Let's touch base on kernel methods
Empirical risk minimization with linear model $f(x) = w.x$ given data points is a traditional way of learning coefficients $w$. Ridge regression, Lasso, Elastic net, Perceptron, SVM, and Logistic Regression are examples of ERM methods. Let’s say our data points are $(x_1,y_1) , (x_2,y_2), .., (x_n, y_n)$, then ERM is defined by:
$$ \min_{w\in \mathbb{R}^d} \sum_{i=1}^n L(y_i,w.x_i)+\lambda \text{Reg}(w) $$
You can find $w$ using least square error and (stochastic) gradient descent. But how about nonlinear models? Can we do the same on data points that are not linearly separable?
How to extend this to nonlinear models?
We can do regression (predict $y$ from data $x\in \mathbb{R}^d$) using a nonlinear function $f(x)$ of the form:
$$ f(x) = w. \phi (x) $$
for some feature map $\phi: \mathbb{R}^d \rightarrow \mathbb{R}^m$ for some $M$.
This captures polynomials. Then, for one dimensional $x$ we can write $f(x)$ as:
$$ d=1 : \begin{align} f(x) = & c_0+c_1x+c_2x^2 + .. + c_px^p \\\ =& \begin{bmatrix} c_0 \\\ c_1 \\\ \vdots \\\ c_p \end{bmatrix}^T. \begin{bmatrix} 1 \\\ x \\\ \vdots \\\ x^p \end{bmatrix} \\\ =& w.\phi(x) \qquad x \in \mathbb{R} , \phi(x) \in \mathbb{R}^d \end{align} $$
For high dimensional $x$:
$$ d>1 : \\\ f(x) = c_0+\sum_{i=1}^d c_ix_i+\sum_{i,j}c_{i,j}x_ix_j + .. + \sum_{i_1, .. i_p}c_{i}x_{i_1}x_{i_2}..x_{i_p} $$
$$ \begin{align} \phi(x)=& \begin{bmatrix} 1 \\\ x_i \\\ x_ix_j \\\ \vdots \\\ x_{i_1}x_{i_2}..x_{i_p} \end{bmatrix} \in \begin{bmatrix} \mathbb{R}^1 \\\ \mathbb{R}^d \\\ \mathbb{R}^{d^2} \\\ \vdots \\\ \mathbb{R}^{d^p} \end{bmatrix} \end{align} $$
So far, we have captured nonlinearity in our data points with $\phi$. However, our original function $f(x)$ is still linear concerning $w$.
Let’s go over some examples:
$$ d=1: f(x) = e^x = 1+ x + \frac{x^2}{2} + \frac{x^3}{3!} + .. = \sum_{m=0}^{\inf} \frac{x^m}{m!} $$
$$ \phi(x)= \begin{bmatrix} 1 \\\ x \\\ \vdots \\\ x^p \end{bmatrix} $$
$$ d>1: f(x) = e^{w.x} = 1+w.x + \frac{(w.x)^2}{2} + .. = \sum_{m=0}^{\inf} \frac{(w.x)^m}{m!} $$
Which is derived from Taylor expansion for the exponential function.
Can we learn $w$ for $f(x)=w.\phi(x)$ without having to compute $\phi(x) \in \mathbb{R}^m$ directly: $\phi: \mathbb{R}^d \rightarrow \mathbb{R}^m$ ?
Yes, as long as we can compute the inner product between feature vectors. Let’s call this kernel function :
$$ k(x,x’) = \phi(x).\phi(x’) $$
Now, lets’ do non-linear regularized least squares: $f(x) = w.\phi(x)$ where we are going to fit $(x_i,y_i), i=1,..,n$.
$$ \min_{w\in \mathbb{R}^M} \sum_{i=1}^n (y_i-w.\phi(x))^2+\frac{\lambda}{2} ||w||^2 $$
Note that this is a quadratic function in $w$:
$$ L(w) = \sum_{i=1}^n (y_i-w.\phi(x_i))^2+\frac{\lambda}{2} ||w||^2 $$
To minimize that solve: $\nabla L (w^*)=0$:
$$ \nabla L(w) = \sum_{i=1}^n (y_i-w.\phi(x_i))(-\phi (x_i))+ \lambda||w|| $$
Then we have:
$$ w^* = \frac{1}{\lambda} \sum_{i=1}^n (y_i-w^*.\phi(x_i))\phi (x_i) $$
let’s call $\alpha_i = y_i-w^*.\phi(x_i)$ a residual $\in \mathbb{R}$:
$$ w^* = \frac{1}{\lambda} \sum_{i=1}^n \alpha_i \phi (x_i) $$
Then, solution $w^*$ lies on the span of feature vectors: $\phi(x_1),\phi(x_2),..,\phi(x_n) \in \mathbb{R} ^ m$
How to find residuals?
$$ \begin{align} \alpha_i = & y_i - w^*.\phi(x_i) \\ =& y_i- \frac{1}{\lambda}(\sum_{j=1}^n \alpha_j \phi(x_j) ). \phi(x_i)\\ =&y_i-\frac{1}{\lambda} \sum_{j=1}^n \alpha_j \phi(x_j) . \phi(x_i) \end{align} $$
we know that $K(x_i,x_j) = K(x_j,x_i)$ :
$$ \begin{align} \alpha_i = y_i-\frac{1}{\lambda} \sum_{j=1}^n \alpha_j K(x_i,x_j) \end{align} $$
Hence:
$$ \begin{bmatrix} \alpha_1 \\\ \vdots \\\ \alpha_n \end{bmatrix} = \begin{bmatrix} y_1 \\\ \vdots \\\ y_n \end{bmatrix} - \frac{1}{\lambda} \begin{bmatrix} K(x_1,x_1) & \cdots K(x_1,x_n)\\\ \vdots & \vdots \\\ K(x_n,x_1) & \cdots K(x_n,x_n) \end{bmatrix} \begin{bmatrix} \alpha_1 \\\ \vdots \\\ \alpha_n \end{bmatrix} $$
And in a matrix form:
$$ \alpha = y - \frac{1}{\lambda} K \alpha $$ $$ \rightarrow \alpha = (\lambda I + k )^{-1}y. $$
Finally, our solution is:
$$ \begin{align} w^* = & \frac{1}{\lambda} \sum_{i=1}^n \alpha_i \phi (x_i)\\\ =&\frac{1}{\lambda} [\phi(x_1),\phi(x_2), .., \phi(x_n)]\begin{bmatrix} \alpha_1 \\\ \vdots \\\ \alpha_n \end{bmatrix} \qquad \Phi \in \mathbb{R}^{d\times n} \end{align} $$
How to predict? (compute $f(x)$)
$$ \begin{align} f(x) = & w^* . \phi(x)\\\ =&\frac{1}{\lambda} (\sum_i \alpha_i \phi (x_i)). \phi(x) \\\ =&\frac{1}{\lambda} \sum_i \alpha_i K(x_i,x) \end{align} $$
This material was covered in the introduction to machine learning CPCS 481/581, Yale University, Andre Wibisono where I (joint with Siddharth Mitra) was TF.+++ date = “2022-03-28” title = “Sparsity and Graphs” math = “true” thumbnail = “/images/sds365.png” twitterThumbnail = “images/mle-379x201.png” description = “posts” publish = “Journal of Machine Learning Research” category = “journal” weight=3 author = “Javid Dadashkarimi” keyword1= “submodular optimization.” keyword2= “continuous optimization.” +++
Graphs allow us to encode structural assumptions about data. Graphs are the natural language for describing all kinds of problems and data. The graphs discussed in this post will quantify statistical relationships between random variables. In particular, there is a graph associated with any multivariate distribution. There is always a graph associated with random variable $\omega$.
There are two types of graphs: Directed versus Undirected graphs.
A graph $G=(V,E)$ has vertices $V$ and edges $E$. If $X=(X_1,.., X_p)$ is a random variable, we will study graphs with $p$ vertices, one for each $X_j$. The graph will encode conditional independence relations among the variables. Directed graphical models are central for causal inference.
Example
We have a three-dimensional Gaussian $(X,Y,Z)$ with covariance:
$$ \Sigma = \begin{pmatrix} 1.3 & -2 & 2.1\\ -2 & 2.6 & -2.4 \\ 2.1 & -2.4 & 3.1 \end{pmatrix} $$
So, all pairs are correlated. For example, $X$ and $Y$ have a negative correlation.
How to sample
Now we know how to sample from this distribution. We factor it; we take the squared root of that matrix (i.e., Cholesky decomposition), generate standard normal random variables, and then scale by that decomposition. That gives us a sample from that Gaussian:
|
|
However, the marginal is still a normal distribution when we condition one random variable. which is not tilted in any direction.
This is revealed in the “precision matrix”:
$$ \Omega = \Sigma^{-1} = \begin{pmatrix} 1 & 0.9 & 0\\ 0.9 & 2 & 0.9 \\ 0 & 0.9 & 1 \end{pmatrix} $$
The zeros lead to conditional independence assumptions. These conditional independence assumptions were hidden in the covariance matrix.
Undirected graphs
Here, $V={X,Y,Z}$ and $E={(X,Y),(Y,Z)}$. This encodes the independence relation: $$ X \perp \!\!\! \perp Z ~ | ~ Y $$ This means that $X$ and $Z$ are independent conditioned on $Y$.
Here is another example.
$$ \begin{align} X_1 \perp \!\!\! \perp X_4 ~ | ~ X_2,X_3 \\ X_2 \perp \!\!\! \perp X_3 ~ | ~ X_1,X_4 \end{align} $$
In the following graph, $C=\{3,7\}$ separates $A=\{1,2\}$ and $B=\{4,8\}$. Therefore:
$$ \begin{align} \{X_1,X_2\} \perp \!\!\! \perp \{X_4,X_8\} ~ | ~ \{X_3,X_7 \} \end{align} $$
Special case
If $(i,j) \not\in E$ then: $$ X_i \perp \!\!\! \perp X_j | \{X_k: k \neq i,j\} $$
lack of an edge from $i$ to $j$ implies that $X_i$ and $X_j$ are independent given all of the other random variables.
Graph Estimation
A graph $G$ represents the class of distributions, $P(G)$, the distributions that are Markov concerning $G$.
Graph estimation: Given n samples $X_1, .., X_n \sim P$, estimate the graph $G$.
Gaussian case
Let $\Omega = \Sigma^{-1}$ be the precision matrix. A zero in $\Omega$ indicates a lack of the corresponding edge in the graph.
For example:
$$ \Omega = \Sigma^{-1} = \begin{pmatrix} * & * & 0\\ * & * & * \\ 0 & * & * \end{pmatrix} $$
And when we have four random variables, we can similarly read off the graph: $$ \Omega = \Sigma^{-1} = \begin{pmatrix} * & * & * & 0\\ * & * & 0 & *\\ * & 0 & * & *\\ 0 & * & * & * \end{pmatrix} $$
$$ \begin{align} X_1 \perp \!\!\! \perp X_4 ~ | ~ X_2,X_3 \end{align} $$
The machine learning problem
How do we estimate the graph from a sample of data?
Gaussian case: Algorithms
Two approaches:
- parallel lasso
- graphical lasso
In parallel lasso:
- For each $j = 1, .., p$ (in parallel): Regress $X_j$ on all other variables using the lasso.
- Put an edge between $X_i$ and $X_j$ if each appears in the regression of the other.
In graphical lasso:
- Assume a multivariate Gaussian model
- Subtract out the sample mean
- Minimize the negative log-likelihood of the data, subject to a constraint on the sum of the absolute values of the inverse covariance.
The glasso optimizes the parameters of $\Omega = \Sigma^{-1}$ by minimizing: $$ \text{trace}(\Omega S_n) - \log |\Omega| + \lambda \sum_{j\neq k}|\Omega_{jk}| $$
where $|\Omega|$ is the determinant and $S_n$ is the sample covariance:
$$ S_n = \frac{1}{n} \sum_{i=1}^n x_ix_i^T $$
If we assume the mean equals $0$, then the probability density at a data point $x$ is:
$$ \begin{aligned} p(x) = & \frac{1}{\sqrt{(2\pi)^p|\Sigma|}} \exp \Big( -\frac{1}{2} X^T \Sigma^{-1}X\Big) \\ =& \frac{1}{\sqrt{(2\pi)^p|\Sigma|}} \exp \Big( -\frac{1}{2} X^T \Omega X\Big) \\ = & \frac{1}{\sqrt{(2\pi)^p|\Sigma|}} \exp \Big( -\frac{1}{2} \text{trace}(\Omega XX^T)\Big) \\ \end{aligned} $$
Therefore, using $\log |A| = -\log |A^{-1}|$, up to an additive constant: $$ -\log p(x) = \frac{1}{2} \log |\Sigma| + \frac{1}{2} \text{trace}(\Omega XX^T) = -\frac{1}{2} \log |\Omega|+ \frac{1}{2} \text{trace}(\Omega XX^T) $$
Summing over all the data we have:
$$ \begin{aligned} -\sum_{i=1}^n \log p(x_i) =& \frac{1}{2} \sum_{i=1}^n \text{trace}(\Omega X_iX_i^T) -\frac{n}{2} \log |\Omega| \\ = & \frac{n}{2} \text{trace} (\Omega S_n) - \frac{n}{2} \log |\Omega| \end{aligned} $$
Rescaling by $2/n$ and adding the $\mathcal{L_1}$ penalty, we get the objective function: $$ \mathcal{O}(\Omega) = \text{trace} (\Omega S_n) - \log |\Omega|+ \lambda \sum_{k\neq j} |\Omega_{jk}| $$
This is a convex function of $\Omega$.
Discrete Graphical Models
There are some challenges in handling discrete data:
- Models don’t have closed forms.
- We need to use Gibbs sampling, variational inference
- No analog of the graphical lasso
Lets say $G=(V,E)$ be an undirected Graph on $m=|V|$ vertices:
Recall: edge could be a clique, a triangle is a clique, but a square is not a clique (i.e., we need cross edges).
A positive distribution over random variables $Z_1,.., Z_p$ that satisfies the Markov properties of graph $G$ can be represented as:
$$ p(Z) \propto \prod_{c\in \mathcal{C}} \psi_c(Z_c) $$
It’s a product of factors, one for each clique in the graph where $\mathcal{C}$ is the set of clubs in the graph $G$. This is equivalent to our previous statement that a graph corresponds to a set of conditional independence assumptions.
Recall: Positive distributions can be represented by an exponential family:
$$ p(Z;\beta) \propto \exp\Big( \sum_{c\in \mathcal{C}} \beta_c \phi_c(Z_c)\Big) $$
Particular case: Ising Model (discrete Gaussian):
$$ p(Z;\beta) \propto \exp\Big( \sum_{i\in V} \beta_i Z_i +\sum_{(i,j)\in E} \beta_{ij}Z_i Z_j \Big) $$
Note that we can write a multivariate Gaussian as follows: $$ p(z) \propto \exp\Big( \sum_{i\in V} \beta_i z_i +\sum_{(i,j)\in E} \beta_{ij} z_i z_j\Big) $$
If I change the sample space from natural numbers to binary, I go from a Gaussian to an Ising model. Another way to think about this is in terms of sufficient statistics, the first term corresponds to $x$, and the second term corresponds to $x^2$.
can you see what $\beta_i$ and $\beta_{ij}$ are?
From edges to cliques
Take $\beta_j=0$ for simplicity. If we have a triangle $(i,j,k)$ in the graph, then the potential function corresponds to:
$$ \psi_{ijk}(Z_i,Z_j,Z_k) = e^{\beta_{ij} Z_i Z_j } e^{\beta_{jk} Z_j Z_k} e^{\beta_{ik} Z_i Z_k } $$
This is a prevalent model in Immunology, like the risk of an outbreak.
Recall from a few weeks ago
$$ P_{\beta}(Z_1,..,Z_n) \propto \exp\Big( \sum_{s\in V}\beta_s Z_s+ \sum_{(s,t)\in E}\beta_{st}Z_sZ_t \Big) $$
where $Z_i = \{0,1\}$.
We discussed that we couldn’t compute this probability because of the normalizing constant. Here are some techniques to compute these:
Gibbs sampler (stochastic approximation)
- Choose vertex $s\in V$ at random
- Sample $u\sim \text{Uniform}(0,1)$ and update:
$$ Z_s = \begin{cases} 1 & u \leq (1+\exp(-\beta_s-\sum_{t\in N(s)}\beta_{st}z_t))^{-1}\\ 0 & \text{ otherwise } \end{cases} $$
where $\beta_{st}$ denotes strength of influence node $s$ has on node $t$. This is precisely like Logistic regression.
- Iterate
Mean field variational algorithm (deterministic approximation)
- Choose vertex $s\in V$ at random
- Update:
$$ \mu_s = (1+\exp(-\beta_s-\sum_{t\in N(s)}\beta_{st}z_t))^{-1} $$ 3. Iterate
Graph Estimation
The problem is this: Given n i.i.d. samples from an Ising distribution, ${Z_i , i = 1, .. ,n}$ (each is a p-vector of {0, 1} values) identify underlying graph. Then, we have some data points like the following graphs:
We want to go from the data points to estimate that graph.
Consider the Ising model: $$ \psi_{ijk}(Z_i,Z_j,Z_k) = e^{\beta_{ij} Z_i Z_j } e^{\beta_{jk} Z_j Z_k} e^{\beta_{ik} Z_i Z_k } $$
Conditioned on $(z_2,.., z_p$), variable $Z_1 \in \{0,1\}$ has probability a logistic function gives the mass function:
$$ P(Z_1=1 | z_2,..,z_p) = \text{sigmoid} \Big( \beta_1+ \sum_{j\in N(1)} \beta_{1,j} z_j\Big) $$
But we only know the neighbors’ values if we know the graph. What we do is:
Parallel Lasso (sparse logistic regressions)
- Perform $L_1$ regularized logistic regression of each node $Z_i$ on $Z_{-i}={Z_j, j\neq i}$ to estimate neighbors $\hat{N}(i)$.
- Two versions:
- create an edge $(i,j)$ if $j\in \hat{N}(i)$
and
$i\in \hat{N}(j)$ (Smaller graph). - create an edge $(i,j)$ if $j\in \hat{N}(i)$
or
$i\in \hat{N}(j)$ (Bigger graph).
In theory, these methods work the same, and you can use either. There is no graphical lasso because there is no closed form for the Ising model. This is a different way of looking at the relationship between random variables than clustering.
Scaling behavior: Performance with data size
How many data points do you need to estimate the graph correctly? Assume the model is correct, either Gaussian or Ising model. Maximum degree $d$ of the $p$ variables. Sample size $n$ must satisfy:
- Ising model: $n\geq d^3 \log p$
- Graphical lasso: $n\geq d^2 \log p$
- Parallel lasso: $n\geq d \log p$
- Lower bound: $n\geq d \log p$
So why should we use a graphical lasso when a parallel Lasso needs less data? Answer: Each method makes different incoherence assumptions: Correlations between unrelated variables are not too significant.
Number of edges $p^d$ and the number of bits to write down is $\log p^d = d \log p$
Graph Laplacian
Then, the graph Laplacian $L$ is the square $n \times n $ matrix defined as $L=D-A$. The graph Laplacian gets its name from being the discrete analog of the Laplacian operator from calculus.
Polynomials of the Laplacian
$$ P_w(L) \in \mathbb{R}^{n\times n} = w_0 I_n + w_1 L + w_2 L^2+ .. + w_d L^d = \sum_{i=0}^d w_i L^i $$
Each polynomial of this form can alternately be represented by its vector of coefficients: $w=[w_0,..,w_d]$. If $\text{dist}(u,v)> I $, then the $(u,v)$ entry of $L^i$ is zero, which is analogous to a CNN filter (kernel). The weights $w_i$ play the role of filter coefficients. Degree $d$ of polynomial plays the role of the size of the kernel.
The Laplacian is a Mercer kernel
- It’s symmetric: $L_{uv} = L_{vu}$.
- It’s Positive-definite: $$ f^TLf = \sum_{(u,v) \in E} (f_u-f_v)^2 \geq 0 $$
Whence equivariance
A transformation $f: \mathbb{R}^n \rightarrow \mathbb{R}^n $ is equivariant if: $$ f(Px) = Pf(x) $$
for any permutation matrix $P$, where $PP^T=I$. The transformed data and Laplacian are: $$ \begin{aligned} x\rightarrow Px \\ L \rightarrow PLP^T \\ L^i \rightarrow PL^iP^T \end{aligned} $$
The transformed polynomial kernels are: $$ \begin{aligned} f(Px) = & \sum_{i=0}^d w_i (PL^iP^T) Px \\ =& \sum_{i=0}^d w_i PL^ix \\ =& P\sum_{i=0}^d w_i L^ix \\ =& Pf(x) \\ \end{aligned} $$
We covered this post in the intermediate machine learning SDS 365/565, Yale University, John Lafferty, where I was TF.


CrimPapers
Articles published on 29 April 2020
On 29 April 2020, 14 new articles were published in 10 journals.
The Other Side of Assimilation: How Immigrants Are Changing American Life by David Piacenti
The Formerly Incarcerated, Advocacy, Activism, and Community Reintegration by Justin M. Smith
Exploring the Bottom-Up Reform of Sex Offender Registration in China: Carceral Feminism and Populist Authoritarianism
The Coca Diplomacy As the End of the War on Drugs. the Impact of International Cooperation on the Crime Policy of the Plurinational State of Bolivia
Continuing Education: Toward A Life‐course Perspective on Social Learning by Peggy C. Giordano
Conceptual and Empirical Obstacles in Defining Ms‐13 by Maya P. Barak,
kenneth Sebastian León,
edward R. Maguire
At Risk of Rights: Rehabilitation, Sentence Management and the Structural Violence of Prison
Total Extraction: Exploitative Behaviors of the Carceral Apparatus Towards Carceral Officers in Central Appalachia
Editors’ Introduction to the Special Issue, “Governing Through Human Rights and Critical Criminology”
Risk Relevance of Psychometric Assessment and Evaluator Ratings of Dynamic Risk Factors in High‐risk Violent Offenders by Tamsin Higgs,
mark E. Olver,
kevin Nunes,
franca Cortoni
Policing the Port, Watching the City. Manifestations of Organised Crime in the Port of Genoa by Anna Sergi
A Turning Point, Securitization, and Policing in the Context of Covid-19: Building A New Social Contract Between State and Nation?
Differential Mnemonic Consistency Differs Between Experienced and Fabricated Incidents by Thorsten Lange, Raoul Bell, Axel Buchner
She Left the Party: College Students’ Meanings of Sexual Consent by Anne Groggel, Maddie Burdick, Angel Barraza


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#assign id = "true">
Mark a property as the @Id
property.
@Entity public class Contact { @Id long id; ... }
When we annotate a property with @Id
Ebean will automatically assign an appropriate
Id generator
if the type is a number type or UUID type
.
That is, with Ebean it is effectively redundant to also add the @GeneratedValue
annotation.
@Id long id; // ... is effectively the same as @Id @GeneratedValue long id;
Ebean additionally has an @Identity annotation
when we want to use to specify the starts value
, cache value
and
generated by default|always
options for database identity or sequence.
We want to use this to specifically set the cache
option when the associated
table is expected to get a high load of inserts and we want to let databases that support it
to have higher than normal id caching to reduce contention around generated id values/sequences.
Postgres, Oracle and NuoDB all support setting the identity cache option. The downside to larger
cache for id values is the potential for larger gaps in the id values if the database is restarted.
@Id @Identity(start=10000, cache=500) long id;
We can also specify @Identity
on the class - typically when the id property
is inherited from a MappedSuperclass.
@Entity @Identity(start=10000, cache=500) public class Contact { @Id long id; ... }
If the @Id type is UUID
then Ebean will automatically assign an appropriate Id generator
to that property.
// an appropriate UUID based Id generator // is automatically assigned @Id UUID id;
If the @Id type is String
then Ebean assumes that the id value is supplied by the
application. No Id generator is assigned to the Id property in this case.
@Id String code;
An example of this could be ISO Country codes used as a primary key for Country.
We can model entity beans without any @Id
property. The 2 cases for this are
when modelling views and when modelling tables that are "insert only" representing a log
of events or similar.
When the entity bean is modelled to a @View
often the view will not have
column(s) that equates to a primary key.
@Entity @View(name = "machine_stats") public class MachineStats { @ManyToOne Machine machine; LocalDate date; @Sum // equivalent to @Aggregation("sum(totalKms)") long totalKms; @Sum long hours; @Max BigDecimal rate; @Aggregation("sum(cost)") BigDecimal totalCost; @Aggregation("max(totalKms)") BigDecimal maxKms;
When modelling a "append only" table where we do not need a primary key
then we can desire not having an @Id
property and no actual primary key.
We can desire this when we want to avoid the cost of the primary key index (that the
application will never need or use).
For Postgres we would often use table partitioning for this type of "append only" events
type table via @DbPartition
.
/** * Append only table with no Id property, no primary key (no primary key index). */ @DbPartition(mode = DAY, property = "eventTime") @Entity @Table(name = "machine_log") public class MachineLog { @Index @NotNull protected Instant eventTime; @DbForeignKey(noConstraint = true) @ManyToOne(optional = false) protected Machine machine; ...
There is support for registering and using a custom Id generator.
We register the IdGenerator with ServerConfig by using addClass()
or add(IdGenerator idGenerator)
or setIdGenerators(List
.
Note that if you are using classpath scanning to find entity beans that this will also automatically find implementations of IdGenerator and automatically register them so in this case you don't need to register the IdGenerator manually with ServerConfig.
We can then tell Ebean to use our custom IdGenerator by using the @GeneratedValue
with
the generator being the name of our custom IdGenerator.
Note that @Id
properties never invoke lazy loading.
All the supported databases use either Identity
or Sequences
(or allow both)
and Ebean will choose the appropriate Id generation strategy based on the database platform.
| Database | Strategy |
|---|---|
| H2 | Identity (also supports sequences) |
| Postgres | Identity (as serial, also supports sequences) |
| MySql | Identity |
| Oracle | Sequences (also supports Identity) |
| DB2 | Identity (also supports sequences) |
| SQL Server | Identity |
| SQLite | Identity |
| SqlAnywhere | Identity |


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Can be packed into camping kits and trekking bags as your companion for all adventure sports, into forests or the hills!


Class MessageFactory
This class creates WONDER-compliant messages. Please note that all functions in this class are static, so there is no need to create MessageFactory objects.
Defined in: wonder_full.js.
createAnswerMessage - Creates an Answer message, the connectionDescription field will be empty and has to be filled before sending.
- Parameters:
- {Identity} from
- - The Identity that figures as sender of the message.
- {Identity[]} to
- - The Array of Identity that figures as receiver of the message.
- {string} contextId
- - The contextId of the conversation related to the invitation.
- {ResourceConstraints} constraints
- - The resource constraints for the resources initialized on conversation start.
- {Identity} hosting Optional
- - The host of the conversation (optional). [NOT IMPLEMENTED, by default the host will be the one starting the conversation]
- {Identity[]} connected
- - Array of Identity that are already connected to the conversation. Used to establish the order in the connection flow for multiparty.
- Returns:
- The created Message
createCandidateMessage - Creates a Message containing an ICE candidate
- Parameters:
- {Identity} from
- - The Identity that figures as sender of the message.
- {Identity[]} to
- - The Array of Identity that figures as receiver of the message.
- {string} contextId
- - The contextId of the conversation related to the invitation.
- {string} label
- - The label of the candidate.
- {string} id
- - The id of the candidate.
- {string} candidate
- - The ICE candidate string.
- {boolean} lastCandidate
- - Boolean indicating if the candidate is the last one. If true, include the full SDP in the candidate parameter for compatibility with domains that don't support trickling.
- Returns:
- The created Message
createInvitationMessage - Creates an Invitation message, the connectionDescription field will be empty and has to be filled before sending.
- Parameters:
- {Identity} from
- - The Identity that figures as sender of the message.
- {Identity[]} to
- - The Array of Identity that figures as receiver of the message.
- {string} contextId
- - The contextId of the conversation related to the invitation.
- {ResourceConstraints} constraints
- - The resource constraints for the resources initialized on conversation start.
- {string} conversationURL Optional
- - The URL of the conversation (optional).
- {string} subject Optional
- - The subject of the conversation. (optional).
- {Identity} hosting Optional
- - The host of the conversation (optional). [NOT IMPLEMENTED, by default the host will be the one starting the conversation]
- agenda
- peers
- Returns:
- The created Message
createUpdatedMessage - Creates an Updated message, the newConnectionDescription field will be empty and has to be filled before sending.
- Parameters:
- {Identity} from
- - The Identity that figures as sender of the message.
- {Identity[]} to
- - The Array of Identity that figures as receiver of the message.
- {string} contextId
- - The contextId of the conversation related to the invitation.
- {ResourceConstraints} newConstraints
- - The resource constraints for the resources to update.
- Returns:
- The created Message
createUpdateMessage - Creates an Update message, the newConnectionDescription field will be empty and has to be filled before sending.
- Parameters:
- {Identity} from
- - The Identity that figures as sender of the message.
- {Identity[]} to
- - The Array of Identity that figures as receiver of the message.
- {string} contextId
- - The contextId of the conversation related to the invitation.
- {ResourceConstraints} newConstraints
- - The resource constraints for the resources to update.
- Returns:
- The created Message


{% extends "base.html" %}
{% block body_class %}index{% endblock %}
{% block content %}
{{ render_markdown("""
# Ethical Review
Supporting documentation on the ethical review process.
- [Consent form](/assets/supporting-material/pyarelal_consent_r1_2023_03_08.pdf)
- [Recruitment materials](/assets/supporting-material/pyarelal_recruitment_material_r1_2023_02_25.pdf)
""") }}
{% endblock %}


Xiuli
Constructor Summary
| Public Constructor | ||
| public |
|
Member Summary
| Public Members | ||
| public |
callback: * |
|
| public |
clicked: * |
|
| public |
current: * |
|
| public |
data: * |
|
| public |
elementIds: *[] |
|
| public |
elements: {} |
|
| public |
main: * |
|
| public |
mainTrans: * |
|
| public |
root: * |
Method Summary
| Public Methods | ||
| public |
add(el: *, move: *) |
|
| public |
goto(tId: *, data: *) |
|
| public |
next(data: *) |
|
| public |
onTransitionend(fn: *) |
|
| public |
pre(data: *) |
Public Constructors
public constructor() source
Public Members
public callback: * source
public clicked: * source
public current: * source
public data: * source
public elementIds: *[] source
public elements: {} source
public main: * source
public mainTrans: * source
public root: * source
Public Methods
public add(el: *, move: *) source
Params:
| Name | Type | Attribute | Description |
| el | * | ||
| move | * |
public goto(tId: *, data: *) source
Params:
| Name | Type | Attribute | Description |
| tId | * | ||
| data | * |
public next(data: *) source
Params:
| Name | Type | Attribute | Description |
| data | * |
public onTransitionend(fn: *) source
Params:
| Name | Type | Attribute | Description |
| fn | * |
public pre(data: *) source
Params:
| Name | Type | Attribute | Description |
| data | * |


Tristan Harward & Laura Morris
October 22nd, 2016 • Rock Hill, South Carolina
Welcome to our wedding web site! Thank you so much for visiting and for being a part of our lives. We hope you can join us for a weekend of family, friends, and fun in the South Carolina sun.
Tip: don't miss the Things to Do section!


The divide-and-conquer paradigm is often used to find an optimal solution of a problem. Its basic idea is to decompose a given problem into two or more similar, but simpler, subproblems, to solve them in turn, and to compose their solutions to solve the given problem. Here, we use the above paradigm to solve "The measure problem " and "The contour Problem" of a set of Iso-Rectangles(axis aligned rectangles). The algorithm followed was given by Ralf Hartmut Guting and has a time and space complexity of O(nlogn) where n is the cardinality of the set of rectangles.
The order of the input of a particular rectangle formed by the lines x=a , y= b , x=c and y=d where a < c and b < d should specifically be "a b c d".
The algorithm assumes non degeneracy of rectangles meaning that the rectangles will not degerate into a line segment or a point.
The code does not account for decimal co-ordinates as vertices of the rectangles.
(Acta Informatica)(21,271-291) (1984)Optimal Divide-and-Conquer to Compute Measure and Contour for a Set of Iso-Rectangles, Ralf Hartmut Guting


using-bootstrap-grid-system
Get beyond the Ordinary Business is not usual. Today we see multiple shifts occurring simultaneously. Shifts that are broad, complex, and at high velocity and are driving new levels of disruption and value creation. The risks of not acting or acting in yesterday’s innovation paradigms are far too great. The current situation demands doing something beyond the ordinary. Organizations must confront the new realities and develop an intrinsic ability to apply innovation. What we do The AIE moves organizations from the problem statement to tangible business outcomes by applying innovation in ways that are meaningful to the business and able to drive tangible value and competitive edge. As thought leaders and innovation experts, we empower enterprises to:
Who we are Capgemini’s Applied Innovation Exchange (AIE) brings together a framework for action, a global network of exchanges, and a rich and diverse ecosystem to deliver a high-performance innovation engagement experience through a portfolio of more than 20+ distinct services that span the breadth of the applied innovation process. What we do The AIE moves organizations from the problem statement to tangible business outcomes by applying innovation in ways that are meaningful to the business and able to drive tangible value and competitive edge. As thought leaders and innovation experts, we empower enterprises to:
Get beyond the Ordinary Business is not usual. Today we see multiple shifts occurring simultaneously. Shifts that are broad, complex, and at high velocity and are driving new levels of disruption and value creation. The risks of not acting or acting in yesterday’s innovation paradigms are far too great. The current situation demands doing something beyond the ordinary. Organizations must confront the new realities and develop an intrinsic ability to apply innovation. What we do The AIE moves organizations from the problem statement to tangible business outcomes by applying innovation in ways that are meaningful to the business and able to drive tangible value and competitive edge. As thought leaders and innovation experts, we empower enterprises to:


rndc-confgen [-a] [-b keysize] [-c keyfile] [-h] [-k keyname] [-p port] [-r randomfile] [-s address] [-t chrootdir] [-u user]
rndc-confgen generates configuration files for rndc. It can be used as a convenient alternative to writing the rndc.conf file and the corresponding controls and key statements in named.conf by hand. Alternatively, it can be run with the -a option to set up a rndc.key file and avoid the need for a rndc.conf file and a controls statement altogether.
Do automatic rndc configuration. This creates a file rndc.key in /etc (or whatever sysconfdir was specified as when BIND was built) that is read by both rndc and named on startup. The rndc.key file defines a default command channel and authentication key allowing rndc to communicate with named with no further configuration.
Running rndc-confgen -a allows BIND 9 and rndc to be used as drop-in replacements for BIND 8 and ndc, with no changes to the existing BIND 8 named.conf file.
Specifies the size of the authentication key in bits. Must be between 1 and 512 bits; the default is 128.
Used with the -a option to specify an alternate location for rndc.key.
Prints a short summary of the options and arguments to rndc-confgen.
Specifies the key name of the rndc authentication key. This must be a valid domain name. The default is rndc-key.
Specifies the command channel port where named listens for connections from rndc. The default is 953.
Specifies a source of random data for generating the authorization. If the operating system does not provide a /dev/random or equivalent device, the default source of randomness is keyboard input. randomdev specifies the name of a character device or file containing random data to be used instead of the default. The special value keyboard indicates that keyboard input should be used.
Specifies the IP address where named listens for command channel connections from rndc. The default is the loopback address <IP_ADDRESS>.
Used with the -a option to specify a directory where named will run chrooted. An additional copy of the rndc.key will be written relative to this directory so that it will be found by the chrooted named.
Used with the -a option to set the owner of the rndc.key file generated. If -t is also specified only the file in the chroot area has its owner changed.


Sea.js Manual & Documentation
Home
|
Index
Table of Contents
Map Plugin
Map Plugin
Bookmarklet:
seajs map tool
The page is under construction.


Scratch is about to become way more awesome. Here are some of the features that you will get if you install the Mega Scratch Userscript:
Are you as hyped as we are? Good! Let's start the installation (don't worry, it's easy).
The first step to installing the Mega Scratch Userscript is to get a userscript manager. If you're using Firefox, Greasemonkey is a great option. If you're on Google Chrome, use Tampermonkey. There is also a version of Tampermonkey for Opera.
These userscript managers are what allow us to install the script. The next step will be super simple. All you have to do is click the button below, and then click on "install", "yes", or a similar option.
Your browser doesn't seem to have local storage support, which is required for MSU to work. Please upgrade to the latest version of your browser or switch to Google Chrome or Mozilla Firefox.
MSU is pretty easy to use (it does everything almost automatically!) but once you've installed it, you may want to adjust it to your preferences.
This is the settings dialog. You can get to it by opening your user menu on the top right of Scratch, then choosing
"MSU Settings"
On the left there are a list of things that MSU can do. You can enable or disable them with the checkboxes. By
selecting something on the left, you can get to its settings page, where you can adjust things to be the way you want.
Once you select your settings, you should refresh the page or navigate to a new page for the new things you chose to take
effect.
Want to make Scratch awesome? Feel free to help! The more people we can get on board, the better Scratch can turn out to be. Maybe some of our scripts can even become official features! :D
The best way to contribute is simply to submit your userscripts (for Scratch, of course). Then someone else can help add it to the mega script. There are two ways to submit a userscript. The first is through the forum topic. Simply post your userscript's code, as well as what it does. Assuming it's half decent, we'll add it to the mega script! The other option is to add it to the Github Repo yourself. In order to do that, you will have to have a github account (they are free to make if you don't have one!) You will have to become a part of the MSU (Mega Scratch Userscript) organization on github. You'll have to go to the forum topic to request an invite. Once you are invited, you have the ability to add your code to the repo! However, you have to be sure you follow the correct folder structure. Go into the /originals directory and create a new folder whose title is your scratch username. Inside that folder, you can add any userscripts you have created. Each one should have a .user.js file extension.
Once you add your userscript to the repo, your job is done! Soon someone will add it to the Mega script so that everyone can use it! :D
If you have any problems with your script or in the rest of MSU, feel free to open an issue on GitHub. This lets us know that something needs attention, so we can fix it right away.


Human Resources for Health
Availability
TO MEET THRESHOLDS BY 2030, REQUIRES:
FEASIBILITY
-
% increase to meet 22.8/10,000 threshold
-
% increase to meet 34.5/10,000 threshold
-
% increase to meet 59.4/10,000 threshold
Feasibility of achieving thresholds:
Most likely
Somewhat likely
Least likely
2010
2015
2020
2025
2030
2035
140
120
100
80
60
40
20
0
CURRENT
DENSITY
0.0/10,000
Benchmarks*
59.4/10,000
34.5/10,000
22.8/10,000
POPULATION (MILLIONS)
DENSITY OF SBAs PER 10,000 POPULTAION (ESTIMATED 2010)
Accessibility
GEOGRAPHICAL DISTRIBUTION OF PHYSICIANS
(density per 10,000 population)
NATIONAL LOW
-
Physicians
NATIONAL AVERAGE
-
Physicians
NATIONAL HIGH
-
Physicians
Acceptibility
The ratio of nurses to physicians is
-
the OECD average.
-
Nurses
1
Physician
Female physicians
Quality
Is there evidence that the country has mechanisms in place to:
ACCREDIT
training institutions for:
Dentists
Midwives
Nurses
Pharmacists
Physicians
REGULATE
:
Dentists
Midwives
Nurses
Pharmacists
Physicians
LICENSE/RE-LICENSE
:
Dentists
Midwives
Nurses
Pharmacists
Physicians


Tahir Update (1.0.5):
Alt+K now un-blurs & re-blurs an img.
Add websites to whitelist/exception list
Several technical improvements
Read full update.
Dismiss
Tahir
Settings
Images:
BG Images:
Videos:
iFrames:
Blur Amount:
Grayscale:
Whitelist:
add to whitelist
remove from whitelist
Shortcuts
Alt+L: turn on / off
Alt+K: over image to selectively unblur/reblur (close this first)


Defines the default content type for type A
.
Defines common HTTP Content-Type header values, according to the current available Codec.
Contains typeclasses for ContentTypeOf.
Default Writeable.
Defines all standard HTTP headers.
Default Writeable with lowwe priority.
Represent a media range as defined by the RFC 2616.
Common HTTP MIME types
Defines all standard HTTP status codes.
Transform a value of type A to a Byte Array.
Default Content-Type typeclasses.
Defines common HTTP Content-Type header values, according to the current available Codec.
Defines all standard HTTP headers.
Common HTTP MIME types
Defines all standard HTTP Status.
Helper utilities for Writeable
.
Contains standard HTTP constants. For example:


{% extends "base.html" %} {% load humanize %} {% block content %}
These are the various architectures we support.
Everything is built in amd64 (x86_64) based virtual machines. The i386 builds are done in a 32 bit chroot (using linux32
to mimic a 32 bit kernel as well). Packages that are architecture independent (also known as "Arch: all" packages) are built on i386.Create new
| Architecture | Build arch:all packages |
|---|---|
| {{ architecture.name }} | {{ architecture.builds_arch_all }} |


Discuss how understanding the principles of neuroplasticity benefits people
Our core beliefs subtly, but very strongly influence everything we do, so when we know we can grow and improve, this is almost permission for our brains to do so. Understanding neuroplasticity is the key to a growth mindset which, in turn, is the key to improvement of any kind. We can also essentially hack the body into a state of neuroplasticity
What are some of the ways to increase your neuroplasticity and how might you engage with these principles of neuroplasticity for your own benefit?
Skin in the game
raising the stakes helps to increase motivation, which in turn increases neuroplasticity
Practice
“neurons that fire together, wire together” repetitive practice takes advantage of neuroplasticity. This goes without saying, I will be practicing all the time
Sleep
8+, preferably 9 hours of sleep a night.  I already try to get 8+ hours of sleep a night but this is just further evidence of its importance
Reading fiction
I have been very slack with reading recently, this is even more motivation to prioritise reading fiction,
Practicing an instrument
learning an instrument has profound effects on neuroplasticity. I play guitar, piano and can drum a little too, but i haven’t been doing much practice at all since I left school. I will re-engage with my guitar and piano playing.
Using mnemonic devices
I often do this when rote learning. For example, remembering that IDs are referenced in css with a #, I think of a really annoying person holding up their “hashtag fake ID” (weird mnemonic device but it works!)
Learning information in context
helps the brain to connect information to existing information. This is very conducive to EDA because most of what I’m learning is immediately put into practice, or better yet, learnt ‘just-in-time’ because I needed to put it into practice.
Link to a resource that you found particularly useful or engaging.
Discuss Growth Mindset and why it is relevant
A growth mindset is one of the most basic and important tools for success. It is the belief that you can change and learn; the belief that the future you will be different from who you are today. With a growth mindset, challenges are surmountable, problem are solvable and inadequacies are room for improvement. A growth mindset is the very first precondition to success, without it you can’t even start to improve.
In this exploration, did anything surprise you? Change for you?
One piece of an article I found very interesting was the idea that “You Can Have Both Mindsets”. I believe I have a growth mindset when It comes to academic and physical pursuits. But socially, I realise I’m more fixed. Realising that I can change and, in fact, have changed, dramatically as a social person will help me be better able to grow.
How will you integrate a growth mindset into your learning journey?
I will take small steps to improve the aspects of myself that need improvement, such as organisation and focus. I will not be intimidated by hard tasks, Understanding that a smarter, better version of me will be equipped for them.
Link to a resource that you found particularly useful or engaging.
This one


KEDU
!
Home
Register
Pricing
KeduBot
Testimonials
Complaint Manager
An easy-to-use complaint app. You can even track the history of your support complaints.
Full Name:
Email Address:
Subject:
Today's Date:
Message:
Submit
Complaint History


Copyright (c) 2003-2004, Simon Brown
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the
distribution.
- Neither the name of Pebble nor the names of its contributors may
be used to endorse or promote products derived from this software
without specific prior written permission.
 IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
|


While games written with Cocos2d-html5 should work offline, but some browsers won't allow this to happen. Browsers that deny access to certain functions such as XMLHttpRequest that fails for "file:// " protocol, but Cocos2d-html5 engine depend on this to read many files such as a .plist file.
Some versions of Firefox, Safari and Opera are notable exceptions.
If you wish to use other browsers, you need a webserver. It doesn't mean a seperate computer, you can download a free webserver program to your computer. Here is some popular webserver program:
Once you install a webserver, go to your installation directory, there should be a directory called "htdocs" or "www", place Cocos2d-html5 files in there. Then point your browser to http://localhost/.Cocos2d-html5 should now work without any errors


{{ define "desc" }}
Just blog. Nothing else. Well, maybe a few articles here and there will
do. Maybe even some hyperlinks, eh
{{ end }}
{{ define "index" }}
{{ template "head" }}
tblog demo
tblog demo
{{ template "desc" }}
{{ index . }}
{{ end }}
{{ define "head" }}
{{ end }}


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 4
Beans all’uccelletto (with a light tomato sauce) makes an irresistible accompaniment to stuffed pork, along with creamy garlic scallop potatoes.
For the cannellini beans, preheat the oven to 200C/400F/Gas 6. Mix the onions, garlic and celery in the oil to coat them, season with black pepper and roast for 25 minutes. Remove from the oven and add the tomatoes, sugar, vinegar, cannellini beans and sage and cook for a further 20 minutes at 180C/350F/Gas 4. Remove from the oven and allow to cool slightly.
For the dressing, whisk together all the ingredients in a small bowl. Mix the dressing with the bean mixture.
For the creamy garlic scallop potatoes, preheat the oven to 170C/340F/Gas 3½. Place the cream, milk, garlic, nutmeg in a pan and season well with salt and pepper. Heat gently until just boiling.
Meanwhile, layer up the potatoes in a lightly buttered medium size shallow ovenproof dish. Pour over the hot cream mixture. Cover with foil and cook in the oven for 45 minutes to 1 hour, or until the potatoes are soft. Remove the foil and cook for a further 30-40 minutes, or until golden-brown and bubbling.
For the pork medallions, slice the pork fillet on a slant into eight pieces. Using the palm of your hand, gently flatten the pieces of pork into medallion shapes. Using a small, sharp knife, cut two slits into the centre of each medallion. Pull a small fig and sage leaf through each cut. Season with pepper.
Heat a non-stick frying pan with the butter and oil. When foaming, lay in the stuffed pork medallions. Cook over a medium heat for 2 -3 minutes each side, then set aside and keep warm. Pour in the masala, stirring as it boils and cook until the volume of liquid has reduced and thickened to a sauce consistency.
To serve, spoon the cannellini beans onto serving plates. Arrange the pork medallions on top and drizzle the marsala juices around. Serve the creamed potatoes alongside.


 You can do this on the lightcurve page. {% endif %}
{{analysis.data.coords | length}} point sources detected via the DAOFind algorithm. Select the point source for which you are interested in creating a lightcurve.
{% include 'partials/point_source_plot.html' %}Select the standard filter that most closely matches the filter used for this image.
The default datetime value is parsed from FITS DATE-OBS
field. Datetimes should be ISO 8601 format.
Geographical coordinates of observation.
Elevation above sea level, in meters.


Logout
Music Collector
Collections
Vinyl Records
Magnetic tapes for reel-to-reel
Compact cassette
Add to collections
Groups
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Create group
Concerts
Seen concerts
Tickets
Autographs
Add to concerts
Search
Search in collections
Search in concerts


Exploring the Tail Behavior of ESPN’s Win Probability Model
It’s College Football Playoff season, which means I’ve been watching a lot of games lately. And I find myself complaining pretty often about how badly calibrated I think ESPN’s win probability model is. In particular, I’ve noted a bunch of examples – or at least enough for it to feel like a bunch – of games where ESPN’s model gives a team a win probability that feels way too extreme in a situation where they’re clearly winning. I’m not talking about giving a team an 80% chance when they should have a 60% chance. The cases I’ve been curious about are something more like teams getting a 99.7% chance of winning when, at least in my opinion, they should be getting something more like a 98% chance.
Introduction
For classification problems like predicting win probabilities, model calibration is, at the highest level, the answer to the question “When my model says that Michigan has a 62% chance to win, do they actually end up winning about 62% of the time?” A well-calibrated model will have relatively low error over the long run. As we get more and more data, we’d expect that the amount of error in our calibration numbers would go down, and hopefully the predicted probabilities start to converge to the actual win probabilities as the games play out. For more on calibration, check out this cool FiveThirtyEight post.
You might be reading this thinking that the difference (both in absolute terms and ratio terms) between 60 and 80 percent is way bigger than the difference between, say, 98 and 99.7. And you’d be right. But I’d encourage you to think about it like this: The team that’s the underdog in the latter case has either a 2% chance (the first case) or a 0.3% chance (the second case). If you’re applying the same ratio of win probabilities back of the napkin math, that increase feels a lot bigger.
For the statistically inclined folks in the back, the “right” way to do this is just to use odds ratios, which would show that going from 98% to 99.7% is a massive magnitude odds (or log-odds) increase.
So in a nutshell, what I’ve been curious about is the tail behavior of the ESPN model – I’m trying to answer the question of how ESPN’s model does at predicting events we know are unlikely. How often, for instance, does a team that ESPN gives a 0.5% mid-game chance of winning actually end up winning? My suspicion, based on my anecdotal evidence from watching games and complaining over the years, has been that the model is badly calibrated in the tails. I’ve been on the record arguing that ESPN’s model gives win probabilities greater than 99% way too often, and can usually be heard saying things like “Well, they’re almost definitely going to win. But 99%? I’m not sure…”
So then, to the question. I looked into a couple of things: 1. How well-calibrated is their model in general? 2. When ESPN gives a team a very high chance of winning (>98%), how often do they actually win? 3. Does the model perform better or worse for ranked teams?
Calibration
First, how well-calibrated is the model in general? I usually like to look at calibration plots to evaluate models, similar to the ones in the FiveThirtyEight post above.
This first plot is the overall calibration of the model at kickoff time. What we’re looking for are the points to roughly lie along the dashed line, which is the line y = x
.
Two main things to notice in that plot:
- The model, on aggregate, is quite well calibrated.
- The model looks like it’s off by a bit in the lower tail, where it appears to be predicting win probabilities that are too low. That’s a sample size issue. For instance, there were 64 games where the model gave the home team a worse than 5% chance to win, and the home team ended up winning 6.25% in those games. But generating a confidence interval for that proportion gives us a range of 1.25%-12%, which is too wide to scold the model for that mistake
We can also look at the same plot, broken down by the teams playing. For instance, the following plot is broken down by whether neither team is ranked, one team is, or both teams are:
In this case, even with relatively wide error bars, we see that the model seems to perform worse for games where both teams are ranked. And it’s pretty clearly the best in games where neither team is ranked.
Edge Cases
Next, I was curious about how often teams given very high chances of winning ended up doing so. Anecdotally, I’ve found myself complaining the most about games like the Oregon - Oregon State game from 2022 where ESPN gives Oregon a 98.3% chance of winning when they’re up 18 with 6:53 left in the third. Of course, I’m leaning into confirmation bias. But it’s hard to not think to yourself that with more than 20 minutes of football to go, Oregon State only wins that game in a wild comeback less than two in one hundred times. I’m not sure what I’d view as a more “correct” estimate of their win probability, but seventeen in a thousand seems low to me. Maybe even thirty in a thousand (3%) would be better.
One note is that the 3% probability I’d probably lobby for doesn’t feel that different from the 1.7% that ESPN gave, but that’s an odds ratio of 1.79, which is a big difference in practice. For instance, that’s a similar odds ratio to what you’d get if you went from a 35% chance to a 50% chance, which is significant. In FiveThirtyEight world, that’s the difference between being right in the middle of the “toss-up” category vs. being solidly in the “lean Oregon” category.
So anyways, back to Oregon - Oregon State. I was curious about games like that: Games where, with more than, say, five minutes to go and one team leading by at most three scores (24 points), how often ESPN was right when they gave the leading team a better than 98% chance of winning the game.
As it turns out, ESPN’s model is doing pretty well in the tails on the whole. See the table below:
| Ranked | Overall Win % | Win % CI Lower Bound | Win % CI Upper Bound | N |
|---|---|---|---|---|
| Both | 0.54% | 0% | 1.63% | 184 |
| One | 1% | 0.4% | 1.7% | 1002 |
| Neither | 1.07% | 0.7% | 1.52% | 2427 |
| All | 1.02% | 0.72% | 1.36% | 3613 |
Ranked
corresponds to how many of the teams in the game were ranked (i.e. “both” means “both teams were ranked”). “all” is all of the data pooled together.
The main takeaway from the table above is that when ESPN gives a team a <2% chance of winning a game, that tends to not be a severe underestimate as I was expecting. Across the crosstabs I checked, even the high end of a 95% confidence interval for the proportion of the time that the underdog would go on to win was below the 2% threshold.
Wrapping Up
All told, I didn’t end up confirming my suspicions. At least from a cursory look through the data, ESPN’s model seems to be performing quite well in the tails. Or, at the very least, it’s not making predictions that are as ridiculous as I had thought they were. I still have my suspicions and will surely continue finding individual cases that don’t make sense to me intuitively, but after poking around a little I at least feel less concerned about the model making egregious predictions – as far as I can tell, it’s doing a pretty good job on average.
Future Work
A couple of other things jump out at me as being worth exploring:
- How well did the model do vs. my intuitions? In games where I was on the record as thinking the win probabilities given were far too high (or low), how do I perform?
- How does ESPN’s model perform by other common ML metric standards? For instance, does its AUC outperform (e.g.) Vegas? (Almost certainly not). Or how negative is the model’s Brier Skill Score when using Vegas as a baseline?
- Does the model perform better or worse for certain teams? Maybe some teams are being consistently overrated or underrated by the model.
Appendix
You can find the code to reproduce this analysis on my Github.


Adds two FileHandlers to the global logger object.
| Parameters: | directory – path to output directory of the conversion |
|---|---|
| Returns: | logs all >=INFO-level messages to directory/INFO_LOG
h2: logs all >=DEBUG-level messages to directory/DEBUG_LOG
|
| Return type: | h1 |


| render_site {rmarkdown} | R Documentation |
Render all of the R Markdown documents within a directory as a website.
render_site( input = ".", output_format = "all", envir = parent.frame(), quiet = FALSE, encoding = "UTF-8" ) clean_site(input = ".", preview = TRUE, quiet = FALSE, encoding = "UTF-8") site_generator(input = ".", output_format = NULL) site_config(input = ".", encoding = "UTF-8") default_site_generator(input, ...)
input |
Website directory (or the name of a file within the directory). |
output_format |
R Markdown format to convert to (defaults to "all"). |
envir |
The environment in which the code chunks are to be evaluated
during knitting (can use |
quiet |
|
encoding |
Ignored. The encoding is always assumed to be UTF-8. |
preview |
Whether to list the files to be removed rather than actually removing them. Defaulting to TRUE to prevent removing without notice. |
... |
Currently unused. |
The render_site
function enables you to render a collection of
markdown documents within a directory as a website. There are two
requirements for a directory to be rendered as a website:
It must contain either an "index.Rmd" or "index.md" file.
It must contain a site configuration file ("_site.yml").
The most minimal valid website is an empty "index.Rmd" and an empty
"_site.yml". With this configuration a single empty webpage would be
generated via a call to render_site
. If you add additional markdown
documents to the directory they will also be rendered. By default a site is
rendered in the following fashion:
R Markdown (.Rmd) and plain markdown (.md) files in the root directory are rendered. Note however that markdown files beginning with "_" are not rendered (this is a convention to designate files that are included by top level documents).
All output and supporting files are copied to a "_site" subdirectory of the website directory (this is configurable, see discussion below).
The following files are not copied to the "_site" sub-directory:
Files beginning with "." (hidden files).
Files beginning with "_"
Files known to contain R source code (e.g. ".R", ".s", ".Rmd"), R data (e.g. ".RData", ".rds"), or configuration data (e.g. ".Rproj", "rsconnect")).
Note that you can override which files are included or excluded via settings in "_site.yml" (described below).
Normally R Markdown renders documents as self-contained HTML.
However, render_site
ensures that dependencies (e.g. CSS,
JavaScript, images, etc.) remain in external files. CSS/JavaScript
libraries are copied to a "site_libs" sub-directory and plots/images are
copied to "_files" sub-directories.
You can remove the files generated by render_site
using the
clean_site
function.
render_site
returns the name of the site output file (relative
to the input directory). clean_site
returns the names of the
generated files removed during cleaning. site_config
returns the
contents of _site.yml as an R list. default_site_generator
returns
the default site generator for R Markdown websites.
A "_site.yml" file can be used to configure the behavior of site generation. Here is an example configuration file:
name: my-website output_dir: _site include: ["demo.R"] exclude: ["docs.txt", "*.csv"] navbar: title: "My Website" left: - text: "Home" href: index.html - text: "About" href: about.html output: html_document: toc: true highlight: textmate
The name
field provides a suggested URL path for your website when it
is published (by default this is just the name of the directory containing
the site). The output_dir
indicates which directory to copy site
content into ("_site" is the default if none is specified). Note that this
can be "." to keep all content within the root website directory alongside
the source code.
The include
and exclude
fields enable you to override the
default behavior vis-a-vis what files are copied into the "_site" directory
(wildcards can be used as in the above example).
The navbar
field can be used to define a navigation bar for websites
based on the html_document
format.
Finally, the output
field enables you to specify output options that
are common to all documents within the website (you can also still provide
local options within each document that override any common options).
new_session: true
causes each file to be rendered in a new R session.
This prevents the masking problem that arises when different files use
functions from different packages (namespaces) that share a common name, such
as here::here
and lubridate::here
or dplyr::filter
and
MASS::filter
. The default behaviour of render_site
is to use a
common R session.
autospin: true
causes .R
files to be spinned and rendered
(as well as .Rmd
files). If autospin
is set to false (the default),
.R
files will not be spinned nor rendered. autospin
can also
enumerate a list of .R files to be spinned and rendered.
The behavior of the default site generation function
(rmarkdown::default_site
) is described above. It is also possible to
define a custom site generator that has alternate behavior. A site generator
is an R function that is bound to by including it in the "site:" field of the
"index.Rmd" or "index.md" file. For example:
title: "My Book" output: bookdown::gitbook site: bookdown::bookdown_site
A site generation function should return a list with the following elements:
name
The name for the website (e.g. the parent directory
name).
output_dir
The directory where the website output is written
to. This path should be relative to the site directory (e.g. "." or
"_site")
render
An R function that can be called to generate the
site. The function should accept the input_file
,
output_format
, envir
, and quiet
arguments.
clean
An R function that returns relative paths to the files
generated by render_site
(these files are the ones which will be
removed by the clean_site
function.
Note that the input_file
argument will be NULL
when the entire
site is being generated. It will be set to a specific file name if a
front-end tool is attempting to preview it (e.g. RStudio IDE via the Knit
button).
When quiet = FALSE
the render
function should also print a line
of output using the message
function indicating which output
file should be previewed, for example:
if (!quiet) message("\nOutput created: ", output)
Emitting this line enables front-ends like RStudio to determine which file they should open to preview the website.
See the source code of the rmarkdown::default_site
function for a
example of a site generation function.


The ggf-hover-opacity class adds transparency to the image when you move the mouse over it:
The ggf-hover-opacity-off class rempves transparency from an image when you move the mouse over it:


Enabling SSL/TLS
Transport Layer Security (TLS) is a protocol for encrypting data that is sent over the internet. It provides three important features:
- Data Privacy: The data that is exchanged between a server and client is not visible to anyone else.
- Data Integrity: The data that is exchanged between a server and client cannot be modified by anyone else.
- Server Authenticity: The server can prove its identity to the client and so prove the origin of sent data.
TLS evolved out of Netscape’s Secure Sockets Layer (SSL) protocol in order to fix most of its security flaws. The industry still uses the terms somewhat interchangeably for historical reasons. Any web site that you visit starting with https:// rather than http:// is using TLS/SSL for communication between your browser and their server.
This guide will demonstrate how to enable the TLS protocol directly on your Kitura server. Another way to enable TLS is via an SSL/TLS proxy. In this case an intermediary application between your server and clients performs the encryption and decryption. Some cloud providers offer this service by default, which may be a more appropriate solution.
Install OpenSSL
In this guide, we will use OpenSSL to generate our self-signed certificate.
OpenSSL is included by default on Linux. On MacOS we can install it using Homebrew:
brew install openssl
Step 1: Create our certificate and key pair
To enable TLS, a server needs a certificate and a corresponding secret key. Certificates are files that bind together information about the identity of the owner of a site and the public half of an asymmetric key pair (usually RSA). Certificates are usually digitally signed by a certificate authority (CA) who verifies that the identity information in the certificate is correct. This creates a chain of certificates between the site owner certificate and a CA certificate and transitive trust. Assuming that we trust the CA, we can trust the validity of the server certificate.
If you want to create a CA-signed certificate chain, Let’s Encrypt is a Certificate Authority (CA), provided by the non-profit Internet Security Research Group (ISRG) that makes it easy to generate and install free TLS/SSL certificates.
In this guide, we will use OpenSSL to generate a self-signed certificate.
If you don't have a server, follow our Create a server guide.
Create a Credentials folder in the root of your project:
mkdir Credentials && cd Credentials
Inside this folder we create a 2048 bit RSA private key using OpenSSL:
openssl genrsa -out key.pem 2048
We use this private key to create a certificate:
openssl req -new -sha256 -key key.pem -out csr.csr
At this stage you provide additional information that will be stored in the certificate.
These choices are optional, except for Common Name, which must be the server's hostname. In our case, this is "localhost".
Next we use OpenSSL to convert the certificate to PEM format:
openssl req -x509 -sha256 -days 365 -key key.pem -in csr.csr -out cert.pem
Finally we encrypt the key and certificate together using a password. For simplicity we will use "password".
openssl pkcs12 -export -out cert.pfx -inkey key.pem -in cert.pem
Step 2: Setting up your SSL configuration
Now we have created our certificate, we are going to set up our SSL configuration so that it can direct Kitura to the required files.
Open your Application.swift file:
open Sources/Application/Application.swift
We are going to use FileKit to access our certificate file, so we need to add FileKit to our dependencies.
We also need to add FileKit to our import statements:
import FileKit
Now we initialize an SSLConfig struct. On macOS we use Apple Secure Transport under the covers, which requires the path to our cert.pfx file and the password we used to encrypt the file. On Linux we use OpenSSL under the covers, which requires the paths to the certificate and private key PEM files.
We use #if to check which operating system we're running on and provide the appropriate files to our configuration.
Inside the App class, add the following code:
#if os(Linux)
let sslConfig = SSLConfig(withCACertificateDirectory: nil,
usingCertificateFile: FileKit.projectFolder + "/Credentials/cert.pem",
withKeyFile: FileKit.projectFolder + "/Credentials/key.pem",
usingSelfSignedCerts: true)
#else // on macOS
let sslConfig = SSLConfig(withChainFilePath: FileKit.projectFolder + "/Credentials/cert.pfx",
withPassword: "password",
usingSelfSignedCerts: true)
#endif
Step 3: Configure our Kitura server to use SSL
Now we have our SSL configuration, we need to pass it to Kitura. We do this as part of the addHTTPServer function, using the withSSL parameter.
Inside the run function, replace:
Kitura.addHTTPServer(onPort: 8080, with: router)
With the following code:
Kitura.addHTTPServer(onPort: 8080, with: router, withSSL: sslConfig)
To test this, start your server and open your browser at the Kitura landing page:
At this point your browser might stop you with a warning that the SSL certificate it is validating is self-signed. Since you are accessing your own server this isn’t a problem at all. In the advanced settings, you can tell your web-browser to accept the self-signed SSL certificate and continue. In general though, your browser should only trust server certificates which are issued by a valid CA.
Notice the https in your URL. You are running Kitura using SSL/TLS! This means that the data your application transmits is secure and the server your users are connecting to is authenticated.


Last session, the party beat a hasty retreat from the cultists of Zedar, in pursuit of the dragon's hoard1 waiting for them. When they reached the door, they found that a powerful magical ward barred the door. Naturally, Sir Patty Cakes and Krunch responded by trying to smash it really hard until it broke. However, their destructive instincts were thwarted when a voice spoke up on the other side - Garruk had returned! He opened the door, and as the party started the arduous task of determining exactly how staggeringly wealthy they had just become, he told them that he had been taken as a prisoner by Zedar.
Garruk was somewhat taken aback to discover that the party had already defeat Zedar in combat. As the party related the story of their battle, it occurred to them that they should probably tell someone about the fact that they just saved the kingdom. They used the Sending stone Faren had given them to tell him that they had temporarily defeated Zedar, and he quickly teleported in. They explained what had happened, and he demanded that they open the Zedar bag - but it was empty. Zedar had managed to escape. Fortunately, Silk skillfully deflected Faren's anger at the narrow escape2. Faren then teleported back to his tower, saying he would use his magical powers to locate Xykon’s Zedar's phylactery.
Left to their own devices again, and slightly miffed by their encounter with Faren, the party decided to go break something. Since they were ethical adventurers, they decided to go break something that was probably evil, and attacked the necronaut3. It was a short and brutal fight, but they managed to put it down. Show up this week to join the fight against the Cult of Zedar!


About me
Hello I am Priyanka Kamble. I am aspiring programmer focused on developing Responsive Front End to provide
unique perspectives on how end-users interact with websites and software platforms.
Innovative problem-solver who is passionate about developing
apps with a focus on responsive design and development. Strengths in creativity, teamwork, and building
projects from ideation to execution.
Technologies: HTML, CSS, Javascript, Jquery, Bootstrap, Node.js, Express.js, MySql Server, Heroku.


Research assistant
Ryan Hodge is an undergraduate Computer Science student at the University of South Carolina, originally hailing from Cincinnati, Ohio. He is a member of the South Carolina Honors College, as well as the Editor-in-Chief of USC's satirical newspaper. He joined the ERGS team in 2015 as an Research Assistant, and is currently working on code generation for the HDF5 backend of Cyclus. During his time at USC, Ryan has also been awarded a research internship with the Open Orbiter program at the University of North Dakota, where he developed the concept and simulation of an agent-based cybersecurity system for spacecraft.
Education & Employment
University of South Carolina
B.S.C.S. Computer Science
Columbia, SC, 2014 - 2018- Prospective Computer Science major with minor in Mathematics
Open Orbiter Small Satellite Initiative, University of North Dakota Aerospace
Research Intern
Grand Forks, ND, 2016 - 2016- Research Topic: Concept and simulation of an agent-based cybersecurity system for orbiting spacecraft
University Housing at the University of South Carolina
Senior Housing Representative
Columbia, SC, 2015 - 2016- Customer service and special projects
Camp Livingston: The Robert Krohn Livingston Memorial Camp
Communications Intern
Bennington, IN, 2015 - 2015- Photography, social media management, and virtual tour creation
INTERalliance of Greater Cincinnati
User Experience Intern at Fifth Third Bancorp
Cincinnati, OH, 2014 - 2014- One of a handful of high school students placed through the INTERalliance Internship Program
Service
Wildcat Dash 5K Run/Walk Co-Founder and Coordinator
2014Publications
[Download bibtex]
Memberships
Association for Computer Machinery (ACM)
Student Member, 2016 - presentSouth Carolina Honors College
Member, 2014 - presentUSC Satirical News
Editor-in-Chief, 2014 - presentHillel at USC
Vice President, 2014 - presentSkills
Programming Languages
- Intermediate: Java, C++, Python
Markup Languages
- Intermediate: HTML, CSS
Specialized Software
- Intermediate: Linux, NetLogo


I Alone Level-up: Chapter 10
Panicking, Jinwoo tried to get a grasp of the situation; suddenly, he heard the sound of sand shifting .
ssssssss
The sand on the ground next to him was sinking into the ground .
“Uh, uh oh!”
Using all his strength, Jinwoo struggled to not be sucked into the whirlpool . As the sunken area became wider, Jinwoo barely escaped the sinkhole using his two hands .
“Pant, pant, pant”
As he sat panting, he looked down into the newly created depression on the ground . The sand at the lowest point of the hole was boiling strangely . Jinwoo shuddered as he realized a single misstep would’ve resulted in him falling down into that hole .
“Wait…”
His eyes narrowed .
“It’s not boiling . ”
Taking a closer look, he realized something big was wriggling beneath the sand at the bottom of the depression . Jinwoo stood up instinctively . Something did not feel right .
Confirming his suspicions, a pillar of sand shot up from the hole as he was walking backwards .
Shaaaah!
 Jinwoo’s eyes widened .
“A-a bug?”
A giant centipede had risen out of the sand .
Screeeech!
The fully revealed form of the monster stood taller than a 5-story building .
Jinwoo gulped .
‘This is impossible…’
And like so, it was an impossibly large existence . He had never heard of a centipede of this size before . But it wasn’t just the size of the centipede that shocked Jinwoo .
“Why… why is there a name floating above that thing’s head?”
Was he dreaming?
He closed his eyes and opened them again . Nothing had changed . The centipede’s name floated above its head in red letters . Like a video game monster .
‘Poison-fanged Giant Sand Centipede[1]’
Alongside its uncanny and dangerous looking figure, it had a name that screamed at him to run . Especially the “poison-fanged” part . Two fangs the size of a child caught Jinwoo’s eye . It was very clear from its name what would happen if he was pierced by them .
Jinwoo saw the centipede’s mouth open and close repeatedly without rest .
“Looks like it’s hungry . ”
At that time, a sound rang through his head .
ring~
| Penalty Quest: Survive. |
| Goal: Survive for the required time . . Required time: 4 Hours. Remaining time: 4 Hours 0 Min 0 Sec |
‘You’re kidding, right…?’
But the second the remaining time decreased to 3 Hours 59 Min 59 Sec, the centipede started moving across the sand as if it was waiting .
sliiiiiide
“Wait, what?!”
Jinwoo quickly turned and started running .
Tripping, he fell and rolled on the ground and quickly shook the sand off his head . His eyes widened looking at the sight before him . There were more pillars of sand appearing in front of him .
shaaaah
shaaaaaaah
Before he realized it, seven centipede heads had risen from the ground and were staring down at him .
Screech!
Screeeeech!
Jinwoo’s face paled as he heard their terrifying shrieks .
“Shit…”
Exactly 4 hours later, he reappeared in the hospital room .
collapse-
Lying on the floor of his room, he coughed in agony .
*cough* *cough* *spit* *spit*
The inside of his mouth was dry . His eyes stung from the sand . Jinwoo sprawled out on the ground, moaning . He did not have the strength to even move a finger .
“Hah… hah… what… was… that…?”
A new message appeared before the panting Jinwoo .
ring~
| You’ve completed the “Penalty Quest” . . |
His brows furrowed .
Penalty Quest?
Did he do something to deserve punishment?
Searching his memories, he remembered the [Daily Quest] that he half-heartedly gave up on .
‘It can’t be…’
He recalled the part of the quest that talked about receiving a penalty if he did not complete it .
“Hah… hah… you mean… that wasn’t . a hallucination . ?”
Indeed .
It was not a hallucination .
Nor a dream .
There was no need to check by pinching his cheeks . The pain of running until his breath ran out; the pain of being scratched by the centipede’s legs; he felt them all even right now .
It was all real . He had almost met his death back there .
“This… is… too… much… hah… hah…”
To think that something like a penalty would throw him into the jaws of death . At the same time, a foreboding dread washed over him . If what just happened now was due to the daily quest, it meant that all of this was not over yet .
As if on cue, the electronic sound rang .
ring~
Startled at first, he relaxed as he realized it was not another quest .
. .
| The reward for completing the Penalty Quest has arrived . . |
| Would you like to verify? (Y/N). |
‘Reward…?’
The word “reward” briefly caught his attention . Unfortunately, Jinwoo lacked the strength to care about anything at the moment . It was a testament to how tough the Penalty Quest had been .
‘Reward or whatever… I just want to rest for now…’
And with that, he fell into a deep slumber .
“Oh no! Why is he on the ground?!”
Next morning, his assigned nurse was greeted with a shocking scene when she entered the room . The patient was lying on the floor, and there was sand scattered about the ground . Even the patient’s clothes were filled with sand . The nurse threw the treatment chart onto the bed and pressed the call button .
“Someone come help!”
Soon, Dr . Joo Chiui arrived .
“What’s the ma- why is Mr . Sung on the ground?!”
“I’m not sure! He was fine until last night! I found him like this just now . ”
“Let’s first get him on to the bed . One . Two!”
The two put Jinwoo onto the bed . In the rough movement, the medical chart that was thrown on the bed made a small papercut on the back of Jinwoo’s hand, but no one noticed .
“Now, let’s check him . ”
Joo Chiui went over Jinwoo’s condition, but found no abnormalities .
“What’s this? He’s just sleeping . ”
He sighed in relief . The Hunter Association had requested special care for this patient . He had been nervous in fear that something had happened to him .
“Let’s just let him rest . He seems to be in deep sleep . ”
After he spoke, Joo Chiui began to leave when he noticed the sand in the room .
“Er… Looks like we should clean-up this room a little . Ms . Yura, I’ll leave it to you . ”
“Understood, doctor . ”
As the doctor left the room, nurse Choi Yura also relaxed in relief . It was a good thing nothing bad had happened . After all, there were no doctors or nurses who wished ill upon their patients . The hospital she worked at was specially dedicated to the treatment of Hunters . Having seen the unfortunate end of many Hunters before, she did not want to see yet another Hunter be hurt under her care .
“Phew-”
Calmed down, she began to figure out how to clean the room when she noticed something on the ground . It was a drop of blood .
“Oh my . ”
The drop of blood had fallen from the patient’s hand that stretched out from the bed over the ground . Surprised, she looked for a wound on Jinwoo’s hand and held it in inspection . Turning over to the back of his hand,
“ . This is…?”
She saw traces of fresh blood, but no scar . Wiping away the blood, the hand was completely clean .
‘Did… did his cut heal in an instant?’
With concern, Yura looked over to Jinwoo’s face .
He appeared to be sleeping without a care in the world .
-7 . Daily Quest-
ring~
[The Daily Quest has arrived . ]
Jinwoo’s eyes shot open . Sitting straight up from the bed, the first thing Jinwoo did was to check the time; it was a little past 4:30 in the afternoon . There was roughly 7 hours and 30 minutes of breathing room until midnight .
‘Good . ’
He opened the message box .
ring~
| Daily Quest: The Preparation To Become Powerful. |
| Do 100 Push-ups:. (Incomplete) (0/100). Do 100 Sit-ups: (Incomplete) (0/100). . Do 100 Squats: (Incomplete) (0/100). Run 10 kilometers: (Incomplete) (0/10). *Warning: Failing to complete the Daily Quest will result in a Penalty Quest . |
“Same thing?” He uttered reflexively . No, perhaps this was a good thing . Compared to a possibly difficult quest, this was much better . Even if it strained his body, it was at least something that could be completed with time .
He went on the floor and began the push-ups .
“One . ”
He couldn’t afford to be dragged to some strange place and be forced to run from death again .
“Two . ”
He got lucky last night . He didn’t feel like he would get lucky again .
“Three . ”
He paused to look up at the quest creen; the numbers were rising accordingly with each push-up .
ring~
| You’ve succeeded in doing 1 push-up . . |
| Do 100 Push-ups:. (Incomplete) (7/100) |
ring~
| You’ve succeeded in doing 1 push-up . . |
| Do 100 Push-ups:. (Incomplete) (8/100) |
As if testing the System, he performed a half-hearted push-up, going only part way down, but it did not register on the screen . He understood why his weak attempts last night did nothing .
‘Sigh… really?’
Although exasperated, he did not let-up the strength in his arms . 50 push-ups remaining . After that would be sit-ups .
Then squats .
Then running… .
. .
3 hours later .
| Do 100 Push-ups:. (Complete) (100/100). Do 100 Sit-ups: (Complete) (100/100). . Do 100 Squats: (Complete) (100/100). Run 10 kilometers: (Incomplete) (9/10) |
“Pant, pant, pant, pant,”
Having run a lap around the area outside, Jinwoo bowed over huffing . His heart felt like it was about to explode, but he knew he could not stop here .
‘If I think about the effort up until now…’
He entered the hospital gates and completed the last stretch of his run .
ring~
| Distance ran: 10km . . |
| You’ve succeeded in running 10km . . |
It was finally over .
“Pant… pant…”
He fell to his knees . His lungs were on fire . His forehead and back were soaked .
“Pant… pant…”
Sweating profusely, he attempted to control his breathing when a familiar message arrived .
ring~
| You’ve completed the ‘Daily Quest: The Preparation To Become Powerful’. |
| The reward for completing the quest has arrived . . |
| Would you like to verify? (Y/N). |
He almost wanted to forget about it like yesterday but changed his mind . He wanted to confirm just what this damn reward was .
“Yes . ”
ring~
| You’ve received the below rewards . . |
| Reward 1 . Status Recovery. |
| Reward 2 . Stat points +3. |
| Reward 3 . Random Box x 1. |
| Accept all?. |
‘Why is there so many?’
Was his first thought after looking at the reward screen . But after thinking about it, he noticed some useful rewards and rewards he was curious about .
Feeling like he was about to pass out any moment from now, the first reward caught his eyes . Having experienced the penalty involved in failure, he knew there was a good chance that its rewards were just as real .
‘It couldn’t hurt . Probably won’t throw me in the Penalty Zone after offering me a reward . ’
“Accept . ”
.
Translator’s Notes:
[1]: “Poison-fanged Giant Sand Centipede”: The name translation is almost word for word literal .


As a research community, we have long gone hungry for ways to assess research. With journal-based evaluation, we were feeding off of flies – perfect for a frog but not enough for a researcher. Today, we have entered into the golden era of the Altmetrics Grocery Store, filled with a diversity of fresh produce.
For All Your Grocery and Hardware Needs: Maison Laurent! (1905). On Flickr, by Ana y Esteban.
The dangers of going shopping when hungry are tried and true. But there’s no need to wander the grocery store once you have directions to the food you need. We can erect signs in the aisles to figure out where to go and what to buy. We can develop standard categories for the growing set of altmetrics to give us the right – i.e., best suited for our particular need – measurements of the type of impact for the research outputs at hand.
What is in a category? First, it would subdivide the metrics into like components. Secondly, it would inform the most appropriate use of the new metrics in a way that is prudent and apropos to the activity captured. And thirdly, the classifications overall provide a cohesive structure to think about the whole kit and caboodle.
As we discuss in the article Altmetrics in Evolution, published Tuesday in Information Standards Quarterly, PLOS embarked on an effort to renegotiate all its existing categories used in displaying its ALMs and forming the analytic infrastructure that power the journals’ navigational tools. Timed with the publication of the article, this week we have launched a redesigned metrics tab that uses these new categories:
Redesigned metrics tab for Why Most Published Research Findings Are False.
You may have noticed that the renamed categories are the same as those used by ImpactStory with one important difference: we feel that we can’t clearly distinguish between metrics showing scholarly vs. public impact. But we hope that the article (and this blog post) help spark a discussion. Because we want to maker it easier for people going into different grocery stores to find the stuff they want. Not every store should look the same, but maybe we can agree that we find cereals in one aisle and ice cream somewhere else.
The article is part of a special ISQ issue on Altmetrics, with a total of five articles on the topic, an editorial by one of us (Martin), and a report about the recently announced Sloan Altmetrics grant to NISO. The articles touch on a variety of aspects:
- Scott Chamberlain: Consuming Article-Level Metrics: Observations and Lessons
- Robin Chin Roemer and Rachel Borchardt: Institutional Altmetrics and Academic Libraries
- Mike Taylor: Exploring the Boundaries: How Altmetrics Can Expand Our Vision of Scholarly Communication and Social Impact
- William Gunn: Social Signals Reflect Academic Impact: What it Means When a Scholar Adds a Paper to Mendeley
When you are done reading the articles, and are still hungry, head to the PLOS ALM website to register for the ALM workshop that takes place October 10-11 in San Francisco. Because you know that San Francisco is famous for its food, and a number of famous chefs have already signed up to participate.
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Hi! I am Katie, a current art student and multi-media artist. Welcome to my website!
Like most artists, I am still learning and always wanting to branch out and explore mediums and medias!
...such a cool website 10/10 - totally real reviewer
Barr 2021©


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Assam government joins hands with Times of India Group to host the 65th Filmfare Awards in Guwahati
by TIMESOFINDIA.COMThe prestigious black lady is all set to enthrall Bollywood stars yet again as the 65th Filmfare Awards were announced today. For the first time ever the prestigious award function will be hosted outside Mumbai. Assam government has signed an MoU with the Times Group to host the extravagant Bollywood event in Guwahati next year.
Mr. Vineet Jain, Managing Director, Bennett, Coleman and Company Limited and Mr. Deepak Lamba, CEO, Worldwide Media Pvt. Ltd were present along with Mr. Sarbananda Sonowal, Honourable Chief Minister, Govt of Assam, & Mr. Chandan Brahma, Honourable Minister of Tourism, Govt of Assam, Mr. Jayanta Malla Baruah, Honorable Chairman, Assam Tourism Development Corporation, and Dr. M. Angamuthu, IAS, Commissioner & Secretary, Tourism Department, Govt. of Assam to sign the MoU, making the official announcement that the 65th Filmfare Awards will be hosted in Guwahati.
With Filmfare 2020 shifting to Guwahati, locals will be in for a treat as several Bollywood celebrities will own the stage and this event may boost tourism in the state too.


|
M4521 BSP V3.01.000
The Board Support Package for M4521
|
USB Host core configuration file. More...
Go to the source code of this file.
Macros | |
| #define | DEV_MAX_NUM 8 |
| #define | URB_MAX_NUM 12 |
| #define | ED_MAX_NUM 12 |
| #define | TD_MAX_NUM 32 |
| #define | MAX_ENDPOINTS 16 |
| #define | MAX_DRIVER_PER_DEV 3 |
| #define | MAX_TD_PER_OHCI_URB 8 |
| #define | MAX_HUB_DEVICE 2 |
| #define | ISO_FRAME_COUNT |
| #define | OHCI_ISO_DELAY 8 |
| #define | SUPPORT_HUB_CLASS |
USB Host core configuration file.
Definition in file usbh_config.h.
| #define DEV_MAX_NUM 8 |
Maximum number of connected devices
Definition at line 32 of file usbh_config.h.
| #define ED_MAX_NUM 12 |
Maximum number of OHCI EDs in memory pool
Definition at line 34 of file usbh_config.h.
| #define ISO_FRAME_COUNT |
Transfer frames per Isohronous TD
Definition at line 43 of file usbh_config.h.
| #define MAX_DRIVER_PER_DEV 3 |
Maximum number of drivers for a device
Definition at line 38 of file usbh_config.h.
| #define MAX_ENDPOINTS 16 |
Maximum number of endpoints per device
Definition at line 37 of file usbh_config.h.
| #define MAX_HUB_DEVICE 2 |
Maximum number of connected Hub devices
Definition at line 40 of file usbh_config.h.
| #define MAX_TD_PER_OHCI_URB 8 |
Maximum number of OHCI TDs per URB
Definition at line 39 of file usbh_config.h.
| #define OHCI_ISO_DELAY 8 |
Delay isochronous transfer frame time
Definition at line 44 of file usbh_config.h.
| #define SUPPORT_HUB_CLASS |
Support Hub driver
Definition at line 49 of file usbh_config.h.
| #define TD_MAX_NUM 32 |
Maximum number of OHCI TDs in memory pool
Definition at line 35 of file usbh_config.h.
| #define URB_MAX_NUM 12 |
Maximum number of URBs in memory pool
Definition at line 33 of file usbh_config.h.


Using Natural Language Processing (NLP) and neural networks to build a chatbot for fleet management Siemens.
Using RNN, LSTM, GRU and ARIMA models for energy load forecasting of energy consumption at Siemens.


Use the Left mouse button to drag a selection on the graph to zoom.
Use the Right mouse button to reset the zoom.
You can use the zoomTo method on the Range Selector object to call a zoom by passing the starting and ending timestamps as parameters in this way:
RangeSelector.zoomTo(start,end);
The range selector object callback returns the event with the position object allowing you to work with the currently zoomed timestamps:


Before Change
]
def execute_codegen(self, targets):
bases, sources = self._calculate_sources(targets, lambda t: isinstance(t, SpindleThriftLibrary))
scalate_workdir = os.path.join(self.workdir, "scalate_workdir")
safe_mkdir(self.namespace_out)
safe_mkdir(scalate_workdir)
After Change
def execute_codegen(self, targets):
sources = self._calculate_sources(targets, lambda t: isinstance(t, SpindleThriftLibrary))
bases = set(
target.target_base
for target in self.context.targets(lambda t: isinstance(t, SpindleThriftLibrary))
)
scalate_workdir = os.path.join(self.workdir, "scalate_workdir")
safe_mkdir(self.namespace_out)
safe_mkdir(scalate_workdir)


Recently my father and I have been adding IoT devices and other implementations of the Samsung SmartThings application. Our most recent project has been getting the right light switches that dim and can be controlled by Wi-Fi signals.
We finally found the correct light switches a few weeks ago. We were distracted by the "Notification Light" that came with the switches. But we then pivoted to setting up whole scenes and "Moods" for the lights.
Firstly, we had to set up a "Movie" scene that would dim everything and turn on the TV. We set it up so that when we say "It's Movie Time" the Kitchen and Living Room lights dim, the color LED's behind the TV turn on to Blue, and the TV turns on the TV with the Surround Sound.
Lastly, my father wanted to add a "Spring" mood for the kitchen. During the rainier times my dad decided to make a "Blue Sky" theme. So when asked for a "Spring" mood, the kitchen lights switch to varying shades of blue and lighter colors. This was as far as we got with our scenes, we are thinking of adding more that start music, or open the garage in the morning.


XSConstants Class Reference
List of all members.
|
Public Types |
| enum | COMPONENT_TYPE {
ATTRIBUTE_DECLARATION = 1,
ELEMENT_DECLARATION = 2,
TYPE_DEFINITION = 3,
ATTRIBUTE_USE = 4,
ATTRIBUTE_GROUP_DEFINITION = 5,
MODEL_GROUP_DEFINITION = 6,
MODEL_GROUP = 7,
PARTICLE = 8,
WILDCARD = 9,
IDENTITY_CONSTRAINT = 10,
NOTATION_DECLARATION = 11,
ANNOTATION = 12,
FACET = 13,
MULTIVALUE_FACET = 14
} |
| enum | DERIVATION_TYPE {
DERIVATION_NONE = 0,
DERIVATION_EXTENSION = 1,
DERIVATION_RESTRICTION = 2,
DERIVATION_SUBSTITUTION = 4,
DERIVATION_UNION = 8,
DERIVATION_LIST = 16
} |
| enum | SCOPE { SCOPE_ABSENT = 0,
SCOPE_GLOBAL = 1,
SCOPE_LOCAL = 2
} |
| enum | VALUE_CONSTRAINT { VALUE_CONSTRAINT_NONE = 0,
VALUE_CONSTRAINT_DEFAULT = 1,
VALUE_CONSTRAINT_FIXED = 2
} |
Member Enumeration Documentation
- Enumerator:
-
| ATTRIBUTE_DECLARATION |
The object describes an attribute declaration. |
| ELEMENT_DECLARATION |
The object describes an element declaration. |
| TYPE_DEFINITION |
The object describes a complex type or simple type definition. |
| ATTRIBUTE_USE |
The object describes an attribute use definition. |
| ATTRIBUTE_GROUP_DEFINITION |
The object describes an attribute group definition. |
| MODEL_GROUP_DEFINITION |
The object describes a model group definition. |
| MODEL_GROUP |
A model group. |
| PARTICLE |
The object describes a particle. |
| WILDCARD |
The object describes a wildcard. |
| IDENTITY_CONSTRAINT |
The object describes an identity constraint definition. |
| NOTATION_DECLARATION |
The object describes a notation declaration. |
| ANNOTATION |
The object describes an annotation. |
| FACET |
The object describes a constraining facet. |
| MULTIVALUE_FACET |
The object describes enumeration/pattern facets. |
- Enumerator:
-
| SCOPE_ABSENT |
The scope of a declaration within named model groups or attribute groups is absent .
The scope of such declaration is determined when it is used in the construction of complex type definitions. |
| SCOPE_GLOBAL |
A scope of global identifies top-level declarations. |
| SCOPE_LOCAL |
Locally scoped declarations are available for use only within the complex type. |
- Enumerator:
-
| VALUE_CONSTRAINT_NONE |
Indicates that the component does not have any value constraint. |
| VALUE_CONSTRAINT_DEFAULT |
Indicates that there is a default value constraint. |
| VALUE_CONSTRAINT_FIXED |
Indicates that there is a fixed value constraint for this attribute. |
The documentation for this class was generated from the following file:


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20.06.2016 12:34
Technical Fault
named 'Broadband network 3XX' on 21.06.2016 was included by
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18.06.2016 12:34
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named 'Billing Cycle for May' for the duration of 24.05.2016 - 24.06.2016 was included by
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2021-05-28
This keyboard was developed for keyboarding the Balochi language in the Arabic Script.
Balochi Phonetic Keyman Keyboard is copyright ©2017-2021 SIL International. It is distributed under The MIT License (MIT)
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


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