Algorithms for Walking, Running, Swimming, Flying, and Manipulation
© Russ Tedrake, 2023
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Note: These are working notes used for a course being taught at MIT. They will be updated throughout the Spring 2023 semester. Lecture videos are available on YouTube.
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In chapter 2, we spent some time thinking about the phase portrait of the simple pendulum, and concluded with a challenge: can we design a nonlinear controller to reshape the phase portrait, with a very modest amount of actuation, so that the upright fixed point becomes globally stable? With enough actuators and unbounded torque, feedback-cancellation solutions (e.g., invert gravity) can work well, but can also require an unnecessarily large amount of control effort. The energy-based swing-up control solutions presented for the Acrobot and cart-pole systems are considerably more appealing, but required some cleverness and might not scale to more complicated systems. Here we investigate another approach to the problem, using computational optimal control to synthesize a feedback controller directly.
In this chapter, we will introduce optimal control - a control design
process using optimization. This approach is powerful for a number of
reasons. First and foremost, it is very general - allowing us to specify
the goal of control equally well for fully- or under-actuated, linear or
nonlinear, deterministic or stochastic, and continuous or discrete
systems. Second, it permits concise descriptions of potentially very
complex desired behaviours, specifying the goal of control as a scalar
objective (plus a list of constraints). Finally, and most importantly,
optimal control is very amenable to numerical solutions.
The fundamental idea in optimal control is to formulate the goal of control as the long-term optimization of a scalar cost function. Let's introduce the basic concepts by considering a system that is even simpler than the simple pendulum.
Consider the double integrator system $$\ddot{q} = u, \quad |u| \le 1.$$ If you would like a mechanical analog of the system (I always do), then you can think about this as a unit mass brick moving along the $x$-axis on a frictionless surface, with a control input which provides a horizontal force, $u$. The task is to design a control system, $u = \pi(\bx,t)$, $\bx=[q,\dot{q}]^T$ to regulate this brick to $\bx = [0,0]^T$.
In order to formulate this control design problem using optimal control, we must define a scalar objective which scores the long-term performance of running each candidate control policy, $\pi(\bx,t)$, from each initial condition, $(\bx_0,t_0)$, and a list of constraints that must be satisfied. For the task of driving the double integrator to the origin, one could imagine a number of optimal control formulations which would accomplish the task, e.g.:
Note that the input limits, $|u|\le1$ are also required to make this problem well-posed; otherwise both optimizations would result in the optimal policy using infinite control input to approach the goal infinitely fast. Besides input limits, another common approach to limiting the control effort is to add an additional quadratic cost on the input (or "effort"), e.g. $\int \left[ \bu^T(t) {\bf R} \bu(t) \right] dt,$ ${\bf R}\succ0$. This could be added to either formulation above. We will examine many of these formulations in some detail in the examples worked out at the end of this chapter.
Optimal control has a long history in robotics. For instance, there has been a great deal of work on the minimum-time problem for pick-and-place robotic manipulators, and the linear quadratic regulator (LQR) and linear quadratic regulator with Gaussian noise (LQG) have become essential tools for any practicing controls engineer. With increasingly powerful computers and algorithms, the popularity of numerical optimal control has grown at an incredible pace over the last decades.
For more intuition, let's do an informal derivation of the solution to the minimum time problem for the double integrator with input constraints: \begin{align*} \minimize_{\pi} \quad & t_f\\ \subjto \quad & \bx(t_0) = \bx_0, \\ & \bx(t_f) = {\bf 0}, \\ & \ddot{q}(t) = u(t), \\ & |u(t)| \le 1. \end{align*} What behavior would you expect an optimal controller exhibit?
Your intuition might tell you that the best thing that the brick can do, to reach the goal in minimum time with limited control input, is to accelerate maximally towards the goal until reaching a critical point, then hitting the brakes in order to come to a stop exactly at the goal. This would be called a bang-bang control policy; these are often optimal for systems with bounded input, and it is in fact optimal for the double integrator although we will not prove it until we have developed more tools.
Let's work out the details of this bang-bang policy. First, we can figure out the states from which, when the brakes are fully applied, the system comes to rest precisely at the origin. Let's start with the case where $q(0) < 0$, and $\dot{q}(0)>0$, and "hitting the brakes" implies that $u=-1$ . Integrating the equations, we have \begin{gather*} \ddot{q}(t) = u = -1 \\\dot{q}(t) = \dot{q}(0) - t \\ q(t) = q(0) + \dot{q}(0) t - \frac{1}{2} t^2. \end{gather*} Substituting $t = \dot{q}(0) - \dot{q}$ into the solution reveals that the system orbits are parabolic arcs: \[ q = -\frac{1}{2} \dot{q}^2 + c_{-}, \] with $c_{-} = q(0) + \frac{1}{2}\dot{q}^2(0)$.
Similarly, the solutions for $u=1$ are $q = \frac{1}{2} \dot{q}^2 + c_{+}$, with $c_{+}=q(0)-\frac{1}{2}\dot{q}^2(0)$.
Perhaps the most important of these orbits are the ones that pass directly through the origin (e.g., $c_{-}=0$). Following our initial logic, if the system is going slower than this $\dot{q}$ for any $q$, then the optimal thing to do is to slam on the accelerator ($u=-\text{sgn}(q)$). If it's going faster than the $\dot{q}$ that we've solved for, then still the best thing to do is to brake; but inevitably the system will overshoot the origin and have to come back. We can summarize this policy with: \[ u = \begin{cases} +1 & \text{if } (\dot{q} < 0 \text{ and } q \le \frac{1}{2} \dot{q}^2) \text{ or } (\dot{q}\ge 0 \text{ and } q < -\frac{1}{2} \dot{q}^2) \\ 0 & \text{if } q=0 \text{ and } \dot{q}=0 \\ -1 & \text{otherwise} \end{cases} \]
and illustrate some of the optimal solution trajectories:
And for completeness, we can compute the optimal time to the goal by solving for the amount of time required to reach the switching surface plus the amount of time spent moving along the switching surface to the goal. With a little algebra, you will find that the time to the goal, $J(\bx)$, is given by \[ J(\bx) = \begin{cases} 2\sqrt{\frac{1}{2}\dot{q}^2-q} - \dot{q} & \text{for } u=+1 \text{ regime}, \\ 0 & \text{for } u=0, \\ \dot{q} + 2\sqrt{\frac{1}{2}\dot{q}^2+q} & \text{for } u=-1, \end{cases} \] plotted here:
Notice that the function is continuous (though not smooth), even though the policy is discontinuous.
As we begin to develop theoretical and algorithmic tools for optimal control, we will see that some formulations are much easier to deal with than others. One important example is the dramatic simplification that can come from formulating objective functions using additive cost, because they often yield recursive solutions. In the additive cost formulation, the long-term objective for a trajectory can be written as $$\int_0^T \ell(x(t),u(t)) dt,$$ where $\ell()$ is the instantaneous cost (also referred to as the "running cost"), and $T$ can be either a finite real number or $\infty$. We will call a problem specification with a finite $T$ a "finite-horizon" problem, and $T=\infty$ an "infinite-horizon" problem. Problems and solutions for infinite-horizon problems tend to be more elegant, but care is required to make sure that the integral converges for the optimal controller (typically by having an achievable goal that allows the robot to accrue zero-cost, or by having costs that discount/decay to zero as time increases).
The "quadratic cost" formulation we considered in the double integrator example above is clearly written as an additive cost. At first glance, our "minimum-time" formulation doesn't appear to be of this form, but we actually can write it as an additive cost problem using an infinite horizon and the instantaneous cost $$\ell(x,u) = \begin{cases} 0 & \text{if } x=0, \\ 1 & \text{otherwise.} \end{cases}$$
We will examine a number of approaches to solving optimal control problems throughout the next few chapters. For the remainder of this chapter, we will focus on additive-cost problems and their solution via dynamic programming.
For systems with continuous states and continuous actions, dynamic programming is a set of theoretical ideas surrounding additive-cost optimal control problems. For systems with a finite, discrete set of states and a finite, discrete set of actions, dynamic programming also represents a set of very efficient numerical algorithms which can compute optimal feedback controllers. Many of you will have seen it introduced before as a tool for graph search.
Imagine you have a directed graph with states (or nodes) $\{s_1,s_2,...\} \in S$ and "actions" associated with edges labeled as $\{a_1,a_2,...\} \in A$, as in the following trivial example:
Let us also assume that each edge has an associate weight or cost, using $\ell(s,a)$ to denote the cost of being in state $s$ and taking action $a$. Furthermore we will denote the transition "dynamics" using \[ s[n+1] = f(s[n],a[n]). \] For instance, in the graph above, $f(s_1,a_1) = s_2$.
There are many algorithms for finding (or approximating) the optimal
path from a start to a goal on directed graphs. In dynamic programming,
the key insight is that we can find the shortest path from every node by
solving recursively for the optimal cost-to-go (the cost that will
be accumulated when running the optimal controller) from every node to the
goal. One such algorithm starts by initializing an estimate $\hat{J}^*=0$
for all $s_i$, then proceeds with an iterative algorithm which sets
\begin{equation} \forall i \quad \hat{J}^*(s_i) \Leftarrow \min_{a \in A}
\left[ \ell(s_i,a) + \hat{J}^*\left({f(s_i,a)}\right) \right].
\label{eq:value_update} \end{equation} This notation is more reasonable if
we ensure that the update $f(s,a)$ is defined for every state-action pair;
we can add zero-cost self transitions at the goal node for every action. In
software, $\hat{J}^*$ can be represented as a vector with dimension equal
to the number of discrete states. This algorithm, appropriately known as
value iteration, is guaranteed to converge to the optimal
cost-to-go up to a constant factor, $\hat{J}^* \rightarrow J^* + c$
Value iteration is an amazingly simple algorithm, but it accomplishes something quite amazing: it efficiently computes the long-term cost of an optimal policy from every state by iteratively evaluating the one-step cost. If we know the optimal cost-to-go, then it's easy to extract the optimal policy, $a = \pi^*(s)$: \begin{equation} \pi^*(s_i) = \argmin_a \left[ \ell(s_i,a) + J^*\left( f(s_i,a) \right) \right]. \label{eq:policy_update} \end{equation} It's a simple algorithm, but playing with an example can help our intuition.
Imagine a robot living in a grid (finite state) world. Wants to get
to the goal location. Possibly has to negotiate cells with obstacles.
Actions are to move up, down, left, right, or do nothing.
You can run value iteration for the double integrator (using
barycentric interpolation to interpolate between nodes) in
Please do take some time to try different cost functions by editing the code yourself.
Let's take a minute to appreciate how amazing this is. Our solution to finding the optimal controller for the double integrator wasn't all that hard, but it required some mechanical intuition and solutions to differential equations. The resulting policy was non-trivial -- bang-bang control with a parabolic switching surface. The value iteration algorithm doesn't use any of this directly -- it's a simple algorithm for graph search. But remarkably, it can generate effectively the same policy with just a few moments of computation.
It's important to note that there are some differences between the computed policy and the optimal policy that we derived, due to discretization errors. We will ask you to explore these in the problems.
The real value of this numerical solution, however, is unlike our analytical solution for the double integrator, we can apply this same algorithm to any number of dynamical systems virtually without modification. Let's apply it now to the simple pendulum, which was intractable analytically.
You can run value iteration for the simple pendulum (using barycentric
interpolation to interpolate between nodes) in
Again, you can easily try different cost functions by editing the code yourself.
I find the graph search algorithm extremely satisfying as a first step, but also become quickly frustrated by the limitations of the discretization required to use it. In many cases, we can do better; coming up with algorithms which work more natively on continuous dynamical systems. We'll explore those extensions in this section.
It's important to understand that the value iteration equations,
equations (\ref{eq:value_update}) and (\ref{eq:policy_update}), are more
than just an algorithm. They are also sufficient conditions for
optimality: if we can produce a $J^*$ and $\pi^*$ which satisfy these
equations, then $\pi^*$ must be an optimal controller. There are an
analogous set of conditions for the continuous systems. For a system
$\dot{\bx} = f(\bx,\bu)$ and an infinite-horizon additive cost
$\int_0^\infty \ell(\bx,\bu)dt$, we have: \begin{gather} 0 = \min_\bu \left[
\ell(\bx,\bu) + \pd{J^*}{\bx}f(\bx,\bu) \right], \label{eq:HJB} \\
\pi^*(\bx) = \argmin_\bu \left[ \ell(\bx,\bu) + \pd{J^*}{\bx}f(\bx,\bu)
\right]. \end{gather} Equation \ref{eq:HJB} is known as the
Hamilton-Jacobi-Bellman (HJB) equation.
Consider a system $\dot{\bx}=f(\bx,\bu)$ and an infinite-horizon additive cost $\int_0^\infty \ell(\bx,\bu)dt$, with $f$ and $\ell$ continuous functions, and $\ell$ a strictly-positive-definite function that obtains zero only at a unique state $\bx^*$. Suppose $J(\bx)$ is a solution to the HJB equation: $J$ is continuously differentiable in $\bx$ and is such that \[ 0 = \min_{\bu \in U} \left[\ell(\bx,\bu) + \pd{J}{\bx}f(\bx,\bu) \right],\quad \text{for all } \bx. \] Further assume that $J(\bx)$ is positive definite and that $\pi(\bx)$ is the minimizer for all $\bx$. Then, under some technical conditions on the existence and boundedness of solutions, we have that $J(\bx) - J(\bx^*)$ is the optimal cost-to-go and $\pi$ is an optimal policy.
Given an open subset $\Omega\subset\mathbb R^n$, with a selected
element $x^*$, a subset $U\subset\mathbb R^m$, continuous functions
$f:~\Omega\times U\to\mathbb R^n$, $g:~\Omega\times U\to[0,\infty)$,
continuously differentiable function $V:~\Omega\to[0,\infty)$, and a
function $\mu:~\Omega\to U$, such that Here is a more formal version from a personal communication with Sasha Megretski.
Then, for every $x_0\in\Omega$, the
functional \[ J(x,u)=\int_0^\infty g(x(t),u(t))dt,\] defined on the
set of all piecewise continuous pairs $(x,u)$ satisfying (\ref{e1}),
achieves its minimal value of $V(x_0)-V(x^*)$ at every piecewise
continuous solution of (\ref{e1}), (\ref{e2}).
Proof. First, observe that, for all piecewise continuous $(x,u)$ satisfying (\ref{e1}),
To finish the proof, for an arbitrary piecewise continuous solution of (\ref{e1}), equation (\ref{e3}), together with non-negativity of $g_V$, implies \[ J(x,u)=\int_0^Tg(x(t),u(t))dt\ge V(x_0)-V(x(T)).\] When $J(x,u)<\infty$, applying this to $T=T_k$, where $T_k$ are described in observation (II), and taking the limit as $k\to\infty$ (and $x(T_k)\to x^*$) yields $J(x,u)\ge V(x_0)-V(x^*)$. $\Box$
It is possible to prove sufficiency under different assumptions, too. The particular assumptions here were chosen to ensure that $J(\bx(0)) < \infty$ implies that $\bx(t) \rightarrow \bx^*$. As Sasha says, "without something like this, all sorts of counter-examples emerge."
As a tool for verifying optimality, the HJB equations are actually surprisingly easy to work with: we can verify optimality for an infinite-horizon objective without doing any integration; we simply have to check a derivative condition on the optimal cost-to-go function $J^*$. Let's see this play out on the double integrator example.
Consider the problem of regulating the double integrator (this time without input limits) to the origin using a quadratic cost: $$ \ell(\bx,\bu) = q^2 + \dot{q}^2 + u^2. $$ I claim (without derivation) that the optimal controller for this objective is $$\pi(\bx) = -q - \sqrt{3}\dot{q}.$$ To convince you that this is indeed optimal, I have produced the following cost-to-go function: $$J(\bx) = \sqrt{3} q^2 + 2 q \dot{q} + \sqrt{3} \dot{q}^2.$$
Taking \begin{gather*} \pd{J}{q} = 2\sqrt{3} q + 2\dot{q}, \qquad \pd{J}{\dot{q}} = 2q + 2\sqrt{3}\dot{q}, \end{gather*} we can write \begin{align*} \ell(\bx,\bu) + \pd{J}{\bx}f(\bx,\bu) &= q^2 + \dot{q}^2 + u^2 + (2\sqrt{3} q + 2\dot{q}) \dot{q} + (2q + 2\sqrt{3}\dot{q}) u \end{align*} This is a convex quadratic function in $u$, so we can find the minimum with respect to $u$ by finding where the gradient with respect to $u$ evaluates to zero. \[ \pd{}{u} \left[ \ell(\bx,\bu) + \pd{J}{\bx} f(\bx,\bu) \right] = 2u + 2q + 2\sqrt{3}\dot{q}. \] Setting this equal to $0$ and solving for $u$ yields: $$u^* = -q - \sqrt{3} \dot{q},$$ thereby confirming that our policy $\pi$ is in fact the minimizer. Substituting $u^*$ back into the HJB reveals that the right side does in fact simplify to zero. I hope you are convinced!
Note that evaluating the HJB for the time-to-go of the minimum-time problem for the double integrator will also reveal that the HJB is satisfied wherever that gradient is well-defined. This is certainly mounting evidence in support of our bang-bang policy being optimal, but since $\pd{J}{\bx}$ is not defined everywhere, it does not actually satisfy the requirements of the sufficiency theorem as stated above. In some sense, the assumption in the sufficiency theorem that $\pd{J}{\bx}$ is defined everywhere makes it very weak.
We still face a few barriers to actually using the HJB in an algorithm. The first barrier is the minimization over $u$. When the action set was discrete, as in the graph search version, we could evaluate the one-step cost plus cost-to-go for every possible action, and then simply take the best. For continuous action spaces, in general we cannot rely on the strategy of evaluating a finite number of possible $\bu$'s to find the minimizer.
All is not lost. In the quadratic cost double integrator example above, we were able to solve explicitly for the minimizing $\bu$ in terms of the cost-to-go. It turns out that this strategy will actually work for a number of the problems we're interested in, even when the system (which we are given) or cost function (which we are free to pick, but which should be expressive) gets more complicated.
Recall that I've already tried to convince you that a majority of the systems of interest are control affine, e.g. I can write \[ f(\bx,\bu) = f_1(\bx) + f_2(\bx)\bu. \] We can make another dramatic simplification by restricting ourselves to instantaneous cost functions of the form \[ \ell(\bx,\bu) = \ell_1(\bx) + \bu^T {\bf R} \bu, \qquad {\bf R}={\bf R}^T \succ 0. \] In my view, this is not very restrictive - many of the cost functions that I find myself choosing to write down can be expressed in this form. Given these assumptions, we can write the HJB as \[ 0 = \min_{\bu} \left[ \ell_1(\bx) + \bu^T {\bf R} \bu + \pd{J}{\bx} \left[ f_1(\bx) + f_2(\bx)\bu \right]\right]. \] Since this is a positive quadratic function in $\bu$, if the system does not have any constraints on $\bu$, then we can solve in closed-form for the minimizing $\bu$ by taking the gradient of the right-hand side: \[ \pd{}{\bu} = 2\bu^T {\bf R} + \pd{J}{\bx} f_2(\bx) = 0, \] and setting it equal to zero to obtain \[ \bu^* = -\frac{1}{2}{\bf R}^{-1}f_2^T(\bx) \pd{J}{\bx}^T.\] If there are linear constraints on the input, such as torque limits, then more generally this could be solved (at any particular $\bx$) as a quadratic program.
Exploiting the ability to solve for the optimal action in closed form
is also nice for generating benchmark problems for numerical optimal
control, as it can be used for "converse optimal control"
Consider a one-dimensional system of the form $\dot{x} = f_1(x) + xu,$ and the running cost function $\ell(x,u) = x^2 + u^2.$ The optimal policy is $u^* = -\frac{1}{2}x\pd{J^*}{x},$ leading to the HJB $$0 = x^2 - \frac{1}{4}x^2\left[\pd{J^*}{x}\right]^2 + \pd{J^*}{x} f_1(x).$$ Choosing $J^*(x) = x^2$, we find that this is the cost-to-go when $f_1(x) = -\frac{1}{2}x + \frac{1}{2}x^3$ and $u^* = -x^2.$
What happens in the case where our system is not control affine or if
we really do need to specify an instantaneous cost function on $\bu$
that is not simply quadratic? If the goal is to produce an iterative
algorithm, like value iteration, then one common approach is to make a
(positive-definite) quadratic approximation in $\bu$ of the HJB, and
updating that approximation on every iteration of the algorithm. This
broad approach is often referred to as differential dynamic
programming (c.f.
The other major barrier to using the HJB in a value iteration algorithm is that the estimated optimal cost-to-go function, $\hat{J}^*$, must somehow be represented with a finite set of numbers, but we don't yet know anything about the potential form it must take. In fact, knowing the time-to-goal solution for minimum-time problem with the double integrator, we see that this function might need to be non-smooth for even very simple dynamics and objectives.
One natural way to parameterize $\hat{J}^*$ -- a scalar valued-function defined over the state space -- is to define the values on a mesh. This approach then admits algorithms with close ties to the relatively very advanced numerical methods used to solve other partial differential equations (PDEs), such as the ones that appear in finite element modeling or fluid dynamics. One important difference, however, is that our PDE lives in the dimension of the state space, while many of the mesh representations from the other disciplines are optimized for two or three dimensional space. Also, our PDE may have discontinuities (or at least discontinuous gradients) at locations in the state space which are not known apriori.
A slightly more general view of the problem would describe the mesh (and the associated interpolation functions) as just one form of representations for function approximation. Using a neural network to represent the cost-to-go also falls under the domain of function approximation, perhaps representing the other extreme in terms of complexity; using neural networks in approximate dynamic programming is common in reinforcement learning, which we will discuss more later in the book.
If we approximate $J^*$ with a finitely-parameterized function $\hat{J}_\balpha^*$, with parameter vector $\balpha$, then this immediately raises many important questions. If the true cost-to-go function does not live in the prescribed function class -- e.g., there does not exist an $\balpha$ which satisfies the sufficiency conditions for all $\bx$ -- then we can might instead write a cost function to match the optimality conditions as closely as possible. But our choice of cost function is important. Are errors in all states equally important? If we are balancing an Acrobot, presumably having an accurate cost-to-go in the vicinity of the upright condition is more important that some rarely visited state? Even if we can define a cost function that we like, then can we say anything about the convergence of our algorithm in this more general case?
Let us start by considering a least-squares approximation of the value iteration update.
Using the least squares solution in a value iteration update is sometimes referred to as fitted value iteration, where $\bx_k$ are some number of samples taken from the continuous space and for discrete-time systems the iterative approximate solution to \begin{gather*} J^*(\bx_0) = \min_{u[\cdot]} \sum_{n=0}^\infty \ell(\bx[n],\bu[n]), \\ \text{ s.t. } \bx[n+1] = f(\bx[n], \bu[n]), \bx[0] = \bx_0\end{gather*} becomes \begin{gather} J^d_k = \min_\bu \left[ \ell(\bx_k,\bu) + \hat{J}^*_\alpha\left({f(\bx_k,\bu)}\right) \right], \\ \balpha \Leftarrow \argmin_\balpha \sum_k \left(\hat{J}^*_\balpha(\bx_k) - J^d_k \right)^2. \label{eq:fitted_value_iteration} \end{gather} Since the desired values $J^d_k$ are only an initial guess of the cost-to-go, we will apply this algorithm iteratively until (hopefully) we achieve some numerical convergence.
Note that the update in \eqref{eq:fitted_value_iteration} is not quite the same as doing least-squares optimization of $$\sum_k \left(\hat{J}^*_\balpha(\bx_k) - \min_\bu \left[ \ell(\bx_k,\bu) + \hat{J}^*_\alpha\left({f(\bx_k,\bu)}\right) \right] \right)^2,$$ because in this equation $\alpha$ has an effect on both occurences of $\hat{J}^*$. In \eqref{eq:fitted_value_iteration}, we cut that dependence by taking $J_k^d$ as fixed desired values; this version performs better in practice. Like many things, this is an old idea that has been given a new name in the deep reinforcement learning literature -- people think of the $\hat{J}^*_\alpha$ on the right hand side as being the output from a fixed "target network". For nonlinear function approximators, the update to $\alpha$ in \eqref{eq:fitted_value_iteration} is often replaced some steps of gradient descent.
In general, the convergence and accuracy guarantees for value
iteration with generic function approximators are quite weak. But we
do have some results for the special case of linear function
approximators. A linear function approximator takes the form: \[
\hat{J}^*_\balpha(\bx) = \sum_i \alpha_i \psi_i(\bx) = \bpsi^T(\bx)
\balpha, \] where $\bpsi(\bx)$ is a vector of potentially nonlinear
features. Common examples of features include polynomials, radial
basis functions, or most interpolation schemes used on a mesh. The
distinguishing feature of a linear function approximator is the ability
to exactly solve for $\balpha$ in order to represent a desired function
optimally, in a least-squares sense. For linear function
approximators, this is simply: \begin{gather*} \balpha \Leftarrow
\begin{bmatrix} \bpsi^T(\bx_1) \\ \vdots \\
\bpsi^T(\bx_K)\end{bmatrix}^+ \begin{bmatrix} J^d_1 \\ \vdots \\ J^d_K
\end{bmatrix}, \end{gather*} where the $^+$ notation refers to a
Moore-Penrose pseudoinverse. In finite state and action settings,
fitted value iteration with linear function approximators is known to
converge to the globally optimal $\balpha^*$
There can be some subtleties required for achieving convergence in practice. In the next chapter, we'll see why exponentially-discounted cost functions are often used help the value iterations converge even for infinite-horizon formulations.
Imagine that we use a mesh to approximate the cost-to-go function
over that state space with $K$ mesh points $\bx_k$. We would like to
perform the value iteration update: \begin{equation} \forall k,
\hat{J}^*(\bx_k) \Leftarrow \min_\bu \left[ \ell(\bx_k,\bu) +
\hat{J}^*\left({f(\bx_k,\bu)}\right) \right],
\label{eq:mesh_value_iteration} \end{equation} but must deal with the
fact that $f(\bx_k,\bu)$ might not result in a next state that is
directly at a mesh point. Most interpolation schemes for a mesh can be
written as some weighted combination of the values at nearby mesh
points, e.g. \[ \hat{J}^*(\bx) = \sum_i \beta_i(\bx) \hat{J}^*(\bx_i),
\quad \sum_i \beta_i = 1 \] with $\beta_i$ the relative weight of the
$i$th mesh point. In
Let us try reproducing our double-integrator value iteration examples using neural networks:
For solutions to systems with continuous-time dynamics, I have to uncover one of the details that I've so far been hiding to keep the notation simpler. Let us consider a problem with a finite-horizon: \begin{gather*} \min_{\bu[\cdot]} \sum_{n=0}^N \ell(\bx[n],\bu[n]), \\ \text{ s.t. } \bx[n+1] = f(\bx[n], \bu[n]), \bx[0] = \bx_0\end{gather*} In fact, the way that we compute this is by solving the time-varying cost-to-go function backwards in time: \begin{gather*}J^*(\bx,N) = \min_\bu \ell(\bx, \bu) \\ J^*(\bx,n-1) = \min_\bu \left[ \ell(\bx, \bu) + J^*(f(\bx,\bu), n) \right]. \end{gather*} The convergence of the value iteration update is equivalent to solving this time-varying cost-to-go backwards in time until it reaches a steady-state solution (the infinite-horizon solution). Which explains why value iteration only converges if the optimal cost-to-go is bounded.
Now let's consider the continuous-time version. Again, we have a
time-varying cost-to-go, $J^*(\bx,t)$. Now $$\frac{dJ^*}{dt} =
\pd{J^*}{\bx}f(\bx,\bu) + \pd{J^*}{t},$$ and our sufficiency condition
is $$0 = \min_\bu \left[\ell(\bx, \bu) + \pd{J^*}{\bx}f(\bx,\bu) +
\pd{J^*}{t} \right].$$ But since $\pd{J^*}{t}$ doesn't depend on $\bu$,
we can pull it out of the $\min$ and write the (true) HJB:
$$-\pd{J^*}{t} = \min_\bu \left[\ell(\bx, \bu) + \pd{J^*}{\bx}f(\bx,\bu)
\right].$$ The standard numerical recipe
Probably most visible and consistent campaign using numerical HJB
solutions in applied control (at least in robotics) has come from Claire Tomlin's group
at Berkeley. Their work leverages Ian Mitchell's Level
Set Toolbox, which solves the Hamilton-Jacobi PDEs on a Cartesian
mesh using the technique cartooned above, and even includes the
minimum-time problem for the double integrator as a tutorial
example
There are many many nice extensions to the basic formulations that we've
presented so far. I'll try to list a few of the most important ones here.
I've also had a number of students in this course explore very interesting
extensions; for example
Throughout this chapter, we have focused primarily on minimizing an infinite horizon integral of costs. Whenever you write an infinite sum (or integral), one should immediately question whether that sum converges to a finite value. For many control problems, it does -- for instance, if we set the cost to be zero at the goal and the controller can drive the system to the goal at a rate that is faster cost is accrued. However, once we enter the realm of approximate optimal control, we can run into problems. For instance, if we can a discrete state and action approximation of the dynamics, it may not be possible for the discretized system to arrive exactly at the zero-cost goal.
A common way to protect oneself from this is to add an exponential
discount factor to the integral cost. This is a natural choice since it
preserves the recursive structure of the Bellman equation. In discrete
time, it takes the form $$\min_{\bu[\cdot]} \sum_{n=0}^\infty \gamma^n
\ell(\bx[n], \bu[n]),$$ which leads to the Bellman equation $$J^*(\bx) =
\min_\bu \left[ \ell(\bx, \bx) + \gamma J^*(f(\bx,\bu) \right].$$ In
continuous time we have
A word of warning: this discount factor does change the optimal policy; and it is not without its pitfalls. We'll work out a specific case of the discounted cost for the LQR problem, and see that the discounted controller is exactly a controller which assumes that the plant is more stable than it actually is. It is possible to find an optimal controller for the discounted cost that results in an unstable controller.
A more robust alternative is to consider an average-cost formulations... (details coming soon!)
One of the most amazing features of the dynamic programming, additive cost approach to optimal control is the relative ease with which it extends to optimizing stochastic systems.
For discrete systems, we can generalize our dynamics on a graph by adding action-dependent transition probabilities to the edges. This new dynamical system is known as a Markov Decision Process (MDP), and we write the dynamics completely in terms of the transition probabilities \[\Pr(s[n+1] = s' | s[n] = s, a[n] = a). \] For discrete systems, this is simply a big lookup table. The cost that we incur for any execution of system is now a random variable, and so we formulate the goal of control as optimizing the expected cost, e.g. \begin{equation} J^*(s[0]) = \min_{a[\cdot]} E \left[ \sum_{n=0}^\infty \ell(s[n],a[n]) \right]. \label{eq:stochastic_dp_optimality_cond} \end{equation} Note that there are many other potential objectives, such as minimizing the worst-case error, but the expected cost is special because it preserves the dynamic programming recursion: \[ J^*(s) = \min_a E \left[\ell(s,a) + J^*(s')\right] = \min_a \left[ \ell(s,a) + \sum_{s'} \Pr(s'|s,a) J^*(s') \right].\] Remarkably, if we use these optimality conditions to construct our value iteration algorithm \[ \hat{J}(s) \Leftarrow \min_a \left[ \ell(s,a) + \sum_{s'} \Pr(s'|s,a) \hat{J}(s') \right],\] then this algorithm has the same strong convergence guarantees of its counterpart for deterministic systems. And it is essentially no more expensive to compute!
There is a particularly nice observation to be made here. Let's assume that we have discrete control inputs and discrete-time dynamics, but a continuous state space. Recall the fitted value iteration on a mesh algorithm described above. In fact, the resulting update is exactly the same as if we treated the system as a discrete state MDP with $\beta_i$ representing the probability of transitioning to state $x_i$! This sheds some light on the impact of discretization on the solutions -- discretization error here will cause a sort of diffusion corresponding to the probability of spreading across neighboring nodes.
This is a preview of a much more general toolkit that we develop later for stochastic and robust control.
There are some cases where we can obtain quite strong approximate dynamic programming solutions, even for nonlinear systems, by changing the HJB equality into an inequality. This approach follows naturally from our study of Lyapunov functions in an upcoming chapter, and builds on the computational tools we develop there. Feel free to skip ahead.
Let's consider the discrete time, state, and action version of value iteration: \begin{equation} \hat{J}^*(s_i) \Leftarrow \min_{a \in A} \left[ \ell(s_i,a) + \hat{J}^*\left({f(s_i,a)}\right) \right],\end{equation} which finds the optimal policy for the infinite horizon cost function $\sum_{n=0}^\infty \ell(s[n],a[n]).$
Surprisingly, adding a discount factor can help with this. Consider the infinite-horizon discounted cost: $\sum_{n=0}^\infty \gamma^n \ell(s[n],a[n])$, where $0 < \gamma \le 1$ is the discount factor. The corresponding value iteration update is \begin{equation} \hat{J}^*(s_i) \Leftarrow \min_{a \in A} \left[ \ell(s_i,a) + \gamma\hat{J}^*\left({f(s_i,a)}\right) \right].\end{equation} For simplicity, assume that there exists a state $s_i$ that is a zero-cost fixed point under the optimal policy; e.g. $\ell(s_i, \pi^*(s_i)) = 0$ and $f(s_i, \pi^*(s_i)) = s_i$.
The figure above shows an autonomous car moving at constant velocity $v>0$ on a straight road. Let $x$ be the (longitudinal) position of the car along the road, $y$ its (transversal) distance from the centerline, and $\theta$ the angle between the centerline and the direction of motion. The only control action is the steering velocity $u$, which is constrained in the interval $[u_{\text{min}}, u_{\text{max}}]$ (where $u_{\text{min}}<0$ and $u_{\text{max}}>0$). We describe the car dynamics with the simple kinematic model \begin{align*}\dot x &= v \cos \theta, \\ \dot y &= v \sin \theta, \\ \dot \theta &= u.\end{align*} Let $\bx = [x, y, \theta]^T$ be the state vector. To optimize the car trajectory we consider a quadratic objective function $$J = \int_{0}^{\infty} [\bx^T(t) \bQ \bx(t) + R u^2(t)] dt,$$ where $\bQ$ is a constant positive-semidefinite (hence symmetric) matrix and $R$ is a constant nonnegative scalar (note that $R=0$ is allowed here).
In this exercise we will see how seemingly simple cost functions can give surprising results. Consider the single-integrator system $\dot x = u$ with initial state $x(0)=0$. We would like to find the control signal $u(t)$ that minimizes the seemingly innocuous cost function $$J = \int_0^T x^2(t) + (u^2(t) - 1)^2 dt,$$ with $T$ finite. To this end, we consider a square-wave control parameterized by $\tau>0$: $$u_\tau(t) = \begin{cases} 1 &\text{if} & t \in [0, \tau) \cup [3 \tau, 5 \tau) \cup [7 \tau, 9 \tau) \cup \cdots \\ -1 &\text{if} & t \in [\tau, 3 \tau) \cup [5 \tau, 7 \tau) \cup [9 \tau, 11 \tau) \cup \cdots \end{cases}.$$
Consider the scalar control differential equation $$\dot{x} = x + u,$$ and the infinite horizon cost function $$J = \int_0^{\infty} [3x^2(t) + u^2(t)] dt.$$ As we will see in the chapter on linear-quadratic regulation, the optimal cost-to-go for a problem of this kind assumes the form $J^* = S x^2$. It is not hard to see that this, in turn, implies that the optimal controller has the form $u^* = - K x$.
In this exercise we analyze the performance of the value-iteration algorithm, considering its application to the minimum time problem for the double integrator. , you will find everything you need for this analysis. Take the time to go through the notebook and understand the code in it, then answer the following questions.
In this exercise we solve the dynamic programming using a linear program, considering its application to the grid world. , you will write a linear program for solving the optimal cost-to-go in the grid world.
In this excercise we will implement value iteration for a continuous time system with continuous actions and states. We will achieve by training a neural network to approximate our value function. You will work exclusively in to implement the algorithms described in the Continuous Dynamic Programming section.
| Previous Chapter | Table of contents | Next Chapter |


Traditional - Germany
This game is played on a 8x8 square board, with the following setup:
|
| An example If the green soldier advances to d7, Black looses the game. If he captures, he jumps to d6 and is captured by the last green King. The other cells are attacked by the soldier itself. |
According to Ralf Gering, Gothic Checkers is actually known as "Altdeutsche Dame" (or in Old German:"Damm-Spel"). He also says: The old books don't say how far the kings are permitted to move. A new book by R. F. Müller which is quite reliable as judged by the other rules it gives (of draughts variants and Salta) says that that the king may only move one square (so you would be correct!). Another book which is even newer claims the same but the author used obviously Müller's book as his source. The strange thing is that Altdeutsche Dame obviously influenced Turkish Draughts (which was invented shortly after the Turks tried to conquer the capital of the Holy Roman Empire of the German Nation, Vienna, in 1683) which has long-ranging kings. Did the Turks invent the long range independently? The France introduced the long-ranging king's move in about 1650 and the new rule rapidly spread to Germany. Perhaps the early variant of Altdeutsche Dame had indeed the short move (as explained by you, i.e. the move of the modern Chess King), but later adopted the long range (i.e. the move of the modern Chess Queen). Another strange thing is that the old game books state that you must choose the move which captures most enemy pieces. This is a rather modern rule usually associated with the long-range. A mixture of "old" and "new" rules (if the short move is correct) would mean that Altdeutsche Dame was invented around 1650, probably in what is now Austria near the Turkish border. It appears that the rules are not completely known from the old sources as a German draughts expert I met 15 years ago claimed that the rules are "RE-constructed.


This vignette illustrates how to use mashr
to estimate the change in some quantity measured in multiple conditions compared with a common control condition.
We assume that we have measurements in multiple conditions, and want to estimate the deviation in each condition from the control: that is, the difference in mean between that condition and the control condition. When we compare every condition to the same control then the observed deviations are correlated with one another (even under the null where there are no true differences among conditions). These correlations, if not properly accounted for, can lead to many false positives in a multivariate analysis. This vignette illustrates how to properly account for such correlations.
Here is the write-up for the details of the model. When there is no control condition in the study, we can compare the quantity in different conditions with the mean. We illustrate an example in the no baseline vignette.
To deal with these correlations, mashr allows the user to specify the reference condition using mash_update_data
, after setting up the data in mash_set_data
.
Note: The correlations in deviations induced by comparing to a common baseline/control occur even if the measurements in different conditions are entirely independent. If the measurements in different conditions are also correlated with one another (eg in eQTL applications this can occur due to sample overlap among the different conditions) then this induces additional correlations into the analysis that should also be taken into account. In common baseline
analysis, such additional correlations can be specified by the user (we have not yet implemented methods to estimate this additional correlation from the data).
Here we simulate data for illustration. This simulation routine creates a dataset with 8 conditions and 12000 samples, the last condition is the control condition. 90% of the samples have no deviations from the control condition. The remaining 10% of the samples are “non-null”, and consist of equal numbers of three different types of deviations: equal among conditions \(1, \cdots, 7\), present only in condition 1, independent across conditions \(1, \cdots, 7\).
Our goal is to estimate the deviations in condition \(1, \cdots, 7\) compared with the control condition.
library(mashr)
# Loading required package: ashr
set.seed(1)
simdata = sim_contrast2(nsamp = 12000, ncond = 8)
We demonstrate the right way and the wrong to do the analysis
Read in the data, and set the control condition
data = mash_set_data(simdata$Chat, simdata$Shat)
data.L = mash_update_data(data, ref = 8)
The updated mash data object (data.L
) includes the induced correlation internally.
We proceed the analysis using just the simple canonical covariances as in the initial introductory vignette.
U.c = cov_canonical(data.L)
mashcontrast.model = mash(data.L, U.c, algorithm.version = 'R')
# - Computing 12000 x 181 likelihood matrix.
# - Likelihood calculations took 1.28 seconds.
# - Fitting model with 181 mixture components.
# - Model fitting took 797.26 seconds.
# - Computing posterior matrices.
# - Computation allocated took 1.48 seconds.
print(get_loglik(mashcontrast.model),digits=10)
# [1] -105727.2259
Use get_significant_results
to find the indices of effects that are ‘significant’:
length(get_significant_results(mashcontrast.model))
# [1] 58
The number of false positive is 3.
We fit the mash model ignoring the induced correlation.
L = contrast_matrix(8, ref=8)
data.wrong = mash_set_data(Bhat = simdata$Chat %*% t(L), Shat = 1)
m = mash(data.wrong, U.c)
# - Computing 12000 x 181 likelihood matrix.
# - Likelihood calculations took 0.36 seconds.
# - Fitting model with 181 mixture components.
# - Model fitting took 577.91 seconds.
# - Computing posterior matrices.
# - Computation allocated took 0.81 seconds.
print(get_loglik(m),digits = 10)
# [1] -113111.4118
We can see that the log likelihood is lower, since it does not consider the induced correlation.
There are 1572 significant effects, 1320 of them are false positives. The number of false positives is much more than the one include the induced correlation.


- 1. What is Intelligent Kids?
- Education and entertainment websit for kids
- 2. What are the goals of this site??
-
- Provide educational tool for kids at home.
- Provide different subject to learn
- Try to solve the problem of variation in learning abilities among children.
- Use different tool to attract the interest of kids for learning
- 3. How is the target?
- Our target is children in both gender boys and girls between 2-10 years.
- 3. How is the target of this website?
- Our target is children in both gender boys and girls between 2-10 years.
- 4. How to contact with the devlopers of this site?
- You can find our information in CONTACT US at the navigation bar of each page.
- 5. Are there any site similer to our sites?
- Of course, there are many here is two examples:
FAQ?


Standard DOM events are fired and listeners are registered using the
standard addEventListener
function.
Buttons can be disabled with the standard disabled
attribute,
and the corresponding DOM property.
By default, v-button
elements can only be focused with the
keyboard (using the tab order) or programmatically.
The pointer-focus
attribute makes the button focusable also
with the mouse or finger.
Additional styles can be combined together to create more variations, such as a small primary button.
Custom button themes can be generated using the Vaadin Valo theme engine (i.e. "vaadin-theme").
Install vaadin-theme globally as a service (requires Node.js):
$ npm install -g vaadin-theme
Create a Sass file, e.g. "custom-theme.scss" which describes the new theme, using the Valo theme API:
$v-theme-name: "my-theme"; $v-included-components: button; $v-font-family: Helvetica Neue, sans-serif; $v-font-weight: 500; $v-border: 0; $v-border-radius: 0; $v-background-color: #00b4f0; $v-bevel: false; $v-shadow: false; $v-gradient: false; $v-unit-size: 40px; @import "vaadin-theme";
Use the command line to compile the file into a new theme:
$ vaadin-theme custom-theme.scss
The output is generated into a file named "custom-theme.css" (i.e. the same file name as the input but a different file type), in the same folder as the input. You can also specify an explicit output path as a second argument for the command:
$ vaadin-theme custom-theme.scss path/to/output.css
Include the new file in your HTML page as any other CSS stylesheet:
<link rel="stylesheet" href="path/to/custom-theme.css">
Specify the theme
attribute for the buttons you wish to
use the custom theme. The attribute value should match the
$v-theme-name
Sass variable value:
<v-button theme="my-theme">Custom theme<v-button>


This example shows how to use the gmf-elevation directive to get elevation under the mouse position.
gmf-elevation


Saison
|
Selected Style and Target Specs
|
16C-Belgian And French Ale-Saison
|
Minimum OG:
|
1.048 SG
|
Maximum OG:
|
1.065 SG
|
|
Minimum FG:
|
1.002 SG
|
Maximum FG:
|
1.012 SG
|
|
Minimum IBU:
|
20 IBU
|
Maximum IBU:
|
35 IBU
|
|
Minimum Color:
|
5.0 SRM
|
Maximum Color:
|
14.0 SRM
|
|
Wort Volume Before Boil:
|
14.00 US gals
|
Wort Volume After Boil:
|
12.00 US gals
|
|
Volume Transferred:
|
11.00 US gals
|
Water Added To Fermenter:
|
0.0 qts
|
|
Volume At Pitching:
|
11.00 US gals
|
Volume Of Finished Beer:
|
10.00 US gals
|
|
Expected Pre-Boil Gravity:
|
1.048 SG
|
Expected OG:
|
1.056 SG
|
|
Expected FG:
|
1.012 SG
|
Apparent Attenuation:
|
77.4 %
|
|
Expected ABV:
|
5.8 %
|
Expected ABW:
|
4.5 %
|
|
Expected IBU: (using Tinseth):
|
22.6 IBU
|
Expected Color: (using Morey):
|
4.7 SRM
|
|
BU:GU ratio:
|
0.41
|
Approx Color:
|
|
|
Mash Efficiency:
|
70.0 %
|
|
|
|
Boil Duration:
|
90.0 mins
|
|
|
|
Fermentation Temperature:
|
64 ˚F
|
|
|
|
Ingredient
|
Amount
|
%
|
MCU
|
When
|
|
Belgian Pilsen Malt
|
16lb 0oz
|
66.7 %
|
2.1
|
In Mash/Steeped
|
|
German Dark Munich Malt
|
4lb 0oz
|
16.7 %
|
3.0
|
In Mash/Steeped
|
|
German Wheat Malt
|
2lb 0oz
|
8.3 %
|
0.3
|
In Mash/Steeped
|
|
Sugar - White Sugar/Sucrose
|
2lb 0oz
|
8.3 %
|
0.0
|
In Mash/Steeped
|
|
Variety
|
Alpha
|
Amount
|
IBU
|
Form
|
When
|
|
US Sterling
|
7.0 %
|
2.00 oz
|
22.6
|
Loose Pellet Hops
|
60 Min From End
|
|
US Sterling
|
7.0 %
|
1.00 oz
|
0.0
|
Loose Pellet Hops
|
At turn off
|
|
Yeast Strain
|
Amount
|
Used
|
|
White Labs WLP565-Belgian Saison I
|
2 packs
|
|
|
Target Profile:
|
No Water Profile Chosen
|
|
Mash pH:
|
5.2
|
|
pH Adjusted with:
|
Unadjusted
|
|
Total Calcium (ppm):
|
0
|
Total Magnesium (ppm):
|
0
|
|
Total Sodium (ppm):
|
0
|
Total Sulfate (ppm):
|
0
|
|
Total Chloride (ppm):
|
0
|
Total Bicarbonate (ppm):
|
0
|
|
Mash Type:
|
Full Mash
|
|
Schedule Name:
|
No Chosen Schedule
|
|
Step Type
|
Temperature
|
Duration
|


We were promised a world of cryptocurrencies that could be exchanged peer-to-peer over decentralized networks. But what has arisen are centralized gatekeepers that operate more like traditional banks, and blockchains that can’t readily communicate with one another. But that is all about to change. Enter Ferrum: the world’s first peer-to-peer network with built in support to read across all blockchains
Ferrum is a decentralized exchange that allows for exchange of crypto accross blockchains without counterparty risk
TRANSACTION LAYER FOR ALL
Ferrum is a cheap and fast transaction layer for cryptos that are not presently scalable and are expensive to transact
DEMOCRATIZING VALUE
Developers of almost any kind can write plug-ins to onboard their crypto onto
the Ferrum ecosystem
THE TRANSACTION LAYER FOR THE INTERNET OF VALUE
Most cryptocurrencies can be transacted in the Ferrum Network. Users can import their BTC or ETH into Ferrum, and buy a coffee or a motorcycle with transactions that are extremely cheap and fast
Bringing
networks
together
Ferrum enables networks to talk to each other. Networks big and small can interact with each other through Ferrum. Developers of even the smallest blockchains can join their network to Ferrum's decentralized exchange through plug-ins
DECENTRALIZED EXCHANGE BETTER THAN ATOMIC SWAP
Atomic swap allows direct exchange between networks. It is an interesting technology, but with some serious limitations.
Near-instant transactions
Transaction only as fast as slowest network
Near zero fees
One fee per network in swap
Can be used for a network without smart contract support
Requires smart contracts
Can represent other networks
Can support fiat currencies
BRIDGING THE GAP BETWEEN
FIAT & CRYPTO
CURRENCIES
Ferrum is designed to import value including fiat currencies
Ferrum can tokenize fiat currencies such as USD, EUR, Yuan, and YEN
Fiat currencies can interact with cryptocurrencies
HOW IS THIS POSSIBLE?
This sounds too good to be true? By utilizing directed acyclic graph (DAG) ledger technology, plug-ins, and concepts such as Proof of Burn, External Proof, and Futures, Ferrum is able to read across networks and represent almost any digital asset, allowing those digital assets to be instantaneously exchanged and transacted in the Ferrum network with near-zero fees. We have published the technical yellow paper for the technically minded who want to dive deep.
THE PURE TOKEN
The PUR token is the rocket fuel for the Ferrum Network. A small amount of it is required to use the Network


AOC goes on the attack after ABC pulls pin on Olympic radio broadcast rights for first time since 1952
The Australian Olympic Committee (AOC) has criticised the ABC's "monumentally short-sighted" decision to not buy the live radio broadcast rights for the 2020 Olympic Games in Tokyo.
Key points:
- The ABC made the decision not to buy the rights to the Olympics due to budgetary pressures
- The AOC described the decision as "a great letdown to Australians", particularly those in regional areas
- ABC radio caller Quentin Hull described the decision as "a national shame"
That means that, for the first time since the 1952 Helsinki Olympics, the ABC will not have a live radio broadcast of the Games, with the national broadcaster citing "budget pressures and the changing broadcast environment" as reasons not to bid for the non-commercial radio rights.
"This is an incredibly tough decision, especially given our 67-year run as the official non-commercial Olympic Games radio broadcaster," the ABC said in a statement.
"Due to competing budget priorities coupled with the fact that Australians can access Olympic Games coverage in many other ways, we have chosen not to pursue rights in 2020."
The AOC called on the ABC to reverse the decision and said it was "prepared to put this case to the chair of the ABC directly".
"The AOC believes the decision is monumentally short-sighted and a great let down to Australians who rely on their national broadcaster — from the smallest of communities to our suburbs," AOC chief executive Matt Carroll said.
"We live in times when we need to inspire even more Australians to participate in sport to defeat the growing scourges of obesity and mental illness."
The AOC said it was a particularly ill-advised move with Tokyo 2020 taking place in a time zone that is more conducive to listening and watching than Rio four years ago.
Carroll also suggested covering things like the Olympics on live radio was pivotal to the ABC's charter.
"The Olympics can inspire Australians to embrace something valuable and, as the ABC's charter indicates, to contribute to a sense of national identity. There's no bigger event in the world to fulfil this ambition," he said.
"There is no sporting movement in Australia that covers the multicultural and geographical diversity of our country as the Olympic movement. … These are things go to the heart of the ABC charter."
'ABC will lose part of its DNA'
The ABC's statement made clear ABC News and ABC Grandstand would still be providing daily updates throughout the Games.
ABC radio caller Alister Nicholson said he felt "absolutely flat" and "empty in the stomach" after hearing the news.
"That idea that I would be part of the sports department that didn't any longer cover the Olympic Games is really deflating," he told ABC Radio Melbourne.
"I can't say too much about it. Obviously this is a decision that has been made a lot higher up the chain than myself.
"I know that it is a difficult environment budgetary-wise for the ABC at the moment, and it's not an inexpensive thing to mount a broadcast of the Olympic Games."
Nicholson described the ABC team that was sent to the 2016 Rio Games as a "trimmed back … skeleton team" that he was lucky enough to be a part of.
"I'm not so much disappointed for myself, I'm disappointed for the athletes and for the public," he said.
"You think about all the great stories there are to tell — and yes, we will continue to cover in some capacity — we won't have broadcast rights in the sense that we will be able to call those great moments."
Fellow broadcaster Quentin Hull tweeted: "The ABC will lose part of its DNA by not being at the Tokyo Olympics.
"The coverage has always shed a light on amazing stories of not only Australians, but human achievement the world over.
"It's a national shame."


Matt White
Senior at Sac State studying Computer Science
Aspiring Web Developer and Software Engineer seeking employment in the California Bay Area
Previous work experience
Documentation Intern at Smart ERP Solutions (3 Months, Summer 2013)
Quality Assurance Intern at Smart ERP Solutions (3 Months, Summer 2014)
Student Assistant at Legislative Data Center (LDC) (3 Months, Summer 2015)
Some languages I've enjoyed learning so far!
Some languages I'm excited to start learning!


--- layout: default title: Makers Guesthouse | A home of Makers in Siem Reap lang: en permalink: / --- {% include components/container_generic.html heading_upper = "A home of Makers, Makers Guesthouse" heading_lower = "Welcome" text_align = "left" img_src = "/img/intro.jpg" link_button_enable = true link_button_text = "Book room" link_button_href = "https://hotels.cloudbeds.com/reservation/Xnbrf3" content = '
Proudly being the first Makerspace in Siem Reap, Cambodia, Makers Guesthouse is the place for Makers, operated by a Maker, where Makers and non-Makers, can enjoy reasonable accommodation in a quiet neighborhood, visit the Angkor Wat in a 20 minutes Tuk Tuk ride, and build things.
All rooms have private shower, fans, and A/C. No-frills, but clean and enough space for big suitcase(s). Practical amenities are provided for free, such as hair dryers, USB chargers, AC adapters, voice assistant speakers, and managed WiFi.
' %}The first in the town, we offer a place to learn technologies and build things. We provide tools, facilities, and instruments.
We also teach basics for beginners. Even if you are not a maker (yet), you can learn IoT, computer programmings, 3D CAD, and, most importantly, the joy of building things and making something work.
' %}Internet access is an essential infrastructure for everyone. Our WiFi network is monitored 24/7, and managed by a professional network engineer. SINET, our ISP, is known as the best ISP in the country, with symmetric bandwidth, 60 Mbps, for upload and download, dedicated & stable bandwidth over optical fibre, and one of the best domestic and international network backbone in Cambodia. The access line is NOT shared with neighbors, but dedicated to the property, i.e. "true" FTTH.
' %}We solve our own issues and challenges in guesthouse business. The solutions are equally appliciable to other local guesthouse businesses, which are often limited in budget. We are planning to provide such solutions, tailored for each business. The guesthouse is a showcase of our solutions, where you will see what you can do in your business with modern technologies.
' %}


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Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam.
Renovation Contrary to what people might imagine when we talk about bathroom remodeling, it should not have anything to do with
Renovation Contrary to what people might imagine when we talk about bathroom remodeling, it should not have anything to do with
Renovation Contrary to what people might imagine when we talk about bathroom remodeling, it should not have anything to do with
Renovation Contrary to what people might imagine when we talk about bathroom remodeling, it should not have anything to do with
Renovation Contrary to what people might imagine when we talk about bathroom remodeling, it should not have anything to do with
Renovation Contrary to what people might imagine when we talk about bathroom remodeling, it should not have anything to do with
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CALL US FREE ON
100 FAIRCHILDES AVE, CROYDON,
LONDON, CR0 0AJ
INFO@RENOVATIONS.CO.UK


Fondu ICO
Interactive White Paper [English, Russian]
Author: Nikita Kolmogorov
Introduction
This paper is a proposal to the whole crypto and open source community to save millions of dollars by providing emerging and underrated startups with the key services they need to emerge. Swamped with parasitical speculators, imitators, and scammers that continue to leach on the industry by misleading investors and developers alike, the ICO market is sick and we need to fix it.
As we all know, automation continues to bring efficiency to businesses and applications around the world. Why should the ICO market be any different? We are proposing a unique solution to this problem by allowing our community to create semi-automatic open sourced services to create and manage all kinds of ICOs. With minimal steps, users will be able to fill in their ICO profile, configure the smart-contract with a series of simple questions, and start raising money with a market-ready website.
If you have ever conducted an ICO, you know that writing a smart contract can be as costly as $10,000 to $75,000, and marketing costs can run as high as $30,000 to $70,000. Often, developers find that once they have spent all their money, they are left with only a generic website and a slightly modified ERC20 token smart-contract. Thus raising the question: why can't it be done automatically?
Product
The solution that we propose consists of three simple modules: the ICO constructor, the ICO presenter and the Investor Personal Profile. Let us explain each of them in detail.
- The ICO constructor: a simple webpage with forms to be filled in with critical ICO information such as the token name, team details, product description, logo, screenshots, social network contacts, legal details, whitepaper, roadmap, etc. The Smart-contract constructor is a part of this module, allowing you to fill simple forms, answer a series of multiple-choice questions, and get a smart-contract deployed to the ETH and/or EOS blockchain immediately.
- The ICO presenter: after constructing the ICO, you will get a personalized profile page that acts as the main landing page for the ICO and contains all the necessary information for investors including live ICO progress (displaying the smart-contract state in the real time), important milestones, and the contract address. Everything is hosted on Fondu platform and has zero costs associated with its upkeep.
- The Investor Personal Profile: one of the most difficult to implement and expensive aspects of running any ICO, this module allows potential investors to pre-register for the sale and seek approval. As well as being a perfect tool to provide KYC and whitelisting functionality to the ICO conductors, we intend to make this module a part of the initial release, as it will allow for drastically reduced costs associated with running an ICO while improving the overall investor user experience.
The main goal of our project is to democratize the ICO concept for all, enabling anyone to theoretically access smart-contracts in order to conduct any type of crowd funding. Whether it is something positive, like funding a wedding with the help of guests (instead of them buying presents), or rather unfortunate, like funding a complex medical operation for someone in need, everybody ought to be able to launch an ICO without headache and a large capital investment.
In case you’re wondering how difficult it is to build what we have just described, look at what I've written in 5 minutes while drinking my morning coffee:
{{ title || titleSample }}
{{ description || descriptionSample }}
Team:
{{ team || teamSample }}
Legal:
{{ legal || legalSample }}
It wasn't difficult to setup your first ICO profile, was it? Perhaps it will be more difficult to create a smart-contract constructor.
{{code}}
What's next?
- Run
./deploy.sh
in the root folder of the zip you've downloaded - Follow the steps of the deployer
- Enjoy your smart-contract deployed!
Congratulations you have just written your first smart contract! $70,000 they say — and you just did it for free! Download the resulting code and follow the deployment instructions, our product is fully capable of deploying smart contracts to the local, test and main Ethereum networks! For free and without hassle! The worst offenders amongst ICO's copy\paste generic code with the names of their tokens, their owner ETH addresses and with the supply amount changed. Real ICO visionaries are getting robbed in broad daylight and we want to help stop it.
So far, you have learned about our mission and our project: The Simple ICO Constructor, the ICO Presenter and the Investor Personal Profile. With these services, launching an ICO will not take more than 15 minutes of configuration process. Obviously, in order to achieve such an ambitious goal we need an experienced team of developers and contributors. So who is behind the Fondu team?
Team
Nikita Kolmogorov [TG, FB]
Creator of @voicybot (1,500,000 users, open source), @banofbot (150,000 chats, open source), Toreador.io (over $1,000,000 of trading capital), @golden_borodutch — Telegram channel with over 12,000 subscribers with the focus on cryptocurrencies, IT and self-development. One of the main contributors to @arbeit_bot (open source).
Since learning to code at the young age of 12, he has since participated in 35+ projects as a developer, IT enthusiast, manager, lead and consultant. Today, his main focus is working on his personal projects, and consulting startups interested in launching Minimal Viable Products through establishing technical superiority.
Nikita is a strong believer that coding talents can be used for the greater good and that the reward comes not only from building quality products but also only after his work successfully creates value for the largest amount of people possible.
Disclaimer: the only person currently working at Fondu is Nikita Kolmogorov. The following team members are potential only, having agreed to participate in the project only in the case of successful funding. The following people are not responsible for the project and cannot be held responsible for any actions taken by Fondu.
Nikita Pechenov [TG]
Nikita has over 10 year of commercial development experience. He has worked with numerous companies as a contractor for projects ranging from entertainment mini-apps to large business-architecture. He has worked with Amwey, Sberbank, Alfabank, Tinkoff and OneTwoTrip. Nikita currently works as a front-end engineer, however he also continues to maintain his back-end development abilities. At the moment he is a consultant for BestRate and Toreador.
Artyom Chebotaryov [TG, VK]
Creator of @VoterBiBot, a sweepstakes bot for Telegram groups and channels. Artyom is a full-stack developer specializing in Python Django and Vue.js. He has been involved in a series of different IT ventures as a coder: starting as an electronics parts websites parser, building software solutions for Neolab, building event-oriented Telegram bots, and finally building an alternative to Monitor (courtesy of Meduza).
Artyom started his developer career 2 years ago. He has since fallen head over heels in love with programming because of the opportunity to create valuable people oriented products and for the freedom that comes from working as a freelancer. He loves back-end development and works with big data as a hobby.
Aleksandr Mihhailov [TG]
A goal oriented full stack developer with 7+ years of development experience. He is always looking to work on exciting projects with exciting people and strives to participate in social-oriented projects. Aleksandr likes learning new programming languages and techniques.
He has spent most of his career picking up and mastering new skills. For instance, he has extensive experience with the MEAN stack, Ruby, TypeScript and various databases. Finally, he has participated in the development of adcash, Tactic, motiva.
Artem Biryukov [TG]
Artem began his developer career in 6th grade with Pascal and has since transitioned to more serious programming tasks such as solving problems for businesses and building valuable products for various markets. Fondu has attracted him as a fascinating idea with its potential to solve multiple industry-wide issues and as a means to gather blockchain related experience.
Artem has contributed to Mozilla open source repositories, rust-bindgen, servo and Rocket. He is experienced in Node.js, JavaScript (Vue, React), Python, Rust, Go, as well as various databases such as Docker, and GraphQL.
Alexey Kakotkin [TG]
Alexey started to develop software in high school, and has been doing commercial development for the last 3 years. He started programming as a way to stretch his knowledge and challenge his brain. Alexey wants to participate in Fondu to build his blockchain portfolio.
Alexey has been working on various hobby projects with the most recent being a personal blogging platform. His main tech stack at the moment is React, Python, Java, C#, Go — as represented by his GitLab profile.
Rauf Salikhov [TG]
Rauf started development 2 years ago as a part of a small freelance project. Since then he has grown as a programmer to use more advanced and sophisticated technologies including the MEAN stack and Vue.
Unfortunately, while no trace of his previous projects are left on the Internet, he has considerable experience developing networks for various businesses such as real-estate agencies, car dealerships, and beauty product distributors. He has also begun looking into mobile development, starting with Swift.
Michel Erohov [TG]
A front-end developer who started his career in 2014, Michel participated in the shut down project Controlio. He currently works at BBDO. Fondu has interested him as an opportunity to contribute to the community and increase his presence in the open source community.
Advisors
Disclaimer: the only role advisors have in Fondu is advising the developer team. They do it completely pro bono. No risk should be ever associated with them and they should not be held responsible for the company’s actions. The only people who are responsible for Fondu are the actual project team of contributors and developers. Although all the listed advisors have agreed to participate in the project after successful funding, not all of them currently advise the project.
Max Frolov [TG, LI]
Visionary, ICO and legal advisor, Max managed projects in a company group that develops applications for the Russian stock market. He participated in the listing of foreign securities on the Saint Petersburg Exchange, off-market exchanges, and ETF clearing. He was a part of the initial crypto exchange launch first attempted in Russian jurisdiction but which is now setting up in Switzerland.
Max was responsible for business-architecture for solutions, efforts and release planning as well as brokers communication at SPB Exchange, NPRTS and Voskhod.
Aler Denisov [TG]
Blockchain enthusiast and technical advisor at Fondu, Aler has broad experience developing various projects. He is the founder of Vibrance devshop that has developed apps for multiple high-end brands. He recently participated in the development of Ace Token, Team Token, Alt Estate, Musereum.
Aler has contributed his wisdom to making Fondu smart-contracts more secure, reliable and human-readable. Finally, he has agreed to advise on the technical aspect of the smart contract constructor as well as general service architecture.
Vlad Sofronov [TG, LI]
Vlad Sofronov is VP for Partner Relations at ICOBox, the world's largest provider of SaaS ICO solutions. For most of his professional career he has been implementing groundbreaking technologies in new fields, including a multi-year stint as a highly sought-after VFX supervisor in the film industry. For the past 5 years Vlad has been actively involved in cryptocurrency trading and mining, and, over the past year, has been deeply engaged in studying and participating in ICOs.
Because of his thorough understanding of the new technology, he was invited to come onboard at ICOBox, where he now manages relations with all of the ICOBox's global partners, acts as the company's ambassador at public events, and is extremely busy supporting ICOs of the company's strategic clients. He has also been a part of AML Bitcoin, Storiqa, PAVO, Cinematix, Tradelize, MODULE, Goldmint. Having such a wide expertise in conducting ICO's, Vlad is a very valuable advisor at Fondu, helping with the planning and decision making process.
Andrew Ahachinsky [FB, LI]
Longevity specialist and founder and CEO at Eterly, Andrew has been a driving visionary and business advisor. With experience conducting ICO’s, he shapes the development of his products toward the needs of customers instead of following hype and the status quo.
Yuriy Kashnikov [TG]
Yuriy is a software engineer at Exyte, Russia. Yuriy works on blockchain and related technologies bridging the latest research results with real world applications. He previously graduated with a PhD from the University of Versailles, France, and worked on HPC problems at the Exascale research center in Paris. He participated in the development of Kayuri, Dragon King, Dorado and microCAR.
Together with Yuri Strot, he is developing the ICO generator and they have agreed to share their experiences solving technical and visionary problems as advisors for Fondu.
Mike Goryunov [TG]
Mike started his marketing career in 2009 and has been working in business development since 2014. His latest project is 3commas, a crypto exchange trading terminal, analytics and trading bot. There he is responsible for partner relations and marketing.
3commas is one of the leading crypto-trading platforms with over 40 000 users and 13 integrated exchanges. Mike and the 3commas team will advise Fondu on technical and business levels, particularly on how to drive a complete product to market.
ICO
Even though we wish that a project of such scale could be fuelled by the enthusiasm of the contributors alone, there is a strong necessity for Fondu to attract investors. We have to act quickly, otherwise the community risks losing millions of dollars to scammers who continue to simply reuse stolen smart-contract code again and again, while charging enormously large fees.
In order to release Fondu to market as quickly as possible, we need to hire developers, designers, and managers full-time. Our best estimate for our first year of operations is around $250,000. However, the estimated amount of money saved within the first should exceed the estimate expenses. If every 10th ICO pledges $1,000 (1/70 of the average smart contract and website budget) to Fondu, this ICO will succeed.
The terms of the ICO are simple, all one needs to do is to transfer ETH to the ETH address below to receive FDU tokens in return. For every 1 ETH sent you will receive 250 FDU tokens until July 1st, 2019. Then the crowd funding ICO campaign will be over.
The Fondu ICO token sale will end on July 1st, 2019. All the proceeds will be converted to USD and will be used to pay developers and contributors to the Fondu repositories. The use and purpose of FDU tokens is yet to be wholly defined, however we promise to present the use case for the Fondu platform in the near future. When buying FDU tokens, please execute extreme caution and make sure you understand that by purchasing FDU tokens, you are supporting our community and an asset that might hold no value at all in the future.
ICO progress (proof):
{{ progress }}
{{ raised }}
Send Ethereum to the address on the qr code or to this address to buy FDU:
0x7a5eb747f7921b057fa081e52597bc2cf0a03c4a
Note on open source
We intend to make Fondu fully available for free for everyone. Our intent is to give our excellent developers a chance to give back to the open source community. Our goal is to empower anyone to start their own ICO for any reason, just like anybody can create a GitHub repository. On the topic of GitHub, we are completely transparent and our smart-contract code and any code in general can always be found on GitHub under MIT license, meaning our code is open source to all those who wish to use it. As developers, we can only hope that our project delivers value to those in need, as we seek to empower and democratize the ICO process for all. Every single line of code we write within Fondu will always be open to public. Thanks!
Conclusion
We strongly believe that by possessing such an incredible amount of experience and knowledge between our developer and advisory teams, that we will be able to launch the first generation release version of the product within 2 months after the crowd sale reaches our initial target of $50,000 raised. Let nobody wait for the first ICO made on our open source free platform to be made available. The only operating constraint ahead is funding, beyond what has already been invested by us personally. We also hope that by acting together as a community we can change the way ICO's are perceived by the general public, to democratize the crypto climate away from the extravagant exceptions exploited by speculators and imitators in order to level the playing field and experience for non technical users everywhere. Only through this initiative can the rapid adoption of cryptocurrency and blockchain technology be made possible.
The Fondu developer team would like to thank you in advance, and please let us know if you have any questions by contacting us in our public Telegram group: @project_f. Team members are online as much as possible to answer all inquiries. You're awesome – stay this way!
© 2018, Fondu


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What is Prime Number?
A prime number (or a prime) is a natural number greater than 1 that is not a product of two smaller natural numbers. A natural number greater than 1 that is not prime is called a composite number.
Is this number prime?
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T
- Entity to be managed by this DAO, needs to inherit from Entity
public abstract class DataAccessObject<T extends Entity>
extends java.lang.Object
Inherit from this class to create a straight-forwards DAO to handle basic read and write database operations for your entities.
| Constructor and Description |
|---|
DataAccessObject(DatabaseProvider dbProvider)
Create an instance of this DAO.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
cacheEntity(long id,
T object)
Store an entity in the object cache.
|
void |
delete(T entity)
Deletes a known entity from the database.
|
protected void |
ensureForeignKeyConsistency(T entity)
Handle relationships to other entities.
|
T |
findById(long id)
Retrieve a single entity of this DAO's type from the database.
|
java.util.List<T> |
getAll()
Retrieve all entities of this DAO's type from the database.
|
protected T |
getCachedEntity(long id)
Retrieve an entity from the object cache.
|
protected DatabaseProvider |
getDbProvider()
Retrieve the database provider.
|
protected abstract java.lang.String |
getDefaultOrderBy()
Get the default sorting order statement.
|
protected java.lang.String |
getDeleteStatement()
Get the SQL statement to delete an existing entity from the database.
|
protected T |
getEntityFromResultSet(java.sql.ResultSet result)
Converts a single database query result into the actual entity object.
|
protected abstract java.lang.String |
getEntityName()
Get the name of the managed entity.
|
protected abstract java.lang.String[] |
getFieldList()
Get the list of database fields that are used for this entity.
|
protected java.lang.String |
getIdColumnName()
Get the ID database column for this entity.
|
protected java.lang.String |
getInsertStatement()
Get the SQL statement to insert a new entity into the database.
|
protected java.lang.String |
getSelectAllStatement()
Get the SQL statement to retrieve all entities of this DAO's type.
|
protected java.lang.String |
getSelectByIdStatement()
Get the SQL statement to retrieve a single entity via its ID.
|
protected java.lang.String |
getTableName()
Get the name of the database table to be used for this DAO's entity.
|
protected java.lang.String |
getUpdateStatement()
Get the SQL statement to update an existing entity in the database.
|
protected boolean |
isEntityCached(long id)
Check if an entity is already cached.
|
protected abstract T |
mapFieldsToEntity(java.sql.ResultSet result)
This method is called when a database query result needs to be converted into an actual
entity object.
|
void |
save(T entity)
Save an entity to the database.
|
protected abstract void |
setInsertUpdateParameters(java.sql.PreparedStatement statement,
T entity)
This method is called when the DAO is executing an insert or update statement to
poopulate the field values.
|
public DataAccessObject(DatabaseProvider dbProvider)
dbProvider
- The provider that arranges the database connectionpublic java.util.List<T> getAll()
getDefaultOrderBy()
protected DatabaseProvider getDbProvider()
Usually used as getDbProvider().getDatabaseConnection();
right before
a database statement is issued.
protected java.lang.String getSelectAllStatement()
This statement contains no placeholders at all.
protected T getEntityFromResultSet(java.sql.ResultSet result)
Also takes care of internal caching to prevent the creation of multiple objects for the same entity.
result
- Query result, iterated to the row to be extractedprotected java.lang.String getIdColumnName()
Overwrite this method only if you really need to change the name of the ID column.
protected abstract java.lang.String[] getFieldList()
The order is order of these fields is important and you will need it for the implementation
of setInsertUpdateParameters(java.sql.PreparedStatement, T)
.
protected java.lang.String getTableName()
Only overwrite this method, if you don't want the name to be the same as the entity's.
protected abstract java.lang.String getDefaultOrderBy()
This will be used by some of the standard SQL statements.
protected boolean isEntityCached(long id)
Note: The class mostly deals with caching itself.
id
- Cache entry key, usually the entity's IDprotected T getCachedEntity(long id)
Note: The class mostly deals with caching itself.
id
- Cache entry key, usually the entity's IDisEntityCached(long)
protected abstract T mapFieldsToEntity(java.sql.ResultSet result)
The ResultSet has already been iterated to the correct row. Retrieve all of your fields from the result and place the values inside your newly created entity object.
Note: You don't have to set the ID yourself, the DAO will take care of it.
result
- Database query result, iterated to the right rowprotected void cacheEntity(long id, T object)
Note: The class mostly deals with caching itself.
id
- Cache entry key, usually the entity's IDobject
- The actual object to be cachedprotected abstract java.lang.String getEntityName()
If you need to change the name of the database table name, overwrite getTableName()
.
public T findById(long id)
id
- ID of the entity to retrieveprotected java.lang.String getSelectByIdStatement()
This statement contains a single placeholder for the entity's ID.
public void save(T entity)
You can call this with a new or an already known entity. If it isn't in the database yet, it will be inserted and an ID will be assigned to it. Otherwise the existing entry will be updated.
entity
- Entity to store in the databasepublic void delete(T entity)
entity
- Entity to deleteprotected java.lang.String getDeleteStatement()
This statement contains a single placeholder for the entity's ID.
protected java.lang.String getInsertStatement()
This statement contains n placeholders, one for each field of the entity. The order follows
the one introduced in getFieldList()
.
protected java.lang.String getUpdateStatement()
This statement contains n+1 placeholders, where 1..n are for the entity's fields. The order follows
the one introduced in getFieldList()
. The n+1th parameter is for the entity's ID.
protected abstract void setInsertUpdateParameters(java.sql.PreparedStatement statement, T entity) throws java.sql.SQLException
Retrieve all values from your entity and assign them to the statement's parameters
in the same order as you returned the field values in getFieldList()
.
The parameters are counted from 1..n, where n is the total number of fields.
statement
- SQL stamement to be populated with parameter valuesentity
- Entity to take the parameter values fromjava.sql.SQLException
- when accessing a parameter that doesn't existprotected void ensureForeignKeyConsistency(T entity)
If your entity has relationships with other entities, use this method to make sure they are stored to the database before a foreign key constraint can fail.
You might have to adjust your constructor to make sure you have access to a DAO for the related Entity.
entity
- Entity that is about to be stored to the database


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20 November 2009 - The Ministry of Health of Egypt has reported a new confirmed human case of avian influenza A(H5N1).
The case is a 21 year-old male from Sedy Beshir District, Alexandria Governorate. His symptoms started on 11 November. He was admitted to Maamoura Chest Hospital on 15 November, where he received oseltamivir treatment. The patient is in a stable condition.
Investigations into the source of infection indicated that the case had close contact with dead and/or sick poultry and was involved in slaughtering sick birds.
The cases were confirmed by the Egyptian Central Public Health Laboratories.
Of the 88 cases confirmed to date in Egypt, 27 have been fatal.


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HLA
Compact but easy to read, not confusing
Line based
Easy to parse
Close to 1:1 mapping for debugging, non disconnect. No large compounds.
All x86 ops have a 1:1 X# op, but there can be more X# ops which have mult x86 ops


{% extends "app.html" %} {% block content_extraclass %}campaigns pricing{% endblock %} {% block content_title %}Pricing{% endblock %} {# TODO: Write a component that'll make this form work #} {% block content_main %}
Create campaign with these options


--- title: Clone a model instance. type: docs repo: https://github.com/rstudio/keras menu: main: name: Clone a model instance. - keras parent: keras-reference aliases: - /reference/keras/clone_model.html - /keras/reference/clone_model.html - /guide/keras/reference/clone_model.html - /tools/tools/keras/reference/clone_model.html - /installation/keras/reference/clone_model.html - /tutorials/keras/reference/clone_model.html - /guide/tools/keras/reference/clone_model.html - /deploy/keras/reference/clone_model.html - /tools/keras/reference/clone_model.html - /tutorials/tools/keras/reference/clone_model.html ---
Model cloning is similar to calling a model on new inputs, except that it creates new layers (and thus new weights) instead of sharing the weights of the existing layers.
clone_model(model, input_tensors = NULL)
| model | Instance of Keras model (could be a functional model or a Sequential model). |
| input_tensors | Optional list of input tensors to build the model upon. If not provided, placeholders will be created. |


#include <db_cxx.h>int Db::sync(u_int32_t flags);
The Db::sync method flushes any cached information to disk.
If the database is in memory only, the Db::sync method has no effect and will always succeed.
The flags parameter is currently unused, and must be set to 0.
See Db::close for a discussion of Berkeley DB and cached data.
The Db::sync method either returns errno or throws an exception that encapsulates an errno on failure, and 0 on success.
The Db::sync method may fail and throw an exception for any of the errors specified for the following Berkeley DB and C library functions: Db::cursor, Db::get, Db::sync, DBcursor->c_close(3), __account_page(3), close(3), dbenv->db_paniccall(3), fcntl(3), fflush(3), fprintf(3), free(3), func(3), DbLockTab::get, DbLock::put, DbLockTab::vec, DbLog::put, malloc(3), memcpy(3), memmove(3), DbMpoolFile::get, DbMpoolFile::put, DbMpoolFile::set, DbMpoolFile::sync, memset(3), munmap(3), open(3), realloc(3), sigfillset(3), sigprocmask(3), strerror(3), unlink(3), vfprintf(3), vsnprintf(3), and write(3).
In addition, the Db::sync method may fail and throw an exception or return errno for the following conditions:


1 seriously injured in Roxbury shooting
by EndPlayBOSTON - One person has life-threatening injuries after a shooting near Cedar and Centre streets in Roxbury late Thursday.
Boston police said the man was shot just before midnight and was rushed to a local hospital with life-threatening injuries. Another man, who police believe was stabbed, sustained non-life threatening injuries.
Related Headlines
Police, who spent hours overnight talking with potential witnesses and collecting evidence, have not said what led to the violence.
Officials have not made any arrests and it's unclear if they have any suspects.
Police said the investigation is ongoing.


| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | package org.apache.commons.collections4.functors; | |
| 18 | ||
| 19 | import java.util.Collection; | |
| 20 | ||
| 21 | import org.apache.commons.collections4.Predicate; | |
| 22 | ||
| 23 | /** | |
| 24 | * Predicate implementation that returns true if any of the | |
| 25 | * predicates return true. | |
| 26 | * If the array of predicates is empty, then this predicate returns false. | |
| 27 | *
 | |
| 28 | * NOTE: In versions prior to 3.2 an array size of zero or one | |
| 29 | * threw an exception. | |
| 30 | * | |
| 31 | * @since 3.0 | |
| 32 | * @version $Id: AnyPredicate.java 1543167 2013-11-18 21:21:32Z ggregory $ | |
| 33 | */ | |
| 34 | public final class AnyPredicate<T> extends AbstractQuantifierPredicate<T> { | |
| 35 | ||
| 36 | /** Serial version UID */ | |
| 37 | private static final long serialVersionUID =<PHONE_NUMBER>934647542L; | |
| 38 | ||
| 39 | /** | |
| 40 | * Factory to create the predicate. | |
| 41 | *
 | |
| 42 | * If the array is size zero, the predicate always returns false. | |
| 43 | * If the array is size one, then that predicate is returned. | |
| 44 | * | |
| 45 | * @param <T> the type that the predicate queries | |
| 46 | * @param predicates the predicates to check, cloned, not null | |
| 47 | * @return the any predicate | |
| 48 | * @throws IllegalArgumentException if the predicates array is null | |
| 49 | * @throws IllegalArgumentException if any predicate in the array is null | |
| 50 | */ | |
| 51 | @SuppressWarnings("unchecked") | |
| 52 | public static <T> Predicate<T> anyPredicate(final Predicate<? super T>... predicates) { | |
| 53 |
1
1. anyPredicate : removed call to org/apache/commons/collections4/functors/FunctorUtils::validate → KILLED |
FunctorUtils.validate(predicates); |
| 54 |
1
1. anyPredicate : negated conditional → KILLED |
if (predicates.length == 0) { |
| 55 |
1
1. anyPredicate : mutated return of Object value for org/apache/commons/collections4/functors/AnyPredicate::anyPredicate to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return FalsePredicate.<T>falsePredicate(); |
| 56 | } | |
| 57 |
1
1. anyPredicate : negated conditional → KILLED |
if (predicates.length == 1) { |
| 58 |
1
1. anyPredicate : mutated return of Object value for org/apache/commons/collections4/functors/AnyPredicate::anyPredicate to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return (Predicate<T>) predicates[0]; |
| 59 | } | |
| 60 |
1
1. anyPredicate : mutated return of Object value for org/apache/commons/collections4/functors/AnyPredicate::anyPredicate to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return new AnyPredicate<T>(FunctorUtils.copy(predicates)); |
| 61 | } | |
| 62 | ||
| 63 | /** | |
| 64 | * Factory to create the predicate. | |
| 65 | *
 | |
| 66 | * If the collection is size zero, the predicate always returns false. | |
| 67 | * If the collection is size one, then that predicate is returned. | |
| 68 | * | |
| 69 | * @param <T> the type that the predicate queries | |
| 70 | * @param predicates the predicates to check, cloned, not null | |
| 71 | * @return the all predicate | |
| 72 | * @throws IllegalArgumentException if the predicates array is null | |
| 73 | * @throws IllegalArgumentException if any predicate in the array is null | |
| 74 | */ | |
| 75 | @SuppressWarnings("unchecked") | |
| 76 | public static <T> Predicate<T> anyPredicate(final Collection<? extends Predicate<T>> predicates) { | |
| 77 | final Predicate<? super T>[] preds = FunctorUtils.validate(predicates); | |
| 78 |
1
1. anyPredicate : negated conditional → KILLED |
if (preds.length == 0) { |
| 79 |
1
1. anyPredicate : mutated return of Object value for org/apache/commons/collections4/functors/AnyPredicate::anyPredicate to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return FalsePredicate.<T>falsePredicate(); |
| 80 | } | |
| 81 |
1
1. anyPredicate : negated conditional → KILLED |
if (preds.length == 1) { |
| 82 |
1
1. anyPredicate : mutated return of Object value for org/apache/commons/collections4/functors/AnyPredicate::anyPredicate to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return (Predicate<T>) preds[0]; |
| 83 | } | |
| 84 |
1
1. anyPredicate : mutated return of Object value for org/apache/commons/collections4/functors/AnyPredicate::anyPredicate to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return new AnyPredicate<T>(preds); |
| 85 | } | |
| 86 | ||
| 87 | /** | |
| 88 | * Constructor that performs no validation. | |
| 89 | * Use anyPredicate if you want that. | |
| 90 | * | |
| 91 | * @param predicates the predicates to check, not cloned, not null | |
| 92 | */ | |
| 93 | public AnyPredicate(final Predicate<? super T>... predicates) { | |
| 94 | super(predicates); | |
| 95 | } | |
| 96 | ||
| 97 | /** | |
| 98 | * Evaluates the predicate returning true if any predicate returns true. | |
| 99 | * | |
| 100 | * @param object the input object | |
| 101 | * @return true if any decorated predicate return true | |
| 102 | */ | |
| 103 | public boolean evaluate(final T object) { | |
| 104 |
3
1. evaluate : changed conditional boundary → KILLED 2. evaluate : Changed increment from 1 to -1 → KILLED 3. evaluate : negated conditional → KILLED |
for (final Predicate<? super T> iPredicate : iPredicates) { |
| 105 |
1
1. evaluate : negated conditional → KILLED |
if (iPredicate.evaluate(object)) { |
| 106 |
1
1. evaluate : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return true; |
| 107 | } | |
| 108 | } | |
| 109 |
1
1. evaluate : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return false; |
| 110 | } | |
| 111 | ||
| 112 | } | |
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