Building occupant sensing : occupancy prediction and behavior recognition by Zhu, Qingchang

“…To achieve these goals in smart buildings, it is necessary to study the problem of occupant sensing by leveraging machine learning methods to understand occupants based on sensor signals. …”
Get full text
Get full text

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. …”

Measuring the predictability of life outcomes with a scientific mass collaboration by Salganik, Matthew J., Lundberg, Ian, Kindel, Alexander T., Ahearn, Caitlin E., Al-Ghoneim, Khaled, Almaatouq, Abdullah, Altschul, Drew M., Brand, Jennie E., Carnegie, Nicole Bohme, Compton, Ryan James, Datta, Debanjan, Davidson, Thomas, Filippova, Anna, Gilroy, Connor, Goode, Brian J., Jahani, Eaman, Kashyap, Ridhi, Kirchner, Antje, McKay, Stephen, Morgan, Allison C., Pentland, Alex, Polimis, Kivan, Raes, Louis, Rigobon, Daniel E., Roberts, Claudia V., Stanescu, Diana M., Suhara, Yoshihiko, Usmani, Adaner, Wang, Erik H., Adem, Muna, Alhajri, Abdulla, AlShebli, Bedoor, Amin, Redwane, Amos, Ryan B., Argyle, Lisa P., Baer-Bositis, Livia, Buchi, Moritz, Chung, Bo-Ryehn, Eggert, William, Faletto, Gregory, Fan, Zhilin, Freese, Jeremy, Gadgil, Tejomay, Gagne ́, Josh, Gao, Yue, Halpern-Manners, Andrew, Hashim, Sonia P., Hausen, Sonia, He, Guanhua, Higuera, Kimberly, Hogan, Bernie, Horwitz, Ilana M., Hummel, Lisa M., Jain, Naman, Jin, Kun, Jurgens, David, Kaminski, Patrick, Karapetyan, Areg, Kim, E. H., Leizman, Ben, Liu, Naijia, Moser, Malte, Mack, Andrew E., Mahajan, Mayank, Mandell, Noah, Marahrens, Helge, Mercado-Garcia, Diana, Mocz, Viola, Mueller-Gastell, Katariina, Musse, Ahmed, Niu, Qiankun, Nowak, William, Omidvar, Hamidreza, Or, Andrew, Ouyang, Karen, Pinto, Katy M., Porter, Ethan, Porter, Kristin E., Qian, Crystal, Rauf, Tamkinat, Sargsyan, Anahit, Schaffner, Thomas, Schnabel, Landon, Schonfeld, Bryan, Sender, Ben, Tang, Jonathan D., Tsurkov, Emma, van Loon, Austin, Varol, Onur, Wang, Xiafei, Wang, Zhi, Wang, Julia, Wang, Flora, Weissman, Samantha, Whitaker, Kirstie, Wolters, Maria K., Woon, Wei Lee, Wu, James, Wu, Catherine, Yang, Kengran, Yin, Jingwen, Zhao, Bingyu, Zhu, Chenyun, Brooks-Gunn, Jeanne, Engelhardt, Barbara E., Hardt, Moritz, Knox, Dean, Levy, Karen, Narayanan, Arvind, Stewart, Brandon M., Watts, Duncan J., McLanahan, Sara

“…Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. …”
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

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

Offline eLearning for undergraduates in health professions : a systematic review of the impact on knowledge, skills, attitudes and satisfaction by Wark, Petra A., Rasmussen, Kristine, Belisario, José Marcano, Molina, Joseph Antonio, Loong, Stewart Lee, Cotic, Ziva, Papachristou, Nikos, Riboli–Sasco, Eva, Car, Lorainne Tudor, Musulanov, Eve Marie, Zhang, Yanfeng, Kunz, Holger, George, Pradeep Paul, Heng, Bee Hoon, Wheeler, Erica Lynette, Al Shorbaji, Najeeb, Svab, Igor, Atun, Rifat, Majeed, Azeem, Car, Josip

“…To inform investments in offline eLearning, we need to establish its effectiveness in terms of gaining knowledge and skills, students’ satisfaction and attitudes towards eLearning. Methods: We conducted a systematic review of offline eLearning for students enrolled in undergraduate, health–related university degrees. …”
Get full text
Get full text
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

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

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

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

Trasformation from amorphous carbon to graphene by Liang, Chen

“…In this report, a simple annealing method is used to produce the graphene. A thin Ni film is deposited on a Si substrate by the e-beam system. …”
Get full text

Improved electrical performance of Erbium silicide Schottky diodes formed by pre-RTA amorphization of Si by Tang, L. J., Tan, Eu Jin, Pey, Kin Leong, Chi, Dong Zhi, Lee, Pooi See

“…Erbium silicide Schottky diodes formed on Si(001) substrate using rapid thermal annealing method show degraded Schottky-barrier height ϕ_Beff and ideality factor due to the presence of silicide-induced microstructural defects which are likely sources of trap states. …”
Get full text
Get full text

Investigation of process parameter variation on properties of magnetron sputtered p-type Bi0.5Sb1.5Te3 thermoelectric thin films by Law, Justin Hui Ching.

“…Furthermore, the annealing method using the magnetron sputtering machine itself was inefficient and produced minor effects on the surface morphology and the crystallography of the materials. …”
Get full text

Night vision with unmanned surface vehicle (USV) by Ang, Claryl Hui Ern

“…Besides that, this research also involves sourcing code on a deep learning method for transferring night-time images to daytime images. …”
Get full text

Active learning for ontological event extraction incorporating named entity recognition and unknown word handling by Han, Xu, Kim, Jung-jae, Kwoh, Chee Keong

“…We also apply our active learning method for the task of named entity recognition. …”
Get full text
Get full text

Great enhancement effect of 20-40 nm Ag NPs on solar-blind UV response of the mixed-phase MgZnO detector by Han, Shun, Xia, Hao, Lu, Youming, Hu, Sirong, Zhang, Dao Hua, Xu, Wangying, Fang, Ming, Liu, Wenjun, Cao, Peijiang, Zhu, Deliang

“…Here, the effects of Ag nanoparticles (NPs) with different sizes on UV response characteristics of the device are studied, the Ag NPs with different sizes that are made from a simple vacuum anneal method. Ag NPs with different sizes could modulate the peak response position of the mixed-phase MgZnO detector from near UV range (350 nm) to deep UV range (235 nm), and the enhancement effect of the Ag NPs on the UV response differs much with the crystal structure and the basic UV response of the MgZnO thin film. …”
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