Inverse Modeling for MEG/EEG data

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur

Data in Business Process Models. A Preliminary Empirical Study

Traditional activity-centric process modeling languages treat data as simple black boxes acting as input or output for activities. Many alternate and emerging process modeling paradigms, such as case handling and artifact-centric process modeling, give data a more central role. This is achieved by introducing lifecycles and states for data objects, which is beneficial when modeling data-or knowledge-intensive processes. We assume that traditional activity-centric process modeling languages lack the capabilities to adequately capture the complexity of such processes. To verify this assumption we conducted an online interview among BPM experts. The results not only allow us to identify various profiles of persons modeling business processes, but also the problems that exist in contemporary modeling languages w.r.t. The modeling of business data. Overall, this preliminary empirical study confirms the necessity of data-awareness in process modeling notations in general

Superquadrics for segmentation and modeling range data

We present a novel approach to reliable and efficient recovery of part-descriptions in terms of superquadric models from range data. We show that superquadrics can directly be recovered from unsegmented data, thus avoiding any presegmentation steps (e.g., in terms of surfaces). The approach is based on the recover-andselect paradigm. We present several experiments on real and synthetic range images, where we demonstrate the stability of the results with respect to viewpoint and noise

Modeling social networks from sampled data

Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great deal of attention on random graph models of networks whose nodes represent individual social actors and whose edges represent a specified relationship between the actors. Most inference for social network models assumes that the presence or absence of all possible links is observed, that the information is completely reliable, and that there are no measurement (e.g., recording) errors. This is clearly not true in practice, as much network data is collected though sample surveys. In addition even if a census of a population is attempted, individuals and links between individuals are missed (i.e., do not appear in the recorded data). In this paper we develop the conceptual and computational theory for inference based on sampled network information. We first review forms of network sampling designs used in practice. We consider inference from the likelihood framework, and develop a typology of network data that reflects their treatment within this frame. We then develop inference for social network models based on information from adaptive network designs. We motivate and illustrate these ideas by analyzing the effect of link-tracing sampling designs on a collaboration network.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS221 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Deep Generative Modeling of LiDAR Data

Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot mapping and localization. In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map. Our approach can generate high quality samples, while simultaneously learning a meaningful latent representation of the data. We demonstrate significant improvements against state-of-the-art point cloud generation methods. Furthermore, we propose a novel data representation that augments the 2D signal with absolute positional information. We show that this helps robustness to noisy and imputed input; the learned model can recover the underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201

Modeling of Traceability Information System for Material Flow Control Data.

This paper focuses on data modeling for traceability of material/work flow in information layer of manufacturing control system. The model is able to trace all associated data throughout the product manufacturing from order to final product. Dynamic data processing of Quality and Purchase activities are considered in data modeling as well as Order and Operation base on lots particulars. The modeling consisted of four steps and integrated as one final model. Entity-Relationships Modeling as data modeling methodology is proposed. The model is reengineered with Toad Data Modeler software in physical modeling step. The developed model promises to handle fundamental issues of a traceability system effectively. It supports for customization and real-time control of material in flow in all levels of manufacturing processes. Through enhanced visibility and dynamic store/retrieval of data, all traceability usages and applications is responded. Designed solution is initially applicable as reference data model in identical lot-base traceability system

Modeling Blank Data Entries in Data Envelopment Analysis

We show how Data Envelopment Analysis (DEA) can handle missing data. When blank data entries are coded by appropriate dummy values, the DEA model automatically excludes the missing data from the analysis. We extend this result to weight-restricted DEA models by presenting a simple modification to the usual weight restrictions, which automatically relaxes the weight restriction in case of missing data. Our approach is illustrated by a case study, describing an application to international sustainable development indices.Data Envelopment Analysis, Weight Restrictions, Missing Data, Blank Entries