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Qing Zhang

Publications and source records attributed to Qing Zhang.

At least 181 records · Page 10Linked to original sources
arXiv2019

Adjoint L-functions for GL(3) and U(2,1)

We show that the finite part of the adjoint $L$ function (including contributions from all nonarchimedean places, including ramified places) is holomorphic in $\Re(s) \ge 1/2$ for a cuspidal automorphic representation of $GL_3$ over a number field. This improves the main result of [H16]. We obtain more general results for twisted adjoint $L$ functions of both $GL_3$ and quasisplit unitary groups. For unitary groups, we explicate the relationship between poles of twisted adjoint $L$ functions, endoscopy, and the structure of the stable base change lifting.

math.NT↗
arXiv2018

On a converse theorem for $G_2$ over finite fields

In this paper, we prove certain multiplicity one theorems and define twisted gamma factors for irreducible generic cuspidal representations of split $G_2$ over finite fields $k$ of odd characteristic. Then we prove the first converse theorem for exceptional groups, namely, $GL_1$ and $GL_2$-twisted gamma factors will uniquely determine an irreducible generic cuspidal representation of $G_2(k)$.

math.RT↗
arXiv2018

Graph-Adaptive Pruning for Efficient Inference of Convolutional Neural Networks

In this work, we propose a graph-adaptive pruning (GAP) method for efficient inference of convolutional neural networks (CNNs). In this method, the network is viewed as a computational graph, in which the vertices denote the computation nodes and edges represent the information flow. Through topology analysis, GAP is capable of adapting to different network structures, especially the widely used cross connections and multi-path data flow in recent novel convolutional models. The models can be adaptively pruned at vertex-level as well as edge-level without any post-processing, thus GAP can directly get practical model compression and inference speed-up. Moreover, it does not need any customized computation library or hardware support. Finetuning is conducted after pruning to restore the model performance. In the finetuning step, we adopt a self-taught knowledge distillation (KD) strategy by utilizing information from the original model, through which, the performance of the optimized model can be sufficiently improved, without introduction of any other teacher model. Experimental results show the proposed GAP can achieve promising result to make inference more efficient, e.g., for ResNeXt-29 on CIFAR10, it can get 13X model compression and 4.3X practical speed-up with marginal loss of accuracy.

cs.CV↗
arXiv2018

A Novel Integrated Framework for Learning both Text Detection and Recognition

In this paper, we propose a novel integrated framework for learning both text detection and recognition. For most of the existing methods, detection and recognition are treated as two isolated tasks and trained separately, since parameters of detection and recognition models are different and two models target to optimize their own loss functions during individual training processes. In contrast to those methods, by sharing model parameters, we merge the detection model and recognition model into a single end-to-end trainable model and train the joint model for two tasks simultaneously. The shared parameters not only help effectively reduce the computational load in inference process, but also improve the end-to-end text detection-recognition accuracy. In addition, we design a simpler and faster sequence learning method for the recognition network based on a succession of stacked convolutional layers without any recurrent structure, this is proved feasible and dramatically improves inference speed. Extensive experiments on different datasets demonstrate that the proposed method achieves very promising results.

cs.CV↗
arXiv2018

Pyramid Embedded Generative Adversarial Network for Automated Font Generation

In this paper, we investigate the Chinese font synthesis problem and propose a Pyramid Embedded Generative Adversarial Network (PEGAN) to automatically generate Chinese character images. The PEGAN consists of one generator and one discriminator. The generator is built using one encoder-decoder structure with cascaded refinement connections and mirror skip connections. The cascaded refinement connections embed a multiscale pyramid of downsampled original input into the encoder feature maps of different layers, and multi-scale feature maps from the encoder are connected to the corresponding feature maps in the decoder to make the mirror skip connections. Through combining the generative adversarial loss, pixel-wise loss, category loss and perceptual loss, the generator and discriminator can be trained alternately to synthesize character images. In order to verify the effectiveness of our proposed PEGAN, we first build one evaluation set, in which the characters are selected according to their stroke number and frequency of use, and then use both qualitative and quantitative metrics to measure the performance of our model comparing with the baseline method. The experimental results demonstrate the effectiveness of our proposed model, it shows the potential to automatically extend small font banks into complete ones.

cs.CV↗
arXiv2018

Applying Deep Learning To Airbnb Search

The application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This paper discusses the work done in applying neural networks in an attempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!

cs.LG↗
arXiv2018

Sub-20 nm Nanopores Sculptured by a Single Nanosecond Laser Pulse

Single-molecule based 3rd generation DNA sequencing technologies have been explored with tremendous effort, among which nanopore sequencing is considered as one of the most promising to achieve the goal of $1,000 genome project towards personalized medicine. Solid state nanopore is consented to be complementary to protein nanopore and subjected to extensive investigations in the past decade. However, the prevailing solid-state nanopore preparation still relies on focused ion or electron beams, which are expensive and time consuming. Here we demonstrate the fabrication of nanopores down to 19 nm with a single nanosecond laser pulse. The laser drilling process is understood based upon a 2D axisymmetric transient heat transfer model, which predicts the laser fluence-dependent pore size distribution and shape with excellent agreement to electron microscopy and tomography analysis. As-drilled nanopore devices (26 nm) exhibit adequate sensitivity to detect single DNA molecule translocations and discriminate unfolded or folded events. Sub-10 nm nanopores can be readily achieved upon a thin layer of alumina deposition by atomic layer deposition, which further improves the DNA translocation signal to noise ratio considerably. Our work provides a solution for fast, low-cost and efficient large-scale fabrication of solid state nanopore devices for the 3rd generation nanopore sequencing.

physics.bio-ph↗
arXiv2018

On Multi-resident Activity Recognition in Ambient Smart-Homes

Increasing attention to the research on activity monitoring in smart homes has motivated the employment of ambient intelligence to reduce the deployment cost and solve the privacy issue. Several approaches have been proposed for multi-resident activity recognition, however, there still lacks a comprehensive benchmark for future research and practical selection of models. In this paper we study different methods for multi-resident activity recognition and evaluate them on same sets of data. The experimental results show that recurrent neural network with gated recurrent units is better than other models and also considerably efficient, and that using combined activities as single labels is more effective than represent them as separate labels.

cs.CV↗
arXiv2018

Room Temperature Continuous-wave Excited Biexciton Emission in CsPbBr3 Nanocrystals

Biexcitons are a manifestation of many-body excitonic interactions crucial for quantum information and quantum computation in the construction of coherent combinations of quantum states. However, due to their small binding energy and low transition efficiency, most biexcitons in conventional semiconductors exist either at cryogenic temperature or under femtosecond pulse laser excitation. Here we demonstrate room temperature, continuous wave driven biexciton states in CsPbBr3 perovskite nanocrystals through coupling with a plasmonic nanogap. The room temperature CsPbBr3 biexciton excitation fluence (~100 mW/cm2) is reduced by ~10^13 times in the Ag nanowire-film nanogaps. The giant enhancement of biexciton emission is driven by coherent biexciton-plasmon Fano interference. These results provide new pathways to develop high efficiency non-blinking single photon sources, entangled light sources and lasers based on biexciton states.

cond-mat.mes-hall↗
arXiv2018

VOCs monitoring using microwave capacitive resonator and conductive polymer -- MWCNTs nanocomposites for environmental applications

This paper presents a chemical microwave flexible sensor based on a resonant electromagnetic transducer in micro-strip technology with poly (3,4-ethylenedioxythiophene) polystyrene sulfonate -- multi wall carbon nanotubes (PEDOT:PSS-MWCNTs) as sensitive material for Volatile Organic Compounds (VOCs) detection. The results show a high sensitivity to ethanol and toluene vapors on the S parameters of a passive resonator over a large frequency range. It is equal to -1.59 kHz/ppm and -1.45 kHz/ppm for ethanol and toluene vapors respectively. This kind of sensor can be integrated into real-time multi-sensing platform adaptable for the Internet of Things (IoT).

physics.ins-det↗
arXiv2018

Chemical gas sensor based on a novel capacitive microwave flexible transducer and composite polymer carbon nanomaterials

This study presents the results on the feasibility of a resonant planar chemical capacitive sensor in the microwave frequency range suitable for gas detection and for wireless communications applications. The objective is to develop a low cost ultra-sensitive sensor that can be integrated into a real time multi-sensing platform. The first demonstrators target the detection of harmful gases such as volatile organic compounds (VOCs) to monitor environmental pollution.

physics.ins-det↗
arXiv2017

A local converse theorem for $\textrm{Sp}_{2r}$

In this paper, we prove the local converse theorem for $\textrm{Sp}_{2r}(F)$ over a $p$-adic field $F$. More precisely, given two irreducible supercuspidal representations of $\textrm{Sp}_{2r}(F)$ with the same central character such that they are generic with the same additive character and they have the same gamma factors when twisted with generic irreducible representations of $\textrm{GL}_n(F)$ for all $1\le n\le r$, then these two representations must be isomorphic. Our proof is based on the local analysis of the local integrals which define local gamma factors. A key ingredient of the proof is certain partial Bessel function property developed by Cogdell-Shahidi-Tsai recently. The same method can give the local converse theorem for $\textrm{U}(r,r)$.

math.RT↗
arXiv2017

A local converse theorem for $\textrm{U}_{2r+1}$

Let $E/F$ be a quadratic extension of $p$-adic fields and $\textrm{U}_{2r+1}$ be the unitary group associated with $E/F$. We prove the following local converse theorem for $\textrm{U}_{2r+1}$: given two irreducible generic supercuspidal representations $π,π_0$ of $\textrm{U}_{2r+1}$ with the same central character, if $γ(s,π\times τ,ψ)=γ(s,π_0\times τ,ψ)$ for all irreducible generic representation $τ$ of $\textrm{GL}_n(E)$ and for all $n$ with $1\le n\le r$, then $π\cong π_0$. The proof depends on analysis of the local integrals which define local gamma factors and uses certain properties of partial Bessel functions developed by Cogdell-Shahidi-Tsai recently.

math.RT↗
arXiv2017

Strong Exciton-Photon Coupling and lasing behavior in All-Inorganic CsPbBr3 Micro/nanowire Fabry-Perot cavity

Macroscopic spontaneous coherence of exciton-polariton in semiconductor cavity is one important research field in condensed matter physics. All-inorganic micro/nanowire Fabry-Pérot cavity with excellent optical performance makes it possible to realize strong coupling and lasing of exciton-photon at room temperature. In this work, we demonstrated strong coupling of exciton-photon and polariton lasing in CsPbBr3 micro/nanowires synthesized by CVD method. By exploring spatial resolved PL spectra of CsPbBr3 cavity, we observed mode volume dependent coupling strength , as well as significant increase in group index. Moreover, low threshold polariton lasing was achieved at room temperature within strong coupling regime; the polariton characteristic is confirmed by comparing lasing spectra with waveguided output spectra and the dramatically reduced lasing threshold. Our present results provide new avenues to achieve high coupling strengths potentially enabling application of exciting phenomena such as Bose-Einstein condensation of polaritons, efficient light-emitting diodes and lasers.

cond-mat.mes-hall↗
arXiv2017

Surface Plasmon Enhanced Strong Exciton-Photon Coupling in Hybrid Inorganic-Organic Perovskites Nanowires

Manipulating strong light-matter interaction in semiconductor microcavities is crucial for developing high-performance exciton polariton devices with great potentials in next-generation all-solid state quantum technologies. In this work, we report surface plasmon enhanced strong exciton-photon interaction in CH3NH3PbBr3 perovskite nanowires. Characteristic anti-crossing behaviors, indicating Rabi splitting energy up to ~ 560 meV, are observed near exciton resonance in hybrid semiconductor-insulator-metallic waveguide cavity at room temperature. An exciton-photon coupling strength enhancement factor of ~ 1.4 times is evaluated, which is mainly attributed to surface plasmon induced localized excitation field redistribution. Further, systematic studies on nanowires and insulator dimension dependence of exciton-photon interaction are presented. These results provide new avenues to achieve extremely high coupling strengths and push forward the development of electrically pumped and ultra-low threshold small lasers.

cond-mat.mes-hall↗
arXiv2017

Exciton-Polaritons in Hybrid Inorganic-organic Perovskite Fabry-Pérot Microcavity

Exciton-polaritons in semiconductor microcavities generate fascinating effects such as long-range spatial coherence and Bose-Einstein Condensation (BEC), which are attractive for their potential use in low threshold lasers, vortices and slowing light, etc. However, currently most of exciton-polariton effects either occur at cryogenic temperature or rely on expensive cavity fabrication procedures. Further exploring new semiconductor microcavities with stronger exciton photon interaction strength is extensively needed. Herein, we demonstrate room temperature photon exciton strong coupling in hybrid inorganic-organic CH3NH3PbBr3 Fabry-Pérot microcavities for the first time. The vacuum Rabi splitting energy is up to ~390 meV, which is ascribed to large oscillator strength and photon confinement in reduced dimension of optical microcavities. With increasing pumping energy, exciton-photon coupling strength is weakened due to carrier screening effect, leading to occurrence of photonic lasing instead of polartion lasing. The demonstrated strong coupling between photons and excitons in perovskite microcavities would be helpful for development of high performance polariton-based incoherent and coherent light sources, nonlinear optics, and slow light applications.

cond-mat.mes-hall↗
arXiv2017

Cell-to-cell variability and robustness in S-phase duration from genome replication kinetics

Genome replication, a key process for a cell, relies on stochastic initiation by replication origins, causing a variability of replication timing from cell to cell. While stochastic models of eukaryotic replication are widely available, the link between the key parameters and overall replication timing has not been addressed systematically.We use a combined analytical and computational approach to calculate how positions and strength of many origins lead to a given cell-to-cell variability of total duration of the replication of a large region, a chromosome or the entire genome.Specifically, the total replication timing can be framed as an extreme-value problem, since it is due to the last region that replicates in each cell. Our calculations identify two regimes based on the spread between characteristic completion times of all inter-origin regions of a genome. For widely different completion times, timing is set by the single specific region that is typically the last to replicate in all cells. Conversely, when the completion time of all regions are comparable,an extreme-value estimate shows that the cell-to-cell variability of genome replication timing has universal properties. Comparison with available data shows that the replication program of three yeast species falls in this extreme-value regime.

q-bio.GN↗