4,374 research outputs found
The Career of Vernon Briggs, Jr.: A Liberal Economistâs Struggle to Reduce Immigration
[Excerpt] At the conclusion of Cornellâs spring semester in 2007, Briggs ended his 47 years of college teaching. As he retired, Cornell honored him with emeritus status. Since then, he has occasionally given public talks and written articles on the need for immigration reform. He says his work still draws motivation from a principle he left with his students at the end of the last lecture in each of his classes over his entire career: âThe mode through which the impossible comes to pass is effort.â
That quote from Justice Oliver Wendell Homes was passed on to Briggs by Michigan State University professor Charles Killingsworth. In his long, remarkable career, Briggs has honored Holmes, Killingsworth, and his profession by passing it on â in word and deed â to countless others
A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization
The frequency-domain data of a multivariable system in different operating
points is used to design a robust controller with respect to the measurement
noise and multimodel uncertainty. The controller is fully parametrized in terms
of matrix polynomial functions and can be formulated as a centralized,
decentralized or distributed controller. All standard performance
specifications like , and loop shaping are considered in a
unified framework for continuous- and discrete-time systems. The control
problem is formulated as a convex-concave optimization problem and then
convexified by linearization of the concave part around an initial controller.
The performance criterion converges monotonically to a local optimal solution
in an iterative algorithm. The effectiveness of the method is compared with
fixed-structure controllers using non-smooth optimization and with full-order
optimal controllers via simulation examples. Finally, the experimental data of
a gyroscope is used to design a data-driven controller that is successfully
applied on the real system
Space-Efficient Biconnected Components and Recognition of Outerplanar Graphs
We present space-efficient algorithms for computing cut vertices in a given
graph with vertices and edges in linear time using bits. With the same time and using bits, we can compute the
biconnected components of a graph. We use this result to show an algorithm for
the recognition of (maximal) outerplanar graphs in time using
bits
On Temporal Graph Exploration
A temporal graph is a graph in which the edge set can change from step to
step. The temporal graph exploration problem TEXP is the problem of computing a
foremost exploration schedule for a temporal graph, i.e., a temporal walk that
starts at a given start node, visits all nodes of the graph, and has the
smallest arrival time. In the first part of the paper, we consider only
temporal graphs that are connected at each step. For such temporal graphs with
nodes, we show that it is NP-hard to approximate TEXP with ratio
for any . We also provide an explicit
construction of temporal graphs that require steps to be
explored. We then consider TEXP under the assumption that the underlying graph
(i.e. the graph that contains all edges that are present in the temporal graph
in at least one step) belongs to a specific class of graphs. Among other
results, we show that temporal graphs can be explored in steps if the underlying graph has treewidth and in
steps if the underlying graph is a grid. In the second part of the
paper, we replace the connectedness assumption by a weaker assumption and show
that -edge temporal graphs with regularly present edges and with random
edges can always be explored in steps and steps with high
probability, respectively. We finally show that the latter result can be used
to obtain a distributed algorithm for the gossiping problem.Comment: This is an extended version of an ICALP 2015 pape
Reach and speed of judgment propagation in the laboratory
In recent years, a large body of research has demonstrated that judgments and
behaviors can propagate from person to person. Phenomena as diverse as
political mobilization, health practices, altruism, and emotional states
exhibit similar dynamics of social contagion. The precise mechanisms of
judgment propagation are not well understood, however, because it is difficult
to control for confounding factors such as homophily or dynamic network
structures. We introduce a novel experimental design that renders possible the
stringent study of judgment propagation. In this design, experimental chains of
individuals can revise their initial judgment in a visual perception task after
observing a predecessor's judgment. The positioning of a very good performer at
the top of a chain created a performance gap, which triggered waves of judgment
propagation down the chain. We evaluated the dynamics of judgment propagation
experimentally. Despite strong social influence within pairs of individuals,
the reach of judgment propagation across a chain rarely exceeded a social
distance of three to four degrees of separation. Furthermore, computer
simulations showed that the speed of judgment propagation decayed exponentially
with the social distance from the source. We show that information distortion
and the overweighting of other people's errors are two individual-level
mechanisms hindering judgment propagation at the scale of the chain. Our
results contribute to the understanding of social contagion processes, and our
experimental method offers numerous new opportunities to study judgment
propagation in the laboratory
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Disrupting Illicit Supply Networks: New Applications of Operations Research and Data Analytics to End Modern Slavery
Report from a 2017 National Science Foundation workshop on promising research directions for applications of operations research and data analytics toward the disruption of illicit supply networks like human trafficking. The workshop was funded by the NSFâs Operations Engineering (ENG) and the Law & Social Sciences Program (SBE) under grant # CMMI-1726895. The report addresses the opportunity to apply advances from the fields of operations research, management science, analytics, machine learning, and data science toward the development of disruptive interventions against illicit networks. Such an extension of the current research agenda for trafficking would move understanding of such dynamic systems from descriptive characterization and predictive estimation toward improved dynamic operational control.Bureau of Business Researc
Stochastic Properties of Static Friction
The onset of frictional motion is mediated by rupture-like slip fronts, which
nucleate locally and propagate eventually along the entire interface causing
global sliding. The static friction coefficient is a macroscopic measure of the
applied force at this particular instant when the frictional interface loses
stability. However, experimental studies are known to present important scatter
in the measurement of static friction; the origin of which remains unexplained.
Here, we study the nucleation of local slip at interfaces with slip-weakening
friction of random strength and analyze the resulting variability in the
measured global strength. Using numerical simulations that solve the
elastodynamic equations, we observe that multiple slip patches nucleate
simultaneously, many of which are stable and grow only slowly, but one reaches
a critical length and starts propagating dynamically. We show that a
theoretical criterion based on a static equilibrium solution predicts
quantitatively well the onset of frictional sliding. We develop a Monte-Carlo
model by adapting the theoretical criterion and pre-computing modal convolution
terms, which enables us to run efficiently a large number of samples and to
study variability in global strength distribution caused by the stochastic
properties of local frictional strength. The results demonstrate that an
increasing spatial correlation length on the interface, representing geometric
imperfections and roughness, causes lower global static friction. Conversely,
smaller correlation length increases the macroscopic strength while its
variability decreases. We further show that randomness in local friction
properties is insufficient for the existence of systematic precursory slip
events. Random or systematic non-uniformity in the driving force, such as
potential energy or stress drop, is required for arrested slip fronts. Our
model and observations..
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