arXiv subjects

Hao Lyu

Publications and source records attributed to Hao Lyu.

24 records · Page 2Linked to original sources

arXiv2022

Self-interfering dynamics in Bose-Einstein condensates with engineered dispersions

Optical lattice and spin-orbit coupling are typical experimental approaches to engineer dispersion. We reveal a self-interfering dynamics in a noninteracting Bose-Einstein condensate with the engineered dispersion by optical lattice or spin-orbit coupling. The self-interference results from the co-occupation of positive and negative effect mass regimes in the engineered dispersion. The physical origination of the self-interference is explained by the Wigner distribution function of the self-interfering wave-packet. We characterize detail features of the self-interference pattern.

cond-mat.quant-gas↗

arXiv2021

Creating moving gap solitons in spin-orbit-coupled Bose-Einstein condensates

A simple and efficient method to create gap solitons is proposed in a spin-orbit-coupled spin-1 Bose-Einstein condensate. We find that a free expansion along the spin-orbit coupling dimension can generate two moving gap solitons, which are identified from a generalized massive Thirring model. The dynamics of gap solitons can be controlled by adjusting spin-orbit coupling parameters.

cond-mat.quant-gas↗

arXiv2021

Wave based damage detection in solid structures using artificial neural networks

The identification of structural damages takes a more and more important role within the modern economy, where often the monitoring of an infrastructure is the last approach to keep it under public use. Conventional monitoring methods require specialized engineers and are mainly time consuming. This research paper considers the ability of neural networks to recognize the initial or alteration of structural properties based on the training processes. The presented work here is based on Convolutional Neural Networks (CNN) for wave field pattern recognition, or more specifically the wave field change recognition. The CNN model is used to identify the change within propagating wave fields after a crack initiation within the structure. The paper describes the implemented method and the required training procedure to get a successful crack detection accuracy, where the training data are based on the dynamic lattice model. Although the training of the model is still time consuming, the proposed new method has an enormous potential to become a new crack detection or structural health monitoring approach within the conventional monitoring methods.

cs.LG↗

arXiv2020

Spin-orbit-coupling-assisted roton softening and superstripes in a Rydberg-dressed Bose-Einstein Condensate

Rotons can exist in ultracold atomic gases either with long-range interactions or with spin-orbitcoupled dispersions. We find that two different kinds of rotons coexist in a joint system combining long-range interactions and spin-orbit coupling. One roton originates from spin-orbit coupling and two others come from long-range interactions. Their softening can be controlled separately. The interesting new phenomenon which we find is that spin-orbit-coupled roton can push down the energy of one long-range-interaction roton. The spin-orbit coupling accelerates the softening of this roton. The post phase of spin-orbit-coupling-assisted roton softening and instability is identified as a superstripe.

cond-mat.quant-gas↗

arXiv2016

Crowdsourcing Information Extraction for Biomedical Systematic Reviews

Information extraction is a critical step in the practice of conducting biomedical systematic literature reviews. Extracted structured data can be aggregated via methods such as statistical meta-analysis. Typically highly trained domain experts extract data for systematic reviews. The high expense of conducting biomedical systematic reviews has motivated researchers to explore lower cost methods that achieve similar rigor without compromising quality. Crowdsourcing represents one such promising approach. In this work-in-progress study, we designed a crowdsourcing task for biomedical information extraction. We briefly report the iterative design process and the results of two pilot testings. We found that giving more concrete examples in the task instruction can help workers better understand the task, especially for concepts that are abstract and confusing. We found a few workers completed most of the work, and our payment level appeared more attractive to workers from low-income countries. In the future, we will further evaluate our results with reference to gold standard extractions, thus assessing the feasibility of tasking crowd workers with extracting biomedical intervention information for systematic reviews.

cs.HC↗

arXiv2016

Modelling Context with User Embeddings for Sarcasm Detection in Social Media

We introduce a deep neural network for automated sarcasm detection. Recent work has emphasized the need for models to capitalize on contextual features, beyond lexical and syntactic cues present in utterances. For example, different speakers will tend to employ sarcasm regarding different subjects and, thus, sarcasm detection models ought to encode such speaker information. Current methods have achieved this by way of laborious feature engineering. By contrast, we propose to automatically learn and then exploit user embeddings, to be used in concert with lexical signals to recognize sarcasm. Our approach does not require elaborate feature engineering (and concomitant data scraping); fitting user embeddings requires only the text from their previous posts. The experimental results show that our model outperforms a state-of-the-art approach leveraging an extensive set of carefully crafted features.

cs.CL↗