135,094 research outputs found
Scalable inference of topic evolution via models for latent geometric structures
We develop new models and algorithms for learning the temporal dynamics of
the topic polytopes and related geometric objects that arise in topic model
based inference. Our model is nonparametric Bayesian and the corresponding
inference algorithm is able to discover new topics as the time progresses. By
exploiting the connection between the modeling of topic polytope evolution,
Beta-Bernoulli process and the Hungarian matching algorithm, our method is
shown to be several orders of magnitude faster than existing topic modeling
approaches, as demonstrated by experiments working with several million
documents in under two dozens of minutes.Comment: NeurIPS 201
A New Class of Time Dependent Latent Factor Models with Applications
In many applications, observed data are influenced by some combination of
latent causes. For example, suppose sensors are placed inside a building to
record responses such as temperature, humidity, power consumption and noise
levels. These random, observed responses are typically affected by many
unobserved, latent factors (or features) within the building such as the number
of individuals, the turning on and off of electrical devices, power surges,
etc. These latent factors are usually present for a contiguous period of time
before disappearing; further, multiple factors could be present at a time. This
paper develops new probabilistic methodology and inference methods for random
object generation influenced by latent features exhibiting temporal
persistence. Every datum is associated with subsets of a potentially infinite
number of hidden, persistent features that account for temporal dynamics in an
observation. The ensuing class of dynamic models constructed by adapting the
Indian Buffet Process --- a probability measure on the space of random,
unbounded binary matrices --- finds use in a variety of applications arising in
operations, signal processing, biomedicine, marketing, image analysis, etc.
Illustrations using synthetic and real data are provided
Organizing the Aggregate: Languages for Spatial Computing
As the number of computing devices embedded into engineered systems continues
to rise, there is a widening gap between the needs of the user to control
aggregates of devices and the complex technology of individual devices. Spatial
computing attempts to bridge this gap for systems with local communication by
exploiting the connection between physical locality and device connectivity. A
large number of spatial computing domain specific languages (DSLs) have emerged
across diverse domains, from biology and reconfigurable computing, to sensor
networks and agent-based systems. In this chapter, we develop a framework for
analyzing and comparing spatial computing DSLs, survey the current state of the
art, and provide a roadmap for future spatial computing DSL investigation.Comment: 60 pages; Review chapter to appear as a chapter in book "Formal and
Practical Aspects of Domain-Specific Languages: Recent Developments
An Example for BeSpaceD and its Use for Decision Support in Industrial Automation
We describe our formal methods-based spatial reasoning framework BeSpaceD and
its application in decision support for industrial automation. In particular we
are supporting analysis and decisions based on formal models for industrial
plant and mining operations. BeSpaceD is a framework for deciding geometric and
topological properties of spatio-temporal models. We present an example and
report on our ongoing experience with applications in different projects around
software and cyber-physical systems engineering. The example features
abstracted aspects of a production plant model. Using the example we motivate
the use of our framework in the context of an existing software platform
supporting monitoring, incident handling and maintenance of industrial
automation facilities in remote locations
Towards Decision Support for Smart Energy Systems based on Spatio-temporal Models
This report presents our SmartSpace event handling framework for managing
smart-grids and renewable energy installations. SmartSpace provides decision
support for human stakeholders. Based on different datasources that feed into
our framework, a variety of analysis and decision steps are supported. These
decision steps are ultimately used to provide adequate information to human
stakeholders. The paper discusses potential data sources for decisions around
smart energy systems and introduces a spatio-temporal modeling technique for
the involved data. Operations to reason about the formalized data are provided.
Our spatio-temporal models help to provide a semantic context for the data.
Customized rules allow the specification of conditions under which information
is provided to stakeholders. We exemplify our ideas and present our
demonstrators including visualization capabilities
Haptic Assembly and Prototyping: An Expository Review
An important application of haptic technology to digital product development
is in virtual prototyping (VP), part of which deals with interactive planning,
simulation, and verification of assembly-related activities, collectively
called virtual assembly (VA). In spite of numerous research and development
efforts over the last two decades, the industrial adoption of haptic-assisted
VP/VA has been slower than expected. Putting hardware limitations aside, the
main roadblocks faced in software development can be traced to the lack of
effective and efficient computational models of haptic feedback. Such models
must 1) accommodate the inherent geometric complexities faced when assembling
objects of arbitrary shape; and 2) conform to the computation time limitation
imposed by the notorious frame rate requirements---namely, 1 kHz for haptic
feedback compared to the more manageable 30-60 Hz for graphic rendering. The
simultaneous fulfillment of these competing objectives is far from trivial.
This survey presents some of the conceptual and computational challenges and
opportunities as well as promising future directions in haptic-assisted VP/VA,
with a focus on haptic assembly from a geometric modeling and spatial reasoning
perspective. The main focus is on revisiting definitions and classifications of
different methods used to handle the constrained multibody simulation in
real-time, ranging from physics-based and geometry-based to hybrid and unified
approaches using a variety of auxiliary computational devices to specify,
impose, and solve assembly constraints. Particular attention is given to the
newly developed 'analytic methods' inherited from motion planning and protein
docking that have shown great promise as an alternative paradigm to the more
popular combinatorial methods.Comment: Technical Report, University of Connecticut, 201
Toward the Automatic Generation of a Semantic VRML Model from Unorganized 3D Point Clouds
This paper presents our experience regarding the creation of 3D semantic
facility model out of unorganized 3D point clouds. Thus, a knowledge-based
detection approach of objects using the OWL ontology language is presented.
This knowledge is used to define SWRL detection rules. In addition, the
combination of 3D processing built-ins and topological Built-Ins in SWRL rules
aims at combining geometrical analysis of 3D point clouds and specialist's
knowledge. This combination allows more flexible and intelligent detection and
the annotation of objects contained in 3D point clouds. The created WiDOP
prototype takes a set of 3D point clouds as input, and produces an indexed
scene of colored objects visualized within VRML language as output. The context
of the study is the detection of railway objects materialized within the
Deutsche Bahn scene such as signals, technical cupboards, electric poles, etc.
Therefore, the resulting enriched and populated domain ontology, that contains
the annotations of objects in the point clouds, is used to feed a GIS system.Comment: arXiv admin note: substantial text overlap with arXiv:1301.4991,
arXiv:1301.478
Supporting Finite Element Analysis with a Relational Database Backend, Part I: There is Life beyond Files
In this paper, we show how to use a Relational Database Management System in
support of Finite Element Analysis. We believe it is a new way of thinking
about data management in well-understood applications to prepare them for two
major challenges, - size and integration (globalization). Neither extreme size
nor integration (with other applications over the Web) was a design concern 30
years ago when the paradigm for FEA implementation first was formed. On the
other hand, database technology has come a long way since its inception and it
is past time to highlight its usefulness to the field of scientific computing
and computer based engineering. This series aims to widen the list of
applications for database designers and for FEA users and application
developers to reap some of the benefits of database development
Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, Applications, and Prospects
The past few decades have witnessed the great progress of unmanned aircraft
vehicles (UAVs) in civilian fields, especially in photogrammetry and remote
sensing. In contrast with the platforms of manned aircraft and satellite, the
UAV platform holds many promising characteristics: flexibility, efficiency,
high-spatial/temporal resolution, low cost, easy operation, etc., which make it
an effective complement to other remote-sensing platforms and a cost-effective
means for remote sensing. Considering the popularity and expansion of UAV-based
remote sensing in recent years, this paper provides a systematic survey on the
recent advances and future prospectives of UAVs in the remote-sensing
community. Specifically, the main challenges and key technologies of
remote-sensing data processing based on UAVs are discussed and summarized
firstly. Then, we provide an overview of the widespread applications of UAVs in
remote sensing. Finally, some prospects for future work are discussed. We hope
this paper will provide remote-sensing researchers an overall picture of recent
UAV-based remote sensing developments and help guide the further research on
this topic
Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery
We develop necessary and sufficient conditions and a novel provably
consistent and efficient algorithm for discovering topics (latent factors) from
observations (documents) that are realized from a probabilistic mixture of
shared latent factors that have certain properties. Our focus is on the class
of topic models in which each shared latent factor contains a novel word that
is unique to that factor, a property that has come to be known as separability.
Our algorithm is based on the key insight that the novel words correspond to
the extreme points of the convex hull formed by the row-vectors of a suitably
normalized word co-occurrence matrix. We leverage this geometric insight to
establish polynomial computation and sample complexity bounds based on a few
isotropic random projections of the rows of the normalized word co-occurrence
matrix. Our proposed random-projections-based algorithm is naturally amenable
to an efficient distributed implementation and is attractive for modern
web-scale distributed data mining applications.Comment: Typo corrected; Revised argument in Lemma 3 and
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