arXiv subjects

Yanan Guo

Publications and source records attributed to Yanan Guo.

18 recordsLinked to original sources

arXiv2026

Fuzz'EMup: Leveraging EM Side-Channel Emanation to Guide Black-Box Embedded Firmware Fuzzing

As IoT and embedded devices proliferate across various domains, securing their firmware has become critical. Fuzzing offers a systematic approach to uncovering vulnerabilities in firmware, and coverage feedback can improve its effectiveness by guiding exploration. However, many devices make coverage information impossible to obtain by preventing firmware extraction, instrumentation, or accurate emulation; in such cases, testers are left with only inefficient black-box fuzzing. In this paper, we present an approach that leverages electromagnetic (EM) side-channel emanations to guide firmware fuzzing in purely black-box settings. However, turning raw EM measurements into reliable guidance is challenging: EM traces are noisy, and timing jitter causes corresponding features in different traces to shift in time. We address these challenges by combining frequency band selection based on the activity-to-idle signal contrast with dynamic time warping to align per-input traces and detect sustained divergence, while maintaining scalability by organizing executions in a tree structure based on their divergence times. We evaluate our approach on four real firmware targets and demonstrate that EM-derived feedback enhances path exploration, yielding higher code coverage than unguided fuzzing.

cs.CR↗

arXiv2026

Reliable Associative Lookup in Content-Addressable Memory

Content Addressable Memory (CAM) is an important memory paradigm, which performs fast search by comparing an input query against all stored entries in parallel, achieving $O(1)$ lookup complexity. CAM is typically built upon conventional memory technologies, such as SRAM and Non-Volatile Memory (NVM). Accordingly, CAM can also be subject to the reliability challenges of these underlying technologies. In traditional memory systems, protection codes play a critical role in ensuring reliability and have been extensively studied. However, protection codes for CAM have remained largely unexplored. This paper takes an initial step toward addressing this longstanding gap by introducing a non-traditional code design.

cs.AR↗

arXiv2026

GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks

GPU memory errors are a critical threat to deep learning (DL) frameworks, leading to crashes or even security issues. We introduce GPU-Fuzz, a fuzzer locating these issues efficiently by modeling operator parameters as formal constraints. GPU-Fuzz utilizes a constraint solver to generate test cases that systematically probe error-prone boundary conditions in GPU kernels. Applied to PyTorch, TensorFlow, and PaddlePaddle, we uncovered 13 unknown bugs, demonstrating the effectiveness of GPU-Fuzz in finding memory errors.

cs.CR↗

arXiv2026

Overcoming sensitivity-bandwidth trade-off in mid-infrared spectroscopy by a microresonator-anchored swept laser

Optical frequency combs have revolutionized high-precision spectroscopy, yet an intrinsic trade-off between spectroscopic signal-to-noise ratio (sSNR) and measurement bandwidth ($B$) fundamentally constrains sensitive, broadband measurements. While broadband swept lasers offer a potential solution, generating broadband, ultrafast and linearly sweeping lasers with a narrow linewidth remains a significant challenge, particularly in the fingerprint mid-infrared (mid-IR) band. Here we overcome this limitation by using a microresonator-anchored ultrafast sweeping Fourier domain mode-locked (FDML) laser for mid-IR spectroscopy. We introduce a dual-microresonator-anchor approach: a microcomb provides frequency calibration and a high-Q microresonator resolves the instantaneous FDML lasing lineshape. The strategy enables accurate correction of the FDML laser's sweep nonlinearity and broad linewidth in the near-IR, allowing the FDML laser to function as a high-fidelity mid-IR light via difference frequency generation. The system achieves a record sSNR$\times$$B$ of 1.3$\times$10$^5$ THz$\cdot \sqrt{\rm Hz}$ and methane sensing precision of 9 ppb$\cdot$m$\cdot$$\sqrt{\rm s}$, while retaining GHz resolution to distinguish methane isotope. We further demonstrate broadband, coherent swept laser phase spectroscopy in the mid-IR, tolerating losses up to 78 dB. This work leverages advances in integrated photonics to overcome the fundamental limitations of precision spectroscopy, paving the way for next-generation, broadband, and ultra-sensitive mid-IR spectroscopic sensing systems.

physics.optics↗

arXiv2026

High-Q AlN microresonators for nonlinear near-infrared and near-visible photonics

High Q-factors of microresonators are crucial for nonlinear integrated photonics, as many nonlinear dynamics have quadratic or even cubic dependence on Q-factors. The unique material properties make AlN microresonators invaluable for microcomb generation, Raman lasing and visible integrated photonics. However, the loss level of AlN falls behind other integrated platforms. By optimizing the fabrication, we demonstrate record Q-factors of 5.4$\times$10$^6$ and 2.2$\times$10$^6$ for AlN microresonators in the near-infrared and near-visible, respectively. Polarized-mode-interaction was used to create anomalous dispersion to support bright AlN Dirac solitons. Measurement of polarization-dependent spectra reveals the polarization hybridization of the Dirac soliton. In a microresonator with normal dispersion, Raman assisted four-wave-mixing (RFWM) was observed to initiate platicon formation, adding an approach to generate normal dispersion microcombs. A design of width-varying waveguides was used to ensure both efficient coupling and high Q-factor for racetrack microresonators at 780 nm. The microresonator was pumped to generate near-visble Raman laser at 820 nm with a fundamental linewidth narrower than 220 Hz. Our work unlocks new opportunities for integrated AlN photonics by improving Q-factors and uncovering nonlinear dynamics in AlN microresonators.

physics.optics↗

arXiv2025

SCREME: A Scalable Framework for Resilient Memory Design

The continuing advancement of memory technology has not only fueled a surge in performance, but also substantially exacerbate reliability challenges. Traditional solutions have primarily focused on improving the efficiency of protection schemes, i.e., Error Correction Codes (ECC), under the assumption that allocating additional memory space for parity data is always expensive and therefore not a scalable solution. We break the stereotype by proposing an orthogonal approach that provides additional, cost-effective memory space for resilient memory design. In particular, we recognize that ECC chips (used for parity storage) do not necessarily require the same performance level as regular data chips. This offers two-fold benefits: First, the bandwidth originally provisioned for a regular-performance ECC chip can instead be used to accommodate multiple low-performance chips. Second, the cost of ECC chips can be effectively reduced, as lower performance often correlates with lower expense. In addition, we observe that server-class memory chips are often provisioned with ample, yet underutilized I/O resources. This further offers the opportunity to repurpose these resources to enable flexible on-DIMM interconnections. Based on the above two insights, we finally propose SCREME, a scalable memory framework leverages cost-effective, albeit slower, chips -- naturally produced during rapid technology evolution -- to meet the growing reliability demands driven by this evolution.

cs.AR↗

arXiv2025

Multi-view Normal and Distance Guidance Gaussian Splatting for Surface Reconstruction

3D Gaussian Splatting (3DGS) achieves remarkable results in the field of surface reconstruction. However, when Gaussian normal vectors are aligned within the single-view projection plane, while the geometry appears reasonable in the current view, biases may emerge upon switching to nearby views. To address the distance and global matching challenges in multi-view scenes, we design multi-view normal and distance-guided Gaussian splatting. This method achieves geometric depth unification and high-accuracy reconstruction by constraining nearby depth maps and aligning 3D normals. Specifically, for the reconstruction of small indoor and outdoor scenes, we propose a multi-view distance reprojection regularization module that achieves multi-view Gaussian alignment by computing the distance loss between two nearby views and the same Gaussian surface. Additionally, we develop a multi-view normal enhancement module, which ensures consistency across views by matching the normals of pixel points in nearby views and calculating the loss. Extensive experimental results demonstrate that our method outperforms the baseline in both quantitative and qualitative evaluations, significantly enhancing the surface reconstruction capability of 3DGS. Our code will be made publicly available at (https://github.com/Bistu3DV/MND-GS/).

cs.CV↗

arXiv2025

FiLA-Video: Spatio-Temporal Compression for Fine-Grained Long Video Understanding

Recent advancements in video understanding within visual large language models (VLLMs) have led to notable progress. However, the complexity of video data and contextual processing limitations still hinder long-video comprehension. A common approach is video feature compression to reduce token input to large language models, yet many methods either fail to prioritize essential features, leading to redundant inter-frame information, or introduce computationally expensive modules.To address these issues, we propose FiLA(Fine-grained Vision Language Model)-Video, a novel framework that leverages a lightweight dynamic-weight multi-frame fusion strategy, which adaptively integrates multiple frames into a single representation while preserving key video information and reducing computational costs. To enhance frame selection for fusion, we introduce a keyframe selection strategy, effectively identifying informative frames from a larger pool for improved summarization. Additionally, we present a simple yet effective long-video training data generation strategy, boosting model performance without extensive manual annotation. Experimental results demonstrate that FiLA-Video achieves superior efficiency and accuracy in long-video comprehension compared to existing methods.

cs.CV↗

arXiv2025

3D Heterogeneous Integration of Silicon Nitride and Aluminum Nitride on Sapphire toward Ultra-wideband Photonics Integrated Circuits

Extending two-dimensional photonic integrated circuits (PICs) to three-dimensional (3D) configurations promises great potential for scaling up integration, enhancing functionality, and improving performance of PICs. Silicon-based 3D PICs have made substantial progress due to CMOS compatibility. However, the narrow bandgap of silicon (1.1 eV) limits their use in short-wavelength applications, such as chemical and biological sensing, underwater optical communications, and optical atomic clocks. In this work, we developed a 3D photonics platform by heterogeneously integrating silicon nitride (SiN) and aluminum nitride (AlN) PICs on sapphire (Al$_2$O$_3$). The broadband transparency of these materials allow our platform to operate over a multi-octave wavelength ranging from ultraviolet to infrared. Leveraging this platform, we demonstrated efficient optical nonlinearity in an AlN microcavity, low-loss and tunable SiN waveguide-based optical components, and optical linking between AlN and SiN PICs layers in the visible and near-infrared spectrum, hinting at potential applications in integrated quantum systems. Our work presents an ultra-wideband 3D PICs platform, providing new opportunities for broadband and short-wavelength applications of PICs.

physics.optics↗

arXiv2025

Self-injection locking dynamics with Raman actions in AlN microresonators

Self-injection locking (SIL) of semiconductor lasers to on-chip microcavities enables significant laser noise purification and diverse nonlinear optical actions. Realizing nonlinear SIL in new material platforms is essential for advancing photonic integrated circuits. Here, we demonstrate nonlinear SIL in AlN microcavities that generates stimulated Raman lasers (SRLs) and microcombs. We achieve SRL emission with an output power exceeding 10 mW and a fundamental linewidth below 70 Hz in the 1750 nm band. The Kerr effect further mediates stimulated emissions at the 2nd-Stokes and anti-Stokes frequencies. Multi-time-scale thermal relaxations during turnkey SIL enable GHz-level frequency sweeps of the SRL and pump. Raman actions also render a Stokes platicon microcomb state with co-emission in the pump and Stokes bands. Hybrid-integrated crystalline microresonators can be a versatile platform to investigate nonlinear photon-phonon interactions.

physics.optics↗

arXiv2024

FILA: Fine-Grained Vision Language Models

Recently, there has been growing interest in the capability of multimodal large language models (MLLMs) to process high-resolution images. A common approach currently involves dynamically cropping the original high-resolution image into smaller sub-images, which are then fed into a vision encoder that was pre-trained on lower-resolution images. However, this cropping approach often truncates objects and connected areas in the original image, causing semantic breaks. To address this limitation, we introduce HyViLM, designed to process images of any resolution while retaining the overall context during encoding. Specifically, we: (i) Design a new visual encoder called Hybrid Encoder that not only encodes individual sub-images but also interacts with detailed global visual features, significantly improving the model's ability to encode high-resolution images. (ii) Propose an optimal feature fusion strategy for the dynamic cropping approach, effectively leveraging information from different layers of the vision encoder. Compared with the state-of-the-art MLLMs under the same setting, our HyViLM outperforms existing MLLMs in nine out of ten tasks. Specifically, HyViLM achieves a 9.6% improvement in performance on the TextVQA task and a 6.9% enhancement on the DocVQA task.

cs.CV↗

arXiv2024

Hybrid bundle-adjusting 3D Gaussians for view consistent rendering with pose optimization

Novel view synthesis has made significant progress in the field of 3D computer vision. However, the rendering of view-consistent novel views from imperfect camera poses remains challenging. In this paper, we introduce a hybrid bundle-adjusting 3D Gaussians model that enables view-consistent rendering with pose optimization. This model jointly extract image-based and neural 3D representations to simultaneously generate view-consistent images and camera poses within forward-facing scenes. The effective of our model is demonstrated through extensive experiments conducted on both real and synthetic datasets. These experiments clearly illustrate that our model can effectively optimize neural scene representations while simultaneously resolving significant camera pose misalignments. The source code is available at https://github.com/Bistu3DV/hybridBA.

cs.CV↗

arXiv2023

QRCC: Evaluating Large Quantum Circuits on Small Quantum Computers through Integrated Qubit Reuse and Circuit Cutting

Quantum computing has recently emerged as a promising computing paradigm for many application domains. However, the size of quantum circuits that can be run with high fidelity is constrained by the limited quantity and quality of physical qubits. Recently proposed schemes, such as wire cutting and qubit reuse, mitigate the problem but produce sub-optimal results as they address the problem individually. In addition, gate cutting, an alternative circuit-cutting strategy that is suitable for circuits computing expectation values, has not been fully explored in the field. In this paper, we propose QRCC, an integrated approach that exploits qubit reuse and circuit-cutting (including wire cutting and gate cutting) to run large circuits on small quantum computers. Circuit-cutting techniques introduce non-negligible post-processing overhead, which increases exponentially with the number of cuts. QRCC exploits qubit reuse to find better cutting solutions to minimize the cut numbers and thus the post-processing overhead. Our evaluation results show that on average we reduce the number of cuts by 29% and additional reduction when considering gate cuts.

quant-ph↗

arXiv2022

Adversarial Prefetch: New Cross-Core Cache Side Channel Attacks

Modern x86 processors have many prefetch instructions that can be used by programmers to boost performance. However, these instructions may also cause security problems. In particular, we found that on Intel processors, there are two security flaws in the implementation of PREFETCHW, an instruction for accelerating future writes. First, this instruction can execute on data with read-only permission. Second, the execution time of this instruction leaks the current coherence state of the target data. Based on these two design issues, we build two cross-core private cache attacks that work with both inclusive and non-inclusive LLCs, named Prefetch+Reload and Prefetch+Prefetch. We demonstrate the significance of our attacks in different scenarios. First, in the covert channel case, Prefetch+Reload and Prefetch+Prefetch achieve 782 KB/s and 822 KB/s channel capacities, when using only one shared cache line between the sender and receiver, the largest-to-date single-line capacities for CPU cache covert channels. Further, in the side channel case, our attacks can monitor the access pattern of the victim on the same processor, with almost zero error rate. We show that they can be used to leak private information of real-world applications such as cryptographic keys. Finally, our attacks can be used in transient execution attacks in order to leak more secrets within the transient window than prior work. From the experimental results, our attacks allow leaking about 2 times as many secret bytes, compared to Flush+Reload, which is widely used in transient execution attacks.

cs.CR↗

arXiv2021

On-chip parallel processing of quantum frequency combs for high-dimensional hyper-entanglement generation

High-dimensional encoding and hyper-entanglement are unique features that distinguish optical photons from other quantum information carriers, leading to improved system efficiency and novel quantum functions. However, the disparate requirements to control different optical degrees of freedom have prevent the development of complete integrated platforms that is capable of leveraging the complementary benefits of high-dimensional encoding and hyper-entanglement at the same time. Here we demonstrate the chip-scale solution to the generation and manipulation of high-dimensional hyper-entanglement. This is achieved by the parallel processing of multiple quantum frequency combs in the path domain. Cavity-enhanced parametric down-conversion with Sagnac configuration is implemented to ensure the spectral indistinguishability. Simultaneous entanglement in path and frequency is realized with high dimensions. On-chip reconfiguration of the entanglement structure is also demonstrated. We further present quantum interference in both entanglement degrees of freedom with high visibility. Our work provides the critical step for the efficient and parallel processing of quantum information with integrated photonics.

quant-ph↗

arXiv2021

Proposal of Analog In-Memory Computing with Magnified Tunnel Magnetoresistance Ratio and Universal STT-MRAM Cell

In-memory computing (IMC) is an effectual solution for energy-efficient artificial intelligence applications. Analog IMC amortizes the power consumption of multiple sensing amplifiers with analog-to-digital converter (ADC), and simultaneously completes the calculation of multi-line data with high parallelism degree. Based on a universal one-transistor one-magnetic tunnel junction (MTJ) spin transfer torque magnetic RAM (STT-MRAM) cell, this paper demonstrates a novel tunneling magnetoresistance (TMR) ratio magnifying method to realize analog IMC. Previous concerns include low TMR ratio and analog calculation nonlinearity are addressed using device-circuit interaction. Peripheral circuits are minimally modified to enable in-memory matrix-vector multiplication. A current mirror with feedback structure is implemented to enhance analog computing linearity and calculation accuracy. The proposed design maximumly supports 1024 2-bit input and 1-bit weight multiply-and-accumulate (MAC) computations simultaneously. The 2-bit input is represented by the width of the input (IN) pulses, while the 1-bit weight is stored in STT-MRAM and the x7500 magnified TMR (m-TMR) ratio is obtained by latching. The proposal is simulated using 28-nm CMOS process and MTJ compact model. The integral nonlinearity is reduced by 57.6% compared with the conventional structure. 9.47-25.4 TOPS/W is realized with 2-bit input, 1-bit weight and 4-bit output convolution neural network (CNN).

cs.ET↗

arXiv2019

Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data

One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data. To cope with this problem, we propose a novel data augmentation approach based on semi-supervised conditional Generative Adversarial Networks (scGANs), which aims to automatically learn a mapping strategy from a random noise space to original data distribution. The proposed approach has the capability of well synthesizing 'realistic' high-dimensional data, while requiring no additional annotation process. To handle the mode collapse problem of GANs, we further introduce an ensemble strategy to enhance the diversity of the generated data. The systematic experiments conducted on a widely used Munich-Passau snore sound corpus demonstrate that the scGANs-based systems can remarkably outperform other classic data augmentation systems, and are also competitive to other recently reported systems for ASSC.

cs.LG↗

arXiv2016

Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition

The ever-growing popularity of Kinect and inertial sensors has prompted intensive research efforts on human action recognition. Since human actions can be characterized by multiple feature representations extracted from Kinect and inertial sensors, multiview features must be encoded into a unified space optimal for human action recognition. In this paper, we propose a new unsupervised feature fusion method termed Multiview Cauchy Estimator Feature Embedding (MCEFE) for human action recognition. By minimizing empirical risk, MCEFE integrates the encoded complementary information in multiple views to find the unified data representation and the projection matrices. To enhance robustness to outliers, the Cauchy estimator is imposed on the reconstruction error. Furthermore, ensemble manifold regularization is enforced on the projection matrices to encode the correlations between different views and avoid overfitting. Experiments are conducted on the new Chinese Academy of Sciences - Yunnan University - Multimodal Human Action Database (CAS-YNU-MHAD) to demonstrate the effectiveness and robustness of MCEFE for human action recognition.

cs.CV↗