4,998 research outputs found
Scalable, Time-Responsive, Digital, Energy-Efficient Molecular Circuits using DNA Strand Displacement
We propose a novel theoretical biomolecular design to implement any Boolean
circuit using the mechanism of DNA strand displacement. The design is scalable:
all species of DNA strands can in principle be mixed and prepared in a single
test tube, rather than requiring separate purification of each species, which
is a barrier to large-scale synthesis. The design is time-responsive: the
concentration of output species changes in response to the concentration of
input species, so that time-varying inputs may be continuously processed. The
design is digital: Boolean values of wires in the circuit are represented as
high or low concentrations of certain species, and we show how to construct a
single-input, single-output signal restoration gate that amplifies the
difference between high and low, which can be distributed to each wire in the
circuit to overcome signal degradation. This means we can achieve a digital
abstraction of the analog values of concentrations. Finally, the design is
energy-efficient: if input species are specified ideally (meaning absolutely 0
concentration of unwanted species), then output species converge to their ideal
concentrations at steady-state, and the system at steady-state is in (dynamic)
equilibrium, meaning that no energy is consumed by irreversible reactions until
the input again changes.
Drawbacks of our design include the following. If input is provided
non-ideally (small positive concentration of unwanted species), then energy
must be continually expended to maintain correct output concentrations even at
steady-state. In addition, our fuel species - those species that are
permanently consumed in irreversible reactions - are not "generic"; each gate
in the circuit is powered by its own specific type of fuel species. Hence
different circuits must be powered by different types of fuel. Finally, we
require input to be given according to the dual-rail convention, so that an
input of 0 is specified not only by the absence of a certain species, but by
the presence of another. That is, we do not construct a "true NOT gate" that
sets its output to high concentration if and only if its input's concentration
is low. It remains an open problem to design scalable, time-responsive,
digital, energy-efficient molecular circuits that additionally solve one of
these problems, or to prove that some subset of their resolutions are mutually
incompatible.Comment: version 2: the paper itself is unchanged from version 1, but the
arXiv software stripped some asterisk characters out of the abstract whose
purpose was to highlight words. These characters have been replaced with
underscores in version 2. The arXiv software also removed the second
paragraph of the abstract, which has been (attempted to be) re-inserted.
Also, although the secondary subject is "Soft Condensed Matter", this
classification was chosen by the arXiv moderators after submission, not
chosen by the authors. The authors consider this submission to be a
theoretical computer science paper
Experiences of the Support Designs in the Two Large Underground Openings in India
Support requirements for two caverns arc worked out by empirical and the numerical approaches. The adequacy of the shotcrete-rock bolt support system is monitored by measuring the deformations of caverns walls and roof. The measurements are compared with the results obtained from the numerical approach. Brief geology, the supports, and the results of performance monitoring are presented in the paper
Why We Read Wikipedia
Wikipedia is one of the most popular sites on the Web, with millions of users
relying on it to satisfy a broad range of information needs every day. Although
it is crucial to understand what exactly these needs are in order to be able to
meet them, little is currently known about why users visit Wikipedia. The goal
of this paper is to fill this gap by combining a survey of Wikipedia readers
with a log-based analysis of user activity. Based on an initial series of user
surveys, we build a taxonomy of Wikipedia use cases along several dimensions,
capturing users' motivations to visit Wikipedia, the depth of knowledge they
are seeking, and their knowledge of the topic of interest prior to visiting
Wikipedia. Then, we quantify the prevalence of these use cases via a
large-scale user survey conducted on live Wikipedia with almost 30,000
responses. Our analyses highlight the variety of factors driving users to
Wikipedia, such as current events, media coverage of a topic, personal
curiosity, work or school assignments, or boredom. Finally, we match survey
responses to the respondents' digital traces in Wikipedia's server logs,
enabling the discovery of behavioral patterns associated with specific use
cases. For instance, we observe long and fast-paced page sequences across
topics for users who are bored or exploring randomly, whereas those using
Wikipedia for work or school spend more time on individual articles focused on
topics such as science. Our findings advance our understanding of reader
motivations and behavior on Wikipedia and can have implications for developers
aiming to improve Wikipedia's user experience, editors striving to cater to
their readers' needs, third-party services (such as search engines) providing
access to Wikipedia content, and researchers aiming to build tools such as
recommendation engines.Comment: Published in WWW'17; v2 fixes caption of Table
Fully-dynamic Approximation of Betweenness Centrality
Betweenness is a well-known centrality measure that ranks the nodes of a
network according to their participation in shortest paths. Since an exact
computation is prohibitive in large networks, several approximation algorithms
have been proposed. Besides that, recent years have seen the publication of
dynamic algorithms for efficient recomputation of betweenness in evolving
networks. In previous work we proposed the first semi-dynamic algorithms that
recompute an approximation of betweenness in connected graphs after batches of
edge insertions.
In this paper we propose the first fully-dynamic approximation algorithms
(for weighted and unweighted undirected graphs that need not to be connected)
with a provable guarantee on the maximum approximation error. The transfer to
fully-dynamic and disconnected graphs implies additional algorithmic problems
that could be of independent interest. In particular, we propose a new upper
bound on the vertex diameter for weighted undirected graphs. For both weighted
and unweighted graphs, we also propose the first fully-dynamic algorithms that
keep track of such upper bound. In addition, we extend our former algorithm for
semi-dynamic BFS to batches of both edge insertions and deletions.
Using approximation, our algorithms are the first to make in-memory
computation of betweenness in fully-dynamic networks with millions of edges
feasible. Our experiments show that they can achieve substantial speedups
compared to recomputation, up to several orders of magnitude
Optimal discrete stopping times for reliability growth tests
Often, the duration of a reliability growth development test is specified in advance and the decision to terminate or continue testing is conducted at discrete time intervals. These features are normally not captured by reliability growth models. This paper adapts a standard reliability growth model to determine the optimal time for which to plan to terminate testing. The underlying stochastic process is developed from an Order Statistic argument with Bayesian inference used to estimate the number of faults within the design and classical inference procedures used to assess the rate of fault detection. Inference procedures within this framework are explored where it is shown the Maximum Likelihood Estimators possess a small bias and converges to the Minimum Variance Unbiased Estimator after few tests for designs with moderate number of faults. It is shown that the Likelihood function can be bimodal when there is conflict between the observed rate of fault detection and the prior distribution describing the number of faults in the design. An illustrative example is provided
Defending against Sybil Devices in Crowdsourced Mapping Services
Real-time crowdsourced maps such as Waze provide timely updates on traffic,
congestion, accidents and points of interest. In this paper, we demonstrate how
lack of strong location authentication allows creation of software-based {\em
Sybil devices} that expose crowdsourced map systems to a variety of security
and privacy attacks. Our experiments show that a single Sybil device with
limited resources can cause havoc on Waze, reporting false congestion and
accidents and automatically rerouting user traffic. More importantly, we
describe techniques to generate Sybil devices at scale, creating armies of
virtual vehicles capable of remotely tracking precise movements for large user
populations while avoiding detection. We propose a new approach to defend
against Sybil devices based on {\em co-location edges}, authenticated records
that attest to the one-time physical co-location of a pair of devices. Over
time, co-location edges combine to form large {\em proximity graphs} that
attest to physical interactions between devices, allowing scalable detection of
virtual vehicles. We demonstrate the efficacy of this approach using
large-scale simulations, and discuss how they can be used to dramatically
reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio
Fractional diffusion modeling of ion channel gating
An anomalous diffusion model for ion channel gating is put forward. This
scheme is able to describe non-exponential, power-law like distributions of
residence time intervals in several types of ion channels. Our method presents
a generalization of the discrete diffusion model by Millhauser, Salpeter and
Oswald [Proc. Natl. Acad. Sci. USA 85, 1503 (1988)] to the case of a
continuous, anomalous slow conformational diffusion. The corresponding
generalization is derived from a continuous time random walk composed of
nearest neighbor jumps which in the scaling limit results in a fractional
diffusion equation. The studied model contains three parameters only: the mean
residence time, a characteristic time of conformational diffusion, and the
index of subdiffusion. A tractable analytical expression for the characteristic
function of the residence time distribution is obtained. In the limiting case
of normal diffusion, our prior findings [Proc. Natl. Acad. Sci. USA 99, 3552
(2002)] are reproduced. Depending on the chosen parameters, the fractional
diffusion model exhibits a very rich behavior of the residence time
distribution with different characteristic time-regimes. Moreover, the
corresponding autocorrelation function of conductance fluctuations displays
nontrivial features. Our theoretical model is in good agreement with
experimental data for large conductance potassium ion channels
Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles
In micro-blogging platforms, people connect and interact with others.
However, due to cognitive biases, they tend to interact with like-minded people
and read agreeable information only. Many efforts to make people connect with
those who think differently have not worked well. In this paper, we
hypothesize, first, that previous approaches have not worked because they have
been direct -- they have tried to explicitly connect people with those having
opposing views on sensitive issues. Second, that neither recommendation or
presentation of information by themselves are enough to encourage behavioral
change. We propose a platform that mixes a recommender algorithm and a
visualization-based user interface to explore recommendations. It recommends
politically diverse profiles in terms of distance of latent topics, and
displays those recommendations in a visual representation of each user's
personal content. We performed an "in the wild" evaluation of this platform,
and found that people explored more recommendations when using a biased
algorithm instead of ours. In line with our hypothesis, we also found that the
mixture of our recommender algorithm and our user interface, allowed
politically interested users to exhibit an unbiased exploration of the
recommended profiles. Finally, our results contribute insights in two aspects:
first, which individual differences are important when designing platforms
aimed at behavioral change; and second, which algorithms and user interfaces
should be mixed to help users avoid cognitive mechanisms that lead to biased
behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User
Interfaces 201
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