Adaptive Real-Time Decoding of Brain Signals for Long-Term Control of a Neuro-Prosthetic Device

Changes in the statistical properties of neural signals recorded at the brain machine interface (BMI) pose significant challenges for accurate long-term control of prostheses interfaced directly with the brain by continuously altering the relationship between neural responses and desired action. In this thesis, we develop and test an adaptive decoding algorithm that can recover from changes in the statistical properties of neural signals within minutes. The adaptive decoding algorithm uses a Kalman filter as part of a dual-filter design to continuously optimize the relationship between the observed neural responses and the desired action of the prosthesis. Performance of the algorithm was evaluated by simulating the encoding of arm movement by neurons in the primary motor cortex under stationary conditions as well as nonstationary conditions depicting loss and/or replacement of neurons in the population. The time taken for the system to fully recover (3-12 minutes) was faster than other adaptive systems (Rotermund et al 2006) and resulted in errors that were well matched to the initial system performance. The algorithm adapts to the local properties of the stimulus and is able to decode movements with high accuracy outside the trained movement space. This implementation lends itself favorably toward a portable, robust long-term decoding approach at the brain machine interface capable of providing accurate real-time decoding of neural signals over periods of weeks to months without outside intervention

Technical Reports (2004 - 2009)

Authors of Technical Reports (2005-2009): Choueiry, Berthe Cohen, Myra Deogun, Jitender Dwyer, Matthew Elbaum, Sebastian Goddard, Steve Henninger, Scott Jiang, Hong Lu, Ying Ramamurthy, Byrav Rothermel, Gregg Scott, Stephen Seth, Sharad Soh, Leen-Kiat Srisa-an, Witty Swanson, David Variyam, Vinodchandran Wang, Jun Xu, Lison

Reallocation Problems in Scheduling

In traditional on-line problems, such as scheduling, requests arrive over time, demanding available resources. As each request arrives, some resources may have to be irrevocably committed to servicing that request. In many situations, however, it may be possible or even necessary to reallocate previously allocated resources in order to satisfy a new request. This reallocation has a cost. This paper shows how to service the requests while minimizing the reallocation cost. We focus on the classic problem of scheduling jobs on a multiprocessor system. Each unit-size job has a time window in which it can be executed. Jobs are dynamically added and removed from the system. We provide an algorithm that maintains a valid schedule, as long as a sufficiently feasible schedule exists. The algorithm reschedules only a total number of O(min{log^* n, log^* Delta}) jobs for each job that is inserted or deleted from the system, where n is the number of active jobs and Delta is the size of the largest window.Comment: 9 oages, 1 table; extended abstract version to appear in SPAA 201

DALiuGE: A Graph Execution Framework for Harnessing the Astronomical Data Deluge

The Data Activated Liu Graph Engine - DALiuGE - is an execution framework for processing large astronomical datasets at a scale required by the Square Kilometre Array Phase 1 (SKA1). It includes an interface for expressing complex data reduction pipelines consisting of both data sets and algorithmic components and an implementation run-time to execute such pipelines on distributed resources. By mapping the logical view of a pipeline to its physical realisation, DALiuGE separates the concerns of multiple stakeholders, allowing them to collectively optimise large-scale data processing solutions in a coherent manner. The execution in DALiuGE is data-activated, where each individual data item autonomously triggers the processing on itself. Such decentralisation also makes the execution framework very scalable and flexible, supporting pipeline sizes ranging from less than ten tasks running on a laptop to tens of millions of concurrent tasks on the second fastest supercomputer in the world. DALiuGE has been used in production for reducing interferometry data sets from the Karl E. Jansky Very Large Array and the Mingantu Ultrawide Spectral Radioheliograph; and is being developed as the execution framework prototype for the Science Data Processor (SDP) consortium of the Square Kilometre Array (SKA) telescope. This paper presents a technical overview of DALiuGE and discusses case studies from the CHILES and MUSER projects that use DALiuGE to execute production pipelines. In a companion paper, we provide in-depth analysis of DALiuGE's scalability to very large numbers of tasks on two supercomputing facilities.Comment: 31 pages, 12 figures, currently under review by Astronomy and Computin

A Column Generation Algorithm for Choice-Based Network Revenue Management

In the last few years, there has been a trend to enrich traditional revenue management models built upon the independent demand paradigm by accounting for customer choice behavior. This extension involves both modeling and computational challenges. One way to describe choice behavior is to assume that each customer belongs to a segment, which is characterized by a consideration set, i.e., a subset of the products provided by the firm that a customer views as options. Customers choose a particular product according to a multinomial-logit criterion, a model widely used in the marketing literature. In this paper, we consider the choice-based, deterministic, linear programming model (CDLP) of Gallego et al. [6], and the follow-up dynamic programming (DP) decomposition heuristic of van Ryzin and Liu [16], and focus on the more general version of these models, where customers belong to overlapping segments. To solve the CDLP for real-size networks, we need to develop a column generation algorithm. We prove that the associated column generation subproblem is indeed NP-Complete, and propose a simple, greedy heuristic to overcome the complexity of an exact algorithm. Our computational results show that the heuristic is quite effective, and that the overall approach has good practical potential and leads to high quality solutions.Operations Management Working Papers Serie

Branching on multi-aggregated variables

open5siopenGamrath, Gerald; Melchiori, Anna; Berthold, Timo; Gleixner, Ambros M.; Salvagnin, DomenicoGamrath, Gerald; Melchiori, Anna; Berthold, Timo; Gleixner, Ambros M.; Salvagnin, Domenic