19,655 research outputs found
Adaptive versus non-adaptive strategies for quantum channel discrimination
We provide a simple example that illustrates the advantage of adaptive over
non-adaptive strategies for quantum channel discrimination. In particular, we
give a pair of entanglement-breaking channels that can be perfectly
discriminated by means of an adaptive strategy that requires just two channel
evaluations, but for which no non-adaptive strategy can give a perfect
discrimination using any finite number of channel evaluations.Comment: 11 page
Robustness of airline alliance route networks
The aim of this study is to analyze the robustness of the three major airline alliances’ (i.e., Star Alliance, oneworld and SkyTeam) route networks. Firstly, the normalization of a multi-scale measure of vulnerability is proposed in order to perform the analysis in networks with different sizes, i.e., number of nodes. An alternative node selection criterion is also proposed in order to study robustness and vulnerability of such complex networks, based on network efficiency. And lastly, a new procedure – the inverted adaptive strategy – is presented to sort the nodes in order to anticipate network breakdown. Finally, the robustness of the three alliance networks are analyzed with (1) a normalized multi-scale measure of vulnerability, (2) an adaptive strategy based on four different criteria and (3) an inverted adaptive strategy based on the efficiency criterion. The results show that Star Alliance has the most resilient route network, followed by SkyTeam and then oneworld. It was also shown that the inverted adaptive strategy based on the efficiency criterion – inverted efficiency – shows a great success in quickly breaking networks similar to that found with betweenness criterion but with even better results.Peer ReviewedPostprint (author’s final draft
An Adaptive Strategy for Active Learning with Smooth Decision Boundary
We present the first adaptive strategy for active learning in the setting of
classification with smooth decision boundary. The problem of adaptivity (to
unknown distributional parameters) has remained opened since the seminal work
of Castro and Nowak (2007), which first established (active learning) rates for
this setting. While some recent advances on this problem establish adaptive
rates in the case of univariate data, adaptivity in the more practical setting
of multivariate data has so far remained elusive. Combining insights from
various recent works, we show that, for the multivariate case, a careful
reduction to univariate-adaptive strategies yield near-optimal rates without
prior knowledge of distributional parameters
Estimation of communication-delays through adaptive synchronization of chaos
This paper deals with adaptive synchronization of chaos in the presence of
time-varying communication-delays. We consider two bidirectionally coupled
systems that seek to synchronize through a signal that each system sends to the
other one and is transmitted with an unknown time-varying delay. We show that
an appropriate adaptive strategy can be devised that is successful in
dynamically identifying the time-varying delay and in synchronizing the two
systems. The performance of our strategy with respect to the choice of the
initial conditions and the presence of noise in the communication channels is
tested by using numerical simulations. Another advantage of our approach is
that in addition to estimating the communication-delay, the adaptive strategy
could be used to simultaneously identify other parameters, such as e.g., the
unknown time-varying amplitude of the received signal.Comment: Accepted for publication in Chaos, Solitons & Fractal
Skim reading: an adaptive strategy for reading on the web
It has been suggested that readers spend a great deal of time skim reading on the Web and that if readers skim read they reduce their comprehension of what they have read. There have been a number of studies exploring skim reading, but relatively little exists on the skim reading of hypertext and Webpages. In the experiment documented here, we utilised eye tracking methodology to explore how readers skim read hypertext and how hyperlinks affect reading behaviour. The results show that the readers read faster when they were skim reading and comprehension was reduced. However, the presence of hyperlinks seemed to assist the readers in picking out important information when skim reading. We suggest that readers engage in an adaptive information foraging strategy where they attempt to minimise comprehension loss while maintaining a high reading speed. Readers use hyperlinks as markers to suggest important information and use them to read through the text in an efficient and effective way. This suggests that skim reading may not be as damaging to comprehension when reading hypertext, but it does mean that the words we choose to hyperlink become very important to comprehension for those skim reading text on the Web
Finding a most biased coin with fewest flips
We study the problem of learning a most biased coin among a set of coins by
tossing the coins adaptively. The goal is to minimize the number of tosses
until we identify a coin i* whose posterior probability of being most biased is
at least 1-delta for a given delta. Under a particular probabilistic model, we
give an optimal algorithm, i.e., an algorithm that minimizes the expected
number of future tosses. The problem is closely related to finding the best arm
in the multi-armed bandit problem using adaptive strategies. Our algorithm
employs an optimal adaptive strategy -- a strategy that performs the best
possible action at each step after observing the outcomes of all previous coin
tosses. Consequently, our algorithm is also optimal for any starting history of
outcomes. To our knowledge, this is the first algorithm that employs an optimal
adaptive strategy under a Bayesian setting for this problem. Our proof of
optimality employs tools from the field of Markov games
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Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions
The Eco-Approach and Departure (EAD) application has been proved to be environmentally efficient for a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, traffic conditions and signal timings are usually dynamic and uncertain due to mixed vehicle types, various driving behaviors and limited sensing range, which is challenging in EAD development. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions. Stochastic graph models are built to link the vehicle and external (e.g., traffic, signal) data and dynamic programing is applied to identify the optimal speed for each vehicle-state efficiently. From energy perspective, adaptive strategy using traffic data could double the effective sensor range in eco-driving. A hybrid reinforcement learning framework is also developed for EAD in mixed traffic condition using both short-term benefit and long-term benefit as the action reward. Micro-simulation is conducted in Unity to validate the method, showing over 20% energy saving.View the NCST Project Webpag
TOFEC: Achieving Optimal Throughput-Delay Trade-off of Cloud Storage Using Erasure Codes
Our paper presents solutions using erasure coding, parallel connections to
storage cloud and limited chunking (i.e., dividing the object into a few
smaller segments) together to significantly improve the delay performance of
uploading and downloading data in and out of cloud storage.
TOFEC is a strategy that helps front-end proxy adapt to level of workload by
treating scalable cloud storage (e.g. Amazon S3) as a shared resource requiring
admission control. Under light workloads, TOFEC creates more smaller chunks and
uses more parallel connections per file, minimizing service delay. Under heavy
workloads, TOFEC automatically reduces the level of chunking (fewer chunks with
increased size) and uses fewer parallel connections to reduce overhead,
resulting in higher throughput and preventing queueing delay. Our trace-driven
simulation results show that TOFEC's adaptation mechanism converges to an
appropriate code that provides the optimal delay-throughput trade-off without
reducing system capacity. Compared to a non-adaptive strategy optimized for
throughput, TOFEC delivers 2.5x lower latency under light workloads; compared
to a non-adaptive strategy optimized for latency, TOFEC can scale to support
over 3x as many requests
Case study: Bio-inspired self-adaptive strategy for spike-based PID controller
A key requirement for modern large scale
neuromorphic systems is the ability to detect and diagnose faults
and to explore self-correction strategies. In particular, to perform
this under area-constraints which meet scalability requirements
of large neuromorphic systems. A bio-inspired online fault
detection and self-correction mechanism for neuro-inspired PID
controllers is presented in this paper. This strategy employs a
fault detection unit for online testing of the PID controller; uses a
fault detection manager to perform the detection procedure
across multiple controllers, and a controller selection mechanism
to select an available fault-free controller to provide a corrective
step in restoring system functionality. The novelty of the
proposed work is that the fault detection method, using synapse
models with excitatory and inhibitory responses, is applied to a
robotic spike-based PID controller. The results are presented for
robotic motor controllers and show that the proposed bioinspired
self-detection and self-correction strategy can detect
faults and re-allocate resources to restore the controller’s
functionality. In particular, the case study demonstrates the
compactness (~1.4% area overhead) of the fault detection
mechanism for large scale robotic controllers.Ministerio de Economía y Competitividad TEC2012-37868-C04-0
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