Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios.Comment: This revised version fixes two small typos in the published versio

Summary paper on the ‘carbon accounting’ methodology applied to the assessment of the Scottish Government’s 2010-11 budget

Hailed by WWF Scotland as a “World First”, the Scottish Government in late September 2009 published a Carbon Assessment of their draft 2010-11 budget. Undertaken a year in advance of this assessment becoming a statutory requirement under the Climate Change (Scotland) Act 2009, this exercise produced some interesting results and generated a lot of interest. This article is intended to provide an overview of the exercise that was undertaken, and to highlight and address some outstanding issues that surround the assessment

Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet

Leaf area index (LAI) and water content (WC) in the root zone are two major hydro-meteorological parameters that exhibit a dominant control on water, energy and carbon fluxes, and are therefore important for any regional eco-hydrological or climatological study. To investigate the potential for retrieving these parameter from hyperspectral remote sensing, we have investigated plant spectral reflectance (400-2,500 nm, ASD FieldSpec3) for two major agricultural crops (sugar beet and spring barley) in the mid-latitudes, treated under different water and nitrogen (N) conditions in a greenhouse experiment over the growing period of 2008. Along with the spectral response, we have measured soil water content and LAI for 15 intensive measurement campaigns spread over the growing season and could demonstrate a significant response of plant reflectance characteristics to variations in water content and nutrient conditions. Linear and non-linear dimensionality analysis suggests that the full band reflectance information is well represented by the set of 28 vegetation spectral indices (SI) and most of the variance is explained by three to a maximum of eight variables. Investigation of linear dependencies between LAI and soil WC and pre-selected SI's indicate that: (1) linear regression using single SI is not sufficient to describe plant/soil variables over the range of experimental conditions, however, some improvement can be seen knowing crop species beforehand; (2) the improvement is superior when applying multiple linear regression using three explanatory SI's approach. In addition to linear investigations, we applied the non-linear CART (Classification and Regression Trees) technique, which finally did not show the potential for any improvement in the retrieval process

Teachers\u27 Perceptions of Students\u27 Creativity Characteristics

Teachers’ perceptions of an ideal student were investigated in terms of their FourSight preferences (i.e. Ideator, Clarifier, Developer, and Implementer). Based on these preferences, 275 teachers who were currently working in Western New York region described their “ideal” student with 66 adjectives of Torrance Ideal Child Checklist. Results showed that for each of FourSight preferences, teachers have a tendency to support characteristics associated with their own preference. More specifically, teachers with a stronger Ideator tendency encouraged the students’ Ideator characteristics more compared to Developer and Implementer styles. Teachers with a Clarifier tendency do not seem to favor students’ Ideator characteristics as much as those with an Ideator tendency. Significant findings also indicated that teachers with an Ideator tendency tend to define themselves as more creative than those with a Clarifier, Developer, or Implementer tendency. However, surprisingly, teachers who considered themselves as smart tend to encourage the Ideator student characteristics more in their classrooms than those who view themselves as creative. Results underscore the importance of creativity training in educational settings that emphasize cognitive style characteristics

RandomBoost: Simplified Multi-class Boosting through Randomization

We propose a novel boosting approach to multi-class classification problems, in which multiple classes are distinguished by a set of random projection matrices in essence. The approach uses random projections to alleviate the proliferation of binary classifiers typically required to perform multi-class classification. The result is a multi-class classifier with a single vector-valued parameter, irrespective of the number of classes involved. Two variants of this approach are proposed. The first method randomly projects the original data into new spaces, while the second method randomly projects the outputs of learned weak classifiers. These methods are not only conceptually simple but also effective and easy to implement. A series of experiments on synthetic, machine learning and visual recognition data sets demonstrate that our proposed methods compare favorably to existing multi-class boosting algorithms in terms of both the convergence rate and classification accuracy.Comment: 15 page

Target identification strategies in plant chemical biology

The current needs to understand gene function in plant biology increasingly require more dynamic and conditional approaches opposed to classic genetic strategies. Gene redundancy and lethality can substantially complicate research, which might be solved by applying a chemical genetics approach. Now understood as the study of small molecules and their effect on biological systems with subsequent target identification, chemical genetics is a fast developing field with a strong history in pharmaceutical research and drug discovery. In plant biology however, chemical genetics is still largely in the starting blocks, with most studies relying on forward genetics and phenotypic analysis for target identification, whereas studies including direct target identification are limited. Here, we provide an overview of recent advances in chemical genetics in plant biology with a focus on target identification. Furthermore, we discuss different strategies for direct target identification and the possibilities and challenges for plant biology

eXplainable Modeling (XM): Data Analysis for Intelligent Agents

Intelligent agents perform key tasks in several application domains by processing sensor data and taking actions that maximize reward functions based on internal models of the environment and the agent itself. In this paper we present eXplainable Modeling (XM), a Python software which supports data analysis for intelligent agents. XM enables to analyze state-models, namely models of the agent states, discovered from sensor traces by data-driven methods, and to interpret them for improved situation awareness. The main features of the tool are described through the analysis of a real case study concerning aquatic drones for water monitoring

Trauma-Informed Risk Assessment in Correctional Settings

This paper outlines a model which infuses trauma-informed principles into the existing Risk-Needs-Responsivity model of risk assessment commonly used in correctional settings. The connection between certain types of trauma and criminality is established. Despite this, many risk assessment procedures do not include screening for trauma, or trauma-specific interventions. An overview of the lasting effects of childhood maltreatment is included. Trauma-informed practices and assessment recommendations are also provided, along with recommendations for additional resources