Stefanos Manganaris

IBM

The aim of the study was to determine circulating cytokines, cortisol and Insulin-like Growth Factor (IGF)-1, known for their involvement in inflammation, in male patients with First Episode Psychosis (FEP) and subjects at Ultra High Risk (UHR) for Psychosis. The FEP group presented increased pro-inflammatory cytokines (TNF-α, IFN-γ, ΤNF-β) as well...

Erythropoiesis in mammals concludes with the dramatic process of enucleation that results in reticulocyte formation. The mechanism of enucleation has not yet been fully elucidated. A common problem encountered when studying the localization of key proteins and structures within enucleating erythroblasts by microscopy is the difficulty to observe a...

To understand the role of cytoskeleton and membrane signaling molecules in erythroblast enucleation, we developed a novel analysis protocol of multiparameter high-speed cell imaging in flow. This protocol enabled us to observe F-actin and phosphorylated myosin regulatory light chain (pMRLC) assembled into a contractile actomyosin ring (CAR) between...

Sound marketing decisions often require understanding the cause-and-effect relationships between treatment and outcomes. Market research traditionally approaches such questions by designing randomized experiments that aim to isolate the effects of the specific treatment from other effects. We review an alternate methodology that is well suited to o...

IBM's emergency response service provides real-time intrusion detection (RTID) services through the Internet for a variety of clients. As the number of clients increases, the volume of alerts generated by the RTID sensors becomes intractable. This problem is aggravated by the fact that some sensors may generate hundreds or even thousands of innocen...

IBM's emergency response service provides real-time intrusion detection (RTID) services through the Internet for a variety of clients. As the number of clients increases, the volume of alerts generated by the RTID sensors becomes intractable. This problem is aggravated by the fact that some sensors may generate hundreds or even thousands of innocen...

Many real-life problems require a partial classification of the data. We use the term "partial classification" to describe the discovery of models that show char- acteristics of the data classes, but may not cover all classes and all examples of any given class. Complete classification may be infeasible or undesirable when there are a very large nu...

Many real-life problems require a partial classification of the data. We use the term "partial classification" to describe the discovery of models that show characteristics of the data classes, but may not cover all classes and all examples of any given class. Complete classification may be infeasible or undesirable when there are a very large numb...

Learning how to classify sensor data is one of the basic learning tasks in engineering. Data from sensors are usually made available over time, and are classified according to the behavior they exhibit in specific time intervals. This paper addresses the problem of classifying finite, univariate time series that are governed by unknown deterministi...

Learning how to classify sensor data is one of the basic learning tasks in engineering. Data from sensors are usually made available over time and are classified according to the behavior they exhibit in specific time intervals. I address the problem of classifying finite, univariate, time series that are governed by unknown deterministic processes...

Density estimation is a central problem in data mining and knowledge discovery, with applications from data visualization and exploratory data analysis to supervised and unsupervised concept learning. This paper presents a simple nonparametric method for univariate density estimation that uses Bayesian inference and the minimum-message length princ...

Learning how to classify sensor data is one of the basic learning tasks in engineering. Data from sensors are usually made available over time and are classified according to the behavior they exhibit in specific time intervals. I address the problem of classifying finite, univariate, time series that are governed by unknown deterministic processes...

Most concept induction algorithms process concept instances described in terms of properties that remain constant over time. In temporal domains, instances are best described in terms of properties whose values vary with time. Data engineering is called upon in temporal domains to transform the raw data into an appropriate form for concept inductio...

We address the problem of classifying time series according to their morphological features in the time domain. In a supervised machine-learning framework, we induce a classification procedure from a set of preclassified examples. For each class, we infer a model that captures its morphological features, using Bayesian model induction and the minim...

This paper addresses the problem of detecting and diagnosing faults in physical systems, for which suitable system models are not available. An architecture is proposed that integrates the on-line acquisition and exploitation of monitoring and diagnostic knowledge. The focus is on the component of the architecture that discovers classes of behavior...

The derivation of useful behaviors from component connection models of the physical structure of complex engineering systems is discussed. A component connection (CC) modeling scheme that compiles defined models to Qsim constraint representations for behavior generation is used. A number of issues arise in generating globally precise behaviors from...

In this paper we address the problem of detecting and diagnosing faults in physical systems, for which neither prior expertise for the task nor suitable system models are available. We propose an architecture that integrates the on-line acquisition and exploitation of monitoring and diagnostic knowledge. The focus of the paper is on the component o...

Many real-life problems require a partial classification of the data. We use the term "partial classification" to describe the discovery of models that show characteristics of the data classes, but may not cover all classes and all examples of any given class. Complete classification may be infeasible or undesirable when there are a very large numb...

Thesis (Ph. D. in Computer Science)--Vanderbilt University, 1997. Bibliography: leaves 180-191.