arXiv subjects

Yuan Lu

Publications and source records attributed to Yuan Lu.

At least 73 records · Page 4Linked to original sources

arXiv2025

Magnetic-field controlled organic spintronic memristor for neural network computation

Memristors are emerging as key electronic components that retain resistance states without power. Their non-volatile nature and ability to mimic synaptic behavior make them ideal for next-generation memory technologies and neuromorphic computing systems inspired by the human brain. In this study, we present a novel organic spintronic memristor based on a La0.67Sr0.33MnO3 (LSMO)/poly(vinylidene fluoride) (PVDF)/Co heterostructure, exhibiting biologically inspired synaptic behavior. Driven by fluorine atom migration within the PVDF layer, the device demonstrates both long-term depression (LTD) and long-term potentiation (LTP) under controlled electrical polarization. Distinctively, the resistance states can also be modulated by an external magnetic field via the tunneling magnetoresistance (TMR) effect, introducing a non-electrical means of tuning synaptic plasticity. This magnetic control mechanism enables multi-state modulation without compromising device performance or endurance. Furthermore, convolutional neural network (CNN) simulations incorporating this magnetic tuning capability reveal enhanced pattern recognition accuracy and improved training stability, especially at high learning rates. These findings underscore the potential of organic spintronic memristors as high-performance, low-power neuromorphic elements, particularly suited for applications in flexible and wearable electronics.

cond-mat.mes-hall↗

arXiv2025

Interpretable Multimodal Zero-Shot ECG Diagnosis via Structured Clinical Knowledge Alignment

Electrocardiogram (ECG) interpretation is essential for cardiovascular disease diagnosis, but current automated systems often struggle with transparency and generalization to unseen conditions. To address this, we introduce ZETA, a zero-shot multimodal framework designed for interpretable ECG diagnosis aligned with clinical workflows. ZETA uniquely compares ECG signals against structured positive and negative clinical observations, which are curated through an LLM-assisted, expert-validated process, thereby mimicking differential diagnosis. Our approach leverages a pre-trained multimodal model to align ECG and text embeddings without disease-specific fine-tuning. Empirical evaluations demonstrate ZETA's competitive zero-shot classification performance and, importantly, provide qualitative and quantitative evidence of enhanced interpretability, grounding predictions in specific, clinically relevant positive and negative diagnostic features. ZETA underscores the potential of aligning ECG analysis with structured clinical knowledge for building more transparent, generalizable, and trustworthy AI diagnostic systems. We will release the curated observation dataset and code to facilitate future research.

cs.LG↗

arXiv2025

Giant shot noise in superconductor/ferromagnet junctions with orbital-symmetry-controlled spin-orbit coupling

By measuring the shot noise, a consequence of charge quantization, in superconductor/insulator/ferromagnet (V/MgO/Fe) junctions, we discover a giant increase, orders of magnitude larger than expected. The origin of this giant noise is a peculiar realization of a superconducting proximity effect, where a simple superconductor influences its neighbors. Our measurements reveal largely unexplored implications of orbital-symmetry-controlled proximity effects. The importance of orbital symmetries and the accompanying spin-orbit coupling is manifested by an unexpected emergence of another superconducting region strikingly different from the parent superconductor. Unlike vanadium's common spin-singlet superconductivity, the broken inversion symmetry in V/MgO/Fe junctions and the resulting interfacial spin-orbit coupling leads to the formation of spin-triplet superconductivity across the ferromagnetic iron. Here we show that the enhanced shot noise, known from Josephson junctions with two superconductors, is measured even in a single superconductor, this discovery motivates revisiting how the spin-orbit coupling and superconducting proximity effects can transform many materials.

cond-mat.supr-con↗

arXiv2025

Large Tunable Thermoelectric Effects in Superconducting Spin Valves with Commercially Available Materials

Recent studies have revealed magnetically controllable thermoelectric effects in superconductor/ferromagnet (S/F) structures. A tunable cryogenic thermoelectric generator needs not only a high conversion factor between electricity and heat, but also a large change in the thermoelectric output when switching the magnetic state of the device. However, the reported modifications in thermoelectric power are either minimal, involve superconductors with relatively low critical temperatures (below 1 K), or do not utilize commercially available spintronic materials. Here, we experimentally measure and numerically model thermoelectric effects in fully epitaxial F/S/F junctions based on commercially available, easily grown materials, as well as their dependence on the magnetic configuration of the F electrodes. We observe sizeable Seebeck coefficients for the parallel alignment of the ferromagnetic electrodes, reaching values of about $100$~$μ$V/K. Importantly, we find a decrease of the thermoelectric signal of more than an order of magnitude when switching from a parallel to an antiparallel configuration, constituting a large thermoelectric spin-valve effect. Theoretical modeling based on a self-consistent non-equilibrium Keldysh-Usadel Green's function theory, combined with micromagnetic simulations, qualitatively reproduce the experimental findings. The thermoelectric effect is optimized when there is a large spin-dependent electron-hole asymmetry in the superconductor combined with spin-dependent transmission through the interfaces. These findings pave the way for the development of efficient and versatile cryogenic thermoelectric heat engines.

cond-mat.supr-con↗

arXiv2025

MiniMax-Speech: Intrinsic Zero-Shot Text-to-Speech with a Learnable Speaker Encoder

We introduce MiniMax-Speech, an autoregressive Transformer-based Text-to-Speech (TTS) model that generates high-quality speech. A key innovation is our learnable speaker encoder, which extracts timbre features from a reference audio without requiring its transcription. This enables MiniMax-Speech to produce highly expressive speech with timbre consistent with the reference in a zero-shot manner, while also supporting one-shot voice cloning with exceptionally high similarity to the reference voice. In addition, the overall quality of the synthesized audio is enhanced through the proposed Flow-VAE. Our model supports 32 languages and demonstrates excellent performance across multiple objective and subjective evaluations metrics. Notably, it achieves state-of-the-art (SOTA) results on objective voice cloning metrics (Word Error Rate and Speaker Similarity) and has secured the top position on the public TTS Arena leaderboard. Another key strength of MiniMax-Speech, granted by the robust and disentangled representations from the speaker encoder, is its extensibility without modifying the base model, enabling various applications such as: arbitrary voice emotion control via LoRA; text to voice (T2V) by synthesizing timbre features directly from text description; and professional voice cloning (PVC) by fine-tuning timbre features with additional data. We encourage readers to visit https://minimax-ai.github.io/tts_tech_report for more examples.

eess.AS↗

arXiv2025

Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning

Electrocardiogram (ECG) interpretation requires specialized expertise, often involving synthesizing insights from ECG signals with complex clinical queries posed in natural language. The scarcity of labeled ECG data coupled with the diverse nature of clinical inquiries presents a significant challenge for developing robust and adaptable ECG diagnostic systems. This work introduces a novel multimodal meta-learning method for few-shot ECG question answering, addressing the challenge of limited labeled data while leveraging the rich knowledge encoded within large language models (LLMs). Our LLM-agnostic approach integrates a pre-trained ECG encoder with a frozen LLM (e.g., LLaMA and Gemma) via a trainable fusion module, enabling the language model to reason about ECG data and generate clinically meaningful answers. Extensive experiments demonstrate superior generalization to unseen diagnostic tasks compared to supervised baselines, achieving notable performance even with limited ECG leads. For instance, in a 5-way 5-shot setting, our method using LLaMA-3.1-8B achieves an accuracy of 84.6%, 77.3%, and 69.6% on single verify, choose and query question types, respectively. These results highlight the potential of our method to enhance clinical ECG interpretation by combining signal processing with the nuanced language understanding capabilities of LLMs, particularly in data-constrained scenarios.

cs.LG↗

arXiv2025

Investigating the Scaling Effect of Instruction Templates for Training Multimodal Language Model

Current multimodal language model (MLM) training approaches overlook the influence of instruction templates. Previous research deals with this problem by leveraging hand-crafted or model-generated templates, failing to investigate the scaling effect of instruction templates on MLM training. In this work, we propose a programmatic instruction template generator capable of producing over 15K unique instruction templates by filling randomly sampled positional synonyms into weighted sampled meta templates, enabling us to comprehensively explore MLM's performance across various template scales in the training process. Our investigation into scaling instruction templates for MLM training demonstrates that MLM capabilities do not consistently improve with increasing template scale. Instead, optimal performance is achieved at a medium template scale. Models trained with data augmented at the optimal template scale achieve performance gains of up to 10% over those trained on the original data and achieve the best overall performance compared with the similar-scale MLMs tuned on at most 75 times the scale of our augmented dataset. The code will be publicly available at https://github.com/shijian2001/TemplateScaling.

cs.CV↗

arXiv2025

Photon statistics of time dependent electronic excitation of spin injected quantum dots

The time dynamics of spin-injected, electrically contacted quantum dots were investigated with a focus on the time evolution of photon statistics. Photon statistics can provide insights into whether the device functions as an effective single-photon emitter or exhibits higher-order emissions. Through these investigations, we found that the shape of the electrical excitation pulse has a direct impact on photon statistics. Specifically, the rising edge of the pulse corresponds to a significantly higher number of higher-order photon states, which decay much faster than single photons associated with the falling edge of the electrical pulse. This relationship implies that the pulse shape can be tailored to optimize the device as either a better single-photon source or a generator of higher-order photon states, with potential applications in creating deterministic higher-order photon Fock states. The ability to easily modify the pulse shape is a unique feature of electrically excited quantum dots.

quant-ph↗

arXiv2024

Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling

Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time investment. To address these critical issues, we propose ECG-ReGen, a retrieval-based approach for ECG-to-text report generation and question answering. Our method leverages a self-supervised learning for the ECG encoder, enabling efficient similarity searches and report retrieval. By combining pre-training with dynamic retrieval and Large Language Model (LLM)-based refinement, ECG-ReGen effectively analyzes ECG data and answers related queries, with the potential of improving patient care. Experiments conducted on the PTB-XL and MIMIC-IV-ECG datasets demonstrate superior performance in both in-domain and cross-domain scenarios for report generation. Furthermore, our approach exhibits competitive performance on ECG-QA dataset compared to fully supervised methods when utilizing off-the-shelf LLMs for zero-shot question answering. This approach, effectively combining self-supervised encoder and LLMs, offers a scalable and efficient solution for accurate ECG interpretation, holding significant potential to enhance clinical decision-making.

cs.LG↗

arXiv2024

Kronos: A Secure and Generic Sharding Blockchain Consensus with Optimized Overhead

Sharding enhances blockchain scalability by dividing the network into shards, each managing specific unspent transaction outputs or accounts. As an introduced new transaction type, cross-shard transactions pose a critical challenge to the security and efficiency of sharding blockchains. Currently, there is a lack of a generic sharding consensus pattern that achieves both security and low overhead. In this paper, we present Kronos, a secure sharding blockchain consensus achieving optimized overhead. In particular, we propose a new secure sharding consensus pattern, based on a buffer managed jointly by shard members. Valid transactions are transferred to the payee via the buffer, while invalid ones are rejected through happy or unhappy paths. Kronos is proved to achieve security with atomicity under malicious clients with optimal intra-shard overhead $kB$ ($k$ for involved shard number and $B$ for a Byzantine fault tolerance (BFT) cost). Besides, we propose secure cross-shard certification methods based on batch certification and reliable cross-shard transfer. The former combines hybrid trees or vector commitments, while the latter integrates erasure coding. Handling $b$ transactions, Kronos is proved to achieve reliability with low cross-shard overhead $O(n b λ)$ ($n$ for shard size and $λ$ for the security parameter). Notably, Kronos imposes no restrictions on BFT and does not rely on time assumptions, offering optional constructions in various modules. We implement Kronos using two prominent BFT protocols: asynchronous Speeding Dumbo and partial synchronous Hotstuff. Extensive experiments demonstrate Kronos scales the consensus nodes to thousands, achieving a substantial throughput of 320 ktx/sec with 2.0 sec latency. Compared with the past solutions, Kronos outperforms, achieving up to a 12* improvement in throughput and a 50% reduction in latency.

cs.CR↗

arXiv2024

Elliptic anisotropy of open-charm hadrons from parton scatterings in p--Pb collisions at energies available at the CERN Large Hadron Collider

The elliptic azimuthal anisotropy coefficient ($v_{2}$) of open-charm hadron at midrapidity ($|η<1|$) was studied in p--Pb collisions at $\sqrt{s_{\mathrm{NN}}}=$ 8.16 TeV using a multi-phase transport (AMPT) model included an additional charm quark--antiquark pair production trigger. The model provides a simultaneously description of the measured $p_{\mathrm{T}}$ spectrum and $v_{2}$ of $D^{0}$ meson, as well as the $v_{2}$ of light flavor meson $K_{s}^{0}$. We found that the $D^{0}$ and $K_{s}^{0}$ $v_{2}$ are both significantly affected by different parton scatterings among charm and light quarks. In addition, the predictions for the $v_{2}$ of other charm hadrons including $D^{+}$, $D_{s}^{+}$ and $Λ_{c}^{+}$ in p--Pb collisions are given for the first time. The $v_{2}$ of open-charm hadron reasonably follows the number-of-constituent-quark (NCQ) scaling up to 2.5 GeV, strongly indicating the importance of partonic degrees of freedom for the collectivity of heavy flavors in high-multiplicity p--Pb collisions. These findings may hint the formation of deconfined matter in small collision systems, and provide referential value for future measurements of azimuthal anisotropy at the LHC energies.

hep-ph↗

arXiv2024

Stockformer: A Price-Volume Factor Stock Selection Model Based on Wavelet Transform and Multi-Task Self-Attention Networks

As the Chinese stock market continues to evolve and its market structure grows increasingly complex, traditional quantitative trading methods are facing escalating challenges. Particularly, due to policy uncertainty and the frequent market fluctuations triggered by sudden economic events, existing models often struggle to accurately predict market dynamics. To address these challenges, this paper introduces Stockformer, a price-volume factor stock selection model that integrates wavelet transformation and a multitask self-attention network, aimed at enhancing responsiveness and predictive accuracy regarding market instabilities. Through discrete wavelet transform, Stockformer decomposes stock returns into high and low frequencies, meticulously capturing long-term market trends and short-term fluctuations, including abrupt events. Moreover, the model incorporates a Dual-Frequency Spatiotemporal Encoder and graph embedding techniques to effectively capture complex temporal and spatial relationships among stocks. Employing a multitask learning strategy, it simultaneously predicts stock returns and directional trends. Experimental results show that Stockformer outperforms existing advanced methods on multiple real stock market datasets. In strategy backtesting, Stockformer consistently demonstrates exceptional stability and reliability across market conditions-whether rising, falling, or fluctuating-particularly maintaining high performance during downturns or volatile periods, indicating a high adaptability to market fluctuations. To foster innovation and collaboration in the financial analysis sector, the Stockformer model's code has been open-sourced and is available on the GitHub repository: https://github.com/Eric991005/Multitask-Stockformer.

q-fin.TR↗

arXiv2024

Mask-coding-assisted continuous-variable quantum direct communication with orbital angular momentum multiplexing

Quantum secure direct communication (QSDC) is a approach of communication to transmit secret messages base on quantum mechanics. Different from the quantum key distribution, secret messages can be transmitted directly on quantum channel with QSDC. Higher channel capacity and noise suppression capabilities are key to achieving long-distance quantum communication. Here we report a continuous-variable QSDC scheme based on mask-coding and orbital angular momentum, in which the mask-coding is employed to protect the security of the transmitting messages and to suppression the influence of excess noise. And the combination of orbital angular momentum and the information block transmission can effectively improve the secrecy capacity. In the 800 information blocks * 1310 bits length 10-km experiment, the results show that the statistical average of bit error rate has decreased by 36% and reached 0.38% comparing to existing solutions, and the system excess noise is 0.0184 SNU, and the final secrecy capacity is 6.319 * 106 bps. Therefore, this scheme reduces error bits while increasing secrecy capacity, providing a solution for long-distance large-scale quantum communication.

quant-ph↗

arXiv2024

JUMBO: Fully Asynchronous BFT Consensus Made Truly Scalable

Recent progresses in asynchronous Byzantine fault-tolerant (BFT) consensus, e.g. Dumbo-NG (CCS' 22) and Tusk (EuroSys' 22), show promising performance through decoupling transaction dissemination and block agreement. However, when executed with a larger number $n$ of nodes, like several hundreds, they would suffer from significant degradation in performance. Their dominating scalability bottleneck is the huge authenticator complexity: each node has to multicast $\bigO(n)$ quorum certificates (QCs) and subsequently verify them for each block. This paper systematically investigates and resolves the above scalability issue. We first propose a signature-free asynchronous BFT consensus FIN-NG that adapts a recent signature-free asynchronous common subset protocol FIN (CCS' 23) into the state-of-the-art framework of concurrent broadcast and agreement. The liveness of FIN-NG relies on our non-trivial redesign of FIN's multi-valued validated Byzantine agreement towards achieving optimal quality. FIN-NG greatly improves the performance of FIN and already outperforms Dumbo-NG in most deployment settings. To further overcome the scalability limit of FIN-NG due to $\bigO(n^3)$ messages, we propose JUMBO, a scalable instantiation of Dumbo-NG, with only $\bigO(n^2)$ complexities for both authenticators and messages. We use various aggregation and dispersal techniques for QCs to significantly reduce the authenticator complexity of original Dumbo-NG implementations by up to $\bigO(n^2)$ orders. We also propose a ``fairness'' patch for JUMBO, thus preventing a flooding adversary from controlling an overwhelming portion of transactions in its output.

cs.DC↗

arXiv2024

Unraveling spin texture and spin-orbit coupling contributions in spin triplet superconductivity

Over the past decade, it has been proposed theoretically and confirmed experimentally that long-range spin triplet (LRT) superconductivity can be generated in ferromagnet-superconductor hybrids either by the presence of spin textures (ST-LRT) or thanks to spin-orbit coupling (SOC-LRT). Nevertheless, there has been no theoretical or experimental investigation to date suggesting that both contributions could simultaneously exist within an experimental system. To disentangle these contributions, we present a comprehensive study of superconducting quasiparticle interference effects taking place inside a ferromagnetic layer interfacing a superconductor, through the investigation of above-gap conductance anomalies (CAs) related to MacMillan-Rowell resonances. The bias dependence of the CAs has been studied under a wide range of in-plane (IP) and out-of-plane (OOP) magnetic fields in two types of epitaxial, V/MgO/Fe-based ferromagnet-superconductor junctions with interfacial spin-orbit coupling. We observe an anisotropy of the CAs amplitude under small IP and OOP magnetic fields while remaining weakly affected by high fields, and implement micromagnetic simulations to help us distinguish between the ST-LRT and SOC-LRT contributions. Our findings suggest that further exploration of Fabry-Pérot type interference effects in electron transport could yield valuable insights into the hybridization between superconductors and ferromagnets induced by spin-orbit coupling and spin textures.

cond-mat.supr-con↗

arXiv2024

Dumbo-NG: Fast Asynchronous BFT Consensus with Throughput-Oblivious Latency

Despite recent progresses of practical asynchronous Byzantine fault tolerant (BFT) consensus, the state-of-the-art designs still suffer from suboptimal performance. Particularly, to obtain maximum throughput, most existing protocols with guaranteed linear amortized communication complexity require each participating node to broadcast a huge batch of transactions, which dramatically sacrifices latency. Worse still, the f slowest nodes' broadcasts might never be agreed to output and thus can be censored (where f is the number of faults). Implementable mitigation to the threat either uses computationally costly threshold encryption or incurs communication blow-up, thus causing further efficiency issues. We present Dumbo-NG, a novel asynchronous BFT consensus (atomic broadcast) to solve the remaining practical issues. Its technical core is a non-trivial direct reduction from asynchronous atomic broadcast to multi-valued validated Byzantine agreement (MVBA) with quality property. Most interestingly, the new protocol structure empowers completely concurrent execution of transaction dissemination and asynchronous agreement. This brings about two benefits: (i) the throughput-latency tension is resolved to approach peak throughput with minimal increase in latency; (ii) the transactions broadcasted by any honest node can be agreed to output, thus conquering the censorship threat with no extra cost. We implement Dumbo-NG and compare it to the state-of-the-art asynchronous BFT with guaranteed censorship resilience including Dumbo (CCS'20) and Speeding-Dumbo (NDSS'22). We also apply the techniques from Speeding-Dumbo to DispersedLedger (NSDI'22) and obtain an improved variant of DispersedLedger called sDumbo-DL for comprehensive comparison. Extensive experiments reveal: Dumbo-NG realizes better peak throughput performance and its latency can almost remain stable when throughput grows.

cs.CR↗

arXiv2023

Square Moiré Superlattices in Twisted Two-Dimensional Halide Perovskites

Moiré superlattices have emerged as a new platform for studying strongly correlated quantum phenomena, but these systems have been largely limited to van der Waals layer two-dimensional (2D) materials. Here we introduce moiré superlattices leveraging ultra-thin, ligand-free halide perovskites, facilitated by ionic interactions. Square moiré superlattices with varying periodic lengths are clearly visualized through high-resolution transmission electron microscopy. Twist-angle-dependent transient photoluminescence microscopy and electrical characterizations indicate the emergence of localized bright excitons and trapped charge carriers near a twist angle of ~10°. The localized excitons are accompanied by enhanced exciton emission, attributed to an increased oscillator strength by a theoretically forecasted flat band. This work illustrates the potential of extended ionic interaction in realizing moiré physics at room temperature, broadening the horizon for future investigations.

cond-mat.mtrl-sci↗

arXiv2023

AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference

In real-world scenarios, although data entities may possess inherent relationships, the specific graph illustrating their connections might not be directly accessible. Latent graph inference addresses this issue by enabling Graph Neural Networks (GNNs) to operate on point cloud data, dynamically learning the necessary graph structure. These graphs are often derived from a latent embedding space, which can be modeled using Euclidean, hyperbolic, spherical, or product spaces. However, currently, there is no principled differentiable method for determining the optimal embedding space. In this work, we introduce the Attentional Multi-Embedding Selection (AMES) framework, a differentiable method for selecting the best embedding space for latent graph inference through backpropagation, considering a downstream task. Our framework consistently achieves comparable or superior results compared to previous methods for latent graph inference across five benchmark datasets. Importantly, our approach eliminates the need for conducting multiple experiments to identify the optimal embedding space. Furthermore, we explore interpretability techniques that track the gradient contributions of different latent graphs, shedding light on how our attention-based, fully differentiable approach learns to choose the appropriate latent space. In line with previous works, our experiments emphasize the advantages of hyperbolic spaces in enhancing performance. More importantly, our interpretability framework provides a general approach for quantitatively comparing embedding spaces across different tasks based on their contributions, a dimension that has been overlooked in previous literature on latent graph inference.

cs.LG↗