1,887 research outputs found
Image Encryption Algorithm Based on DNA Encoding and Chaotic Maps
We propose a new image encryption algorithm based on DNA sequences combined with chaotic maps. This algorithm has two innovations: (1) it diffuses the pixels by transforming the nucleotides into corresponding base pairs a random number of times and (2) it confuses the pixels by a chaotic index based on a chaotic map. For any size of the original grayscale image, the rows and columns are fist exchanged by the arrays generated by a logistic chaotic map. Secondly, each pixel that has been confused is encoded into four nucleotides according to the DNA coding. Thirdly, each nucleotide is transformed into the corresponding base pair a random number of time(s) by a series of iterative computations based on Chebyshev’s chaotic map. Experimental results indicate that the key account of this algorithm is 1.536 × 10127, the correlation coefficient of a 256 × 256 Lena image between, before, and after the encryption processes was 0.0028, and the information entropy of the encrypted image was 7.9854. These simulation results and security analysis show that the proposed algorithm not only has good encryption effect, but also has the ability to repel exhaustive, statistical, differential, and noise attacks
TetraÂpyridineÂbis(trichloroÂacetato)nickel(II)
The title compound, [Ni(C2Cl3O2)2(C5H5N)4], was prepared by the reaction of pyridine and trichloroÂacetatonickel(II) in ethanol solution at room temperature. The NiII atom is located on a twofold rotation axis and has a slightly distorted octaÂhedral coordination made up of four N atoms of the pyridine ligands and two O atoms of trichloroÂacetate anions. The molÂecular structure and packing are stabilized by intra- and interÂmolecular C—H⋯O hydrogen-bonding interÂactions
N-(4-ChloroÂbenzylÂidene)-4-methoxyÂaniline
The title compound, C14H12ClNO, was prepared by the reaction of 4-methoxyÂaniline and 4-chloroÂbenzaldehyde in ethanol at 367 K. The molÂecule is almost planar, with a dihedral angle between the two benzene rings of 9.1 (2)° and an r.m.s. deviation from the mean plane through all non-H atoms in the molÂecule of 0.167 Å
LHC Search of New Higgs Boson via Resonant Di-Higgs Production with Decays into 4W
Searching for new Higgs particle beyond the observed light Higgs boson
h(125GeV) will unambiguously point to new physics beyond the standard model. We
study the resonant production of a CP-even heavy Higgs state in the
di-Higgs channel via, , at the LHC Run-2 and
the high luminosity LHC (HL-LHC). We analyze two types of the decay modes,
one with the same-sign di-leptons () and the
other with tri-leptons (). We
perform a full simulation for the signals and backgrounds, and estimate the
discovery potential of the heavy Higgs state at the LHC Run-2 and the HL-LHC,
in the context of generical two-Higgs-doublet models (2HDM). We determine the
viable parameter space of the 2HDM as allowed by the theoretical constraints
and the current experimental limits. We systematically analyze the allowed
parameter space of the 2HDM which can be effectively probed by the heavy Higgs
searches of the LHC, and further compare this with the viable parameter region
under the current theoretical and experimental bounds.Comment: v3: JHEP published version, 34pp, 10 Figs(36 plots) and 9 Tables.
Only minor typos fixed, references added. v2: JHEP version. All results and
conclusions un-changed, discussions and references added. (This update is
much delayed due to author's traveling and flu.
5,6-Diphenyl-3-(3-pyridÂyl)-1,2,4-triazine
In the molÂecule of the title compound, C20H14N4, the triazine ring is attached to two phenyl rings and one pyridine ring. In the crystal, molÂecules are linked by interÂmolecular C—H⋯N hydrogen bonds. The crystal packing is also stabilized by C—H⋯π interÂactions
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the
presented items, plays a key role in online services. With two-decade rapid
development, nowadays the cumulated user behavior sequences on mature Internet
service platforms have become extremely long since the user's first
registration. Each user not only has intrinsic tastes, but also keeps changing
her personal interests during lifetime. Hence, it is challenging to handle such
lifelong sequential modeling for each individual user. Existing methodologies
for sequential modeling are only capable of dealing with relatively recent user
behaviors, which leaves huge space for modeling long-term especially lifelong
sequential patterns to facilitate user modeling. Moreover, one user's behavior
may be accounted for various previous behaviors within her whole online
activity history, i.e., long-term dependency with multi-scale sequential
patterns. In order to tackle these challenges, in this paper, we propose a
Hierarchical Periodic Memory Network for lifelong sequential modeling with
personalized memorization of sequential patterns for each user. The model also
adopts a hierarchical and periodical updating mechanism to capture multi-scale
sequential patterns of user interests while supporting the evolving user
behavior logs. The experimental results over three large-scale real-world
datasets have demonstrated the advantages of our proposed model with
significant improvement in user response prediction performance against the
state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets:
https://github.com/alimamarankgroup/HPM
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