155 research outputs found
Local Subspace-Based Outlier Detection using Global Neighbourhoods
Outlier detection in high-dimensional data is a challenging yet important
task, as it has applications in, e.g., fraud detection and quality control.
State-of-the-art density-based algorithms perform well because they 1) take the
local neighbourhoods of data points into account and 2) consider feature
subspaces. In highly complex and high-dimensional data, however, existing
methods are likely to overlook important outliers because they do not
explicitly take into account that the data is often a mixture distribution of
multiple components.
We therefore introduce GLOSS, an algorithm that performs local subspace
outlier detection using global neighbourhoods. Experiments on synthetic data
demonstrate that GLOSS more accurately detects local outliers in mixed data
than its competitors. Moreover, experiments on real-world data show that our
approach identifies relevant outliers overlooked by existing methods,
confirming that one should keep an eye on the global perspective even when
doing local outlier detection.Comment: Short version accepted at IEEE BigData 201
Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes
Anomaly detection describes methods of finding abnormal states, instances or
data points that differ from a normal value space. Industrial processes are a
domain where predicitve models are needed for finding anomalous data instances
for quality enhancement. A main challenge, however, is absence of labels in
this environment. This paper contributes to a data-centric way of approaching
artificial intelligence in industrial production. With a use case from additive
manufacturing for automotive components we present a deep-learning-based image
processing pipeline. Additionally, we integrate the concept of domain
randomisation and synthetic data in the loop that shows promising results for
bridging advances in deep learning and its application to real-world,
industrial production processes.Comment: Conference paper for: 17. Fachtagung "Entwurf komplexer
Automatisierungssysteme (EKA)", Magdeburg/Germany, June 202
Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
Decades of progress in simulation-based surrogate-assisted optimization and
unprecedented growth in computational power have enabled researchers and
practitioners to optimize previously intractable complex engineering problems.
This paper investigates the possible benefit of a concurrent utilization of
multiple simulation-based surrogate models to solve complex discrete
optimization problems. To fulfill this, the so-called Self-Adaptive
Multi-surrogate Assisted Efficient Global Optimization algorithm (SAMA-DiEGO),
which features a two-stage online model management strategy, is proposed and
further benchmarked on fifteen binary-encoded combinatorial and fifteen ordinal
problems against several state-of-the-art non-surrogate or single surrogate
assisted optimization algorithms. Our findings indicate that SAMA-DiEGO can
rapidly converge to better solutions on a majority of the test problems, which
shows the feasibility and advantage of using multiple surrogate models in
optimizing discrete problems
ENIGMA-anxiety working group : rationale for and organization of large-scale neuroimaging studies of anxiety disorders
Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders
ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries
This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors
- …