Large scale first-hand tweets motivate automatic event detection on Twitter. Previous approaches model events by clustering tweets, words or segments. On the other hand, event clusters represented by tweets are easier to understand than those represented by words/segments. However, compared to words/segments, tweets are sparser and therefore makes clustering less effective. This article proposes to represent events with triple structures called frames, which are as efficient as, yet can be easier to understand than words/segments. Frames are extracted based on shallow syntactic information of tweets with an unsupervised open information extraction method, which is introduced for domain-independent relation extraction in a single pass over web scale data. This is then followed by bursty frame element extraction functions as feature selection by filtering frame elements with bursty frequency pattern via a probabilistic model. After being clustered and ranked, high-quality events are yielded and then reported by linking frame elements back to frames. Experimental results show that frame-based event detection leads to improved precision over a state-of-the-art baseline segment-based event detection method. Superior readability of frame-based events as compared with segment-based events is demonstrated in some example outputs.

The present study examined the evaluation of aging using the photic driving response, a measure used in routine EEG examinations. We examined 60 normal participants without EEG abnormalities, classified into three age groups (2029, 3059 and over 60 years; 20 participants per group). EEG was measured at rest and during photic stimulation (PS). We calculated Z-scores as a measure of enhancement and suppression due to visual stimulation at rest and during PS and tested for between-group and intraindividual differences. We examined responses in the alpha frequency and harmonic frequency ranges separately, because alpha suppression can affect harmonic frequency responses that overlap the alpha frequency band. We found a negative correlation between Z-scores for harmonics and age by fitting the data to a linear function (CC: -0.740). In contrast, Z-scores and alpha frequency were positively correlated (CC: 0.590).

An age-group classification method based on a fusion of different classifiers with different two-dimensional feature extraction algorithms is proposed. Theoretically, an integration of multiple classifiers can provide better performance compared to a single classifier. In this paper, we extract effective features from one sample image using different dimensional reduction methods, construct multiple classifiers in each subspace, and combine them to reduce age-group classification errors. As for the dimensional reduction methods, two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) are used. These algorithms are antisymmetric in the treatment of the rows and the columns of the images. We prepared the row-based and column-based algorithms to make two different classifiers with different error tendencies. By combining these classifiers with different errors, the performance can be improved. Experimental results show that our fusion-based age-group classification method achieves better performance than existing two-dimensional algorithms alone.