Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of its use (...) in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search forgenerative model-based andgeometric manipulation-based DA methods. Additionally, we categorize TL techniques in DL-based BCIs intoexplicitandimplicitmethods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs. (shrink)

Entering the Stream to Enlightenment: Experiences of the Stages of the Buddhist Path in Contemporary Sri Lanka, by Yuki Sirimane. Equinox Publishing Ltd., 2016. 366pp. Hb. £75.00; Pb. £24.99. ISBN-13: 978-1-7817-9203-2 ; 978-1-7817-9204-9.

Ultra-high-performance concrete (UHPC) is gaining popularity due to its superior mechanical properties, including high strength, toughness, durability, and excellent seismic performance. However, the risk of explosive spalling in UHPC structures exposed to fire poses a significant challenge, fundamentally hindering comprehensive studies on fire safety and limiting the broader engineering applications of UHPC. In this study, the mixture proportion of UHPC was developed and optimized by comparing the usage of yellow river and standard sands, different types of chemical admixtures, and different (...) curing regimes. Furthermore, a sensitive case study was also conducted to optimize the calcined bauxite content to improve UHPC's fire resistance. For these purposes, 189 UHPC cubes, incorporating variations in particle types, superplasticizer types, and curing regimes, were tested under three thermal levels, such as 200℃,400℃, and 600℃. The microstructural evolution of heated cube specimens was then observed using a scanning electron microscope (SEM) examination. Calcined bauxite was then considered to replace the standard sand to improve the fire resistance of UHPC and enhance the residual compressive strength of cube specimens under thermal conditions. For this purpose, 0%, 20%, 40%, 60%, 80%, and 100% calcined bauxite content was designed as six case studies. Furthermore, the degradation of UHPC strength was also investigated for the corresponding six case studies by conducting the uniaxial compressive experiments. Finally, the microstructural evolution under both thermal conditions and calcined bauxite content was observed and discussed. (shrink)

1. 21-segi wa t'ongsŏphyŏng injae ŭi p'iryosŏng -- 2. Ch'oe Han-gi, t'ongsŏphyŏng ŭl mal hada -- 3. T'ongsŏphyŏng injae ŭi chagyŏk chokŏn -- 4. T'ongsŏphyŏng injae ŭi lidŏsip -- 5. Naganŭn mal.