4,746 research outputs found
How Well Do Text Embedding Models Understand Syntax?
Text embedding models have significantly contributed to advancements in
natural language processing by adeptly capturing semantic properties of textual
data. However, the ability of these models to generalize across a wide range of
syntactic contexts remains under-explored. In this paper, we first develop an
evaluation set, named \textbf{SR}, to scrutinize the capability for syntax
understanding of text embedding models from two crucial syntactic aspects:
Structural heuristics, and Relational understanding among concepts, as revealed
by the performance gaps in previous studies. Our findings reveal that existing
text embedding models have not sufficiently addressed these syntactic
understanding challenges, and such ineffectiveness becomes even more apparent
when evaluated against existing benchmark datasets. Furthermore, we conduct
rigorous analysis to unearth factors that lead to such limitations and examine
why previous evaluations fail to detect such ineffectiveness. Lastly, we
propose strategies to augment the generalization ability of text embedding
models in diverse syntactic scenarios. This study serves to highlight the
hurdles associated with syntactic generalization and provides pragmatic
guidance for boosting model performance across varied syntactic contexts.Comment: Accepted to EMNLP-Findings 2023, datasets and code are release
Dual orthogonally-polarized lasing assisted by imaginary Fermi arcs in organic microcavities
The polarization control of micro/nano lasers is an important topic in
nanophotonics. Up to now, the simultaneous generation of two distinguishable
orthogonally-polarized lasing modes from a single organic microlaser remains a
critical challenge. Here, we demonstrate simultaneously orthogonally-polarized
dual lasing from a microcavity filled with an organic single crystal exhibiting
selective strong coupling. We show that the non-Hermiticity due to
polarization-dependent losses leads to the formation of real and imaginary
Fermi arcs with exceptional points. Simultaneous orthogonally-polarized lasing
becomes possible thanks to the eigenstate mixing by the photonic spin-orbit
coupling at the imaginary Fermi arcs. Our work provides a novel way to develop
linearly-polarized lasers and paves the way for the future fundamental research
in topological photonics, non-Hermitian optics, and other fields.Comment: arXiv admin note: text overlap with arXiv:2110.1345
Polymorphisms of the _ENPP1_ gene are not associated with type 2 diabetes or obesity in the Chinese Han population
*Objective:* Type 2 Diabetes mellitus is a metabolic disorder characterized by chronic hyperglycemia and with a major feature of insulin resistance. Genetic association studies have suggested that _ENPP1_ might play a potential role in susceptibility to type 2 diabetes and obesity. Our study aimed to examine the association between _ENPP1_ and type 2 diabetes and obesity.

*Design:* Association study between two SNPs, rs1044498 (K121Q) and rs7754561 of ENPP1 and diabetes and obesity in the Chinese Han population.

*Subjects:* 1912 unrelated patients (785 male and 1127 female with a mean age 63.8 ± 9 years), 236 IFG/IGT subjects (83 male and 153 female with a mean age 64 ± 9 years) and 2041 controls (635 male and 1406 female with a mean age 58 ± 9 years).
 
*Measurements:* Subjects were genotyped for two SNPs using TaqMan technology on an ABI7900 system and tested by regression analysis.

*Results:* By logistic regression analysis, rs1044498 (K121Q) and rs7754561 showed no statistical association with type 2 diabetes, obesity under additive, dominant and recessive models either before or after adjusting for sex and age. Haplotype analysis found a marginal association of haplotype C-G (p=0.05) which was reported in the previous study.

*Conclusion:* Our investigation did not replicated the positive association found previously and suggested that the polymorphisms of _ENPP1_ might not play a major role in the susceptibility to type 2 diabetes or obesity in the Chinese Han population
Multiscale Point Correspondence Using Feature Distribution and Frequency Domain Alignment
In this paper, a hybrid scheme is proposed to find the reliable point-correspondences between two images, which combines the distribution of invariant spatial feature description and frequency domain alignment based on two-stage coarse to fine refinement strategy. Firstly, the source and the target images are both down-sampled by the image pyramid algorithm in a hierarchical multi-scale way. The Fourier-Mellin transform is applied to obtain the transformation parameters at the coarse level between the image pairs; then, the parameters can serve as the initial coarse guess, to guide the following feature matching step at the original scale, where the correspondences are restricted in a search window determined by the deformation between the reference image and the current image; Finally, a novel matching strategy is developed to reject the false matches by validating geometrical relationships between candidate matching points. By doing so, the alignment parameters are refined, which is more accurate and more flexible than a robust fitting technique. This in return can provide a more accurate result for feature correspondence. Experiments on real and synthetic image-pairs show that our approach provides satisfactory feature matching performance
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