SOFTWARE API DEFINITION FOR Analog Comparator Controller. More...
Classes | |
| struct | Acc |
| Acc hardware registers. More... | |
Macros | |
| #define | ACC_CR_SWRST (0x1u << 0) |
| (ACC_CR) Software Reset More... | |
| #define | ACC_MR_SELMINUS_Pos 0 |
| #define | ACC_MR_SELMINUS_Msk (0x7u << ACC_MR_SELMINUS_Pos) |
| (ACC_MR) Selection for Minus Comparator Input More... | |
| #define | ACC_MR_SELMINUS(value) ((ACC_MR_SELMINUS_Msk & ((value) << ACC_MR_SELMINUS_Pos))) |
| #define | ACC_MR_SELMINUS_TS (0x0u << 0) |
| (ACC_MR) Select TS More... | |
| #define | ACC_MR_SELMINUS_VREFP (0x1u << 0) |
| (ACC_MR) Select VREFP More... | |
| #define | ACC_MR_SELMINUS_DAC0 (0x2u << 0) |
| (ACC_MR) Select DAC0 More... | |
| #define | ACC_MR_SELMINUS_DAC1 (0x3u << 0) |
| (ACC_MR) Select DAC1 More... | |
| #define | ACC_MR_SELMINUS_AFE0_AD0 (0x4u << 0) |
| (ACC_MR) Select AFE0_AD0 More... | |
| #define | ACC_MR_SELMINUS_AFE0_AD1 (0x5u << 0) |
| (ACC_MR) Select AFE0_AD1 More... | |
| #define | ACC_MR_SELMINUS_AFE0_AD2 (0x6u << 0) |
| (ACC_MR) Select AFE0_AD2 More... | |
| #define | ACC_MR_SELMINUS_AFE0_AD3 (0x7u << 0) |
| (ACC_MR) Select AFE0_AD3 More... | |
| #define | ACC_MR_SELPLUS_Pos 4 |
| #define | ACC_MR_SELPLUS_Msk (0x7u << ACC_MR_SELPLUS_Pos) |
| (ACC_MR) Selection For Plus Comparator Input More... | |
| #define | ACC_MR_SELPLUS(value) ((ACC_MR_SELPLUS_Msk & ((value) << ACC_MR_SELPLUS_Pos))) |
| #define | ACC_MR_SELPLUS_AFE0_AD0 (0x0u << 4) |
| (ACC_MR) Select AFE0_AD0 More... | |
| #define | ACC_MR_SELPLUS_AFE0_AD1 (0x1u << 4) |
| (ACC_MR) Select AFE0_AD1 More... | |
| #define | ACC_MR_SELPLUS_AFE0_AD2 (0x2u << 4) |
| (ACC_MR) Select AFE0_AD2 More... | |
| #define | ACC_MR_SELPLUS_AFE0_AD3 (0x3u << 4) |
| (ACC_MR) Select AFE0_AD3 More... | |
| #define | ACC_MR_SELPLUS_AFE0_AD4 (0x4u << 4) |
| (ACC_MR) Select AFE0_AD4 More... | |
| #define | ACC_MR_SELPLUS_AFE0_AD5 (0x5u << 4) |
| (ACC_MR) Select AFE0_AD5 More... | |
| #define | ACC_MR_SELPLUS_AFE1_AD0 (0x6u << 4) |
| (ACC_MR) Select AFE1_AD0 More... | |
| #define | ACC_MR_SELPLUS_AFE1_AD1 (0x7u << 4) |
| (ACC_MR) Select AFE1_AD1 More... | |
| #define | ACC_MR_ACEN (0x1u << 8) |
| (ACC_MR) Analog Comparator Enable More... | |
| #define | ACC_MR_ACEN_DIS (0x0u << 8) |
| (ACC_MR) Analog comparator disabled. More... | |
| #define | ACC_MR_ACEN_EN (0x1u << 8) |
| (ACC_MR) Analog comparator enabled. More... | |
| #define | ACC_MR_EDGETYP_Pos 9 |
| #define | ACC_MR_EDGETYP_Msk (0x3u << ACC_MR_EDGETYP_Pos) |
| (ACC_MR) Edge Type More... | |
| #define | ACC_MR_EDGETYP(value) ((ACC_MR_EDGETYP_Msk & ((value) << ACC_MR_EDGETYP_Pos))) |
| #define | ACC_MR_EDGETYP_RISING (0x0u << 9) |
| (ACC_MR) Only rising edge of comparator output More... | |
| #define | ACC_MR_EDGETYP_FALLING (0x1u << 9) |
| (ACC_MR) Falling edge of comparator output More... | |
| #define | ACC_MR_EDGETYP_ANY (0x2u << 9) |
| (ACC_MR) Any edge of comparator output More... | |
| #define | ACC_MR_INV (0x1u << 12) |
| (ACC_MR) Invert Comparator Output More... | |
| #define | ACC_MR_INV_DIS (0x0u << 12) |
| (ACC_MR) Analog comparator output is directly processed. More... | |
| #define | ACC_MR_INV_EN (0x1u << 12) |
| (ACC_MR) Analog comparator output is inverted prior to being processed. More... | |
| #define | ACC_MR_SELFS (0x1u << 13) |
| (ACC_MR) Selection Of Fault Source More... | |
| #define | ACC_MR_SELFS_CE (0x0u << 13) |
| (ACC_MR) The CE flag is used to drive the FAULT output. More... | |
| #define | ACC_MR_SELFS_OUTPUT (0x1u << 13) |
| (ACC_MR) The output of the analog comparator flag is used to drive the FAULT output. More... | |
| #define | ACC_MR_FE (0x1u << 14) |
| (ACC_MR) Fault Enable More... | |
| #define | ACC_MR_FE_DIS (0x0u << 14) |
| (ACC_MR) The FAULT output is tied to 0. More... | |
| #define | ACC_MR_FE_EN (0x1u << 14) |
| (ACC_MR) The FAULT output is driven by the signal defined by SELFS. More... | |
| #define | ACC_IER_CE (0x1u << 0) |
| (ACC_IER) Comparison Edge More... | |
| #define | ACC_IDR_CE (0x1u << 0) |
| (ACC_IDR) Comparison Edge More... | |
| #define | ACC_IMR_CE (0x1u << 0) |
| (ACC_IMR) Comparison Edge More... | |
| #define | ACC_ISR_CE (0x1u << 0) |
| (ACC_ISR) Comparison Edge (cleared on read) More... | |
| #define | ACC_ISR_SCO (0x1u << 1) |
| (ACC_ISR) Synchronized Comparator Output More... | |
| #define | ACC_ISR_MASK (0x1u << 31) |
| (ACC_ISR) Flag Mask More... | |
| #define | ACC_ACR_ISEL (0x1u << 0) |
| (ACC_ACR) Current Selection More... | |
| #define | ACC_ACR_ISEL_LOPW (0x0u << 0) |
| (ACC_ACR) Low-power option. More... | |
| #define | ACC_ACR_ISEL_HISP (0x1u << 0) |
| (ACC_ACR) High-speed option. More... | |
| #define | ACC_ACR_HYST_Pos 1 |
| #define | ACC_ACR_HYST_Msk (0x3u << ACC_ACR_HYST_Pos) |
| (ACC_ACR) Hysteresis Selection More... | |
| #define | ACC_ACR_HYST(value) ((ACC_ACR_HYST_Msk & ((value) << ACC_ACR_HYST_Pos))) |
| #define | ACC_WPMR_WPEN (0x1u << 0) |
| (ACC_WPMR) Write Protection Enable More... | |
| #define | ACC_WPMR_WPKEY_Pos 8 |
| #define | ACC_WPMR_WPKEY_Msk (0xffffffu << ACC_WPMR_WPKEY_Pos) |
| (ACC_WPMR) Write Protection Key More... | |
| #define | ACC_WPMR_WPKEY(value) ((ACC_WPMR_WPKEY_Msk & ((value) << ACC_WPMR_WPKEY_Pos))) |
| #define | ACC_WPMR_WPKEY_PASSWD (0x414343u << 8) |
| (ACC_WPMR) Writing any other value in this field aborts the write operation of the WPEN bit.Always reads as 0. More... | |
| #define | ACC_WPSR_WPVS (0x1u << 0) |
| (ACC_WPSR) Write Protection Violation Status More... | |
SOFTWARE API DEFINITION FOR Analog Comparator Controller.
| #define ACC_ACR_HYST | ( | value | ) | ((ACC_ACR_HYST_Msk & ((value) << ACC_ACR_HYST_Pos))) |
| #define ACC_ACR_HYST_Msk (0x3u << ACC_ACR_HYST_Pos) |
(ACC_ACR) Hysteresis Selection
| #define ACC_ACR_HYST_Pos 1 |
| #define ACC_ACR_ISEL (0x1u << 0) |
(ACC_ACR) Current Selection
| #define ACC_ACR_ISEL_HISP (0x1u << 0) |
(ACC_ACR) High-speed option.
| #define ACC_ACR_ISEL_LOPW (0x0u << 0) |
(ACC_ACR) Low-power option.
| #define ACC_CR_SWRST (0x1u << 0) |
(ACC_CR) Software Reset
| #define ACC_IDR_CE (0x1u << 0) |
(ACC_IDR) Comparison Edge
| #define ACC_IER_CE (0x1u << 0) |
(ACC_IER) Comparison Edge
| #define ACC_IMR_CE (0x1u << 0) |
(ACC_IMR) Comparison Edge
| #define ACC_ISR_CE (0x1u << 0) |
(ACC_ISR) Comparison Edge (cleared on read)
| #define ACC_ISR_MASK (0x1u << 31) |
(ACC_ISR) Flag Mask
| #define ACC_ISR_SCO (0x1u << 1) |
(ACC_ISR) Synchronized Comparator Output
| #define ACC_MR_ACEN (0x1u << 8) |
(ACC_MR) Analog Comparator Enable
| #define ACC_MR_ACEN_DIS (0x0u << 8) |
(ACC_MR) Analog comparator disabled.
| #define ACC_MR_ACEN_EN (0x1u << 8) |
(ACC_MR) Analog comparator enabled.
| #define ACC_MR_EDGETYP | ( | value | ) | ((ACC_MR_EDGETYP_Msk & ((value) << ACC_MR_EDGETYP_Pos))) |
| #define ACC_MR_EDGETYP_ANY (0x2u << 9) |
(ACC_MR) Any edge of comparator output
| #define ACC_MR_EDGETYP_FALLING (0x1u << 9) |
(ACC_MR) Falling edge of comparator output
| #define ACC_MR_EDGETYP_Msk (0x3u << ACC_MR_EDGETYP_Pos) |
(ACC_MR) Edge Type
| #define ACC_MR_EDGETYP_Pos 9 |
| #define ACC_MR_EDGETYP_RISING (0x0u << 9) |
(ACC_MR) Only rising edge of comparator output
| #define ACC_MR_FE (0x1u << 14) |
(ACC_MR) Fault Enable
| #define ACC_MR_FE_DIS (0x0u << 14) |
(ACC_MR) The FAULT output is tied to 0.
| #define ACC_MR_FE_EN (0x1u << 14) |
(ACC_MR) The FAULT output is driven by the signal defined by SELFS.
| #define ACC_MR_INV (0x1u << 12) |
(ACC_MR) Invert Comparator Output
| #define ACC_MR_INV_DIS (0x0u << 12) |
(ACC_MR) Analog comparator output is directly processed.
| #define ACC_MR_INV_EN (0x1u << 12) |
(ACC_MR) Analog comparator output is inverted prior to being processed.
| #define ACC_MR_SELFS (0x1u << 13) |
(ACC_MR) Selection Of Fault Source
| #define ACC_MR_SELFS_CE (0x0u << 13) |
(ACC_MR) The CE flag is used to drive the FAULT output.
| #define ACC_MR_SELFS_OUTPUT (0x1u << 13) |
(ACC_MR) The output of the analog comparator flag is used to drive the FAULT output.
| #define ACC_MR_SELMINUS | ( | value | ) | ((ACC_MR_SELMINUS_Msk & ((value) << ACC_MR_SELMINUS_Pos))) |
| #define ACC_MR_SELMINUS_AFE0_AD0 (0x4u << 0) |
(ACC_MR) Select AFE0_AD0
| #define ACC_MR_SELMINUS_AFE0_AD1 (0x5u << 0) |
(ACC_MR) Select AFE0_AD1
| #define ACC_MR_SELMINUS_AFE0_AD2 (0x6u << 0) |
(ACC_MR) Select AFE0_AD2
| #define ACC_MR_SELMINUS_AFE0_AD3 (0x7u << 0) |
(ACC_MR) Select AFE0_AD3
| #define ACC_MR_SELMINUS_DAC0 (0x2u << 0) |
(ACC_MR) Select DAC0
| #define ACC_MR_SELMINUS_DAC1 (0x3u << 0) |
(ACC_MR) Select DAC1
| #define ACC_MR_SELMINUS_Msk (0x7u << ACC_MR_SELMINUS_Pos) |
(ACC_MR) Selection for Minus Comparator Input
| #define ACC_MR_SELMINUS_Pos 0 |
| #define ACC_MR_SELMINUS_TS (0x0u << 0) |
(ACC_MR) Select TS
| #define ACC_MR_SELMINUS_VREFP (0x1u << 0) |
(ACC_MR) Select VREFP
| #define ACC_MR_SELPLUS | ( | value | ) | ((ACC_MR_SELPLUS_Msk & ((value) << ACC_MR_SELPLUS_Pos))) |
| #define ACC_MR_SELPLUS_AFE0_AD0 (0x0u << 4) |
(ACC_MR) Select AFE0_AD0
| #define ACC_MR_SELPLUS_AFE0_AD1 (0x1u << 4) |
(ACC_MR) Select AFE0_AD1
| #define ACC_MR_SELPLUS_AFE0_AD2 (0x2u << 4) |
(ACC_MR) Select AFE0_AD2
| #define ACC_MR_SELPLUS_AFE0_AD3 (0x3u << 4) |
(ACC_MR) Select AFE0_AD3
| #define ACC_MR_SELPLUS_AFE0_AD4 (0x4u << 4) |
(ACC_MR) Select AFE0_AD4
| #define ACC_MR_SELPLUS_AFE0_AD5 (0x5u << 4) |
(ACC_MR) Select AFE0_AD5
| #define ACC_MR_SELPLUS_AFE1_AD0 (0x6u << 4) |
(ACC_MR) Select AFE1_AD0
| #define ACC_MR_SELPLUS_AFE1_AD1 (0x7u << 4) |
(ACC_MR) Select AFE1_AD1
| #define ACC_MR_SELPLUS_Msk (0x7u << ACC_MR_SELPLUS_Pos) |
(ACC_MR) Selection For Plus Comparator Input
| #define ACC_MR_SELPLUS_Pos 4 |
| #define ACC_WPMR_WPEN (0x1u << 0) |
(ACC_WPMR) Write Protection Enable
| #define ACC_WPMR_WPKEY | ( | value | ) | ((ACC_WPMR_WPKEY_Msk & ((value) << ACC_WPMR_WPKEY_Pos))) |
| #define ACC_WPMR_WPKEY_Msk (0xffffffu << ACC_WPMR_WPKEY_Pos) |
(ACC_WPMR) Write Protection Key
| #define ACC_WPMR_WPKEY_PASSWD (0x414343u << 8) |
(ACC_WPMR) Writing any other value in this field aborts the write operation of the WPEN bit.Always reads as 0.
| #define ACC_WPMR_WPKEY_Pos 8 |
| #define ACC_WPSR_WPVS (0x1u << 0) |
(ACC_WPSR) Write Protection Violation Status


Assists in constructing queries for Google Base
Located in /Gdata/Gbase/Query.php (line 40)
Zend_Gdata_Query | --Zend_Gdata_Gbase_Query
| Class | Description |
|---|---|
| Zend_Gdata_Gbase_ItemQuery | Assists in constructing queries for Google Base Customer Items Feed |
| Zend_Gdata_Gbase_SnippetQuery | Assists in constructing queries for Google Base Snippets Feed |
The default URI for POST methods
Inherited from Zend_Gdata_Query
Zend_Gdata_Query::$_category
Zend_Gdata_Query::$_params
Zend_Gdata_Query::$_url
Inherited From Zend_Gdata_Query
Zend_Gdata_Query::__construct()
Zend_Gdata_Query::getAlt()
Zend_Gdata_Query::getAuthor()
Zend_Gdata_Query::getCategory()
Zend_Gdata_Query::getMaxResults()
Zend_Gdata_Query::getParam()
Zend_Gdata_Query::getPublishedMax()
Zend_Gdata_Query::getPublishedMin()
Zend_Gdata_Query::getQuery()
Zend_Gdata_Query::getQueryString()
Zend_Gdata_Query::getQueryUrl()
Zend_Gdata_Query::getStartIndex()
Zend_Gdata_Query::getUpdatedMax()
Zend_Gdata_Query::getUpdatedMin()
Zend_Gdata_Query::resetParameters()
Zend_Gdata_Query::setAlt()
Zend_Gdata_Query::setAuthor()
Zend_Gdata_Query::setCategory()
Zend_Gdata_Query::setMaxResults()
Zend_Gdata_Query::setParam()
Zend_Gdata_Query::setPublishedMax()
Zend_Gdata_Query::setPublishedMin()
Zend_Gdata_Query::setQuery()
Zend_Gdata_Query::setStartIndex()
Zend_Gdata_Query::setUpdatedMax()
Zend_Gdata_Query::setUpdatedMin()
Zend_Gdata_Query::__get()
Zend_Gdata_Query::__set()
Documentation generated on Wed, 25 Aug 2010 03:44:17 +0400 by phpDocumentor 1.4.2


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I am an aspiring software engineer and am interested in full-stack development and data analytics.
I don't have a niche yet and can see myself in a variety of roles.
I'm currently working towards a bachelors in Computer Science at University of Colorado Boulder.
I am taking sopomore/early junior-level CS classes.
For 2 years, I studied Mechanical Engineering at Carnegie Mellon University.
I have written software to build web applications, analyze and visualize data, simulate and control robots.
I'm familiar with the electrical, mechanical and control aspects of robotics.
I've built robots for ocean research at UCSD, in the CMU Robotics Club, and for the FIRST Robotics Competition and Botball.


Chapter 6 Empirical risk minimization
6.1 Excess risk and overfitting error
Let \(g:\mathcal{X}\mapsto\{0,1\}\) be a classifier. Given data \(D_n=((X_1,y_1),...,(X_n,y_n))\) we can estimate \(R(g)=P(g(X)\ne y)\) by the empirical mean \(R_n(g)= \frac{1}{n} \sum_{i=1}^{n} \mathbb{1}_{g(X_i)\ne y_i}\). Then by Hoeffding’s Inequality ?? we have
\[ \begin{aligned} && P(\left|R_n(g)-R(g)\right|\ge \varepsilon)&\le 2e^{-2n\varepsilon^2} \\ \end{aligned} \] and equivalently with probability \(\ge 1-\delta\):
\[ \begin{aligned} && \left|R_n(g)-R(g)\right|&\le \sqrt{ \frac{\log( \frac{2}{\delta})}{2n}} \\ \end{aligned} \]
Suppose now that we have \(N\) classifiers \(g^{(1)},...,g^{(n)}\). Now let \(g_n\) denote the data-based classifier that minimizes the empirical risk \(R_n(g^{(j)})\) among our \(N\) classifiers. For its risk \(R(g_n)=P(g_n(X)\ne y|D_n)\) we can establish two quantities of interest:
We can further establish two basic inequalities. The first one is trivial:
\[ \begin{equation} \begin{aligned} && R(g_n)-R_n(g_n)&\le \max_{j=1,...,N} \left| R(g^{(j)})-R_n(g^{(j)}) \right| && \text{(overfitting)} \\ \end{aligned} \tag{6.1} \end{equation} \]
To derive the second one, note that we can rewrite the term in ?? as
\[ \begin{aligned} && R(g_n)-\min_{j=1,..,N}R(g^{(j)})&=R(g_n)-R_n(g_n)+R_n(g_n)-\min_{j=1,..,N}R(g^{(j)}) \\ \end{aligned} \] where \(R(g_n)-R_n(g_n)\) just correspond to the overfitting error again. For the second term denote \(\bar{g}=\arg\min_{j}R(g^{(j)})\) and note that
\[ \begin{aligned} && R_n(g_n)-\min_{j=1,..,N}R(g^{(j)})&\le R_n(\bar{g})-\min_{j=1,..,N}R(g^{(j)}) \\ \end{aligned} \]
since by definition \(g_n\) minimizes the empirical risk and hence \(R_n(g_n)\le R_n(\bar{g})\). Once again we can trivially establish that
\[ \begin{aligned} && R_n(\bar{g})-\min_{j=1,..,N}R(g^{(j)})&\le \max_{j=1,...,N} \left| R(g^{(j)})-R_n(g^{(j)})\right|\\ \end{aligned} \]
which is the just the bound for the overfitting error already established in (6.1). Hence, we take everything together to arrive at the second basic inequality:
\[ \begin{equation} \begin{aligned} && R(g_n)-\min_{j=1,..,N}R(g^{(j)})&\le 2\max_{j=1,...,N} \left| R(g^{(j)})-R_n(g^{(j)})\right| && \text{(excess risk)} \\ \end{aligned} \tag{6.2} \end{equation} \]
So both the excess risk and the overfitting error may be bounded in term of:
\[ \begin{aligned} && \max_{j=1,...,N} \left| R(g^{(j)})-R_n(g^{(j)})\right| \\ \end{aligned} \]
Now let us actually derive a bound. We have
\[ \begin{aligned} && P\left(\max_{j=1,...,N} \left| R(g^{(j)})-R_n(g^{(j)})\right|\ge\varepsilon\right)&=P\left(\bigcup_{j=1,...,N} \left\{ \left| R(g^{(j)})-R_n(g^{(j)})\right|\ge\varepsilon\right\}\right) \\ && &\le \sum_{j=1}^{N}P\left(\left| R(g^{(j)})-R_n(g^{(j)})\right|\ge\varepsilon\right) \\ && &\le 2Ne^{-2n\varepsilon^2} \\ \end{aligned} \] where the first inequality follows from the union bound and the second one from Hoeffding’s Inequality ??. Equivalently, we finally have that with probability \(\ge 1-\delta\)
\[ \begin{aligned} && \max_{j=1,...,N} \left| R(g^{(j)})-R_n(g^{(j)})\right|&\le \sqrt{ \frac{\log ( \frac{2N}{\delta})}{2n}} \\ \end{aligned} \] and hence the following bound:
\[ \begin{equation} \begin{aligned} && R(g_n)-R_n(g_n)&\le\sqrt{ \frac{\log ( \frac{2N}{\delta})}{2n}} && \text{(overfitting)} \\ \end{aligned} \tag{6.3} \end{equation} \]
\[ \begin{equation} \begin{aligned} && R(g_n)-\min_{j=1,..,N}R(g^{(j)})&\le 2\sqrt{ \frac{\log ( \frac{2N}{\delta})}{2n}} && \text{(excess risk)} \\ \end{aligned} \tag{6.4} \end{equation} \]
6.1.1 Data splitting
Let \(g_1^{(1)},...,g_n^{(N)}\) be data-dependent classifiers depending on \(D_n\) and suppose that an independent data set is available for testing \(D'_m=((X'_1,y'_1),...,(X'_m,y'_m))\). Then we may estimate the true risk \(R(g_n^{(j)})=P(g_n^{(j)}(X)\ne y|D_n)\) by the empirical risk (test error):
\[ \begin{equation} \begin{aligned} && R'_m(g_n^{(j)})&= \frac{1}{m} \sum_{i=1}^{m} \mathbb{1}_{g_n^{(j)}(X'_i)\ne y'_i}\\ \end{aligned} \tag{6.5} \end{equation} \]
Then using the results from above, where have for the empirical risk minimizer \(g_{n,m}=\arg\min_{j=1,...,N}R'_m(g_n^{(j)})\) that with probability \(1-\delta\):
\[ \begin{aligned} && R(g_{n,m})-R'_m(g_{n,m})&=\sqrt{ \frac{\log ( \frac{2N}{\delta})}{2m}} && \text{(overfitting)} \\ \end{aligned} \]
\[ \begin{aligned} && R(g_{n,m})-\min_{j=1,..,N}R(g_n^{(j)})&\le 2\sqrt{ \frac{\log ( \frac{2N}{\delta})}{2m}} && \text{(excess risk)} \\ \end{aligned} \]
6.1.2 Leave-one-out cross-validation
Instead of data-splitting (once) we can use leave-one-out cross-validation to get an estimate of the true risk of our data-based classifier. As before we have \(D_n=((X_1,y_1),...,(X_n,y_n))\). Now let \(D_{n,i}=((X_1,y_1),...,(X_{i-1},y_{i-1}),(X_{i+1},y_{i+1}),...,(X_n,y_n))\) denote the subsample that contains all observations except \(i\). Then we can define:
\[ \begin{aligned} && R_n^{(D)}(g_n)&= \frac{1}{n} \sum_{i=1}^{n} \mathbb{1}_{g_{n-1}(X_i,D_{n,i})\ne y_i} \\ \end{aligned} \]
Now since \(g_{n-1}\) does not depend on \((X_i,y_i)\) we have that:
\[ \begin{aligned} && \mathbb{E} \left[ R_n^{(D)}(g_n) \right]&= \mathbb{E} \left[ R(g_{n-1}) \right]\\ \end{aligned} \]
But since \(\mathbb{E} \left[ R(g_{n-1}) \right]\ne\mathbb{E} \left[ R(g_{n}) \right]\) we introduce a small amount of bias. In general it is not easy to establish bounds for the leave-one-out estimator, but empirically it performs well.
6.1.3 Reliable case
Let \(g^{(1)},...,g^{(N)}\) be non-data-dependent classifiers depending on \(D_n\). Now assume one of the candidate classifier has zero risk, that is \(\min_jR(g^{(j)})=0\). We refer to this as the reliable case. Note that this implies that \(\min_jR_n(g^{(j)})=0\) and also that both the excess risk and overfitting error are equal to the true risk, \(R(g_n)\). Hence, we would like to bound this quantity.
\[ \begin{aligned} && P(R(g_n) \ge \varepsilon)&\le P \left( \exists j\in 1,...,N: R(g^{(j)})\ge \varepsilon; R_n(g^{(j)})=0 \right)\\ && &\le N P \left(R(g)\ge \varepsilon \ \& \ R_n(g)=0 \right) \\ && &\le N(1-\varepsilon)^N \le N e^{-n\varepsilon} \\ \end{aligned} \] where the second inequality follows from the union bound.


This is a shrt response to the idea Input Field should allow more types (number, range, etc...) : View Idea. In the beginning not all verisfications are available, but many validations.
Link to blog: Design Studio SDK: Verification Input Field


Uses of Class
llnl.gnem.core.util.TimeT
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Packages that use TimeT Package Description gov.llnl.gnem.apps.coda.common.service.util llnl.gnem.core.io.SAC llnl.gnem.core.util llnl.gnem.core.waveform.seismogram -
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Uses of TimeT in gov.llnl.gnem.apps.coda.common.service.util
Methods in gov.llnl.gnem.apps.coda.common.service.util with parameters of type TimeT Modifier and Type Method Description static boolean
WaveformUtils. cutSeismograms(TimeSeries a, TimeSeries b, TimeT startTime, TimeT endTime)
static double[]
WaveformUtils. getMaxTime(TimeSeries waveform, TimeT originTime)
This method mimics the getMaxTime method of SacSeismogram, which is NOT purely the same as the methods in the TimeSeries chain. -
Uses of TimeT in llnl.gnem.core.io.SAC
Fields in llnl.gnem.core.io.SAC declared as TimeT Modifier and Type Field Description TimeT
SACFileReader. timeT
Methods in llnl.gnem.core.io.SAC that return TimeT Modifier and Type Method Description TimeT
SACHeader. getBeginTime()
Get the begin Epoch time as a TimeT objectTimeT
SACHeader. getEndTime()
Get the end Epoch time as a TimeT objectTimeT
SACHeader. getOriginTime()
Get the origin Epoch time as a TimeT objectTimeT
SACHeader. getReferenceTime()
Convert the date header fields into a TimeT object Note: if the nzyear...TimeT
SACFileReader. getStartTime()
Methods in llnl.gnem.core.io.SAC with parameters of type TimeT Modifier and Type Method Description void
SACHeader. setBeginTime(TimeT begintime)
b is defined by the start of the seismogram relative to the reference time (nzyear, ...,nzmsec) NOTE the reference time should be already be definedvoid
SACHeader. setOriginTime(TimeT origintime)
Set the origin time relative to the sac file's reference timevoid
SACHeader. setTime(TimeT timet)
Set the nz(date) fields based on a TimeT object -
Uses of TimeT in llnl.gnem.core.util
Fields in llnl.gnem.core.util declared as TimeT Modifier and Type Field Description static TimeT
Epoch. MAX_POSSIBLE_TIME
static TimeT
Epoch. MIN_POSSIBLE_TIME
Methods in llnl.gnem.core.util that return TimeT Modifier and Type Method Description TimeT
TimeT. add(double v)
Produce a new TimeT object whose Epoch time is the sum of the Epoch times in this object and the input double (interpreted as an Epoch time).TimeT
TimeT. add(TimeT other)
Produce a new TimeT object whose Epoch time is the sum of the Epoch times in this object and the input object.TimeT
Epoch. getbeginning()
Gets the start of the Epoch as a TimeT objectTimeT
Epoch. getEndtime()
Gets the endtime attribute of the Epoch objectTimeT
Epoch. getOffdate()
Gets the offdate attribute of the Epoch objectTimeT
Epoch. getOndate()
Gets the ondate attribute of the Epoch objectTimeT
Epoch. getTime()
Gets the time attribute of the Epoch objectstatic TimeT
TimeT. getTimeFromDateString(String dateStr)
static TimeT
TimeT. jdateToTimeT(int jdate)
TimeT
TimeT. roundToDay()
Round the epoch time to the nearest whole day.TimeT
TimeT. roundToHour()
Round the epoch time to the nearest whole hour.TimeT
TimeT. roundToMin()
Round the epoch time to the nearest whole minute.TimeT
TimeT. roundToSec()
Round the epoch time to the nearest whole second.TimeT
TimeT. subtract(double v)
TimeT
TimeT. subtract(TimeT other)
Subtract the input TimeT from this TimeT and return the resulting epoch time as a double.Methods in llnl.gnem.core.util with parameters of type TimeT Modifier and Type Method Description TimeT
TimeT. add(TimeT other)
Produce a new TimeT object whose Epoch time is the sum of the Epoch times in this object and the input object.boolean
Epoch. ContainsTime(TimeT aTime)
Determine whether an instant in time falls within this Epochboolean
TimeT. ge(TimeT T)
Return true if this epoch time is greater than or equal to the input epoch timeboolean
TimeT. gt(TimeT T)
Return true if this epoch time is greater than the input epoch timeboolean
TimeT. le(TimeT T)
Return true if this epoch time is less than or equal to the input epoch timeboolean
TimeT. lt(TimeT T)
Return true if this epoch time is less than the input epoch timevoid
Epoch. setEndtime(TimeT end)
Sets the end time of this Epoch objectvoid
Epoch. setTime(TimeT start)
Sets the start time of this Epoch objectTimeT
TimeT. subtract(TimeT other)
Subtract the input TimeT from this TimeT and return the resulting epoch time as a double.double
TimeT. subtractD(TimeT other)
Subtract the input TimeT from this TimeT and return the resulting epoch time as a double.Constructors in llnl.gnem.core.util with parameters of type TimeT Constructor Description Epoch(TimeT t1, TimeT t2)
Constructor for the Epoch objectTimeT(TimeT other)
Copy constructor for the TimeT object -
Uses of TimeT in llnl.gnem.core.waveform.seismogram
Methods in llnl.gnem.core.waveform.seismogram that return TimeT Modifier and Type Method Description TimeT
SeismicSignal. getEndtime()
Gets the endtime attribute of the CssSeismogram objectTimeT
TimeSeries. getEndtime()
Gets the endtime attribute of the CssSeismogram objectTimeT
SeismicSignal. getTime()
Gets the start time of the CssSeismogram as a TimeT objectTimeT
TimeSeries. getTime()
Gets the start time of the CssSeismogram as a TimeT objectMethods in llnl.gnem.core.waveform.seismogram with parameters of type TimeT Modifier and Type Method Description TimeSeries
TimeSeries. crop(TimeT start, TimeT end)
void
TimeSeries. cut(TimeT start, TimeT end)
Truncate the Seismogram to be a subsection of itself.void
TimeSeries. cutAfter(TimeT end)
void
TimeSeries. cutBefore(TimeT start)
int
SeismicSignal. getPointsIn(TimeT start, TimeT end)
int
TimeSeries. getPointsIn(TimeT start, TimeT end)
float[]
TimeSeries. getSubSection(TimeT start, double duration)
Gets a float array which is a subsection of the CssSeismogram's time series.float[]
TimeSeries. getSubSection(TimeT start, TimeT end)
Gets a float array which is a subsection of the Seismogram's time series.float[]
TimeSeries. getSubSection(TimeT start, TimeT end, float[] result)
void
TimeSeries. reverseAt(TimeT mirrorTime)
void
TimeSeries. setTime(TimeT v)
Sets the time attribute of the CssSeismogram objectConstructors in llnl.gnem.core.waveform.seismogram with parameters of type TimeT Constructor Description TimeSeries(float[] data, double samprate, TimeT time)
TimeSeries(BinaryData data, double samprate, TimeT time)
-


1. Wait until at least 3 numbers appeared
2. Click the right numbers to remove them
3. If you hit a wrong button a random number will be added immediately
4. If you are able to clear all the numbers your level will be increased by one
5. The higher your level the faster new numbers will appear
6. If there is a number multiple times you can remove all of them with one button click


I am originally from Chicago but have been living in middle Tennessee for the last 15 years and proudly call it home. As a marketer by nature and profession I have developed a strong business acumen in various industries such as call center, healthcare, and payment processing to name a few. Always intrigued by all the moving pieces of any organization I push myself to become an engrained contributing member.
At the turn of the year, I made a conscientious decision to leave my most recent role as a project manager to pursue my career as a software developer and have not looked back since. This journey has already been one of the most fulfilling experiences and I am just getting started. The desire to continously learn and improve on a skillset that can create endless opportunties, has been one of my biggest motivators while attending Nashville Software School
As my prelude to software development comes to an end at NSS, I openly welcome the future. As a (fairly) new husband and father, stability and growth are important factors in searching for my next career endeavor. Ideally, would love to be a part of an organization that values its culture and workforce which is examplified through high client retention.
-JuansList (Currently in development)-Using C# on the .NET framework to render a Razor MVC application to create a business listing site that allows companies to claim their business and edit it. A .NET API was created to allow for authentication and authorization of role based views.
I am always open to interactive discussions and learning something new.
If you feel that I could be a good fit to your organization I would love to hear from you!
Please email me at contact@edgarbarajas.com.


This is a test of vmax without Javascript. This block is supported to be 50 percent of the width or height of the viewport, whichever is larger. This is not correct in IE9 and messes up in iOS Safari when the device is rotated.


Setting Up Raspberry Pi With an OLED Display
I purchased a SSD1351 from Adafruit for $40 and it has just been lying around collecting dust since all of my Arduino projects either didn’t require a display or the project parts I purchased already had one included. That isn’t cheap for something to not have a very dutiful purpose sitting around so I decided to scope out a project. It wasn’t any surprise that I wasn’t the only one that came up with the idea of slapping it into my most resource heavy Raspberry Pi to monitor the system statistics.
However, the reason I am writing this, is that there are so many displays out there with various wirings and settings that I had to piece together a number of different sources before I gathered the information for a solution that worked for me. I am putting those details here to hopefully save others from the hunting and researching I had to do to get it all running. However, first, here is a pretty picture of it sitting in my Raspberry Pi array:
First, go to this page and setup Raspberry Pi for SPI: Adafruit: Configuring SPI - Installing Kernel Support (with Raspi-Config)
Next, install all the necessary libraries and the Luma display examples:
|
|
Next, wire up your Raspberri Pi to your PIN outs from the OLED display according to this page: Luma.OLED: Display drivers - SPI.
Lastly, run a demo program and see if everything worked:
|
|


The Show Assignments by DLC plugin works fine for me (im using Firefox) , but my Idea is to have a few Icons
in the Screen to toggle between completed & incompleted
like this:
assignment plugin idea.JPG
perhaps between both by the different DLC`s .
Dont forget , there are coming 3 additional DLC`s and each of them brings new assignments to the Battlelog !
so it would certainly be somewhat easier to maintain an overview.
Thx for any response.....Bub3812....XboxOne..User.
dapil
Plugin Developer
Great idea! I will look into it.
Topicstarter
RicoSchulz
Thx for your support , hope you can find a way to bring it together with the working plugin .
that would be awesome , but i believe in your skills .
dapil
Plugin Developer
Would you prefer having it as an option in settings or as buttons on the page?
BrainFooLong
Administrator
Moved.
Topicstarter
RicoSchulz
hi.
it sounds , you found a way to do it .
Nice.
for me it would be better to have it as little buttons on the page , perhaps like the buttons on the weaponstats
page.


BENNY & JOON A film review by Mark R. Leeper Copyright 1993 Mark R. Leeper
Capsule review: Can one schizophrenic find love with another? BENNY & JOON says yes if the world of normal people does not tear them apart. This is a film that will click with you or it won't. I did not click for me. Rating: 0 (-4 to +4).
There is a sort of film that works really well for a very narrow audience and will leave almost everybody else wondering, "What was that all about?" It is a film with a narrow band of powerful appeal. One of the films that I thought really spoke to me was HAROLD AND MAUDE (1972). Other were JOHNNY GOT HIS GUN (1971) and KNIGHTRIDERS (1981). One that missed me entirely was BIRDY (1984). I think I can see what others like about BIRDY, but the film does not work very well for me. Another film that just does not do it for me is BENNY & JOON.
Benny (played by Aidan Quinn) has no time for a life of his own. Benny's life is split between his auto repair business and taking care of his sister Joon (played by Mary Stuart Masterson). Joon is ... well, Joon is somewhat different. Perhaps writer Barry Berman intended Joon to be schizophrenic, though it is never actually said that is the case. Whatever her problem is, she spends her table passionately painting, setting things on fire, placing emergency calls to Benny to pick up peanut butter, directing traffic with ping-pong paddles and snorkel--that sort of thing.
Then Joon loses a poker game and for Benny to pay off, he has to take in a second person whose mental processes are different from the norm, Sam, the cousin of a friend. Sam (played by Johnny Depp) is barely literate but has patterned himself on Buster Keaton and Charlie Chaplin. He dresses like Keaton and can even mimic Keaton's inventive style of seeing unconventional uses for everyday objects. The tension of the story comes from Joon's doctor wanting to institutionalize Joon, Benny wanting to keep Joon around, Joon wanting to keep Sam around, and Benny wanting to be rid of Sam.
Where the story does not work for me is on the contrivance and in the view of mental illness. With the exception of short bouts of panic, Joon appears to be on a perpetual high. Sam's illness appears to make him a comic genius--though most, and for all I know, all, of his routines are borrowed from silent films. Sam's illness increases his creativity and the film arranges for the right props to be at the right place and time to be used. The film says that mental illness has its positive side and that like in MANHUNTER and THE SILENCE OF THE LAMBS, only the mentally ill can understand the mentally ill. The film is an interesting attempt but I rate it only a 0 on the -4 to +4 scale.
[Viewed at the Hazlet Multiplex Cinemas, Hazlet, NJ.]
Mark R. Leeper att!mtgzfs3!leeper<EMAIL_ADDRESS>.
The review above was posted to the
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Related links: index of all rec.arts.movies.reviews reviews


--- title: "HTML Submission Guide" updated: 2015-12-23 00:00:00 layout: default group: calls menu: sub ordered: 20 status: published ---
This year we have added an additional option to allow for the submission of articles formatted using HTML. Authors choosing to submit using HTML still need to comply with the page limit and layout requirements of the conference. It’s the author’s responsibility to ensure that reviewers can easily access their submission. This page provides a guide to helpful tools that can make HTML submissions easier.
Your HTML submission with any extensions if accepted will be available on the ISWC web page in preprint form with all the new elements that you’ve included.
HTML submissions should be submitted to EasyChair as a ZIP archive that contains the complete content of the paper. It should include a main “index.html” as well as any additional, referenced files..
This is an experiment! So help us help you. Please reach out to us if you have questions or need clarifications. We also ask you to do this with plenty of time before submission.
The following tools can help produce well formatted academic papers using HTML; they are capable of rendering papers in the LNCS layout, and a conversion to LaTeX is possible:


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In this rotation, we learned how to operation a sewing machine and create our own clothes. This is the process by which all of the clothes we wear is made, so it is important to understand how it happens.  To do this, I first selected the fabric that I would use. I decided to choose two different ones, one for each leg. What followed was a series of multiple different sewing jobs in order to turn the fabric into a pair of pants. I had to sew each fabric into a pant leg, sew those two together to make a single pair of pants, and sew the top and bottom ends of the pants for a clean finish. After two class periods of sewing, I had something that resembled a pair of pants. I then had to use some leftover fabric to create a drawstring to run through the pants to tighten or loosen it. I finally had a pair of pants that I could wear to look super fashionable. I think the hardest part of the process was keeping the pants aligned on the sewing machine to make a straight edge. There were many times where I felt my sewing going off and potentially messing up the pants. Making these pants has given me a newfound appreciation for the people who make our clothes, because I realize now that just making a single article of clothing takes a lot of time and effort. In the end, I found the process to be enjoyable, and I take great pride in the spectacular pair of pants that I have created.
Image 1: An inside view of the pants.


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- Gray and pink dotted nodes and edges are processors and paths the baton did not use.
- Pink dashed lines are exceptions that are bubbling up the stack.
- Dashed red lines and step numbers indicate exceptions that are raised and handled.
- Diamond shaped (♦) nodes are return values from pipelines.
- A color gradient from green -> red indicate the relative time used by the previous processor.
- Mouse-over the colored lines/step number to see the amount of time used.
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- "escape" closes the step viewer. Click on a step number to re-open it.


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TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
The TensorFlow API is composed of a set of Python modules that enable constructing and executing TensorFlow graphs. The tensorflow package provides access to the complete TensorFlow API from within R. Here’s a simple example of making up some data in two dimensions and then fitting a line to it:
library(tensorflow)
# Create 100 phony x, y data points, y = x * 0.1 + 0.3
x_data <- runif(100, min=0, max=1)
y_data <- x_data * 0.1 + 0.3
# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but TensorFlow will
# figure that out for us.)
W <- tf$Variable(tf$random_uniform(shape(1L), -1.0, 1.0))
b <- tf$Variable(tf$zeros(shape(1L)))
y <- W * x_data + b
# Minimize the mean squared errors.
loss <- tf$reduce_mean((y - y_data) ^ 2)
optimizer <- tf$train$GradientDescentOptimizer(0.5)
train <- optimizer$minimize(loss)
# Launch the graph and initialize the variables.
sess = tf$Session()
sess$run(tf$global_variables_initializer())
# Fit the line (Learns best fit is W: 0.1, b: 0.3)
for (step in 1:201) {
sess$run(train)
if (step %% 20 == 0)
cat(step, "-", sess$run(W), sess$run(b), "\n")
}
The first part of this code builds the data flow graph. TensorFlow does not actually run any computation until the session is created and the run
function is called.
To whet your appetite further, we suggest you check out what a classical machine learning problem looks like in TensorFlow. In the land of neural networks the most “classic” classical problem is the MNIST handwritten digit classification. We offer two introductions here, one for machine learning newbies, and one for pros. If you’ve already trained dozens of MNIST models in other software packages, please take the red pill. If you’ve never even heard of MNIST, definitely take the blue pill. If you’re somewhere in between, we suggest skimming blue, then red.
Images licensed CC BY-SA 4.0; original by W. Carter
If you’re already sure you want to learn and install TensorFlow you can skip these and charge ahead. Don’t worry, you’ll still get to see MNIST – we’ll also use MNIST as an example in our technical tutorial where we elaborate on TensorFlow features.
To get started, install the tensorflow R package from GitHub as follows:
devtools::install_github("rstudio/tensorflow")
Then, use the install_tensorflow()
function to install TensorFlow:
library(tensorflow)
install_tensorflow()
You can confirm that the installation succeeded with:
sess = tf$Session()
hello <- tf$constant('Hello, TensorFlow!')
sess$run(hello)
This will provide you with a default installation of TensorFlow suitable for getting started with the tensorflow R package. See the article on installation to learn about more advanced options.
Once you’ve installed the base TensorFlow system and the tensorflow R package, you will likely want work though the series of tutorials that cover TensorFlow basics:
These articles cover the core concepts of TensorFlow in more depth as well describe the details of using the TensorFlow API from R:
These articles provide more in depth treatments of various topics:
Finally, to learn more about neural networks you might enjoy the TensorFlow playground, which lets you tinker with a neural network in your browser.


politic
Trump team rejects Georgia recount confirming Biden win
President-elect Joe Biden beat his Republican rival in the Peach State by around 12,000 votes according to the recount.
politic
President-elect Joe Biden beat his Republican rival in the Peach State by around 12,000 votes according to the recount.
politic
President-elect Joe Biden beat his Republican rival in the Peach State by around 12,000 votes according to the recount.
politic
President-elect Joe Biden beat his Republican rival in the Peach State by around 12,000 votes according to the recount.
politic
President-elect Joe Biden beat his Republican rival in the Peach State by around 12,000 votes according to the recount.
politic
President-elect Joe Biden beat his Republican rival in the Peach State by around 12,000 votes according to the recount.
politic
President-elect Joe Biden beat his Republican rival in the Peach State by around 12,000 votes according to the recount.
'I'm a Doctor in Illinois, We're Scared of Running Out of Hospital Beds'
'I'm a Doctor in Illinois, We're Scared of Running Out of Hospital Beds'
'I'm a Doctor in Illinois, We're Scared of Running Out of Hospital Beds'
'I'm a Doctor in Illinois, We're Scared of Running Out of Hospital Beds'
'I'm a Doctor in Illinois, We're Scared of Running Out of Hospital Beds'
'I'm a Doctor in Illinois, We're Scared of Running Out of Hospital Beds'
The move comes after Pfizer-BioNTech claimed their vaccine is 95 percent effective in preventing COVID.
The move comes after Pfizer-BioNTech claimed their vaccine is 95 percent effective in preventing COVID.
The move comes after Pfizer-BioNTech claimed their vaccine is 95 percent effective in preventing COVID.
The move comes after Pfizer-BioNTech claimed their vaccine is 95 percent effective in preventing COVID.
The move comes after Pfizer-BioNTech claimed their vaccine is 95 percent effective in preventing COVID.
The move comes after Pfizer-BioNTech claimed their vaccine is 95 percent effective in preventing COVID.
The move comes after Pfizer-BioNTech claimed their vaccine is 95 percent effective in preventing COVID.
The move comes after Pfizer-BioNTech claimed their vaccine is 95 percent effective in preventing COVID.
The move comes after Pfizer-BioNTech claimed their vaccine is 95 percent effective in preventing COVID.
The move comes after Pfizer-BioNTech claimed their vaccine is 95 percent effective in preventing COVID.
The move comes after Pfizer-BioNTech claimed their vaccine is 95 percent effective in preventing COVID.
Science Says Porn Is a Huge Problem. Lawmakers Must Stop Ignoring It
BY TERRY SCHILLING
vs
Science Says Porn Is a Huge Problem. Lawmakers Must Stop Ignoring It
BY TERRY SCHILLING
Science Says Porn Is a Huge Problem. Lawmakers Must Stop Ignoring It
BY TERRY SCHILLING
Science Says Porn Is a Huge Problem. Lawmakers Must Stop Ignoring It
BY TERRY SCHILLING
Science Says Porn Is a Huge Problem. Lawmakers Must Stop Ignoring It
BY TERRY SCHILLING
Science Says Porn Is a Huge Problem. Lawmakers Must Stop Ignoring It
BY TERRY SCHILLING
Science Says Porn Is a Huge Problem. Lawmakers Must Stop Ignoring It
BY TERRY SCHILLING
Science Says Porn Is a Huge Problem. Lawmakers Must Stop Ignoring It
BY TERRY SCHILLING
Science Says Porn Is a Huge Problem. Lawmakers Must Stop Ignoring It
BY TERRY SCHILLING
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trump has 70 days to undermine joe biden, even if he eventually concedes
cover
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trump has 70 days to undermine joe biden, even if he eventually concedes
cover
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trump has 70 days to undermine joe biden, even if he eventually concedes
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trump has 70 days to undermine joe biden, even if he eventually concedes
u. s.
California's Tier Map Explained as Purple Counties Face Month-Long Curfew
More than 90 percent of California's population are living in "purple counties" based on the tiered system.
u. s.
California's Tier Map Explained as Purple Counties Face Month-Long Curfew
More than 90 percent of California's population are living in "purple counties" based on the tiered system.
u. s.
California's Tier Map Explained as Purple Counties Face Month-Long Curfew
More than 90 percent of California's population are living in "purple counties" based on the tiered system.
u. s.
California's Tier Map Explained as Purple Counties Face Month-Long Curfew
More than 90 percent of California's population are living in "purple counties" based on the tiered system.
u. s.
California's Tier Map Explained as Purple Counties Face Month-Long Curfew
More than 90 percent of California's population are living in "purple counties" based on the tiered system.
u. s.
California's Tier Map Explained as Purple Counties Face Month-Long Curfew
More than 90 percent of California's population are living in "purple counties" based on the tiered system.
u. s.
California's Tier Map Explained as Purple Counties Face Month-Long Curfew
More than 90 percent of California's population are living in "purple counties" based on the tiered system.
u. s.
California's Tier Map Explained as Purple Counties Face Month-Long Curfew
More than 90 percent of California's population are living in "purple counties" based on the tiered system.
Age of Anxiety: 10 Stats on How Stressed Americans Are During COVID-19
Age of Anxiety: 10 Stats on How Stressed Americans Are During COVID-19
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre
Age of Anxiety: 10 Stats on How Stressed Americans Are During COVID-19
Age of Anxiety: 10 Stats on How Stressed Americans Are During COVID-19
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre
Age of Anxiety: 10 Stats on How Stressed Americans Are During COVID-19
Age of Anxiety: 10 Stats on How Stressed Americans Are During COVID-19
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre
Age of Anxiety: 10 Stats on How Stressed Americans Are During COVID-19
Age of Anxiety: 10 Stats on How Stressed Americans Are During COVID-19
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre
Russell Wilson Equals NFL Record Held by Drew Brees and Brett Favre


Adventure Guildsman:
Welcome! You're looking for something to do, aren't you, mate?
You have a distinguished air about you! I'll wager you're looking for a unique experience, am I right? I knew I was! Now, listen very carefully...
Quest Information
Raid Monster Information
Use Life Crystals
Quest


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I love playing with kendamas while I listen to dubstep.


This sample demonstrates how to take advantage of ADAL JS for adding Azure AD authentication to your AngularJS apps.


Holst:
He is faster than I thought. Follow him while I go tell the head of the security. It looks as though he is running toward the northwestern side. If he passed the northwestern gate, the gate guards must have seen him. Go ask them.


Eroding Porsche 911 Art Car Is Completely Drivable
A Porsche 911 for someone that thinks the coupe needs more crystals.
by Chris BruceWhen is a damaged Porsche 911 something special? Apparently, the answer is when artist Daniel Arsham is the one destroying the bodywork by adding something unique. His style goes far beyond taking a hammer or saw to the body. Instead, Arsham uses a crystal erosion technique that adds a sparkling, rough finish to the alterations of the exterior. The result is a weird, yet eye-catching appearance.
Gallery: Crystal-Etched Porsche 911 By Daniel Arsham
Arsham removes sections of the body and replaces them with roughly hewn pieces of crystal. The spots include a large section of the hood, fenders, doors, and rear bumper. To match the appearance, the vast majority of the body is white, including the wheels, except for skinny pieces of black trim. The crystal alterations don't extend to the cabin.
 It's not clear what Arsham intends to do with this car, though. Since there's nothing preventing driving this 911 on the street, we imagine the model could end up in someone's garage eventually.
Save Thousands On A New Porsche 911
MSRP $ 92,350
MSRP $ 92,350
Save on average over $3,400 off MSRP* with
Motor1.com Car Buying Service
SEE PRICING
The eroded Porsche is currently on display at the Selfridges department store in London as part of an exhibit called The House by Daniel Arsham. The idea is to imagine the family home from the year 3019. It includes a variety of his fashion and decorative goods for sale. The shop indicates that the car is on display there through Christmas, so if you're in the area, check it out.
Source: DuPont Registry


Stewart Platform - Dynamics Study
Table of Contents
1 Compare external forces and forces applied by the actuators
In this section, we wish to compare the effect of forces/torques applied by the actuators with the effect of external forces/torques on the displacement of the mobile platform.
1.1 Comparison with fixed support
Let’s generate a Stewart platform.
stewart = initializeStewartPlatform(); stewart = initializeFramesPositions(stewart, 'H', 90e-3, 'MO_B', 45e-3); stewart = generateGeneralConfiguration(stewart); stewart = computeJointsPose(stewart); stewart = initializeStrutDynamics(stewart); stewart = initializeJointDynamics(stewart, 'type_F', 'universal_p', 'type_M', 'spherical_p'); stewart = initializeCylindricalPlatforms(stewart); stewart = initializeCylindricalStruts(stewart); stewart = computeJacobian(stewart); stewart = initializeStewartPose(stewart); stewart = initializeInertialSensor(stewart, 'type', 'none');
We don’t put any flexibility below the Stewart platform such that its base is fixed to an inertial frame. We also don’t put any payload on top of the Stewart platform.
ground = initializeGround('type', 'none'); payload = initializePayload('type', 'none'); controller = initializeController('type', 'open-loop');
The transfer function from actuator forces \(\bm{\tau}\) to the relative displacement of the mobile platform \(\mathcal{\bm{X}}\) is extracted.
%% Options for Linearized options = linearizeOptions; options.SampleTime = 0; %% Name of the Simulink File mdl = 'stewart_platform_model'; %% Input/Output definition clear io; io_i = 1; io(io_i) = linio([mdl, '/Controller'], 1, 'openinput'); io_i = io_i + 1; % Actuator Force Inputs [N] io(io_i) = linio([mdl, '/Relative Motion Sensor'], 1, 'openoutput'); io_i = io_i + 1; % Position/Orientation of {B} w.r.t. {A} %% Run the linearization G = linearize(mdl, io, options); G.InputName = {'F1', 'F2', 'F3', 'F4', 'F5', 'F6'}; G.OutputName = {'Edx', 'Edy', 'Edz', 'Erx', 'Ery', 'Erz'};
Using the Jacobian matrix, we compute the transfer function from force/torques applied by the actuators on the frame \(\{B\}\) fixed to the mobile platform:
Gc = minreal(G*inv(stewart.kinematics.J')); Gc.InputName = {'Fnx', 'Fny', 'Fnz', 'Mnx', 'Mny', 'Mnz'};
We also extract the transfer function from external forces \(\bm{\mathcal{F}}_{\text{ext}}\) on the frame \(\{B\}\) fixed to the mobile platform to the relative displacement \(\mathcal{\bm{X}}\) of \(\{B\}\) with respect to frame \(\{A\}\):
%% Input/Output definition clear io; io_i = 1; io(io_i) = linio([mdl, '/Disturbances'], 1, 'openinput', [], 'F_ext'); io_i = io_i + 1; % External forces/torques applied on {B} io(io_i) = linio([mdl, '/Relative Motion Sensor'], 1, 'openoutput'); io_i = io_i + 1; % Position/Orientation of {B} w.r.t. {A} %% Run the linearization Gd = linearize(mdl, io, options); Gd.InputName = {'Fex', 'Fey', 'Fez', 'Mex', 'Mey', 'Mez'}; Gd.OutputName = {'Edx', 'Edy', 'Edz', 'Erx', 'Ery', 'Erz'};
The comparison of the two transfer functions is shown in Figure 1.
Figure 1: Comparison of the transfer functions from \(\bm{\mathcal{F}}\) to \(\mathcal{\bm{X}}\) and from \(\bm{\mathcal{F}}_{\text{ext}}\) to \(\mathcal{\bm{X}}\) (png, pdf)
This can be understood from figure 2 where \(\mathcal{F}_{x}\) and \(\mathcal{F}_{x,\text{ext}}\) have clearly the same effect on \(\mathcal{X}_{x}\).
Figure 2: Schematic representation of the stewart platform on a rigid support
1.2 Comparison with a flexible support
We now add a flexible support under the Stewart platform.
ground = initializeGround('type', 'flexible');
And we perform again the identification.
%% Input/Output definition clear io; io_i = 1; io(io_i) = linio([mdl, '/Controller'], 1, 'openinput'); io_i = io_i + 1; % Actuator Force Inputs [N] io(io_i) = linio([mdl, '/Relative Motion Sensor'], 1, 'openoutput'); io_i = io_i + 1; % Position/Orientation of {B} w.r.t. {A} %% Run the linearization G = linearize(mdl, io, options); G.InputName = {'F1', 'F2', 'F3', 'F4', 'F5', 'F6'}; G.OutputName = {'Edx', 'Edy', 'Edz', 'Erx', 'Ery', 'Erz'}; Gc = minreal(G*inv(stewart.kinematics.J')); Gc.InputName = {'Fnx', 'Fny', 'Fnz', 'Mnx', 'Mny', 'Mnz'}; %% Input/Output definition clear io; io_i = 1; io(io_i) = linio([mdl, '/Disturbances'], 1, 'openinput', [], 'F_ext'); io_i = io_i + 1; % External forces/torques applied on {B} io(io_i) = linio([mdl, '/Relative Motion Sensor'], 1, 'openoutput'); io_i = io_i + 1; % Position/Orientation of {B} w.r.t. {A} %% Run the linearization Gd = linearize(mdl, io, options); Gd.InputName = {'Fex', 'Fey', 'Fez', 'Mex', 'Mey', 'Mez'}; Gd.OutputName = {'Edx', 'Edy', 'Edz', 'Erx', 'Ery', 'Erz'};
The comparison between the obtained transfer functions is shown in Figure 3.
Figure 3: Comparison of the transfer functions from \(\bm{\mathcal{F}}\) to \(\mathcal{\bm{X}}\) and from \(\bm{\mathcal{F}}_{\text{ext}}\) to \(\mathcal{\bm{X}}\) (png, pdf)
The addition of a flexible support can be schematically represented in Figure 4. We see that \(\mathcal{F}_{x}\) applies a force both on \(m\) and \(m^{\prime}\) whereas \(\mathcal{F}_{x,\text{ext}}\) only applies a force on \(m\). And thus \(\mathcal{F}_{x}\) and \(\mathcal{F}_{x,\text{ext}}\) have clearly not the same effect on \(\mathcal{X}_{x}\).
Figure 4: Schematic representation of the stewart platform on top of a flexible support
1.3 Conclusion
The transfer function from forces/torques applied by the actuators on the payload \(\bm{\mathcal{F}} = \bm{J}^T \bm{\tau}\) to the pose of the mobile platform \(\bm{\mathcal{X}}\) is the same as the transfer function from external forces/torques to \(\bm{\mathcal{X}}\) as long as the Stewart platform’s base is fixed.
2 Comparison of the static transfer function and the Compliance matrix
In this section, we see how the Compliance matrix of the Stewart platform is linked to the static relation between \(\mathcal{\bm{F}}\) to \(\mathcal{\bm{X}}\).
2.1 Analysis
Initialization of the Stewart platform.
stewart = initializeStewartPlatform(); stewart = initializeFramesPositions(stewart, 'H', 90e-3, 'MO_B', 45e-3); stewart = generateGeneralConfiguration(stewart); stewart = computeJointsPose(stewart); stewart = initializeStrutDynamics(stewart); stewart = initializeJointDynamics(stewart, 'type_F', 'universal_p', 'type_M', 'spherical_p'); stewart = initializeCylindricalPlatforms(stewart); stewart = initializeCylindricalStruts(stewart); stewart = computeJacobian(stewart); stewart = initializeStewartPose(stewart); stewart = initializeInertialSensor(stewart, 'type', 'none');
No flexibility below the Stewart platform and no payload.
ground = initializeGround('type', 'none'); payload = initializePayload('type', 'none'); controller = initializeController('type', 'open-loop');
Estimation of the transfer function from \(\mathcal{\bm{F}}\) to \(\mathcal{\bm{X}}\):
%% Options for Linearized options = linearizeOptions; options.SampleTime = 0; %% Name of the Simulink File mdl = 'stewart_platform_model'; %% Input/Output definition clear io; io_i = 1; io(io_i) = linio([mdl, '/Controller'], 1, 'openinput'); io_i = io_i + 1; % Actuator Force Inputs [N] io(io_i) = linio([mdl, '/Relative Motion Sensor'], 1, 'openoutput'); io_i = io_i + 1; % Position/Orientation of {B} w.r.t. {A} %% Run the linearization G = linearize(mdl, io, options); G.InputName = {'F1', 'F2', 'F3', 'F4', 'F5', 'F6'}; G.OutputName = {'Edx', 'Edy', 'Edz', 'Erx', 'Ery', 'Erz'};
Gc = minreal(G*inv(stewart.kinematics.J')); Gc.InputName = {'Fnx', 'Fny', 'Fnz', 'Mnx', 'Mny', 'Mnz'};
Let’s first look at the low frequency transfer function matrix from \(\mathcal{\bm{F}}\) to \(\mathcal{\bm{X}}\).
| 4.7e-08 | -7.2e-19 | 5.0e-18 | -8.9e-18 | 3.2e-07 | 9.9e-18 |
| 4.7e-18 | 4.7e-08 | -5.7e-18 | -3.2e-07 | -1.6e-17 | -1.7e-17 |
| 3.3e-18 | -6.3e-18 | 2.1e-08 | 4.4e-17 | 6.6e-18 | 7.4e-18 |
| -3.2e-17 | -3.2e-07 | 6.2e-18 | 5.2e-06 | -3.5e-16 | 6.3e-17 |
| 3.2e-07 | 2.7e-17 | 4.8e-17 | -4.5e-16 | 5.2e-06 | -1.2e-19 |
| 4.0e-17 | -9.5e-17 | 8.4e-18 | 4.3e-16 | 5.8e-16 | 1.7e-06 |
And now at the Compliance matrix.
| 4.7e-08 | -2.0e-24 | 7.4e-25 | 5.9e-23 | 3.2e-07 | 5.9e-24 |
| -7.1e-25 | 4.7e-08 | 2.9e-25 | -3.2e-07 | -5.4e-24 | -3.3e-23 |
| 7.9e-26 | -6.4e-25 | 2.1e-08 | 1.9e-23 | 5.3e-25 | -6.5e-40 |
| 1.4e-23 | -3.2e-07 | 1.3e-23 | 5.2e-06 | 4.9e-22 | -3.8e-24 |
| 3.2e-07 | 7.6e-24 | 1.2e-23 | 6.9e-22 | 5.2e-06 | -2.6e-22 |
| 7.3e-24 | -3.2e-23 | -1.6e-39 | 9.9e-23 | -3.3e-22 | 1.7e-06 |
2.2 Conclusion
The low frequency transfer function matrix from \(\mathcal{\bm{F}}\) to \(\mathcal{\bm{X}}\) corresponds to the compliance matrix of the Stewart platform.


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Story: The use of the !goin-!goin, followed by an account of a Bushman dance
The use of the !goin-!goin, followed by an account of a Bushman dance
Title
The use of the !goin-!goin, followed by an account of a Bushman dance
Collection
Summary
The employment of the !gõin–!gõin (the !gõin–!gõin consists of a [variously–sized] blade of wood, attached by a little cord to a short stick. The latter is held in the hand of the performer, and the blade of wood (attached to the string) is then, by means of the stick–handle, whirled about in the air, producing a strong whirring sound. When several of these instruments are used at a time, a considerable effect must be produced), followed by the account of a Bushman dance, in which the men dance, one woman beats the drum, and the rest of the women sit, clapping their hands for the dancers. By /han≠kass’o (L VIII.-1. 6108-6127). The !goin-!goin (or 'bullroarer') is beaten to attract an abundance of bees and provide honey for the people to eat.
Comments
1) See also The preparation of the drum; ears of springbok are tied to the feet of the men who dance and The preparation of the springbok's ears and How the prepared springbok's ears are tied on, 2) This story is found in Book VIII-1
Contributors
|han≠kass'o (Klein Jantje) (VIII)
Date
24 January 1878 (started)
Categories
Custom and daily life, Artefact and dress
Keywords
!goin-!goin (and finding and eating honey) , !goin-!goin (beating it) , !goin-!goin (used for calling bees) , !goin-!goin (method of playing) , !goin-!goin (and a dance) , honey ('cutting') , honey (and calling bees) , honey (and beating the !goin-!goin) , dance (and the !goin-!goin) , dance (an account of) , dance (the performance of) , dance (a description of) , dance (and music) , dance (and drumming) , dance (and the actions of dancers) , dance (actions after) , dance (fatigue after) , dance (and gender) , dance (men dance) , dance (women drum and clap) , music (an instrument called the !goin-!goin) , music (and a dance) , musical instrument (!goin-!goin) , musical instrument (method of playing the !goin-!goin) , musical instrument (a woman beats a drum in a dance) , musical instrument (used for finding honey) , musical instrument (beating it) , musical instrument (used for calling bees) , musical instrument (and dance) , bees (!goin-!goin used for calling bees and finding honey) , drum (beaten by a woman in a dance) , drum (an account of a dance)
Story Pages
6108-6127


CrimPapers
Articles published on 27 August 2018
On 27 August 2018, 7 new articles were published in 7 journals.
Enhancing Data-Driven Law Enforcement Efforts: Exploring How Risk Terrain Modeling and Conjunctive Analysis Fit in A Crime and Traffic Safety Framework
Thank You to Our 2017 Reviewers
Constraints to Conservation At Okomu National Park: A Ranger’s Perspective by O. Digun-Aweto, O.p Fawole, M. Saayman
Immigrant Threat and Latino/a Disadvantage: Disentangling the Impact of Immigration Attitudes on Ethnic Sentencing Disparities by Melanie M. Holland, Richard J. Stringer
Rethinking Campus Sexual Assault: We Must Be Leaders in Anti-Bias Practices, Civil Rights and Human Rights
Is Childhood Sexual Victimization Associated With Cognitive Distortions, Self-Esteem, and Emotional Congruence With Children? by Carolyn Blank, Kevin L. Nunes, Sacha Maimone, Chantal A. Hermann, Ian V. McPhail
The Ipswich (queensland) Safe City Program: An Evaluation


This year we are also offering post-conference 4-hours workshops on Sunday, July 1. All topics will give you a huge dose of knowledge about Java or/and Big Data. The workshops will be conducted in Polish.
Workshop Day is hosted by our partner Sages. Sages cooperates with experts from different areas of IT who share their knowledge in conferences, community workshops and commercial trainings. If you have any questions regarding workshops, please check FAQ or contact Sages at<EMAIL_ADDRESS>The number of seats is limited to 30 per workshop. Registration is required. Workshop fee includes the entrance to Confitura conference (Sat, 30 June 2018).
Today blockchain technology and cryptocurrencies are very hot topic. It is good to know how this database works and in which systems it may or may not be useful. During the workshop we will launch a private blockchain instance using the Ethereum implementation and prepare a client application in Java that will use it for data storage. We will also dispel the myths around blockchain.
Apache Kafka has become the leading data streaming technology and is used in production by over 2000 companies like Netflix, Airbnb, Uber and LinkedIn. Kafka is used for building real-time data pipelines and streaming apps. It is horizontally scalable, fault-tolerant and very fast. This workshop will explore the basic concepts of messaging systems based on publish - subscribe messaging in Kafka.
So you've went through all the word counts and other hello-world type of codes, but you have some real data to analyse and you found answers to your issues missing we can help! This tutorial goes beyond the basic examples and shows how to deal with common issues of Data Scientist and Engineers using Spark for real (big) data sets. We focus on the Spark SQL, i.e. DataSets and DataFrames, show why it's good for both Java/Scala nad Python users, how to avoid typical errors and how to deal with them.
Natural Language Processing (NLP) is gaining a lot of popularity in recent years, thanks to advancements in state-of-the-art methods and algorithms, processing power available and rising expectations of end users of technology products. During this workshop we will make the first steps towards processing natural language, discuss the main stages necessary to perform typical tasks and make an overview of available methods and tools for processing English and Polish. Finally, we will talk about current research directions and perspectives for months and years to come.
Post-conference workshops will take place in Golden Floor Plaza, 123A Jerozolimskie Avenue, Warsaw, Poland.


- Tuesday, December 12 3:26pm
Using the char-rnn neural network, I created an AI model that generates company names. Based off a Wikipedia list of companies in the US, this is my most successful AI experiment yet. Below you can see a list of the best, and worst, that it came up with. (If you want to try it out for yourself, click here.)
Here is a list of the best it came up with:
It's not perfect, as shown by these ones:
Some seem OK, but are quite strange:
Neural Networks can be pretty useful. But we can also use them to have fun with so-called "computer creativity". With the dataset I used, it didn't have a large amount of information to learn from, so I think it did fairly well with it's creations. I also tried it with Canadian companies, but the French names confused it and produced gibberish.
If you have an idea for the next thing the network should creation, send me an email! (admin@bustedsocket.com)


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Utility to make sure the marketplace, backend data, and IWC are
behaving appropriately for use with Webtop
OZP Bus:
Reconnect to the OZP Bus and reload marketplace data from system.api and
dashboard data from the data.api
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Loading ...Invalid Dashboard Data
If you see invalid apps for any of the dashboards, it means an application found
in the dashboard data has a name the matches up with an application retrieved from the marketplace,
but the application id doesn't match. This happens because the application ids change each time the
sample marketplace data is reloaded in the backend. Hit this button to synchronize the ids in the
dashboard data (data.api) with those in the marketplace (system.api).
Reload the data.api with demo dashboard data located here


Software Programmer - Data Science - Cryptography
I developed an interest in computer science my senior year of highschool thanks to one of my professors, who I attribute a lot of my current success to, thanks to his constant support and teachings. I went to Pierce College to fulfill undergrade requirements before tranferring to CSUN.
At CSUN while selecting electives which sounded engaging I picked up an interest in Software Programming, Data Mining, and Cryptography. I am currently pursuing my Masters Degree at CSUN.
Python
Java
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Email: MichaelGW123@gmail.com


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MISSION: IMPOSSIBLE 2 (2000) / ** 1/2
Directed by John Woo. Screenplay by Robert Towne, from a story by Brannon Braga and Robert D Moore, based on the television series by Bruce Geller. Starring Tom Cruise, Dougray Scott, Thandie Newton. Running time: 125 minutes. Rated PG for mild language by the MFCB. Reviewed on May 26th, 2000.
By SHANNON PATRICK SULLIVAN
When the original movie version of "Mission: Impossible" was released in 1996, there was little doubt that a sequel would soon follow. The film provided the perfect framework for a long-running franchise in the form of the international espionage agency known as the Impossible Missions Force. Not only did the use of this multi-purpose organization provide reasonable breadth of storytelling, but it meant future films weren't contingent on a recurring cast. Sequels could always use a different IMF if need be. Even the star, Tom Cruise, could be replaced in a manner rather more palatable than, say, the revolving door Batmans of the past decade.
Of course, for "Mission: Impossible 2", only the first option has been employed. Cruise does indeed return to reprise his role as Ethan Hunt, one of the IMF's top agents. Hunt, once again, is a cool, poised man of action, possessing a seemingly endless array of skills. But unlike the first "Mission: Impossible", which cast Hunt in the role of a desperate agent abandoned by his organization, little is really done with Cruise's character in this sequel. Character development is slim to none, and a romance with a fellow agent feels lifted out of a James Bond picture. There is an unsatisfying sense of stagnation to Ethan in "MI2"; it's almost as if scriptwriter Robert Towne is taking a page from the "Mission: Impossible" TV series in limiting Hunt's evolution. The problem is that this is a feature film, not a television programme. It's fine for TV characters to develop slowly, because with twenty-two episodes a year they have the time. A movie franchise which only churns out a new installment every few years cannot afford the same luxury.
The same problem plagues the other holdover from the first film, Luther Stickell (Ving Rhames). Stickell was engaging in "Mission: Impossible"; here he's shoehorned into the background, often isolated even from the other IMF agents. Stickell could just as easily have been replaced with a totally new character without damaging the film, and as a result his return here feels almost token. The third member of this version of Hunt's team, Billy Baird (John Polson) is similarly faceless; even Emilio Estevez's short-lived agent in the first film was more interesting than this guy.
The same cannot quite be said of the last of our heroes, Nyah Hall (Thandie Newton). A world-class thief, we first encounter Nyah in action trying to steal a valuable necklace, and it makes for one of the movie's most watchable scenes. Nyah is a promising character -- neither a typical damsel-in-distress nor some kind of uber-confident goddess. Unfortunately, that potential gets lost in the movie's second half, in which Newton has far less screen time. It's also regrettable that Nyah's role in the proceedings means that she rarely gets to demonstrate her skills; the script mostly just calls for her to stand around and look vaguely nervous. Nonetheless, I would enjoy seeing the character return, in a more proactive role, in future "Mission: Impossible" films.
This time around, the team has been assembled to investigate the disappearance of the mysterious genetically-engineered Chimera virus and its antidote, Bellorophon. These have been stolen by a rogue IMF agent named Sean Ambrose (Dougray Scott, who does a good job of acting evil but can conjure no other personality traits from the character) posing as Hunt himself. Ambrose and his goons intend to auction off Chimera and Bellorophon to the highest bidder. New IMF recruit Nyah is also Ambrose's former lover, putting her in a unique position to get close to him and give Hunt and his team a chance to reclaim the stolen goods.
The plot of the original "Mission: Impossible" was criticised by some for being too labyrinthine, although I enjoyed the complexity and intrigue it offered. The sequel goes too far the other way. This is all standard spy stuff -- viruses, high-stakes auctions, jilted ex-lovers -- and the "rogue agent" subplot has been overused of late, in everything from "GoldenEye" to, indeed, the first "Mission: Impossible"!
And this isn't the only element of the movie that feels lifted from its predecessor. Remember that terrific scene with Tom Cruise dangling inches above a pressure-sensitive floor? Well, something similar occurs here, albeit with none of the tension or flair of the original. Indeed, "MI2" is sadly lacking in invention and suspense, with many of the action scenes being just routine material. There is little of the twisting and turning which characterised the first film. There, when a character's secrets were revealed they were astounding; here, they're easily predicted.
It is fortunate, then, that "Mission: Impossible 2" has renowned director John Woo at its helm. Woo knows how to inject some much-needed adrenaline into the picture, employing some creative action shots which only slightly overuse slow-motion effects. Even Woo cannot save the tedious and overlong middle third of the movie. But he is in fine form during the closing half hour, pumping up the tempo with an entertaining motorcycle chase and a heart-pounding hand-to-hand duel between Hunt and Ambrose. Also an asset is a fine multifaceted score by Hans Zimmer.
But ultimately, it's hard to shake the feeling that "Mission: Impossible 2" is mostly an exercise in sound and fury. And while this is certainly nothing new for a summer blockbuster, I at least demand something with a bit of originality for my entertainment dollar. "MI2" just doesn't offer that; it seems content to just be adequate, relying on the memory of the superior first movie (and of course Cruise's star power) to fill the seats.
"MI2" isn't a bad film, but it is a poor sibling to the first "Mission: Impossible". That movie was fun consistently, this one only in flashes. I would not be dismayed if a third film in the series were to go into production. But I hope any such project will strive to bring some freshness and invention to the franchise. With "Mission: Impossible 2", complacency has already set in.
Copyright © 2000 Shannon Patrick Sullivan. Archived at The Popcorn Gallery, http://www.physics.mun.ca/~sps/movies/MissionImpossible2.html
-- _______________________________________________________________________ / Shannon Patrick Sullivan | "We are all in the gutter, but some of us \ |<EMAIL_ADDRESS>| are looking at the stars." - Oscar Wilde | \___________________________|__________________________________________/ | Popcorn Gallery Movie Reviews www.physics.mun.ca/~sps/movies.html | | Doctor Who: A Brief History of Time (Travel) /drwho.html |
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Which of these inputs will cost the most for Shellsort when using divide-by-twos increments on an array with a size where n is a power of 2?
A sorted array will result in every sublist also being sorted, which is fast.
An array with random input is unlikely to give the worst case.
A reverse-sorted array turns out not to cause any particular problem.
When the array size is a power of 2, the divide-by-twos increment series will keep the even-position items separate from the odd-position items until the final pass, resulting an a really expensive final pass.


I am currently a Master of Arts Gender and Development Studies Student and I also hold a Bachelor of Science degree from the University of Nairobi.
I have over fifteen years experience in development work in East Africa mostly supporting design of proposals and design of theories of change, results frameworks and evalaution of development projects.
I decided to take a class on programming so that I can gain new skills that I can use to develop my own applications and websites as well as contribute to innovation for greater socail impact on communities.


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We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. See Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford, Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical Association, 109:505, 11-23, <doi:10.1080/01621459.2013.870904>.
| Version: | 0.1 |
| Depends: | R (≥ 2.10) |
| Imports: | nleqslv, stats |
| Published: | 2016-12-20 |
| Author: | Ming Wang, Yongtao Guan, Kun Xu |
| Maintainer: | Ming Wang <willnju at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| CRAN checks: | SPPcomb results |
| Reference manual: | SPPcomb.pdf |
| Package source: | SPPcomb_0.1.tar.gz |
| Windows binaries: | r-devel: SPPcomb_0.1.zip, r-release: SPPcomb_0.1.zip, r-oldrel: SPPcomb_0.1.zip |
| OS X binaries: | r-release: SPPcomb_0.1.tgz, r-oldrel: SPPcomb_0.1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=SPPcomb to link to this page.


Functions over the Symmetric Group¶
SnFFT represents a function over Sn as an array of Float64 values.
Because this representation doesn’t explicitly store the permutation that corresponds to each value of the function, SnFFT has a set of standards that it uses to define the correspondence between indices of this array and the permutations.
These standards will be explained for each of the three types of fast Fourier transforms that SnFFT implements.
Dense Functions¶
A dense function over Sn will have a length of n! because the dense fast Fourier transform doesn’t rely on any prior knowledge of the function.
The dense fast Fourier transform assumes that the value stored at index i is the value associated with the permutation that permutation_index() maps to i.
See example3() for more details.
-
snf
(N, PA, VA)¶
# Parameters:
# N::Int
# - the problem size
# PA::Array{Array{Int, 1}, 1}
# - PA[i] is a Permutation of N
# VA::Array{Float64, 1}
# - VA[i] is the Value associated with PA[i]
# Return Values:
# SNF::Array{Float64, 1}
# - SNF[i] is the value associated with the permutation that permutation_index() maps to i
# - this is the format for the SNF parameter of sn_fft()
# Notes:
# - any permutation of N not represented in PA will be assigned a value of zero
Bandlimited Functions¶
A bandlimited function over Sn that is invariant at Sn - k will have n!/(n - k)! blocks of identical values of length (n - k)! when it is represented in the format that the dense fast Fourier transform uses.
This representation both wastes space and makes the calculation of the fast Fourier transform much slower.
Consequently, SnFFT uses a representation of such a function that stores one value from each block.
The bandlimited fast Fourier transform assumes the the value stored at index i is the value associated with all of the permutations that permutation_index() maps to (i - 1) * (n - k)! + 1 to i * (n - k)!.
See example7() for more details.
-
snf_bl
(N, K, PA, VA)¶
# Parameters:
# N::Int
# - the problem size
# K::Int
# - the problem is homogenous at N-K
# PA::Array{Array{Int, 1}, 1}
# - PA[i] is a Permutation of N
# VA::Array{Float64, 1}
# - VA[i] is the Value associated with PA[i]
# Return Values:
# SNF::Array{Float64, 1}
# - SNF[i] is the value associated with all of the permutations that permutation_index() maps to any value in the range ((i - 1) * factorial(N - K) + 1):(i * factorial(N - K))
# - this is the format for the SNF parameter of sn_fft_bl()
# Notes:
# - any homogenous coset that doesn't have a representative permutation in PA will be assigned a value of zero
Sparse Functions¶
A sparse function over Sn is represented by two components.
The first is a set of values and the second is a set of indices.
The sparse fast Fourier transform assumes the the value at index i is the value associated with the permutation that permutation_index() maps the index at index i.
See example5() for more details.
-
snf_sp
(N, PA, VA)¶
# Parameters:
# N::Int
# - the problem size
# PA::Array{Array{Int, 1}, 1}
# - PA[i] is a Permutation of N
# VA::Array{Float64, 1}
# - VA[i] is the Value associated with PA[i]
# Return Values:
# SNF::Array{Float64, 1}
# - SNF[i] is the value associated with the permutation that permutation_index() maps to NZL[i]
# - this is the format for the SNF parameter of sn_fft_sp()
# NZL::Array{Int, 1}
# - NZL must in increasing order
# - this is the format for the NZL parameter of sn_fft_sp()
# Notes:
# - the values in VA should be non-zero


quotek::data::
news
¶news is a container which aims to store data comming from remote financial news feeds, being RSS, twitter, etc..
Public Members
date
¶Stores the timestamp on which the news was published.
source
¶Stores the source/author of the news.
title
¶Stores the title of the news (if appliable).
content
¶Stores the main content of the news.
Stores the related tags of the news (if appliable).


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Zeeland again! Day trip from Nieuwvliet to Vlissingen
by Monika Suchoszek
Longer weekend was ahead of us and since Luxembourg, out first desired destination, was placed on the list of countries with code orange (after visiting this country self-quarantine is recommended by the government) we decided to spontaneously change our plans and go again to the Netherlands. This time we had 4 days to discover more of this beautiful region, called Zeeland. Surprisingly, it took me few days of very intense research to find a free spot on the camping and I was not looking at any specific location, just somewhere in a reasonable walking distance from a nice beach. Small camping sites are where you should be looking at, they are more flexible if you want to visit them for a shorter period of time (many had only a possibility to book the whole week or minimum 5 nights) and they are not that crowded which is now very important to stay safe and enjoy the holidays. We were lucky with the camping site! Everyone has enough space for themselves, there was huge football field in the middle, enclosure with chickens, sheep, a pony and even a few loud goats :) On the top of that many beautiful flower beds were creating really lovely atmosphere!
We arrived at the camping in the early afternoon and we decided to leave everything there and had a walk to the beach (around 2km). The beach was crowded only close to the main entrance and around the beach restaurant but if you are willing to walk maybe 100-200m, it will get quieter. The beach is wide enough to allow people for a sufficient distances between each other. It was a sunny day but even then I felt a cold breeze on my skin (most of the people had windbreaks or sun&wind shelters, smart!).
Since the sun was getting lower and temperature dropped, it was a perfect time for an evening run. After arriving back at the camping and quick change of clothes, I was ready to explore the surroundings :) First I was following small street until I have reached cycling line along the creek called Zwarte Gat, then I followed it until the nature reserve ‘Waterdunen’ where I started to run along the coast, towards our camping. At this time of the day, before sunset, it was really spectacular there. Only people waiting for the sunset, horses on the beach and almost no cyclist or walkers on the path. Truly amazing!
Since we couldn’t find any bicycles to rent next day, we decided to go by car to Breskens, leave the car on the parking and from there take a ferry to Vlissingen on the other site of the coast. It was quite crowded on the ferry and not everyone was wearing a mask, even though it was obligatory on the boat. It wasn’t long until we got to Vlissingen, about 20 minutes on the ferry.
First, we walked along the coast (~2 km) to the city center, passing old cannons, a windmill with the nearby monument called “Uncle Beach” indicating the place where was a fight to free Vlissingen in 1944. The first characteristic building you see getting closer to the center is “Het Arsenaal”, pirate-theme amusement park dedicated mostly to kids. There is a marine full of yachts next to it, many restaurants and shops. We walked to the church, along the shopping street but everything was closed (besides the supermarkets) since we were there on Sunday.
“MuZEEum”, Vlissingen’s Maritime and Naval History museum, we decided to visit as well. The museum is located just next to the marina. Nowadays you need to make a reservation online since there is a limited number of visitors allowed every 30min. We did it spontaneously, while having a lunch and were lucky to find a few spots available. We could go there in less than an hour. It was overall very good decision to visit this place. First we stored our backpacks in the lockers, then we got a audio guides in Dutch and English to listen stories about exhibition. We didn’t get lost in the museum, stories about the ships were not too long and interesting. We got to know how ships were cruised around the world centuries ago, a bit about the local history of the harbour in Vlissingen and the biggest accidents resulting in the wreck of the ships laying now on the bottom of the ocean. You can get to the tower of the museum and admire the panoramic view of the marine and the coast. On the way to the top of the tower, there is one floor with the museum pieces that are sill not ready to be on the exhibition or under repair. You can see the place where someone is working patiently to have all details of the boats recreated before we can admire a new showpiece.
After an obligatory ice cream, we headed back to the harbour. We didn’t have to wait long for the ferry to Breskens, and while we were heading back the sky became darker and sea rougher but it actually made photos of the boats more interesting :)
Practical info:
-
We stayed at the camping site: Camping De Catshoek
-
Ferry Breskens - Vlissingen was 8.35 euro per person, return ticket for pedestrians. If you have a bike with you, the price is 10.35euro Big parking next to the ferry terminal is free, always a good news :)
-
Ticket to Zeeland Maritime Museum Vlissingen - 10.50 euro per person MuZEEum, the ticket also grant you access to The Casemates, parts of an old fortress within walking distance of the museum (which we skipped). Audio guides (we got them in English and Dutch) are included and are very necessary since there are not many descriptions and besides that, it was very easy to understand and remember more about the history of the showpieces, visiting time: 1.5hour
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Revature empowers businesses around the globe with best-in-class talent and technologies. Our people work on the most innovative, challenging, and rewarding software development projects for leading Fortune 500 companies, government organizations, and top systems integrators. We offer three distinct advantages that help put our clients—and our people—ahead in the market: a one-of-a-kind talent delivery program, unparalleled expertise, and better control of your talent pipeline.
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Obsolete Members for QWheelEvent
The following members of class QWheelEvent are obsolete. They are provided to keep old source code working. We strongly advise against using them in new code.
Public Functions
(obsolete) | QWheelEvent(const QPointF &pos, const QPointF &globalPos, int delta, Qt::MouseButtons buttons, Qt::KeyboardModifiers modifiers, Qt::Orientation orient = Qt::Vertical) |
(obsolete) | QWheelEvent(const QPointF &pos, int delta, Qt::MouseButtons buttons, Qt::KeyboardModifiers modifiers, Qt::Orientation orient = Qt::Vertical) |
(obsolete) int | delta() const |
(obsolete) Qt::Orientation | orientation() const |
Member Function Documentation
QWheelEvent::QWheelEvent(const QPointF &pos, const QPointF &globalPos, int delta, Qt::MouseButtons buttons, Qt::KeyboardModifiers modifiers, Qt::Orientation orient = Qt::Vertical)
Constructs a wheel event object.
Use the constructor taking angleDelta and pixelDelta QPoints instead.
The pos provides the location of the mouse cursor within the widget. The position in global coordinates is specified by globalPos. delta contains the rotation distance, modifiers holds the keyboard modifier flags at the time of the event, and orient holds the wheel's orientation.
See also pos(), pixelDelta(), and angleDelta().
QWheelEvent::QWheelEvent(const QPointF &pos, int delta, Qt::MouseButtons buttons, Qt::KeyboardModifiers modifiers, Qt::Orientation orient = Qt::Vertical)
Constructs a wheel event object.
Use the constructor taking angleDelta and pixelDelta QPoints instead.
The position, pos, is the location of the mouse cursor within the widget. The globalPos() is initialized to QCursor::pos() which is usually, but not always, correct. Use the other constructor if you need to specify the global position explicitly.
The buttons describe the state of the mouse buttons at the time of the event, delta contains the rotation distance, modifiers holds the keyboard modifier flags at the time of the event, and orient holds the wheel's orientation.
See also pos(), pixelDelta(), and angleDelta().
int QWheelEvent::delta() const
This function has been deprecated, use pixelDelta() or angleDelta() instead.
Qt::Orientation QWheelEvent::orientation() const
Returns the wheel's orientation.
Use angleDelta() instead.


Media Designer
Find Out MoreHello, my name is Sabrina Rowland-Côté, I live the capital of Canada, Ottawa. Originally from Sherbrooke, Quebec. Fully bilingual, written and spoken. Currently enrolled in the Interactive Media Design program at Algonquin College as a second year student. I've also studied Business Commerce and Architectural Technology, and recieved my certificate in Media and Communications.
Let's get started!Through the design process
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Intelligently Deduplicating Records in Rails
I recently needed to write a deduplicate method in a Rails app I built. In the application, each artist has_one
song. However, over the course of several months, several songs had been added to the database with the same artist_id
as an existing song.
Based on this helpful post, my deduplicate method looked like this:
1 2 3 4 5 6 7 8 |
|
This method groups every song by the song’s artist. For groupings with more than one song (i.e. songs that have the same artist_id
), it keeps the first song and deletes all the rest.
This works great, but it naively decides which duplicate record to destroy and which to keep.
To make the deduplicating method “smarter” we can use sort
and sort_by
to influence which duplicate will be kept. For example, if we wanted to keep the duplicate song with the highest play count, we could add this line before the shift
method call:
1
|
|
This would ensure that the song “kept” using the shift
method is the song with the highest play count.
It’s also possible to sort records by boolean values using sort_by
. This was useful for my method, because in cases where an artist had two songs, I preferred to keep the song that had been reviewed by a trusted editor vs. one that had not.
The sort
method doesn’t work with booleans, so we instead use sort_by
and replace the booleans with integers to achieve the sort. I used the following line of code before the shift
call:
1
|
|
Now, I have a concise, reusable method I can use to intelligently de-duplicate my songs.
Charles Worthington (@cew821) is a freelance product designer living in Washington, DC. He would love to hear from you!