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Xiaoxi Li

Publications and source records attributed to Xiaoxi Li.

49 records · Page 3Linked to original sources

Information Sale on Network

This paper studies a stylized model of a monopoly data seller when information-sharing network exists among data buyers. We show that, if the buyers' prior information is sufficiently noisy, the optimal selling strategy is characterized by a maximum independent set, which is the largest set of buyers who do not have information-sharing link at all. In addition, the precision of the seller's data decreases in the number of information-sharing links among buyers, but it is higher than the socially efficient level of precision.

econ.TH↗

UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models

Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in the area of information retrieval and natural language processing. Existing methods for GDR and GAR rely on separate retrieval and reader modules, which hinder simultaneous optimization. To overcome this, we present \textbf{UniGen}, a \textbf{Uni}fied \textbf{Gen}erative framework for retrieval and question answering that integrates both tasks into a single generative model leveraging the capabilities of large language models. UniGen employs a shared encoder and two distinct decoders for generative retrieval and question answering. To facilitate the learning of both tasks, we introduce connectors, generated by large language models, to bridge the gaps between query inputs and generation targets, as well as between document identifiers and answers. Furthermore, we propose an iterative enhancement strategy that leverages generated answers and retrieved documents to iteratively improve both tasks. Through extensive experiments on the MS MARCO and NQ datasets, we demonstrate the effectiveness of UniGen, showcasing its superior performance in both the retrieval and the question answering tasks.

cs.IR↗

The USTC-NELSLIP Systems for Simultaneous Speech Translation Task at IWSLT 2021

This paper describes USTC-NELSLIP's submissions to the IWSLT2021 Simultaneous Speech Translation task. We proposed a novel simultaneous translation model, Cross Attention Augmented Transducer (CAAT), which extends conventional RNN-T to sequence-to-sequence tasks without monotonic constraints, e.g., simultaneous translation. Experiments on speech-to-text (S2T) and text-to-text (T2T) simultaneous translation tasks shows CAAT achieves better quality-latency trade-offs compared to \textit{wait-k}, one of the previous state-of-the-art approaches. Based on CAAT architecture and data augmentation, we build S2T and T2T simultaneous translation systems in this evaluation campaign. Compared to last year's optimal systems, our S2T simultaneous translation system improves by an average of 11.3 BLEU for all latency regimes, and our T2T simultaneous translation system improves by an average of 4.6 BLEU.

cs.CL↗

Robust Bayesian variable selection for gene-environment interactions

Gene-environment (G$\times$E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G$\times$E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G$\times$E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies and analysis of both the diabetes data with SNP measurements from the Nurses' Health Study and TCGA melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.

stat.ME↗

Semi-parametric Bayesian variable selection for gene-environment interactions

Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Study of gene-environment (G$\times$E) interactions is important for elucidating the disease etiology. Existing Bayesian methods for G$\times$E interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. Many studies have shown the advantages of penalization methods in detecting G$\times$E interactions in "large p, small n" settings. However, Bayesian variable selection, which can provide fresh insight into G$\times$E study, has not been widely examined. We propose a novel and powerful semi-parametric Bayesian variable selection model that can investigate linear and nonlinear G$\times$E interactions simultaneously. Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main-effects-only case within the Bayesian framework. Spike and slab priors are incorporated on both individual and group levels to identify the sparse main and interaction effects. The proposed method conducts Bayesian variable selection more efficiently than existing methods. Simulation shows that the proposed model outperforms competing alternatives in terms of both identification and prediction. The proposed Bayesian method leads to the identification of main and interaction effects with important implications in a high-throughput profiling study with high-dimensional SNP data.

stat.ME↗

Helical quantum Hall phase in graphene on SrTiO$_3$

The ground state of charge neutral graphene under perpendicular magnetic field was predicted to be a quantum Hall topological insulator with a ferromagnetic order and spin-filtered, helical edge channels. In most experiments, however, an otherwise insulating state is observed and is accounted for by lattice-scale interactions that promote a broken-symmetry state with gapped bulk and edge excitations. We tuned the ground state of the graphene zeroth Landau level to the topological phase via a suitable screening of the Coulomb interaction with a SrTiO$_3$ high-$k$ dielectric substrate. We observed robust helical edge transport emerging at a magnetic field as low as 1 tesla and withstanding temperatures up to 110 kelvins over micron-long distances. This new and versatile graphene platform opens new avenues for spintronics and topological quantum computation.

cond-mat.mes-hall↗

Current-driven magnetization switching in a van der Waals ferromagnet Fe3GeTe2

The recent discovery of ferromagnetism in two-dimensional (2D) van der Waals (vdW) materials holds promises for novel spintronic devices with exceptional performances. However, in order to utilize 2D vdW magnets for building spintronic nanodevices such as magnetic memories, key challenges remain in terms of effectively switching the magnetization from one state to the other electrically. Here, we devise a bilayer structure of Fe3GeTe2/Pt, in which the magnetization of few-layered Fe3GeTe2 can be effectively switched by the spin-orbit torques (SOTs) originated from the current flowing in the Pt layer. The effective magnetic fields corresponding to the SOTs are further quantitatively characterized using harmonic measurements. Our demonstration of the SOT-driven magnetization switching in a 2D vdW magnet could pave the way for implementing low-dimensional materials in the next-generation spintronic applications.

cond-mat.mes-hall↗

Gate tunable giant anisotropic resistance in ultra-thin GaTe

In crystals, the duplication of atoms often follows different periodicity along different directions. It thus gives rise to the so called anisotropy, which is usually even more pronounced in two dimensional (2D) materials due to the absence of $\textbf{z}$ dimension. Indeed, in the emerging 2D materials, electrical anisotropy has been one of the focuses in recent experimental efforts. However, key understandings of the in-plane anisotropic resistance in low-symmetry 2D materials, as well as demonstrations of model devices taking advantage of it, have proven difficult. Here, we show that, in few-layered semiconducting GaTe, electrical conductivity along $\textbf{x}$ and $\textbf{y}$ directions of the 2D crystal can be gate tuned from a ratio of less than one order to as large as 10$^{3}$. This effect is further demonstrated to yield an anisotropic memory resistor behaviour in ultra-thin GaTe, when equipped with an architecture of van der Waals floating gate. Our findings of gate tunable giant anisotropic resistance (GAR) effect pave the way for potential applications in nano-electronics such as multifunctional directional memories in the 2D limit.

cond-mat.mes-hall↗

Gate-controlled reversible rectifying behaviour in tunnel contacted atomically-thin MoS$_{2}$ transistor

Atomically-thin 2D semiconducting materials integrated into van der Waals heterostructures have enabled architectures that hold great promise for next generation nanoelectronics. However, challenges still remain to enable their full acceptance as compliant materials for integration in logic devices. Two key-components to master are the barriers at metal/semiconductor interfaces and the mobility of the semiconducting channel, which endow the building-blocks of ${pn}$ diode and field effect transistor. Here, we have devised a reverted stacking technique to intercalate a wrinkle-free h-BN tunnel layer between MoS$_{2}$ channel and contacting electrodes. Vertical tunnelling of electrons therefore makes it possible to suppress the Schottky barriers and Fermi level pinning, leading to homogeneous gate-control of the channel chemical potential across the bandgap edges. The observed unprecedented features of ambipolar ${pn}$ to ${np}$ diode, which can be reversibly gate tuned, paves the way for future logic applications and high performance switches based on atomically thin semiconducting channel.

physics.app-ph↗

Uniform Value for Recursive Games with Compact Actions

Mertens, Neyman and Rosenberg [MOR, 2009] used the Mertens and Neyman theorem [IJGT, 1981] to prove the existence of uniform value for absorbing games with finite state space and compact action sets. We provide an analogous proof for another class of stochastic games, recursive games with finite state space and compact action sets. Moreover, both players have stationary epsilon-optimal strategies.

math.OC↗

Uniform value for some nonexpansive optimal control problems with general evaluations

We study the long-run properties of optimal control problems in continuous time, where the running cost of a control problem is evaluated by a probability measure over R_+. Li, Quincampoix and Renault [DCDS-A, 2016] introduced an asymptotic regularity condition for a sequence of probability measures to study the limit properties of the value functions with respect to the evaluation. In the particular case of t-horizon Cesàro mean or rho-discounted Abel mean, this condition implies that the horizon t tends to infinity or the discount factor rho tends to zero. For the control system defined on a compact domain and satisfying some nonexpansive condition, Li, Quincampoix and Renault [DCDS-A, 2016] proved the existence of general limit value, i.e. the value function uniform converges as the evaluation becomes more and more regular. Within the same context, we prove the existence of general uniform value, i.e. for any epsilon>0, there is an optimal control that guarantees the general limit value up to epsilon for all control problems where the cost is evaluated by sufficiently regular probability measures.

math.OC↗

Recursive games: Uniform value, Tauberian theorem and the Mertens conjecture "$Maxmin=\lim v_n=\lim v_λ$"

We study two-player zero-sum recursive games with a countable state space and finite action spaces at each state. When the family of $n$-stage values $\{v_n,n\geq 1\}$ is totally bounded for the uniform norm, we prove the existence of the uniform value. Together with a result in Rosenberg and Vieille (2000), we obtain a uniform Tauberian theorem for recursive games: $(v_n)$ converges uniformly if and only if $(v_λ)$ converges uniformly. We apply our main result to finite recursive games with signals (where players observe only signals on the state and on past actions). When the maximizer is more informed than the minimizer, we prove the Mertens conjecture $Maxmin=\lim_{n\to\infty} v_n=\lim_{λ\to 0}v_λ$. Finally, we deduce the existence of the uniform value in finite recursive game with symmetric information.

math.OC↗

Limit value for optimal control with general means

We consider optimal control problem with an integral cost which is a mean of a given function. As a particular case, the cost concerned is the Cesàro average. The limit of the value with Cesàro mean when the horizon tends to infinity is widely studied in the literature. We address the more general question of the existence of a limit when the averaging parameter converges, for values defined with means of general types. We consider a given function and a family of costs defined as the mean of the function with respect to a family of probability measures -- the evaluations -- on R_+. We provide conditions on the evaluations in order to obtain the uniform convergence of the associated value function (when the parameter of the family converges). Our main result gives a necessary and sufficient condition in term of the total variation of the family of probability measures on R_+. As a byproduct, we obtain the existence of a limit value (for general means) for control systems having a compact invariant set and satisfying suitable nonexpansive property.

math.OC↗