Book Thing design

Answer Fac­tory design

Answer Fac­tory design

Answer Fac­tory design

“The Den­dritic Cell Algo­rithm (DCA) is inspired by the func­tion of the den­dritic cells of the human immune sys­tem. In nature, den­dritic cells are the intru­sion detec­tion agents of the human body, polic­ing the tis­sue and organs for poten­tial invaders in the form of pathogens. In this research, and abstract model of DC behav­iour is devel­oped and sub­se­quently used to form an algo­rithm, the DCA. The abstrac­tion process was facil­i­tated through close col­lab­o­ra­tion with lab­o­ra­tory– based immu­nol­o­gists, who per­formed bespoke exper­i­ments, the results of which are used as an inte­gral part of this algo­rithm. The DCA is a pop­u­la­tion based algo­rithm, with each agent in the sys­tem rep­re­sented as an ‘arti­fi­cial DC’. Each DC has the abil­ity to com­bine mul­ti­ple data streams and can add con­text to data sus­pected as anomalous.…”

“Retriev­ing images from large and var­ied repos­i­to­ries using visual con­tents has been one of major research items, but a chal­leng­ing task in the image man­age­ment com­mu­nity. In this paper we present an effi­cient approach for region-​​based image clas­si­fi­ca­tion and retrieval using a fast multi-​​level neural net­work model. The advan­tages of this neural model in image clas­si­fi­ca­tion and retrieval domain will be high­lighted. The pro­posed approach accom­plishes its goal in three main steps. First, with the help of a mean-​​shift based seg­men­ta­tion algo­rithm, sig­nif­i­cant regions of the image are iso­lated. Sec­ondly, color and tex­ture fea­tures of each region are extracted by using color moments and 2D wavelets decom­po­si­tion tech­nique. Thirdly the multi-​​level neural clas­si­fier is trained in order to clas­sify each region in a given image into one of five pre­de­fined cat­e­gories, i.e., “Sky”, “Build­ing”, “Sand­nRock”, “Grass” and “Water”. …”

“The study of evo­lu­tion of net­works has received increased inter­est with the recent dis­cov­ery that many real-​​world net­works pos­sess many things in com­mon, in par­tic­u­lar the man­ner of evo­lu­tion of such net­works. By adding a dimen­sion of time to graph analy­sis, evolv­ing graphs present oppor­tu­ni­ties and chal­lenges to extract valu­able infor­ma­tion. This paper intro­duces the Evolv­ing Graph Markup Lan­guage (EGML), an XML appli­ca­tion for rep­re­sent­ing evolv­ing graphs and related results. Along with EGML, a soft­ware tool is pro­vided for the study of evolv­ing graphs. New evolv­ing graph draw­ing tech­niques based on the force-​​directed graph lay­out algo­rithm are also explored. Our evolv­ing graph tech­niques reduce ver­tex move­ments between graph instances, so that an evolv­ing graph can be viewed with smooth transitions”

“One of the most visu­ally demon­stra­ble and straight­for­ward uses of fil­ter­ing is in the field of Com­puter Vision. In this doc­u­ment we will try to out­line the issues encoun­tered while design­ing and imple­ment­ing a par­ti­cle and kalman fil­ter based track­ing system.”

“The econ­omy has started cre­at­ing jobs—albeit at a slow rate—in recent months. But those with new master’s degrees often aren’t at the front of the line to get them, say experts. One rea­son: They fre­quently com­pete for jobs that require those advanced degrees with older work­ers who have the advan­tage of more work experience.”

“In this paper, a sim­ple Neural con­troller has been used to achieve sta­ble walk­ing in a NAO biped robot, with 22 degrees of free­dom that imple­mented in a vir­tual physics-​​based sim­u­la­tion envi­ron­ment of Robocup soc­cer sim­u­la­tion envi­ron­ment. The algo­rithm uses a Mat­suoka base neural oscil­la­tor to gen­er­ate con­trol sig­nal for the biped robot. To find the best angu­lar tra­jec­tory and opti­mize net­work para­me­ters, a new population-​​based search algo­rithm, called the Har­mony Search (HS) algo­rithm, has been used. The algo­rithm con­cep­tu­al­ized a group of musi­cians together try­ing to search for bet­ter state of har­mony. Sim­u­la­tion results demon­strate that the mod­i­fi­ca­tion of the step period and the walk­ing motion due to the sen­sory feed­back sig­nals improves the sta­bil­ity of the walk­ing motion.”

“Want to kill your firm quickly? Then study the cur­rent issue of Har­vard Busi­ness Review. Imbibe its phi­los­o­phy, its atti­tudes and its val­ues. Imple­ment every­thing it says. In so doing, you will be well on the way to turn­ing your orga­ni­za­tion into a fully-​​fledged 20^th Cen­tury orga­ni­za­tion, with a life expectancy of around 5–10 years.”

seems like some­thing to look at

“We have described three types of geo­met­ric object that have nat­ural encod­ings as reg­u­lar label­ings of a max­i­mal or near-​​maximal pla­nar graph. Although much about these label­ings is known, there are still many ques­tions remaining:…”

“We study the phase behav­ior of bowl-​​shaped par­ti­cles using com­puter sim­u­la­tions. These par­ti­cles were found exper­i­men­tally to form a meta-​​stable worm-​​like fluid phase in which the bowl-​​shaped par­ti­cles have a strong ten­dency to stack on top of each other [M.Marechal et al, Nano Let­ters 10, 1907 (2010)]. In this work, we show that the tran­si­tion from the low-​​density fluid to the worm-​​like phase has an inter­est­ing effect on the equa­tion of state. The sim­u­la­tion results also show that the worm-​​like fluid phase trans­forms spon­ta­neously into a colum­nar phase for bowls that are suf­fi­ciently deep. Fur­ther­more, we describe the phase behav­ior as obtained from free energy cal­cu­la­tions employ­ing Monte Carlo sim­u­la­tions. The colum­nar phase is sta­ble for bowl shapes rang­ing from infi­nitely thin bowls to sur­pris­ingly shal­low bowls. … the phase dia­gram fea­tures four novel crys­tal phases and a region where the sta­ble fluid con­tains worm-​​like stacks.”

“… We have devel­oped a novel method of study­ing the melt­ing of RNAs with tem­per­a­ture by com­pu­ta­tion­ally sam­pling the dis­tri­b­u­tion of the RNA struc­tures at var­i­ous tem­per­a­tures using the RNA fold­ing soft­ware Vienna. In this study, we com­pared the ther­mosens­ing prop­erty of 100 ran­domly selected mRNAs and three well known thermometers…”