arXiv subjects

Yuxuan He

Publications and source records attributed to Yuxuan He.

25 records · Page 2Linked to original sources

arXiv2023

Exploring Ultralight Scalar Assistance in Sterile Neutrino Dark Matter: Cold Spectrum and Unusual X/Gamma-ray Signatures

We present a scalar-driven sterile neutrino production model where the interaction with the ultralight scalar field modifies the oscillation production of sterile neutrinos in the early universe. The model effectively suppresses the production of sterile neutrinos at low temperatures due to the heavy scalar mass, resulting in a colder matter power spectrum that avoids constraints from small-scale structure observations. In this model, the dominant dark matter relic is from sterile neutrinos, with only a small fraction originating from the ultralight scalar. Furthermore, the model predicts a detectable X/gamma-ray flux proportional to the cubic density of local sterile neutrinos for a light scalar mass due to the light scalar coupling to sterile neutrinos. This distinguishes our model from normal decaying dark matter, which has a linear dependence on the density. In addition, the model predicts a potential low-energy monochromatic neutrino signal that can be detectable by future neutrino telescopes.

hep-ph↗

arXiv2023

Unveiling Time-Varying Signals of Ultralight Bosonic Dark Matter at Collider and Beam Dump Experiments

The ultralight boson represents a promising dark matter candidate exhibiting unique wave-like behaviors. These properties could transfer to the dark mediator, such as the kinetic mixing dark photon, which can be a link between the dark and Standard Model sectors, resulting in periodic oscillations of its mass. We propose a method to detect ultralight dark matter using dark mediators in collider and beam dump experiments, distinguishing it from conventional atomic, molecular, and optical methods. The time-varying nature of dark mediator mass exhibits a double-peak spectrum, reducing traditional constraints by 1 to 2 orders of magnitude, due to decreased luminosity exposure in each resonant mass bin. To enhance sensitivity, we utilize event time-stamps in the CMS Open Data and demonstrate that this technique boosts sensitivity by approximately one order of magnitude compared to the time-blind method. Moreover, it proves effective in detecting the invisible decay of the dark mediator.

hep-ph↗

arXiv2023

A Unified One-Step Solution for Aspect Sentiment Quad Prediction

Aspect sentiment quad prediction (ASQP) is a challenging yet significant subtask in aspect-based sentiment analysis as it provides a complete aspect-level sentiment structure. However, existing ASQP datasets are usually small and low-density, hindering technical advancement. To expand the capacity, in this paper, we release two new datasets for ASQP, which contain the following characteristics: larger size, more words per sample, and higher density. With such datasets, we unveil the shortcomings of existing strong ASQP baselines and therefore propose a unified one-step solution for ASQP, namely One-ASQP, to detect the aspect categories and to identify the aspect-opinion-sentiment (AOS) triplets simultaneously. Our One-ASQP holds several unique advantages: (1) by separating ASQP into two subtasks and solving them independently and simultaneously, we can avoid error propagation in pipeline-based methods and overcome slow training and inference in generation-based methods; (2) by introducing sentiment-specific horns tagging schema in a token-pair-based two-dimensional matrix, we can exploit deeper interactions between sentiment elements and efficiently decode the AOS triplets; (3) we design ``[NULL]'' token can help us effectively identify the implicit aspects or opinions. Experiments on two benchmark datasets and our released two datasets demonstrate the advantages of our One-ASQP. The two new datasets are publicly released at \url{https://www.github.com/Datastory-CN/ASQP-Datasets}.

cs.AI↗

arXiv2023

Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance

Natural language understanding(NLU) is challenging for finance due to the lack of annotated data and the specialized language in that domain. As a result, researchers have proposed to use pre-trained language model and multi-task learning to learn robust representations. However, aggressive fine-tuning often causes over-fitting and multi-task learning may favor tasks with significantly larger amounts data, etc. To address these problems, in this paper, we investigate model-agnostic meta-learning algorithm(MAML) in low-resource financial NLU tasks. Our contribution includes: 1. we explore the performance of MAML method with multiple types of tasks: GLUE datasets, SNLI, Sci-Tail and Financial PhraseBank; 2. we study the performance of MAML method with multiple single-type tasks: a real scenario stock price prediction problem with twitter text data. Our models achieve the state-of-the-art performance according to the experimental results, which demonstrate that our method can adapt fast and well to low-resource situations.

cs.CL↗

arXiv2022

Heavy long-lived coannihilation partner from inelastic Dark Matter model and its signatures at the LHC

The coannihilation mechanism is a well-motivated alternative to the simple thermal freeze-out mechanism, where the dark matter relic density can be obtained through the coannihilation with a partner particle of similar mass with dark matter. When the partner particle is neutral, the inelastic nature of dark matter can help it to escape the direct detection limits. In this work, we focus on the coannihilation scenario in which the annihilation cross section is dominated by the partner-partner pair annihilation. We pay special interest on the parameter space where the coannihilation partner is long-lived, which leads to displaced signatures at the collider. In such case, it opens the heavy mass parameter space for the coannihilation dark matter, comparing with those dominated by the partner-dark matter annihilation. Specifically, we propose an inelastic scalar dark matter model with a broken symmetry, which realizes the domination of partner-partner pair annihilation. Then, we study two different realizations of the coannihilation partner decay and the existing constraints from the relic abundance, direct and indirect dark matter detection and the collider searches. We focus on the channel that the long-lived coannihilation partner decays to dark matter plus leptons. The high-luminosity LHC can reach good sensitivities for such heavy dark matter and coannihilation partner around 100--700 GeV.

hep-ph↗

arXiv2021

Auto-KWS 2021 Challenge: Task, Datasets, and Baselines

Auto-KWS 2021 challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to a customized keyword spotting task. Compared with other keyword spotting tasks, Auto-KWS challenge has the following three characteristics: 1) The challenge focuses on the problem of customized keyword spotting, where the target device can only be awakened by an enrolled speaker with his specified keyword. The speaker can use any language and accent to define his keyword. 2) All dataset of the challenge is recorded in realistic environment. It is to simulate different user scenarios. 3) Auto-KWS is a "code competition", where participants need to submit AutoML solutions, then the platform automatically runs the enrollment and prediction steps with the submitted code.This challenge aims at promoting the development of a more personalized and flexible keyword spotting system. Two baseline systems are provided to all participants as references.

cs.SD↗

arXiv2019

FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions

The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact computational processes are missed if we do not consider the lifecycle stages upstream from model training and deployment. Yet, very little has been done to date to provide system-level support to data scientists who wish to develop and deploy responsible machine learning methods. We aim to fill this gap and present FairPrep, a design and evaluation framework for fairness-enhancing interventions. FairPrep is based on a developer-centered design, and helps data scientists follow best practices in software engineering and machine learning. As part of our contribution, we identify shortcomings in existing empirical studies for analyzing fairness-enhancing interventions. We then show how FairPrep can be used to measure the impact of sound best practices, such as hyperparameter tuning and feature scaling. In particular, our results suggest that the high variability of the outcomes of fairness-enhancing interventions observed in previous studies is often an artifact of a lack of hyperparameter tuning. Further, we show that the choice of a data cleaning method can impact the effectiveness of fairness-enhancing interventions.

cs.LG↗