Search

Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. This gap restricts the scope of experiments vital for advancing both fundamental and clinical neuroscience. Recent advances in real-time machine learning technologies, particularly in analyzing neural time series as nonlinear stochastic dynamical systems, are beginning to bridge this gap. These technologies enable immediate interpretation of and interaction with neural systems, offering new insights into neural computation. However, several significant challenges remain. Issues such as slow convergence rates, high-dimensional data complexities, structured noise, non-identifiability, and a general lack of inductive biases tailored for neural dynamics are key hurdles. Overcoming these challenges is crucial for the full realization of real-time neural data analysis for the causal investigation of neural computation and advanced perturbation based brain machine interfaces. In this paper, we provide a comprehensive perspective on the current state of the field, focusing on these persistent issues and outlining potential paths forward. We emphasize the importance of large-scale integrative neuroscience initiatives and the role of meta-learning in overcoming these challenges. These approaches represent promising research directions that could redefine the landscape of neuroscience experiments and brain-machine interfaces, facilitating breakthroughs in understanding brain function, and treatment of neurological disorders., Comment: This article is accepted for publication in the 2024 European Signal Processing Conference (EUSIPCO)

Automatic Video Dubbing (AVD) aims to take the given script and generate speech that aligns with lip motion and prosody expressiveness. Current AVD models mainly utilize visual information of the current sentence to enhance the prosody of synthesized speech. However, it is crucial to consider whether the prosody of the generated dubbing aligns with the multimodal context, as the dubbing will be combined with the original context in the final video. This aspect has been overlooked in previous studies. To address this issue, we propose a Multimodal Context-aware video Dubbing model, termed \textbf{MCDubber}, to convert the modeling object from a single sentence to a longer sequence with context information to ensure the consistency of the global context prosody. MCDubber comprises three main components: (1) A context duration aligner aims to learn the context-aware alignment between the text and lip frames; (2) A context prosody predictor seeks to read the global context visual sequence and predict the context-aware global energy and pitch; (3) A context acoustic decoder ultimately predicts the global context mel-spectrogram with the assistance of adjacent ground-truth mel-spectrograms of the target sentence. Through this process, MCDubber fully considers the influence of multimodal context on the prosody expressiveness of the current sentence when dubbing. The extracted mel-spectrogram belonging to the target sentence from the output context mel-spectrograms is the final required dubbing audio. Extensive experiments on the Chem benchmark dataset demonstrate that our MCDubber significantly improves dubbing expressiveness compared to all advanced baselines. The code and demos are available at https://github.com/XiaoYuanJun-zy/MCDubber., Comment: Accepted by NCMMSC2024

Entanglement distribution is a critical technique that enables numerous quantum applications. Most fiber-based long-distance experiments reported to date have utilized photon pair sources generated in bulk optical crystals, with the entanglement encoded in the polarization degree of freedom. Here, we create time-energy entanglement for photon pairs generated from an on-chip silicon ring resonator via SFWM process and report the distribution of the entanglement over standard optical fiber with distance >81 km. Our work paves the way for future large-scale quantum networks with connect of distant quantum nodes., Comment: 8 pages, 4 figures

Excited states (ESs) of two- and three-dimensional (2D and 3D) solitons of the semivortex (SV) and mixed-mode (MM) types, supported by the interplay of the spin-orbit coupling (SOC) and local nonlinearity in binary Bose-Einstein condensates, are unstable, on the contrary to the stability of the SV and MM solitons in their fundamental states. We propose a stabilization strategy for these states in 3D, combining SOC and long-range Rydberg-Rydberg interactions (RRI), in the presence of a spatially-periodic potential, that may include a parity-time (PT)-symmetric component. ESs of the SV solitons, which carry integer vorticities S and S+1 in their two components, exhibit robustness up to S= 4. ESs of MM solitons feature an interwoven necklace-like structure, with the components carrying opposite fractional values of the orbital angular momentum. Regions of the effective stability of the 3D solitons of the SV and MM types (both fundamental ones and ESs), are identified as functions of the imaginary component of the PT-symmetric potential and strengths of the SOC and RRI terms., Comment: to be published in Chaos, Solitons & Fractals

The chiral magnetic wave (CMW) is a collective mode in quark-gluon plasma originated from the chiral magnetic effect (CME) and chiral separation effect. Its detection in heavy-ion collisions is challenging due to significant background contamination. In Ref.[1], we have constructed a neural network which can accurately identify the CME-related signal from the final-state pion spectra. In this paper, we generalize such a neural network to the case of CMW search. We show that, after a updated training, the neural network can effectively recognize the CMW-related signal. Additionally, we assess the performance of the neural network compared to other known methods for CMW search., Comment: 6 pages, 6 figures. Published in Chin.Phys.C

State-space graphical models and the variational autoencoder framework provide a principled apparatus for learning dynamical systems from data. State-of-the-art probabilistic approaches are often able to scale to large problems at the cost of flexibility of the variational posterior or expressivity of the dynamics model. However, those consolidations can be detrimental if the ultimate goal is to learn a generative model capable of explaining the spatiotemporal structure of the data and making accurate forecasts. We introduce a low-rank structured variational autoencoding framework for nonlinear Gaussian state-space graphical models capable of capturing dense covariance structures that are important for learning dynamical systems with predictive capabilities. Our inference algorithm exploits the covariance structures that arise naturally from sample based approximate Gaussian message passing and low-rank amortized posterior updates -- effectively performing approximate variational smoothing with time complexity scaling linearly in the state dimensionality. In comparisons with other deep state-space model architectures our approach consistently demonstrates the ability to learn a more predictive generative model. Furthermore, when applied to neural physiological recordings, our approach is able to learn a dynamical system capable of forecasting population spiking and behavioral correlates from a small portion of single trials.

This study investigates the computational processing of euphemisms, a universal linguistic phenomenon, across multiple languages. We train a multilingual transformer model (XLM-RoBERTa) to disambiguate potentially euphemistic terms (PETs) in multilingual and cross-lingual settings. In line with current trends, we demonstrate that zero-shot learning across languages takes place. We also show cases where multilingual models perform better on the task compared to monolingual models by a statistically significant margin, indicating that multilingual data presents additional opportunities for models to learn about cross-lingual, computational properties of euphemisms. In a follow-up analysis, we focus on universal euphemistic "categories" such as death and bodily functions among others. We test to see whether cross-lingual data of the same domain is more important than within-language data of other domains to further understand the nature of the cross-lingual transfer.

The fisheye camera, with its unique wide field of view and other characteristics, has found extensive applications in various fields. However, the fisheye camera suffers from significant distortion compared to pinhole cameras, resulting in distorted images of captured objects. Fish-eye camera distortion is a common issue in digital image processing, requiring effective correction techniques to enhance image quality. This review provides a comprehensive overview of various methods used for fish-eye camera distortion correction. The article explores the polynomial distortion model, which utilizes polynomial functions to model and correct radial distortions. Additionally, alternative approaches such as panorama mapping, grid mapping, direct methods, and deep learning-based methods are discussed. The review highlights the advantages, limitations, and recent advancements of each method, enabling readers to make informed decisions based on their specific needs.

The consensus algorithm is crucial in blockchain for ensuring the validity and security of transactions across the decentralized network. However, achieving consensus among nodes and packaging blocks in blockchain networks is a complex task that requires efficient and secure consensus algorithms. The DPoS consensus algorithm has emerged as a popular choice due to its fast transaction processing and high throughput. Despite these advantages, the algorithm still suffers from weaknesses such as centralization and vulnerability to long-range attacks, which can compromise the integrity of the blockchain network. To combat these problems, we developed an Enhanced Anti-Long-Range Attack DPoS algorithm (HL-DPoS). First, we split nodes into pieces to reduce centralization issues while giving witness nodes the power to report and benefit from malicious node's reports, maintaining high efficiency and high security. Second, we propose a validation method in HL-DPoS that compares consensuses transactions with the longest chain to detect long-range attacks. Algorithm analysis and simulation experiment results demonstrate that our HL-DPoS consensus algorithm improves security while achieving better consensus performance.

We present numerical results for three-dimensional (3D) solitons with symmetries of the semi-vortex (SV) and mixed-mode (MM) types, which can be created in spinor Bose-Einstein condensates of Rydberg atoms under the action of the spin-orbit coupling (SOC). By means of systematic numerical computations, we demonstrate that the interplay of SOC and long-range spherically symmetric Rydberg interactions stabilize the 3D solitons, improving their resistance to collapse. We find how the stability range depends on the strengths of the SOC and Rydberg interactions and the soft-core atomic radius., Comment: to be published in Scientific Reports

Rydberg electromagnetically-induced transparency has been widely studied as a medium supporting light propagation under the action of nonlocal nonlinearities. Recently, optical potentials based on moir\'e lattices (MLs) were introduced for exploring unconventional physical states. Here, we predict a possibility of creating fully three-dimensional (3D) light bullets (LBs) in cold Rydberg gases under the action of ML potentials. The nonlinearity includes local self-defocusing and long-range focusing terms, the latter one induced by the Rydberg-Rydberg interaction. We produce zero-vorticity LB families of the fundamental, dipole, and quadrupole types, as well as vortex LBs. They all are gap solitons populating finite bandgaps of the underlying ML spectrum. Stable subfamilies are identified utilizing the combination of the anti-Vakhitov-Kolokolov criterion, computation of eigenvalues for small perturbations, and direct simulations.

In recent years, analysis methods for quantum states based on randomized measurements have been investigated extensively. Still, in the experimental implementations these methods were typically used for characterizing strongly entangled states and not to analyze the different families of multiparticle or weakly entangled states. In this work, we experimentally prepare various entangled states with path-polarization hyper-entangled photon pairs, and study their entanglement properties using the full toolbox of randomized measurements. First, we successfully characterize the correlations of a series of GHZ-W mixed states using the second moments of the random outcomes, and demonstrate the advantages of this method by comparing it with the well-known three-tangle and squared concurrence. Second, we generate bound entangled chessboard states of two three-dimensional systems and verify their weak entanglement with a criterion derived from moments of randomized measurements., Comment: 10 pages, 6 figures, Comments are welcome

Latent Gaussian process (GP) models are widely used in neuroscience to uncover hidden state evolutions from sequential observations, mainly in neural activity recordings. While latent GP models provide a principled and powerful solution in theory, the intractable posterior in non-conjugate settings necessitates approximate inference schemes, which may lack scalability. In this work, we propose cvHM, a general inference framework for latent GP models leveraging Hida-Mat\'ern kernels and conjugate computation variational inference (CVI). With cvHM, we are able to perform variational inference of latent neural trajectories with linear time complexity for arbitrary likelihoods. The reparameterization of stationary kernels using Hida-Mat\'ern GPs helps us connect the latent variable models that encode prior assumptions through dynamical systems to those that encode trajectory assumptions through GPs. In contrast to previous work, we use bidirectional information filtering, leading to a more concise implementation. Furthermore, we employ the Whittle approximate likelihood to achieve highly efficient hyperparameter learning., Comment: Published at ICML 2023

Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance. Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese. We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work.

Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorithms for extracting latent neural trajectories from neural recordings. However, despite the potential of real time alternatives to give immediate feedback to experimentalists, and enhance experimental design, they have received markedly less attention. In this work, we introduce the exponential family variational Kalman filter (eVKF), an online recursive Bayesian method aimed at inferring latent trajectories while simultaneously learning the dynamical system generating them. eVKF works for arbitrary likelihoods and utilizes the constant base measure exponential family to model the latent state stochasticity. We derive a closed-form variational analogue to the predict step of the Kalman filter which leads to a provably tighter bound on the ELBO compared to another online variational method. We validate our method on synthetic and real-world data, and, notably, show that it achieves competitive performance, Comment: Published at ICLR 2023

The formation of bulk metallic glass requires the constituent elements to have a negative heat of mixing but has no restrictions on its magnitude. An understanding of this issue is lacking due to the absence of a valid method for describing chemical ordering of metallic glasses. For example, the radial distribution function is ineffective in identifying the elemental preferences of packed atoms. Here, we show that using molecular-dynamics simulation, the chemical medium-range ordering of liquid alloys can be evaluated from persistent homology. This inherently arising chemical medium-range order in metallic glasses is exclusively regulated by the activation and inhibition of the constituent components, making the topology of metallic glasses a Turing pattern. The connecting schemes of atoms of the same species form three distinct regions, reflecting different correlations at the short and medium length scales, while the difference in the schemes corresponds to chemical ordering. By changing the elemental types, it is demonstrated that the chemical medium-range order strongly depends on the relative depth of the interatomic-potential wells. The study separates metallic glasses from crystals under the condition of negative heat of mixing by emphasizing their fundamental difference in interatomic potentials.

Defect engineering is promising to tailor the physical properties of two-dimensional (2D) semiconductors for function-oriented electronics and optoelectronics. Compared with the extensively studied 2D binary materials, the origin of defects and their influence on physical properties of 2D ternary semiconductors have not been clarified. In this work, we thoroughly studied the effect of defects on the electronic structure and optical properties of few-layer hexagonal Znln2S4 via versatile spectroscopic tools in combination with theoretical calculations. It has been demonstrated that the Zn-In anti-structural defects induce the formation of a series of donor and acceptor levels inside the bandgap, leading to rich recombination paths for defect emission and extrinsic absorption. Impressively, the emission of donor-acceptor pair (DAP) in Znln2S4 can be significantly tailored by electrostatic gating due to efficient tunability of Fermi level (Ef). Furthermore, the layer-dependent dipole orientation of defect emission in Znln2S4 was directly revealed by back focal plane (BFP) imagining, where it presents obviously in-plane dipole orientation within a dozen layers thickness of Znln2S4. These unique features of defects in Znln2S4 including extrinsic absorption, rich recombination paths, gate tunability and in-plane dipole orientation will definitely benefit to the advanced orientation-functional optoelectronic applications.