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Fault tolerant on-chip photonic quantum computation is enormously helped by (a) deterministic generation of the needed thousands to millions of photon qubits from (b) quantum emitters in designed spatially ordered arrays to enable networks for implementing many-qubit logic circuits. Scaling up photonic quantum information processing systems has, however, been prevented by the lack of such quantum emitters until the demonstration of the platform of mesa-top single quantum dots (MTSQDs) -- controlled shape, size, and volume single QD -- located in designed regular arrays. Here we demonstrate 2 qubit CNOT gate operation -- a universal gate necessary to enable quantum circuits of arbitrary complexity -- in polarization basis using photons emitted from individual MTSQDs. A Bell state fidelity of 0.825$\pm$0.010 is achieved with two photon interference (TPI) visibility of 0.947$\pm$0.0015 at 4K without Purcell enhancement. The results make a strong case for developing MTSQD arrays for utility scale optical quantum information processing platforms., Comment: 17 pages, 8 figures

The thermalization of an isolated quantum system is described by quantum mechanics and thermodynamics, while these two subjects are still not fully consistent with each other. This leaves a less-explored region where both quantum and thermal effects cannot be neglected, and the ultracold atom platform provides a suitable and versatile testbed to experimentally investigate these complex phenomena. Here we perform experiments based on ultracold atoms in optical lattices and observe a divergence of thermalization rates of quantum matters when the temperature approaches zero. By ramping an external parameter in the Hamiltonian, we observe the time delay between the internal relaxation and the external ramping. This provides us with a direct comparison of the thermalization rates of different quantum phases. We find that the quantum coherence and bosonic stimulation of superfluid induces the divergence while the finite temperature and the many-body interactions are suppressing the divergence. The quantum coherence and the thermal effects are competing with each other in this isolated thermal quantum system, which leads to the transition of thermalization rate from divergence to convergence.

In time-series classification, understanding model decisions is crucial for their application in high-stakes domains such as healthcare and finance. Counterfactual explanations, which provide insights by presenting alternative inputs that change model predictions, offer a promising solution. However, existing methods for generating counterfactual explanations for time-series data often struggle with balancing key objectives like proximity, sparsity, and validity. In this paper, we introduce TX-Gen, a novel algorithm for generating counterfactual explanations based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). TX-Gen leverages evolutionary multi-objective optimization to find a diverse set of counterfactuals that are both sparse and valid, while maintaining minimal dissimilarity to the original time series. By incorporating a flexible reference-guided mechanism, our method improves the plausibility and interpretability of the counterfactuals without relying on predefined assumptions. Extensive experiments on benchmark datasets demonstrate that TX-Gen outperforms existing methods in generating high-quality counterfactuals, making time-series models more transparent and interpretable., Comment: Accepted to EXPLAINS 2024

Motivated by the experimental measurements, we investigate the $p$-$\Omega$ correlation functions and interactions. By solving the inverse scattering problem, we derive the $p$-$\Omega$ potentials from a quark model. The effects of Coulomb interaction and spin-averaging are discussed. According to our results, the depletion of the $p$-$\Omega$ correlation functions, attributed to the $J^P = 2^+$ bound state not observed in the ALICE Collaboration's measurements [Nature \textbf{588}, 232 (2020)], can be explained by the contribution of the attractive $J^P = 1^+$ component in spin-averaging. Additionally, there is a subtle sub-unity part of the correlation function, which can also be seen in the experimental data, supporting the existence of the $p$-$\Omega$ bound state. So far, we have completed the consistent description of the $p$-$\Omega$ system from the perspective of the quark model in terms of energy spectrum, scattering phase shift, and correlation function. The existence of the $p$-$\Omega$ bound state has been confirmed from these three aspects. In Appendix, we learn the relationship between correlation functions and interaction potentials by using simplified square potential models and find a periodic-like variation., Comment: 9 pages, 6 figures

In this letter, we propose a novel mechanism for charmonium scattering that utilizes the internal structure of charmonium. By capturing light flavor quarks and anti-quarks from the vacuum, charm and anti-charm quarks form virtual charmed mesons, which mediate an effective one-boson exchange process. This approach accurately reproduces the di-$J/\psi$ invariant mass spectrum observed by CMS and LHCb, demonstrating its validity. Our mechanism offers a comprehensive framework for understanding charmonium scattering and is applicable to the scattering problems involving all fully heavy hadrons, an area of increasing interest.

This study investigates the nature of the $X_0(2900)$ and $\chi_{c0}(3930)$ based on experimental results of the process $B^+$$\to$$D^+ D^- K^+$. We focus on the S-wave $D^{*-}K^{*+}$ and $D_s^+D_s^-$ molecular states, which can be related to the $X_0(2900)$ and $\chi_{c0}(3930)$, respectively. Using effective Lagrangians, we construct the potential kernel of the $D^{*-}K^{*+}$$-$$D^{-}K^{+}$ and $D_s^+D_s^-$$-$$D^+D^-$ interactions with a one-boson-exchange model, and determine the scattering amplitudes and their poles through a quasipotential Bethe-Salpeter equation approach. By incorporating the potential kernel into the three-body decay process $B^+$$\to$$D^+ D^- K^+$, we evaluate the $D^-K^+$ and $D^+D^-$ invariant mass spectra, as well as the Dalitz plot, with Monte Carlo simulation. A satisfactory fit to the $D^-K^+$ and $D^+D^-$ invariant mass spectra is achieved after introducing additional Breit-Wigner resonances, the $X_1(2900)$, $\psi(3770)$, and $\chi_{c2}(3930)$ states. Prominent signals of the $X_0(2900)$ and $\chi_{c0}(3930)$ states appear as peaks in the $D^-K^+$ and $D^+D^-$ invariant mass spectra near 2900 and 3930 MeV, respectively. Clear event concentration of the $X_0(2900)$ and $\chi_0(3930)$ states is evident as strips in the Dalitz plot. The results suggest that both $X_0(2900)$ and $\chi_{c0}(3930)$ can be interpreted as molecular states, with the inclusion of $X_1(2900)$ and $\chi_2(3930)$ necessary to describe structures in the regions near 2900 and 3930 MeV., Comment: 9 pages, 5figures

We introduce a novel metric for measuring semantic continuity in Explainable AI methods and machine learning models. We posit that for models to be truly interpretable and trustworthy, similar inputs should yield similar explanations, reflecting a consistent semantic understanding. By leveraging XAI techniques, we assess semantic continuity in the task of image recognition. We conduct experiments to observe how incremental changes in input affect the explanations provided by different XAI methods. Through this approach, we aim to evaluate the models' capability to generalize and abstract semantic concepts accurately and to evaluate different XAI methods in correctly capturing the model behaviour. This paper contributes to the broader discourse on AI interpretability by proposing a quantitative measure for semantic continuity for XAI methods, offering insights into the models' and explainers' internal reasoning processes, and promoting more reliable and transparent AI systems., Comment: 25 pages, accepted at the world conference of explainable AI, 2024, Malta

Continuous-Time Dynamic Graph (CTDG) precisely models evolving real-world relationships, drawing heightened interest in dynamic graph learning across academia and industry. However, existing CTDG models encounter challenges stemming from noise and limited historical data. Graph Data Augmentation (GDA) emerges as a critical solution, yet current approaches primarily focus on static graphs and struggle to effectively address the dynamics inherent in CTDGs. Moreover, these methods often demand substantial domain expertise for parameter tuning and lack theoretical guarantees for augmentation efficacy. To address these issues, we propose Conda, a novel latent diffusion-based GDA method tailored for CTDGs. Conda features a sandwich-like architecture, incorporating a Variational Auto-Encoder (VAE) and a conditional diffusion model, aimed at generating enhanced historical neighbor embeddings for target nodes. Unlike conventional diffusion models trained on entire graphs via pre-training, Conda requires historical neighbor sequence embeddings of target nodes for training, thus facilitating more targeted augmentation. We integrate Conda into the CTDG model and adopt an alternating training strategy to optimize performance. Extensive experimentation across six widely used real-world datasets showcases the consistent performance improvement of our approach, particularly in scenarios with limited historical data., Comment: Accepted by KDD 2024

Harmonic mode-locking, realized actively or passively, is an effective technique for increasing the repetition rate of lasers, with important applications in optical sampling, laser micro-machining and frequency metrology. It is critically important to understand how a harmonically mode-locked pulse train responds to external perturbations and noise, so as to make sure that it is stable and resistant to noise. Here, in a series of carefully designed experiments, we elucidate the retiming dynamics of laser pulses generated in a soliton fiber laser harmonically mode-locked at ~2 GHz to the acoustic resonance in a photonic crystal fiber (PCF) core. We characterize the self-driven optomechanical lattice along the PCF using a homodyne set-up, and reveal that each soliton undergoes damped oscillatory retiming within its trapping potential after an abrupt perturbation. In addition we show, through statistical analysis of the intra-cavity pulse spacing, how the trapping potentials are effective for suppressing timing jitter. The experimental results are well described using a dynamic model including dissipation, which provides valuable insight into the stability and noise performance of optomechanically mode-locked laser systems, and may also be useful for studying complex inter-soliton interactions.

In this study, the impact of triangle singularity is investigated in the isospin-breaking process $J/\psi \to \Lambda \bar{\Lambda} \pi$. The triangle singularity is found to play a significant role in the process, resulting in the creation of a resonance-like structure around 1.4 GeV in the $\Lambda\pi(\bar{\Lambda}\pi)$ invariant mass spectrum. To amplify the impact of this triangle singularity, the presence of two $\Sigma^*(\frac{1}{2}^-)$ states around 1.4 GeV and 1.6 GeV is essential, yet these states have not been definitively identified in the current baryon spectrum. We recommend that experiments, particularly the Beijing Spectrometer (BESIII) and the future Super Tau-Charm Factory (STCF), to investigate the process $J/\psi \to \Lambda \bar{\Lambda} \pi$ to offer direct evidences for our predicted triangle singularity and additional evidence regarding the $\Sigma(\frac{1}{2}^-)$ states.

We uncover emergent universality arising in the equilibration dynamics of multimode continuous-variable systems. Specifically, we study the ensemble of pure states supported on a small subsystem of a few modes, generated by Gaussian measurements on the remaining modes of a globally pure bosonic Gaussian state. We find that beginning from sufficiently complex global states, such as random Gaussian states and product squeezed states coupled via a deep array of linear optical elements, the induced ensemble attains a universal form, independent of the choice of measurement basis: it is composed of unsqueezed coherent states whose displacements are distributed normally and isotropically, with variance depending on only the particle-number density of the system. We further show that the emergence of such a universal form is consistent with a generalized maximum entropy principle, which endows the limiting ensemble, which we call the "Gaussian Scrooge distribution", with a special quantum information-theoretic property of having minimal accessible information. Our results represent a conceptual generalization of the recently introduced notion of "deep thermalization" in discrete-variable quantum many-body systems -- a novel form of equilibration going beyond thermalization of local observables -- to the realm of continuous-variable quantum systems. Moreover, it demonstrates how quantum information-theoretic perspectives can unveil new physical phenomena and principles in quantum dynamics and statistical mechanics., Comment: 6+24 pages

We have investigated the $qss\bar{q}q$ ($q = u$ or $d$) system to find possible pentaquark explanations for the $\Xi$ resonances. The bound state calculation is carried out within the framework of the quark delocalization color screening model. The scattering processes are also studied to examine the possible resonance states. The current results indicate that the $\Xi(1950)$ can be interpreted as $\Lambda \bar{K}^*$ state with $J^P = 1/2^-$. Three states are identified that match the $\Xi(2250)$, which are $\Sigma^* \bar{K}^*$ state with $J^P = 3/2^-$, $\Sigma^* \bar{K}^*$ state with $J^P =5/2^-$, and $\Xi^* \rho$ state with $J^P =5/2^-$. This may explain the conflicting experimental values for the width of the $\Xi(2250)$. A new $\Xi$ resonance is predicted, whose mass and width are 2066--2079 MeV and 186--189 MeV, respectively. These results contribute to understanding the nature of the $\Xi$ resonances and to the future search for new $\Xi$ resonances. Moreover, it is meaningful to further investigate the $\Xi$ resonances from an unquenched picture on the basis of pentaquark investigation., Comment: 15 pages, 7 figures. arXiv admin note: text overlap with arXiv:2312.04977, arXiv:2309.15380

The energy market encompasses the behavior of energy supply and trading within a platform system. By utilizing centralized or distributed trading, energy can be effectively managed and distributed across different regions, thereby achieving market equilibrium and satisfying both producers and consumers. However, recent years have presented unprecedented challenges and difficulties for the development of the energy market. These challenges include regional energy imbalances, volatile energy pricing, high computing costs, and issues related to transaction information disclosure. Researchers widely acknowledge that the security features of blockchain technology can enhance the efficiency of energy transactions and establish the fundamental stability and robustness of the energy market. This type of blockchain-enabled energy market is commonly referred to as an energy blockchain. Currently, there is a burgeoning amount of research in this field, encompassing algorithm design, framework construction, and practical application. It is crucial to organize and compare these research efforts to facilitate the further advancement of energy blockchain. This survey aims to comprehensively review the fundamental characteristics of blockchain and energy markets, highlighting the significant advantages of combining the two. Moreover, based on existing research outcomes, we will categorize and compare the current energy market research supported by blockchain in terms of algorithm design, market framework construction, and the policies and practical applications adopted by different countries. Finally, we will address current issues and propose potential future directions for improvement, to provide guidance for the practical implementation of blockchain in the energy market.

We present the design, implementation and engineering experience in building and deploying MegaScale, a production system for training large language models (LLMs) at the scale of more than 10,000 GPUs. Training LLMs at this scale brings unprecedented challenges to training efficiency and stability. We take a full-stack approach that co-designs the algorithmic and system components across model block and optimizer design, computation and communication overlapping, operator optimization, data pipeline, and network performance tuning. Maintaining high efficiency throughout the training process (i.e., stability) is an important consideration in production given the long extent of LLM training jobs. Many hard stability issues only emerge at large scale, and in-depth observability is the key to address them. We develop a set of diagnosis tools to monitor system components and events deep in the stack, identify root causes, and derive effective techniques to achieve fault tolerance and mitigate stragglers. MegaScale achieves 55.2% Model FLOPs Utilization (MFU) when training a 175B LLM model on 12,288 GPUs, improving the MFU by 1.34x compared to Megatron-LM. We share our operational experience in identifying and fixing failures and stragglers. We hope by articulating the problems and sharing our experience from a systems perspective, this work can inspire future LLM systems research.

This paper presents the speech restoration and enhancement system created by the 1024K team for the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. Our system consists of a generative adversarial network (GAN) in complex-domain for speech restoration and a fine-grained multi-band fusion module for speech enhancement. In the blind test set of SSI, the proposed system achieves an overall mean opinion score (MOS) of 3.49 based on ITU-T P.804 and a Word Accuracy Rate (WAcc) of 0.78 for the real-time track, as well as an overall P.804 MOS of 3.43 and a WAcc of 0.78 for the non-real-time track, ranking 1st in both tracks., Comment: Accepted to ICASSP 2024; Rank 1st in ICASSP 2024 Speech Signal Improvement (SSI) Challenge

In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification. The proposed algorithm, namely Time-CF, leverages shapelets and TimeGAN to provide counterfactual explanations for arbitrary time series classifiers. We validate the proposed method on several real-world univariate time series classification tasks from the UCR Time Series Archive. The results indicate that the counterfactual instances generated by Time-CF when compared to state-of-the-art methods, demonstrate better performance in terms of four explainability metrics: closeness, sensibility, plausibility, and sparsity., Comment: The paper has been accepted by the XAI4Sci workshop of AAAI 2024

Realization of the long sought on-chip scalable photonic quantum information processing networks has been thwarted by the absence of spatially-ordered and scalable on-demand single photon emitters with emission figures-of-merit exceeding the required thresholds across large numbers. The positioning must meet the required degree of accuracy that enables fabricating their interconnection to create the desired functional network. Here we report on the realization of large-area spatially-ordered arrays of mesa-top single quantum dots (MTSQDs) that are demonstrated [1] to be on-demand single photon emitters with characteristics that meet the requirements for implementing quantum photonic circuits/platforms aimed at quantum key distribution, linear optical quantum computing, simulations of quantum many-body problems, and metrology/sensing. The reported GaAs/InGaAs/GaAs MTSQD arrays, grown via SESRE (substrate-encoded size-reducing epitaxy) are in multiple arrays of up to 100x100 with 5um pitch, across a centimeter radius area. We show illustrative large-area images of the emission intensity (brightness) and color-coded wavelength distribution exhibiting ~3.35nm standard deviation. Scanning transmission electron microscopy shows a remarkable control on the QD location to within ~3nm accuracy laterally and ~1nm vertically. The primary remaining challenge is the control on the uniformity of the currently wet-chemically etched as-patterned nanomesa lateral size across the substrate, a surmountable technical issue. Thus, SESRE offers the most promising approach to realizing on-chip scalable spatially-ordered arrays of on-demand bright single quantum emitters meeting the figures-of-merit required for on-chip fully integrated quantum photonic circuit platforms-monolithic (such as based upon AlGaAs on insulator) or hybrid that leverage the silicon-on-insulator (SOI) photonic integrated circuit (PIC).

Speech bandwidth extension (BWE) has demonstrated promising performance in enhancing the perceptual speech quality in real communication systems. Most existing BWE researches primarily focus on fixed upsampling ratios, disregarding the fact that the effective bandwidth of captured audio may fluctuate frequently due to various capturing devices and transmission conditions. In this paper, we propose a novel streaming adaptive bandwidth extension solution dubbed BAE-Net, which is suitable to handle the low-resolution speech with unknown and varying effective bandwidth. To address the challenges of recovering both the high-frequency magnitude and phase speech content blindly, we devise a dual-stream architecture that incorporates the magnitude inpainting and phase refinement. For potential applications on edge devices, this paper also introduces BAE-NET-lite, which is a lightweight, streaming and efficient framework. Quantitative results demonstrate the superiority of BAE-Net in terms of both performance and computational efficiency when compared with existing state-of-the-art BWE methods., Comment: Accepted to ICASSP 2024