2,457 research outputs found
Managing Unbounded-Length Keys in Comparison-Driven Data Structures with Applications to On-Line Indexing
This paper presents a general technique for optimally transforming any
dynamic data structure that operates on atomic and indivisible keys by
constant-time comparisons, into a data structure that handles unbounded-length
keys whose comparison cost is not a constant. Examples of these keys are
strings, multi-dimensional points, multiple-precision numbers, multi-key data
(e.g.~records), XML paths, URL addresses, etc. The technique is more general
than what has been done in previous work as no particular exploitation of the
underlying structure of is required. The only requirement is that the insertion
of a key must identify its predecessor or its successor.
Using the proposed technique, online suffix tree can be constructed in worst
case time per input symbol (as opposed to amortized
time per symbol, achieved by previously known algorithms). To our knowledge,
our algorithm is the first that achieves worst case time per input
symbol. Searching for a pattern of length in the resulting suffix tree
takes time, where is the
number of occurrences of the pattern. The paper also describes more
applications and show how to obtain alternative methods for dealing with suffix
sorting, dynamic lowest common ancestors and order maintenance
Universal Compressed Text Indexing
The rise of repetitive datasets has lately generated a lot of interest in
compressed self-indexes based on dictionary compression, a rich and
heterogeneous family that exploits text repetitions in different ways. For each
such compression scheme, several different indexing solutions have been
proposed in the last two decades. To date, the fastest indexes for repetitive
texts are based on the run-length compressed Burrows-Wheeler transform and on
the Compact Directed Acyclic Word Graph. The most space-efficient indexes, on
the other hand, are based on the Lempel-Ziv parsing and on grammar compression.
Indexes for more universal schemes such as collage systems and macro schemes
have not yet been proposed. Very recently, Kempa and Prezza [STOC 2018] showed
that all dictionary compressors can be interpreted as approximation algorithms
for the smallest string attractor, that is, a set of text positions capturing
all distinct substrings. Starting from this observation, in this paper we
develop the first universal compressed self-index, that is, the first indexing
data structure based on string attractors, which can therefore be built on top
of any dictionary-compressed text representation. Let be the size of a
string attractor for a text of length . Our index takes
words of space and supports locating the
occurrences of any pattern of length in
time, for any constant . This is, in particular, the first index
for general macro schemes and collage systems. Our result shows that the
relation between indexing and compression is much deeper than what was
previously thought: the simple property standing at the core of all dictionary
compressors is sufficient to support fast indexed queries.Comment: Fixed with reviewer's comment
Dynamic Planar Point Location in External Memory
In this paper we describe a fully-dynamic data structure for the planar point location problem in the external memory model. Our data structure supports queries in O(log_B n(log log_B n)^3)) I/Os and updates in O(log_B n(log log_B n)^2)) amortized I/Os, where n is the number of segments in the subdivision and B is the block size. This is the first dynamic data structure with almost-optimal query cost. For comparison all previously known results for this problem require O(log_B^2 n) I/Os to answer queries. Our result almost matches the best known upper bound in the internal-memory model
Evolution of Flux Noise in Superconducting Qubits with Weak Magnetic Fields
The microscopic origin of magnetic flux noise in superconducting
circuits has remained an open question for several decades despite extensive
experimental and theoretical investigation. Recent progress in superconducting
devices for quantum information has highlighted the need to mitigate sources of
qubit decoherence, driving a renewed interest in understanding the underlying
noise mechanism(s). Though a consensus has emerged attributing flux noise to
surface spins, their identity and interaction mechanisms remain unclear,
prompting further study. Here we apply weak in-plane magnetic fields to a
capacitively-shunted flux qubit (where the Zeeman splitting of surface spins
lies below the device temperature) and study the flux-noise-limited qubit
dephasing, revealing previously unexplored trends that may shed light on the
dynamics behind the emergent noise. Notably, we observe an enhancement
(suppression) of the spin-echo (Ramsey) pure dephasing time in fields up to
. With direct noise spectroscopy, we further observe a
transition from a to approximately Lorentzian frequency dependence below
10 Hz and a reduction of the noise above 1 MHz with increasing magnetic field.
We suggest that these trends are qualitatively consistent with an increase of
spin cluster sizes with magnetic field. These results should help to inform a
complete microscopic theory of flux noise in superconducting circuits
The set covering problem revisited: an empirical study of the value of dual information
This paper investigates the role of dual information on the performances of heuristics designed for solving the set covering problem. After solving the linear programming relaxation of the problem, the dual information is used to obtain the two main approaches proposed here: (i) The size of the original problem is reduced and then the resulting model is solved with exact methods. We demonstrate the effectiveness of this approach on a rich set of benchmark instances compiled from the literature. We conclude that set covering problems of various characteristics and sizes may reliably be solved to near optimality without resorting to custom solution methods. (ii) The dual information is embedded into an existing heuristic. This approach is demonstrated on a well-known local search based heuristic that was reported to obtain successful results on the set covering problem. Our results demonstrate that the use of dual information significantly improves the efficacy of the heuristic in terms of both solution time and accuracy
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Multimodal biometrics score level fusion using non-confidence information
Multimodal biometrics refers to automatic authentication methods that depend on multiple modalities of measurable physical characteristics. It alleviates most of the restrictions of single biometrics. To combine the multimodal biometrics scores, three different categories of fusion approaches including rule based, classification based and density based approaches are available. When choosing an approach, one has to consider not only the fusion performance, but also system requirements and other circumstances. In the context of verification, classification errors arise from samples in the overlapping region (or non- confidence region) between genuine users and impostors. In score space, a further separation of the samples outside the non-confidence region does not result in further verification improvements. Therefore, information contained in the non-confidence region might be useful for improving the fusion process. Up to this point, no attempts are reported in the literature that tries to enhance the fusion process using this additional information. In this work, the use of this information is explored in rule based and density based approaches mentioned above
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