arXiv subjects

Chao Zhang

Publications and source records attributed to Chao Zhang.

At least 955 records · Page 53Linked to original sources

arXiv2020

Quantum Convolutional Neural Networks for High Energy Physics Data Analysis

This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed architecture demonstrates the quantum advantage of learning faster than the classical convolutional neural networks (CNNs) under a similar number of parameters. In addition to faster convergence, the QCNN achieves greater test accuracy compared to CNNs. Based on experimental results, it is a promising direction to study the application of QCNN and other quantum machine learning models in high energy physics and additional scientific fields.

cs.LG↗

arXiv2020

Gaussian Estimates for Heat Kernels of Higher Order Schrödinger Operators with Potentials in Generalized Schechter Classes

Let $m\in\mathbb N$, $P(D):=\sum_{|α|=2m}(-1)^m a_αD^α$ be a $2m$-order homogeneous elliptic operator with real constant coefficients on $\mathbb{R}^n$, and $V$ a measurable function on $\mathbb{R}^n$. In this article, the authors introduce a new generalized Schechter class concerning $V$ and show that the higher order Schrödinger operator $\mathcal{L}:=P(D)+V$ possesses a heat kernel that satisfies the Gaussian upper bound and the Hölder regularity when $V$ belongs to this new class. The Davies--Gaffney estimates for the associated semigroup and their local versions are also given. These results pave the way for many further studies on the analysis of $\mathcal{L}$.

math.AP↗

arXiv2020

Boundedness of differential transforms for fractional Poisson type operators generated by parabolic operators

In this paper we analyze the convergence of the following type of series $$ T_N^αf(x,t)=\sum_{j=N_1}^{N_2} v_j(P_{a_{j+1}}^αf(x,t)-P_{a_j}^αf(x,t)),\quad (x,t)\in \mathbb R^{n+1}, \ N=(N_1, N_2)\in \mathbb Z^2,\ α>0, $$ where $\{P_τ^α\}_{τ>0}$ is the fractional Poisson-type operators generated by the parabolic operator $L=\partial_t-Δ$ with $Δ$ being the classical Laplacian, $\{v_j\}_{j\in \mathbb Z}$ a bounded real sequences and $\{a_j\}_{j\in \mathbb Z}$ an increasing real sequence. Our analysis will consist {of} the boundedness, in $L^p(\mathbb{R}^n)$ and in $BMO(\mathbb{R}^n)$, of the operators $T^α_N$ and its maximal operator $ T^*f(x)= \sup_{N\in \mathbb Z^2} |T^α_N f(x)|.$ It is also shown that the local size of the maximal differential transform operators is the same with the order of a singular integral for functions $f$ having local support. Moreover, if $\{v_j\}_{j\in \mathbb Z}\in \ell^p(\mathbb Z)$, we get an intermediate size between the local size of singular integrals and Hardy-Littlewood maximal operator.

math.CA↗

arXiv2020

Cutting and tearing thin elastic sheets: two novel single-period cracks and the first period-doubling crack

Two novel single-period cracks were observed in experiments of cutting a folded sheet with a blunt object and tearing a thin brittle sheet under the guidance of a meterstick. Additionally, we observed a period-doubling crack in the tearing experiment. We cut and tore the sheet in different directions. The experimental results suggested that the anisotropy of the thin sheet played an important role in the formation of these two types of saw-tooth cracks. We demonstrated that the formation of the period-doubling crack was closely correlated with the changing of the contact region between the sheet and the meterstick. We also showed that the growth process of crack made by cutting was a logistic growth process (S-curve), while the cracks made by tearing propagated in the form of approximate power-law function.

cond-mat.soft↗

arXiv2020

Experimental high-dimensional quantum teleportation

Quantum teleportation provides a way to transmit unknown quantum states from one location to another. In the quantum world, multilevel systems which enable high-dimensional systems are more prevalent. Therefore, to completely rebuild the quantum states of a single particle remotely, one needs to teleport multilevel (high-dimensional) states. Here, we demonstrate the teleportation of high-dimensional states in a three-dimensional six-photon system. We exploit the spatial mode of a single photon as the high-dimensional system, use two auxiliary entangled photons to realize a deterministic three-dimensional Bell state measurement. The fidelity of teleportation process matrix is F=0.596\pm0.037. Through this process matrix, we can prove that our teleportation is both nonclassical and genuine three dimensional. Our work paves the way to rebuild complex quantum systems remotely and to construct complex quantum networks.

quant-ph↗

arXiv2020

Discriminative Neural Clustering for Speaker Diarisation

In this paper, we propose Discriminative Neural Clustering (DNC) that formulates data clustering with a maximum number of clusters as a supervised sequence-to-sequence learning problem. Compared to traditional unsupervised clustering algorithms, DNC learns clustering patterns from training data without requiring an explicit definition of a similarity measure. An implementation of DNC based on the Transformer architecture is shown to be effective on a speaker diarisation task using the challenging AMI dataset. Since AMI contains only 147 complete meetings as individual input sequences, data scarcity is a significant issue for training a Transformer model for DNC. Accordingly, this paper proposes three data augmentation schemes: sub-sequence randomisation, input vector randomisation, and Diaconis augmentation, which generates new data samples by rotating the entire input sequence of L2-normalised speaker embeddings. Experimental results on AMI show that DNC achieves a reduction in speaker error rate (SER) of 29.4% relative to spectral clustering.

eess.AS↗

arXiv2020

Simulating Noisy Quantum Circuits with Matrix Product Density Operators

Simulating quantum circuits with classical computers requires resources growing exponentially in terms of system size. Real quantum computer with noise, however, may be simulated polynomially with various methods considering different noise models. In this work, we simulate random quantum circuits in 1D with Matrix Product Density Operators (MPDO), for different noise models such as dephasing, depolarizing, and amplitude damping. We show that the method based on Matrix Product States (MPS) fails to approximate the noisy output quantum states for any of the noise models considered, while the MPDO method approximates them well. Compared with the method of Matrix Product Operators (MPO), the MPDO method reflects a clear physical picture of noise (with inner indices taking care of the noise simulation) and quantum entanglement (with bond indices taking care of two-qubit gate simulation). Consequently, in case of weak system noise, the resource cost of MPDO will be significantly less than that of the MPO due to a relatively small inner dimension needed for the simulation. In case of strong system noise, a relatively small bond dimension may be sufficient to simulate the noisy circuits, indicating a regime that the noise is large enough for an `easy' classical simulation. Moreover, we propose a more effective tensor updates scheme with optimal truncations for both the inner and the bond dimensions, performed after each layer of the circuit, which enjoys a canonical form of the MPDO for improving simulation accuracy. With truncated inner dimension to a maximum value $κ$ and bond dimension to a maximum value $χ$, the cost of our simulation scales as $\sim NDκ^3χ^3$, for an $N$-qubit circuit with depth $D$.

quant-ph↗

arXiv2020

EKOR strata for Shimura varieties with parahoric level structure

In this paper we study the geometry of reduction modulo $p$ of the Kisin-Pappas integral models for certain Shimura varieties of abelian type with parahoric level structure. We give some direct and geometric constructions for the EKOR strata on these Shimura varieties, using the theories of $G$-zips and mixed characteristic local $\mathcal{G}$-Shtukas. We establish several basic properties of these strata, including the smoothness, dimension formula, and closure relation. Moreover, we apply our results to the study of Newton strata and central leaves on these Shimura varieties.

math.AG↗

arXiv2020

Observation of two-dimensional superconductivity at the LaAlO3/KTaO3(110) heterointerface

We report on the observation of a Tc ~0.9 K superconductivity at the interface between LaAlO3 film and the 5d transition-metal oxide KTaO3(110) single crystal. The interface shows a large anisotropy of the upper critical field, and its superconducting transition is consistent with a Berezinskii-Kosterlitz-Thouless transition. Both facts suggest that the superconductivity is two-dimensional (2D) in nature. The carrier density measured at 5 K is ~7 time 10^13 cm-2. The superconducting layer thickness and coherence length are estimated to be ~8 and ~30 nm, respectively. Our result provides a new platform for the study of 2D superconductivity at oxide interfaces.

cond-mat.supr-con↗

arXiv2020

Blur Removal via Blurred-Noisy Image Pair

Complex blur such as the mixup of space-variant and space-invariant blur, which is hard to model mathematically, widely exists in real images. In this paper, we propose a novel image deblurring method that does not need to estimate blur kernels. We utilize a pair of images that can be easily acquired in low-light situations: (1) a blurred image taken with low shutter speed and low ISO noise; and (2) a noisy image captured with high shutter speed and high ISO noise. Slicing the blurred image into patches, we extend the Gaussian mixture model (GMM) to model the underlying intensity distribution of each patch using the corresponding patches in the noisy image. We compute patch correspondences by analyzing the optical flow between the two images. The Expectation Maximization (EM) algorithm is utilized to estimate the parameters of GMM. To preserve sharp features, we add an additional bilateral term to the objective function in the M-step. We eventually add a detail layer to the deblurred image for refinement. Extensive experiments on both synthetic and real-world data demonstrate that our method outperforms state-of-the-art techniques, in terms of robustness, visual quality, and quantitative metrics.

cs.CV↗

arXiv2020

Exploring non-singular black holes in gravitational perturbations

We calculate gravitational perturbation quasinormal modes (QNMs) of non-singular Bardeen black holes (BHs) and singularity-free BHs in conformal gravity and examine their spectra in wave dynamics comparing with standard BHs in general relativity. After testing the validity of the approximate signal-to-noise ratio (SNR) calculation for different space based interferometers, we discuss the SNR of non-singular BHs in single-mode gravitational waveform detections in LISA, TianQin and TaiJi. We explore the impact of the Bardeen parameter and the conformal factor on the behavior of the SNR and find that in comparison with the standard Schwarzschild BHs, the increase of non-singular parameters leads to higher SNR for more massive non-singular BHs. We also examine the effect of the galactic confusion noise on the SNR and find that a dip appears in SNR due to such noise. For non-singular BHs, with the increase of the non-singular parameters the dip emerges for more massive BHs. It suggests a wider range of mass of non-singular black holes whose SNR will not be lowered by galactic noise, which implies the SNR for the super massive black holes centered at our galaxy will less likely to be influenced by galactic noise in non-singular black holes models than singular case in general relativity. We conduct Fisher analysis which suggests that the non-singular black holes parameters can be detected accurately with measurement errors as small as $\sim10^{-4}-10^{-5}$. The detections of non-singular BHs are expected to be realized more likely by LISA or TaiJi.

gr-qc↗

arXiv2020

Full Counting Statistics of Phonon Transport in Disordered Systems

The coherent potential approximation (CPA) within full counting statistics (FCS) formalism is shown to be a suitable method to investigate average electric conductance, shot noise as well as higher order cumulants in disordered systems. We develop a similar FCS-CPA formalism for phonon transport through disordered systems. As a byproduct, we derive relations among coefficients of different phonon current cumulants. We apply the FCS-CPA method to investigate phonon transport properties of graphene systems in the presence of disorders. For binary disorders as well as Anderson disorders, we calculate up to the $8$-th phonon transmission moments and demonstrate that the numerical results of the FCS-CPA method agree very well with that of the brute force method. The benchmark shows that the FCS-CPA method achieves $20$ times more speedup ratio. Collective features of phonon current cumulants are also revealed.

cond-mat.mes-hall↗

arXiv2020

Improving Prosody Modelling with Cross-Utterance BERT Embeddings for End-to-end Speech Synthesis

Despite prosody is related to the linguistic information up to the discourse structure, most text-to-speech (TTS) systems only take into account that within each sentence, which makes it challenging when converting a paragraph of texts into natural and expressive speech. In this paper, we propose to use the text embeddings of the neighboring sentences to improve the prosody generation for each utterance of a paragraph in an end-to-end fashion without using any explicit prosody features. More specifically, cross-utterance (CU) context vectors, which are produced by an additional CU encoder based on the sentence embeddings extracted by a pre-trained BERT model, are used to augment the input of the Tacotron2 decoder. Two types of BERT embeddings are investigated, which leads to the use of different CU encoder structures. Experimental results on a Mandarin audiobook dataset and the LJ-Speech English audiobook dataset demonstrate the use of CU information can improve the naturalness and expressiveness of the synthesized speech. Subjective listening testing shows most of the participants prefer the voice generated using the CU encoder over that generated using standard Tacotron2. It is also found that the prosody can be controlled indirectly by changing the neighbouring sentences.

eess.AS↗

arXiv2020

Augmented Signal Processing in Liquid Argon Time Projection Chambers with a Deep Neural Network

The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.

physics.ins-det↗

arXiv2020

Semi-Grant-Free NOMA: Ergodic Rates Analysis with Random Deployed Users

Semi-grant-free (Semi-GF) non-orthogonal multiple access (NOMA) enables grant-free (GF) and grant-based (GB) users to share the same resource blocks, thereby balancing the connectivity and stability of communications. This letter analyzes ergodic rates of Semi-GF NOMA systems. First, this paper exploits a Semi-GF protocol, denoted as dynamic protocol, for selecting GF users into the occupied GB channels via the GB user's instantaneous received power. Under this protocol, the closed-form analytical and approximated expressions for ergodic rates are derived. The numerical results illustrate that the GF user (weak NOMA user) has a performance upper limit, while the ergodic rate of the GB user (strong NOMA user) increases linearly versus the transmit signal-to-noise ratio.

cs.IT↗

arXiv2020

Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data

Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization. To mitigate this issue, we propose a regularized fine-tuning method. Our method introduces two types of regularization for better calibration: (1) On-manifold regularization, which generates pseudo on-manifold samples through interpolation within the data manifold. Augmented training with these pseudo samples imposes a smoothness regularization to improve in-distribution calibration. (2) Off-manifold regularization, which encourages the model to output uniform distributions for pseudo off-manifold samples to address the over-confidence issue for OOD data. Our experiments demonstrate that the proposed method outperforms existing calibration methods for text classification in terms of expectation calibration error, misclassification detection, and OOD detection on six datasets. Our code can be found at https://github.com/Lingkai-Kong/Calibrated-BERT-Fine-Tuning.

cs.CL↗

arXiv2020

Planck Galactic Cold Clumps in Two Regions: the First Quadrant and the Anti-Center Direction Region

Sixty five Planck Galactic cold clumps (PGCCs) from the first quadrant (IQuad) and thirty nine of PGCCs from the Anti-Center direction region (ACent) were observed in $^{12}$CO, $^{13}$CO and C$^{18}$O J=1-0 lines using the PMO 13.7-m telescope. All the targets were detected with all the three lines, except for 12 IQuad and 8 ACent PGCCs without C$^{18}$O detection. Seventy six and 49 velocity components were obtained in IQuad and ACent respectively. One-hundred and forty-six cores were extracted from 76 IQuad clumps and 100 cores from 49 ACent clumps. The average T$_{\mathrm{ex}}$ of IQuad cores and ACent cores are 12.4 K and 12.1 K, respectively. The average line width of $^{13}$CO of IQuad cores and ACent cores are 1.55 km s$^{-1}$ and 1.77 km s$^{-1}$, respectively. Among the detected cores, 24 in IQuad and 13 in ACent have asymmetric line profiles. The small blue excesses, $\sim$0.03 in IQuad and 0.01 in ACent, indicate that the star formation is not active in these PGCC cores. Power-law fittings of core mass function to the high mass end give indexes of -0.57 in IQuad and -1.02 in ACent which are flatter than the slope of initial mass function given by \citeauthor{1955ApJ...121..161S}. The large turnover masses with value of 28 M$_{\odot}$ for IQuad cores and 77 M$_{\odot}$ for ACent cores suggest low star formation efficiencies in PGCCs. The correlation between virial mass and gas mass indicates that most of PGCC cores in both regions are not likely pressure-confined.

astro-ph.GA↗

arXiv2020

Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction

We study the problem of event extraction from text data, which requires both detecting target event types and their arguments. Typically, both the event detection and argument detection subtasks are formulated as supervised sequence labeling problems. We argue that the event extraction models so trained are inherently label-hungry, and can generalize poorly across domains and text genres.We propose a reading comprehension framework for event extraction.Specifically, we formulate event detection as a textual entailment prediction problem, and argument detection as a question answer-ing problem. By constructing proper query templates, our approach can effectively distill rich knowledge about tasks and label semantics from pretrained reading comprehension models. Moreover, our model can be fine-tuned with a small amount of data to boost its performance. Our experiment results show that our method performs strongly for zero-shot and few-shot event extraction, and it achieves state-of-the-art performance on the ACE 2005 benchmark when trained with full supervision.

cs.CL↗