Sensing the Fiber Bent via Deep Learning



Detecting User Emotions in Twitter through Collective Classification
İLERİ, İBRAHİM; Karagöz, Pınar (2016-11-11)
The explosion in the use of social networks has generated a big amount of data including user opinions about varying subjects. For classifying the sentiment of user postings, many text-based techniques have been proposed in the literature. As a continuation of sentiment analysis, there are also studies on the emotion analysis. Due to the fact that many different emotions are needed to be dealt with at this point, the problem gets more complicated as the number of emotions to be detected increases. In this s...
Detecting Image Communities
Esen, Ersin; Ozkan, Savas; Atil, Ilkay; Arabaci, Mehmet Ali; Tankiz, Seda (2014-06-20)
In this work, we propose a novel community detection method that is specifically designed for image communities. We define image community as a coherent subgroup of images within a large set of images. In order to detect image communities, we construct an image graph by utilizing visual affinity between each image pair and then prune most of the links. Instead of affinity values, we prefer ranking of neighboring images and get rid of range mismatch of affinity values. The resulting directed graph is process...
Detecting the long-distance structure of the X(3872)
Guo, Feng-Kun; Hidalgo-Duque, Carlos; Nieves, Juan; Özpineci, Altuğ; Valderrama, Manuel Pavon (Elsevier BV; 2014-07-09)
In this work we focus on the analysis of the X(3872) -> D-0(D) over bar (0)pi(0) decay assuming a molecular picture for the X(3872) state within an effective field theory (EFT) approach. This decay is sensitive to the long-distance structure of the X(3872); in sharp contrast with the main decay channels: J/psi pi pi and J/psi 3 pi. We show that the final state interactions in the D (D) over bar system can be important and that the measurement of this partial decay width can provide a constrain on the low en...
Recognizing actions from still images
In this paper, we approach the problem of understanding human actions from still images. Our method involves representing the pose with a spatial and orientational histogramming of rectangular regions on a parse probability map. We use LDA to obtain a more compact and discriminative feature representation and binary SVMs for classification. Our results over a new dataset collected for this problem show that by using a rectangle histogramming approach, we can discriminate actions to a great extent. We also s...
Positioning colloids at the surfaces of cholesteric liquid crystal droplets
Büküşoğlu, Emre; Zhou, Ye; A. Martinez-Gonzalez, Jose; Rahimi, Mohammad; Wang, Qi; de Pablo, Juan Jose; Abbott, Nicholas L (2016-01-01)
We report on the internal configurations of aqueous dispersions of droplets of cholesteric liquid crystals (LCs; 5-50 mu m-in-diameter; comprised of 4-cyano-4'-pentylbiphenyl and 4-(1-methylheptyloxycarbonyl)-phenyl-4-hexyloxybenzoate) and their influence on the positioning of surface-adsorbed colloids (0.2 or 1 mm-in-diameter polystyrene (PS)). When N = 2D/P was less than 4, where D is the droplet diameter and P is the cholesteric pitch, the droplets adopted a twisted bipolar structure (TBS) and colloids w...
Citation Formats
E. Yüce, “Sensing the Fiber Bent via Deep Learning,” presented at the NanoTR, Ankara, Türkiye, 2022, Accessed: 00, 2022. [Online]. Available: