319 research outputs found
A Bayesian Abduction Model For Sensemaking
This research develops a Bayesian Abduction Model for Sensemaking Support (BAMSS) for information fusion in sensemaking tasks. Two methods are investigated. The first is the classical Bayesian information fusion with belief updating (using Bayesian clustering algorithm) and abductive inference. The second method uses a Genetic Algorithm (BAMSS-GA) to search for the k-best most probable explanation (MPE) in the network. Using various data from recent Iraq and Afghanistan conflicts, experimental simulations were conducted to compare the methods using posterior probability values which can be used to give insightful information for prospective sensemaking. The inference results demonstrate the utility of BAMSS as a computational model for sensemaking. The major results obtained are: (1) The inference results from BAMSS-GA gave average posterior probabilities that were 103 better than those produced by BAMSS; (2) BAMSS-GA gave more consistent posterior probabilities as measured by variances; and (3) BAMSS was able to give an MPE while BAMSS-GA was able to identify the optimal values for kMPEs. In the experiments, out of 20 MPEs generated by BAMSS, BAMSS-GA was able to identify 7 plausible network solutions resulting in less amount of information needed for sensemaking and reducing the inference search space by 7/20 (35%). The results reveal that GA can be used successfully in Bayesian information fusion as a search technique to identify those significant posterior probabilities useful for sensemaking. BAMSS-GA was also more robust in overcoming the problem of bounded search that is a constraint to Bayesian clustering and inference state space in BAMSS
Deep Learning in Lane Marking Detection: A Survey
Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm
Logical Abstractions for Noisy Variational Quantum Algorithm Simulation
Due to the unreliability and limited capacity of existing quantum computer
prototypes, quantum circuit simulation continues to be a vital tool for
validating next generation quantum computers and for studying variational
quantum algorithms, which are among the leading candidates for useful quantum
computation. Existing quantum circuit simulators do not address the common
traits of variational algorithms, namely: 1) their ability to work with noisy
qubits and operations, 2) their repeated execution of the same circuits but
with different parameters, and 3) the fact that they sample from circuit final
wavefunctions to drive a classical optimization routine. We present a quantum
circuit simulation toolchain based on logical abstractions targeted for
simulating variational algorithms. Our proposed toolchain encodes quantum
amplitudes and noise probabilities in a probabilistic graphical model, and it
compiles the circuits to logical formulas that support efficient repeated
simulation of and sampling from quantum circuits for different parameters.
Compared to state-of-the-art state vector and density matrix quantum circuit
simulators, our simulation approach offers greater performance when sampling
from noisy circuits with at least eight to 20 qubits and with around 12
operations on each qubit, making the approach ideal for simulating near-term
variational quantum algorithms. And for simulating noise-free shallow quantum
circuits with 32 qubits, our simulation approach offers a reduction
in sampling cost versus quantum circuit simulation techniques based on tensor
network contraction.Comment: ASPLOS '21, April 19-23, 2021, Virtual, US
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
We present a novel hybrid algorithm for Bayesian network structure learning,
called H2PC. It first reconstructs the skeleton of a Bayesian network and then
performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
The algorithm is based on divide-and-conquer constraint-based subroutines to
learn the local structure around a target variable. We conduct two series of
experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is
currently the most powerful state-of-the-art algorithm for Bayesian network
structure learning. First, we use eight well-known Bayesian network benchmarks
with various data sizes to assess the quality of the learned structure returned
by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in
terms of goodness of fit to new data and quality of the network structure with
respect to the true dependence structure of the data. Second, we investigate
H2PC's ability to solve the multi-label learning problem. We provide
theoretical results to characterize and identify graphically the so-called
minimal label powersets that appear as irreducible factors in the joint
distribution under the faithfulness condition. The multi-label learning problem
is then decomposed into a series of multi-class classification problems, where
each multi-class variable encodes a label powerset. H2PC is shown to compare
favorably to MMHC in terms of global classification accuracy over ten
multi-label data sets covering different application domains. Overall, our
experiments support the conclusions that local structural learning with H2PC in
the form of local neighborhood induction is a theoretically well-motivated and
empirically effective learning framework that is well suited to multi-label
learning. The source code (in R) of H2PC as well as all data sets used for the
empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author
An overview of the main machine learning models - from theory to algorithms
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn the context of solving highly complex problems, Artificial Intelligence shows an exponential
growth over the past years allowing the Machine Learning to augment and sometimes to outperform
the human learning. From driverless cars to automatic recommendation on Netflix, we are
surrounded by AI, even if we do not notice it. Furthermore, companies have recently adopted new
frameworks in their routines which are mainly composed by algorithms able to solve complex
problems in a short period of time.
The growth of AI technologies has been absolutely stunning and yes, it is only possible because
a sub-field of AI called Machine Learning is growing even faster. In a small scale, Machine Learning
may be seen as a simple system able to find patterns on data and learn from it. However, it is
precisely that learning process that in a large scale will allow machines to mimic the human behavior
and perform tasks that would eventually require human intelligence. Just for us to have an idea,
according to Forbes the global Machine Learning market was evaluated in 21B in 2024. Naturally, Machine Learning has become an attractive and
profitable scientific area that demands continuous learning since there is always something new
being discovered.
During the last decades, a huge number of algorithms have been proposed by the research
community, which sometimes may cause some confusion of how and when to use each one of them.
That is exactly what is pretended in this thesis, over the next chapters we are going to review the
main Machine Learning models and their respective advantages/disadvantages
Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems
Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Data Science: Measuring Uncertainties
With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems
- …