“The extremal index para­me­ter theta char­ac­ter­izes the degree of local depen­dence in the extremes of a sta­tion­ary time series and has impor­tant appli­ca­tions in a num­ber of areas, such as hydrol­ogy, telecom­mu­ni­ca­tions, finance and envi­ron­men­tal studies.…Further, a pro­ce­dure for the auto­matic selec­tion of its tun­ing para­me­ter is devel­oped and dif­fer­ent types of con­fi­dence inter­vals that prove use­ful in prac­tice pro­posed. The per­for­mance of the esti­ma­tor is exam­ined through sim­u­la­tions, which show its highly com­pet­i­tive behav­ior. Finally, the esti­ma­tor is applied to three real data sets of daily crude oil prices, daily returns of the S&P 500 stock index, and high-​​frequency, intra-​​day traded vol­umes of a stock. These appli­ca­tions demon­strate addi­tional diag­nos­tic fea­tures of sta­tis­ti­cal plots based on the new estimator.”

“I am lis­ten­ing to a pre­sen­ta­tion at the Homer Hoyt meet­ings on the condo melt­down in South Florida. Devel­op­ers planned on build­ing 95,000 units in the city of Miami between 2002 and 2007. In the 2000 cen­sus, the whole city had 163,000 units.”

“Research into prop­er­ties of het­ero­ge­neous arti­fi­cial mate­ri­als, con­sist­ing of arrange­ments of rigid scat­ter­ers embed­ded in a medium with dif­fer­ent elas­tic prop­er­ties, has been intense through­out last two decades. The capa­bil­ity to pre­vent the trans­mis­sion of waves in pre­de­ter­mined bands of fre­quen­cies –called bandgaps– becomes one of the most inter­est­ing prop­er­ties of these sys­tems, and leads to the pos­si­bil­ity of design­ing devices to con­trol wave prop­a­ga­tion. The under­ly­ing phys­i­cal mech­a­nism is destruc­tive Bragg inter­fer­ence. Here we show a tech­nique that enables the cre­ation of a wide bandgap in these mate­ri­als, based on frac­tal geome­tries. We have focused our work in the acoustic case where these mate­ri­als are called Phononic/​Sonic Crys­tals (SC) but, the tech­nique could be applied any types of crys­tals and wave types in ranges of fre­quen­cies where the physics of the process is linear.”

“The opti­miza­tion for­mu­la­tion con­sid­ered in this paper uses a penal­ized neg­a­tive Pois­son log-​​likelihood objec­tive func­tion with non­neg­a­tiv­ity con­straints (since Pois­son inten­si­ties are nat­u­rally non­neg­a­tive). In par­tic­u­lar, the pro­posed approach incor­po­rates key ideas of using sep­a­ra­ble qua­dratic approx­i­ma­tions to the objec­tive func­tion at each iter­a­tion and penal­iza­tion terms related to l1 norms of coef­fi­cient vec­tors, total vari­a­tion semi­norms, and partition-​​based mul­ti­scale esti­ma­tion methods.”

“The obtained results and trends sug­gest a num­ber of fur­ther inves­ti­ga­tions. For instance, it would be inter­est– ing to con­sider other net­work mod­els and mea­sure­ments, as well as to assess the effect of dif­fer­ent types of hard– ware, com­pil­ers and oper­at­ing systems.”

“Ran­dom num­bers play a cru­cial role in sci­ence and indus­try. Many numer­i­cal meth­ods require the use of ran­dom num­bers, in par­tic­u­lar the Monte Carlo method. There­fore it is of para­mount impor­tance to have effi­cient ran­dom num­ber gen­er­a­tors. The dif­fer­ences, advan­tages and dis­ad­van­tages of true and pseudo ran­dom num­ber gen­er­a­tors are dis­cussed with an empha­sis on the intrin­sic details of mod­ern and fast pseudo ran­dom num­ber gen­er­a­tors. Fur­ther­more, stan­dard tests to ver­ify the qual­ity of the ran­dom num­bers pro­duced by a given gen­er­a­tor are out­lined. Finally, stan­dard sci­en­tific libraries with built-​​in gen­er­a­tors are pre­sented, as well as dif­fer­ent approaches to gen­er­ate nonuni­form ran­dom num­bers. Poten­tial prob­lems that one might encounter when using large par­al­lel machines are discussed.”

“In many net­works, ver­tices have hid­den attrib­utes, or types, that are cor­re­lated with the net­works topol­ogy. If the topol­ogy is known but these attrib­utes are not, and if learn­ing the attrib­utes is costly, we need a method for choos­ing which ver­tex to query in order to learn as much as pos­si­ble about the attrib­utes of the other ver­tices. We assume the net­work is gen­er­ated by a sto­chas­tic block model, but we make no assump­tions about its assor­ta­tiv­ity or dis­as­sor­ta­tiv­ity. We choose which ver­tex to query using two meth­ods: 1) max­i­miz­ing the mutual infor­ma­tion between its attrib­utes and those of the oth­ers (a well-​​known approach in active learn­ing) and 2) max­i­miz­ing the aver­age agree­ment between two inde­pen­dent sam­ples of the con­di­tional Gibbs dis­tri­b­u­tion. Exper­i­men­tal results show that both these meth­ods do much bet­ter than sim­ple heuris­tics. They also con­sis­tently iden­tify cer­tain ver­tices as impor­tant by query­ing them early on.”

“We pro­pose a fluc­tu­a­tion analy­sis to quan­tify spa­tial cor­re­la­tions in com­plex net­works. The approach con­sid­ers the sequences of degrees along short­est paths in the net­works and quan­ti­fies the fluc­tu­a­tions in anal­ogy to time series. In this work, the Barabasi-​​Albert (BA) model, the Cay­ley tree at the per­co­la­tion tran­si­tion, a frac­tal net­work model, and exam­ples of real-​​world net­works are stud­ied. While the fluc­tu­a­tion func­tions for the BA model show expo­nen­tial decay, in the case of the Cay­ley tree and the frac­tal net­work model the fluc­tu­a­tion func­tions dis­play a power-​​law behav­ior. The frac­tal net­work model com­prises long-​​range anti-​​correlations. The results sug­gest that the fluc­tu­a­tion expo­nent pro­vides com­ple­men­tary infor­ma­tion to the frac­tal dimension.”

“Wealth is no proof of moral char­ac­ter; nor poverty of the want of it.

On the con­trary, wealth is often the pre­sump­tive evi­dence of dis­hon­esty; and poverty the neg­a­tive evi­dence of inno­cence. If there­fore prop­erty, whether lit­tle or much, be made a cri­te­rion, the means by which that prop­erty has been acquired ought to be made a cri­te­rion also.”

“This means that each time the doc­u­ment is updated, the client will also store the pre­vi­ous ver­sion as an attach­ment to the lat­est ver­sion. At any time, a user can load any of the old versions.”

“Com­mu­nity struc­ture is one of the key prop­er­ties of com­plex net­works and plays a cru­cial role in their topol­ogy and func­tion. While an impres­sive amount of work has been done on the issue of com­mu­nity detec­tion, very lit­tle atten­tion has been so far devoted to the inves­ti­ga­tion of com­mu­ni­ties in real net­works. We present a sys­tem­atic empir­i­cal analy­sis of the sta­tis­ti­cal prop­er­ties of com­mu­ni­ties in large infor­ma­tion, com­mu­ni­ca­tion, tech­no­log­i­cal, bio­log­i­cal, and social net­works. We find that the meso­scopic orga­ni­za­tion of net­works of the same cat­e­gory is remark­ably sim­i­lar. This is reflected in sev­eral char­ac­ter­is­tics of com­mu­nity struc­ture, which can be used as “fin­ger­prints” of spe­cific net­work categories.…”

“… We find that SF net­works gen­er­ate oscil­la­tions much more eas­ily than ER net­works do, and this may explain why SF net­works are more evolv­able than ER net­works are for oscil­la­tory phe­no­types. In spite of their greater evolv­abil­ity, we find that net­works with SFout topolo­gies are also more robust to muta­tions than ER net­works. Fur­ther­more, the SFout topolo­gies are more robust to changes in ini­tial con­di­tions (envi­ron­men­tal robust­ness). For both topolo­gies, we find that once a pop­u­la­tion of net­works has reached the tar­get state, fur­ther neu­tral evo­lu­tion can lead to an increase in both the muta­tional robust­ness and the envi­ron­men­tal robust­ness to changes in ini­tial conditions.”