8,313 research outputs found
Superconductivity induced by spark erosion in ZrZn2
We show that the superconductivity observed recently in the weak itinerant
ferromagnet ZrZn2 [C. Pfleiderer et al., Nature (London) 412, 58 (2001)] is due
to remnants of a superconducting layer induced by spark erosion. Results of
resistivity, susceptibility, specific heat and surface analysis measurements on
high-quality ZrZn2 crystals show that cutting by spark erosion leaves a
superconducting surface layer. The resistive superconducting transition is
destroyed by chemically etching a layer of 5 microns from the sample. No
signature of superconductivity is observed in rho(T) of etched samples at the
lowest current density measured, J=675 Am-2, and at T < 45 mK. EDX analysis
shows that spark-eroded surfaces are strongly Zn depleted. The simplest
explanation of our results is that the superconductivity results from an alloy
with higher Zr content than ZrZn2.Comment: Final published versio
Exploiting Resolution-based Representations for MaxSAT Solving
Most recent MaxSAT algorithms rely on a succession of calls to a SAT solver
in order to find an optimal solution. In particular, several algorithms take
advantage of the ability of SAT solvers to identify unsatisfiable subformulas.
Usually, these MaxSAT algorithms perform better when small unsatisfiable
subformulas are found early. However, this is not the case in many problem
instances, since the whole formula is given to the SAT solver in each call. In
this paper, we propose to partition the MaxSAT formula using a resolution-based
graph representation. Partitions are then iteratively joined by using a
proximity measure extracted from the graph representation of the formula. The
algorithm ends when only one partition remains and the optimal solution is
found. Experimental results show that this new approach further enhances a
state of the art MaxSAT solver to optimally solve a larger set of industrial
problem instances
Revisiting the Problem of Searching on a Line
We revisit the problem of searching for a target at an unknown location on a
line when given upper and lower bounds on the distance D that separates the
initial position of the searcher from the target. Prior to this work, only
asymptotic bounds were known for the optimal competitive ratio achievable by
any search strategy in the worst case. We present the first tight bounds on the
exact optimal competitive ratio achievable, parameterized in terms of the given
bounds on D, along with an optimal search strategy that achieves this
competitive ratio. We prove that this optimal strategy is unique. We
characterize the conditions under which an optimal strategy can be computed
exactly and, when it cannot, we explain how numerical methods can be used
efficiently. In addition, we answer several related open questions, including
the maximal reach problem, and we discuss how to generalize these results to m
rays, for any m >= 2
Spectral and Fermi surface properties from Wannier interpolation
We present an efficient first-principles approach for calculating Fermi
surface averages and spectral properties of solids, and use it to compute the
low-field Hall coefficient of several cubic metals and the magnetic circular
dichroism of iron. The first step is to perform a conventional first-principles
calculation and store the low-lying Bloch functions evaluated on a uniform grid
of k-points in the Brillouin zone. We then map those states onto a set of
maximally-localized Wannier functions, and evaluate the matrix elements of the
Hamiltonian and the other needed operators between the Wannier orbitals, thus
setting up an ``exact tight-binding model.'' In this compact representation the
k-space quantities are evaluated inexpensively using a generalized
Slater-Koster interpolation. Because of the strong localization of the Wannier
orbitals in real space, the smoothness and accuracy of the k-space
interpolation increases rapidly with the number of grid points originally used
to construct the Wannier functions. This allows k-space integrals to be
performed with ab-initio accuracy at low cost. In the Wannier representation,
band gradients, effective masses, and other k-derivatives needed for transport
and optical coefficients can be evaluated analytically, producing numerically
stable results even at band crossings and near weak avoided crossings.Comment: 12 pages, 7 figure
Suffix Tree of Alignment: An Efficient Index for Similar Data
We consider an index data structure for similar strings. The generalized
suffix tree can be a solution for this. The generalized suffix tree of two
strings and is a compacted trie representing all suffixes in and
. It has leaves and can be constructed in time.
However, if the two strings are similar, the generalized suffix tree is not
efficient because it does not exploit the similarity which is usually
represented as an alignment of and .
In this paper we propose a space/time-efficient suffix tree of alignment
which wisely exploits the similarity in an alignment. Our suffix tree for an
alignment of and has leaves where is the sum of
the lengths of all parts of different from and is the sum of the
lengths of some common parts of and . We did not compromise the pattern
search to reduce the space. Our suffix tree can be searched for a pattern
in time where is the number of occurrences of in and
. We also present an efficient algorithm to construct the suffix tree of
alignment. When the suffix tree is constructed from scratch, the algorithm
requires time where is the sum of the lengths
of other common substrings of and . When the suffix tree of is
already given, it requires time.Comment: 12 page
Expected length of the longest common subsequence for large alphabets
We consider the length L of the longest common subsequence of two randomly
uniformly and independently chosen n character words over a k-ary alphabet.
Subadditivity arguments yield that the expected value of L, when normalized by
n, converges to a constant C_k. We prove a conjecture of Sankoff and Mainville
from the early 80's claiming that C_k\sqrt{k} goes to 2 as k goes to infinity.Comment: 14 pages, 1 figure, LaTe
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Where Are My Intelligent Assistant's Mistakes? A Systematic Testing Approach
Intelligent assistants are handling increasingly critical tasks, but until now, end users have had no way to systematically assess where their assistants make mistakes. For some intelligent assistants, this is a serious problem: if the assistant is doing work that is important, such as assisting with qualitative research or monitoring an elderly parent’s safety, the user may pay a high cost for unnoticed mistakes. This paper addresses the problem with WYSIWYT/ML (What You See Is What You Test for Machine Learning), a human/computer partnership that enables end users to systematically test intelligent assistants. Our empirical evaluation shows that WYSIWYT/ML helped end users find assistants’ mistakes significantly more effectively than ad hoc testing. Not only did it allow users to assess an assistant’s work on an average of 117 predictions in only 10 minutes, it also scaled to a much larger data set, assessing an assistant’s work on 623 out of 1,448 predictions using only the users’ original 10 minutes’ testing effort
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