3,722 research outputs found
On the Security of the Yi-Tan-Siew Chaos-Based Cipher
This paper presents a comprehensive analysis on the security of the
Yi-Tan-Siew chaotic cipher proposed in [IEEE TCAS-I 49(12):1826-1829 (2002)]. A
differential chosen-plaintext attack and a differential chosen-ciphertext
attack are suggested to break the sub-key K, under the assumption that the time
stamp can be altered by the attacker, which is reasonable in such attacks.
Also, some security Problems about the sub-keys and are
clarified, from both theoretical and experimental points of view. Further
analysis shows that the security of this cipher is independent of the use of
the chaotic tent map, once the sub-key is removed via the proposed
suggested differential chosen-plaintext attack.Comment: 5 pages, 3 figures, IEEEtrans.cls v 1.
Quench Dynamics of Topological Maximally-Entangled States
We investigate the quench dynamics of the one-particle entanglement spectra
(OPES) for systems with topologically nontrivial phases. By using dimerized
chains as an example, it is demonstrated that the evolution of OPES for the
quenched bi-partite systems is governed by an effective Hamiltonian which is
characterized by a pseudo spin in a time-dependent pseudo magnetic field
. The existence and evolution of the topological
maximally-entangled edge states are determined by the winding number of
in the -space. In particular, the maximally-entangled edge
states survive only if nontrivial Berry phases are induced by the winding of
. In the infinite time limit the equilibrium OPES can be
determined by an effective time-independent pseudo magnetic field
\vec{S}_{\mb{eff}}(k). Furthermore, when maximally-entangled edge states are
unstable, they are destroyed by quasiparticles within a characteristic
timescale in proportional to the system size.Comment: 5 pages, 3 figure
A Comparative Study on Regularization Strategies for Embedding-based Neural Networks
This paper aims to compare different regularization strategies to address a
common phenomenon, severe overfitting, in embedding-based neural networks for
NLP. We chose two widely studied neural models and tasks as our testbed. We
tried several frequently applied or newly proposed regularization strategies,
including penalizing weights (embeddings excluded), penalizing embeddings,
re-embedding words, and dropout. We also emphasized on incremental
hyperparameter tuning, and combining different regularizations. The results
provide a picture on tuning hyperparameters for neural NLP models.Comment: EMNLP '1
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