arXiv subjects

Lin Li

Publications and source records attributed to Lin Li.

At least 343 records · Page 19Linked to original sources

arXiv2021

DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction

In E-commerce, vouchers are important marketing tools to enhance users' engagement and boost sales and revenue. The likelihood that a user redeems a voucher is a key factor in voucher distribution decision. User-item Click-Through-Rate (CTR) models are often applied to predict the user-voucher redemption rate. However, the voucher scenario involves more complicated relations among users, items and vouchers. The users' historical behavior in a voucher collection activity reflects users' voucher usage patterns, which is nevertheless overlooked by the CTR-based solutions. In this paper, we propose a Deep Multi-behavior Graph Networks (DMBGN) to shed light on this field for the voucher redemption rate prediction. The complex structural user-voucher-item relationships are captured by a User-Behavior Voucher Graph (UVG). User behavior happening both before and after voucher collection is taken into consideration, and a high-level representation is extracted by Higher-order Graph Neural Networks. On top of a sequence of UVGs, an attention network is built which can help to learn users' long-term voucher redemption preference. Extensive experiments on three large-scale production datasets demonstrate the proposed DMBGN model is effective, with 10% to 16% relative AUC improvement over Deep Neural Networks (DNN), and 2% to 4% AUC improvement over Deep Interest Network (DIN). Source code and a sample dataset are made publicly available to facilitate future research.

cs.IR↗

arXiv2021

CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues

Anaphora and ellipses are two common phenomena in dialogues. Without resolving referring expressions and information omission, dialogue systems may fail to generate consistent and coherent responses. Traditionally, anaphora is resolved by coreference resolution and ellipses by query rewrite. In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding. Given an ongoing dialogue between a user and a dialogue assistant, for the user query, our joint learning model first predicts coreference links between the query and the dialogue context, and then generates a self-contained rewritten user query. To evaluate our model, we annotate a dialogue based coreference resolution dataset, MuDoCo, with rewritten queries. Results show that the performance of query rewrite can be substantially boosted (+2.3% F1) with the aid of coreference modeling. Furthermore, our joint model outperforms the state-of-the-art coreference resolution model (+2% F1) on this dataset.

cs.AI↗

arXiv2021

Conversational Semantic Parsing for Dialog State Tracking

We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositionality, cross-domain knowledge sharing and co-reference. We present TreeDST, a dataset of 27k conversations annotated with tree-structured dialog states and system acts. We describe an encoder-decoder framework for DST with hierarchical representations, which leads to 20% improvement over state-of-the-art DST approaches that operate on a flat meaning space of slot-value pairs.

cs.CL↗

arXiv2021

A Hybrid Rydberg Quantum Gate for Quantum Network

The high fidelity storage, distribution and processing of quantum information prefers qubits with different physical properties. Thus, hybrid quantum gates interfacing different types of qubits are essential for the realization of complex quantum network structures. A Rydberg-atom based physical quantum CZ gate is proposed to hybridly process the polarisation-encoded single-photon optical qubit and the "Schroedinger cat" microwave qubit. The degradation of the fidelity under the influence of various noise channels, such as microwave cavity loss, sponetanous emission of atom states, and non-adiabaticity effect, etc, has been analyised through detailed theoretical analysis by deriving input-output relation of qubit fields. The feasibility and the challenges of the protocol within current technology are also discussed by analysing the possible experimental parameter settings.

quant-ph↗

arXiv2021

Compacting Deep Neural Networks for Internet of Things: Methods and Applications

Deep Neural Networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this paper presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression, 2) Knowledge Distillation (KD), 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future directions.

cs.LG↗

arXiv2021

CFNS Ad-Hoc meeting on Radiative Corrections Whitepaper

Current precision scattering experiments and even more so many experiments planed for the Electron Ion Collider will be limited by systematics. From the theory side, a fundamental source of systematic uncertainty is the correct treatment of radiative effects. To gauge the current state of technique and knowledge, help the cross-pollination between different direction of nuclear physics, and to give input to the yellow report process, the community met in an ad-hoc workshop hosted by the Center for Frontiers in Nuclear Science, Stony Brook University. This whitepaper is a collection of contributions to this workshop.

nucl-th↗

arXiv2020

Time-Scale-Chirp_rate Operator for Recovery of Non-stationary Signal Components with Crossover Instantaneous Frequency Curves

The objective of this paper is to introduce an innovative approach for the recovery of non-stationary signal components with possibly cross-over instantaneous frequency (IF) curves from a multi-component blind-source signal. The main idea is to incorporate a chirp rate parameter with the time-scale continuous wavelet-like transformation, by considering the quadratic phase representation of the signal components. Hence-forth, even if two IF curves cross, the two corresponding signal components can still be separated and recovered, provided that their chirp rates are different. In other words, signal components with the same IF value at any time instant could still be recovered. To facilitate our presentation, we introduce the notion of time-scale-chirp_rate (TSC-R) recovery transform or TSC-R recovery operator to develop a TSC-R theory for the 3-dimensional space of time, scale, chirp rate. Our theoretical development is based on the approximation of the non-stationary signal components with linear chirps and applying the proposed adaptive TSC-R transform to the multi-component blind-source signal to obtain fairly accurate error bounds of IF estimations and signal components recovery. Several numerical experimental results are presented to demonstrate the out-performance of the proposed method over all existing time-frequency and time-scale approaches in the published literature, particularly for non-stationary source signals with crossover IFs.

math.NA↗

arXiv2020

Signal Separation Based on Adaptive Continuous Wavelet Transform and Analysis

Recently the synchrosqueezed transform (SST) was developed as an empirical mode decomposition (EMD)-like tool to enhance the time-frequency resolution and energy concentration of a multi-component non-stationary signal and provides more accurate component recovery. To recover individual components, the SST method consists of two steps. First the instantaneous frequency (IF) of a component is estimated from the SST plane. Secondly, after IF is recovered, the associated component is computed by a definite integral along the estimated IF curve on the SST plane. The reconstruction accuracy for a component depends heavily on the accuracy of the IFs estimation carried out in the first step. More recently, a direct method of the time-frequency approach, called signal separation operation (SSO), was introduced for multi-component signal separation. While both SST and SSO are mathematically rigorous on IF estimation, SSO avoids the second step of the two-step SST method in component recovery (mode retrieval). The SSO method is based on some variant of the short-time Fourier transform. In the present paper, we propose a direct method of signal separation based on the adaptive continuous wavelet-like transform (CWLT) by introducing two models of the adaptive CWLT-based approach for signal separation: the sinusoidal signal-based model and the linear chirp-based model, which are derived respectively from sinusoidal signal approximation and the linear chirp approximation at any time instant. A more accurate component recovery formula is derived from linear chirp local approximation. We present the theoretical analysis of our approach. For each model, we establish the error bounds for IF estimation and component recovery.

math.NA↗

arXiv2020

Iterative roots of exclusive multifunctions

In this paper we investigate iterative roots of strictly monotone upper semi-continuous multifunctions having finitely many jumps. Known results are concerning roots of order 2 for multifunctions of exact one jump. For the general investigation, we introduce a concept `intensity' to formulate the growth of jumps under iteration and find a class of strictly monotone and upper semi-continuous multifunctions of intensity 1, called exclusive multifunctions, each of which has an absorbing interval. Then we use the absorbing interval to construct iterative roots of order $n$ for those exclusive multifunctions.

math.DS↗

arXiv2020

Influence of different exchange-correlation potentials on twisted structures of bilayer XS2 (X= Mo, Cr)

In this work, we employ the LDA, GGA and GGA with four vdW corrections to study crystal and electronic structures of bilayer transition metal dichalcogenides (TMDs) with different twist angles. We find the GGA interlayer distance of bilayer MoS2 has good agreement with experimental value while vdW correction method still needs to be further improved. Our results indicate the GGA interlayer distances of bilayer XS2 (X= Mo, Cr) with twist angles are smaller than that of normal bilayer, which is the opposite in the LDA case. The GGA results show that reduced bandgap is due to the reduction of interlayer distance and, flat valley and conductivity bands appear owing to twist angle. Our study not only supports valuable information for application possibility of twisted two-dimensional (2D) materials but also stimulates more related research.

cond-mat.mtrl-sci↗

arXiv2020

Analysis of an Adaptive Short-Time Fourier Transform-Based Multicomponent Signal Separation Method Derived from Linear Chirp Local Approximation

The synchrosqueezing transform (SST) has been developed as a powerful EMD-like tool for instantaneous frequency (IF) estimation and component separation of non-stationary multicomponent signals. Recently, a direct method of the time-frequency approach, called signal separation operation (SSO), was introduced to solving the problem of multicomponent signal separation. While both SST and SSO are mathematically rigorous on IF estimation, SSO avoids the second step of the two-step SST method in component recovery (mode retrieval). In addition, SSO is simple: the IF of a component is estimated by a time-frequency ridge of the SSO plane; and this component is recovered by simply plugging the time-frequency ridge to the SSO operation. In recent paper "Direct signal separation via extraction of local frequencies with adaptive time-varying parameters", after showing that the SSO operation is related to the adaptive short-time Fourier transform (STFT), the authors obtained a more accurate component recovery formula derived from the linear chirp (also called linear frequency modulation signal) approximation at any local time and they also proposed a recovery scheme to extract the signal components one by one with the time-varying window updated for each component. However the theoretical analysis of the recovery formula derived from linear chirp local approximation has not been studied there. In this paper, we carry out such analysis and obtain error bounds for IF estimation and component recovery. These results provide a mathematical guarantee to the proposed adaptive STFT-based non-stationary multicomponent signal separation method.

math.NA↗

arXiv2020

Timing Detectors with SiPM read-out for the MUSE Experiment at PSI

The Muon Scattering Experiment at the Paul Scherrer Institut uses a mixed beam of electrons, muons, and pions, necessitating precise timing to identify the beam particles and reactions they cause. We describe the design and performance of three timing detectors using plastic scintillator read out with silicon photomultipliers that have been built for the experiment. The Beam Hodoscope, upstream of the scattering target, counts the beam flux and precisely times beam particles both to identify species and provide a starting time for time-of-flight measurements. The Beam Monitor, downstream of the scattering target, counts the unscattered beam flux, helps identify background in scattering events, and precisely times beam particles for time-of-flight measurements. The Beam Focus Monitor, mounted on the target ladder under the liquid hydrogen target inside the target vacuum chamber, is used in dedicated runs to sample the beam spot at three points near the target center, where the beam should be focused.

physics.ins-det↗

arXiv2020

Laser Assisted Solution Synthesis of High Performance Graphene Supported Electrocatalysts

Simple, yet versatile, methods to functionalize graphene flakes with metal (oxide) nanoparticles are in demand, particularly for the development of advanced catalysts. Herein, based on light-induced electrochemistry, a laser-assisted, continuous, solution route for the simultaneous reduction and modification of graphene oxide with catalytic nanoparticles is reported. Electrochemical graphene oxide (EGO) is used as starting material and electron-hole pair source due to its low degree of oxidation, which imparts structural integrity and an ability to withstand photodegradation. Simply illuminating a solution stream containing EGO and metal salt (e.g., H2PtCl6 or RuCl3) with a 248 nm wavelength laser produces reduced EGO (rEGO, oxygen content 4.0 at%) flakes, decorated with Pt (~2.0 nm) or RuO2 (~2.8 nm) nanoparticles. The RuO2-rEGO flakes exhibit superior catalytic activity for the oxygen evolution reaction, requiring a small overpotential of 225 mV to reach a current density of 10 mA cm-2. The Pt-rEGO flakes (10.2 wt% of Pt) show enhanced mass activity for the hydrogen evolution reaction, and similar performance for oxygen reduction reaction compared to a commercial 20 wt% Pt/C catalyst. This simple production method is also used to deposit PtPd alloy and MnOx nanoparticles on rEGO, demonstrating its versatility in synthesizing functional nanoparticle-modified graphene materials.

cond-mat.mtrl-sci↗

arXiv2020

AP20-OLR Challenge: Three Tasks and Their Baselines

This paper introduces the fifth oriental language recognition (OLR) challenge AP20-OLR, which intends to improve the performance of language recognition systems, along with APSIPA Annual Summit and Conference (APSIPA ASC). The data profile, three tasks, the corresponding baselines, and the evaluation principles are introduced in this paper. The AP20-OLR challenge includes more languages, dialects and real-life data provided by Speechocean and the NSFC M2ASR project, and all the data is free for participants. The challenge this year still focuses on practical and challenging problems, with three tasks: (1) cross-channel LID, (2) dialect identification and (3) noisy LID. Based on Kaldi and Pytorch, recipes for i-vector and x-vector systems are also conducted as baselines for the three tasks. These recipes will be online-published, and available for participants to configure LID systems. The baseline results on the three tasks demonstrate that those tasks in this challenge are worth paying more efforts to achieve better performance.

eess.AS↗

arXiv2020

Butterfly-like anisotropic magnetoresistance and angle-dependent Berry phase in Type-II Weyl semimetal WP2

Weyl semimetal emerges as a new topologically nontrivial phase of matter, hosting low-energy excitations of massless Weyl fermions. Here, we present a comprehensive study of the type-II Weyl semimetal WP2. Transport studies show a butterfly-like magnetoresistance at low temperature, reflecting the anisotropy of the electron Fermi surfaces. The four-lobed feature gradually evolves into a two-lobed one upon increasing temperature, mainly due to the reduced relative contribution of electron Fermi surfaces compared to hole Fermi surfaces for the magnetoresistance. Moreover, angle-dependent Berry phase is further discovered from the quantum oscillations, which is ascribed to the effective manipulation of the extremal Fermi orbits by the magnetic field to feel the nearby topological singularities in the momentum space. The revealed topological characters and anisotropic Fermi surfaces of WP2 substantially enrich the physical properties of Weyl semimetals and hold great promises in topological electronic and Fermitronic device applications.

cond-mat.mtrl-sci↗

arXiv2020

Analysis of Adaptive Synchrosqueezing Transform with a Time-varying Parameter

The synchrosqueezing transform (SST) was developed recently to separate the components of non-stationary multicomponent signals. The continuous wavelet transform-based SST (WSST) reassigns the scale variable of the continuous wavelet transform of a signal to the frequency variable and sharpens the time-frequency representation. The WSST with a time-varying parameter, called the adaptive WSST, was introduced very recently in the paper "Adaptive synchrosqueezing transform with a time-varying parameter for non-stationary signal separation". The well-separated conditions of non-stationary multicomponent signals with the adaptive WSST and a method to select the time-varying parameter were proposed in that paper. In addition, simulation experiments in that paper show that the adaptive WSST is very promising in estimating the instantaneous frequency of a multicomponent signal, and in accurate component recovery. However the theoretical analysis of the adaptive WSST has not been studied. In this paper, we carry out such analysis and obtain error bounds for component recovery with the adaptive WSST and the 2nd-order adaptive WSST. These results provide a mathematical guarantee to non-stationary multicomponent signal separation with the adaptive WSST.

eess.SP↗

arXiv2020

Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach

Few-shot learning is a challenging problem that has attracted more and more attention recently since abundant training samples are difficult to obtain in practical applications. Meta-learning has been proposed to address this issue, which focuses on quickly adapting a predictor as a base-learner to new tasks, given limited labeled samples. However, a critical challenge for meta-learning is the representation deficiency since it is hard to discover common information from a small number of training samples or even one, as is the representation of key features from such little information. As a result, a meta-learner cannot be trained well in a high-dimensional parameter space to generalize to new tasks. Existing methods mostly resort to extracting less expressive features so as to avoid the representation deficiency. Aiming at learning better representations, we propose a meta-learning approach with complemented representations network (MCRNet) for few-shot image classification. In particular, we embed a latent space, where latent codes are reconstructed with extra representation information to complement the representation deficiency. Furthermore, the latent space is established with variational inference, collaborating well with different base-learners, and can be extended to other models. Finally, our end-to-end framework achieves the state-of-the-art performance in image classification on three standard few-shot learning datasets.

cs.CV↗

arXiv2020

Null Gravitational Redshift by a Reissner-Nordström Black Hole in the Strong Field Limit

The gravitational shift of electromagnetic frequency in the strong field limit is usually investigated under the common scenario, where the light receiver is far away from the central body while the emitter is in the strong-field region of the lens. In this paper, the gravitational frequency shift of light caused by a Reissner-Nordström (RN) black hole is studied numerically in the traditional strong-field scenario, as well as in the scenario where both the light emission and reception events happen in the strong-field region of the black hole. In order to obtain the numerical results of the gravitational redshift, we first derive the exact null equations of motion in the RN geometry in harmonic coordinates. For a given light observer, a new numerical technique is proposed in the integration of the geodesic equations to determine the spatial position of the emitter, considering the fact that their spatial positions are not always known simultaneously. Our work might be helpful to the related observations for probing strong gravity.

gr-qc↗