4,651 research outputs found
On the energetic origin of self-limiting trenches formed around Ge/Si quantum dots
At high growth temperatures, the misfit strain at the boundary of Ge quantum
dots on Si(001) is relieved by formation of trenches around the base of the
islands. The depth of the trenches has been observed to saturate at a level
that depends on the base-width of the islands. Using finite element
simulations, we show that the self-limiting nature of trench depth is due to a
competition between the elastic relaxation energy gained by the formation of
the trench and the surface energy cost for creating the trench. Our simulations
predict a linear increase of the trench depth with the island radius, in
quantitative agreement with the experimental observations of Drucker and
coworkers
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
Creating impact in real-world settings requires artificial intelligence
techniques to span the full pipeline from data, to predictive models, to
decisions. These components are typically approached separately: a machine
learning model is first trained via a measure of predictive accuracy, and then
its predictions are used as input into an optimization algorithm which produces
a decision. However, the loss function used to train the model may easily be
misaligned with the end goal, which is to make the best decisions possible.
Hand-tuning the loss function to align with optimization is a difficult and
error-prone process (which is often skipped entirely).
We focus on combinatorial optimization problems and introduce a general
framework for decision-focused learning, where the machine learning model is
directly trained in conjunction with the optimization algorithm to produce
high-quality decisions. Technically, our contribution is a means of integrating
common classes of discrete optimization problems into deep learning or other
predictive models, which are typically trained via gradient descent. The main
idea is to use a continuous relaxation of the discrete problem to propagate
gradients through the optimization procedure. We instantiate this framework for
two broad classes of combinatorial problems: linear programs and submodular
maximization. Experimental results across a variety of domains show that
decision-focused learning often leads to improved optimization performance
compared to traditional methods. We find that standard measures of accuracy are
not a reliable proxy for a predictive model's utility in optimization, and our
method's ability to specify the true goal as the model's training objective
yields substantial dividends across a range of decision problems.Comment: Full version of paper accepted at AAAI 201
Identifying RNA contacts from SHAPE-MaP by partial correlation analysis
In a recent paper Siegfried et al. published a new sequence-based structural
RNA assay that utilizes mutational profiling to detect base pairing (MaP).
Output from MaP provides information about both pairing (via reactivities) and
contact (via correlations). Reactivities can be coupled to partition function
folding models for structural inference, while correlations can reveal pairs of
sites that may be in structural proximity. The possibility for inference of 3D
contacts via MaP suggests a novel approach to structural prediction for RNA
analogous to covariance structural prediction for proteins. We explore this
approach and show that partial correlation analysis outperforms na\"ive
correlation analysis. Our results should be applicable to a wide range of
high-throughput sequencing based RNA structural assays that are under
development
Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach
Recent years are seeing an increasing need for on-line monitoring of teams of
cooperating agents, e.g., for visualization, or performance tracking. However,
in monitoring deployed teams, we often cannot rely on the agents to always
communicate their state to the monitoring system. This paper presents a
non-intrusive approach to monitoring by 'overhearing', where the monitored
team's state is inferred (via plan-recognition) from team-members' routine
communications, exchanged as part of their coordinated task execution, and
observed (overheard) by the monitoring system. Key challenges in this approach
include the demanding run-time requirements of monitoring, the scarceness of
observations (increasing monitoring uncertainty), and the need to scale-up
monitoring to address potentially large teams. To address these, we present a
set of complementary novel techniques, exploiting knowledge of the social
structures and procedures in the monitored team: (i) an efficient probabilistic
plan-recognition algorithm, well-suited for processing communications as
observations; (ii) an approach to exploiting knowledge of the team's social
behavior to predict future observations during execution (reducing monitoring
uncertainty); and (iii) monitoring algorithms that trade expressivity for
scalability, representing only certain useful monitoring hypotheses, but
allowing for any number of agents and their different activities to be
represented in a single coherent entity. We present an empirical evaluation of
these techniques, in combination and apart, in monitoring a deployed team of
agents, running on machines physically distributed across the country, and
engaged in complex, dynamic task execution. We also compare the performance of
these techniques to human expert and novice monitors, and show that the
techniques presented are capable of monitoring at human-expert levels, despite
the difficulty of the task
Bulk Aluminum at High Pressure: A First-Principles Study
The behavior of metals at high pressure is of great importance to the fields
of shock physics, geophysics, astrophysics, and nuclear materials. In order to
further understand the properties of metals at high pressures we studied the
equation of state of aluminum using first-principles techniques up to 2500 GPa,
pressures within reach of the planned L.L.N.L. National Ignition Facility. Our
simulations use density-functional theory and density-functional perturbation
theory in the generalized gradient approximation at 0K. We found core overlaps
to become relevant beyond pressures of 1200 GPa. The equations of state for
three phases (fcc, bcc, and hcp) were calculated predicting the fcc-hcp,
fcc-bcc, and hcp-bcc transitions to occur at 215 GPa, 307 GPa, and 435 GPa
respectively. From the phonon dispersions at increasing pressure, we predict a
softening of the lowest transverse acoustic vibrational mode along the [110]
direction, which corresponds to a Born instability of the fcc phase at 725 GPa.Comment: 4 pages, 5 figures, accepted to Phys. Rev. B as a Brief Report. This
version has update many figures. Moreover we provided updated and more
accurate numbers based on further in-depth analyses of potential
computational error
Towards Flexible Teamwork
Many AI researchers are today striving to build agent teams for complex,
dynamic multi-agent domains, with intended applications in arenas such as
education, training, entertainment, information integration, and collective
robotics. Unfortunately, uncertainties in these complex, dynamic domains
obstruct coherent teamwork. In particular, team members often encounter
differing, incomplete, and possibly inconsistent views of their environment.
Furthermore, team members can unexpectedly fail in fulfilling responsibilities
or discover unexpected opportunities. Highly flexible coordination and
communication is key in addressing such uncertainties. Simply fitting
individual agents with precomputed coordination plans will not do, for their
inflexibility can cause severe failures in teamwork, and their
domain-specificity hinders reusability. Our central hypothesis is that the key
to such flexibility and reusability is providing agents with general models of
teamwork. Agents exploit such models to autonomously reason about coordination
and communication, providing requisite flexibility. Furthermore, the models
enable reuse across domains, both saving implementation effort and enforcing
consistency. This article presents one general, implemented model of teamwork,
called STEAM. The basic building block of teamwork in STEAM is joint intentions
(Cohen & Levesque, 1991b); teamwork in STEAM is based on agents' building up a
(partial) hierarchy of joint intentions (this hierarchy is seen to parallel
Grosz & Kraus's partial SharedPlans, 1996). Furthermore, in STEAM, team members
monitor the team's and individual members' performance, reorganizing the team
as necessary. Finally, decision-theoretic communication selectivity in STEAM
ensures reduction in communication overheads of teamwork, with appropriate
sensitivity to the environmental conditions. This article describes STEAM's
application in three different complex domains, and presents detailed empirical
results.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
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