In this paper, we propose a modified design of a hexagonal circular photonic crystal fiber (HC-PCF) which obtains a large negative dispersion and ultrahigh birefringence simultaneously. The optical properties of the proposed HC-PCF were investigated using the finite element method (FEM) incorporated with a circular perfectly matched layer at the boundary. The simulation results showed large negative dispersion of −1044 ps/nm.km and ultrahigh birefringence of 4.321 × 10−2 at the operating wavelength of 1550 nm for the optimum geometrical parameters. Our proposed HC-PCF exhibited the desirable optical properties without non-circular air holes in the core and cladding region which facilitates the fabrication process. The large negative dispersion of the proposed microstructure over the wide spectral range, i.e., 1350 nm to 1600 nm, and high birefringence make it a suitable candidate for high-speed optical broadband communication and different sensing applications.

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As there is a scarcity of large representative corpora for most languages, it is important for Multilingual Language Models (MLLM) to extract the most out of existing corpora. In this regard, script diversity presents a challenge to MLLMs by reducing lexical overlap among closely related languages. Therefore, transliterating closely related languages that use different writing scripts to a common script may improve the downstream task performance of MLLMs. In this paper, we pretrain two AL-BERT models to empirically measure the effect of transliteration on MLLMs. We specifically focus on the Indo-Aryan language family, which has the highest script diversity in the world. Afterward, we evaluate our models on the IndicGLUE benchmark. We perform Mann-Whitney U test to rigorously verify whether the effect of transliteration is significant or not. We find that transliteration benefits the low-resource languages without negatively affecting the comparatively high-resource languages. We also measure the cross-lingual representation similarity (CLRS) of the models using centered kernel alignment (CKA) on parallel sentences of eight languages from the FLORES-101 dataset. We find that the hidden representations of the transliteration-based model have higher and more stable CLRS scores. Our code is available at Github 1 Hugging Face Hub 2 , 3