arXiv subjects

Han Li

Publications and source records attributed to Han Li.

At least 379 records · Page 21Linked to original sources

arXiv2019

Thermal Tensor Renormalization Group Simulations of Square-Lattice Quantum Spin Models

In this work, we benchmark the well-controlled and numerically accurate exponential thermal tensor renormalization group (XTRG) in the simulation of interacting spin models in two dimensions. Finite temperature introduces a thermal correlation length, which justifies the analysis of finite system size for the sake of numerical efficiency. In this paper we focus on the square lattice Heisenberg antiferromagnet (SLH) and quantum Ising models (QIM) on open and cylindrical geometries up to width $W=10$. We explore various one-dimensional mapping paths in the matrix product operator (MPO) representation, whose performance is clearly shown to be geometry dependent. We benchmark against quantum Monte Carlo (QMC) data, yet also the series-expansion thermal tensor network results. Thermal properties including the internal energy, specific heat, and spin structure factors, etc., are computed with high precision, obtaining excellent agreement with QMC results. XTRG also allows us to reach remarkably low temperatures. For SLH we obtain at low temperature an energy per site $u_g^*\simeq -0.6694(4)$ and a spontaneous magnetization $m_S^*\simeq0.30(1)$, which is already consistent with the ground state properties. We extract an exponential divergence vs. $T$ of the structure factor $S(M)$, as well as the correlation length $ξ$, at the ordering wave vector $M=(π,π)$, which represents the renormalized classical behavior and can be observed over a narrow but appreciable temperature window, by analysing the finite-size data by XTRG simulations. For the QIM with a finite-temperature phase transition, we employ several thermal quantities, including the specific heat, Binder ratio, as well as the MPO entanglement to determine the critical temperature $T_c$.

cond-mat.str-el↗

arXiv2019

Two-temperature scales in the triangular-lattice Heisenberg antiferromagnet

The anomalous thermodynamic properties of the paradigmatic frustrated spin-1/2 triangular lattice Heisenberg antiferromagnet (TLH) has remained an open topic of research over decades, both experimentally and theoretically. Here we further the theoretical understanding based on the recently developed, powerful exponential tensor renormalization group (XTRG) method on cylinders and stripes in a quasi one-dimensional (1D) setup, as well as a tensor product operator approach directly in 2D. The observed thermal properties of the TLH are in excellent agreement with two recent experimental measurements on the virtually ideal TLH material Ba$_8$CoNb$_6$O$_{24}$. Remarkably, our numerical simulations reveal two crossover temperature scales, at $T_l/J \sim 0.20$ and $T_h/J\sim 0.55$, with $J$ the Heisenberg exchange coupling, which are also confirmed by a more careful inspection of the experimental data. We propose that in the intermediate regime between the low-temperature scale $T_l$ and the higher one $T_h$, the gapped "roton-like" excitations are activated with a strong chiral component and a large contribution to thermal entropies, which suppress the incipient 120$^\circ$ order that emerges for temperatures below $T_l$.

cond-mat.str-el↗

arXiv2019

Optimized Cost per Click in Taobao Display Advertising

Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to display in tens of milliseconds. Common pricing methods include cost per mille (CPM) and cost per click (CPC). Traditional advertising systems target certain traits of users and ad placements with fixed bids, essentially regarded as coarse-grained matching of bid and traffic quality. However, the fixed bids set by the advertisers competing for different quality requests cannot fully optimize the advertisers' key requirements. Moreover, the platform has to be responsible for the business revenue and user experience. Thus, we proposed a bid optimizing strategy called optimized cost per click (OCPC) which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity. Our approach optimizes advertisers' demands, platform business revenue and user experience and as a whole improves traffic allocation efficiency. We have validated our approach in Taobao display advertising system in production. The online A/B test shows our algorithm yields substantially better results than previous fixed bid manner.

cs.GT↗

arXiv2018

Learning Tree-based Deep Model for Recommender Systems

Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, models such as matrix factorization resort to inner product form (i.e., model user-item preference as the inner product of user, item latent factors) and indexes to facilitate efficient approximate k-nearest neighbor searches. However, it still remains challenging to incorporate more expressive interaction forms between user and item features, e.g., interactions through deep neural networks, because of the calculation cost. In this paper, we focus on the problem of introducing arbitrary advanced models to recommender systems with large corpus. We propose a novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks. Our main idea is to predict user interests from coarse to fine by traversing tree nodes in a top-down fashion and making decisions for each user-node pair. We also show that the tree structure can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction. Experimental evaluations with two large-scale real-world datasets show that the proposed method significantly outperforms traditional methods. Online A/B test results in Taobao display advertising platform also demonstrate the effectiveness of the proposed method in production environments.

stat.ML↗

arXiv2018

Deep Interest Network for Click-Through Rate Prediction

Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding\&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed into fixed-length vectors in a group-wise manner, finally concatenated together to fed into a multilayer perceptron (MLP) to learn the nonlinear relations among features. In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are. The use of fixed-length vector will be a bottleneck, which brings difficulty for Embedding\&MLP methods to capture user's diverse interests effectively from rich historical behaviors. In this paper, we propose a novel model: Deep Interest Network (DIN) which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad. This representation vector varies over different ads, improving the expressive ability of model greatly. Besides, we develop two techniques: mini-batch aware regularization and data adaptive activation function which can help training industrial deep networks with hundreds of millions of parameters. Experiments on two public datasets as well as an Alibaba real production dataset with over 2 billion samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with state-of-the-art methods. DIN now has been successfully deployed in the online display advertising system in Alibaba, serving the main traffic.

stat.ML↗

arXiv2018

Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising

Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a strategic response with respect to other advertisers bidding in the market. In this paper, we formulate bidding optimization with multi-agent reinforcement learning. To deal with a large number of advertisers, we propose a clustering method and assign each cluster with a strategic bidding agent. A practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed and implemented to balance the tradeoff between the competition and cooperation among advertisers. The empirical study on our industry-scaled real-world data has demonstrated the effectiveness of our methods. Our results show cluster-based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than purely self-interested bidding agents.

stat.ML↗

arXiv2018

Cosmic test of sTGC detector prototype made in China for ATLAS experiment upgrade

Following the Higgs particle discovery, the Large Hadron Collider complex will be upgraded in several phases allowing the luminosity to increase to $7 \times 10^{34}cm^{-2}s^{-1}$. In order to adapt the ATLAS detector to the higher luminosity environment after the upgrade, part of the ATLAS muon end-cap system, the Small Wheel, will be replaced by the New Small Wheel. The New Small Wheel includes two kinds of detectors: small-strip Thin Gap Chambers and Micromegas. Shandong University, part of the ATLAS collaboration, participates in the construction of the ATLAS New Small Wheel by developing, producing and testing the performance of part of the small-strip Thin Gap Chambers. This paper describes the construction and cosmic-ray testing of small-strip Thin Gap Chambers in Shandong University.

physics.ins-det↗

arXiv2018

Charge-tuneable biexciton complexes in monolayer WSe$_{2}$

Multi-exciton states such as biexcitons, albeit theoretically predicted, have remained challenging to identify in atomically thin transition metal dichalcogenides so far. Here, we use excitation-power, electric-field and magnetic-field dependence of photoluminescence to report direct experimental evidence of two biexciton complexes in monolayer tungsten diselenide: the neutral and the negatively charged biexciton. We demonstrate bias-controlled switching between these two states, we determine their internal structure and we resolve a fine-structure splitting of 2.5 meV for the neutral biexciton. Our results unveil multi-particle exciton complexes in transition metal dichalcogenides and offer direct routes to their deterministic control in many-body quantum phenomena.

cond-mat.mes-hall↗

arXiv2017

Toward a System Building Agenda for Data Integration

In this paper we argue that the data management community should devote far more effort to building data integration (DI) systems, in order to truly advance the field. Toward this goal, we make three contributions. First, we draw on our recent industrial experience to discuss the limitations of current DI systems. Second, we propose an agenda to build a new kind of DI systems to address these limitations. These systems guide users through the DI workflow, step by step. They provide tools to address the "pain points" of the steps, and tools are built on top of the Python data science and Big Data ecosystem (PyData). We discuss how to foster an ecosystem of such tools within PyData, then use it to build DI systems for collaborative/cloud/crowd/lay user settings. Finally, we discuss ongoing work at Wisconsin, which suggests that these DI systems are highly promising and building them raises many interesting research challenges.

cs.DB↗

arXiv2017

Small generators of cocompact arithmetic Fuchsian groups

In the study of Fuchsian groups, it is a nontrivial problem to determine a set of generators. Using a dynamical approach we construct for any cocompact arithmetic Fuchsian group a fundamental region in $\mathbf{SL}_2(\mathbb{R})$ from which we determine a set of small generators.

math.GR↗

arXiv2017

Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction

CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data. In this paper, we introduce an industrial strength solution with model named Large Scale Piece-wise Linear Model (LS-PLM). We formulate the learning problem with $L_1$ and $L_{2,1}$ regularizers, leading to a non-convex and non-smooth optimization problem. Then, we propose a novel algorithm to solve it efficiently, based on directional derivatives and quasi-Newton method. In addition, we design a distributed system which can run on hundreds of machines parallel and provides us with the industrial scalability. LS-PLM model can capture nonlinear patterns from massive sparse data, saving us from heavy feature engineering jobs. Since 2012, LS-PLM has become the main CTR prediction model in Alibaba's online display advertising system, serving hundreds of millions users every day.

stat.ML↗

arXiv2016

First-principles calculations that clarify energetics and reactions of oxygen adsorption and carbon desorption on 4H-SiC ($11\bar20$) surface

We report static and dynamic first-principles calculations that provide atomistic pictures of the initial stage of the oxidation processes occurring at the ($11\bar20$) surface of 4H-SiC. Our results unveil reaction pathways and their associated free-energy barriers for the adsorption of oxygen and the desorption of carbon atoms. We find that oxygen adsorption shows structural multi-stability and that the surface-bridge sites are the most stable and crucial sites for subsequent oxidation. We find that an approaching O$_2$ molecule is adsorbed, then dissociated and finally migrates toward these surface-bridge sites with a free-energy barrier of 0.7 eV at the ($11\bar20$) surface. We also find that a CO molecule is desorbed from the metastable oxidized structure upon the overcoming of a free-energy barrier of 2.4$\sim$2.6 eV, thus constituting one of the annihilation process of C during the oxidation. The results of the CO molecule desorption on the ($11\bar20$) surface are compared with the ($000\bar1$) surface. A catalytic effect of dangling bonds at the surface, causing a drastic reduction of the CO desorption energy, is found on the ($000\bar1$) surface and the microscopic picture of the effect is ascribed to an electron transfer from the Si to C dangling bonds. The intrinsic ($11\bar20$) surface does not show this catalytic effect, and this is because the surface consists of an equal amount of Si and C dangling bonds and the electron transfer occurs before the desorption.

cond-mat.mtrl-sci↗

arXiv2016

Effective equidistribution of translates of maximal horospherical measures in the space of lattices

Recently Mohammadi and Salehi-Golsefidy gave necessary and sufficient conditions under which certain translates of homogeneous measures converge, and they determined the limiting measures in the cases of convergence. The class of measures they considered includes the maximal horospherical measures. In this paper we prove the corresponding effective equidistribution results in the space of unimodular lattices. We also prove the corresponding results for probability measures with absolutely continuous densities in rank two and three. Then we address the problem of determining the error terms in two counting problems also considered by Mohammadi and Salehi-Golsefidy. In the first problem, we determine an error term for counting the number of lifts of a closed horosphere from an irreducible, finite-volume quotient of the space of positive definite $n\times n$ matrices of determinant one that intersect a ball with large radius. In the second problem, we determine a logarithmic error term for the Manin conjecture of a flag variety over $\mathbb{Q}$.

math.DS↗

arXiv2015

Creation of high mobility two-dimensional electron gases via strain induced polarization at an otherwise nonpolar complex oxide interface

The discovery of two-dimensional electron gases (2DEGs) in SrTiO3-based heterostructures provides new opportunities for nanoelectronics. Herein, we create a new type of oxide 2DEG by the epitaxial-strain-induced polarization at an otherwise nonpolar perovskite-type interface of CaZrO3/SrTiO3. Remarkably, this heterointerface is atomically sharp, and exhibits a high electron mobility exceeding 60,000 cm2V-1s-1 at low temperatures. The 2DEG carrier density exhibits a critical dependence on the film thickness, in good agreement with the polarization induced 2DEG scheme.

cond-mat.mtrl-sci↗

arXiv2013

Effective Limit Distribution of the Frobenius Numbers

The Frobenius number $F(\ba)$ of a lattice point $\ba$ in $\R^d$ with positive coprime coordinates, is the largest integer which can $not$ be expressed as a non-negative integer linear combination of the coordinates of $\ba$. Marklof in \cite{M} proved the existence of the limit distribution of the Frobenius numbers, when $\ba$ is taken to be random in an enlarging domain in $\R^d$. We will show that if the domain has piecewise smooth boundary, the error term for the convergence of the distribution functions is at most a polynomial in the enlarging factor.

math.DS↗

arXiv2013

Design of a 325MHz Beta=0.12 superconducting single spoke cavity for China-ADS

Twelve superconducting single spoke cavities whose Beta is 0.12 (Spoke012) operating at 325MHz, are adopted in Injector I for China-ADS linac. This type of spoke cavity is believed to be one of the key challenges for its very low geometry Beta. So far, the prototype cavity has been designed, fabricated and tested successfully. The design work was finished by using CST-MWS and ANSYS software for the RF and mechanical properties optimization. This paper presents the details of the final design for Spoke012 prototype cavity.

physics.acc-ph↗

arXiv2013

Spectral gap actions and invariant states

We define spectral gap actions of discrete groups on von Neumann algebras and study their relations with invariant states. We will show that a finitely generated ICC group $Γ$ is inner amenable if and only if there exist more than one inner invariant states on the group von Neumann algebra $L(Γ)$. Moreover, a countable discrete group $Γ$ has property $(T)$ if and only if for any action $α$ of $Γ$ on a von Neumann algebra $N$, every $α$-invariant state on $N$ is a weak-$^*$-limit of a net of normal $α$-invariant states.

math.OA↗

arXiv2012

Efficient Superimposition Recovering Algorithm

In this article, we address the issue of recovering latent transparent layers from superimposition images. Here, we assume we have the estimated transformations and extracted gradients of latent layers. To rapidly recover high-quality image layers, we propose an Efficient Superimposition Recovering Algorithm (ESRA) by extending the framework of accelerated gradient method. In addition, a key building block (in each iteration) in our proposed method is the proximal operator calculating. Here we propose to employ a dual approach and present our Parallel Algorithm with Constrained Total Variation (PACTV) method. Our recovering method not only reconstructs high-quality layers without color-bias problem, but also theoretically guarantees good convergence performance.

cs.CV↗