Physical Sciences and Mathematics Commons^™

Modeling Post-Fire Successional Trajectories Under Climate Change In Interior Alaska Using Landis Ii, Shelby A. Weiss

Systems Science Friday Noon Seminar Series

Alaska boreal forest ecosystems are experiencing a greater frequency of wildfire relative to the region’s historic fire regime. These increases in fire frequency, as well as annual burned area, increase the probability of forests re-burning within shorter intervals than were experienced historically. Such changes to the fire regime have the potential to shift successional trajectories in this ecosystem. To better understand potential changes in vegetation composition following short-interval, repeat fires, we are using LANDIS-II, a forest landscape model, to simulate changes in forest composition in response to climate change and increasing fire frequency. This seminar will include a description of …

Systems Thinking As A Design Process, Elizabeth Lockwood

Systems Science Friday Noon Seminar Series

During my master’s degree I analyzed sustainable practices in the built environment. What came from that work was a deep level of understanding that the current practices and rating systems appeared to be technical approaches to a larger system at play. I realized I have a gift to see hidden connections and find links between systems. Currently I use systems mapping as part of the design process to unearth the hidden elements in a system. I believe it is important to understand where designers, clients and stakeholders can insert themselves into a system. Part of this understanding is having empathy …

The Complexities Of Open Data, Hector Dominguez

Systems Science Friday Noon Seminar Series

Hector Dominguez is the current Open Data Coordinator at the City of Portland, and there are several lessons learned and strategies developed in the several months of work in this position. Hector will share some challenges on creating trusted and reliable data and information services, as well as the opportunities to work with Urban Data to resolve city challenges and to support achieving the City's goals in the coming years.

In this talk, Hector will share how modeling and defining the right metrics are not the only factors to implementing a citywide program, but rather, how ethics, communications and strategy …

Latent Space Models For Temporal Networks, Jasper Alt

Systems Science Friday Noon Seminar Series

In many contexts we may expect the structure of networks to be derived from some kind of abstract distance between actors. We refer to this phenomenon as homophily: like nodes connect to like. For example, people with similar beliefs may be more likely to form social relations.

We formalize this notion by positioning the nodes in a latent space representing the possible values of the homophilous attributes. Realistically, we should expect latent attributes like beliefs to change over time in some nontrivial way, and the structures of temporal networks to evolve accordingly. We introduce a model of latent space dynamics …

Prediction: The Quintessential Model Validation Test, Wayne Wakeland

Systems Science Friday Noon Seminar Series

It is essential to objectively test how well policy models predict real world behavior. The method used to support this assertion involves the review of three SD policy models emphasizing the degree to which the model was able to fit the historical outcome data and how well model-predicted outcomes matched real world outcomes as they unfolded. Findings indicate that while historical model agreement is a favorable indication of model validity, the act of making predictions without knowing the actual data, and comparing these predictions to actual data, can reveal model weaknesses that might be overlooked when all of the available …

Systems Ideas For The Scientific And Societal Imperatives Of The Coastal Ocean: Case Of The Bp Oil Gusher In The Gulf Of Mexico, Spring & Summer 2010, Christopher Mooers

Systems Science Friday Noon Seminar Series

In recent decades, great progress has been made in advancing the scientific understanding of the coastal ocean (i.e., the 200 nautical mile Exclusive Economic Zone (EEZ)) across a broad set of disciplines. Simultaneously, the societal use of the coastal ocean has skyrocketed through, for example, increased shipping & boating, sports & commercial fishing, and exploitation of non-living resources, such as, oil & gas extraction and sand & gravel mining. International law and national policy assign coastal nations the responsibility for stewardship (i.e., wise management) of their respective EEZs. The scope of the stewardship and applications can be summarized as (1) …

Integer Optimization And Computational Algebraic Topology, Bala Krishnamoorthy

Systems Science Friday Noon Seminar Series

We present recently discovered connections between integer optimization, or integer programming (IP), and homology. Under reasonable assumptions, these results lead to efficient solutions of several otherwise hard-to-solve problems from computational topology and geometric analysis. The main result equates the total unimodularity of the boundary matrix of a simplicial complex to an algebraic topological condition on the complex (absence of relative torsion), which is often satisfied in real-life applications . When the boundary matrix is totally unimodular, the problem of finding the shortest chain homologous under Z (ring of integers) to a given chain, which is inherently an integer program, can …

On The Effect Of Criticality And Topology On Learning In Random Boolean Networks, Alireza Goudarzi

Systems Science Friday Noon Seminar Series

Random Boolean networks (RBN) are discrete dynamical systems composed of N automata with a binary state, each of which interacts with other automata in the network. RBNs were originally introduced as simplified models of gene regulation. In this presentation, I will present recent work done conjointly with Natali Gulbahce (UCSF), Thimo Rohlf (MPI, CNRS), and Christof Teuscher (PSU). We extend the study of learning in feedforward Boolean networks to random Boolean networks (RBNs) and systematically explore the relationship between the learning capability, the network topology, the system size N, the training sample T, and the complexity of the computational task. …

Random Automata Networks: Why Playing Dice Is Not A Vice, Christof Teuscher

Systems Science Friday Noon Seminar Series

Random automata networks consist of a set of simple compute nodes interacting with each other. In this generic model, one or multiple model parameters, such as the the node interactions and/or the compute functions, are chosen at random. Random Boolean Networks (RBNs) are a particular case of discrete dynamical automata networks where both time and states are discrete. While traditional RBNs are generally credited to Stuart Kauffman (1969), who introduced them as simplified models of gene regulation, Alan Turing proposed unorganized machines as early as 1948. In this talk I will start with Alan Turing's early work on unorganized machines, …

Biologically Inspired Computing: The Darpa Synapse Program & The Hierarchical Temporal Memory, Dan Hammerstrom

Systems Science Friday Noon Seminar Series

This presentation provides an update on biologically inspired computation. In particular, it focuses on two important developments in this area, the DARPA SyNAPSE program (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) and the HTM (Hierarchical Temporal Memory) being developed by Numenta.

The SyNAPSE Program’s ultimate goal is to build a low-power, compact electronic chip combining novel analog circuit design and a neuroscience-inspired architecture that can address a wide range of cognitive abilities: perception, planning, decision making and motor control. According to DARPA program manager Todd Hylton, “Our research progress in this area is unprecedented, No suitable electronic synaptic device that …

Understanding Classification Decisions For Object Detection, Will Landecker, Michael David Thomure, Melanie Mitchell

Systems Science Friday Noon Seminar Series

Computer vision systems are traditionally tested in the object detection paradigm. In these experiments, a vision system is asked whether or not a specific object--for example an animal--occurs in a given image. A system that often answers correctly is said to be very accurate. In this talk, we will discuss some ambiguity that exists in this measure of accuracy. We will also propose a new measure of object-detection accuracy that addresses some of this ambiguity, and apply this measure to the hierarchical "standard model" of visual cortex.