arXiv subjects

Xinxin Wang

Publications and source records attributed to Xinxin Wang.

At least 19 recordsLinked to original sources

arXiv2026

Simultaneous estimation of relative phase and coherence in astronomical interferometry

Astronomical interferometry is a cornerstone technique for high-resolution stellar imaging and observational astrophysics, extracting spatial information from the coherence of light collected by separated telescopes. Since the degree of coherence is complex, a genuine imaging task requires the joint recovery of the modulus and the relative phase, instead of independent singleparameter estimations. We investigate the simultaneous estimation of both parameters based on direct interferometry scheme and continuou-svariable quantum teleportation scheme. We find that in simultaneous estimation the direct interferometry scheme consistently yields a lower quantum Cram\'er-Rao bound, demonstrating its superiority over the continuous-variable quantum teleportation scheme. Furthermore, we establish the conditions under which the classical Cram\'er-Rao bound for Gaussian measurements saturates the quantum Cram\'er-Rao bound, identifying heterodyne detection as a near-optimal measurement scheme in the large mean photon number regime. An analysis of transmission loss reveals that the direct interferometry scheme yields superior precision in the short-baseline regime, whereas the continuous-variable quantum teleportation scheme outperforms it at longer baselines.

quant-ph↗

arXiv2026

Atomically resolved intrinsic superconducting gap in (La,Pr)3Ni2O7 films

Ruddlesden-Popper bilayer nickelates provide an emerging platform for studying high-temperature superconductivity, yet the superconducting pairing symmetry remains under debate. Here, we use atomic-resolution scanning tunnelling microscopy and spectroscopy to investigate superconducting 1.5-unit-cell (La,Pr)3Ni2O7 films grown on SrLaAlO4. A cryogenic ultrahigh-vacuum (UHV) sample transfer preserves an ordered sqrt(2) * sqrt(2) surface and yields reproducible U-shaped spectra with two gap scales of ~14 and ~20 meV and extended flat zero-conductance bottoms. By contrast, samples exposed for a longer time in UHV without cooling during transfer show V-shaped spectra despite retaining the surface reconstruction and a transport superconducting transition onset above 40 K. Wide-energy-range spectra indicate that oxygen loss can mix density-wave-related spectral weight. Our measurements provide an atomic-scale observation of the intrinsic nodeless superconducting gap in bilayer nickelate ultrathin films.

cond-mat.supr-con↗

arXiv2026

Heterogeneous Memory Design Exploration for AI Accelerators with a Gain Cell Memory Compiler

As memory increasingly dominates system cost and energy, heterogeneous on-chip memory systems that combine technologies with complementary characteristics are becoming essential. Gain Cell RAM (GCRAM) offers higher density, lower power, and tunable retention, expanding the design space beyond conventional SRAM. To this end, we create an OpenGCRAM compiler supporting both SRAM and GCRAM. It generates macro-level designs and layouts for commercial CMOS processes and characterizes area, delay, and power across user-defined configurations. The tool enables systematic identification of optimal heterogeneous memory configurations for AI tasks under specified performance metrics.

cs.AR↗

arXiv2025

OpenGCRAM: An Open-Source Gain Cell Compiler Enabling Design-Space Exploration for AI Workloads

Gain Cell memory (GCRAM) offers higher density and lower power than SRAM, making it a promising candidate for on-chip memory in domain-specific accelerators. To support workloads with varying traffic and lifetime metrics, GCRAM also offers high bandwidth, ultra low leakage power and a wide range of retention times, which can be adjusted through transistor design (like threshold voltage and channel material) and on-the-fly by changing the operating voltage. However, designing and optimizing GCRAM sub-systems can be time-consuming. In this paper, we present OpenGCRAM, an open-source GCRAM compiler capable of generating GCRAM bank circuit designs and DRC- and LVS-clean layouts for commercially available foundry CMOS, while also providing area, delay, and power simulations based on user-specified configurations (e.g., word size and number of words). OpenGCRAM enables fast, accurate, customizable, and optimized GCRAM block generation, reduces design time, ensure process compliance, and delivers performance-tailored memory blocks that meet diverse application requirements.

cs.AR↗

arXiv2025

Dynamic Contrastive Learning for Hierarchical Retrieval: A Case Study of Distance-Aware Cross-View Geo-Localization

Existing deep learning-based cross-view geo-localization methods primarily focus on improving the accuracy of cross-domain image matching, rather than enabling models to comprehensively capture contextual information around the target and minimize the cost of localization errors. To support systematic research into this Distance-Aware Cross-View Geo-Localization (DACVGL) problem, we construct Distance-Aware Campus (DA-Campus), the first benchmark that pairs multi-view imagery with precise distance annotations across three spatial resolutions. Based on DA-Campus, we formulate DACVGL as a hierarchical retrieval problem across different domains. Our study further reveals that, due to the inherent complexity of spatial relationships among buildings, this problem can only be addressed via a contrastive learning paradigm, rather than conventional metric learning. To tackle this challenge, we propose Dynamic Contrastive Learning (DyCL), a novel framework that progressively aligns feature representations according to hierarchical spatial margins. Extensive experiments demonstrate that DyCL is highly complementary to existing multi-scale metric learning methods and yields substantial improvements in both hierarchical retrieval performance and overall cross-view geo-localization accuracy. Our code and benchmark are publicly available at https://github.com/anocodetest1/DyCL.

cs.CV↗

arXiv2025

GainSight: A Unified Framework for Data Lifetime Profiling and Heterogeneous Memory Composition

As AI workloads drive increasing memory requirements, domain-specific accelerators need higher-density on-chip memory beyond what current SRAM scaling trends can provide. Simultaneously, the vast amounts of short-lived data in these workloads make SRAM overprovisioned in retention capability. To address this mismatch, we propose a wholesale shift from uniform SRAM arrays to heterogeneous on-chip memory, incorporating denser short-term RAM (StRAM) devices whose limited retention times align with transient data lifetimes. To facilitate this shift, we introduce GainSight, the first comprehensive, open-source framework that aligns dynamic, fine-grained workload lifetime profiles with memory device characteristics to enable generation of optimal StRAM memory compositions. GainSight combines retargetable profiling backends with an architecture-agnostic analytical frontend. The various backends capture cycle-accurate data lifetimes, while the frontend correlates workload patterns with StRAM retention properties to generate optimal memory compositions and project performance. GainSight elevates data lifetime to a first-class design consideration for next-generation AI accelerators, enabling systematic exploitation of data transience for improved on-chip memory density and efficiency. Applying GainSight to MLPerf Inference and PolyBench workloads reveals that 64.3% of first-level GPU cache accesses and 79.01% of systolic array scratchpad accesses exhibit sub-microsecond lifetimes suitable for high-density StRAM, with optimal heterogeneous on-chip memory compositions achieving up to 3x active energy and 4x area reductions compared to uniform SRAM hierarchies. To facilitate adoption and further research, GainSight is open-sourced at https://gainsight.stanford.edu/.

cs.AR↗

arXiv2025

Low-power Spike-based Wearable Analytics on RRAM Crossbars

This work introduces a spike-based wearable analytics system utilizing Spiking Neural Networks (SNNs) deployed on an In-memory Computing engine based on RRAM crossbars, which are known for their compactness and energy-efficiency. Given the hardware constraints and noise characteristics of the underlying RRAM crossbars, we propose online adaptation of pre-trained SNNs in real-time using Direct Feedback Alignment (DFA) against traditional backpropagation (BP). Direct Feedback Alignment (DFA) learning, that allows layer-parallel gradient computations, acts as a fast, energy & area-efficient method for online adaptation of SNNs on RRAM crossbars, unleashing better algorithmic performance against those adapted using BP. Through extensive simulations using our in-house hardware evaluation engine called DFA_Sim, we find that DFA achieves upto 64.1% lower energy consumption, 10.1% lower area overhead, and a 2.1x reduction in latency compared to BP, while delivering upto 7.55% higher inference accuracy on human activity recognition (HAR) tasks.

cs.ET↗

arXiv2025

Highly Efficient Rotation-Invariant Spectral Embedding for Scalable Incomplete Multi-View Clustering

Incomplete multi-view clustering presents significant challenges due to missing views. Although many existing graph-based methods aim to recover missing instances or complete similarity matrices with promising results, they still face several limitations: (1) Recovered data may be unsuitable for spectral clustering, as these methods often ignore guidance from spectral analysis; (2) Complex optimization processes require high computational burden, hindering scalability to large-scale problems; (3) Most methods do not address the rotational mismatch problem in spectral embeddings. To address these issues, we propose a highly efficient rotation-invariant spectral embedding (RISE) method for scalable incomplete multi-view clustering. RISE learns view-specific embeddings from incomplete bipartite graphs to capture the complementary information. Meanwhile, a complete consensus representation with second-order rotation-invariant property is recovered from these incomplete embeddings in a unified model. Moreover, we design a fast alternating optimization algorithm with linear complexity and promising convergence to solve the proposed formulation. Extensive experiments on multiple datasets demonstrate the effectiveness, scalability, and efficiency of RISE compared to the state-of-the-art methods.

cs.LG↗

arXiv2025

SF6 streamer breakdown induced by floating linear metal particles: Following streamers and side streamers

The electrical breakdown of SF6 in the presence of floating metal particles is facilitated by two key factors: the role of floating metal particles and the nonlinear breakdown behavior of high-pressure SF6. However, the microscopic transient processes remain unclear, motivating this paper. Using 2D fluid models, we investigate SF6 streamer breakdown induced by a floating linear metal particle under negative applied voltage. First, We identify a characteristic double-end streamer inception in the combined gap. Then, we propose the following streamer (FS) mechanism to explain the metal particle's role. Two following streamers, FS1 and FS2, arise from the interaction between space charge and metal particle. FS1 facilitates breakdown via the negative space charge field generated by its head. FS2 facilitates breakdown by merging with FS1, accelerating its propagation and enhancing the electric field at the primary streamer head. Finally, we propose the side streamer (SS) mechanism to explain the nonlinear breakdown behavior of high-pressure SF6. The SS is identified as a new forward ionization wave that develops along the sides of the primary streamer, due to photoionization-driven negative ion accumulation. SS facilitates breakdown by merging with the primary streamer, increasing negative space charge and leading to three distinct propagation modes. Higher pressure increases the production rate of negative ions along the streamer sides, making SS more likely to form. Under overvoltage, the facilitating effect of SS diminishes as the background field (E/N)b strengthens, disappearing when (E/N)b exceeds 245 Td. This study provides new insights into the SF6 streamer breakdown mechanisms induced by floating metal particles and offers theoretical references for further investigation on the quantitative characterization.

physics.plasm-ph↗

arXiv2024

Spatial Fluctuation of the Electric Field within SF6 Streamer Channel in Highly Non-Uniform Fields: Phenomenon, Validation, and Mechanism

The electric field within the streamer channel is a critical parameter in the calculation model for the nonlinear breakdown voltage of SF6, motivating the research presented in this paper. By using a 2D fluid model, we investigate the microscopic characteristics of the SF6 streamer channel in highly non-uniform fields and uncover a previously unexplained coherent structure: the spatial fluctuation of the electric field (SFEF). We validate the physical validity of SFEF by modifying model parameters that could potentially introduce non-physical effects. Further comparative analysis reveals that SFEF is driven by an ion-conducting channel formed due to the strong electronegativity of SF6. This ion-conducting channel exhibits local characteristics, which fundamentally arise from the slow response of charged species to local charge relaxation. We identify that some charge separation originates from the accumulation of negative ions at the rear edge of the streamer head due to strong electric field shielding in this region. As the streamer propagates, charge separation is continuously generated and passively carried into the streamer channel, ultimately forming the SFEF. Finally, we confirm that SFEF does not occur in uniform fields, indicating that it is a phenomenon exclusive to highly non-uniform fields. These findings provide a deep insight into the electric field within the SF6 streamer channel and offer a potential avenue for further investigation into the mechanisms of SF6 nonlinear breakdown voltage.

physics.plasm-ph↗

arXiv2024

Privacy-preserving Universal Adversarial Defense for Black-box Models

Deep neural networks (DNNs) are increasingly used in critical applications such as identity authentication and autonomous driving, where robustness against adversarial attacks is crucial. These attacks can exploit minor perturbations to cause significant prediction errors, making it essential to enhance the resilience of DNNs. Traditional defense methods often rely on access to detailed model information, which raises privacy concerns, as model owners may be reluctant to share such data. In contrast, existing black-box defense methods fail to offer a universal defense against various types of adversarial attacks. To address these challenges, we introduce DUCD, a universal black-box defense method that does not require access to the target model's parameters or architecture. Our approach involves distilling the target model by querying it with data, creating a white-box surrogate while preserving data privacy. We further enhance this surrogate model using a certified defense based on randomized smoothing and optimized noise selection, enabling robust defense against a broad range of adversarial attacks. Comparative evaluations between the certified defenses of the surrogate and target models demonstrate the effectiveness of our approach. Experiments on multiple image classification datasets show that DUCD not only outperforms existing black-box defenses but also matches the accuracy of white-box defenses, all while enhancing data privacy and reducing the success rate of membership inference attacks.

cs.LG↗

arXiv2024

Diverse Generation while Maintaining Semantic Coordination: A Diffusion-Based Data Augmentation Method for Object Detection

Recent studies emphasize the crucial role of data augmentation in enhancing the performance of object detection models. However,existing methodologies often struggle to effectively harmonize dataset diversity with semantic coordination.To bridge this gap, we introduce an innovative augmentation technique leveraging pre-trained conditional diffusion models to mediate this balance. Our approach encompasses the development of a Category Affinity Matrix, meticulously designed to enhance dataset diversity, and a Surrounding Region Alignment strategy, which ensures the preservation of semantic coordination in the augmented images. Extensive experimental evaluations confirm the efficacy of our method in enriching dataset diversity while seamlessly maintaining semantic coordination. Our method yields substantial average improvements of +1.4AP, +0.9AP, and +3.4AP over existing alternatives on three distinct object detection models, respectively.

cs.CV↗

arXiv2024

Microscopic characteristics of SF6 partial discharge induced by a floating linear metal particle

Direct current (DC) gas insulated transmission lines (GILs) have been widely used in power transmission, but might be threatened by partial discharge due to the presence of floating impurities (e.g., dust and metal particles) inside the sealed chamber. In this letter, by using a 2D fluid model we characterize the microscopic properties of the partial discharge induced by a floating linear metal particle in SF6 (both the discharge propagation and interaction between space charge and metal particle) under negative high voltage direct current (HVDC) conditions. Due to the strong electronegativity of SF6, the spatiotemporal distributions of the charged species (electrons, positive and negative ions), space charge, and reduced electric field are rather different from those in air. Notably, a negative ion region is observed around the top tip of the metal particle, and it plays an important role in the generation and propagation of primary and secondary streamers in SF6, which may lead to severe motion characteristics of the particle and aliasing of partial discharge signals. Additionally, we analyze the charging process and electric force reversal phenomenon, which may provide a more precise understanding of the underlying mechanisms of the firefly motion previously reported for DC GILs.

physics.plasm-ph↗

arXiv2024

Federated learning in food research

Research in the food domain is at times limited due to data sharing obstacles, such as data ownership, privacy requirements, and regulations. While important, these obstacles can restrict data-driven methods such as machine learning. Federated learning, the approach of training models on locally kept data and only sharing the learned parameters, is a potential technique to alleviate data sharing obstacles. This systematic review investigates the use of federated learning within the food domain, structures included papers in a federated learning framework, highlights knowledge gaps, and discusses potential applications. A total of 41 papers were included in the review. The current applications include solutions to water and milk quality assessment, cybersecurity of water processing, pesticide residue risk analysis, weed detection, and fraud detection, focusing on centralized horizontal federated learning. One of the gaps found was the lack of vertical or transfer federated learning and decentralized architectures.

cs.LG↗

arXiv2023

Host Galaxy Dispersion Measure of Fast Radio Burst

Fast radio bursts are a class of transient radio sources that are thought to originate from extragalactic sources since their dispersion measure greatly exceeds the highest dispersion measure that the Milky Way interstellar medium can provide. Host Galaxies of twenty-two fast radio bursts have already been identified. In this paper, the dispersion measurement of these fast radio bursts produced by the Milky Way interstellar medium, and the intergalactic medium is obtained through known physical models to yield the host galaxy dispersion measure. It is observed that the host galaxy dispersion measure increases with its redshift value. We also obtained that the host galaxy dispersion measure has different distribution between repeaters and non-repeaters. It is noted that the reason for the divergence of the host galaxy dispersion measures should be accounted for by the difference in their local environment.

astro-ph.HE↗

arXiv2023

Training Spiking Neural Networks Using Lessons From Deep Learning

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This paper serves as a tutorial and perspective showing how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks. We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to spiking neural networks (SNNs); the subtle link between temporal backpropagation and spike timing dependent plasticity, and how deep learning might move towards biologically plausible online learning. Some ideas are well accepted and commonly used amongst the neuromorphic engineering community, while others are presented or justified for the first time here. The fields of deep learning and spiking neural networks evolve very rapidly. We endeavour to treat this document as a 'dynamic' manuscript that will continue to be updated as the common practices in training SNNs also change. A series of companion interactive tutorials complementary to this paper using our Python package, snnTorch, are also made available. See https://snntorch.readthedocs.io/en/latest/tutorials/index.html .

cs.NE↗

arXiv2023

Differential Game Analysis for Cooperation Models in Automotive Supply Chain under Low-Carbon Emission Reduction Policies

In the context of reducing carbon emissions in the automotive supply chain, collaboration between vehicle manufacturers and retailers has proven to be an effective measure for enhancing carbon emission reduction within the enterprise. This study aims to evaluate the effectiveness of such collaboration by constructing a differential game model that incorporates carbon trading and consumer preferences for low-carbon products. The model examines the decision-making process of an automotive supply chain comprising a vehicle manufacturer and multiple retailers. By utilizing the Hamilton-Jacobi-Bellman equation, we analyze the equilibrium strategies of the participants under both a decentralized model and a Stackelberg leader-follower game model. In the decentralized model, the vehicle manufacturer optimizes its carbon emission reduction efforts, while each retailer independently determines its low-carbon promotion efforts and vehicle retail price. In the Stackelberg leader-follower game model, the vehicle manufacturer cooperates with the retailers by offering them a subsidy. Consequently, the manufacturer plays as the leader, making decisions on carbon emission reduction efforts and the subsidy rate, while the retailers, as followers, compute their promotion efforts and retail prices accordingly. Through theoretical analysis and numerical experiments considering the manufacturer's and retailers' efforts, the low-carbon reputation of vehicles, and the overall system profits under both models, we conclude that compared to the decentralized model, where each party pursues individual profits, the collaboration in the Stackelberg game yields greater benefits for both parties. Furthermore, this collaborative approach promotes the long-term development of the automotive supply chain.

cs.GT↗

arXiv2023

PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators

Analog compute-in-memory (CIM) systems are promising for deep neural network (DNN) inference acceleration due to their energy efficiency and high throughput. However, as the use of DNNs expands, protecting user input privacy has become increasingly important. In this paper, we identify a potential security vulnerability wherein an adversary can reconstruct the user's private input data from a power side-channel attack, under proper data acquisition and pre-processing, even without knowledge of the DNN model. We further demonstrate a machine learning-based attack approach using a generative adversarial network (GAN) to enhance the data reconstruction. Our results show that the attack methodology is effective in reconstructing user inputs from analog CIM accelerator power leakage, even at large noise levels and after countermeasures are applied. Specifically, we demonstrate the efficacy of our approach on an example of U-Net inference chip for brain tumor detection, and show the original magnetic resonance imaging (MRI) medical images can be successfully reconstructed even at a noise-level of 20% standard deviation of the maximum power signal value. Our study highlights a potential security vulnerability in analog CIM accelerators and raises awareness of using GAN to breach user privacy in such systems.

cs.CR↗