SMLSOM: The shrinking maximum likelihood self-organizing map

Determining the number of clusters in a dataset is a fundamental issue in data clustering. Many methods have been proposed to solve the problem of selecting the number of clusters, considering it to be a problem with regard to model selection. This paper proposes an efficient algorithm that automatically selects a suitable number of clusters based on a probability distribution model framework. The algorithm includes the following two components. First, a generalization of Kohonen's self-organizing map (SOM) is introduced. In Kohonen's SOM, clusters are modeled as mean vectors. In the generalized SOM, each cluster is modeled as a probabilistic distribution and constructed by samples classified based on the likelihood. Second, the dynamically updating method of the SOM structure is introduced. In Kohonen's SOM, each cluster is tied to a node of a fixed two-dimensional lattice space and learned using neighborhood relations between nodes based on Euclidean distance. The extended SOM defines a graph with clusters as vertices and neighborhood relations as links and updates the graph structure by cutting weakly-connection and unnecessary vertex deletions. The weakness of a link is measured using the Kullback--Leibler divergence, and the redundancy of a vertex is measured using the minimum description length. Those extensions make it efficient to determine the appropriate number of clusters. Compared with existing methods, the proposed method is computationally efficient and can accurately select the number of clusters

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

LABORATORY AND FIELD INVESTIGATION OF MIXING, MORPHOLOGY AND OPTICAL PROPERTIES OF SOOT AND SECONDARY ORGANIC AEROSOLS

Soot/ black carbon particles are believed to be the second largest anthropogenic contributor to the Earth’s radiative forcing, and are emitted from combustion processes. Freshly emitted soot has a fractal-like structure in which monomers are arranged into branched chain-like configuration. In the atmosphere, soot mixes with and is processed by interacting with other co-existing particle and vapors. The processing that soot undergo after emission alters its morphology; for example, condensation of vapors results into coated, mixed or compacted soot depending upon the environmental conditions. Changes in soot morphology have a strong and direct influence on its optical properties. The unique and complex structure of soot and its behavior and evolution in the atmosphere are difficult to quantify and to model accurately. Radiative models use simplified assumptions for soot morphology, which results in large uncertainties on the forcing and therefore on the effect that soot has on climate. The temporal and spatial variability of soot morphology and mixing state may also add to already severe biases in absorption measurements in filter-based methods traditionally used to estimate aerosol absorption in the atmosphere. Since optical properties (absorption and scattering) are key input parameters to climate models, biases associated with their measurements add to the uncertainty in the forcing estimates. During my graduate research, I worked on the development of a new multi-wavelength instrument, capable of measuring aerosol absorption and scattering over the entire visible range of the solar spectrum. The instrument combines photoacoustic spectroscopy and nephelometry with a supercontinuum laser to measure aerosol absorption and scattering over a broad wavelength range (~400 nm to ~700 nm). Since the instrument measures the aerosol optical properties while the particles are suspended in air, this instrument is free from biases common in filter based instruments due to changes in morphology of the particles when they are deposited on the filter or due to multiple scattering of light from the filter. To complement the instrument development discussed above, my research also included an in depth study of the evolution of soot morphology and mixing state upon its interaction with organics in the atmosphere. We investigated the samples collected from a forested site during the Carbonaceous Aerosols Radiative Effects Study (CARES), conducted in June 2010 in the Sacramento area, CA. Using a scanning electron microscopy, we characterized the morphology and mixing state of soot due to its interaction with biogenic secondary organic aerosols. Based on our analysis we found that both condensation and coagulation were accountable for the mixing of soot with SOA during the episodes we studied in CARES. We found that during coagulation, the viscosity of secondary organic aerosol (SOA) plays a crucial role in determining the soot-SOA mixing state. The viscosity of SOA can be linked to environmental factors like relative humidity, therefore the mixing of soot particles with biogenic aerosol may be affected by factors controlling the viscosity of SOA. The mixing of soot with biogenic SOA through condensation was further investigated under controlled laboratory conditions in a set of follow-up chamber experiments at the Pacific Northwest National Laboratory during the Soot Aerosol Aging Study (SAAS). In this study, the condensation of α-pinene SOA was examined on diesel soot at different relative humidity conditions. In this study, we find that soot in humid conditions becomes compact, which results into reduction of soot surface area for condensation. The reduction in surface area results in a much slower SOA condensational growth on the soot particles. Compacted soot particles in the atmosphere have been reported in several previous studies and their compaction is attributed to the condensation of organic vapors on soot in humid conditions, ice nucleation or water processing in clouds. Our findings are relevant to mixing scenarios where preprocessed, compacted soot competes with freshly emitted soot for SOA uptake. Based on the findings of our study we expect larger SOA growths on fresh soot particles as compared to compacted particles. The research carried out during my PhD contributes to the field of atmospheric science on several aspects. The results from our study can be used to reduce the uncertainties in radiative forcing estimates

Projection-Based Clustering through Self-Organization and Swarm Intelligence

It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining

A virtual hand assessment system for efficient outcome measures of hand rehabilitation

Previously held under moratorium from 1st December 2016 until 1st December 2021.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control

Apolipoprotein A-I in glucose metabolism and amyloidosis

The role of Apolipoprotein A-I (ApoA-I), the main protein component of HDL, in cholesterol transport and metabolism is well known and has been studied for more than four decades. More recently, ApoA-I protein has been shown to also have a positive role in glucose control by both stimulation of glucose uptake by muscles and by increasing glucose-stimulated pancreatic insulin secretion. Two of the four papers included in this thesis are focused on the role of ApoA-I in glucose control. In paper I, we discovered that pre-incubation of beta cells and isolated murine islets with ApoA-I augmented glucose stimulated insulin secretion. To dissect the cellular mechanisms of action, we used a variety of functional and microscopic approaches. We concluded that ApoA-I’s positive action on beta cells involves ApoA-I internalization into beta cells, Pdx1 nuclear translocation, and increased levels of proinsulin processing enzymes. Altogether, these events lead to an increased number of insulin granules. In paper II, we addressed the impact of hyperglycemia on the function of ApoA-I in glucose control. Prolonged hyperglycemia in poorly controlled diabetes leads to an increase in reactive glucose metabolites that covalently modify proteins, including ApoA-I, by non-enzymatic glycation reaction. To investigate the impact of ApoA-I glycation on its functionality, we chemically glycated ApoA-I with two different metabolites and performed structural and functional studies. We concluded that site-specific, covalent modifications of ApoA-I alter the protein structure, reduce the lipid-binding capability as well as the ability to catalyze cholesterol efflux from macrophages. Glycation modifications eliminated the ApoA-I stimulatory effect on the in vivo and in vitro glucose clearance. Altogether, it was concluded that glycation modification of ApoA-I impairs the ApoA-I protein functionality in lipid and glucose metabolism. The two remaining papers included in this thesis are focused on another aspect of ApoA-I, its ability to aggregate in insoluble fibrils causing a disease known as ApoA-I related amyloidosis. So far, more than twenty known human amyloidogenic variants of the APOA1 gene have been found to lead to progressive accumulation of ApoA-I protein in vital organs, causing their dysfunction and failure. ApoA-I amyloidogenic mutations are associated with low ApoA-I and HDL-cholesterol plasma levels, however, subjects affected by ApoA-I-related amyloidosis do not show a higher risk of cardiovascular diseases. In paper IV , we investigated the structural features, the lipid-binding properties and the functionality of four ApoA-I amyloidogenic variants. We found that these variants are characterized by a higher efficiency at catalyzing cholesterol efflux from macrophages. This finding can at least in part explain why the carriers of ApoA-I amyloidogenic variants do not have a higher risk of developing cardiovascular diseases despite lower levels of HDL-cholesterol. To further expand on these observations, in paper III, we examined the clinical plasma samples obtained from patients carrying two of the variants previously investigated in vitro and from matched control individuals. Patients displayed a unique HDL profile with a higher content of the smaller HDL particles was observed in samples from carriers as compared to controls. In line with previous observations, the HDL from the carriers had an improved cholesterol efflux capacity. Structural analysis revealed that ApoA-i variants in 8.4 nm HDL particles showed an increased protein dynamics in close proximity to the region of the mutations. This region-specific increased protein flexibility may contribute to improved functionality of the ApoA-I variants in catalyzing cholesterol efflux