Character (mathematics)

About: Character (mathematics) is a research topic. Over the lifetime, 46723 publications have been published within this topic receiving 411412 citations.

Local motion phases for learning multi-contact character movements

Abstract: Training a bipedal character to play basketball and interact with objects, or a quadruped character to move in various locomotion modes, are difficult tasks due to the fast and complex contacts happening during the motion. In this paper, we propose a novel framework to learn fast and dynamic character interactions that involve multiple contacts between the body and an object, another character and the environment, from a rich, unstructured motion capture database. We use one-on-one basketball play and character interactions with the environment as examples. To achieve this task, we propose a novel feature called local motion phase, that can help neural networks to learn asynchronous movements of each bone and its interaction with external objects such as a ball or an environment. We also propose a novel generative scheme to reproduce a wide variation of movements from abstract control signals given by a gamepad, which can be useful for changing the style of the motion under the same context. Our scheme is useful for animating contact-rich, complex interactions for real-time applications such as computer games.

The psychotic character: clinical psychiatric considerations.

Abstract: T,he purpose o,f this co~mmunicati~n is to delineate a s~drLome from among the s,o-called borderline conditio,ns, a ,syndro.me whicth i have chasen to aall the p,sychotic character, a counterpart to the r~e~rotic character.** I propose tt~e h~p,ottresis that. the p,sycho,t~c ch,ar~aeier represents a specific and re c(rgniz~b~e cli~lfical entity, very m~r ~s does t3~e neurotic character, a~b,eit with all the lilniMti, m~s of the ~a~ter diagnosis. We are dealilag in tire p!s~cho~r e~aracter with a crystallization of features reflected in predictable modes o,f re.sp,onse to stress situations, modes which are charncteristic of the p,sychofic jl~st as the reactive modes o~ the neurotic c~haracter resemable thas,e ~s,een in the neurotic. T~he need to establish such an entity derives from file confusion and welter of terms used to. describe syndr~o,mes seen by ,all o.f u's and c~es~grnated by one or anothe,r term. We have all felt d~ss~tis~fied with the terries used to describe these c~onditions. Included axnong these, have been sr chag~ro~s~ic en/dties a's \"borderl ine s'tates, ''~ \"azn,bulato~r$ schizophrenia, ''~ \"p~seudoIrenrotic schiT~ophrenia, ''~'~ \"las,eudo-p,sychos s chiz.ophrenia, ''6 \"schizophrenia without psych osLs, TM \"latent psycho,sis, ''~ \"larval p,sycho~sis, ''~ and s,o on. ]n some instances, these condition,s ,have been given names characte,rized by the main p,resenti~rg features, fo,r ir~sltance the \"as-if\" character ~~ or \"rreurot%c ego distortiorrs.\"\" K, asanin, ~z in his .study of thoug'ht disturbances in schizophrenia, found it neces~sary to delineate a group of patients vzho develop,ed psychotic reacltiorrs as the culmina~ng points in a series o~ attelT~ted neurotic s0,h~tions of basic maladj~stanenLs. The confusion anmrrg psychiatrists h~s als~o been reflected amo,ng psych(rlogists vzho, have been frequently n~onplassed in their at-

Bangla Handwritten Character Recognition using Convolutional Neural Network

Abstract: Handwritten character recognition complexity varies among different languages due to distinct shapes, strokes and number of characters. Numerous works in handwritten character recognition are available for English with respect to other major languages such as Bangla. Existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, Convolutional Neural Network (CNN) is found efficient for English handwritten character recognition. In this paper, a CNN based Bangla handwritten character recognition is investigated. The proposed method normalizes the written character images and then employ CNN to classify individual characters. It does not employ any feature extraction method like other related works. 20000 handwritten characters with different shapes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed some other prominent exiting methods.