Planar magneto-photonic and gradient-photonic structures : crystals and metamaterials.

In the field of photonics, two new types of material structures, photonic crystals and metamaterials, are presently of great interest. Both are studied in the present work, which focus on planar magnetic materials in the former and planar gradient metamaterials in the latter. These planar periodic structures are easy to handle and integrate into optical systems. The applications are promising field for future optical telecommunication systems and give rise to new optical, microwave and radio technologies. The photonic crystal part emphasizes the utilization of magnetic material based photonic crystals due to its remarkable magneto-optical characteristics. Bandgaps tuning by magnetic field in bismuth-gadolinium-substituted lutetium iron garnet (Bi0.8 Gd0.2 Lu2.0 Fe5 O12) based one- dimensional photonic crystals are investigated and demonstrated in this work. Magnetic optical switches are fabricated and tested. Waveguide formulation for band structure in magneto photonic crystals is developed. We also for the first time demonstrate and test two- dimensional magneto photonic crystals optical. We observe multi-stopbands in two- dimensional photonic waveguide system and study the origin of multi-stopbands. The second part focus on studying photonic metamaterials and planar gradient photonic metamaterial design. We systematically study the effects of varying the geometry of the fishnet unit cell on the refractive index in optical frequency. It is the first time to design and demonstrate the planar gradient structure in the high optical frequency. Optical beam bending using planar gradient photonic metamaterials is observed. The technologies needed for the fabrication of the planar gradient photonic metamaterials are investigated. Beam steering devices, shifter, gradient optical lenses and etc. can be derived from this design

LEALLA: Learning Lightweight Language-agnostic Sentence Embeddings with Knowledge Distillation

Large-scale language-agnostic sentence embedding models such as LaBSE (Feng et al., 2022) obtain state-of-the-art performance for parallel sentence alignment. However, these large-scale models can suffer from inference speed and computation overhead. This study systematically explores learning language-agnostic sentence embeddings with lightweight models. We demonstrate that a thin-deep encoder can construct robust low-dimensional sentence embeddings for 109 languages. With our proposed distillation methods, we achieve further improvements by incorporating knowledge from a teacher model. Empirical results on Tatoeba, United Nations, and BUCC show the effectiveness of our lightweight models. We release our lightweight language-agnostic sentence embedding models LEALLA on TensorFlow Hub.Comment: EACL 2023 main conference; LEALLA models: https://tfhub.dev/google/collections/LEALL

Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning

Finite-time Lyapunov exponents (FTLEs) provide a powerful approach to compute time-varying analogs of invariant manifolds in unsteady fluid flow fields. These manifolds are useful to visualize the transport mechanisms of passive tracers advecting with the flow. However, many vehicles and mobile sensors are not passive, but are instead actuated according to some intelligent trajectory planning or control law; for example, model predictive control and reinforcement learning are often used to design energy-efficient trajectories in a dynamically changing background flow. In this work, we investigate the use of FTLE on such controlled agents to gain insight into optimal transport routes for navigation in known unsteady flows. We find that these controlled FTLE (cFTLE) coherent structures separate the flow field into different regions with similar costs of transport to the goal location. These separatrices are functions of the planning algorithm's hyper-parameters, such as the optimization time horizon and the cost of actuation. Computing the invariant sets and manifolds of active agent dynamics in dynamic flow fields is useful in the context of robust motion control, hyperparameter tuning, and determining safe and collision-free trajectories for autonomous systems. Moreover, these cFTLE structures provide insight into effective deployment locations for mobile agents with actuation and energy constraints to traverse the ocean or atmosphere.Comment: 22 pages, 12 figure

Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine Translation

In the present study, we propose novel sequence-to-sequence pre-training objectives for low-resource machine translation (NMT): Japanese-specific sequence to sequence (JASS) for language pairs involving Japanese as the source or target language, and English-specific sequence to sequence (ENSS) for language pairs involving English. JASS focuses on masking and reordering Japanese linguistic units known as bunsetsu, whereas ENSS is proposed based on phrase structure masking and reordering tasks. Experiments on ASPEC Japanese–English & Japanese–Chinese, Wikipedia Japanese–Chinese, News English–Korean corpora demonstrate that JASS and ENSS outperform MASS and other existing language-agnostic pre-training methods by up to +2.9 BLEU points for the Japanese–English tasks, up to +7.0 BLEU points for the Japanese–Chinese tasks and up to +1.3 BLEU points for English–Korean tasks. Empirical analysis, which focuses on the relationship between individual parts in JASS and ENSS, reveals the complementary nature of the subtasks of JASS and ENSS. Adequacy evaluation using LASER, human evaluation, and case studies reveals that our proposed methods significantly outperform pre-training methods without injected linguistic knowledge and they have a larger positive impact on the adequacy as compared to the fluency

Origin Distribution Visualization of Floating Population and Determinants Analysis: A Case study of Yiwu City

AbstractBased on registered individual floating population data from 2005 to 2008 of Yiwu, the phenomena that population floating to Yiwu City from 34 province and 91 counties in Jiangxi provinces is analyzed. The study aims at analyzing the “pull” forces of Yiwu City and developing migration models for understanding determinants factors of population migration/floating into Yiwu City from other areas in China. The spatial layout of Yiwu's pull forces is proved as a V-shaped pattern consisting of the two axes by using explorative spatial data analysis and map visualization method. The migration models with (model 3) or without (model 2) migration stock are presented and estimated using standard linear regression model, spatial error model as well as spatial lag model at the county scale in Jiangxi province. Based on the likelihood statistics, the AIC and the Moran's I statistics of residuals, the model with migration stock provides an improved fit over the model without migration stock. The correlation between migration ratio and man land ratio is significant at the 0.5 level according to estimates of model 3 and spatial version of model 2. All the three estimates of model 2 and the OLS results of model 3 confirm the distance-decay effect while results from the spatial version of model 3 failed to support the distance rule in population floating. Contrary to the previous studies at the provincial level, the correlation between per capital net income of rural labor forces and migration ratio is not significant according to the three versions of the two models due to the small disparities of income within the counties in Jiangxi. Examination of specification tests in spatial version of model 3 indicates that there is less significant spatial error dependence in the spatial lag models than spatial lag dependence in the error models, further suggesting a preference for the lag model. Model 2 does not suggest any preference for choosing spatial error model and spatial lag model