We are given the function \( f(x) = \frac{e^x - e^{-x}}{2} \), which is the definition of the hyperbolic sine function, \( \sinh(x) \), i.e., \[ f(x) = \sinh(x). \] The \( k^{\text{th}} \) derivative of \( f(x) = \sinh(x) \) is: \[ f^{(k)}(x) = \frac{d^k}{dx^k} \sinh(x). \] We know that the derivatives of \( \sinh(x) \) follow a periodic pattern:
\( f'(x) = \cosh(x) \)
\( f''(x) = \sinh(x) \)
\( f^{(3)}(x) = \cosh(x) \)
\( f^{(4)}(x) = \sinh(x) \), and so on. This pattern alternates between \( \sinh(x) \) and \( \cosh(x) \) for successive derivatives.
Specifically:
For even derivatives, \( f^{(2n)}(x) = \sinh(x) \)
For odd derivatives, \( f^{(2n+1)}(x) = \cosh(x) \)
Since \( f^{(10)}(x) \) is an even derivative, it will be equal to \( \sinh(x) \). Evaluating this at \( x = 0 \): \[ f^{(10)}(0) = \sinh(0) = 0. \] Thus, the value of \( f^{(10)}(0) \) is 0.
In the given figure, the numbers associated with the rectangle, triangle, and ellipse are 1, 2, and 3, respectively. Which one among the given options is the most appropriate combination of \( P \), \( Q \), and \( R \)?

Consider designing a linear binary classifier \( f(x) = \text{sign}(w^T x + b), x \in \mathbb{R}^2 \) on the following training data: 
Class-2: \( \left\{ \left( \begin{array}{c} 0 \\ 0 \end{array} \right) \right\} \)
Hard-margin support vector machine (SVM) formulation is solved to obtain \( w \) and \( b \). Which of the following options is/are correct?