arXiv subjects

Yuxin Huang

Publications and source records attributed to Yuxin Huang.

At least 37 records · Page 2Linked to original sources

arXiv2024

Modeling the Cosmic Dispersion Measure in the D < 120 Mpc Local Universe

The Local Universe (D < 120 Mpc) has been intensely studied for decades, with highly complete galaxy redshift surveys now publicly available. These data have driven density reconstructions of the underlying matter density field, as well as constrained simulations that aim to reproduce the observed structures. In this paper, we introduce a dispersion measure (DM) model that makes use of this detailed knowledge of our Local Universe within D < 120 Mpc. The model comprises three key components: (i) the DM from the Milky Way halo and the intra-group medium (up to 3.4 Mpc), derived from the HESTIA simulations, a series of constrained hydrodynamic simulations designed to reproduce our Local Group; (ii) the DM contribution from the large-scale intergalactic medium beyond the Local Group (3.4 Mpc < D < 120 Mpc), calculated using the HAMLET reconstructed matter density field; and (iii) the individual DM contributions from Local Universe galaxy halos and clusters based on data from the 2MASS Galaxy Group Catalog and the NASA/IPAC Extragalactic Database. This comprehensive model will be made available as a Python package. As the most realistic model to date for DM in the local volume, it promises to improve the constraints of DM contributions from the Intergalactic Medium and Circumgalactic Medium of FRBs, thereby enhancing the accuracy of cosmic baryon distribution calculations based on DM analysis of FRBs.

astro-ph.CO↗

arXiv2024

A Mixed-Language Multi-Document News Summarization Dataset and a Graphs-Based Extract-Generate Model

Existing research on news summarization primarily focuses on single-language single-document (SLSD), single-language multi-document (SLMD) or cross-language single-document (CLSD). However, in real-world scenarios, news about a international event often involves multiple documents in different languages, i.e., mixed-language multi-document (MLMD). Therefore, summarizing MLMD news is of great significance. However, the lack of datasets for MLMD news summarization has constrained the development of research in this area. To fill this gap, we construct a mixed-language multi-document news summarization dataset (MLMD-news), which contains four different languages and 10,992 source document cluster and target summary pairs. Additionally, we propose a graph-based extract-generate model and benchmark various methods on the MLMD-news dataset and publicly release our dataset and code\footnote[1]{https://github.com/Southnf9/MLMD-news}, aiming to advance research in summarization within MLMD scenarios.

cs.CL↗

arXiv2024

Automated Synthesis of Quantum Algorithms via Classical Numerical Techniques

We apply numerical optimization and linear algebra algorithms for classical computers to the problem of automatically synthesizing algorithms for quantum computers. Using our framework, we apply several common techniques from these classical domains and numerically examine their suitability for and performance on this problem. Our methods are evaluated on single-qubit systems as well as on larger systems. While the first part of our proposed method outputs a single unitary matrix representing the composite effects of a quantum circuit or algorithm, we use existing tools - and assess the performance of these - to factor such a matrix into a product of elementary quantum gates. This enables our pipeline to be truly end-to-end: starting from desired input/output examples, our code ultimately results in a quantum circuit diagram. We release our code to the research community (upon acceptance).

math.NA↗

arXiv2024

FRB Line-of-sight Ionization Measurement From Lightcone AAOmega Mapping Survey: the First Data Release

This paper presents the first public data release (DR1) of the FRB Line-of-sight Ionization Measurement From Lightcone AAOmega Mapping (FLIMFLAM) Survey, a wide field spectroscopic survey targeted on the fields of 10 precisely localized Fast Radio Bursts (FRBs). DR1 encompasses spectroscopic data for 10,468 galaxy redshifts across 10 FRBs fields with z<0.4, covering approximately 26 deg^2 of the sky in total. FLIMFLAM is composed of several layers, encompassing the `Wide' (covering ~ degree or >10 Mpc scales), `Narrow', (several-arcminute or ~ Mpc) and integral field unit (`IFU'; ~ arcminute or ~ 100 kpc ) components. The bulk of the data comprise spectroscopy from the 2dF-AAOmega on the 3.9-meter Anglo-Australian Telescope, while most of the Narrow and IFU data was achieved using an ensemble of 8-10-meter class telescopes. We summarize the information on our selected FRB fields, the criteria for target selection, methodologies employed for data reduction, spectral analysis processes, and an overview of our data products. An evaluation of our data reveals an average spectroscopic completeness of 48.43%, with over 80% of the observed targets having secure redshifts. Additionally, we describe our approach on generating angular masks and calculating the target selection functions, setting the stage for the impending reconstruction of the matter density field.

astro-ph.GA↗

arXiv2024

Self-Aligning Depth-regularized Radiance Fields for Asynchronous RGB-D Sequences

It has been shown that learning radiance fields with depth rendering and depth supervision can effectively promote the quality and convergence of view synthesis. However, this paradigm requires input RGB-D sequences to be synchronized, hindering its usage in the UAV city modeling scenario. As there exists asynchrony between RGB images and depth images due to high-speed flight, we propose a novel time-pose function, which is an implicit network that maps timestamps to $\rm SE(3)$ elements. To simplify the training process, we also design a joint optimization scheme to jointly learn the large-scale depth-regularized radiance fields and the time-pose function. Our algorithm consists of three steps: (1) time-pose function fitting, (2) radiance field bootstrapping, (3) joint pose error compensation and radiance field refinement. In addition, we propose a large synthetic dataset with diverse controlled mismatches and ground truth to evaluate this new problem setting systematically. Through extensive experiments, we demonstrate that our method outperforms baselines without regularization. We also show qualitatively improved results on a real-world asynchronous RGB-D sequence captured by drone. Codes, data, and models will be made publicly available.

cs.CV↗

arXiv2023

Generation and Recombination for Multifocus Image Fusion with Free Number of Inputs

Multifocus image fusion is an effective way to overcome the limitation of optical lenses. Many existing methods obtain fused results by generating decision maps. However, such methods often assume that the focused areas of the two source images are complementary, making it impossible to achieve simultaneous fusion of multiple images. Additionally, the existing methods ignore the impact of hard pixels on fusion performance, limiting the visual quality improvement of fusion image. To address these issues, a combining generation and recombination model, termed as GRFusion, is proposed. In GRFusion, focus property detection of each source image can be implemented independently, enabling simultaneous fusion of multiple source images and avoiding information loss caused by alternating fusion. This makes GRFusion free from the number of inputs. To distinguish the hard pixels from the source images, we achieve the determination of hard pixels by considering the inconsistency among the detection results of focus areas in source images. Furthermore, a multi-directional gradient embedding method for generating full focus images is proposed. Subsequently, a hard-pixel-guided recombination mechanism for constructing fused result is devised, effectively integrating the complementary advantages of feature reconstruction-based method and focused pixel recombination-based method. Extensive experimental results demonstrate the effectiveness and the superiority of the proposed method.The source code will be released on https://github.com/xxx/xxx.

cs.CV↗

arXiv2023

The FRB20190520B Sightline Intersects Foreground Galaxy Clusters

The repeating fast radio burst FRB20190520B is an anomaly of the FRB population thanks to its high dispersion measure (DM$=1205\,$pc/cc) despite its low redshift of $z_\mathrm{frb}=0.241$. This excess has been attributed to a large host contribution of $DM_{host}\approx 900\,$pc/cc, far larger than any other known FRB. In this paper, we describe spectroscopic observations of the FRB20190520B field obtained as part of the FLIMFLAM survey, which yielded 701 galaxy redshifts in the field. We find multiple foreground galaxy groups and clusters, for which we then estimated halo masses by comparing their richness with numerical simulations. We discover two separate $M_{halo} >10^{14}\,M_\odot$ galaxy clusters, at $z=0.1867$ and $z=0.2170$, respectively, that are directly intersected by the FRB sightline within their characteristic halo radius \rvir{}. Subtracting off their estimated DM contributions as well that of the diffuse intergalactic medium, we estimate a host contribution of $DM_{host}=430^{+140}_{-220}\,$pc/cc or $DM_{host}=280^{+140}_{-170}\,$pc/cc (observed frame) depending on whether we assume the halo gas extends to $r_{200}$ or $2\times r_{200}$. This significantly smaller $DM_{host}$ -- no longer the largest known value -- is now consistent with H$α$ emission measures of the host galaxy without invoking unusually high gas temperatures. Combined with the observed FRB scattering timescale, we estimate the turbulent fluctuation and geometric amplification factor of the scattering layer to be $\tilde{F} G\approx4.5 - 11\,(\mathrm{pc^2\;km})^{-1/3}$, suggesting most of the gas is close to the FRB host. This result illustrates the importance of incorporating foreground data for FRB analyses, both for understanding the nature of FRBs and to realize their potential as a cosmological probe.

astro-ph.GA↗

arXiv2023

MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving

Nowadays, autonomous cars can drive smoothly in ordinary cases, and it is widely recognized that realistic sensor simulation will play a critical role in solving remaining corner cases by simulating them. To this end, we propose an autonomous driving simulator based upon neural radiance fields (NeRFs). Compared with existing works, ours has three notable features: (1) Instance-aware. Our simulator models the foreground instances and background environments separately with independent networks so that the static (e.g., size and appearance) and dynamic (e.g., trajectory) properties of instances can be controlled separately. (2) Modular. Our simulator allows flexible switching between different modern NeRF-related backbones, sampling strategies, input modalities, etc. We expect this modular design to boost academic progress and industrial deployment of NeRF-based autonomous driving simulation. (3) Realistic. Our simulator set new state-of-the-art photo-realism results given the best module selection. Our simulator will be open-sourced while most of our counterparts are not. Project page: https://open-air-sun.github.io/mars/.

cs.CV↗

arXiv2023

Modeling Task Relationships in Multi-variate Soft Sensor with Balanced Mixture-of-Experts

Accurate estimation of multiple quality variables is critical for building industrial soft sensor models, which have long been confronted with data efficiency and negative transfer issues. Methods sharing backbone parameters among tasks address the data efficiency issue; however, they still fail to mitigate the negative transfer problem. To address this issue, a balanced Mixture-of-Experts (BMoE) is proposed in this work, which consists of a multi-gate mixture of experts (MMoE) module and a task gradient balancing (TGB) module. The MoE module aims to portray task relationships, while the TGB module balances the gradients among tasks dynamically. Both of them cooperate to mitigate the negative transfer problem. Experiments on the typical sulfur recovery unit demonstrate that BMoE models task relationship and balances the training process effectively, and achieves better performance than baseline models significantly.

cs.LG↗

arXiv2023

Searching for the sources of excess extragalactic dispersion of FRBs

The FLIMFLAM survey is collecting spectroscopic data of field galaxies near fast radio burst (FRB) sightlines to constrain key parameters describing the distribution of matter in the Universe. In this work, we leverage the survey data to determine the source of the excess extragalactic dispersion measure (DM), compared to the Macquart relation estimate of four FRBs: FRB20190714A, FRB20200430A, FRB20200906A, and FRB20210117A. By modeling the gas distribution around the foreground galaxy halos and galaxy groups of the sightlines, we estimate $\rm DM_{halos}$, their contribution to the FRB dispersion measures. The FRB20190714A sightline shows a clear excess of foreground halos which contribute roughly 2/3$^{rd}$ of the observed excess DM, thus implying a baryon-dense sightline. FRB20200906A shows a smaller but non-negligible foreground halo contribution, and further analysis of the IGM is necessary to ascertain the true cosmic contribution to its DM. RB20200430A and FRB20210117A show negligible foreground contributions, implying a large host galaxy excess and/or progenitor environment excess.

astro-ph.GA↗

arXiv2022

ConchShell: A Generative Adversarial Networks that Turns Pictures into Piano Music

We present ConchShell, a multi-modal generative adversarial framework that takes pictures as input to the network and generates piano music samples that match the picture context. Inspired by I3D, we introduce a novel image feature representation method: time-convolutional neural network (TCNN), which is used to forge features for images in the temporal dimension. Although our image data consists of only six categories, our proposed framework will be innovative and commercially meaningful. The project will provide technical ideas for work such as 3D game voice overs, short-video soundtracks, and real-time generation of metaverse background music.We have also released a new dataset, the Beach-Ocean-Piano Dataset (BOPD) 1, which contains more than 3,000 images and more than 1,500 piano pieces. This dataset will support multimodal image-to-music research.

cs.SD↗

arXiv2022

Intellectual Property Evaluation Utilizing Machine Learning

Intellectual properties is increasingly important in the economic development. To solve the pain points by traditional methods in IP evaluation, we are developing a new technology with machine learning as the core. We have built an online platform and will expand our business in the Greater Bay Area with plans.

cs.AI↗

arXiv2022

PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit

PaddleSpeech is an open-source all-in-one speech toolkit. It aims at facilitating the development and research of speech processing technologies by providing an easy-to-use command-line interface and a simple code structure. This paper describes the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to-speech tasks. PaddleSpeech achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods. It also provides recipes and pretrained models to quickly reproduce the experimental results in this paper. PaddleSpeech is publicly avaiable at https://github.com/PaddlePaddle/PaddleSpeech.

eess.AS↗

arXiv2022

Constraining the Cosmic Baryon Distribution with Fast Radio Burst Foreground Mapping

The dispersion measures (DM) of fast radio bursts (FRBs) encode the integrated electron density along the line-of-sight, which is typically dominated by the intergalactic medium (IGM) contribution in the case of extragalactic FRBs. In this paper, we show that incorporating wide-field spectroscopic galaxy survey data in the foreground of localized FRBs can significantly improve constraints on the partition of diffuse cosmic baryons. Using mock DMs and realistic lightcone galaxy catalogs derived from the Millennium simulation, we define spectroscopic surveys that can be carried out with 4m and 8m-class wide field spectroscopic facilities. On these simulated surveys, we carry out Bayesian density reconstructions in order to estimate the foreground matter density field. In comparison with the `true' matter density field, we show that these can help reduce the uncertainties in the foreground structures by $\sim 2-3\times$ compared to cosmic variance. We calculate the Fisher matrix to forecast that $N=30\: (96)$ localized FRBs should be able to constrain the diffuse cosmic baryon fraction to $\sim 10\%\: (\sim 5\%) $, and parameters governing the size and baryon fraction of galaxy circumgalactic halos to within $\sim 20-25\%\: (\sim 8-12\%)$. From the Fisher analysis, we show that the foreground data increases the sensitivity of localized FRBs toward our parameters of interest by $\sim 25\times$. We briefly introduce FLIMFLAM, an ongoing galaxy redshift survey that aims to obtain foreground data on $\sim 30$ localized FRB fields.

astro-ph.CO↗

arXiv2020

Guided multi-branch learning systems for sound event detection with sound separation

In this paper, we describe in detail our systems for DCASE 2020 Task 4. The systems are based on the 1st-place system of DCASE 2019 Task 4, which adopts weakly-supervised framework with an attention-based embedding-level pooling module and a semi-supervised learning approach named guided learning. This year, we incorporate multi-branch learning (MBL) into the original system to further improve its performance. MBL uses different branches with different pooling strategies (including instance-level and embedding-level strategies) and different pooling modules (including attention pooling, global max pooling or global average pooling modules), which share the same feature encoder of the model. Therefore, multiple branches pursuing different purposes and focusing on different characteristics of the data can help the feature encoder model the feature space better and avoid over-fitting. To better exploit the strongly-labeled synthetic data, inspired by multi-task learning, we also employ a sound event detection branch. To combine sound separation (SS) with sound event detection (SED), we fuse the results of SED systems with SS-SED systems which are trained using separated sound output by an SS system. The experimental results prove that MBL can improve the model performance and using SS has great potential to improve the performance of SED ensemble system.

cs.SD↗

arXiv2020

Learning generic feature representation with synthetic data for weakly-supervised sound event detection by inter-frame distance loss

Due to the limitation of strong-labeled sound event detection data set, using synthetic data to improve the sound event detection system performance has been a new research focus. In this paper, we try to exploit the usage of synthetic data to improve the feature representation. Based on metric learning, we proposed inter-frame distance loss function for domain adaptation, and prove the effectiveness of it on sound event detection. We also applied multi-task learning with synthetic data. We find the the best performance can be achieved when the two methods being used together. The experiment on DCASE 2018 task 4 test set and DCASE 2019 task 4 synthetic set both show competitive results.

cs.SD↗

arXiv2020

Towards Understanding Gender Bias in Relation Extraction

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction (AKBC). While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to our knowledge to evaluate social biases in NRE systems. We create WikiGenderBias, a distantly supervised dataset with a human annotated test set. WikiGenderBias has sentences specifically curated to analyze gender bias in relation extraction systems. We use WikiGenderBias to evaluate systems for bias and find that NRE systems exhibit gender biased predictions and lay groundwork for future evaluation of bias in NRE. We also analyze how name anonymization, hard debiasing for word embeddings, and counterfactual data augmentation affect gender bias in predictions and performance.

cs.LG↗

arXiv2020

Multi-Branch Learning for Weakly-Labeled Sound Event Detection

There are two sub-tasks implied in the weakly-supervised SED: audio tagging and event boundary detection. Current methods which combine multi-task learning with SED requires annotations both for these two sub-tasks. Since there are only annotations for audio tagging available in weakly-supervised SED, we design multiple branches with different learning purposes instead of pursuing multiple tasks. Similar to multiple tasks, multiple different learning purposes can also prevent the common feature which the multiple branches share from overfitting to any one of the learning purposes. We design these multiple different learning purposes based on combinations of different MIL strategies and different pooling methods. Experiments on the DCASE 2018 Task 4 dataset and the URBAN-SED dataset both show that our method achieves competitive performance.

eess.AS↗