arXiv subjects

Xi Wang

Publications and source records attributed to Xi Wang.

At least 379 records · Page 21Linked to original sources

arXiv2022

Higgs boson decays $h\rightarrow Z γ$ and $h\rightarrow m_V Z$ in the $U(1)_X$SSM

We present a detailed analysis of the Higgs boson decays $h\rightarrow Z γ$ and $h\rightarrow m_V Z$ in the $U(1)_X$SSM, with $m_V$ denoting the meson ($ω,ρ,ϕ,J/ψ,Υ$). Using the effective Lagrangian method, we calculate the effective constants $C_{γZ}$ (CP-even) and $\tilde{C}_{γZ}$ (CP-odd) for the vertex $h γZ$, which are corrected by the new particle loop diagrams. Numerically, the ratio $Γ_{U(1)_X}(h\rightarrow Z γ)/Γ_{SM}(h\rightarrow Z γ)$ is between $1.02-1.35$. For the vector mesons $ω,ρ,ϕ$ and $J/ψ$, the ratios $Γ_{U(1)_X}(h\rightarrow m_V Z )/Γ_{SM}(h\rightarrow m_V Z)$ are mainly distributed in the range of ($1.01-1.45$). When $m_V$ represents $Υ$, the larger value of the ratio $Γ_{U(1)_X}(h\rightarrow ΥZ )/Γ_{SM}(h\rightarrow ΥZ)$ is mostly located in the range of ($1.002-1.30$). This work can provide a reference for specific types of decay processes which can be searched in LHC.

hep-ph↗

arXiv2022

Study the Higgs mass with the effective potential and Higgs decays in the $U(1)_X$SSM

As the U(1) extension of the minimal supersymmetric standard model, the $U(1)_X$SSM has new super fields such as right-handed neutrinos and three Higgs singlets. In the $U(1)_X$SSM, the lightest CP-even Higgs mass $m_{h^0}$ is researched through the Higgs effective potential with one loop corrections. We also calculate the Higgs decays $h^0\rightarrowγγ$, $h^0\rightarrow VV~(V=W,~Z)$, $h^0\rightarrow l\bar{l}Z$ and $h^0\rightarrow ν\barνZ$. The obtained results are reasonable, which are in favour of the study of the Higgs characteristic and the phenomenology of the $U(1)_X$SSM.

hep-ph↗

arXiv2022

$Z$ boson decays $Z\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$ and Higgs boson decays $h\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$ with lepton flavor violation in a $U(1)$ extension of the MSSM

$U(1)_X$SSM is the extension of the minimal supersymmetric standard model (MSSM) and its local gauge group is $SU(3)_C\times SU(2)_L \times U(1)_Y \times U(1)_X$. We study lepton flavor violating (LFV) decays $Z\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$($Z\rightarrow eμ$, $Z\rightarrow eτ$, and $Z\rightarrow μτ$) and $h\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$($h\rightarrow eμ$, $h\rightarrow eτ$, and $h\rightarrow μτ$), in this model. In the numerical results, the branching ratios of $Z\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$ are from $10^{-9}$ to $10^{-13}$ and the branching ratios of $h\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$ are from $10^{-3}$ to $10^{-9}$, which can approach the present experimental upper bounds. Based on the latest experimental data, we analyze the influence of different sensitive parameters on the branching ratio, and make reasonable predictions for future experiments. The main sensitive parameters and LFV sources are the non-diagonal elements corresponding to the initial and final generations of leptons, which can be seen from the numerical analysis.

hep-ph↗

arXiv2022

Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors

We present a method for inferring diverse 3D models of human-object interactions from images. Reasoning about how humans interact with objects in complex scenes from a single 2D image is a challenging task given ambiguities arising from the loss of information through projection. In addition, modeling 3D interactions requires the generalization ability towards diverse object categories and interaction types. We propose an action-conditioned modeling of interactions that allows us to infer diverse 3D arrangements of humans and objects without supervision on contact regions or 3D scene geometry. Our method extracts high-level commonsense knowledge from large language models (such as GPT-3), and applies them to perform 3D reasoning of human-object interactions. Our key insight is priors extracted from large language models can help in reasoning about human-object contacts from textural prompts only. We quantitatively evaluate the inferred 3D models on a large human-object interaction dataset and show how our method leads to better 3D reconstructions. We further qualitatively evaluate the effectiveness of our method on real images and demonstrate its generalizability towards interaction types and object categories.

cs.CV↗

arXiv2022

PeCLR: Self-Supervised 3D Hand Pose Estimation from monocular RGB via Equivariant Contrastive Learning

Encouraged by the success of contrastive learning on image classification tasks, we propose a new self-supervised method for the structured regression task of 3D hand pose estimation. Contrastive learning makes use of unlabeled data for the purpose of representation learning via a loss formulation that encourages the learned feature representations to be invariant under any image transformation. For 3D hand pose estimation, it too is desirable to have invariance to appearance transformation such as color jitter. However, the task requires equivariance under affine transformations, such as rotation and translation. To address this issue, we propose an equivariant contrastive objective and demonstrate its effectiveness in the context of 3D hand pose estimation. We experimentally investigate the impact of invariant and equivariant contrastive objectives and show that learning equivariant features leads to better representations for the task of 3D hand pose estimation. Furthermore, we show that standard ResNets with sufficient depth, trained on additional unlabeled data, attain improvements of up to 14.5% in PA-EPE on FreiHAND and thus achieves state-of-the-art performance without any task specific, specialized architectures. Code and models are available at https://ait.ethz.ch/projects/2021/PeCLR/

cs.CV↗

arXiv2022

Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources

Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which exceed the capacity of small devices for inference and incurs long training time. In addition, it is difficult to afford long training time and inference time of big models even in high performance servers, as well. As an efficient approach to compress a large deep model (a teacher model) to a compact model (a student model), knowledge distillation emerges as a promising approach to deal with the big models. Existing knowledge distillation methods cannot exploit the elastic available computing resources and correspond to low efficiency. In this paper, we propose an Elastic Deep Learning framework for knowledge Distillation, i.e., EDL-Dist. The advantages of EDL-Dist are three-fold. First, the inference and the training process is separated. Second, elastic available computing resources can be utilized to improve the efficiency. Third, fault-tolerance of the training and inference processes is supported. We take extensive experimentation to show that the throughput of EDL-Dist is up to 3.125 times faster than the baseline method (online knowledge distillation) while the accuracy is similar or higher.

cs.DC↗

arXiv2022

Self-supervised Context-aware Style Representation for Expressive Speech Synthesis

Expressive speech synthesis, like audiobook synthesis, is still challenging for style representation learning and prediction. Deriving from reference audio or predicting style tags from text requires a huge amount of labeled data, which is costly to acquire and difficult to define and annotate accurately. In this paper, we propose a novel framework for learning style representation from abundant plain text in a self-supervised manner. It leverages an emotion lexicon and uses contrastive learning and deep clustering. We further integrate the style representation as a conditioned embedding in a multi-style Transformer TTS. Comparing with multi-style TTS by predicting style tags trained on the same dataset but with human annotations, our method achieves improved results according to subjective evaluations on both in-domain and out-of-domain test sets in audiobook speech. Moreover, with implicit context-aware style representation, the emotion transition of synthesized audio in a long paragraph appears more natural. The audio samples are available on the demo web.

cs.SD↗

arXiv2022

Intercell Moiré Exciton Complexes in Electron Lattices

Excitons, Coulomb-bound electron-hole pairs, play a fundamental role in both optical excitation and correlated phenomena in solids. When an exciton interacts with other quasi-particles, few- and many-body excited states, such as trions, exciton Fermi-polarons, Mahan excitons can appear. Here, we report a new interaction between exciton and charges enabled by unusual quantum confinement in 2D moiré superlattices, which results in novel exciton many-body ground states composed of moiré excitons and correlated electron lattices. Unique to H-stacked (or 60o-twisted) WS2/WSe2 heterobilayer, we found that the interlayer atomic registry and moiré structural reconstruction leads to an interlayer moiré exciton (IME) whose hole in one layer is surrounded by its partner electron's wavefunction spread among three adjacent moiré traps in the other layer. This 3D excitonic structure can enable large in-plane electrical quadrupole moments in addition to the vertical dipole. Upon doping, the electric quadrupole facilitates the binding of IME to the charges in neighboring moiré cells, forming an intercell charged exciton complex. The exciton complex is unveiled by the IME photoluminescence energy jumps when the electron lattices form at both fractional and integer-filled moiré minibands, with replica-like spectral features between successive integer moiré fillings. Our work provides the framework in understanding and engineering emergent exciton many-body states in correlated moiré charge orders.

cond-mat.mes-hall↗

arXiv2022

PrEF: Percolation-based Evolutionary Framework for the diffusion-source-localization problem in large networks

We assume that the state of a number of nodes in a network could be investigated if necessary, and study what configuration of those nodes could facilitate a better solution for the diffusion-source-localization (DSL) problem. In particular, we formulate a candidate set which contains the diffusion source for sure, and propose the method, Percolation-based Evolutionary Framework (PrEF), to minimize such set. Hence one could further conduct more intensive investigation on only a few nodes to target the source. To achieve that, we first demonstrate that there are some similarities between the DSL problem and the network immunization problem. We find that the minimization of the candidate set is equivalent to the minimization of the order parameter if we view the observer set as the removal node set. Hence, PrEF is developed based on the network percolation and evolutionary algorithm. The effectiveness of the proposed method is validated on both model and empirical networks in regard to varied circumstances. Our results show that the developed approach could achieve a much smaller candidate set compared to the state of the art in almost all cases. Meanwhile, our approach is also more stable, i.e., it has similar performance irrespective of varied infection probabilities, diffusion models, and outbreak ranges. More importantly, our approach might provide a new framework to tackle the DSL problem in extreme large networks.

cs.SI↗

arXiv2022

NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality

Text to speech (TTS) has made rapid progress in both academia and industry in recent years. Some questions naturally arise that whether a TTS system can achieve human-level quality, how to define/judge that quality and how to achieve it. In this paper, we answer these questions by first defining the human-level quality based on the statistical significance of subjective measure and introducing appropriate guidelines to judge it, and then developing a TTS system called NaturalSpeech that achieves human-level quality on a benchmark dataset. Specifically, we leverage a variational autoencoder (VAE) for end-to-end text to waveform generation, with several key modules to enhance the capacity of the prior from text and reduce the complexity of the posterior from speech, including phoneme pre-training, differentiable duration modeling, bidirectional prior/posterior modeling, and a memory mechanism in VAE. Experiment evaluations on popular LJSpeech dataset show that our proposed NaturalSpeech achieves -0.01 CMOS (comparative mean opinion score) to human recordings at the sentence level, with Wilcoxon signed rank test at p-level p >> 0.05, which demonstrates no statistically significant difference from human recordings for the first time on this dataset.

eess.AS↗

arXiv2022

Length L-function for Network-Constrained Point Data

Network constrained points are referred to as points restricted to road networks, such as taxi pick up and drop off locations. A significant pattern of network constrained points is referred to as an aggregation; e.g., the aggregation of pick up points may indicate a high taxi demand in a particular area. Although the network K function using the shortest path network distance has been proposed to detect point aggregation, its statistical unit is still radius based. R neighborhood, in particular, has inconsistent network length owing to the complex configuration of road networks which cause unfair counts and identification errors in networks (e.g., the length of the r neighborhood located at an intersection is longer than that on straight roads, which may include more points). In this study, we derived the length L function for network constrained points to identify the aggregation by designing a novel neighborhood as the statistical unit; the total length of this is consistent throughout the network. Compared to the network K function, our method can detect a true to life aggregation scale, identify the aggregation with higher network density, as well as identify the aggregations that the network K function cannot. We validated our method using taxi trips pick up location data within Zhongguancun Area in Beijing, analyzing differences in maximal aggregation between workdays and weekends to understand taxi demand in the morning and evening peak.

stat.OT↗

arXiv2022

Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning

Heatmap regression methods have dominated face alignment area in recent years while they ignore the inherent relation between different landmarks. In this paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the inherent relation. The SLPT generates the representation of each single landmark from a local patch and aggregates them by an adaptive inherent relation based on the attention mechanism. The subpixel coordinate of each landmark is predicted independently based on the aggregated feature. Moreover, a coarse-to-fine framework is further introduced to incorporate with the SLPT, which enables the initial landmarks to gradually converge to the target facial landmarks using fine-grained features from dynamically resized local patches. Extensive experiments carried out on three popular benchmarks, including WFLW, 300W and COFW, demonstrate that the proposed method works at the state-of-the-art level with much less computational complexity by learning the inherent relation between facial landmarks. The code is available at the project website.

cs.CV↗

arXiv2022

Light-Induced Ferromagnetism in Moiré Superlattices

Many-body interactions between carriers lie at the heart of correlated physics. The ability to tune such interactions would open the possibility to access and control complex electronic phase diagrams on demand. Recently, moiré superlattices formed by two-dimensional materials have emerged as a promising platform for quantum engineering such phenomena. The power of the moiré system lies in the high tunability of its physical parameters by tweaking layer twist angle, electrical field, moiré carrier filling, and interlayer coupling. Here, we report that optical excitation can drastically tune the spin-spin interactions between moiré trapped carriers, resulting in ferromagnetic order in WS2/WSe2 moiré superlattices over a small range of doping at elevated temperatures. Near the filling factor v = -1/3 (i.e., one hole per three moiré unit cells), as the excitation power at the exciton resonance increases, a well-developed hysteresis loop emerges in the reflective magnetic circular dichroism (RMCD) signal as a function of magnetic field, a hallmark of ferromagnetism. The hysteresis loop persists down to charge neutrality, and its shape evolves as the moiré superlattice is gradually filled, indicating changes of magnetic ground state properties. The observed phenomenon points to a mechanism in which itinerant photo-excited excitons mediate exchange coupling between moiré trapped holes. This exciton-mediated interaction can be of longer range than direct coupling between moiré trapped holes, and thus magnetic order can arise even in the dilute hole regime under optical excitation. This discovery adds a new and dynamic tuning knob to the rich many-body Hamiltonian of moiré quantum matter.

cond-mat.mes-hall↗

arXiv2021

ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation

Pre-trained language models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. GPT-3 has shown that scaling up pre-trained language models can further exploit their enormous potential. A unified framework named ERNIE 3.0 was recently proposed for pre-training large-scale knowledge enhanced models and trained a model with 10 billion parameters. ERNIE 3.0 outperformed the state-of-the-art models on various NLP tasks. In order to explore the performance of scaling up ERNIE 3.0, we train a hundred-billion-parameter model called ERNIE 3.0 Titan with up to 260 billion parameters on the PaddlePaddle platform. Furthermore, we design a self-supervised adversarial loss and a controllable language modeling loss to make ERNIE 3.0 Titan generate credible and controllable texts. To reduce the computation overhead and carbon emission, we propose an online distillation framework for ERNIE 3.0 Titan, where the teacher model will teach students and train itself simultaneously. ERNIE 3.0 Titan is the largest Chinese dense pre-trained model so far. Empirical results show that the ERNIE 3.0 Titan outperforms the state-of-the-art models on 68 NLP datasets.

cs.CL↗

arXiv2021

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

cs.AI↗

arXiv2021

Bayesian OOD detection with aleatoric uncertainty and outlier exposure

Typical Bayesian approaches to OOD detection use epistemic uncertainty. Surprisingly from the Bayesian perspective, there are a number of methods that successfully use aleatoric uncertainty to detect OOD points (e.g. Hendryks et al. 2018). In addition, it is difficult to use outlier exposure to improve a Bayesian OOD detection model, as it is not clear whether it is possible or desirable to increase posterior (epistemic) uncertainty at outlier points. We show that a generative model of data curation provides a principled account of aleatoric uncertainty for OOD detection. In particular, aleatoric uncertainty signals a specific type of OOD point: one without a well-defined class-label, and our model of data curation gives a likelihood for these points, giving us a mechanism for conditioning on outlier points and thus performing principled Bayesian outlier exposure. Our principled Bayesian approach, combining aleatoric and epistemic uncertainty with outlier exposure performs better than methods using aleatoric or epistemic alone.

stat.ML↗

arXiv2021

Monolithic Integrated Multiband Acoustic Devices on Heterogeneous Substrate for Sub-6 GHz RF-FEMs

Monolithic integration of multiband (1.4~ 6.0 GHz) RF acoustic devices were successfully demonstrated within the same process flow by using the lithium niobate (LN) thin film on silicon carbide (LNOSiC) substrate. A novel surface mode with sinking energy distribution was proposed, exhibiting reduced propagation loss. Surface wave and Lamb wave resonators with suppressed transverse modes and leaky modes were demonstrated, showing scalable resonances from 1.4 to 5.7 GHz, electromechanical coupling coefficients (k2) between 7.9% and 29.3%, and maximum Bode-Q (Qmax) larger than 3200. Arrayed filters with a small footprint (4.0 x 2.5 mm2) but diverse center frequencies (fc) and 3-dB fractional bandwidths (FBW) were achieved, showing fc from 1.4 to 6.0 GHz, FBW between 3.3% and 13.3%, and insertion loss (IL) between 0.59 and 2.10 dB. These results may promote the progress of hundred-filter sub-6 GHz RF front-end modules (RF-FEMs).

eess.SP↗

arXiv2021

Observations of Ultrafast Superfluorescent Beatings in a Cesium Atomic Vapor Excited by Femtosecond Laser Pulses

Spontaneous emission from individual atoms in vapor lasts nanoseconds, if not microseconds, and beatings in this emission involve only directly excited energy sublevels. In contrast, the superfluorescent emissions burst on a much-reduced timescale and their beatings involve both directly and indirectly excited energy sublevels. In this work, picosecond and femtosecond superfluorescent beatings are observed from a dense cesium atomic vapor. Cesium atoms are excited by 60-femtosecond long, 800 nm laser pulses via two-photon processes into their coherent superpositions of the ground 6S and excited 8S states. As a part of the transient four wave mixing process, the yoked superfluorescent blue light at lower transitions of 6S - 7P are recorded and studied. Delayed buildup time of this blue light is measured as a function of the input laser beam power using a high-resolution 2 ps streak camera. The power dependent buildup delay time is consistently doubled as the vapor temperature is lowered to cut the number of atoms by half. At low power and density, a beating with a period of 100 picoseconds representing the ground state splitting is observed. The autocorrelation measurements of the generated blue light exhibit a beating with a quasi-period of 230 fs corresponding to the splitting of the 7P level primarily at lower input laser power. Understanding and, eventually, controlling the intriguing nature of superfluorescent beatings may permit a rapid quantum operation free from the rather slow spontaneous emission processes from atoms and molecules.

physics.atom-ph↗