4,055 research outputs found
An improved probabilistic account of counterfactual reasoning
When people want to identify the causes of an event, assign credit or blame, or learn from their
mistakes, they often reflect on how things could have gone differently. In this kind of reasoning,
one considers a counterfactual world in which some events are different from their real-world
counterparts and considers what else would have changed. Researchers have recently proposed
several probabilistic models that aim to capture how people do (or should) reason about counterfactuals. We present a new model and show that it accounts better for human inferences
than several alternative models. Our model builds on the work of Pearl (2000), and extends
his approach in a way that accommodates backtracking inferences and that acknowledges the
difference between counterfactual interventions and counterfactual observations. We present
six new experiments and analyze data from four experiments carried out by Rips (2010), and
the results suggest that the new model provides an accurate account of both mean human judgments and the judgments of individuals
Non-Compositionality in Sentiment: New Data and Analyses
When natural language phrases are combined, their meaning is often more than
the sum of their parts. In the context of NLP tasks such as sentiment analysis,
where the meaning of a phrase is its sentiment, that still applies. Many NLP
studies on sentiment analysis, however, focus on the fact that sentiment
computations are largely compositional. We, instead, set out to obtain
non-compositionality ratings for phrases with respect to their sentiment. Our
contributions are as follows: a) a methodology for obtaining those
non-compositionality ratings, b) a resource of ratings for 259 phrases --
NonCompSST -- along with an analysis of that resource, and c) an evaluation of
computational models for sentiment analysis using this new resource.Comment: Published in EMNLP Findings 2023; 13 pages total (5 in the main
paper, 3 pages with limitations, acknowledgments and references, 5 pages with
appendices
Balancing utility and cognitive cost in social representation
To successfully navigate its environment, an agent must construct and
maintain representations of the other agents that it encounters. Such
representations are useful for many tasks, but they are not without cost. As a
result, agents must make decisions regarding how much information they choose
to store about the agents in their environment. Using selective social learning
as an example task, we motivate the problem of finding agent representations
that optimally trade off between downstream utility and information cost, and
illustrate two example approaches to resource-constrained social
representation.Comment: Workshop on Information-Theoretic Principles in Cognitive Systems,
NeurIPS 202
A short response-time atomic source for trapped ion experiments
Ion traps are often loaded from atomic beams produced by resistively heated
ovens. We demonstrate an atomic oven which has been designed for fast control
of the atomic flux density and reproducible construction. We study the limiting
time constants of the system and, in tests with , show we can
reach the desired level of flux in 12s, with no overshoot. Our results indicate
that it may be possible to achieve an even faster response by applying an
appropriate one-off heat treatment to the oven before it is used.Comment: 5 pages, 7 figure
Patent semantics : analysis, search and visualization of large text corpora
Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 47-48).Patent Semantics is system for processing text documents by extracting features capturing their semantic content, and searching, clustering, and relating them by those same features. It is set apart from existing methodologies by combining a visualization scheme that integrates retrieval and clustering, providing a variety of ways to find and relate documents depending on their goals. In addition, the system provides an explanatory mechanism that makes the retrieval an understandable process rather than a black box. The domain in which the system currently works is biochemistry and molecular biology patents but it is not intrinsically constrained to any document set.by Christopher G. Lucas.M.Eng.and S.B
Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome
Rising temperatures caused by global warming are affecting the distributions of many plant and animal species across the world. This can lead to structural changes in entire ecosystems, and serious, persistent environmental consequences. However, many of these changes occur in vast and poorly accessible biomes and involve myriad species. As a consequence, conventional methods of measurement and data analysis are resource-intensive, restricted in scope, and in some cases, intractable for measuring species changes in remote areas. In this article, we introduce a method for detecting flowers of tundra plant species in large data sets obtained by aerial drones, making it possible to understand ecological change at scale, in remote areas. We focus on the sedge species E. vaginatum that is dominant at the investigated tundra field site in the Canadian Arctic. Our system is a modified version of the Faster R-CNN architecture capable of real-world plant phenology analysis. Our model outperforms experienced human annotators in both detection and counting, recording much higher recall and comparable level of precision, regardless of the image quality caused by varying weather conditions during the data collection. (K. Stanski, GitHub - karoleks4/flower-detection: Flower detection using object analysis: New ways to quantify plant phenology in a warming tundra biome. GitHub. Accessed: Sep. 17, 2021. [Online]. Available: https://github.com/karoleks4/flower-detection.
Dissecting causal asymmetries in inductive generalization
Suppose we observe something happen in an interaction be- tween two objects A and B. Can we then predict what will hap- pen in an interaction between A and C, or between B and C? Recent research, inspired by work on the “causal asymmetry”, suggests that people use cues to causal agency to guide object- based generalization decisions, even in relatively abstract set- tings. When object A possesses cues to causal agency (e.g. it moves, remains stable throughout the interaction), people tend to predict that what happened will probably also occur in an interaction between A and C, but not between B and C. Here we replicate and extend this work, with the goal of identify- ing the cues that people use to determine that an object is a causal agent. In four experiments, we manipulate three prop- erties of the agent and recipient objects. We find that people anchor their inductive generalizations around the agent object when that object possesses all three cues to causal agency, but removing either cue abolishes the asymmetry
- …