11,301 research outputs found
A geometric view of cryptographic equation solving
This paper considers the geometric properties of the Relinearisation algorithm and of the XL algorithm used in cryptology for equation solving. We give a formal description of each algorithm in terms of projective geometry, making particular use of the Veronese variety. We establish the fundamental geometrical connection between the two algorithms and show how both algorithms can be viewed as being equivalent to the problem of finding a matrix of low rank in the linear span of a collection of matrices, a problem sometimes known as the MinRank problem. Furthermore, we generalise the XL algorithm to a geometrically invariant algorithm, which we term the GeometricXL algorithm. The GeometricXL algorithm is a technique which can solve certain equation systems that are not easily soluble by the XL algorithm or by Groebner basis methods
Recursive n-gram hashing is pairwise independent, at best
Many applications use sequences of n consecutive symbols (n-grams). Hashing
these n-grams can be a performance bottleneck. For more speed, recursive hash
families compute hash values by updating previous values. We prove that
recursive hash families cannot be more than pairwise independent. While hashing
by irreducible polynomials is pairwise independent, our implementations either
run in time O(n) or use an exponential amount of memory. As a more scalable
alternative, we make hashing by cyclic polynomials pairwise independent by
ignoring n-1 bits. Experimentally, we show that hashing by cyclic polynomials
is is twice as fast as hashing by irreducible polynomials. We also show that
randomized Karp-Rabin hash families are not pairwise independent.Comment: See software at https://github.com/lemire/rollinghashcp
XL-NBT: A Cross-lingual Neural Belief Tracking Framework
Task-oriented dialog systems are becoming pervasive, and many companies
heavily rely on them to complement human agents for customer service in call
centers. With globalization, the need for providing cross-lingual customer
support becomes more urgent than ever. However, cross-lingual support poses
great challenges---it requires a large amount of additional annotated data from
native speakers. In order to bypass the expensive human annotation and achieve
the first step towards the ultimate goal of building a universal dialog system,
we set out to build a cross-lingual state tracking framework. Specifically, we
assume that there exists a source language with dialog belief tracking
annotations while the target languages have no annotated dialog data of any
form. Then, we pre-train a state tracker for the source language as a teacher,
which is able to exploit easy-to-access parallel data. We then distill and
transfer its own knowledge to the student state tracker in target languages. We
specifically discuss two types of common parallel resources: bilingual corpus
and bilingual dictionary, and design different transfer learning strategies
accordingly. Experimentally, we successfully use English state tracker as the
teacher to transfer its knowledge to both Italian and German trackers and
achieve promising results.Comment: 13 pages, 5 figures, 3 tables, accepted to EMNLP 2018 conferenc
Density Evolution for Asymmetric Memoryless Channels
Density evolution is one of the most powerful analytical tools for
low-density parity-check (LDPC) codes and graph codes with message passing
decoding algorithms. With channel symmetry as one of its fundamental
assumptions, density evolution (DE) has been widely and successfully applied to
different channels, including binary erasure channels, binary symmetric
channels, binary additive white Gaussian noise channels, etc. This paper
generalizes density evolution for non-symmetric memoryless channels, which in
turn broadens the applications to general memoryless channels, e.g. z-channels,
composite white Gaussian noise channels, etc. The central theorem underpinning
this generalization is the convergence to perfect projection for any fixed size
supporting tree. A new iterative formula of the same complexity is then
presented and the necessary theorems for the performance concentration theorems
are developed. Several properties of the new density evolution method are
explored, including stability results for general asymmetric memoryless
channels. Simulations, code optimizations, and possible new applications
suggested by this new density evolution method are also provided. This result
is also used to prove the typicality of linear LDPC codes among the coset code
ensemble when the minimum check node degree is sufficiently large. It is shown
that the convergence to perfect projection is essential to the belief
propagation algorithm even when only symmetric channels are considered. Hence
the proof of the convergence to perfect projection serves also as a completion
of the theory of classical density evolution for symmetric memoryless channels.Comment: To appear in the IEEE Transactions on Information Theor
Search Problems in Vector Spaces
We consider the following -analog of the basic combinatorial search
problem: let be a prime power and \GF(q) the finite field of
elements. Let denote an -dimensional vector space over \GF(q) and let
be an unknown 1-dimensional subspace of . We will be interested
in determining the minimum number of queries that is needed to find
provided all queries are subspaces of and the answer to a
query is YES if and NO if . This number will be denoted by in the adaptive case
(when for each queries answers are obtained immediately and later queries might
depend on previous answers) and in the non-adaptive case (when all
queries must be made in advance).
In the case we prove if is large enough. While
for general values of and we establish the bounds and provided tends
to infinity
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