Telling Something we can't Know: Experimental Approaches to Verbs Exhibiting Implicit Causality

1995 ◽  
Vol 6 (5) ◽  
pp. 262-270 ◽  
Author(s):  
Steven B. Greene ◽  
Gail McKoon
Keyword(s):  
Social Interaction ◽  
Real Time ◽  
Language Comprehension ◽  
Basic Principles ◽  
Present Evidence ◽  
Grammatical Subject ◽  
On Line ◽  
Experimental Approaches ◽  
Implicit Causality ◽  
Shed Light

An interpersonal verb such as annoy or admire can be categorized according to whether its grammatical subject or grammatical object initiates the interaction described by the verb Such a verb can also be categorized according to whether a derived adjective describes its grammatical subject (e g, annoying) or its grammatical object (e g, admirable) Although there has been much speculation (e g, Brown & Fish, 1983) that these and other characteristics of these verbs shed light on basic principles of human social interaction, we argue that research to date has failed to demonstrate directly any real-time consequences of these verbs during language comprehension We present evidence that the initiating-reacting distinction predicts on-line changes in the accessibility of these verbs' arguments, but that the existence of a derived adjective does not We conclude that tasks that question subjects explicitly about language may fail to reflect the ordinary processes of language comprehension

Activation and Persistence of Implicit Causality Information in Spoken Language Comprehension

2010 ◽  
Vol 57 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Pirita Pyykkönen ◽  
Juhani Järvikivi
Keyword(s):  
Eye Tracking ◽  
Language Comprehension ◽  
Spoken Language ◽  
Visual World ◽  
Implicit Causality ◽  
Causality Information ◽  
Spoken Language Comprehension

A visual world eye-tracking study investigated the activation and persistence of implicit causality information in spoken language comprehension. We showed that people infer the implicit causality of verbs as soon as they encounter such verbs in discourse, as is predicted by proponents of the immediate focusing account ( Greene & McKoon, 1995 ; Koornneef & Van Berkum, 2006 ; Van Berkum, Koornneef, Otten, & Nieuwland, 2007 ). Interestingly, we observed activation of implicit causality information even before people encountered the causal conjunction. However, while implicit causality information was persistent as the discourse unfolded, it did not have a privileged role as a focusing cue immediately at the ambiguous pronoun when people were resolving its antecedent. Instead, our study indicated that implicit causality does not affect all referents to the same extent, rather it interacts with other cues in the discourse, especially when one of the referents is already prominently in focus.

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant
Keyword(s):  
Computer Architecture ◽  
Real Time ◽  
Data Abstraction ◽  
Monitoring Strategy ◽  
Intelligent Monitoring ◽  
Patient Status ◽  
On Line ◽  
Main Components ◽  
And Control ◽  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

Nitrogen Removal in a Real-Time Controlled Sequencing Batch Membrane Bioreactor

2010 ◽  
Vol 5 (3) ◽  
Author(s):  
Cheng-Nan Chang ◽  
Li-Ling Lee ◽  
Han-Hsien Huang ◽  
Ying-Chih Chiu
Keyword(s):  
Real Time ◽  
Nitrogen Removal ◽  
Membrane Bioreactor ◽  
Synthetic Wastewater ◽  
Real Time Control ◽  
Time Control ◽  
Cake Layer ◽  
On Line ◽  
Solid Liquid ◽  
Solid Liquid Separation

The performance of a real-time controlled Sequencing Batch Membrane Bioreactor (SBMBR) for removing organic matter and nitrogen from synthetic wastewater has been investigated in this study under two specific ammonia loadings of 0.0086 and 0.0045g NH4+-N gVSS−1 day−1. Laboratory results indicate that both COD and DOC removal are greater than 97.5% (w/w) but the major benefit of using membrane for solid-liquid separation is that the effluent can be decanted through the membrane while aeration is continued during the draw stage. With a continued aeration, the sludge cake layer is prevented from forming thus alleviating the membrane clogging problem in addition to significant nitrification activities observed in the draw stage. With adequate aeration in the oxic stage, the nitrogen removal efficiency exceeding 99% can be achieved with the SBMBR system. Furthermore, the SBMBR system has also been used to study the occurrence of ammonia valley and nitrate knee that can be used for real-time control of the biological process. Under appropriate ammonia loading rates, applicable ammonia valley and nitrate knee are detected. The real-time control of the SBMBR can be performed based on on-line ORP and pH measurements.

Modifications of rainfall runoff and decision finding models for on-line simulation in real time control

1999 ◽  
Vol 39 (9) ◽  
pp. 201-207
Author(s):  
Andreas Cassar ◽  
Hans-Reinhard Verworn
Keyword(s):  
Real Time ◽  
Urban Drainage ◽  
Rain Event ◽  
Rainfall Runoff ◽  
Real Time Control ◽  
Design Storm ◽  
Time Control ◽  
Timing Data ◽  
On Line ◽  
Rainfall Runoff Model

Most of the existing rainfall runoff models for urban drainage systems have been designed for off-line calculations. With a design storm or a historical rain event and the model system the rainfall runoff processes are simulated, the faster the better. Since very recently, hydrodynamic models have been considered to be much too slow for real time applications. However, with the computing power of today - and even more so of tomorrow - very complex and detailed models may be run on-line and in real time. While the algorithms basically remain the same as for off-line simulations, problems concerning timing, data management and inter process communication have to be identified and solved. This paper describes the upgrading of the existing hydrodynamic rainfall runoff model HYSTEM/EXTRAN and the decision finding model INTL for real time performance, their implementation on a network of UNIX stations and the experiences from running them within an urban drainage real time control project. The main focus is not on what the models do but how they are put into action and made to run smoothly embedded in all the processes necessary in operational real time control.

Immune Checkpoint Inhibitors: Basics and Challenges

2019 ◽  
Vol 26 (17) ◽  
pp. 3009-3025 ◽  
Author(s):  
Bin Li ◽  
Ho Lam Chan ◽  
Pingping Chen
Keyword(s):  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Complete Response ◽  
Modern World ◽  
Basic Principles ◽  
Mutation Status ◽  
Anti Cancer ◽  
Cancer Types ◽  
Shed Light

Cancer is one of the most deadly diseases in the modern world. The last decade has witnessed dramatic advances in cancer treatment through immunotherapy. One extremely promising means to achieve anti-cancer immunity is to block the immune checkpoint pathways – mechanisms adopted by cancer cells to disguise themselves as regular components of the human body. Many review articles have described a variety of agents that are currently under extensive clinical evaluation. However, while checkpoint blockade is universally effective against a broad spectrum of cancer types and is mostly unrestricted by the mutation status of certain genes, only a minority of patients achieve a complete response. In this review, we summarize the basic principles of immune checkpoint inhibitors in both antibody and smallmolecule forms and also discuss potential mechanisms of resistance, which may shed light on further investigation to achieve higher clinical efficacy for these inhibitors.

scholarly journals Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 420
Author(s):  
Phong B. Dao
Keyword(s):  
Real Time ◽  
Feedforward Control ◽  
Design Method ◽  
Mechatronic Systems ◽  
Control Approach ◽  
Runtime Environment ◽  
Multiagent Control ◽  
On Line ◽  
Promising Solution ◽  
Function Approximator

Multiagent control system (MACS) has become a promising solution for solving complex control problems. Using the advantages of MACS-based design approaches, a novel solution for advanced control of mechatronic systems has been developed in this paper. The study has aimed at integrating learning control into MACS. Specifically, learning feedforward control (LFFC) is implemented as a pattern for incorporation in MACS. The major novelty of this work is that the feedback control part is realized in a real-time periodic MACS, while the LFFC algorithm is done on-line, asynchronously, and in a separate non-real-time aperiodic MACS. As a result, a MACS-based LFFC design method has been developed. A second-order B-spline neural network (BSN) is used as a function approximator for LFFC whose input-output mapping can be adapted during control and is intended to become equal to the inverse model of the plant. To provide real-time features for the MACS-based LFFC system, the open robot control software (OROCOS) has been employed as development and runtime environment. A case study using a simulated linear motor in the presence of nonlinear cogging and friction force as well as mass variations is used to illustrate the proposed method. A MACS-based LFFC system has been designed and implemented for the simulated plant. The system consists of a setpoint generator, a feedback controller, and a time-index LFFC that can learn on-line. Simulation results have demonstrated the applicability of the design method.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

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