In recent years, researchers in machine learning have investigated three main aspects of empirical discovery: forming taxonomies, finding qualitative laws, and finding quantitative laws. In this paper, we introduce IDS (Integrated Discovery System), a system that integrates these aspects of scientific discovery. We begin by examining the system's representation, showing how it describes events like chemical reactions as sequences of qualitative states. IDS incrementally processes these...
Topics: DTIC Archive, Nordhausen, Bernd, CALIFORNIA UNIV IRVINE SCHOOL OF INFORMATION AND COMPUTER SCIENCE,...
Dynamic scheduling of manufacturing systems has primarily involved the use of dispatching rules. In the context of conventional job shops, the relative performance of these rules has been found to depend upon the system attributes and no single rule is dominant across all possible scenarios. This indicates the need for developing a scheduling approach which adopts a state-dependent dispatching rule selection policy. The importance of adapting the dispatching rule employed to the current state...
Topics: DTIC Archive, Shaw, Michael J, CARNEGIE-MELLON UNIV PITTSBURGH PA ROBOTICS INST, *JOBS,...
We present a new model of machines and their operation, called temporal automata. Characteristics of this model include explicit representation of process time, symmetric representation of a machine and of the environment in which it operates, the wiring together of asynchronous automata, and the ability to aggregate individual machines to form one machine at a coarser level of granularity. We present the mathematical theory of temporal automata, and provide examples of applying the model. We...
Topics: DTIC Archive, Lavignon, Jean-Francois, STANFORD UNIV CA DEPT OF COMPUTER SCIENCE, *AUTOMATA,...
Maritime security is vital to US security. Enhanced Maritime Domain Awareness (MDA) of potential threats in this dynamic environment can be achieved, yet requires integrated analysis from numerous sources in real time. We will present a learning agent technology that integrates structured and unstructured data and discovers behavior patterns from varied sources such as Automatic Information Systems (AIS), Coast Guard, and police contextual information including: maritime commercial activities,...
Topics: DTIC Archive, NAVAL POSTGRADUATE SCHOOL MONTEREY CA, *DATA FUSION, *SITUATIONAL AWARENESS,...
As computer architectures transition towards exponentially increasing parallelism we are forced to adopt parallelism at a fundamental level in the design of machine learning algorithms. In this paper we focus on parallel graphical model inference. We demonstrate that the natural, synchronous parallelization of belief propagation is highly inefficient. By bounding the achievable parallel performance in chain graphical models we develop a theoretical understanding of the parallel limitations of...
Topics: DTIC Archive, CARNEGIE-MELLON UNIV PITTSBURGH PA OFFICE OF SPONSORED RESEARCH, *ALGORITHMS,...
We present several graph-based algorithms for image processing and classification of high-dimensional data. The first (semi-supervised) method uses a graph adaptation of the classical numerical Merriman-Bence-Osher (MBO) scheme, and can be extended to the multiclass case via the Gibbs simplex. We show examples of the application of the algorithm in the areas of image inpainting (both binary and grayscale), image segmentation and classification on benchmark data sets. We have also applied this...
Topics: DTIC Archive, CALIFORNIA UNIV LOS ANGELES DEPT OF MATHEMATICS, *GRAPHS, *IMAGE PROCESSING,...
During the most recent period of performance, the team has refined the major implemented functions of our MIDCA architecture at the cognitive level (Cox, 2013; Paisner, Cox, Maynord, & Perlis, 2014) and has started work on an analogous metacognitive cycle at the meta - level (Dan nenhauer, Cox, Gupta, Paisner & Perlis, 2014; Perlis & Cox, 2014). We produced substantial progress on the knowledge-rich track of the Note- Assess - Guide interpretation procedure. Additionally we started...
Topics: DTIC Archive, WRIGHT STATE RESEARCH INST DAYTON OH, *COGNITION, *KNOWLEDGE BASED SYSTEMS,...
Topics: DTIC Archive, CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE, *ROBOTS, *AUTONOMOUS...
This paper introduces a new measurement, robustness, to measure the quality of machine-discovered knowledge from real-world databases that change over time. A piece of knowledge is robust if it is unlikely to become inconsistent with new database states. Robustness is different from predictive accuracy in that by the latter, the system considers only the consistency of a rule with unseen data, while by the former, the consistency after deletions and updates of existing data is also considered....
Topics: DTIC Archive, Hsu, Chun-Nan, UNIVERSITY OF SOUTHERN CALIFORNIA MARINA DEL REY INFORMATION SCIENCES...
The objective of this project was to conduct fundamental research that will lead to the development of cognitive communications protocols for satellite and space communications with possible broad applications in defense, homeland-security as well as consumer telecommunications. Such cognitive communications protocols are to be implemented on wideband autonomous cognitive radios (WACRs) that will have the ability to sense state of the radiofrequency (RF) spectrum and the network and...
Topics: DTIC Archive, Jayaweera,Sudharman, University of New Mexico Albuquerque United States, satellite...
Very little of our cognitive behavior (as opposed to more peripheral behaviors) is determined by the fixed, unchangeable parts of our mind. Cognitive behaviors seem to be determined by our knowledge and the environment itself. Thus, recent work in A1 on developing models of the fixed part of the mind (the cognitive architecture) will not be of much use to educators who wish to perform a cognitive task analysis of their subject matter before designing instruction for it. However, it seems...
Topics: DTIC Archive, VanLehn, Kurt A, CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF PSYCHOLOGY, *COGNITION,...
The authors present and analyze a novel regularization technique based on enhancing their data set with corrupted copies of their original data. The motivation is that since the learning algorithm lacks information about which parts of the data are reliable, it has to make more robust classification functions. Using this framework, they propose a simple addition to the gentle boosting algorithm that enables it to work with only a few examples. They test this new algorithm on a variety of data...
Topics: DTIC Archive, Wolf, Lior, MASSACHUSETTS INST OF TECH CAMBRIDGE MA CENTER FOR BIOLOGICAL AND...
Convolutional Architecture for Fast Feature Encoding (CAFFE) [11] is a software package for the training, classifying, and feature extraction of images. The UCF Sports Action dataset is a widely used machine learning dataset that has 200 videos taken in 720x480 resolution of 9 different sporting activities: diving, golf swinging, kicking, lifting, horseback riding, running, skateboarding, swinging (various gymnastics), and walking. In this report we report on a caffe feature extraction pipeline...
Topics: DTIC Archive, KNEXUS RESEARCH CORP NATIONAL HARBOR MD, *FEATURE EXTRACTION, *IMAGE PROCESSING,...
The long-term goal of this research is to develop technology for constructing and using large-scale multifunctional knowledge bases on computers. The research during the past 12 months has produced technology for building and using multifunctional knowledge bases. The prototype technologies are for knowledge engineering, knowledge acquisition, and knowledge access.
Topics: DTIC Archive, Porter, Bruce W, TEXAS UNIV AT AUSTIN DEPT OF COMPUTER SCIENCES, *COMPUTERS,...
With a set of observations, humans acquire concepts that organize those observations and use them to classify future experience. This type of concept formation can occur in the absence of a tutor, and despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling the type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept...
Topics: DTIC Archive, Gennari, John H, CALIFORNIA UNIV IRVINE SCHOOL OF INFORMATION AND COMPUTER SCIENCE,...
Topics: DTIC Archive, Grenander,Ulf, BROWN UNIV PROVIDENCE R I DIV OF APPLIED MATHEMATICS, *LEARNING...
The bandwidth utilization of a single channel-based wireless networks decreases due to congestion and interference from other sources and therefore transmission on multiple channels are needed. In this paper, we propose a distributed dynamic channel allocation scheme for wireless networks using adaptive learning automata whose nodes are equipped with single radio interfaces so that a more suitable channel can be selected. The proposed scheme, Adaptive Pursuit Reward-Inaction, runs periodically...
Topics: DTIC Archive, MISSOURI UNIV-ROLLA DEPT OF MECHANICAL AND AEROSPACE ENGINEERING, *ALLOCATIONS,...
The authors, together with a large and varying number of collaborators, worked on a long-term project aimed at how decisions are and should be made under uncertainty and risk. Learniing programs of different types have come to the center of their attention, both in the course of trying to simulate human recognitive behavior and in constructing wholly machine intelligence-oriented competitive strategies. They describe an interactive environment in which an arbitrary number of human and machine...
Topics: DTIC Archive, Findler,Nicholas V, STATE UNIV OF NEW YORK AT BUFFALO AMHERST GROUP FOR COMPUTER...
This Note describes two workshops held at The RAND Corporation in June and November 1986 in conjunction with a study conducted for the Information Science and Technology Office of the Defense Advanced Research Projects Agency (DARPA), under RAND's National Defense Research Institute (NDRI). The NDRI is a Federally Funded Research and Development Center sponsored by the Office of the Secretary of Defense. The study was undertaken to develop criteria for evaluating and selecting tools used to...
Topics: DTIC Archive, Rothenberg, Jeff, RAND CORP SANTA MONICA CA, *COMPUTER PROGRAMMING, *SYSTEMS...
Rather than approach each problem as a unique event, people often try to solve problems by recalling similar previous experiences as guides to problem solving. This analogical process, which we call case-based reasoning, seems to provide an explanation for the change in problem solving behavior of people over time. This research presents a computer process model of problem solving based on the use of case-based reasoning. The necessary reasoning processes, operational measures of similarity,...
Topics: DTIC Archive, Simpson,R L , Jr, AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH, *DECISION MAKING,...
During the period of this grant, six subprojects were investigated and five subprojects have reports and/or publications at present. The sixth area is the subject of a Ph.D. dissertation expected to be completed in 1988. The areas include: Automatic selection of most efficient programs, determination of confidence factors in expert systems, analysis of errors that learning machines make, real time program synthesis through graph factorization, an extension to Prolog, and finding efficient...
Topics: DTIC Archive, Loveland, Donald W, DUKE UNIV DURHAM NC DEPT OF COMPUTER SCIENCE, *ARTIFICIAL...
Explanation-based learning is a recently developed approach to concept acquisition by computer. In this type of machine learning, a specific problem's solution is generalized into a form that can later be used to solve conceptually similar problems. A number of explanation-based generalization algorithms have been developed. Most do not alter the structure of the explanation of the specific problem - no additional objects nor inference rules are incorporated. Instead, these algorithms...
Topics: DTIC Archive, Shavlik, Jude W, ILLINOIS UNIV AT URBANA COORDINATED SCIENCE LAB, *LEARNING MACHINES,...
The main research topic of this contract, i.e. testing how symbolic Machine Learning (ML) techniques can be applied to Scene Analysis, has been completed by the final implementation of our system that learns features to recognize multi-funt characters. The approach we want to develop can be summarized as follows: Inductive Learning of decision trees from a fixed vocabulary; Learning new descriptors improving the decision tree; Merging the results of 1- and 2- Discriminant methods; Finding...
Topics: DTIC Archive, Kodratoff, Yves, PARIS-SUD 11 UNIV ORSAY (FRANCE), *DECISION THEORY, DISCRIMINATE...
According to current theories of cognitive skill acquisition, new problem solving rules are constructed by proceduralization, production compounding, chunking, syntactic generalization, and a variety of other mechanisms. All these mechanisms are assumed to run rather than quickly, so a rule's acquisition should be a matter of a few seconds at most. Such 'learning events' might be visible in protocol data. This paper discusses a method for locating the initial use of a rule in protocol data. The...
Topics: DTIC Archive, VanLehn, Kurt A, CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF PSYCHOLOGY, *LEARNING...
The goal of the research described here is to develop simulation programs that can be used for formative evaluation during the instructional design process. Such simulations are called pseudo-students, because they simulate human students learning from the given instruction. However, unlike human students, pseudo-students keep a detailed trace of the learning so that the designer can discover the causes of undesirable pedagogical outcomes. For instance, one pseudo-student, Sierra, helped...
Topics: DTIC Archive, Van Lehn, Kurt, CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF PSYCHOLOGY, *LEARNING...
Although there are many machine learning programs that can acquire new problem solving strategies, we do not know exactly how their processes will manifest themselves in human behavior, if at all. In order to find out, a line- by-line protocol analysis was conducted of a subject discovering problem solving strategies. A model was developed that could explain 96% of the lines in the protocol. On this analysis, the subject's learning was confined to 11 rule acquisition events, wherein she...
Topics: DTIC Archive, VanLehn, Kurt, CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE,...
One of the main ideas underlying the interest in neural computing is that it may be possible to develop new computational paradigms that will make important aspects of programing both simple and more robust. The means for doing so usually involves usually involves setting up some universal difference or differential equation whose trajectories define rules for solving problems in curve fitting, interpolation, etc. The work has addressed the use of analog computation methods for optimization as...
Topics: DTIC Archive, Brockett, Roger, HARVARD UNIV CAMBRIDGE MA, *NEURAL NETS, *COMPUTER PROGRAMMING,...
This article demonstrates how knowledge level learning can be performed within the Soar architecture. That is, the authors demonstrate how Soar can acquire new knowledge that is not deductively implied by its existing knowledge. This demonstration employs Soar's chunking mechanism -- a mechanism which acquires new productions from goal-based experience -- as its only learning mechanism. Chunking has previously been demonstrated to be a useful symbol level learning mechanism, able to speed up...
Topics: DTIC Archive, Rosenbloom, Paul S, CARNEGIE-MELLON UNIV PITTSBURGH PA ARTIFICIAL INTELLIGENCE AND...
The ultimate goal of working in cognitive architecture is to provide a foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlying structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem- solving methods and representations appropriate for the tasks, and learn about all aspects of the task and its performance on them. In this article we present Soar, an implemented proposal for such...
Topics: DTIC Archive, Laird, John E, CARNEGIE-MELLON UNIV PITTSBURGH PA ARTIFICIAL INTELLIGENCE AND...
Classification methods from statistical pattern recognition, neural nets, and machine learning were applied to four real-world data sets. Each of these data sets has been previously analyzed and reported in the statistical, medical, or machine learning literature. The data sets are characterized by statistical uncertainty; there is no completely accurate solution to these problems. Training and testing or resampling techniques are used to estimate the true error rates of classification methods....
Topics: DTIC Archive, Weiss, Sholom M, RUTGERS - THE STATE UNIV NEW BRUNSWICK NJ CENTER FOR EXPERT SYSTEMS...
Current theories of cognitive skill acquisition, new problem solving rules are constructed by proceduralization, production compounding, chunking, syntactic generalization, and a variety of other mechanisms. All these mechanisms are assumed to run rather quickly, so a rule's acquisition should be a matter of a few seconds at most. Such 'learning events' might be visible in protocol data. This paper discusses a method for locating the initial use of a rule in protocol data. The method is applied...
Topics: DTIC Archive, VanLehn, Kurt, CARNEGIE-MELLON UNIV PITTSBURGH PA ARTIFICIAL INTELLIGENCE AND...
This paper presents a simple, sound, complete, and systematic algorithm for domain independent STRIPS planning. Simplicity is achieved by starting with a ground procedure and then applying a general, and independently verifiable, lifting transformation. Previous planners have been designed directly as lifted procedures. Our ground procedure is a ground version of Tate's NONLIN procedure. In Tate's procedure one is not required to determine whether a prerequisite of a step in an unfinished plan...
Topics: DTIC Archive, McAllester, David, MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB,...
The objective of this program has been to develop theoretical and semi-empirical methods to enable engineers to design ribbon unwinders with reasonable accuracy and a minimum of development effort. This basic objective has been fulfilled, despite the fact that the degree of predictive accuracy depends to some extent on the design details of the fuze containing the ribbon unwinder. Some of the conclusions are that (1) Ribbon materials had to be in a dead soft condition to be suitable for...
Topics: DTIC Archive, THREE C SYSTEMS INC WYNNEWOOD PA, *FOILS(MATERIALS), *WINDING, *ARMING DEVICES,...
We report on initial experiments with an implemented learning system whose inputs are images of two-dimensional shapes. The system first builds semantic network descriptions of shapes based on Brady's smoothed local symmetry representation. It learns shape models from them using a substantially modified version of Winston's ANALOGY program. A generalization of Gray coding enables the representation to be extended and also allows a single operation, called ablation, to achieve the effects of...
Topics: DTIC Archive, Connell,J H, MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB, *IMAGE...
The purpose of this study was to evaluate the teaching effectiveness of different aspects of the SCHOLAR CAI system. The experiment compared how well students learn using SCHOLAR with (1) the interactive map display of Map- SCHOLAR, (2) a static labeled map, and (3) an unlabeled map. The results of the experiment showed that the students learned significantly more with the interactive map display, than with either the labeled map or the unlabeled map. A new method called backtrace analysis was...
Topics: DTIC Archive, Collins, Allan, Adams, Marilyn Jager, Pew, Richard, BOLT BERANEK AND NEWMAN INC...
In systems which learn a large number of rules (productions), it is important to match the rules efficiently, in order to avoid the machine learning utility problem -- if the learned rules slow down the matcher, the 'learning' can slow the whole system down to a crawl. So we need match algorithms that scale well with the number of productions in the system. Right unlinking was introduced as a way to improve the scalability of the Rete match algorithm. In this paper we build on this idea,...
Topics: DTIC Archive, Doorenbos, Robert B, CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE,...
Recently, there has been an increased interest in machine learning methods that learn from more than one learning task. Such methods have repeatedly found to outperform conventional, single-task learning algorithms when learning tasks are appropriately related. To increase robustness of these approaches, methods are desirable that can reason about the relatedness of individual learning tasks, in order to avoid the danger arising from tasks that are unrelated and thus potentially misleading....
Topics: DTIC Archive, Thrun, Sebastian, CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE,...
A probabilistic analysis of the Rocchio relevance feedback algorithm, one of the most popular learning methods from information retrieval, is presented in a text categorization framework. The analysis results in a probabilistic version of the Rocchio classifier and offers an explanation for the TFIDF word weighting heuristic. The Rocchio classifier, its probabilistic variant and a standard naive Bayes classifier are compared on three text categorization tasks. The results suggest that the...
Topics: DTIC Archive, Joachims, Thorsten, CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE,...
This document is the final report on contract number DAAH04-95-1-0068 awarded to the Computer Vision laboratory at the University of Massachusetts for the purchase of a high performance visualization workstation. It describes the equivalent purchased under this contract and contains brief status summaries of two DOD sponsored research programs making the heaviest use of the equipment. In the first of these, the Daedalus Battlefield Visualization System is being designed to produce real time 3D...
Topics: DTIC Archive, Hanson, Allen R., MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE, *COMPUTATIONS,...
Topics: DTIC Archive, ARMY TANK AUTOMOTIVE RESEARCH DEVELOPMENT AND ENGINEERING CENTER WARREN MI,...
Automatic text document classification is a fundamental problem in machine learning. Given the dynamic nature and the exponential growth of the World Wide Web, one needs the ability to classify not only a massive number of documents, but also documents that belong to wide variety of domains. Some examples of the domains are e-mails, blogs, Wikipedia articles, news articles, newsgroups, online chats, etc. It is the difference in the writing style that differentiates these domains. Text documents...
Topics: DTIC Archive, NAVAL POSTGRADUATE SCHOOL MONTEREY CA, *CLASSIFICATION, *DOCUMENTS, INTERNET,...
Semantic parsing involves deep semantic analysis that maps natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing computing systems that understand natural language input. This thesis presents a new machine learning approach for semantic parsing based on string-kernel-based classification. It takes natural language sentences paired with their formal meaning representations as training data. For every production...
Topics: DTIC Archive, TEXAS UNIV AT AUSTIN DEPT OF COMPUTER SCIENCES, *SEMANTICS, GRAMMARS, LEARNING,...
We describe a novel methodology by which a software agent can learn to predict future events in complex stochastic environments together with an important heuristic-based acceleration technique for computing the prediction. This speed-up enables us to use much more context in our predictions than was previously possible [Darken, 2005]. We present results gathered from a first prototype of our approach.
Topics: DTIC Archive, NAVAL POSTGRADUATE SCHOOL MONTEREY CA MODELING VIRTUAL ENVIRONMENTS AND SIMULATION...
In this paper, we report our experiments at Ad-hoc task, Web Track 2012. In this year, we attempt to use new web parser with noise elimination. The Conditional Boolean BM25 was used as major ranking function. We also introduce Learning-To-Rank to combine multiple features together for ranking, but the performance was poor due to the low quality of training data.
Topics: DTIC Archive, CHINESE ACADEMY OF SCIENCES BEIJING INST OF COMPUTING TECHNOLOGY, *INFORMATION...
In this paper, we describe our solutions to the Session Track at TREC 2012. The main contribution of our work is that we implement the learning to rank model to re-rank the documents retrieved by our search engine. We notice that Huurninket al. have used learning to rank algorithm to model session features at last year's Session Track. Due to lacking of training data, their model did not outperform substantially than others. Intuitively, we use last year's session data for tuning the weights of...
Topics: DTIC Archive, CHINESE ACADEMY OF SCIENCES BEIJING INST OF COMPUTING TECHNOLOGY, *INFORMATION...
Unsupervised machine learning methods such as clustering and change detection are indispensable to various real-world data processing tasks. However, due to its vague formulation, studies of unsupervised learning tend to be ad-hoc, and thus development of unsupervised learning methods is still far behind supervised learning. The project aims at overcoming this difficulty by providing a systematic approach to unsupervised learning based on information measures. The PI and his group developed...
Topics: DTIC Archive, TOKYO INST OF TECHNOLOGY (JAPAN), *LEARNING MACHINES, DATA PROCESSING, JAPAN, MAN...
The last three years have been exceptionally productive. Our research focused on two complementary themes: optimal learning, which addresses the efficient collection of information, and approximate dynamic programming, which is a modeling and algorithmic strategy for solving complex, sequential decision problems. These problems arise in the control of complex machinery, R&D portfolio optimization, materials science (sequential design of experiments), communications, and a wide range of...
Topics: DTIC Archive, PRINCETON UNIV NJ DEPT OF OPERATIONS RESEARCH AND FINANCIAL ENGINEERING, *DYNAMIC...
The integrated development environment of the General Architecture for Text Engineering (GATE), or GATE Developer, is used to annotate entities in a text document consisting of messages in and around the Baghdad area (SYNCOIN data). Highlighting entities, such as person(s), location(s), and organization(s), may result in a more structured format for faster comprehension of the data. The application for entity determination is called a nearly-new information extraction, or ANNIE: a system of...
Topics: DTIC Archive, ARMY RESEARCH LAB ABERDEEN PROVING GROUND MD, *COMPUTER ARCHITECTURE, *INFORMATION...
Heart-rate complexity (HRC) has been proposed as a new vital sign for critical care medicine. The purpose of this research was to develop a reliable method for determining HRC continuously in real time in critically ill patients using multiple waveform channels that also compensates for noisy and unreliable data. Using simultaneously acquired electrocardiogram (Leads I, II, V) and arterial blood pressure waveforms sampled at 360 Hz from 250 patients (over 375 h of patient data), we evaluated a...
Topics: DTIC Archive, ARMY INST OF SURGICAL RESEARCH FORT SAM HOUSTON TX, *CLINICAL MEDICINE, *COMPUTER...
Learning to understand the meaning of natural language is an important problem within language processing that has the potential to revolutionize human interactions with computer systems. Informally, the problem specification is to map natural language text to a formal semantic representation connected to the real world. This problem has applications such as information extraction and understanding robot commands, and also may be helpful for other natural language processing tasks. Human...
Topics: DTIC Archive, CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE, *COMPUTATIONAL...
