Print-Scan Resilient Text Image Watermarking Based on Stroke Direction Modulation for Chinese Document Authentication

Print-scan resilient watermarking has emerged as an attractive way for document security. This paper proposes an stroke direction modulation technique for watermarking in Chinese text images. The watermark produced by the idea offers robustness to print-photocopy-scan, yet provides relatively high embedding capacity without losing the transparency. During the embedding phase, the angle of rotatable strokes are quantized to embed the bits. This requires several stages of preprocessing, including stroke generation, junction searching, rotatable stroke decision and character partition. Moreover, shuffling is applied to equalize the uneven embedding capacity. For the data detection, denoising and deskewing mechanisms are used to compensate for the distortions induced by hardcopy. Experimental results show that our technique attains high detection accuracy against distortions resulting from print-scan operations, good quality photocopies and benign attacks in accord with the future goal of soft authentication

Phonon anomalies in pure and underdoped R{1-x}K{x}Fe{2}As{2} (R = Ba, Sr) investigated by Raman light scattering

We present a detailed temperature dependent Raman light scattering study of optical phonons in Ba{1-x}K{x}Fe{2}As{2} (x ~ 0.28, superconducting Tc ~ 29 K), Sr{1-x}K{x}Fe{2}As{2} (x ~ 0.15, Tc ~ 29 K) and non-superconducting BaFe{2}As{2} single crystals. In all samples we observe a strong continuous narrowing of the Raman-active Fe and As vibrations upon cooling below the spin-density-wave transition Ts. We attribute this effect to the opening of the spin-density-wave gap. The electron-phonon linewidths inferred from these data greatly exceed the predictions of ab-initio density functional calculations without spin polarization, which may imply that local magnetic moments survive well above Ts. A first-order structural transition accompanying the spin-density-wave transition induces discontinuous jumps in the phonon frequencies. These anomalies are increasingly suppressed for higher potassium concentrations. We also observe subtle phonon anomalies at the superconducting transition temperature Tc, with a behavior qualitatively similar to that in the cuprate superconductors.Comment: 5 pages, 6 figures, accepted versio

Density-Dependent Synthetic Gauge Fields Using Periodically Modulated Interactions

We show that density-dependent synthetic gauge fields may be engineered by combining periodically modu- lated interactions and Raman-assisted hopping in spin-dependent optical lattices. These fields lead to a density- dependent shift of the momentum distribution, may induce superfluid-to-Mott insulator transitions, and strongly modify correlations in the superfluid regime. We show that the interplay between the created gauge field and the broken sublattice symmetry results, as well, in an intriguing behavior at vanishing interactions, characterized by the appearance of a fractional Mott insulator.Comment: 5 pages, 5 figure

Exploring spin-orbital models with dipolar fermions in zig-zag optical lattices

Ultra-cold dipolar spinor fermions in zig-zag type optical lattices can mimic spin-orbital models relevant in solid-state systems, as transition-metal oxides with partially filled d-levels, with the interesting advantage of reviving the quantum nature of orbital fluctuations. We discuss two different physical systems in which these models may be simulated, showing that the interplay between lattice geometry and spin-orbital quantum dynamics produces a wealth of novel quantum phases.Comment: 4 pages + supplementary materia

Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks

While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions. In this work, we propose a simple, lightweight approach for better context exploitation in CNNs. We do so by introducing a pair of operators: gather, which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled information to local features. The operators are cheap, both in terms of number of added parameters and computational complexity, and can be integrated directly in existing architectures to improve their performance. Experiments on several datasets show that gather-excite can bring benefits comparable to increasing the depth of a CNN at a fraction of the cost. For example, we find ResNet-50 with gather-excite operators is able to outperform its 101-layer counterpart on ImageNet with no additional learnable parameters. We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics.Comment: NeurIPS 201

Rapid Assessment of Intertidal Wetland Sediments

Urbanization of coastal areas poses a severe threat to ecologically valuable intertidal wetlands. This paper presents a pragmatic approach called Rapid Assessment for Intertidal Wetland Sediments (RAITWS) for evaluating the sediment quality of intertidal wetlands. RAITWS involves construction of reference groups, selection of a subset of environmental variables, matching of test sites to reference groups, prediction of the benthic fauna community structure (e. g. of macroinvertebrates) at test sites, evaluation of the Observation to Expectation ratio (O/E ratio), quantification of environmental variables with series of dynamic numerical models, and interpretation of the O/E findings. The proposed method extends the existing rapid biological assessment approach from static to dynamic applications. In particular, RAITWS provides a fast method of assessing intertidal wetland sites which are undergoing ecological change due to nearby coastal development.Environmental SciencesSCI(E)EI0ARTICLE5574-5852