Search

Your search keyword '"Zhang, Haoran"' showing total 1,201 results
Start Over Author "Zhang, Haoran" Search Limiters Full Text Language english
1,201 results on '"Zhang, Haoran"'

3. Efficient second-harmonic generation of quasi-bound states in the continuum in lithium niobate thin film enhanced by Bloch surface waves

Author

Lin Yun, Ye Yong, Fang Ziliang, Chen Bingyu, Zhang Haoran, Yang Tiefeng, Wei Yuming, Jin Yunxia, Kong Fanyu, Peng Gangding, Cao Hongchao, Guan Heyuan, and Lu Huihui

Subjects
bloch surface wave (bsw), lithium niobate (linbo3 or ln), bound states in the continuum (bic), second-harmonic generation (shg), conversion efficiency (ce), Physics, QC1-999
Abstract

Nonlinear optics has generated a wide range of applications in the fields of optical communications, biomedicine, and materials science, with nonlinear conversion efficiency serving as a vital metric for its progress. However, the weak nonlinear response of materials, high optical loss, and inhomogeneous distribution of the light field hamper the improvement of the conversion efficiency. We present a composite grating waveguide structure integrated into a Bragg reflector platform. This design achieves high Q in the spectral range by exploiting the unique properties exhibited by the bound states in the Bloch surface wave-enhanced continuum, and efficient second-harmonic generation by close-field amplification with the optical field tightly localized in a nonlinear material. By manipulating the symmetry of the grating, a precise tune over the near field within a designated wavelength range can be achieved. Specifically, we select a photonic crystal configuration supporting surface waves, employing TE polarization conditions and an asymmetry factor of −0.1 between the composite gratings. This configuration resonates at a fundamental wavelength of 783.5 nm, exhibiting an impressive Q-factor of 106. Notably, at an incident light intensity of 1.33 GW/cm2, we achieve a normalized electric field strength of up to 940 at the fundamental frequency and a second-harmonic conversion efficiency of up to 6 × 10−3, significantly amplifying the second-harmonic response. The proposed configuration in this investigation has the potential to be integrated into the field of nonlinear optics for sensing frequency conversion applications.

Published
2024
Full Text
View/download PDF

5. Essay Quality Signals as Weak Supervision for Source-Based Essay Scoring

Author

Zhang, Haoran and Litman, Diane

Abstract

Human essay grading is a laborious task that can consume much time and effort. Automated Essay Scoring (AES) has thus been proposed as a fast and effective solution to the problem of grading student writing at scale. However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES model. Unfortunately, such a graded corpus often does not exist, so creating a corpus for machine learning can also be a laborious task. This paper presents an investigation of replacing the use of human-labeled essay grades when training an AES system with two automatically available but weaker signals of essay quality: word count and topic distribution similarity. Experiments using two source-based essay scoring (evidence score) corpora show that while weak supervision does not yield a competitive result when training a neural source-based AES model, it can be used to successfully extract Topical Components (TCs) from a source text, which are required by a supervised feature-based AES model. In particular, results show that feature-based AES performance is comparable with either automatically or manually constructed TCs. [This paper was published in: "Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications," Association for Computational Linguistics, 2021, pp. 85-96.]

Published
2021

17. A Fairness Evaluation of Automated Methods for Scoring Text Evidence Usage in Writing

Author

Litman, Diane, Zhang, Haoran, Correnti, Richard, Matsumura, Lindsay Clare, and Wang, Elaine

Abstract

Automated Essay Scoring (AES) can reliably grade essays at scale and reduce human effort in both classroom and commercial settings. There are currently three dominant supervised learning paradigms for building AES models: feature-based, neural, and hybrid. While feature-based models are more explainable, neural network models often outperform feature-based models in terms of prediction accuracy. To create models that are accurate and explainable, hybrid approaches combining neural network and feature-based models are of increasing interest. We compare these three types of AES models with respect to a different evaluation dimension, namely algorithmic fairness. We apply three definitions of AES fairness to an essay corpus scored by different types of AES systems with respect to upper elementary students' use of text evidence. Our results indicate that different AES models exhibit different types of biases, spanning students' gender, race, and socioeconomic status. We conclude with a step towards mitigating AES bias once detected. [This paper was published in: "AIED 2021," edited by I. Roll et al., Springer Nature Switzerland AG, 2021, pp. 255-67.]

Published
2021
Full Text
View/download PDF

35. Automated Topical Component Extraction Using Neural Network Attention Scores from Source-Based Essay Scoring

Author

Zhang, Haoran and Litman, Diane

Abstract

While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays. [This paper was published in: "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics" (pp. 8569-8584). Association for Computational Linguistics.]

Published
2020

36. 'eRevis(ing)': Students' Revision of Text Evidence Use in an Automated Writing Evaluation System

Author

Wang, Elaine Lin, Matsumura, Lindsay Clare, Correnti, Richard, Litman, Diane, Zhang, Haoran, Howe, Emily, Magooda, Ahm, and Quintana, Rafael

Abstract

We investigate students' implementation of the feedback messages they received in an automated writing evaluation system ("eRevise") that aims to improve students' use of text evidence in their writing. Seven 5th and 6th-grade teachers implemented "eRevise" (n = 143 students). Qualitative analysis of students' essays across first and second drafts suggests that the majority of students made changes to their essays that were in line with the feedback they received, though few of these changes resulted in substantive improvement in essay quality. Twenty percent of students did not attempt to implement the feedback; these students generally made small changes to wording or mechanics. In response to the feedback to add more evidence, students whose essays did not improve or showed only slight improvement frequently added in evidence that was not text based or repeated evidence already present in the first draft. When prompted to explain how the evidence they included connected to their claim, many students paraphrased the evidence, added a short conclusion, or explained generally how the evidence supports claims (not how this was instantiated in their writing). Implications for teaching argument writing and for designing AWE systems that support students to successfully revise their essays are discussed. [This is the in press version of an article published in "Assessing Writing."]

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results