activity
20192022
most citedXENON1T Dark Matter Data Analysis: Signal & Background Models, and Statistical Inference

80 citations · 134 across the 5 of their papers we have counts for

collaborators

9 papers

math.OC20221 cited

Zero-Norm Distance to Controllability of Linear Systems: Complexity, Bounds, and Algorithms

Yuan Zhang, Yuanqing Xia, Yufeng Zhan +1

Determining the distance between a controllable system to the set of uncontrollable systems, namely, the controllability radius problem, has been extensively studied in the past. H…

eess.SY2021

Partial Strong Structural Controllability

Yuan Zhang, Yuanqing Xia

This paper introduces a new controllability notion, termed partial strong structural controllability (PSSC), on a structured system whose entries of system matrices are either fixe…

cs.CL202151 cited

Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations

Jonathan Herzig, Peter Shaw, Ming-Wei Chang +3

Sequence-to-sequence (seq2seq) models are prevalent in semantic parsing, but have been found to struggle at out-of-distribution compositional generalization. While specialized mode…

cs.CL20212 cited

Few-shot Intent Classification and Slot Filling with Retrieved Examples

Dian Yu, Luheng He, Yuan Zhang +3

Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce…

eess.SY2021

PTSC: a New Definition for Structural Controllability under Numerical Perturbations

Yuan Zhang, Yuanqing Xia

This paper proposes a novel notion for structural controllability under structured numerical perturbations, namely the perturbation-tolerant structural controllability (PTSC), on a…

hep-ex2019

Light Dark Matter Search with Ionization Signals in XENON1T

E. Aprile, J. Aalbers, F. Agostini +130

We report constraints on light dark matter (DM) models using ionization signals in the XENON1T experiment. We mitigate backgrounds with strong event selections, rather than requiri…