INDI | ar | er | ir | SUBJ | ar | er | ir |
---|---|---|---|---|---|---|---|
yo | o | o | o | yo | e | a | a |
tú | as | es | es | tú | es | as | as |
ustd | a | e | e | ustd | e | a | a |
nos | amos | emos | emos | nos | emos | amos | amos |
vos | áis | éis | éis | vos | éis | áis | áis |
ustds | an | en | en | ustds | en | an | an |
irregular: tener, hacer, venir, ser, ir, saber, estar, dar, ver, haber
IM A | ar | er | ir | IM N | er | ir |
---|---|---|---|---|---|---|
tú | a | e | e | tú | es | as |
ustd | e | a | a | ustd | e | a |
nos | emos | amos | amos | nos | emos | amos |
vos | ad | ed | id | vos | éis | áis |
ustds | en | an | an | ustds | en | an |
me molesta bothers me,me da rabia makes...
La comedia nueva (Barroco)
Creada por Lope de Vega, la comedia nueva rompe con las reglas clásicas y busca entretener al público respetando sus gustos.
Características principales:
Escrita en verso con variedad métrica.
Tres actos: planteamiento, nudo y desenlace. Ruptura de las unidades clásicas de tiempo, lugar y acción. Mezcla de elementos trágicos y cómicos. Temas variados: mitología, Biblia, historia, vidas de santos, novelas italianas, etc.- Objetivo doble: entretener y reforzar la
#Security issues in wireless
Wireless networks offer convenience and flexibility, but they also come with a range of security issues that can expose users and organizations to various threats. Below are some of the most common security issues in wireless networks:
1.Unauthorized Access (Rogue Access Points):::Description: Attackers can set up fake access points (APs) to trick users into connecting. ::: Impact: Allows attackers to intercept traffic and steal credentials.::Mitigation: Use WPA3 encryption,
Crescimento económico: aumento da riqueza produzida por um país (medido pelo PIB).
Exemplo: aumento da produção industrial, exportações, investimento.
Desenvolvimento: melhoria das condições de vida da população.Inclui educação, saúde, acesso à água potável, igualdade de género, etc.
Medido por indicadores como o IDH (Índice de Desenvolvimento Humano).
Países desenvolvidos (ex.: Noruega,
Light pressure: <1 sec: PDL fluid incompressible, alveolar bone bends, piezoelectric signal generated/1–2 sec: PDL fluid expressed, tooth moves within PDL space/3–5 sec:Blood flow within PDL partially compressed pressure side, dilated tension side/Mins:Blood flow altered, oxygen tension begin change/Hours:Metabolic changes chemical messengers affect cellular activity/<4Hrs: Cellular differentiation begins within PDL/ 2 days: Tooth movement osteoclasts osteoblasts remodel bone
Heavy Pressure:
...Likelihood function: Description of how observed data depends on the model parameters, θ. p(x|θ)
MLE: The estimator δ(x) = argmax p(x|θ) = argmax lnp(x|θ). gaussian log likelihood is and
Asymptotic distribution of MLE when n is large: , where
. E.g.1.gaussian
, e.g.2. Expontial
Bayesian:The forecast . Assume the prior distribution on μ is gaussian
where
when T→∞, mT = μ, & MAP is almost same as MLE(rely almost entirely on data, not prior). MAP estimator: the value θMAP @ which...
1. Different between raster and vector graphics method. what do you prefer
=>Raster: *//it Representation Pixels *//Resolution-dependent *//Larger (esp. high-res images) *//Blurs/pixelates on scaling *//JPG, PNG, BMP
=.>VectorG : *//Mathematical formulas (lines, curves) **//Resolution-independent *//Smaller *//Scales cleanly *//SVG, EPS
If working with photos or detailed images: Raster is better. If designing logos, icons, or anything that needs to scale (e.g., for both mobile and billboard)
//prime
int num, i, isPrime = 1;//after this print and scan num
if (num <= 1) {
isPrime = 0;
} else {
for (i = 2; i <= num / 2; i++) {
if (num % i == 0) {
isPrime = 0;
break; }}}
if (isPrime)
printf("%d is a prime number.\n", num);
else{print no prime}
Unit-1
What is Big Data
Data which are very large in size is called Big Data. Normally we work on data of size MB (WordDoc ,Excel) or maximum GB(Movies, Codes) but data in Peta bytes i.e. 10^15 byte size is called Big Data. It is stated that almost 90% of today's data has been generated in the past 3 years.
Sources of Big Data
These data come from many sources like
Social networking sites: Facebook, Google, LinkedIn all these sites generates huge amount of data on a day to day basis as they have...