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Surface curvat.ures such as Gaussian: mean and principal curvat,urcs are intrinsic surface propert,ies and havc played import,ant, roles in curved surface analysis.in this pq'er, we present a correlat,ion-h;lsecI facc rccognition approach based on the analysis of maximum and minimum principal curvaturcs and t,hcir tlirect,ions. \Ye t,rea.t, face rccognition problem as a 311sha.pe recognit,ion problem of free-form curved surfaces.Our approach is based on a 311 vect,or sets correlat,ion mtt,hod which does not. require eit,her face feature cxtrwtion or siirface segmenhtion. Each face in bot,h input images and the inodel datalmse, is represcntetl a.s an Extendcd Gaussian Image( EGI), construct,ed by m a.p pin g p ri n ci p a1 cu rvat u res and t heir di re c t ion s at e x h surface point,s, onto t x o unit, spheres, each of rvhicti represent.s ridge a n d valley lines respectively. Individual face is t,tien recognized by evaluating t,lie simila.rit.ies among others by using Fisher's spherical correlation on EGI's of faces.The method is tzest,ed for it,s simplicit,y and robust,ness a.nd successively implcmentcd for cach of f x c range images from NK.CC( National Ktsearch Couiicil Chnada) 31) ima.ge d a h files. Results shows t,tia.t.shape informa,t.ion from surface curva.t,ures provides vital cues in dist,inguishing and identifying such fine surface structure a s human faces.

The su x array is a string-indexing structure and a memory e cient alternative to the su x tree. It has many advantages for text processing. Here w e p r opose an e cient algorithm for sorting su xes. We call this algorithm the two-stage su x sort. One of our ideas is to exploit the speci c relationships between adjacent su xes. Our algorithm makes it possible to use the suf-x array for much larger texts and suggests new areas of application. Our experiments on several text data sets including 514-MB Japanese newspapers demonstrate that our algorithm is 4.5 to 6.9 times faster than Quicksort, and 2.5 to 3.6 times faster than Sadakane's algorithm, which is considered to be the fastest algorithm in previous works.

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In January 2019, instant noodle giant Nissin Foods released two animated advertisements online featuring Naomi Osaka, which elicited backlash over “whitewashing” the multiracial professional tennis player who represents Japan. Although Nissin has since pulled the advertisements and officially apologized for portraying Osaka as lighter-skinned, underlying these issues are also those of gender, especially its intersections with race/ethnicity, nation, and sport in Japanese media. Employing discourse analysis of these two advertisements, this article examines how Nissin represented Osaka and what ideologies these representations reflect in Japanese society. Drawing on intersectional and transnational feminist cultural studies approaches, we argue that the advertisements’ representation of Osaka and the ensuing controversy reflect racialized and gendered ideologies in an allegedly homogeneous Japanese society, informed by local media and popular cultures that regularly portray “racially neutral” characters, celebrate lighter-skinned hāfu (half; multiracial) women, and diminish sportswomen’s athletic abilities. This article contributes to communication and sport studies by situating Osaka within broader contexts of how hāfu and sporting women are depicted in Japanese media and how elite sportswomen and multiracial athletes are portrayed in international media. Our article concludes by offering two suggestions on how to research multiracial sportswomen in the media.