BU Open Access Articles
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The Boston University Open Access collection contains scholarly publications written by Boston University faculty. This collection primarily consists of scholarly journal articles and published conference proceedings but open access book chapters, reviews, white papers, conference abstracts, and technical reports are also included. These publications are made available in OpenBU, Boston University’s Open Access Institutional Repository under the provisions of the Boston University Open Access Policy or by other arrangement.
Publications in Boston University’s Open Access collection are primarily one of three versions:
- First Author Drafts. This is the author's manuscript prior to formal peer review.
- Accepted Manuscript. This is the version that exists after peer review, but before final copy editing and publisher formatting. Note: This is NOT publisher proofs.
- Published Version. This is the final version that appeared in a formal publication such as a peer-reviewed journal, book, or published conference proceeding.
Citations are always given to the final published version of record which may differ from the version made available in the repository. Wherever possible, a link or DOI to the final version is provided.
For more information please see the FAQ about Open Access at BU or contact Digital Scholarship Services at disc@bu.edu.
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BU Open Access Articles [1001]
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Remember the curse of dimensionality: the case of goodness-of-fit testing in arbitrary dimension
(Taylor & Francis, 2018)Despite a substantial literature on nonparametric two-sample goodness-of-fit testing in arbitrary dimensions, there is no mention there of any curse of dimensionality. In fact, in some publications, a parametric rate is ... -
Uniform test of algorithmic randomness over a general space
(Elsevier Science BV, 2005-09-05)The algorithmic theory of randomness is well developed when the underlying space is the set of finite or infinite sequences and the underlying probability distribution is the uniform distribution or a computable distribution. ... -
Sequential optimization for efficient high-quality object proposal generation
(IEEE, 2018-05-01)We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel ... -
Clairvoyant embedding in one dimension
(Wiley-Blackwell, 2015-10-01)Let v, w be infinite 0‐1 sequences, and urn:x-wiley:10429832:media:rsa20551:rsa20551-math-0001 a positive integer. We say that urn:x-wiley:10429832:media:rsa20551:rsa20551-math-0002 is urn:x-wiley:10429832:media:rsa20551 ... -
Compatible sequences and a slow Winkler percolation
(Cambridge University Press, 2004-11-01)Two infinite 0–1 sequences are called compatible when it is possible to cast out $0\,$s from both in such a way that they become complementary to each other. Answering a question of Peter Winkler, we show that if the two ... -
A Turing machine resisting isolated bursts of faults
(Springer-Verlag, 2012)We consider computations of a Turing machine under noise that causes consecutive violations of the machine’s transition function. Given a constant upper bound β on the size of bursts of faults, we construct a Turing machine ... -
Learning joint feature adaptation for zero-shot recognition
(2016)Zero-shot recognition (ZSR) aims to recognize target-domain data instances of unseen classes based on the models learned from associated pairs of seen-class source and target domain data. One of the key challenges in ZSR ... -
Chromium poisoning effects on performance of (La, Sr) MnO3-based cathode in anode-supported solid oxide fuel cells
(Electrochemical Society, 2017-01-01)Chromium (Cr) vapor species from chromia-forming alloy interconnects are known to cause cathode performance degradation in solid oxide fuel cells (SOFCs). To understand the impact of Cr-poisoning on cathode performance, ... -
Learning policies for Markov decision processes from data
(2017)We consider the problem of learning a policy for a Markov decision process consistent with data captured on the state-actions pairs followed by the policy. We assume that the policy belongs to a class of parameterized ... -
On the non-existence of unbiased estimators in constrained estimation problems
(2017)We address the problem of existence of unbiased constrained parameter estimators. We show that if the constrained set of parameters is compact and the hypothesized distributions are absolutely continuous with respect to ...
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