Public Articles
SQUID-Bike to digitally measure citywide Bike Lane Infrastructure
and 4 collaborators
Math Econ: Ch. 12 and Class Notes
Inferring distance-based species turnover patterns from plot-based data
Web Design
and 1 collaborator
Which days to Tweet? A study protocol for a randomised controlled trial
off road bumpers for toyota Tundra
Interferometer och enstaka fotoner.
5 Qualities Needed in a Good Divorce Solicitor in Sydney
Materiales y Métodos
Applied 1 Review
ANTROPOLOGÍA DE LA INFANCIA. REVISIÓN Y REFLEXIÓN DE UN NUEVO ABORDAJE PARA SU APLICACION EN EL PERÚ.
Technical Report
and 1 collaborator
Teste
Neste capítulo será discutido os conceitos básicos de processamento de imagem como conceito de imagem digital e modelo de cores, e as bases que envolvem o melhoramento racial bovino.
A visão é o sentido humano mais avançado, imagens são muito importante para a percepção humana. Para a industria jornalistica sempre foi essencial o uso de imagens, foi nesta indústria que surgiu a primeira aplicação de processamento imagens. Até o início do anos 1920 para enviar uma imagem de Nova York a Londres era necessário enviar as imagens por navio, o que demorava no mínimo uma semana, esse tempo era elevado para a imprensa receber as imagens, pois as notícia na época já eram transmitidas por telégrafo. Para solucionar esse problema foi criado um sistema em 1920 que segmenta a imagem e enviava por telégrafo via cabo submarino intercontinental, o que fez com que o tempo de semanas passasse a ser de 3 horas (inserir uma imagem do livro do livro ) . Com o advento do computadores e das linguagens de programação de alto nível a partir da década de 50 e 60 impulsionou o desenvolvimento do processamento digital de imagens, nas mais diversas áreas de aplicação, tornando possível tecnologias como a transmissão de imagens da lua tirada de uma nave espacial e enviada a terra (figura (inserir imagem do livro) (gonzalez e woods) ).
Collective sanctions (CS) and whistleblowing: experimental design
- The perpetrator: he is defined here as the The Least Contributor (TLC). A participant with the smallest contribution to a collective investment is chosen. If there are more than one smallest contributors in a group, the TLC is chosen randomly from them.
- Sanctioning authority: a random member from the group of non-perpetrators. If the TLC is detected (by other members of the group) he is able to apply a continuous (as in \citet{Nikiforakis_2005}) punishment which is costly for him.
- Ingroup members: the rest of the group apart from the perpetrator and a sanctioning authority. They make a binary decision about detection of the TLC. This decision is costly. If no one from the ingroup members take the decision to detect and inform the sanctioning authority about the identity of the TLC he cannot be directly punished by the sanctioning authority. If no whistleblowing takes place the rest depends on the specific treatment. Under Individual Sanctions (IS) the sanctioning authority can not further proceed with the punishment and the game ends. Under Collective Sanctions (CS) the sanctioning authority can choose either to skip the punishment or to punish a random member of the group.
How to use Authorea in the Classroom
Romer Problems 3.1, 3.2, 3.4, 3.5, and 3.14
\(Y(t) = A(t) (1 - a_L) L(t)\)\(\dot A(t) = Ba_L^\gamma L(t) ^\gamma A(t) ^\theta\)
\(\frac{\dot A(t)}{A(t)} = G_A * = \frac{\gamma n}{1 - \theta}\)\(\frac{\dot A(t)}{A(t)} = BA_L^\gamma L(t) ^\gamma A(t) ^{\theta - 1}\)
\(A(t) = [\frac{(1 - \theta) BA_L ^\gamma L(t) ^\gamma}{\gamma n}] ^{\frac{1}{1 - \theta}}\)
\(Y(t) = [[\frac{(1 - \theta) BA_L ^\gamma L(t) ^\gamma}{\gamma n}] ^{\frac{1}{1 - \theta}}] (1 - a_L) L(t)\)
\(\ln Y(t) = \frac{1}{1 - \theta} \ln [\frac{(1 - \theta) B}{\gamma n}] + \frac{\gamma}{1 - \theta} \ln a_L + \ln (1 - a_L) + [(\frac{\gamma}{1 - \theta}) + 1] \ln L(t)\)
\(\frac{\partial \ln Y(t)}{\partial a_L} = \frac{\gamma}{1 - \theta} \frac{1}{a_L} - \frac{1}{1 - a_L} = 0\)
\(a_L * = \frac{\gamma}{(1 - \theta) + \gamma}\)
\(\frac{Y_1(t)}{Y_2(t)} = [\frac{K_1(t)}{K_2(t)}]^\theta\)
\(\frac{\frac{\dot Y_1(t)}{Y_2(t)}}{\frac{Y_1(t)}{Y_2(t)}} = \theta [\frac{\dot K_1(t)}{K_1(t)} - \frac{\dot K_2(t)}{K_2(t)}]\)\(= \theta [g_{K,1} (t) - g_{K,2}(t)] > 0\)
\(\dot g_K = 0 \Longrightarrow g_K = g_A + n\)\(\dot g_A = 0 \Longrightarrow g_A = \frac{(1- \theta)g_A - \gamma n}{\beta}\)\(g_K(t) = c_K [\frac{A(t) L(t)}{K(t)}] ^{1 - \alpha}\)\(c_K \equiv s(1 - a_K)^\alpha (a - a_L)^{1 - \alpha}\)\(g_A(t) = c_A K(t)^\beta L(t) ^\gamma A(t) ^{\theta - 1}\)\(c_A \equiv Ba_K^\beta a_L^\gamma\)
\(\frac{\dot Y(t)}{Y(t)} - \alpha g_K (t) + (1 - \alpha) [g_A (t) + n]\)
\(g_K(t) = [c_K L^{1 - \alpha}] [\frac{A(t)}{K(t)}]^{1 - \alpha}\)\(g_A (t) = [c_a L^\gamma] [\frac{K(t)}{A(t)^\beta}]\)
\([c_K L^{1 - \alpha}] [\frac{A(t)}{K(t)}]^{1 - \alpha} = [c_a L^\gamma] [\frac{K(t)}{A(t)^\beta}]\)\([\frac{A(t)}{K(t)}]^{1 - \alpha + \beta} = [\frac{c_A}{c_K}] L^{\gamma - (1 - \alpha)}\)
\(\frac{A(t)}{K(t)}= [(\frac{c_A}{c_K}) L^{\gamma - (1 - \alpha)}] ^{\frac{1}{1 - \alpha + \beta}}\)
\(g* = [c_K L^{1 - \alpha}] [(\frac{c_A}{c_K})L ^{\gamma - (1 - \alpha)}] ^{\frac{1 - \alpha}{1 - \alpha + \beta}}\)\(g* = [c_K ^\beta c_A ^{1 - \alpha} L^{(1 - \alpha)(\gamma + \alpha)}] ^{\frac{1}{1 - \alpha + \beta}}\)
\(g* = [s^\beta (1 - a_K)^{\alpha \beta} (1 - a_L) ^{(1 - \alpha) \beta} B^{(1 - \alpha)} a_K ^{\beta (1 - \alpha)} a_L ^{\gamma (1 - \alpha)} L^{(1 - \alpha) - (\gamma + \alpha)}] ^{\frac{1}{1 - \alpha + \beta}}\)
\(\frac{\partial \ln g*}{\partial \ln s} = \frac{\beta}{(1 - \alpha + \beta)} > 0\)
\(\frac{\partial \ln g*}{\partial a_K} = \frac{\beta}{(1 - \alpha + \beta)} [\frac{- \alpha}{1 - a_K} + \frac{1 - \alpha}{a_K}] = 0\)
\(\frac{\alpha}{1 - a_K} = \frac {1 - \alpha}{a_K} \Longrightarrow a_K * = 1 - \alpha\)
\(\frac{\frac{\dot Y_N(t)}{L_N}}{\frac{Y_N(t)}{L_N}} = \frac{\dot A_N (t)}{A_N(t)} = 0.03\)
\(A_N (t) = e^{0.03 \tau} A_N (t - \tau)\)
\(\frac{\frac{Y_N(t)}{L_N}}{\frac{Y_S(t)}{L_S}} = \frac{A_N (t) (1 - a_L)}{A_S(t)} \approx \frac{A_N(t)}{A_N (t - \tau)} = e^{0.03 \tau}\)
\(e^{0.03 \tau} = 10 \Longrightarrow \tau \approx 76.8\)
\(sf(k_N *) = (n + g + \delta) k_N *\)
\(A_S (t) - \dot A_N ( t - \tau)\)
\(\frac{\dot A_S(t)}{A_S(t)} = g\)
\(sf(k_S *) = (n + g + \delta) k_S *\)
\(\frac{Y_N(t)}{L_N(t)} \equiv A_N(t) y_N*\)
\(\frac{\frac{Y_N(t)}{L_N(t)}}{\frac{Y_S(t)}{L_S(t)}} = \frac{A_N(t)}{A_N(t - \tau)} = e^{0.03 \tau}\)
不知道要寫什麼
Numerical Flow Simulation - Project 1
and 3 collaborators
GAIA GOSA Volunteer Observatio Program
Riset ITB 2018 : Menurunkan potensi kerugian lahan padi menggunakan kombinasi metode pemetaan resistivitas dan perhitungan water balance di wilayah Kabupaten Subang, Provinsi Jawa Barat
Endogenous Growth Lecture
\(\frac{\dot A}{A} \equiv g_A(t) = Ba_L ^\gamma L(t) ^\gamma A(t) ^{\theta - 1}\)
\(\frac{\dot g_A(t)}{g_A(t)}= \gamma n + (\theta - 1) g_A(t)\)
\(\frac{\dot g_K(t)}{g_K(t)} = (1 - \alpha)[g_A (t) + n - g_K(t)]\)
\(g_A * = (\frac{\beta + \gamma}{1 - (\theta + \beta)}) n\)Or, \(g_K * = g_A * + n\)
web design_saggio breve
and 2 collaborators
E-Calorimeter calibration on MC generation level