We’ll assume - Study guides, Class notes & Summaries

Metric Spaces 1. Equivalence Relations Definition 1.1. A relation on a set S is a subset R ⊆ S × S. If (a, b) ∈ R, we write a ∼R b (or just a ∼ b). Example 1.2. (1) Let S be the set of members of our class, and let (a, b) ∈ R if person a is shorter than person b. (2) Let S = Z and R = {(a, b) ∈ Z | b = 2a}. Thus, 4 ∼ 8 and 8 ∼ 16. Definition 1.3. A relation ∼ on a set S is an equivalence relation if for all a, b, c ∈ S, (1) a ∼ a (reflexivity) (2) a ∼ b =⇒ b ...

Succession Succession: - This aspect of law deals with what happens to an estate (the assets you leave behind) after you pass away. - Estate = the assets, obligations, liabilities, debts, etc. that you leave behind. - Inheritance = what is left once all the debts and liabilities have been paid. - Succession determines who qualifies to inherit. There are 2 types of succession: - 1) Testate = dealing with wills. - 2) Intestate = dealing with no wills. Terminology: - Deceased = person w...

Metric Spaces 1. Equivalence Relations Definition 1.1. A relation on a set S is a subset R ⊆ S × S. If (a, b) ∈ R, we write a ∼R b (or just a ∼ b). Example 1.2. (1) Let S be the set of members of our class, and let (a, b) ∈ R if person a is shorter than person b. (2) Let S = Z and R = {(a, b) ∈ Z | b = 2a}. Thus, 4 ∼ 8 and 8 ∼ 16. Definition 1.3. A relation ∼ on a set S is an equivalence relation if for all a, b, c ∈ S, (1) a ∼ a (reflexivity) (2) a ∼ b =⇒ b ...

CS 234 Winter 2022: Assignment #2 Introduction In this assignment we will implement deep Q-learning, following DeepMind’s paper ([1] and [2]) that learns to play Atari games from raw pixels. The purpose is to demonstrate the effectiveness of deep neural networks as well as some of the techniques used in practice to stabilize training and achieve better performance. In the process, you’ll become familiar with PyTorch. We will train our networks on the Pong-v0 environment from OpenAI g...

Math 475: Partial Differential Equations Shereen Elaidi Fall 2019 Term Contents 1 Introduction 2 1.1 Domains and Boundary Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.1 What are the main boundary conditions? . . . . . . . . . . . . . . . . . . . . . 3 1.2 Classification of PDEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Diffusion 4 2.1 Derivation and Setting Up (n = 1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...

CS 234 ASSIGNMENT 2 2021/2022 – Stanford University. Distributions induced by a policy (13 pts) In this problem, we’ll work with an infinite-horizon MDP M = hS, A, R, T , γi and consider stochastic policies of the form π : S → ∆(A) 1 . Additionally, we’ll assume that M has a single, fixed starting state s 0 ∈ S for simplicity. (a) (written, 3 pts) Consider a fixed stochastic policy and imagine running several rollouts of this policy within the environment. Naturally, depe...

3.1 Introduction (No Questions) 3.2 Algorithms 3.1 Specifying the order in which statements are to be executed in a computer program is called (a) an algorithm (b) transfer of control (c) program control (d) pseudocode ANS: (c) 3.2. The two key attributes of an algorithm are: a) actions and start activity b) flow and order of flow c) actions and order of actions d) flow and start activity ANS: (c) 3.3 Pseudocode 3.3 Which of the following is true of pseudocode programs? ...

Test Bank Psychiatric Nursing Contemporary Practice (6th Edition by Boyd)Table of Contents Chapter 01: Psychiatric-Mental Health Nursing and Evidence-Based Practice .............................................. 1 Chapter 02: Mental Health and Mental Disorders .......................................................................................... 4 Chapter 03: Cultural and Spiritual Issues Related to Mental Health Care ..................................................... 8 Chapter 04: Patie...

CS 234 ASSIGNMENT 2 2021/2022.0 Distributions induced by a policy (13 pts) In this problem, we’ll work with an infinite-horizon MDP M = hS, A, R, T , γi and consider stochastic policies of the form π : S → ∆(A) 1 . Additionally, we’ll assume that M has a single, fixed starting state s 0 ∈ S for simplicity. (a) (written, 3 pts) Consider a fixed stochastic policy and imagine running several rollouts of this policy within the environment. Naturally, depending on the stochastici...

In this section we’ll give a “derivation” of the Schrodinger equation. Our starting point will be the classical nonrelativistic expression for the energy of a particle, which is the sum of the kinetic and potential energies. We’ll assume as usual that the potential is a function of only x. We have E = K + V = 1 2 mv2 + V (x) = p 2 2m + V (x). (3) We’ll now invoke de Broglie’s claim that all particles can be represented as waves with frequency ω and wavenumber k, and that...