arXiv subjects

Song Li

Publications and source records attributed to Song Li.

At least 73 records · Page 4Linked to original sources

arXiv2023

Robust Outlier Bound Condition to Phase Retrieval with Adversarial Sparse Outliers

We consider the problem of recovering an unknown signal $\pmb{x}_0\in \mathbb{R}^{n}$ from phaseless measurements. In this paper, we study the convex phase retrieval problem via PhaseLift from linear Gaussian measurements perturbed by $\ell_{1}$-bounded noise and sparse outliers that can change an adversarially chosen $s$-fraction of the measurement vector. We show that the Robust-PhaseLift model can successfully reconstruct the ground-truth up to global phase for any $s< s^{*}\approx 0.1185$ with $\mathcal{O}(n)$ measurements, even in the case where the sparse outliers may depend on the measurement and the observation. The recovery guarantees are based on the robust outlier bound condition and the analysis of the product of two Gaussian variables. Moreover, we construct adaptive counterexamples to show that the Robust-PhaseLift model fails when $s> s^{*}$ with high probability.

cs.IT↗

arXiv2023

Solid state defect emitters with no electrical activity

Point defects may introduce defect levels into the fundamental band gap of the host semiconductors that alter the electrical properties of the material. As a consequence, the in-gap defect levels and states automatically lower the threshold energy of optical excitation associated with the optical gap of the host semiconductor. It is, therefore, a common assumption that solid state defect emitters in semiconductors ultimately alter the conductivity of the host. Here we demonstrate on a particular defect in 4H silicon carbide that a yet unrecognized class of point defects exists which are optically active but electrically inactive in the ground state.

cond-mat.mtrl-sci↗

arXiv2023

Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter

Multilingual intelligent assistants, such as ChatGPT, have recently gained popularity. To further expand the applications of multilingual artificial intelligence assistants and facilitate international communication, it is essential to enhance the performance of multilingual speech recognition, which is a crucial component of speech interaction. In this paper, we propose two simple and parameter-efficient methods: language prompt tuning and frame-level language adapter, to respectively enhance language-configurable and language-agnostic multilingual speech recognition. Additionally, we explore the feasibility of integrating these two approaches using parameter-efficient fine-tuning methods. Our experiments demonstrate significant performance improvements across seven languages using our proposed methods.

eess.AS↗

arXiv2023

M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction

Researchers are solving the challenges of spatial-temporal prediction by combining Federated Learning (FL) and graph models with respect to the constrain of privacy and security. In order to make better use of the power of graph model, some researchs also combine split learning(SL). However, there are still several issues left unattended: 1) Clients might not be able to access the server during inference phase; 2) The graph of clients designed manually in the server model may not reveal the proper relationship between clients. This paper proposes a new GNN-oriented split federated learning method, named node {\bfseries M}asking and {\bfseries M}ulti-granularity {\bfseries M}essage passing-based Federated Graph Model (M$^3$FGM) for the above issues. For the first issue, the server model of M$^3$FGM employs a MaskNode layer to simulate the case of clients being offline. We also redesign the decoder of the client model using a dual-sub-decoders structure so that each client model can use its local data to predict independently when offline. As for the second issue, a new GNN layer named Multi-Granularity Message Passing (MGMP) layer enables each client node to perceive global and local information. We conducted extensive experiments in two different scenarios on two real traffic datasets. Results show that M$^3$FGM outperforms the baselines and variant models, achieves the best results in both datasets and scenarios.

cs.LG↗

arXiv2023

Carbon cluster emitters in silicon carbide

Defect qubits in 4H-SiC are outstanding candidates for numerous applications in the rapidly emerging field of quantum technology. Carbon clusters can act as emission sources that may appear after thermal oxidation of 4H-SiC or during irradiation which kicks out carbon atoms from their sites. These fluorescent carbon clusters could interfere with the already established vacancy-related qubits that generated with irradiation techniques. In this study, we systematically investigate the electronic structure, formation energy, dissociation energy, vibrational properties, and the full fluorescence spectrum of carbon clusters involving up to four carbon atoms in 4H-SiC by means of density functional theory calculations. All the possible local configurations for these carbon clusters are carefully evaluated. We find the electronic and vibronic properties of the carbon clusters depend strongly on the local configuration of the 4H-SiC lattice. By comparing the calculated and previously observed fluorescence spectra in 4H-SiC, we identify several carbon clusters as stable visible emitters in 4H-SiC. The paired carbon interstitial defects are identified as the source of the 463-nm triplet and the 456.6-nm emitters. The 471.8-nm emitter in 4H-SiC is associated with tri-carbon antisite clusters. Our findings provide plausible explanation for the origin of visible emission lines in 4H-SiC and propose the possible configurations of carbon clusters which are helpful for the quantum information processing application through qubits in 4H-SiC.

quant-ph↗

arXiv2023

Finite-temperature ductility-brittleness and electronic structures of Al$_{n}$Sc (n=1, 2 and 3)

Finite-temperature ductility-brittleness and electronic structures of Al$_3$Sc, Al$_2$Sc and AlSc are studied comparatively by first-principles calculations and ab-initio molecular dynamics. Results show that Al$_3$Sc and Al$_2$Sc are inherently brittle at both ground state and finite temperatures. By contrast, AlSc possesses a significantly superior ductility evaluated from all Pugh's, Pettifor's and Poisson's ductility-brittleness criteria. At ground state, AlSc meets the criteria of ductile according to Pugh's and Poisson's theories, while it is categorized as the brittle in the frame of Pettifor's picture. With the increasing temperature, the ductility of all the studied compounds exhibits a noticeable improvement. In particular, as the temperature rises, the Cauchy pressure of AlSc undergoes a transition from negative to positive. Thus, at high temperatures (T > 600 K), AlSc is unequivocally classified as the ductile from all criteria considered. In all Al$_3$Sc, Al$_2$Sc and AlSc, the Al-Al bond, originated from s-p and p-p orbital hybridizations, and the Al-Sc bond, dominated by p-d covalent hybridization, are the first and second strongest chemical bonds, respectively. To explain the difference in mechanical properties of the studied compounds, the mean bond strength (MBS) is evaluated. The weaker Al-Al bond in AlSc, leading to a smaller MBS, could be the origin for the softer elastic stiffness and superior intrinsic ductility. The longer length of the Al-Al bond in AlSc is responsible for its weaker bond strength. Furthermore, the enhanced metallicity of the Al-Al bond in AlSc would also contribute to its exceptional ductility.

cond-mat.mtrl-sci↗

arXiv2023

Exceptionally strong coupling of defect emission in hexagonal boron nitride to stacking sequences

Van der Waals structures present a unique opportunity for tailoring material interfaces and integrating photonic functionalities. By precisely manipulating the twist angle and stacking sequences, it is possible to elegantly tune and functionalize the electronic and optical properties of layered van der Waals structures. Among these materials, two-dimensional hexagonal boron nitride (hBN) stands out for its remarkable optical properties and wide band gap, making it a promising host for solid state single photon emitters at room temperature. Previous investigations have demonstrated the observation of bright single photon emission in hBN across a wide range of wavelengths. In this study, we unveil an application of van der Waals technology in modulating their spectral shapes and brightness by carefully controlling the stacking sequences and polytypes. Our theoretical analysis reveals remarkably large variations in the Huang-Rhys factors-an indicator of the interaction between a defect and its surrounding lattice-reaching up to a factor of 3.3 for the same defect in different stackings. We provide insights into the underlying mechanism behind these variations, shedding light on the design principles necessary to achieve rational and precise control of defect emission. This work paves the way for enhancing defect identification and facilitating the engineering of highly efficient single photon sources and qubits using van der Waals materials.

cond-mat.mes-hall↗

arXiv2023

Coherent control of an ultrabright single spin in hexagonal boron nitride at room temperature

Hexagonal boron nitride (hBN) is a remarkable two-dimensional (2D) material that hosts solid-state spins and has great potential to be used in quantum information applications, including quantum networks. However, in this application, both the optical and spin properties are crucial for single spins but have not yet been discovered simultaneously for hBN spins. Here, we realize an efficient method for arraying and isolating the single defects of hBN and use this method to discover a new spin defect with a high probability of 85%. This single defect exhibits outstanding optical properties and an optically controllable spin, as indicated by the observed significant Rabi oscillation and Hahn echo experiments at room temperature. First principles calculations indicate that complexes of carbon and oxygen dopants may be the origin of the single spin defects. This provides a possibility for further addressing spins that can be optically controlled.

physics.optics↗

arXiv2023

Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks

Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct networks. In the literature, most works focus on static networks. Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. More specifically, we compute motif matrices to capture the local topological information, then use them in the GCN aggregation process. The generated embedding at each snapshot is a weighted average of embeddings within a time window, where the weights are learnable parameters. Since the trust networks is signed on each edge, balance theory is used to guide the training process. Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the proposed model outperforms those in the literature.

cs.LG↗

arXiv2023

Evaluation of population structure inferred by principal component analysis or the admixture model

Principal component analysis (PCA) is commonly used in genetics to infer and visualize population structure and admixture between populations. PCA is often interpreted in a way similar to inferred admixture proportions, where it is assumed that individuals belong to one of several possible populations or are admixed between these populations. We propose a new method to assess the statistical fit of PCA (interpreted as a model spanned by the top principal components) and to show that violations of the PCA assumptions affect the fit. Our method uses the chosen top principal components to predict the genotypes. By assessing the covariance (and the correlation) of the residuals (the differences between observed and predicted genotypes), we are able to detect violation of the model assumptions. Based on simulations and genome wide human data we show that our assessment of fit can be used to guide the interpretation of the data and to pinpoint individuals that are not well represented by the chosen principal components. Our method works equally on other similar models, such as the admixture model, where the mean of the data is represented by linear matrix decomposition.

stat.ME↗

arXiv2022

Gravitational waveform model based on photon motion for spinning black holes

The waveforms from binary black hole mergers include inspiral, merger, and ringdown parts. Usually, the inspiral waveform can be obtained by calibrating from post-Newtonian approximation; The merger and ringdown ones can be gotten from the quasinormal modes with black hole perturbation theory. However, for more general black holes, the calculation of the quasinormal modes is not trivial. In this paper we use the photon sphere to get the quasinormal modes of spinning black holes. Then we connect the ringdown wave with the inspiral part to get full waveforms and compare with the ones from numerical relativity. We find that they match with each other very well. In principle this method can be extended to some general compact objects. As an example, the ringdown waveforms from parametrized axisymmetric black holes are obtained. We also use this method to get the ringdown signals of the accelerating final black hole; this is due to gravitational recoil during the merger. Even for the extreme cases, the acceleration due to the recoil cannot produce detectable effects.

gr-qc↗

arXiv2022

EventPoint: Self-Supervised Interest Point Detection and Description for Event-based Camera

This paper proposes a self-supervised learned local detector and descriptor, called EventPoint, for event stream/camera tracking and registration. Event-based cameras have grown in popularity because of their biological inspiration and low power consumption. Despite this, applying local features directly to the event stream is difficult due to its peculiar data structure. We propose a new time-surface-like event stream representation method called Tencode. The event stream data processed by Tencode can obtain the pixel-level positioning of interest points while also simultaneously extracting descriptors through a neural network. Instead of using costly and unreliable manual annotation, our network leverages the prior knowledge of local feature extraction on color images and conducts self-supervised learning via homographic and spatio-temporal adaptation. To the best of our knowledge, our proposed method is the first research on event-based local features learning using a deep neural network. We provide comprehensive experiments of feature point detection and matching, and three public datasets are used for evaluation (i.e. DSEC, N-Caltech101, and HVGA ATIS Corner Dataset). The experimental findings demonstrate that our method outperforms SOTA in terms of feature point detection and description.

cs.CV↗

arXiv2022

Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting

This paper describes a spatial-aware speaker diarization system for the multi-channel multi-party meeting. The diarization system obtains direction information of speaker by microphone array. Speaker spatial embedding is generated by xvector and s-vector derived from superdirective beamforming (SDB) which makes the embedding more robust. Specifically, we propose a novel multi-channel sequence-to-sequence neural network architecture named discriminative multi-stream neural network (DMSNet) which consists of attention superdirective beamforming (ASDB) block and Conformer encoder. The proposed ASDB is a self-adapted channel-wise block that extracts the latent spatial features of array audios by modeling interdependencies between channels. We explore DMSNet to address overlapped speech problem on multi-channel audio and achieve 93.53% accuracy on evaluation set. By performing DMSNet based overlapped speech detection (OSD) module, the diarization error rate (DER) of cluster-based diarization system decrease significantly from 13.45% to 7.64%.

eess.AS↗

arXiv2022

Bistable carbon-vacancy defects in $h$-BN

Single photon emitters in hexagonal boron nitride have been extensively studied recently. Although unambiguous identification of the emitters is still under intense research, carbon related defects are believed to play a vital role for the emitter producing zero-phonon-lines in the range of $1.6$ to $2.2$~eV. In this study, we systematically investigate two configurations of carbon-vacancy defects, VNCB and CNVB, by means of density functional theory calculations. We calculated the reaction barrier energies from one defect to the other to determine relative stability. We find the barrier energies are charge dependent and CNVB could easily transform to VNCB in neutral and positive charge states while it is stable when negatively charged. Formation energy calculations show that the VNCB is the dominant defect over CNVB. However, neither VNCB nor CNVB has suitable fluorescence spectra that could reproduce the observed ones. Our results indicate that the origin of the $1.6$-to-$2.2$-eV emitters should be other carbon-related configurations.

cond-mat.mtrl-sci↗

arXiv2022

A Ligand-and-structure Dual-driven Deep Learning Method for the Discovery of Highly Potent GnRH1R Antagonist to treat Uterine Diseases

Gonadotrophin-releasing hormone receptor (GnRH1R) is a promising therapeutic target for the treatment of uterine diseases. To date, several GnRH1R antagonists are available in clinical investigation without satisfying multiple property constraints. To fill this gap, we aim to develop a deep learning-based framework to facilitate the effective and efficient discovery of a new orally active small-molecule drug targeting GnRH1R with desirable properties. In the present work, a ligand-and-structure combined model, namely LS-MolGen, was firstly proposed for molecular generation by fully utilizing the information on the known active compounds and the structure of the target protein, which was demonstrated by its superior performance than ligand- or structure-based methods separately. Then, a in silico screening including activity prediction, ADMET evaluation, molecular docking and FEP calculation was conducted, where ~30,000 generated novel molecules were narrowed down to 8 for experimental synthesis and validation. In vitro and in vivo experiments showed that three of them exhibited potent inhibition activities (compound 5 IC50 = 0.856 nM, compound 6 IC50 = 0.901 nM, compound 7 IC50 = 2.54 nM) against GnRH1R, and compound 5 performed well in fundamental PK properties, such as half-life, oral bioavailability, and PPB, etc. We believed that the proposed ligand-and-structure combined molecular generative model and the whole computer-aided workflow can potentially be extended to similar tasks for de novo drug design or lead optimization.

q-bio.BM↗

arXiv2022

Constraining the deformation of a black hole mimicker from the shadow

We consider a black hole mimicker given by an exact solution of the stationary and axially symmetric field equations in vacuum known as the $δ$-Kerr metric. We study its optical properties based on a ray-tracing code for photon motion and characterize the apparent shape of the shadow of the compact object and compare it with the Kerr black hole. For the purpose of obtaining qualitative estimates related to the observed shadow of the supermassive compact object in the galaxy M87 we focus on values of the object's spin $a$ and inclination angle of observation $θ_0$ close to the measured values. We then apply the model to the shadow of the $δ$-Kerr metric to obtain onstraints on the allowed values of the deformation parameter. We show that based uniquely on one set of observations of the shadow's boundary it is not possible to exclude the $δ$-Kerr solution as a viable source for the geometry in the exterior of the compact object.

gr-qc↗

arXiv2022

Explanation of electron and muon $g-2$ anomalies in AMSB

We propose to jointly explain the electron/muon $g-2$ anomalies in the framework of anomaly mediated SUSY breaking (AMSB) scenario. Two Yukawa deflected AMSB models are proposed and discussed in depth: one with lepton-specific interactions and the other one with messenger-matter interactions. Both models are found to be able to jointly explain the anomalies at $2 σ$ level by naturally realizing the preferred parameter space with $μM_1,μM_2<0$ and very heavy left-handed smuon.

hep-ph↗

arXiv2022

Can electron and muon $g-2$ anomalies be jointly explained in SUSY?

The FNAL+BNL measurements for muon $g-2$ is $4.2σ$ above the SM prediction, and the Berkeley $^{133}$Cs measurement for the fine-structure constant $α_{\rm em}$ leads to the SM prediction for electron $g-2$ which is $2.4σ$ above the experimental value. Hence, a joint explanation of both anomalies requires a positive contribution to muon $g-2$ and a negative contribution to electron $g-2$, which is rather challenging. In this work we explore the possibility of such a joint explanation in the minimal supersymmetric standard model (MSSM). Assuming no universality between smuon and selectron soft masses, we find out a part of parameter space for a joint explanation at $2σ$ level, i.e., $μM_1,μM_2<0$, $m_{L1}, m_{E2}<200$ GeV, $m_{L2}$ being much larger than the soft masses of other sleptons, $|M_1|<125$ GeV and $μ<400$ GeV. This part of parameter space can survive LHC and LEP constraints, but gives an over-abundance for dark matter if the bino-like lightest neutralino is assumed to be the dark matter candidate. With the assumption that the dark matter candidate is a superWIMP (say a pseudo-goldstino in multi-sector SUSY breaking scenarios, whose mass can be as light as GeV and produced from the late-decay of the thermally freeze-out lightest neutralino), the dark matter problem can be avoided. So, we conclude that the MSSM may give a joint explanation for the muon and electron $g-2$ anomalies at $2σ$ level (the muon $g-2$ anomaly can be even ameliorated to $1σ$).

hep-ph↗