3,025 research outputs found
Combining decision procedures for the reals
We address the general problem of determining the validity of boolean
combinations of equalities and inequalities between real-valued expressions. In
particular, we consider methods of establishing such assertions using only
restricted forms of distributivity. At the same time, we explore ways in which
"local" decision or heuristic procedures for fragments of the theory of the
reals can be amalgamated into global ones. Let Tadd[Q] be the
first-order theory of the real numbers in the language of ordered groups, with
negation, a constant 1, and function symbols for multiplication by
rational constants. Let Tmult[Q] be the analogous theory for the
multiplicative structure, and let T[Q] be the union of the two. We
show that although T[Q] is undecidable, the universal fragment of
T[Q] is decidable. We also show that terms of T[Q]can
fruitfully be put in a normal form. We prove analogous results for theories in
which Q is replaced, more generally, by suitable subfields F
of the reals. Finally, we consider practical methods of establishing
quantifier-free validities that approximate our (impractical) decidability
results.Comment: Will appear in Logical Methods in Computer Scienc
User Centered, Application Independent Visualization of National Airspace Data
This paper describes an application independent software tool, IV4D, built to visualize animated and still 3D National Airspace System (NAS) data specifically for aeronautics engineers who research aggregate, as well as single, flight efficiencies and behavior. IV4D was origin ally developed in a joint effort between the National Aeronautics and Space Administration (NASA) and the Air Force Research Laboratory (A FRL) to support the visualization of air traffic data from the Airspa ce Concept Evaluation System (ACES) simulation program. The three mai n challenges tackled by IV4D developers were: 1) determining how to d istill multiple NASA data formats into a few minimal dataset types; 2 ) creating an environment, consisting of a user interface, heuristic algorithms, and retained metadata, that facilitates easy setup and fa st visualization; and 3) maximizing the user?s ability to utilize the extended range of visualization available with AFRL?s existing 3D te chnologies. IV4D is currently being used by air traffic management re searchers at NASA?s Ames and Langley Research Centers to support data visualizations
Hierarchical relational models for document networks
We develop the relational topic model (RTM), a hierarchical model of both
network structure and node attributes. We focus on document networks, where the
attributes of each document are its words, that is, discrete observations taken
from a fixed vocabulary. For each pair of documents, the RTM models their link
as a binary random variable that is conditioned on their contents. The model
can be used to summarize a network of documents, predict links between them,
and predict words within them. We derive efficient inference and estimation
algorithms based on variational methods that take advantage of sparsity and
scale with the number of links. We evaluate the predictive performance of the
RTM for large networks of scientific abstracts, web documents, and
geographically tagged news.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS309 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Augmenting Assessment with Learning Analytics
Learning analytics as currently deployed has tended to consist of large-scale analyses of available learning process data to provide descriptive or predictive insight into behaviours. What is sometimes missing in this analysis is a connection to human-interpretable, actionable, diagnostic information. To gain traction, learning analytics researchers should work within existing good practice particularly in assessment, where high quality assessments are designed to provide both student and educator with diagnostic or formative feedback. Such a model keeps the human in the analytics design and implementation loop, by supporting student, peer, tutor, and instructor sense-making of assessment data, while adding value from computational analyses
Machine learning with screens for detecting bid-rigging cartels
We combine machine learning techniques with statistical screens computed from the distribution of bids in tenders within the Swiss construction sector to predict collusion through bid-rigging cartels. We assess the out of sample performance of this approach and find it to correctly classify more than 80% of the total of bidding processes as collusive or non-collusive. As the correct classification rate, however, differs across truly non-collusive and collusive processes, we also investigate tradeoffs in reducing false positive vs. false negative predictions. Finally, we discuss policy implications of our method for competition agencies aiming at detecting bid- rigging cartel
The Role of Leadersâ Regulatory Focus Towards Creativity and Safety Ambidextrous Behavior. A Conceptual View
Most research has explored ambidexterity at the organisational level and very limited research is available on individual ambidextrous behaviours. This research paper reviews the role of leadersâ regulatory focus in promoting individual ambidexterity in the form of creativity and safety. The main aim is to contribute to ambidexterity and self-regulatory literature by examining the role of leadersâ regulatory focus in managing ambidextrous behaviours. Ambidexterity is the ability to manage conflicting task demands, which poses a fundamental self-regulatory and motivational challenge in the process of pursuing different goals
AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0
The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems
Residual Reinforcement Learning from Demonstrations
Residual reinforcement learning (RL) has been proposed as a way to solve
challenging robotic tasks by adapting control actions from a conventional
feedback controller to maximize a reward signal. We extend the residual
formulation to learn from visual inputs and sparse rewards using
demonstrations. Learning from images, proprioceptive inputs and a sparse
task-completion reward relaxes the requirement of accessing full state
features, such as object and target positions. In addition, replacing the base
controller with a policy learned from demonstrations removes the dependency on
a hand-engineered controller in favour of a dataset of demonstrations, which
can be provided by non-experts. Our experimental evaluation on simulated
manipulation tasks on a 6-DoF UR5 arm and a 28-DoF dexterous hand demonstrates
that residual RL from demonstrations is able to generalize to unseen
environment conditions more flexibly than either behavioral cloning or RL
fine-tuning, and is capable of solving high-dimensional, sparse-reward tasks
out of reach for RL from scratch
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