Distinctive antibody responses to Mycobacterium tuberculosis in pulmonary and brain infection by Spatola, M, Nziza, N, Irvine, EB, Cizmeci, D, Jung, W, Van, LH, Nhat, LTH, Ha, VTN, Phu, NH, Nghia, HDT, Thwaites, GE, Lauffenburger, DA, Fortune, S, Thuong, NTT, Alter, G

“…Antibody studies included analysis of immunoglobulin isotypes (IgG, IgM, IgA) and subclass levels (IgG1–4) and the capacity of <i>M. tuberculosis</i>-specific antibodies to bind to Fc receptors or C1q and to activate innate immune effector functions (complement and natural killer cell activation; monocyte or neutrophil phagocytosis). Machine learning methods were applied to characterize serum and CSF responses in TBM, identify prognostic factors associated with disease severity, and define the key antibody features that distinguish TBM from pulmonary TB. …”

Microbial communities: network reconstruction and control by Fu, A

“…It proposes adaptive learning methods and experimental design rules to transform PAG-inferred structures into fully identified causal models, thus enhancing our understanding of microbial dynamics and providing a systematic approach for future research in causal inference within complex biological systems. …”

Speeding up deep neural network training with decoupled and analytic learning by Zhuang, Huiping

“…A fully decoupled learning method using delayed gradients (FDG) is first proposed which addresses all the three lockings. …”
Get full text

Feature extraction from EEG signals and regularization for brain-computer interface by Mishuhina, Vasilisa

“…The goal of this research is to improve feature extraction and regularization of EEG signals using machine learning methods and hence achieve better results during the classification of the signals for motor imagery BCI (MI-BCI). …”
Get full text

Natural robustness of machine learning in the open world by Wei, Hongxin

“…Secondly, classic machine learning methods are built on the i.i.d. assumption that training and testing data are independent and identically distributed. …”
Get full text

High cycle fatigue characterisation and modelling of 316L stainless steel processed by laser powder bed fusion by Zhang, Meng

“…Lastly, considering the numerous influencing factors arising from the process and the associated failure behaviours, a neuro-fuzzy-based machine learning method was applied to provide an effective unifying approach for high cycle fatigue life prediction. …”
Get full text

Digital problem-based learning in health professions : systematic review and meta-analysis by the digital health education collaboration by Car, Lorainne Tudor, Kyaw, Bhone Myint, Dunleavy, Gerard, Smart, Neil A., Semwal, Monika, Rotgans, Jerome Ingmar, Low-Beer, Naomi, Campbell, James

“…We included studies that compared the effectiveness of DPBL with traditional learning methods or other forms of digital education in improving health professionals’ knowledge, skills, attitudes, and satisfaction. …”
Get full text
Get full text

Sensor-based human activity recognition via zero-shot learning by Wang, Wei

“…For problems under this problem setting, as there are no labeled training instances belonging to the unseen classes, the zero-shot learning methods are used. We focus on three problems under this setting. …”
Get full text
Get full text

Topics in Bayesian machine learning for finance by Spears, T

“…Further, we estimate an approximation to epistemic uncertainty via a pseudo-Bayesian deep learning method. This work demonstrates the utility of the model output for deciding the relative allocation of risk capital across trades. …”

Brain Informatics : 14th International Conference, BI 2021, Virtual Event, September 17-19, 2021, Proceedings / by International Conference on Brain Informatics (14th : 2021 : Online), organiser 643024, Mahmud, Mufti, editor 643025, Kaiser, M. Shamim, editor 643026, Vassanelli, Stefano, editor 643027, Dai, Qionghai, 1964-, editor 643028, Zhong, Ning, 1956-, editor 643029

Brain computer interface for post-stroke motor rehabilitation by Mane, Ravikiran Tanaji

“…Moving ahead, we analyze the classification performance of proposed and baseline deep learning architectures and traditional machine learning methods for MI detection in 25 chronic stroke patients undergoing three different BCI-based motor rehabilitation interventions for 2/4 weeks. …”
Get full text

Learning-enabled decision-making for autonomous driving: framework and methodology by Huang, Zhiyu

“…The personalized cost learning method outperforms general cost modeling methods, leading to a more human-like driving experience. …”
Get full text

Conflict-free urban air mobility planning with an airspace-resource-centric approach by Dai, Wei

“…Motivated by the absence of a precise power consumption model that can be applied to multiple eVTOL aircraft types, we use the ensemble learning method to model the power consumption of eVTOL aircraft. …”
Get full text

Geometry guided supervised representation learning for classification by Li, Yue

“…However, the AE-based representation learning method, FAE-LG, is trained iteratively by using back-propagation (BP) that requires a significant amount of training time. …”
Get full text

Creativity in Intelligent Technologies and Data Science : 4th International Conference, CIT&DS 2021, Volgograd, Russia, September 20-23, 2021 : Proceedings / by CIT&DS (Conference) (4th : 2021 : Volgograd, Russia), organiser 642924, Kravets, Alla G., editor 642925, Shcherbakov, Maxim, editor 642926, Parygin, Danila, editor 642927, Groumpos, Peter P., editor 642928

Structured sparse representations for supervised and unsupervised learning by Zeng, Yijie

“…It is demonstrated that the proposed graph learning method, termed Adaptive Locality-constrained Clustering (ALC), generates more structured graph compared with predefined ones and provides better clustering performance on benchmark datasets. …”
Get full text

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures by Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J. Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., Goya-Maldonado, Roberto

“…Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.…”
Get full text

Query cost estimation in DBMS with deep learning by Acharya, Atul

“…Our experiments showed that the TreeGBM was ∼120 times faster than state-of-the-art learned methods while maintaining good prediction scores. …”
Get full text

E-learning for mobile learning platform by MacInnes, Catriona

“…By studying the way people learn, methods can be created to increase learning potential and efficiency. …”
Get full text

0-1 Knapsack in Nearly Quadratic Time by Jin, Ce

“…To extend this approach to our 0-1 setting, we use a novel pruning method, as well as the two-level color-coding of Bringmann (2017) and the SMAWK algorithm on tall matrices.…”
Get full text