arXiv subjects

Qian Hu

Publications and source records attributed to Qian Hu.

47 records · Page 3Linked to original sources

arXiv2021

Two-stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant

Intelligent personal assistants (IPA) enable voice applications that facilitate people's daily tasks. However, due to the complexity and ambiguity of voice requests, some requests may not be handled properly by the standard natural language understanding (NLU) component. In such cases, a simple reply like "Sorry, I don't know" hurts the user's experience and limits the functionality of IPA. In this paper, we propose a two-stage shortlister-reranker recommender system to match third-party voice applications (skills) to unhandled utterances. In this approach, a skill shortlister is proposed to retrieve candidate skills from the skill catalog by calculating both lexical and semantic similarity between skills and user requests. We also illustrate how to build a new system by using observed data collected from a baseline rule-based system, and how the exposure biases can generate discrepancy between offline and human metrics. Lastly, we present two relabeling methods that can handle the incomplete ground truth, and mitigate exposure bias. We demonstrate the effectiveness of our proposed system through extensive offline experiments. Furthermore, we present online A/B testing results that show a significant boost on user experience satisfaction.

cs.LG↗

arXiv2021

Attribute reduction and rule acquisition of formal decision context based on two new kinds of decision rules

This paper mainly studies the rule acquisition and attribute reduction for formal decision context based on two new kinds of decision rules, namely I-decision rules and II-decision rules. The premises of these rules are object-oriented concepts, and the conclusions are formal concept and property-oriented concept respectively. The rule acquisition algorithms for I-decision rules and II-decision rules are presented. Some comparative analysis of these algorithms with the existing algorithms are examined which shows that the algorithms presented in this study behave well. The attribute reduction approaches to preserve I-decision rules and II-decision rules are presented by using discernibility matrix.

cs.AI↗

arXiv2021

Joint Observations of Space-based Gravitational-wave Detectors: Source Localization and Implication for Parity-violating gravity

Space-based gravitational-wave (GW) detectors, including LISA, Taiji and TianQin, are able to detect mHz GW signals produced by mergers of supermassive black hole binaries, which opens a new window for GW astronomy. In this article, we numerically estimate the potential capabilities of the future networks of multiple space-based detectors using Bayesian analysis. We modify the public package Bilby and employ the sampler PyMultiNest to analyze the simulated data of the space-based detector networks, and investigate their abilities for source localization and testing the parity symmetry of gravity. In comparison with the case of an individual detector, we find detector networks can significantly improve the source localization. While for constraining the parity symmetry of gravity, we find that detector networks and an individual detector follow the similar constraints on the parity-violating energy scale $M_{\rm PV}$. Similar analysis can be applied to other potential observations of various space-based GW detectors.

gr-qc↗

arXiv2020

Metric-Free Individual Fairness with Cooperative Contextual Bandits

Data mining algorithms are increasingly used in automated decision making across all walks of daily life. Unfortunately, as reported in several studies these algorithms inject bias from data and environment leading to inequitable and unfair solutions. To mitigate bias in machine learning, different formalizations of fairness have been proposed that can be categorized into group fairness and individual fairness. Group fairness requires that different groups should be treated similarly which might be unfair to some individuals within a group. On the other hand, individual fairness requires that similar individuals be treated similarly. However, individual fairness remains understudied due to its reliance on problem-specific similarity metrics. We propose a metric-free individual fairness and a cooperative contextual bandits (CCB) algorithm. The CCB algorithm utilizes fairness as a reward and attempts to maximize it. The advantage of treating fairness as a reward is that the fairness criterion does not need to be differentiable. The proposed algorithm is tested on multiple real-world benchmark datasets. The results show the effectiveness of the proposed algorithm at mitigating bias and at achieving both individual and group fairness.

cs.LG↗

arXiv2020

Model-independent test of the parity symmetry of gravity with gravitational waves

Gravitational wave (GW) data can be used to test the parity symmetry of gravity by investigating the difference between left-hand and right-hand circular polarization modes. In this article, we develop a method to decompose the circular polarizations of GWs produced during the inspiralling stage of compact binaries, with the help of stationary phase approximation. The foremost advantage is that this method is simple, clean, independent of GW waveform, and is applicable to the existing detector network. Applying it to the mock data, we test the parity symmetry of gravity by constraining the velocity birefringence of GWs. If a nearly edge-on binary neutron-stars with observed electromagnetic counterparts at 40 Mpc is detected by the second-generation detector network, one could derive the model-independent test on the parity symmetry in gravity: the lower limit of the energy scale of parity violation can be constrained within $\mathcal{O}(10^4{\rm eV})$.

gr-qc↗

arXiv2019

Academic Performance Estimation with Attention-based Graph Convolutional Networks

Student's academic performance prediction empowers educational technologies including academic trajectory and degree planning, course recommender systems, early warning and advising systems. Given a student's past data (such as grades in prior courses), the task of student's performance prediction is to predict a student's grades in future courses. Academic programs are structured in a way that prior courses lay the foundation for future courses. The knowledge required by courses is obtained by taking multiple prior courses, which exhibits complex relationships modeled by graph structures. Traditional methods for student's performance prediction usually neglect the underlying relationships between multiple courses; and how students acquire knowledge across them. In addition, traditional methods do not provide interpretation for predictions needed for decision making. In this work, we propose a novel attention-based graph convolutional networks model for student's performance prediction. We conduct extensive experiments on a real-world dataset obtained from a large public university. The experimental results show that our proposed model outperforms state-of-the-art approaches in terms of grade prediction. The proposed model also shows strong accuracy in identifying students who are at-risk of failing or dropping out so that timely intervention and feedback can be provided to the student.

cs.LG↗

arXiv2019

Experimentally demonstration of the repulsive Casimir force in the gold-cyclohexane-PTFE system

The experimentally demonstration of Casimir force transition from attraction to repulsion is still challenging. Herein, the Casimir forces for a sphere above a plate immersed in different liquids were precisely measured using Atomic force microscope, and the long-range repulsive Casimir force in the gold-cyclohexane-PTFE system is observed for the first time. The experimental data are consistent with the calculation by Lifshitz theory, which offers the direct evidence for the system of ε1<ε3<ε2. It further verifies the reasonability of van Zwol et al. dielectric model to describe the intervening fluids. This study is promising for potential applications on quantum levitation and frictionless devices in MEMS and NEMS by Casimir repulsion.

cond-mat.other↗

arXiv2019

Additive Local Multiplications and zero-preserving maps on $C(X)$

Suppose $X$ is a compact Hausdorff space. In terms of topolocical properties of $X$, we find topological conditions on $X$ that are equivalent to each of the following: 1. every additive local multiplication on $C\left( X\right) $ is a multiplication, 2. every additive local multiplication on $C_{R}\left( X\right) $ is a multiplication, and 3. every additive map on $C\left( X\right) $ that is zero-preserving (i.e., $f\left( x\right) =0$ implies $\left( Tf\right) \left( x\right) =0$) has the form $T\left( f\right) =T\left( 1\right) \operatorname{Re}f+T\left( i\right) \operatorname{Im}f$.

math.FA↗

arXiv2019

Reliable Deep Grade Prediction with Uncertainty Estimation

Currently, college-going students are taking longer to graduate than their parental generations. Further, in the United States, the six-year graduation rate has been 59% for decades. Improving the educational quality by training better-prepared students who can successfully graduate in a timely manner is critical. Accurately predicting students' grades in future courses has attracted much attention as it can help identify at-risk students early so that personalized feedback can be provided to them on time by advisors. Prior research on students' grade prediction include shallow linear models; however, students' learning is a highly complex process that involves the accumulation of knowledge across a sequence of courses that can not be sufficiently modeled by these linear models. In addition to that, prior approaches focus on prediction accuracy without considering prediction uncertainty, which is essential for advising and decision making. In this work, we present two types of Bayesian deep learning models for grade prediction. The MLP ignores the temporal dynamics of students' knowledge evolution. Hence, we propose RNN for students' performance prediction. To evaluate the performance of the proposed models, we performed extensive experiments on data collected from a large public university. The experimental results show that the proposed models achieve better performance than prior state-of-the-art approaches. Besides more accurate results, Bayesian deep learning models estimate uncertainty associated with the predictions. We explore how uncertainty estimation can be applied towards developing a reliable educational early warning system. In addition to uncertainty, we also develop an approach to explain the prediction results, which is useful for advisors to provide personalized feedback to students.

cs.AI↗

arXiv2018

Casimir repulsive-attractive transition between liquid-separated dielectric metamaterial and metal

We study the repulsive-attractive transition, regarded as stable equilibrium, between gold and dielectric metamaterial based on Mie resonance immersed in various fluids due to the interplay of gravity, buoyancy and Casimir force among different geometries consisting of parallel plates and spheres levitated over substrates. A wider range of separation distance of stable equilibrium is obtained with Mie metamaterial than natural materials. We investigate the relationship between separation distance of stable equilibrium and constructive parameters of Mie metamaterial and geometric parameters of the system, and provide simple rules to tune the equilibrium position by modifying constructive parameters of Mie metamaterial. Particularly, the effect of permeability of Mie metamaterial on equilibrium separation is also considered. Our work is promising for potential applications in frictionless suspension in micro/nanofabrication technologies.

physics.app-ph↗

arXiv2018

How Special Is GRB 170817A?

GRB 170817A is the first short gamma-ray burst (GRB) with direct detection of the gravitational-wave radiation and also the spectroscopically identified macronova emission (i.e., AT 2017gfo). The prompt emission of this burst, however, is underluminous in comparison with the other short GRBs with known redshift. In this work, we examine whether GRB 170817A is indeed unique. We firstly show that GRB 130603B/macronova may be the on-axis "analogs" of GRB 170817A/AT 2017gfo, and the extremely dim { but long-lasting} afterglow emission of GRB 170817A may suggest a low number density ($\sim 10^{-5}~{\rm cm^{-3}}$) of its circumburst medium { and a structured outflow}. We then discuss whether GRB 070923, GRB 080121, GRB 090417A, GRB 111005A, and GRB 170817A form a new group of very nearby underluminous GRBs originated from neutron star mergers. If the short events GRB 070923, GRB 080121, and GRB 090417A are indeed at a redshift of $\sim 0.076,~0.046,~0.088$, respectively, their isotropic energies of the prompt emission are $\sim 10^{47}$ erg and thus comparable to the other two events. The non-detection of optical counterparts of GRB 070923, GRB 080121, GRB 090417A, and GRB 111005A, however, strongly suggests that the macronovae from neutron star mergers are significantly diverse in luminosities or, alternatively, there is the other origin channel (for instance, the white dwarf and black hole mergers). We finally suggest that GW170817/GRB 170817A are likely not alone and similar events will be detected by the upgraded/upcoming gravitational-wave detectors and the electromagnetic monitors.

astro-ph.HE↗