29 research outputs found
Pragmatic Radiology Report Generation
When pneumonia is not found on a chest X-ray, should the report describe this
negative observation or omit it? We argue that this question cannot be answered
from the X-ray alone and requires a pragmatic perspective, which captures the
communicative goal that radiology reports serve between radiologists and
patients. However, the standard image-to-text formulation for radiology report
generation fails to incorporate such pragmatic intents. Following this
pragmatic perspective, we demonstrate that the indication, which describes why
a patient comes for an X-ray, drives the mentions of negative observations and
introduce indications as additional input to report generation. With respect to
the output, we develop a framework to identify uninferable information from the
image as a source of model hallucinations, and limit them by cleaning
groundtruth reports. Finally, we use indications and cleaned groundtruth
reports to develop pragmatic models, and show that they outperform existing
methods not only in new pragmatics-inspired metrics (+4.3 Negative F1) but also
in standard metrics (+6.3 Positive F1 and +11.0 BLEU-2).Comment: 18 pages, 1 figure, 18 tables. Code at
https://github.com/ChicagoHAI/llm_radiolog
Machine Explanations and Human Understanding
Explanations are hypothesized to improve human understanding of machine
learning models and achieve a variety of desirable outcomes, ranging from model
debugging to enhancing human decision making. However, empirical studies have
found mixed and even negative results. An open question, therefore, is under
what conditions explanations can improve human understanding and in what way.
Using adapted causal diagrams, we provide a formal characterization of the
interplay between machine explanations and human understanding, and show how
human intuitions play a central role in enabling human understanding.
Specifically, we identify three core concepts of interest that cover all
existing quantitative measures of understanding in the context of human-AI
decision making: task decision boundary, model decision boundary, and model
error. Our key result is that without assumptions about task-specific
intuitions, explanations may potentially improve human understanding of model
decision boundary, but they cannot improve human understanding of task decision
boundary or model error. To achieve complementary human-AI performance, we
articulate possible ways on how explanations need to work with human
intuitions. For instance, human intuitions about the relevance of features
(e.g., education is more important than age in predicting a person's income)
can be critical in detecting model error. We validate the importance of human
intuitions in shaping the outcome of machine explanations with empirical
human-subject studies. Overall, our work provides a general framework along
with actionable implications for future algorithmic development and empirical
experiments of machine explanations.Comment: 29 pages, 15 figure
CoLight: Learning Network-level Cooperation for Traffic Signal Control
Cooperation among the traffic signals enables vehicles to move through
intersections more quickly. Conventional transportation approaches implement
cooperation by pre-calculating the offsets between two intersections. Such
pre-calculated offsets are not suitable for dynamic traffic environments. To
enable cooperation of traffic signals, in this paper, we propose a model,
CoLight, which uses graph attentional networks to facilitate communication.
Specifically, for a target intersection in a network, CoLight can not only
incorporate the temporal and spatial influences of neighboring intersections to
the target intersection, but also build up index-free modeling of neighboring
intersections. To the best of our knowledge, we are the first to use graph
attentional networks in the setting of reinforcement learning for traffic
signal control and to conduct experiments on the large-scale road network with
hundreds of traffic signals. In experiments, we demonstrate that by learning
the communication, the proposed model can achieve superior performance against
the state-of-the-art methods.Comment: 10 pages. Proceedings of the 28th ACM International on Conference on
Information and Knowledge Management. ACM, 201
Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control
Traffic congestion plagues cities around the world. Recent years have witnessed an unprecedented trend in applying reinforcement learning for traffic signal control. However, the primary challenge is to control and coordinate traffic lights in large-scale urban networks. No one has ever tested RL models on a network of more than a thousand traffic lights. In this paper, we tackle the problem of multi-intersection traffic signal control, especially for large-scale networks, based on RL techniques and transportation theories. This problem is quite difficult because there are challenges such as scalability, signal coordination, data feasibility, etc. To address these challenges, we (1) design our RL agents utilizing ‘pressure’ concept to achieve signal coordination in region-level; (2) show that implicit coordination could be achieved by individual control agents with well-crafted reward design thus reducing the dimensionality; and (3) conduct extensive experiments on multiple scenarios, including a real-world scenario with 2510 traffic lights in Manhattan, New York City 1 2
Antifeedant Diterpenoids against Tribolium castaneum from the Stems and Twigs of Ceriops tagal (Rhizophoraceae)
The screening of several Chinese mangrove plants for insecticidal principles showed that ethanol extract of Ceriops tagal stems and twigs possessed significant feeding deterrent activity against the red flour beetle, Tribolium castaneum (Family: Rhizophoraceae). From the ethanol extract, three feeding deterrent diterpenoids were isolated by bioassay-guided fractionation. The compounds were identified as tagalsin A, B, and H on the basis of their phytochemical and spectral data. Tagalsin A, B, and H exhibited strong feeding deterrent activity against T. castaneum adults with EC50 values of 375.3 ppm, 277.3 ppm, and 285.45 ppm, respectively