.org/10.1193/110716eqs194m.

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This paper presents and validates an analytical formulation, denoted as Performance Prediction Equations (PPEs), that relates the seismic response engineering demand parameter (EDP) of buildings to earthquake parameters such as magnitude, epicentral distance, and type of faulting. PPEs are conceptually novel and can be readily included in any hazard calculation program to directly estimate EDP hazard curves. The PPEs presented herein are based on the linearization of response spectrum analysis (RSA) formulation for estimation of the seismic response of multi-degree-of-freedom (MDOF) models for planar structural systems. Equations for mean and variance are provided for floor displacement, interstory drift ratio, and normalized base shear. The input parameters needed to apply the proposed PPEs are the modal properties of the structural system and the selection of an existing ground motion model (GMM). The proposed PPEs are validated against simulated results using a set of planar building models and the Campbell-Bozorgnia 2014 GMM. The comparison confirms that the proposed PPEs provide an accurate estimate of the statistics of the said EDPs.

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Ahmed, Rasel, Shuhaimi Mahadzir, Adrián Mota-Babiloni, Md Al-Amin, Abdullah Yousuf Usmani, Zaid Ashraf Rana, Hayati Yassin, Saboor Shaik, and Fayaz Hussain. "4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer." PLOS ONE 18, no. 2 (February 3, 2023): e0272160. http://dx.doi.org/10.1371/journal.pone.0272160.

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Refrigeration systems are complex, non-linear, multi-modal, and multi-dimensional. However, traditional methods are based on a trial and error process to optimize these systems, and a global optimum operating point cannot be guaranteed. Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. Firstly, the interaction between the system components and their cycle behavior is analyzed by building four surrogate models using RSM. The model fit statistics indicate that they are statistically significant and agree with the design data. Three conflicting scenarios in bi-objective optimization are built focusing on the overall system following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) decision-making methods. The optimal solutions indicate that for the first to third scenarios, the exergetic efficiency (EE) and capital expenditure (CAPEX) are optimized by 33.4% and 7.5%, and the EE and operational expenditure (OPEX) are improved by 27.4% and 19.0%. The EE and global warming potential (GWP) are also optimized by 27.2% and 19.1%, where the proposed HMOGWO outperforms the MOGWO and NSGA-II. Finally, the K-means clustering technique is applied for Pareto characterization. Based on the research outcomes, the combined RSM and HMOGWO techniques have proved an excellent solution to simulate and optimize two-stage VCRS.

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Hysing, E. B., L. Smith, M. Thulin, R. Karlsten, and T. Gordh. "Detection of systemic inflammation in severely impaired chronic pain patients, and effects of a CBT-ACT-based multi-modal pain rehabilitation program." Scandinavian Journal of Pain 16, no. 1 (July 1, 2017): 175–76. http://dx.doi.org/10.1016/j.sjpain.2017.04.033.

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AbstractAimsA few previous studies indicate an ongoing of low-grade systemic inflammation in chronic pain patients (CPP) [1, 2]. In the present study we investigated the plasma inflammatory profile in severely impaired chronic pain patients. In addition we studied if there were any alterations in inflammation patterns at one-year follow up, after the patients had taken part in a CBT-ACT based 4 weeks in-hospital pain rehabilitation program (PRP).Methods Blood samples were collected from 52 well characterized chronic pain patients. Plasma from matched healthy blood donors were used as controls. At one year after the treatment program, 28 of the patients were available for follow up. Instead of only analyzing single inflammation-related substances, we used a new multiplex panel enabling the simultaneous analysis of 92 inflammation-related proteins, mainly cytokines and chemokines (Proseek Inflammation, Olink, Uppsala, Sweden). Multivariate statistics were used for analysis.ResultsClear signs of increased inflammatory activity were detected in the pain patients. Accepting a false discovery rate (FDR) of 5%, there were significant differences in 43 of the 92 inflammatory biomarkers. The expression of 8 biomarkers were 4 times higher in patients compared to controls. Three biomarkers, CXCL5, SIRT2, AXIN1 were more than 8 times higher. The conventional marker for inflammation, CRP, did not differ. Of the 28 patients available for follow up one year after the intervention, all showed lower levels of the inflammatory biomarker initially raised.ConclusionsThe results indicate that CPP suffer from a low grade of chronic systemic inflammation, not detectable by CRP analysis. This may have implications for the general pain hypersensitivity, and other symptoms, often described in this group of patients. We conclude that inflammatory plasma proteins may be measureable molecular markers to distinguishes CPP from pain free controls, and that a CBT-ACT pain rehab program seem to decrease this inflammatory activity.

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Chen, Yujie, Tengfei Ma, Xixi Yang, Jianmin Wang, Bosheng Song, and Xiangxiang Zeng. "MUFFIN: multi-scale feature fusion for drug–drug interaction prediction." Bioinformatics 37, no. 17 (March 15, 2021): 2651–58. http://dx.doi.org/10.1093/bioinformatics/btab169.

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Abstract Motivation Adverse drug–drug interactions (DDIs) are crucial for drug research and mainly cause morbidity and mortality. Thus, the identification of potential DDIs is essential for doctors, patients and the society. Existing traditional machine learning models rely heavily on handcraft features and lack generalization. Recently, the deep learning approaches that can automatically learn drug features from the molecular graph or drug-related network have improved the ability of computational models to predict unknown DDIs. However, previous works utilized large labeled data and merely considered the structure or sequence information of drugs without considering the relations or topological information between drug and other biomedical objects (e.g. gene, disease and pathway), or considered knowledge graph (KG) without considering the information from the drug molecular structure. Results Accordingly, to effectively explore the joint effect of drug molecular structure and semantic information of drugs in knowledge graph for DDI prediction, we propose a multi-scale feature fusion deep learning model named MUFFIN. MUFFIN can jointly learn the drug representation based on both the drug-self structure information and the KG with rich bio-medical information. In MUFFIN, we designed a bi-level cross strategy that includes cross- and scalar-level components to fuse multi-modal features well. MUFFIN can alleviate the restriction of limited labeled data on deep learning models by crossing the features learned from large-scale KG and drug molecular graph. We evaluated our approach on three datasets and three different tasks including binary-class, multi-class and multi-label DDI prediction tasks. The results showed that MUFFIN outperformed other state-of-the-art baselines. Availability and implementation The source code and data are available at https://github.com/xzenglab/MUFFIN.

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Rapoza, Amanda, Meghan Shumway, Gary Baker, and Peter Wilke. "A closer look at rail methodology in the BTS National Transportation Noise Map." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 1 (August 1, 2021): 5372–81. http://dx.doi.org/10.3397/in-2021-3069.

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In 2017, the Bureau of Transportation Statistics released the inaugural national, multi-modal transportation noise map prototype. The noise modeling and mapping effort was envisioned as a way to facilitate the geographic tracking of national trends and provide insight into transportation noise-related questions as changes occur over time - changes between modes, types of vehicles within modes and the geographic shifts of populations. How do changes in aircraft technology change the transportation noise landscape? Does increased high speed rail availability affect highway-related noise? How does a population shift away from urban centers affect the soundscape? The inaugural model included aviation and highway sources. The first update, released in November 2020, includes passenger rail-related noise in addition to aviation and highway sources. Operations in this new mode include commuter rail mainline, high-speed electric, light rail, heavy rail and streetcars, along with commuter rail horns at highway-rail grade crossings. The data for this noise map were modeled based on USDOT methods, with adjustments and simplifications to model on a national scale. This paper focuses on the modeling methods and geospatial approach used to develop the passenger rail noise data layer.

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Thakur, Ganesh. "Study of clinical outcome of Loco-Regional Recurrences in breast cancer: A prospective study." MedPulse International Journal of Medicine 19, no. 3 (2021): 160–64. http://dx.doi.org/10.26611/102119316.

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Background: Advances in the management of breast cancer led to significant improvement in survival. This has led to an increased incidence of Loco-Regional Recurrences. Objective: To report clinical outcome of patients presenting with locoregional recurrence (LRR). Material and Methods: The present prospective observational study was done at Tata Memorial Hospital, Mumbai and Advanced Center for Treatment and Education of Cancer, Navi Mumbai. A total of 100 consecutive patients of local/ regional/ Loco-regional recurrences and fulfilling the study inclusion criteria were invited to participate in this study. Statistical analysis was done using SPSS Statistics version 20.0. Survival period was defined as the period from the date of diagnosis to the development of recurrence or to the date of the last recorded clinical followup.Results: The mean and median overall survival from the time of diagnosis of primary for the entire group was 62.8 and 41.2 months (range 8.0-237.5) respectively. The mean and median disease-free survival was 53.9 and 32.7 months (range 0.33-23.3) respectively from the date of primary diagnosis. Conclusion: Multi-modal therapy comprising of optimal locoregional treatment and systemic therapy achieves durable local control.

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Wang, M., S. Ghan, R. Easter, M. Ovchinnikov, X. Liu, E. Kassianov, Y. Qian, et al. "The multi-scale aerosol-climate model PNNL-MMF: model description and evaluation." Geoscientific Model Development Discussions 3, no. 4 (October 8, 2010): 1625–95. http://dx.doi.org/10.5194/gmdd-3-1625-2010.

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Abstract. Anthropogenic aerosol effects on climate produce one of the largest uncertainties in estimates of radiative forcing of past and future climate change. Much of this uncertainty arises from the multi-scale nature of the interactions between aerosols, clouds and large-scale dynamics, which are difficult to represent in conventional global climate models (GCMs). In this study, we develop a multi-scale aerosol climate model that treats aerosols and clouds across different scales, and evaluate the model performance, with a focus on aerosol treatment. This new model is an extension of a multi-scale modeling framework (MMF) model that embeds a cloud-resolving model (CRM) within each grid column of a GCM. In this extension, the effects of clouds on aerosols are treated by using an explicit-cloud parameterized-pollutant (ECPP) approach that links aerosol and chemical processes on the large-scale grid with statistics of cloud properties and processes resolved by the CRM. A two-moment cloud microphysics scheme replaces the simple bulk microphysics scheme in the CRM, and a modal aerosol treatment is included in the GCM. With these extensions, this multi-scale aerosol-climate model allows the explicit simulation of aerosol and chemical processes in both stratiform and convective clouds on a global scale. Simulated aerosol budgets in this new model are in the ranges of other model studies. Simulated gas and aerosol concentrations are in reasonable agreement with observations, although the model underestimates black carbon concentrations at the surface. Simulated aerosol size distributions are in reasonable agreement with observations in the marine boundary layer and in the free troposphere, while the model underestimates the accumulation mode number concentrations near the surface, and overestimates the accumulation number concentrations in the free troposphere. Simulated cloud condensation nuclei (CCN) concentrations are within the observational variations. Simulated aerosol optical depth (AOD) and single scattering albedo (SSA) are in reasonable agreement with observations, and the spatial distribution of AOD is consistent with observations, while the model underestimates AOD over regions with strong fossil fuel and biomass burning emissions, and overestimates AOD over regions with strong dust emissions. Overall, this multi-scale aerosol climate model simulates aerosol fields as well as conventional aerosol models.

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Bichindaritz, Isabelle, Guanghui Liu, and Christopher Bartlett. "Integrative survival analysis of breast cancer with gene expression and DNA methylation data." Bioinformatics 37, no. 17 (March 3, 2021): 2601–8. http://dx.doi.org/10.1093/bioinformatics/btab140.

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Abstract Motivation Integrative multi-feature fusion analysis on biomedical data has gained much attention recently. In breast cancer, existing studies have demonstrated that combining genomic mRNA data and DNA methylation data can better stratify cancer patients with distinct prognosis than using single signature. However, those existing methods are simply combining these gene features in series and have ignored the correlations between separate omics dimensions over time. Results In the present study, we propose an adaptive multi-task learning method, which combines the Cox loss task with the ordinal loss task, for survival prediction of breast cancer patients using multi-modal learning instead of performing survival analysis on each feature dataset. First, we use local maximum quasi-clique merging (lmQCM) algorithm to reduce the mRNA and methylation feature dimensions and extract cluster eigengenes respectively. Then, we add an auxiliary ordinal loss to the original Cox model to improve the ability to optimize the learning process in training and regularization. The auxiliary loss helps to reduce the vanishing gradient problem for earlier layers and helps to decrease the loss of the primary task. Meanwhile, we use an adaptive weights approach to multi-task learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. Finally, we build an ordinal cox hazards model for survival analysis and use long short-term memory (LSTM) method to predict patients’ survival risk. We use the cross-validation method and the concordance index (C-index) for assessing the prediction effect. Stringent cross-verification testing processes for the benchmark dataset and two additional datasets demonstrate that the developed approach is effective, achieving very competitive performance with existing approaches. Availability and implementation https://github.com/bhioswego/ML_ordCOX.

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Gerber, Mathieu, and Kari Heine. "Online inference with multi-modal likelihood functions." Annals of Statistics 49, no. 6 (December 1, 2021). http://dx.doi.org/10.1214/21-aos2076.

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Wang, Haodong, Quefeng Li, and Yufeng Liu. "Multi-response Regression for Block-missing Multi-modal Data without Imputation." Statistica Sinica, 2024. http://dx.doi.org/10.5705/ss.202021.0170.

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Yu, Jun, Wei Huang, Zuhe Li, Zhenqiu Shu, and Liang Zhu. "Hadamard matrix-guided multi-modal hashing for multi-modal retrieval." Digital Signal Processing, September 2022, 103743. http://dx.doi.org/10.1016/j.dsp.2022.103743.

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Liu, Jing, Shengwei Tian, Long Yu, Jun Long, Tiejun zhou, and Bo Wang. "Attention-based multi-modal fusion sarcasm detection." Journal of Intelligent & Fuzzy Systems, June 26, 2022, 1–12. http://dx.doi.org/10.3233/jifs-213501.

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Sarcasm is a way to express the thoughts of a person. The intended meaning of the ideas expressed through sarcasm is often the opposite of the apparent meaning. Previous work on sarcasm detection mainly focused on the text. But nowadays most information is multi-modal, including text and images. Therefore, the task of targeting multi-modal sarcasm detection is becoming an increasingly hot research topic. In order to better detect the accurate meaning of multi-modal sarcasm information, this paper proposed a multi-modal fusion sarcasm detection model based on the attention mechanism, which introduced Vision Transformer (ViT) to extract image features and designed a Double-Layer Bi-Directional Gated Recurrent Unit (D-BiGRU) to extract text features. The features of the two modalities are fused into one feature vector and predicted after attention enhancement. The model presented in this paper gained significant experimental results on the baseline datasets, which are 0.71% and 0.38% higher than that of the best baseline model proposed on F1-score and accuracy respectively.

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Journal articles on the topic 'Multi-modal statistics'

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