6,601 research outputs found
Microfluidic multipoles: theory and applications
Microfluidic multipoles (MFMs) have been realized experimentally and hold
promise for "open-space" biological and chemical surface processing. Whereas
convective flow can readily be predicted using hydraulic-electrical analogies,
the design of advanced MFMs is constrained by the lack of simple, accurate
models to predict mass transport within them. In this work, we introduce the
first exact solutions to mass transport in multipolar microfluidics based on
the iterative conformal mapping of 2D advection-diffusion around a simple edge
into dipoles and multipolar geometries, revealing a rich landscape of transport
modes. The models were validated experimentally with a library of 3D printed
MFM devices and found in excellent agreement. Following a theory-guided design
approach, we further ideated and fabricated two new classes of spatiotemporally
reconfigurable MFM devices that are used for processing surfaces with
time-varying reagent streams, and to realize a multistep automated immunoassay.
Overall, the results set the foundations for exploring, developing, and
applying open-space MFMs.Comment: 16 pages, 5 figure
Collective motion of cells: from experiments to models
Swarming or collective motion of living entities is one of the most common
and spectacular manifestations of living systems having been extensively
studied in recent years. A number of general principles have been established.
The interactions at the level of cells are quite different from those among
individual animals therefore the study of collective motion of cells is likely
to reveal some specific important features which are overviewed in this paper.
In addition to presenting the most appealing results from the quickly growing
related literature we also deliver a critical discussion of the emerging
picture and summarize our present understanding of collective motion at the
cellular level. Collective motion of cells plays an essential role in a number
of experimental and real-life situations. In most cases the coordinated motion
is a helpful aspect of the given phenomenon and results in making a related
process more efficient (e.g., embryogenesis or wound healing), while in the
case of tumor cell invasion it appears to speed up the progression of the
disease. In these mechanisms cells both have to be motile and adhere to one
another, the adherence feature being the most specific to this sort of
collective behavior. One of the central aims of this review is both presenting
the related experimental observations and treating them in the light of a few
basic computational models so as to make an interpretation of the phenomena at
a quantitative level as well.Comment: 24 pages, 25 figures, 13 reference video link
Reasoning About Liquids via Closed-Loop Simulation
Simulators are powerful tools for reasoning about a robot's interactions with
its environment. However, when simulations diverge from reality, that reasoning
becomes less useful. In this paper, we show how to close the loop between
liquid simulation and real-time perception. We use observations of liquids to
correct errors when tracking the liquid's state in a simulator. Our results
show that closed-loop simulation is an effective way to prevent large
divergence between the simulated and real liquid states. As a direct
consequence of this, our method can enable reasoning about liquids that would
otherwise be infeasible due to large divergences, such as reasoning about
occluded liquid.Comment: Robotics: Science & Systems (RSS), July 12-16, 2017. Cambridge, MA,
US
Physics-Informed Computer Vision: A Review and Perspectives
Incorporation of physical information in machine learning frameworks are
opening and transforming many application domains. Here the learning process is
augmented through the induction of fundamental knowledge and governing physical
laws. In this work we explore their utility for computer vision tasks in
interpreting and understanding visual data. We present a systematic literature
review of formulation and approaches to computer vision tasks guided by
physical laws. We begin by decomposing the popular computer vision pipeline
into a taxonomy of stages and investigate approaches to incorporate governing
physical equations in each stage. Existing approaches in each task are analyzed
with regard to what governing physical processes are modeled, formulated and
how they are incorporated, i.e. modify data (observation bias), modify networks
(inductive bias), and modify losses (learning bias). The taxonomy offers a
unified view of the application of the physics-informed capability,
highlighting where physics-informed learning has been conducted and where the
gaps and opportunities are. Finally, we highlight open problems and challenges
to inform future research. While still in its early days, the study of
physics-informed computer vision has the promise to develop better computer
vision models that can improve physical plausibility, accuracy, data efficiency
and generalization in increasingly realistic applications
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