arXiv subjects

Yu Ma

Publications and source records attributed to Yu Ma.

At least 55 records · Page 3Linked to original sources

arXiv2025

Generative Video Semantic Communication via Multimodal Semantic Fusion with Large Model

Despite significant advancements in traditional syntactic communications based on Shannon's theory, these methods struggle to meet the requirements of 6G immersive communications, especially under challenging transmission conditions. With the development of generative artificial intelligence (GenAI), progress has been made in reconstructing videos using high-level semantic information. In this paper, we propose a scalable generative video semantic communication framework that extracts and transmits semantic information to achieve high-quality video reconstruction. Specifically, at the transmitter, description and other condition signals (e.g., first frame, sketches, etc.) are extracted from the source video, functioning as text and structural semantics, respectively. At the receiver, the diffusion-based GenAI large models are utilized to fuse the semantics of the multiple modalities for reconstructing the video. Simulation results demonstrate that, at an ultra-low channel bandwidth ratio (CBR), our scheme effectively captures semantic information to reconstruct videos aligned with human perception under different signal-to-noise ratios. Notably, the proposed ``First Frame+Desc." scheme consistently achieves CLIP score exceeding 0.92 at CBR = 0.0057 for SNR > 0 dB. This demonstrates its robust performance even under low SNR conditions.

eess.SP↗

arXiv2025

TabText: Language-Based Representations of Tabular Health Data for Predictive Modelling

Tabular medical records remain the most readily available data format for applying machine learning in healthcare. However, traditional data preprocessing ignores valuable contextual information in tables and requires substantial manual cleaning and harmonisation, creating a bottleneck for model development. We introduce TabText, a preprocessing and feature extraction method that leverages contextual information and streamlines the curation of tabular medical data. This method converts tables into contextual language and applies pretrained large language models (LLMs) to generate task-independent numerical representations. These fixed embeddings are then used as input for various predictive tasks. TabText was evaluated on nine inpatient flow prediction tasks (e.g., ICU admission, discharge, mortality) using electronic medical records across six hospitals from a US health system, and on nine publicly available datasets from the UCI Machine Learning Repository, covering tasks such as cancer diagnosis, recurrence, and survival. TabText models trained on unprocessed data from a single hospital (572,964 patient-days, Jan 2018-Dec 2020) achieved accurate performance (AUC 0.75-0.94) when tested prospectively on 265,917 patient-days from Jan 2021-Apr 2022, and generalised well to five additional hospitals not used for training. When augmenting preprocessed tabular records with these contextual embeddings, out-of-sample AUC improved by up to 4 additive percentage points in challenging tasks such as ICU transfer and breast cancer recurrence, while providing little to no benefit for already high-performing tasks. Findings were consistent across both private and public datasets.

cs.LG↗

arXiv2025

Observation of Discrete Time Quasicrystal in Rydberg Atomic Gases

Discrete time quasicrystals (DTQC) constitute a class of non-equilibrium matter characterized by temporal order without strict periodicity, in contrast to conventional time crystals. Investigating these phenomena is essential for expanding our fundamental understanding of far-from-equilibrium quantum matter and spontaneous symmetry breaking beyond periodic regimes. Here, we experimentally observe a DTQC in a driven-dissipative ensemble of strongly interacting Rydberg atoms, displaying non-equilibrium dynamical response with a different finite Abelian group symmetry $\mathbb{Z}{_m} \times \mathbb{Z}{_n}$. By applying a quasi-periodic drive using a dual-frequency drive with incommensurate frequencies, we demonstrate that the system exhibits a robust subharmonic response at multiple incommensurate frequencies, signifying the emergence of a DTQC phase. We map the full phase diagram of the system, which includes the DTQC phase, and demonstrated its rigidity against perturbations in both RF field intensity and laser detuning. Moreover, we observe a cyclic group symmetry effect that constrains the construction of $\mathbb{Z}{_2} \times \mathbb{Z}{_3}$-symmetric DTQC. This work establishes a versatile platform for studying non-equilibrium phases of matter and provides insights into the dynamics of time-translation symmetry breaking in quantum many-body systems.

cond-mat.quant-gas↗

arXiv2025

Quantum synchronization at the critical point of Floquet driven Rydberg atoms

The criticality enhanced correlations and susceptibility allow weak periodic driving to induce collective synchronization due to critical slowing down, providing a unique platform to study non-equilibrium order emergence. This establishes a powerful paradigm for investigating non-equilibrium order formation, yet the fundamental mechanisms of critical-point synchronization remain poorly understood. Here, we utilize a microwave pulse as a seed to induce quantum synchronization at the critical point of Floquet driven Rydberg atoms. In the experiment, the microwave periodic driving on Rydberg states acts as a seeded temporal order in subspace, which triggers synchronization across the entire ensemble. The behavior of the emergent synchronized oscillation is elaborately linked to alterations in the seed, such as the relative phase shift and the frequency difference, which result in phase-dependent seeding and embryonic synchronization. This result opens up new possibilities for studying and harnessing time-dependent quantum many-body phenomena, offering insight into the behavior of complex many-body systems under seeding.

cond-mat.quant-gas↗

arXiv2025

Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems

This paper investigates a joint beamforming and resource allocation problem in downlink reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) systems to minimize the average delay, where data packets for each user arrive at the base station (BS) stochastically. The sequential optimization problem is inherently a Markov decision process (MDP), thus falling within the remit of reinforcement learning. To effectively handle the mixed action space and reduce the state space dimensionality, a hybrid deep reinforcement learning (DRL) approach is proposed. Specifically, proximal policy optimization (PPO)-Theta is employed to optimize the RIS phase shift design, while PPO-N is responsible for subcarrier allocation decisions. The active beamforming at the BS is then derived from the jointly optimized RIS phase shifts and subcarrier allocation decisions. To further mitigate the curse of dimensionality associated with subcarrier allocation, a multi-agent strategy is introduced to optimize the subcarrier allocation indicators more efficiently. Moreover, to achieve more adaptive resource allocation and accurately capture the network dynamics, key factors closely related to average delay, such as the number of backlogged packets in buffers and current packet arrivals, are incorporated into the state space. Furthermore, a transfer learning framework is introduced to enhance the training efficiency and accelerate convergence. Simulation results demonstrate that the proposed algorithm significantly reduces the average delay, enhances resource allocation efficiency, and achieves superior system robustness and fairness compared to baseline methods.

cs.AI↗

arXiv2025

Text-Guided Token Communication for Wireless Image Transmission

With the emergence of 6G networks and proliferation of visual applications, efficient image transmission under adverse channel conditions is critical. We present a text-guided token communication system leveraging pre-trained foundation models for wireless image transmission with low bandwidth. Our approach converts images to discrete tokens, applies 5G NR polar coding, and employs text-guided token prediction for reconstruction. Evaluations on ImageNet show our method outperforms Deep Source Channel Coding with Attention Modules (ADJSCC) in perceptual quality and semantic preservation at Signal-to-Noise Ratios (SNRs) above 0 dB while mitigating the cliff effect at lower SNRs. Our system requires no scenario-specific retraining and exhibits superior cross-dataset generalization, establishing a new paradigm for efficient image transmission aligned with human perceptual priorities.

cs.IT↗

arXiv2025

Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systems

Reconfigurable intelligent surfaces (RIS) are key enablers for 6G wireless systems. This paper studies downlink transmission in an RIS-assisted MISO-OFDMA system, addressing resource allocation challenges. A two-stage unsupervised learning-based framework is proposed to jointly design RIS phase shifts, BS beamforming, and resource block (RB) allocation. The framework includes BeamNet, which predicts RIS phase shifts from CSI, and AllocationNet, which allocates RBs using equivalent CSI derived from BeamNet outputs. Active beamforming is implemented via maximum ratio transmission and water-filling. To handle discrete constraints while ensuring differentiability, quantization and the Gumbel-softmax trick are adopted. A customized loss and phased training enhance performance under QoS constraints. Simulations show the method achieves 99.93% of the sum rate of the SCA baseline with only 0.036% of its runtime, and it remains robust across varying channel and user conditions.

eess.SP↗

arXiv2025

Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences

We consider the problem of retraining machine learning (ML) models when new batches of data become available. Existing approaches greedily optimize for predictive power independently at each batch, without considering the stability of the model's structure or analytical insights across retraining iterations. We propose a model-agnostic framework for finding sequences of models that are stable across retraining iterations. We develop a mixed-integer optimization formulation that is guaranteed to recover Pareto optimal models (in terms of the predictive power-stability trade-off) with good generalization properties, as well as an efficient polynomial-time algorithm that performs well in practice. We focus on retaining consistent analytical insights-which is important to model interpretability, ease of implementation, and fostering trust with users-by using custom-defined distance metrics that can be directly incorporated into the optimization problem. We evaluate our framework across models (regression, decision trees, boosted trees, and neural networks) and application domains (healthcare, vision, and language), including deployment in a production pipeline at a major US hospital. We find that, on average, a 2% reduction in predictive power leads to a 30% improvement in stability.

cs.LG↗

arXiv2025

Diatomic and Polyatomic Heteronuclear Ultralong-Range Rydberg Molecules

Ultra-long-range Rydberg molecules (ULRMs) have attracted significant interest due to their unique electronic properties and potential applications in quantum technologies. We theoretically investigate the formation and characteristics of heteronuclear ULRMs, focusing on Rb-Cs systems. We explore the vibrational energy levels of heteronuclear nD ULRMs and compare them with homonuclear counterparts. We also predict the formation of polyatomic heteronuclear ULRMs, discussing how the binding energy and spectral features evolve as the number of ground-state atoms increases. Our theoretical predictions are presented in terms of molecular spectra and provide insight into the formation dynamics of these systems. The study further explores the potential applications of heteronuclear ULRMs in quantum information processing, quantum simulation, and precision measurements, offering new avenues for future research in many-body physics and quantum technologies.

quant-ph↗

arXiv2024

Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models

Automatic code generation has been a longstanding research topic. With the advancement of general-purpose large language models (LLMs), the ability to code stands out as one important measure to the model's reasoning performance. Usually, a two-stage training paradigm is implemented to obtain a Code LLM, namely the pretraining and the fine-tuning. Within the fine-tuning, supervised fine-tuning (SFT), and reinforcement learning (RL) are often used to improve the model's zero-shot ability. A large number of work has been conducted to improve the model's performance on code-related benchmarks with either modifications to the algorithm or refinement of the dataset. However, we still lack a deep insight into the correlation between SFT and RL. For instance, what kind of dataset should be used to ensure generalization, or what if we abandon the SFT phase in fine-tuning. In this work, we make an attempt to understand the correlation between SFT and RL. To facilitate our research, we manually craft 100 basis python functions, called atomic functions, and then a synthesizing pipeline is deployed to create a large number of synthetic functions on top of the atomic ones. In this manner, we ensure that the train and test sets remain distinct, preventing data contamination. Through comprehensive ablation study, we find: (1) Both atomic and synthetic functions are indispensable for SFT's generalization, and only a handful of synthetic functions are adequate; (2) Through RL, the SFT's generalization to target domain can be greatly enhanced, even with the same training prompts; (3) Training RL from scratch can alleviate the over-fitting issue introduced in the SFT phase.

cs.SE↗

arXiv2024

A Rb-Cs dual-species magneto-optical trap

We describe a three-dimensional (3D) magneto-optical trap (MOT) capable of simultaneously capturing 85Rb and 133Cs atoms. Unlike conventional setups, our system utilizes two separate laser systems that are combined before entering the vacuum chamber, enabling the simultaneous trapping of two different atomic species. Additionally, in our 3D MOT configuration, two (of three) pairs of laser beams are not orthogonal to the chamber surfaces but are aligned at a 45° angle. With a total trapping laser power of 8 mW and repump laser power of 4 mW for Rb atoms, and a total trapping laser power of 7.5 mW and repump laser power of 1.5 mW for Cs atoms, we achieve optical depths (OD) of 3.71 for Rb and 3.45 for Cs, demonstrating efficient trapping for both species. Our 3D MOT setup allows full horizontal optical access to the trapped atomic ensembles without spatial interference from the trapping or repump laser beams. Moreover, the red detuning for trapping both atomic species is smaller than in traditional configurations. This system offers a versatile platform for exploring complex phenomena in ultracold atom physics, such as Rydberg molecule formation and interspecies interactions.

physics.atom-ph↗

arXiv2024

Folded multistability and hidden critical point in microwave-driven Rydberg atoms

The interactions between Rydberg atoms and microwave fields provide a valuable framework for studying the complex dynamics out of equilibrium, exotic phases, and critical phenomena in many-body physics. This unique interplay allows us to explore various regimes of nonlinearity and phase transitions. Here, we observe a phase transition from the state in the regime of bistability to that in multistability in strongly interacting Rydberg atoms by varying the microwave field intensity, accompanying with the breaking of Z3-symmetry. During the phase transition, the system experiences a hidden critical point, in which the multistable states are difficult to be identified. Through changing the initial state of system, we can identify a hidden multistable state and reveal a hidden trajectory of phase transition, allowing us to track to a hidden critical point. In addition, we observe multiple phase transitions in spectra, suggesting higher-order symmetry breaking. The reported results shed light on manipulating multistability in dissipative Rydberg atoms systems and hold promise in the applications of non-equilibrium many-body physics.

cond-mat.quant-gas↗

arXiv2024

Higher-order and fractional discrete time crystals in Floquet-driven Rydberg atoms

Higher-order and fractional discrete time crystals (DTCs) are exotic phases of matter where the discrete time translation symmetry is broken into higher-order and non-integer category. Generation of these unique DTCs has been widely studied theoretically in different systems. However, no current experimental methods can probe these higher-order and fractional DTCs in any quantum many-body systems. We demonstrate an experimental approach to observe higher-order and fractional DTCs in Floquet-driven Rydberg atomic gases. We have discovered multiple $n$-DTCs with integer values of $n$ = 2, 3, and 4, and others ranging up to 14, along with fractional $n$-DTCs with $n$ values beyond the integers. The system response can transition between adjacent integer DTCs, during which the fractional DTCs are investigated. Study of higher-order and fractional DTCs expands fundamental knowledge of non-equilibrium dynamics and is promising for discovery of more complex temporal symmetries beyond the single discrete time translation symmetry.

cond-mat.quant-gas↗

arXiv2024

Towards Large Scale Atomic Manufacturing: Heterodyne Grating Interferometer with Zero Dead-Zone

This paper presents a novel heterodyne grating interferometer designed to meet the precise measurement requirements of next-generation lithography systems and large-scale atomic-level manufacturing. Utilizing a dual-frequency light source, the interferometer enables simultaneous measurement of three degrees of freedom. Key advancements include a compact zero Dead-Zone optical path configuration, significantly enhancing measurement reliability by mitigating the impact of light source fluctuations and air refractive index variations. A comprehensive crosstalk error analysis was conducted, resulting in a robust correction algorithm that reduces errors to below 5%. Performance testing of the prototype, size of 90mm*90mm*40mm, demonstrated exceptional resolution (0.25 nm in the XY-axis and 0.3 nm in the Z-axis), superior linearity (6.9e-5, 8.1e-5 and 16.2e-5 for the X, Y, and Z axes, respectively), high repeatability (0.8 nm/1000 nm for the three axes) and stability (20 nm for the XY-axis and 60 nm for the Z-axis over 1000 seconds). Comparative analysis with existing measurement sensors highlights the proposed method's significant advantages in integration, multidimensional capabilities, and is expected to be widely used in fields such as integrated circuits, atomic-level manufacturing and aerospace technology.

physics.optics↗

arXiv2024

Robust Regression over Averaged Uncertainty

We propose a new formulation of robust regression by integrating all realizations of the uncertainty set and taking an averaged approach to obtain the optimal solution for the ordinary least squares regression problem. We show that this formulation recovers ridge regression exactly and establishes the missing link between robust optimization and the mean squared error approaches for existing regression problems. We further demonstrate that the condition of this equivalence relies on the geometric properties of the defined uncertainty set. We provide exact, closed-form, in some cases, analytical solutions to the equivalent regularization strength under uncertainty sets induced by $\ell_p$ norm, Schatten $p$-norm, and general polytopes. We then show in synthetic datasets with different levels of uncertainties, a consistent improvement of the averaged formulation over the existing worst-case formulation in out-of-sample performance. In real-world regression problems obtained from UCI datasets, similar improvements are seen in the out-of-sample datasets.

cs.LG↗

arXiv2024

Observation of Higgs and Goldstone modes in U(1) symmetry-broken Rydberg atomic systems

Higgs and Goldstone modes manifest as fluctuations in the order parameter of system, offering insights into its phase transitions and symmetry properties. Exploring the dynamics of these collective excitations in a Rydberg atoms system advances various branches of condensed matter, particle physics, and cosmology. Here, we report an experimental signature of Higgs and Goldstone modes in a U(1) symmetry-broken Rydberg atomic gases. By constructing two probe fields to excite atoms, we observe the distinct phase and amplitude fluctuations of Rydberg atoms collective excitations under the particle-hole symmetry. Due to the van der Waals interactions between the Rydberg atoms, we detect a symmetric variance spectrum divided by the divergent regime and phase boundary, capturing the full dynamics of the additional Higgs and Goldstone modes. Studying the Higgs and Goldstone modes in Rydberg atoms allows us to explore fundamental aspects of quantum phase transitions and symmetry breaking phenomena, while leveraging the unique properties of these highly interacting systems to uncover new physics and potential applications in quantum simulation.

cond-mat.quant-gas↗

arXiv2024

Early warning signals of the tipping point in strongly interacting Rydberg atoms

The identification of tipping points is essential for prediction of collapses or other sudden changes in complex systems. Applications include studies of ecology, thermodynamics, climatology, and epidemiology. However, detecting early signs of proximity to a tipping is made challenging by complexity and non-linearity. Strongly interacting Rydberg atom gases offer model systems that offer both complexity and non-linearity, including phase transition and critical slowing down. Here, via an external probe we observe prior warning of the proximity of a phase transition of Rydberg thermal gases. This warning signal is manifested as a deviation from linear growth of the variance with increasing probe intensity. We also observed the dynamics of the critical slowing down behavior versus different time scales, and atomic densities, thus providing insights into the study of a Rydberg atom system's critical behavior. Our experiment suggests that the full critical slowing down dynamics of strongly-interacting Rydberg atoms can be probed systematically, thus providing a benchmark with which to identify critical phenomena in quantum many-body systems.

cond-mat.quant-gas↗

arXiv2024

Rhyme-aware Chinese lyric generator based on GPT

Neural language representation models such as GPT, pre-trained on large-scale corpora, can effectively capture rich semantic patterns from plain text and be fine-tuned to consistently improve natural language generation performance. However, existing pre-trained language models used to generate lyrics rarely consider rhyme information, which is crucial in lyrics. Using a pre-trained model directly results in poor performance. To enhance the rhyming quality of generated lyrics, we incorporate integrated rhyme information into our model, thereby improving lyric generation performance.

cs.CL↗