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The goal of the acoustic scene classification (ASC) task is to classify recordings into one of the predefined acoustic scene classes. However, in real-world scenarios, ASC systems often encounter challenges such as recording device mismatch, low-complexity constraints, and the limited availability of labeled data. To alleviate these issues, in this paper, a data-efficient and low-complexity ASC system is built with a new model architecture and better training strategies. Specifically, we firstly design a new low-complexity architecture named Rep-Mobile by integrating multi-convolution branches which can be reparameterized at inference. Compared to other models, it achieves better performance and less computational complexity. Then we apply the knowledge distillation strategy and provide a comparison of the data efficiency of the teacher model with different architectures. Finally, we propose a progressive pruning strategy, which involves pruning the model multiple times in small amounts, resulting in better performance compared to a single step pruning. Experiments are conducted on the TAU dataset. With Rep-Mobile and these training strategies, our proposed ASC system achieves the state-of-the-art (SOTA) results so far, while also winning the first place with a significant advantage over others in the DCASE2024 Challenge., Comment: submitted to ICASSP 2025

We examine the accretion process in a thin disk surrounding a supermassive black hole within the framework of Einstein-Maxwell-scalar (EMS) gravity. Our investigation aims to elucidate how variations in model parameters affect different physical properties of the disk. When keeping EMS parameters $\beta$ and $q$ constant, we observe a reduction in radiation flux and temperature as $\alpha$ increases. However, the luminosity and radiative efficiency exhibit relatively minor variation. Conversely, under fixed $\alpha$ and $q$, an escalation in $\beta$ leads to heightened levels of radiation flux, temperature, luminosity, and radiative efficiency. These results underscore the diverse influences of model parameters on observable metrics, providing valuable insights for the astronomical study of distinct black holes., Comment: 27 pages,23 figures

Recently, a flurry of works have found strong competition between charge density wave (CDW) and superconductivity (SC) in the doped Hubbard and $t$-$J$ models on the square lattice. Interestingly, some recent results suggest that the electron-phonon coupling may suppress CDW order and enhance SC. In this work, we consider the square-lattice Hubbard model with the Holstein or Su-Schrieffer-Heeger electron-phonon coupling at the large-$U$ and antiadiabatic (infinite phonon frequency) limit, which gives an effective $t$-$J$ model with either a density attractive interaction $V$ or a $J_P$ term that contributes a larger spin exchange and a density repulsive interaction. To explore how these effective couplings may suppress CDW and give a SC, we implement the density matrix renormalization group simulation on the $t$-$t'$-$J$ model with $V$ or $J_P$ coupling. We focus on the {\it six-leg} cylinder system with the next-nearest-neighbor hopping $t'$, which hosts partially filled stripe and $d$-wave SC in phase diagram. By tuning $t'/t > 0$ and $V$ or $J_P$, we establish two quantum phase diagrams. In the SC phases, the increased $V$ or $J_P$ coupling can enhance the quasi-long-range SC order, consistent with some previous findings. Nonetheless, no SC emerges when the partially filled stripe phase disappears with increased $V$ or $J_P$. Instead, the system has a transition to either a phase-separation-like regime or a filled stripe phase. On the other hand, with increased $t'/t$, not only the partially filled stripe but the phase separation and filled stripe can also be tuned to SC phase. Our results suggest that although $V$ and $J_P$ couplings may strengthen hole binding, the hole dynamics controlled by $t'/t$ appears to play more crucial role for obtaining a SC in $t$-$J$ model., Comment: 12 pages, 10 figures

Landau levels (LLs) are of great importance for understanding the quantum Hall effect and associated many-body physics. Recently, their three-dimensional (3D) counterparts, i.e., dispersionless 3D LLs with well-defined quantum numbers, have attracted significant attention but have not yet been reported. Here we theoretically propose and experimentally observe 3D LLs with a sharply quantized spectrum in a diamond acoustic lattice, where the eigenstates are characterized by SU(3) quantum numbers. The engineered inhomogeneous hopping strengths not only introduce pseudomagnetic fields that quantize the nodal lines into LLs but also provide three bosonic degrees of freedom, embedding a generic SU(3) symmetry into the LLs. Using a phased array of acoustic sources, we selectively excite distinct eigenstates within the degenerate LL multiplets and visualize their 3D eigenmodes. Importantly, our approach enables the precise reconstruction of SU(3) quantum numbers directly from eigenmode correlations. Our results establish SU(3) LLs as a tractable model in artificial platforms, and pave the way for synthesizing LLs with zero dispersion and countable quantum numbers in arbitrary dimensions., Comment: 6 pages, 4 figures

Anomalous Sound Detection (ASD) has gained significant interest through the application of various Artificial Intelligence (AI) technologies in industrial settings. Though possessing great potential, ASD systems can hardly be readily deployed in real production sites due to the generalization problem, which is primarily caused by the difficulty of data collection and the complexity of environmental factors. This paper introduces a robust ASD model that leverages audio pre-trained models. Specifically, we fine-tune these models using machine operation data, employing SpecAug as a data augmentation strategy. Additionally, we investigate the impact of utilizing Low-Rank Adaptation (LoRA) tuning instead of full fine-tuning to address the problem of limited data for fine-tuning. Our experiments on the DCASE2023 Task 2 dataset establish a new benchmark of 77.75% on the evaluation set, with a significant improvement of 6.48% compared with previous state-of-the-art (SOTA) models, including top-tier traditional convolutional networks and speech pre-trained models, which demonstrates the effectiveness of audio pre-trained models with LoRA tuning. Ablation studies are also conducted to showcase the efficacy of the proposed scheme.

We investigate the Josephson diode effect (JDE) within quasi-2D planar systems featuring the $C_{1v}$ spin-orbit coupling (SOC) and Zeeman fields in the normal region. Our analysis is based on experimental observations conducted on $MoTe_{2}$ planar Josephson junctions (JJ) subjected to out-of-plane magnetic fields. We emphasize the pivotal role of symmetry breaking in current directionality for the occurrence of the JDE. Specifically, we observe the emergence of asymmetric Andreev bound states (ABSs) and $0$-$\pi$-like transitions with $\varphi_0$-shifts in the current phase relations (CPRs) in systems with specific symmetry breaking induced by SOC and Zeeman fields, leading to different critical current magnitudes in opposite directions. Additionally, we explore the influence of parameters such as the strength of SOC, Zeeman field magnitude and orientation, conduction channels with different transverse momenta, and junction lengths on the JDE efficiencies. Our results indicate the potential for diverse approaches to modulate efficiencies and provide insights that can aid in the discovery of materials and design of Josephson diodes with significantly enhanced efficiency.

Machine anomalous sound detection (ASD) has emerged as one of the most promising applications in the Industrial Internet of Things (IIoT) due to its unprecedented efficacy in mitigating risks of malfunctions and promoting production efficiency. Previous works mainly investigated the machine ASD task under centralized settings. However, developing the ASD system under decentralized settings is crucial in practice, since the machine data are dispersed in various factories and the data should not be explicitly shared due to privacy concerns. To enable these factories to cooperatively develop a scalable ASD model while preserving their privacy, we propose a novel framework named CoopASD, where each factory trains an ASD model on its local dataset, and a central server aggregates these local models periodically. We employ a pre-trained model as the backbone of the ASD model to improve its robustness and develop specialized techniques to stabilize the model under a completely non-iid and domain shift setting. Compared with previous state-of-the-art (SOTA) models trained in centralized settings, CoopASD showcases competitive results with negligible degradation of 0.08%. We also conduct extensive ablation studies to demonstrate the effectiveness of CoopASD., Comment: Accepted by GLOBECOM 2024

Prostate cancer is the second most common cancer in males worldwide, and mpMRI is commonly used for diagnosis. However, interpreting mpMRI is challenging and requires expertise from radiologists. This highlights the urgent need for automated grading in mpMRI. Existing studies lack integration of clinical prior information and suffer from uneven training sample distribution due to prevalence. Therefore, we propose a solution that incorporates prior knowledge, addresses the issue of uneven medical sample distribution, and maintains high interpretability in mpMRI. Firstly, we introduce Prior Knowledge-Based Feature Extraction, which mathematically models the PI-RADS criteria for prostate cancer as diagnostic information into model training. Secondly, we propose Adaptive Recall Feedback Loss to address the extremely imbalanced data problem. This method adjusts the training dynamically based on accuracy and recall in the validation set, resulting in high accuracy and recall simultaneously in the testing set.Thirdly, we design an Enhanced Cascade Prostate Cancer Classifier that classifies prostate cancer into different levels in an interpretable way, which refines the classification results and helps with clinical intervention. Our method is validated through experiments on the PI-CAI dataset and outperforms other methods with a more balanced result in both accuracy and recall rate.

Whisper and other large-scale automatic speech recognition models have made significant progress in performance. However, their performance on many low-resource languages, such as Kazakh, is not satisfactory. It is worth researching how to utilize low-cost data to improve the performance of Whisper on under-represented languages. In this study, we utilized easily accessible unpaired speech and text data and combined the language model GPT with Whisper on Kazakh. We implemented end of transcript (EOT) judgment modification and hallucination penalty to improve the performance of speech recognition. Further, we employed the decoding average token log probability as a criterion to select samples from unlabeled speech data and used pseudo-labeled data to fine-tune the model to further improve its performance. Ultimately, we achieved more than 10\% absolute WER reduction in multiple experiments, and the whole process has the potential to be generalized to other under-represented languages., Comment: Accepted by INTERSPEECH 2024;Minor typo correction

We realize a high-performance compact optically pumped cesium beam clock using Faraday laser simultaneously as pumping and detection lasers. The Faraday laser, which is frequency stabilized by modulation transfer spectroscopy (MTS) technique, has narrow linewidth and superior frequency stability. Measured by optical heterodyne method between two identical systems, the linewidth of the Faraday laser is 2.5 kHz after MTS locking, and the fractional frequency stability of the Faraday laser is optimized to $1.8\times{10}^{-12}/\sqrt{\tau}$. Based on this high-performance Faraday laser, the cesium beam clock realizes a signal-to-noise ratio (SNR) in 1 Hz bandwidth of $39600$ when the cesium oven temperature is 130{\deg}C. Frequency-compared with Hydrogen maser, the fractional frequency stability of the Faraday laser pumped cesium beam clock can reach $1.3\times{10}^{-12}/\sqrt{\tau}$ and drops to $1.4\times{10}^{-14}$ at 10000 s when the cesium oven temperature is 110{\deg}C. %, which is the best reported result compared with other cesium beam clocks. This Faraday laser pumped cesium beam clock demonstrates its excellent performance, and its great potential in the fields of timekeeping, navigation, and communication. Meanwhile, the Faraday laser, as a high-performance optical frequency standard, can also contribute to the development of other applications in quantum metrology, precision measurement and atomic physics.

What happens if we train a new Large Language Model (LLM) using data that are at least partially generated by other LLMs? The explosive success of LLMs means that a substantial amount of content online will be generated by LLMs rather than humans, which will inevitably enter the training datasets of next-generation LLMs. We evaluate the implications of such "regurgitative training" on LLM performance. Through fine-tuning GPT-3.5 with data generated either by itself or by other LLMs in a machine translation task, we find strong evidence that regurgitative training clearly handicaps the performance of LLMs. The same performance loss of regurgitative training is observed on transformer models that we train from scratch. We find suggestive evidence that the performance disadvantage of regurgitative training can be attributed to at least two mechanisms: (1) higher error rates and (2) lower lexical diversity in LLM-generated data as compared to real data. Based on these mechanisms, we propose and evaluate three different strategies to mitigate the performance loss of regurgitative training. First, we devise data-driven metrics to gauge the quality of each LLM-generated data instance, and then carry out an ordered training process where high-quality data are added before low-quality ones. Second, we combine data generated by multiple different LLMs (as an attempt to increase lexical diversity). Third, we train an AI detection classifier to differentiate between LLM- and human-generated data, and include LLM-generated data in the order of resemblance to human-generated data. All three strategies can improve the performance of regurgitative training to some extent but are not always able to fully close the gap from training with real data. Our results highlight the value of real, human-generated data in training LLMs, which cannot be easily substituted by synthetic, LLM-generated data.

Large pre-trained models have demonstrated dominant performances in multiple areas, where the consistency between pre-training and fine-tuning is the key to success. However, few works reported satisfactory results of pre-trained models for the machine anomalous sound detection (ASD) task. This may be caused by the inconsistency of the pre-trained model and the inductive bias of machine audio, resulting in inconsistency in data and architecture. Thus, we propose AnoPatch which utilizes a ViT backbone pre-trained on AudioSet and fine-tunes it on machine audio. It is believed that machine audio is more related to audio datasets than speech datasets, and modeling it from patch level suits the sparsity of machine audio. As a result, AnoPatch showcases state-of-the-art (SOTA) performances on the DCASE 2020 ASD dataset and the DCASE 2023 ASD dataset. We also compare multiple pre-trained models and empirically demonstrate that better consistency yields considerable improvement., Comment: Accepted by INTERSPEECH 2024

The evolution of speech technology has been spurred by the rapid increase in dataset sizes. Traditional speech models generally depend on a large amount of labeled training data, which is scarce for low-resource languages. This paper presents GigaSpeech 2, a large-scale, multi-domain, multilingual speech recognition corpus. It is designed for low-resource languages and does not rely on paired speech and text data. GigaSpeech 2 comprises about 30,000 hours of automatically transcribed speech, including Thai, Indonesian, and Vietnamese, gathered from unlabeled YouTube videos. We also introduce an automated pipeline for data crawling, transcription, and label refinement. Specifically, this pipeline uses Whisper for initial transcription and TorchAudio for forced alignment, combined with multi-dimensional filtering for data quality assurance. A modified Noisy Student Training is developed to further refine flawed pseudo labels iteratively, thus enhancing model performance. Experimental results on our manually transcribed evaluation set and two public test sets from Common Voice and FLEURS confirm our corpus's high quality and broad applicability. Notably, ASR models trained on GigaSpeech 2 can reduce the word error rate for Thai, Indonesian, and Vietnamese on our challenging and realistic YouTube test set by 25% to 40% compared to the Whisper large-v3 model, with merely 10% model parameters. Furthermore, our ASR models trained on Gigaspeech 2 yield superior performance compared to commercial services. We believe that our newly introduced corpus and pipeline will open a new avenue for low-resource speech recognition and significantly facilitate research in this area., Comment: Under review

As a robust and large-scale multilingual speech recognition model, Whisper has demonstrated impressive results in many low-resource and out-of-distribution scenarios. However, its encoder-decoder structure hinders its application to streaming speech recognition. In this paper, we introduce Simul-Whisper, which uses the time alignment embedded in Whisper's cross-attention to guide auto-regressive decoding and achieve chunk-based streaming ASR without any fine-tuning of the pre-trained model. Furthermore, we observe the negative effect of the truncated words at the chunk boundaries on the decoding results and propose an integrate-and-fire-based truncation detection model to address this issue. Experiments on multiple languages and Whisper architectures show that Simul-Whisper achieves an average absolute word error rate degradation of only 1.46% at a chunk size of 1 second, which significantly outperforms the current state-of-the-art baseline., Comment: Accepted by INTERSPEECH 2024

Motivated by the order fractionalization in Kitaev-Kondo model, we propose an exactly solvable spin-charge ladder model to study the order fractionalization with discrete symmetry. The spin-charge ladder is composed of a spin chain and a superconducting wire coupled via an Ising-type interaction, and we obtain the exact solution in the flat band limit. The exact solution reveals the $Z_{2}$ order fractionalization with dual symmetry breaking and intertwined order parameters. We investigate the topological phase transition of the spin-charge ladder via the spectral chiral index, and identify the correlated topological superconductor (TSC*) phase with gapped $Z_{2}$ Kondo flux excitations. We demonstrate Majorana spinons generated odd frequency pairing in the superconducting wire. We also discuss the order fractionalization in the perspective of $Z_{2}$ lattice gauge theory.

$^{133}$Cs, which is the only stable cesium (Cs) isotope, is one of the most investigated elements in atomic spectroscopy and was used to realize the atomic clock in 1955. Among all atomic clocks, the cesium atomic clock has a special place, since the current unit of time is based on a microwave transition in the Cs atom. In addition, the long lifetime of the $6{\text{P}}_{3/2}$ state and simple preparation technique of Cs vapor cells have great relevance to quantum and atom optics experiments, which suggests the use of the $6{\text{S}} - 6{\text{P}}$ D2 transition as an optical frequency standard. In this work, using one laser as the local oscillator and Cs atoms as the quantum reference, we realized two atomic clocks in the optical and microwave frequencies, respectively. Both clocks could be freely switched or simultaneously output. The optical clock based on the vapor cell continuously operated with a frequency stability of $3.89 \times {10^{ - 13}}$ at 1 s, decreasing to $2.17 \times {10^{ - 13}}$ at 32 s, which was frequency stabilized by modulation transfer spectroscopy and estimated by an optical comb. Then, applying this stabilized laser for an optically pumped Cs beam atomic clock to reduce the laser frequency noise, we obtained a microwave clock with a frequency stability of $1.84 \times {10^{ - 12}}/\sqrt \tau $, reaching $5.99 \times {10^{ - 15}}$ at $10^5$ s. This study demonstrates an attractive feature for the commercialization and deployment of optical and microwave clocks and will guide further development of integrated atomic clocks with better stability. Thus, this study lays the groundwork for future quantum metrology and laser physics., Comment: 8 pages, 4 figures

We investigate the accretion process in a thin disk surrounding a supermassive black hole in Einstein-Maxwell-dilaton-axion (EMDA) gravity by using the Novikov-Thorne model. The results reveal that as the dilaton parameter $r_2$ increase, the energy flux, radiation temperature, spectra luminosity, and radiative efficiency of the disk all increases. By narrowing down the dilaton parameter range to $0\leqslant \frac{r_2}{M}\leqslant0.4$, we discover that in the high-frequency region, the Kerr-Sen black hole demonstrates higher energy output compared to the Kerr black hole. We also investigated the Kerr-Sen black hole shadow in a uniform plasma environment. For a fixed inclination angle of $\theta_0=90^\circ$, $\frac{r_2}{M} = \frac{a}{M} = 0.5$, the shadow increase as the homogeneous plasma parameter $k$ increase. Conversely, when $k = 0.1$ and $\frac{a}{M} = 0.5$, an increase in $r_2$ leads to a decrease in the shadow. Furthermore, by using observational data from M87* and Sgr A*, we compared the theoretical and observed shadow diameters to constrain the range of the model parameters., Comment: 28 pages,17 figures

In the wake of the surging tide of deep learning over the past decade, Automatic Speech Recognition (ASR) has garnered substantial attention, leading to the emergence of numerous publicly accessible ASR systems that are actively being integrated into our daily lives. Nonetheless, the impartial and replicable evaluation of these ASR systems encounters challenges due to various crucial subtleties. In this paper we introduce the SpeechColab Leaderboard, a general-purpose, open-source platform designed for ASR evaluation. With this platform: (i) We report a comprehensive benchmark, unveiling the current state-of-the-art panorama for ASR systems, covering both open-source models and industrial commercial services. (ii) We quantize how distinct nuances in the scoring pipeline influence the final benchmark outcomes. These include nuances related to capitalization, punctuation, interjection, contraction, synonym usage, compound words, etc. These issues have gained prominence in the context of the transition towards an End-to-End future. (iii) We propose a practical modification to the conventional Token-Error-Rate (TER) evaluation metric, with inspirations from Kolmogorov complexity and Normalized Information Distance (NID). This adaptation, called modified-TER (mTER), achieves proper normalization and symmetrical treatment of reference and hypothesis. By leveraging this platform as a large-scale testing ground, this study demonstrates the robustness and backward compatibility of mTER when compared to TER. The SpeechColab Leaderboard is accessible at https://github.com/SpeechColab/Leaderboard

Recently, evidence of superconductivity (SC) has been reported in pressurized La$_4$Ni$_3$O$_{10}$. Here we study the possible pairing mechanism and pairing symmetry in this material. Through fitting the density-functional-theory band structure, we provide a six-orbital tight-binding model. In comparison with the band structure of La$_3$Ni$_2$O$_7$, the additional non-bonding $d_{z^2}$ band is importance to the pairing mechanism here. When the multi-orbital Hubbard interactions are included, our random-phase-approximation based study yields an $s^{\pm}$-wave pairing. The dominant FS nesting with nesting vector $\mathbf{Q}_1\approx (\pi,\pi)$ is between the $\gamma$-pocket contributed by the bonding $d_{z^2}$ band top and the $\alpha_1$-pocket contributed by the non-bonding $d_{z^2}$ band bottom, leading to the strongest pairing gap amplitude and opposite gap signs within the two regimes. The dominant real-space pairing is the interlayer pairing between the $d_{z^2}$ orbitals. We have also studied the doping dependence of the pairing symmetry and $T_c$., Comment: 5 pages, 5 figures

Recently, many studies are focused on generalized global symmetry, a mixture of both invertible and non-invertible symmetries in various space-time dimensions. The complete structure of generalized global symmetry is described by higher fusion category theory. In this paper, We first review the construction of fusion 2-category symmetry $\Sigma \cal B$ where $\cal B$ is a a braided fusion category. In particular, we elaborate on the monoidal structure of $\Sigma \cal B$ which determines fusion rules and controls the dynamics of topological operators/defects. We then take $\Sigma \mathrm{sVec}$ as an example to demonstrate how we calculate fusion rule, quantum dimension and 10j-symbol of the fusion 2-category. With our algorithm, all these data can be efficiently encoded and computed in computer program. The complete program will be uploaded to github soon. Our work can be thought as explicitly computing the representation theory of $\cal B$, in analogy to, for example the representation theory of $SU(2)$. The choice of basis bimodule maps are in analogy to the Clebsch-Gordon coefficients and the 10j-symbol are in analogy to the 6j-symbol., Comment: 42 pages, 3 figures, All the 10j-symbols of $\Sigma \mathrm{sVec}$ and the complete computer program has been uploaded on github: https://github.com/WJXI/2sVec.git

Montmorillonite-based barriers are key elements of the engineered barrier systems (EBS) in geological disposal facilities (GDF). Their performance at temperatures above 100 {\deg}C is not sufficiently understood to assess the possibility of raising the temperature limits in GDF designs that could reduce construction costs and CO2 footprint. The present work provides new fundamental insights through molecular dynamics (MD) simulations of Na-montmorillonite's water-clay interactions and swelling pressure at temperatures 298-500 K and basal spacings of 1.5-3.5 nm. At temperatures above 100 {\deg}C, the swelling behaviour is governed by the attractive van der Waals force and the repulsive hydration force instead of the repulsive electrostatic (double layer) force. The swelling pressure reduction with increasing temperature is related to the weakened hydration repulsion and electric double layer repulsion, which result from the deterioration of the interlayer water layer structure and the shrinkage of the electric double layer. The applicability and breakdown of the classic Derjaguin-Landau-Verwey-Overbeek (DLVO) theory at elevated temperatures are examined. By excluding the osmotic contribution in the DLVO theory, the summation of the van der Waals interaction in DLVO and an additional non-DLVO hydration interaction can predict our MD system's swelling under high temperatures. The findings of this study provide a fundamental understanding of the swelling behaviour and the underlying molecular-level mechanisms of the clay microstructure under extreme conditions., Comment: 50 pages, 16 figures

The detection of Alzheimer's disease (AD) from spontaneous speech has attracted increasing attention while the sparsity of training data remains an important issue. This paper handles the issue by knowledge transfer, specifically from both speech-generic and depression-specific knowledge. The paper first studies sequential knowledge transfer from generic foundation models pretrained on large amounts of speech and text data. A block-wise analysis is performed for AD diagnosis based on the representations extracted from different intermediate blocks of different foundation models. Apart from the knowledge from speech-generic representations, this paper also proposes to simultaneously transfer the knowledge from a speech depression detection task based on the high comorbidity rates of depression and AD. A parallel knowledge transfer framework is studied that jointly learns the information shared between these two tasks. Experimental results show that the proposed method improves AD and depression detection, and produces a state-of-the-art F1 score of 0.928 for AD diagnosis on the commonly used ADReSSo dataset., Comment: 8 pages, 4 figures. Accepted by ASRU 2023

We use the finite projected entangled pair state (PEPS) method to investigate the global phase diagram of the spin-1/2 square-lattice $J_1$-$J_2$-$J_3$ antiferromagnetic (AFM) Heisenberg model. The ground state phase diagram is established with a rich variety of phases: AFM, gapless quantum spin liquid, valence-bond solid (VBS), stripe, and incommensurate spiral phases. The nature of the VBS region is revealed, containing a plaquette VBS and a mixed columnar-plaquette VBS, with the emergence of short-range incommensurate spin correlations in some region. The long-range incommensurate magnetic phase is also explicitly characterized as a planar spiral with incommensurate spatial periodicities. Most interestingly, there exists several multicritical points connecting different phases. These findings elucidate the true nature of the long-standing square-lattice $J_1$-$J_2$-$J_3$ antiferromagnet at zero-temperature. Our results also pave the way to accurately simulate complex two-dimensional quantum systems that may host nonuniform features by means of finite PEPS., Comment: 13 pages; 19 figures

Motivated by the novel phenomena observed in the layered material $\rm SrCu_2(BO_3)_2$, the Shastry-Sutherland model (SSM) has been extensively studied as the minimal model for $\rm SrCu_2(BO_3)_2$. However, the nature of its quantum phase transition from the plaquette valence-bond solid (PVBS) to antiferromagnetic (AFM) phase is under fierce debate, posing a challenge to understand the underlying quantum criticality. Via the state-of-the-art tensor network simulations, we study the ground state of the SSM on large-scale size up to $20 \times 20$ sites. We identify the continuous transition nature accompanied by an emergent O(4) symmetry between the PVBS and AFM phase, which strongly suggests a deconfined quantum critical point (DQCP). Furthermore, we map out the phase diagram of an extended SSM that can be continuously tuned to the SSM, which demonstrates the same DQCP phenomena along a whole critical line. Our results indicate a compelling scenario for understanding the origin of the proposed proximate DQCP in recent experiments of $\rm SrCu_2(BO_3)_2$., Comment: 5+6 pages; 4+5 figures; 3 tables