24,982 research outputs found
Malicious cryptography techniques for unreversable (malicious or not) binaries
Fighting against computer malware require a mandatory step of reverse
engineering. As soon as the code has been disassemblied/decompiled (including a
dynamic analysis step), there is a hope to understand what the malware actually
does and to implement a detection mean. This also applies to protection of
software whenever one wishes to analyze them. In this paper, we show how to
amour code in such a way that reserse engineering techniques (static and
dymanic) are absolutely impossible by combining malicious cryptography
techniques developped in our laboratory and new types of programming (k-ary
codes). Suitable encryption algorithms combined with new cryptanalytic
approaches to ease the protection of (malicious or not) binaries, enable to
provide both total code armouring and large scale polymorphic features at the
same time. A simple 400 Kb of executable code enables to produce a binary code
and around mutated forms natively while going far beyond the old
concept of decryptor.Comment: 17 pages, 2 figures, accepted for presentation at H2HC'1
Temporally delayed linear modelling (TDLM) measures replay in both animals and humans
There are rich structures in off-task neural activity which are hypothesised to reflect fundamental computations across a broad spectrum of cognitive functions. Here, we develop an analysis toolkit - Temporal Delayed Linear Modelling (TDLM) for analysing such activity. TDLM is a domain-general method for finding neural sequences that respect a pre-specified transition graph. It combines nonlinear classification and linear temporal modelling to test for statistical regularities in sequences of task-related reactivations. TDLM is developed on the non-invasive neuroimaging data and is designed to take care of confounds and maximize sequence detection ability. Notably, as a linear framework, TDLM can be easily extended, without loss of generality, to capture rodent replay in electrophysiology, including in continuous spaces, as well as addressing second-order inference questions, e.g., its temporal and spatial varying pattern. We hope TDLM will advance a deeper understanding of neural computation and promote a richer convergence between animal and human neuroscience
Piecewise smooth systems near a co-dimension 2 discontinuity manifold: can one say what should happen?
We consider a piecewise smooth system in the neighborhood of a co-dimension 2
discontinuity manifold . Within the class of Filippov solutions, if
is attractive, one should expect solution trajectories to slide on
. It is well known, however, that the classical Filippov
convexification methodology is ambiguous on . The situation is further
complicated by the possibility that, regardless of how sliding on is
taking place, during sliding motion a trajectory encounters so-called generic
first order exit points, where ceases to be attractive.
In this work, we attempt to understand what behavior one should expect of a
solution trajectory near when is attractive, what to expect
when ceases to be attractive (at least, at generic exit points), and
finally we also contrast and compare the behavior of some regularizations
proposed in the literature.
Through analysis and experiments we will confirm some known facts, and
provide some important insight: (i) when is attractive, a solution
trajectory indeed does remain near , viz. sliding on is an
appropriate idealization (of course, in general, one cannot predict which
sliding vector field should be selected); (ii) when loses attractivity
(at first order exit conditions), a typical solution trajectory leaves a
neighborhood of ; (iii) there is no obvious way to regularize the
system so that the regularized trajectory will remain near as long as
is attractive, and so that it will be leaving (a neighborhood of)
when looses attractivity.
We reach the above conclusions by considering exclusively the given piecewise
smooth system, without superimposing any assumption on what kind of dynamics
near (or sliding motion on ) should have been taking place.Comment: 19 figure
On capacity of optical communications over a lossy bosonic channel with a receiver employing the most general coherent electro-optic feedback control
We study the problem of designing optical receivers to discriminate between
multiple coherent states using coherent processing receivers---i.e., one that
uses arbitrary coherent feedback control and quantum-noise-limited direct
detection---which was shown by Dolinar to achieve the minimum error probability
in discriminating any two coherent states. We first derive and re-interpret
Dolinar's binary-hypothesis minimum-probability-of-error receiver as the one
that optimizes the information efficiency at each time instant, based on
recursive Bayesian updates within the receiver. Using this viewpoint, we
propose a natural generalization of Dolinar's receiver design to discriminate
coherent states each of which could now be a codeword, i.e., a sequence of
coherent states each drawn from a modulation alphabet. We analyze the
channel capacity of the pure-loss optical channel with a general
coherent-processing receiver in the low-photon number regime and compare it
with the capacity achievable with direct detection and the Holevo limit
(achieving the latter would require a quantum joint-detection receiver). We
show compelling evidence that despite the optimal performance of Dolinar's
receiver for the binary coherent-state hypothesis test (either in error
probability or mutual information), the asymptotic communication rate
achievable by such a coherent-processing receiver is only as good as direct
detection. This suggests that in the infinitely-long codeword limit, all
potential benefits of coherent processing at the receiver can be obtained by
designing a good code and direct detection, with no feedback within the
receiver.Comment: 17 pages, 5 figure
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