Neighborhood semantics, also known as Scott–Montague semantics, is a formal semantics for modal logics. It is a generalization, developed independently by Dana Scott and Richard Montague, of the more widely known relational semantics for modal logic. Whereas a relational frame consists of a set W of worlds (or states) and an accessibility relation R intended to indicate which worlds are alternatives to (or, accessible from) others, a neighborhood frame still has a set W of worlds, but has instead of an accessibility relation a neighborhood function that assigns to each element of W a set of subsets of W. Intuitively, each family of subsets assigned to a world are the propositions necessary at that world, where 'proposition' is defined as a subset of W (i.e. the set of worlds at which the proposition is true). Specifically, if M is a model on the frame, then where is the truth set of A. Neighborhood semantics is used for the classical modal logics that are strictly weaker than the normal modal logic K. (Wikipedia).
An Overview of Relations and Functions for Linguists - Semantics in Linguistics
In this video on #semantics in #linguistics we introduce relations and functions, talk about properties of relations and types of functions. Join this channel to get access to perks: https://www.youtube.com/channel/UCGYSfZbPp3BiAFs531PBY7g/join Instagram: http://instagram.com/TrevTutorOf
From playlist Semantics in Linguistics
In this video, you’ll learn more about when to use "whose" and "who's" correctly in American English. Visit https://www.gcflearnfree.org/grammar/whos-or-whose/1/ for our text-based lesson. We hope you enjoy!
From playlist Grammar
In this video I briefly touch on the derivative as the representative of the instantaneous slope and mention the use of the concept of a limit to explain the slope at a point on a curve.
From playlist Biomathematics
The Generalized Neighborhood Base Construction
The generalized neighborhood base construction of a topology is a tool for creating topological spaces some of which end up being important counterexamples in the study of general topological spaces. The construction takes its inspiration from the ability to form a base for topology from a
From playlist The CHALKboard 2022
From playlist Belong: What It's Like to Live in the Hyphen
What is the definition of domain of a relation
👉 Learn about every thing you need to know to understand the domain and range of functions. We will look at functions represented as equations, tables, mapping, and ordered pairs. The domain is the set of all possible x or input values where the range is the set of all possible y or outp
From playlist What is the Domain and Range of the Function
What are bounded functions and how do you determine the boundness
👉 Learn about the characteristics of a function. Given a function, we can determine the characteristics of the function's graph. We can determine the end behavior of the graph of the function (rises or falls left and rises or falls right). We can determine the number of zeros of the functi
From playlist Characteristics of Functions
NOUN PHRASES - ENGLISH GRAMMAR
We discuss noun phrases. Noun phrases consist of a head noun, proper name, or pronoun. Noun phrases can be modified by adjective phrases or other noun phrases. Noun phrases take determiners as specifiers. We also draw trees for noun phrase. you want to support the channel, hit the "JOIN"
From playlist English Grammar
PSY 523 Word Recognition Part 2
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer/Fall 2016 PSY 523 Psychology and Language lectures covering material from Harley's The Psychology of Language: From Data to Theory. Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofdo
From playlist PSY 523 Psychology and Language
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer/Fall 2016 PSY 523 Psychology and Language lectures covering material from Harley's The Psychology of Language: From Data to Theory. Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofdo
From playlist PSY 523 Psychology and Language
Graham Taylor: "Feature Learning for Comparing Examples"
Graduate Summer School 2012: Deep Learning, Feature Learning "Feature Learning for Comparing Examples" Graham Taylor, University of Guelph Institute for Pure and Applied Mathematics, UCLA July 13, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/graduate-summ
From playlist GSS2012: Deep Learning, Feature Learning
CERIAS Security: Providing Privacy through Plausibly Deniable Search 3/4
Clip 3/4 Speaker: Mummoorthy Murugesan · Purdue University Query-based web search is becoming an integral part of many people's daily activities. Most do not realize that their search history can be used to identify them (and their interests). In July 2006, AOL released an anonymized sea
From playlist The CERIAS Security Seminars 2009
A Literature Review on Graph Neural Networks
For slides and more information on the paper, visit https://aisc.ai.science/events/2020-04-15-spotlight Discussion lead: Nabila Abraham Survey papers: 1. Graph Neural Networks: A Review of Methods and Applications https://arxiv.org/abs/1812.08434 2. Representation Learning on Graphs: M
From playlist Literature Review
Lecturer: Dr. Erin M. Buchanan Summer 2019 https://www.patreon.com/statisticsofdoom This video is part of my human language modeling class. This video covers latent semantic analysis to move from collocations onto vector space models. You will learn about vector space models and how to
From playlist Human Language (ANLY 540)
Detecting semantic shift in large corpora by exploiting temporal random indexing
During the last decade, the surge in available data spanning different epochs has inspired a new analysis of cultural, social, and linguistic phenomena from a temporal perspective. In this talk, I will describe Temporal Random Indexing (TRI) a method that enables the analysis of the time e
From playlist Turing Seminars
CS224W: Machine Learning with Graphs | 2021 | Lecture 19.1 - Pre-Training Graph Neural Networks
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3mnajzE Jure Leskovec Computer Science, PhD There are two challenges in applying GNNs to scientific domains: scarcity of labeled data and out-of-distribution predi
From playlist Stanford CS224W: Machine Learning with Graphs
Paper Read Aloud: Interactive Refinement of Cross-Lingual Word Embeddings
An experiment! I recorded this a while ago but didn't post it until now because ... 2020. A long time ago, a blind student once asked me to record myself reading my papers when he found that I do that anyway during my editing process, so I finally did it. This is an experiment, feedback
From playlist Papers Read Aloud
Jana Kosecka: "Perception and Learning for Autonomous Driving" (Part 1/2)
Watch part 2/2 here: https://youtu.be/hEiGNopcGRY Mathematical Challenges and Opportunities for Autonomous Vehicles Tutorials 2020 "Perception and Learning for Autonomous Driving" (Part 1/2) Jana Kosecka - George Mason University Institute for Pure and Applied Mathematics, UCLA Septembe
From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020
What is the definition of a function
👉 Learn about every thing you need to know to understand the domain and range of functions. We will look at functions represented as equations, tables, mapping, and ordered pairs. The domain is the set of all possible x or input values where the range is the set of all possible y or outp
From playlist What is the Domain and Range of the Function