arXiv subjects

Liu Liu

Publications and source records attributed to Liu Liu.

At least 163 records · Page 9Linked to original sources

arXiv2025

POINT: a web-based platform for pharmacological investigation enhanced by multi-omics networks and knowledge graphs

Network pharmacology (NP) explores pharmacological mechanisms through biological networks. Multi-omics data enable multi-layer network construction under diverse conditions, requiring integration into NP analyses. We developed POINT, a novel NP platform enhanced by multi-omics biological networks, advanced algorithms, and knowledge graphs (KGs) featuring network-based and KG-based analytical functions. In the network-based analysis, users can perform NP studies flexibly using 1,158 multi-omics biological networks encompassing proteins, transcription factors, and non-coding RNAs across diverse cell line-, tissue- and disease-specific conditions. Network-based analysis-including random walk with restart (RWR), GSEA, and diffusion profile (DP) similarity algorithms-supports tasks such as target prediction, functional enrichment, and drug screening. We merged networks from experimental sources to generate a pre-integrated multi-layer human network for evaluation. RWR demonstrated superior performance with a 33.1% average ranking improvement over the second-best algorithm, PageRank, in identifying known targets across 2,002 drugs. Additionally, multi-layer networks significantly improve the ability to identify FDA-approved drug-disease pairs compared to the single-layer network. For KG-based analysis, we compiled three high-quality KGs to construct POINT KG, which cross-references over 90% of network-based predictions. We illustrated the platform's capabilities through two case studies. POINT bridges the gap between multi-omics networks and drug discovery; it is freely accessible at http://point.gene.ac/.

q-bio.MN↗

arXiv2025

Towards Label-Only Membership Inference Attack against Pre-trained Large Language Models

Membership Inference Attacks (MIAs) aim to predict whether a data sample belongs to the model's training set or not. Although prior research has extensively explored MIAs in Large Language Models (LLMs), they typically require accessing to complete output logits (\ie, \textit{logits-based attacks}), which are usually not available in practice. In this paper, we study the vulnerability of pre-trained LLMs to MIAs in the \textit{label-only setting}, where the adversary can only access generated tokens (text). We first reveal that existing label-only MIAs have minor effects in attacking pre-trained LLMs, although they are highly effective in inferring fine-tuning datasets used for personalized LLMs. We find that their failure stems from two main reasons, including better generalization and overly coarse perturbation. Specifically, due to the extensive pre-training corpora and exposing each sample only a few times, LLMs exhibit minimal robustness differences between members and non-members. This makes token-level perturbations too coarse to capture such differences. To alleviate these problems, we propose \textbf{PETAL}: a label-only membership inference attack based on \textbf{PE}r-\textbf{T}oken sem\textbf{A}ntic simi\textbf{L}arity. Specifically, PETAL leverages token-level semantic similarity to approximate output probabilities and subsequently calculate the perplexity. It finally exposes membership based on the common assumption that members are `better' memorized and have smaller perplexity. We conduct extensive experiments on the WikiMIA benchmark and the more challenging MIMIR benchmark. Empirically, our PETAL performs better than the extensions of existing label-only attacks against personalized LLMs and even on par with other advanced logit-based attacks across all metrics on five prevalent open-source LLMs.

cs.CR↗

arXiv2025

Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits

Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years. However, quantum circuits with random structures have poor trainability due to the exponentially vanishing gradient with respect to the circuit depth and the qubit number. This result leads to a general standpoint that deep quantum circuits would not be feasible for practical tasks. In this work, we propose an initialization strategy with theoretical guarantees for the vanishing gradient problem in general deep quantum circuits. Specifically, we prove that under proper Gaussian initialized parameters, the norm of the gradient decays at most polynomially when the qubit number and the circuit depth increase. Our theoretical results hold for both the local and the global observable cases, where the latter was believed to have vanishing gradients even for very shallow circuits. Experimental results verify our theoretical findings in the quantum simulation and quantum chemistry.

quant-ph↗

arXiv2025

HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting

Generative models have gained significant attention in multivariate time series forecasting (MTS), particularly due to their ability to generate high-fidelity samples. Forecasting the probability distribution of multivariate time series is a challenging yet practical task. Although some recent attempts have been made to handle this task, two major challenges persist: 1) some existing generative methods underperform in high-dimensional multivariate time series forecasting, which is hard to scale to higher dimensions; 2) the inherent high-dimensional multivariate attributes constrain the forecasting lengths of existing generative models. In this paper, we point out that discrete token representations can model high-dimensional MTS with faster inference time, and forecasting the target with long-term trends of itself can extend the forecasting length with high accuracy. Motivated by this, we propose a vector quantized framework called Hierarchical Discrete Transformer (HDT) that models time series into discrete token representations with l2 normalization enhanced vector quantized strategy, in which we transform the MTS forecasting into discrete tokens generation. To address the limitations of generative models in long-term forecasting, we propose a hierarchical discrete Transformer. This model captures the discrete long-term trend of the target at the low level and leverages this trend as a condition to generate the discrete representation of the target at the high level that introduces the features of the target itself to extend the forecasting length in high-dimensional MTS. Extensive experiments on five popular MTS datasets verify the effectiveness of our proposed method.

cs.LG↗

arXiv2025

TAP-CAM: A Tunable Approximate Matching Engine based on Ferroelectric Content Addressable Memory

Pattern search is crucial in numerous analytic applications for retrieving data entries akin to the query. Content Addressable Memories (CAMs), an in-memory computing fabric, directly compare input queries with stored entries through embedded comparison logic, facilitating fast parallel pattern search in memory. While conventional CAM designs offer exact match functionality, they are inadequate for meeting the approximate search needs of emerging data-intensive applications. Some recent CAM designs propose approximate matching functions, but they face limitations such as excessively large cell area or the inability to precisely control the degree of approximation. In this paper, we propose TAP-CAM, a novel ferroelectric field effect transistor (FeFET) based ternary CAM (TCAM) capable of both exact and tunable approximate matching. TAP-CAM employs a compact 2FeFET-2R cell structure as the entry storage unit, and similarities in Hamming distances between input queries and stored entries are measured using an evaluation transistor associated with the matchline of CAM array. The operation, robustness and performance of the proposed design at array level have been discussed and evaluated, respectively. We conduct a case study of K-nearest neighbor (KNN) search to benchmark the proposed TAP-CAM at application level. Results demonstrate that compared to 16T CMOS CAM with exact match functionality, TAP-CAM achieves a 16.95x energy improvement, along with a 3.06% accuracy enhancement. Compared to 2FeFET TCAM with approximate match functionality, TAP-CAM achieves a 6.78x energy improvement.

cs.ET↗

arXiv2025

UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models

Previous studies on robotic manipulation are based on a limited understanding of the underlying 3D motion constraints and affordances. To address these challenges, we propose a comprehensive paradigm, termed UniAff, that integrates 3D object-centric manipulation and task understanding in a unified formulation. Specifically, we constructed a dataset labeled with manipulation-related key attributes, comprising 900 articulated objects from 19 categories and 600 tools from 12 categories. Furthermore, we leverage MLLMs to infer object-centric representations for manipulation tasks, including affordance recognition and reasoning about 3D motion constraints. Comprehensive experiments in both simulation and real-world settings indicate that UniAff significantly improves the generalization of robotic manipulation for tools and articulated objects. We hope that UniAff will serve as a general baseline for unified robotic manipulation tasks in the future. Images, videos, dataset, and code are published on the project website at:https://sites.google.com/view/uni-aff/home

cs.RO↗

arXiv2024

Generalizable Articulated Object Perception with Superpoints

Manipulating articulated objects with robotic arms is challenging due to the complex kinematic structure, which requires precise part segmentation for efficient manipulation. In this work, we introduce a novel superpoint-based perception method designed to improve part segmentation in 3D point clouds of articulated objects. We propose a learnable, part-aware superpoint generation technique that efficiently groups points based on their geometric and semantic similarities, resulting in clearer part boundaries. Furthermore, by leveraging the segmentation capabilities of the 2D foundation model SAM, we identify the centers of pixel regions and select corresponding superpoints as candidate query points. Integrating a query-based transformer decoder further enhances our method's ability to achieve precise part segmentation. Experimental results on the GAPartNet dataset show that our method outperforms existing state-of-the-art approaches in cross-category part segmentation, achieving AP50 scores of 77.9% for seen categories (4.4% improvement) and $39.3\%$ for unseen categories (11.6% improvement), with superior results in 5 out of 9 part categories for seen objects and outperforming all previous methods across all part categories for unseen objects.

cs.CV↗

arXiv2024

Silicon photonic MEMS switches based on split waveguide crossings

The continuous push for high-performance photonic switches is one of the most crucial premises for the sustainable scaling of programmable and reconfigurable photonic circuits for a wide spectrum of applications. Conventional optical switches rely on the perturbative mechanisms of mode coupling or mode interference, resulting in inherent bottlenecks in their switching performance concerning size, power consumption and bandwidth. Here we propose and realize a silicon photonic 2x2 elementary switch based on a split waveguide crossing (SWX) consisting of two halves. The propagation direction of the incident light is manipulated to implement the OFF/ON states by splitting/combining the two halves of the SWX, showing excellent performance with low excess loss and low crosstalk over an ultrawide bandwidth. Both elementary switch and a 64x64 switch array based on Benes topology are fabricated and characterized, demonstrating great potential for practical scenarios such as photonic interconnect/routing, Lidar and spectroscopy, photonic computing, as well as microwave photonics.

physics.optics↗

arXiv2024

ELSA: Evaluating Localization of Social Activities in Urban Streets using Open-Vocabulary Detection

Existing Open Vocabulary Detection (OVD) models exhibit a number of challenges. They often struggle with semantic consistency across diverse inputs, and are often sensitive to slight variations in input phrasing, leading to inconsistent performance. The calibration of their predictive confidence, especially in complex multi-label scenarios, remains suboptimal, frequently resulting in overconfident predictions that do not accurately reflect their context understanding. To understand these limitations, multi-label detection benchmarks are needed. A particularly challenging domain for such benchmarking is social activities. Due to the lack of multi-label benchmarks for social interactions, in this work we present ELSA: Evaluating Localization of Social Activities. ELSA draws on theoretical frameworks in urban sociology and design and uses in-the-wild street-level imagery, where the size of groups and the types of activities vary significantly. ELSA includes more than 900 manually annotated images with more than 4,300 multi-labeled bounding boxes for individual and group activities. We introduce a novel confidence score computation method NLSE and a novel Dynamic Box Aggregation (DBA) algorithm to assess semantic consistency in overlapping predictions. We report our results on the widely-used SOTA models Grounding DINO, Detic, OWL, and MDETR. Our evaluation protocol considers semantic stability and localization accuracy and further exposes the limitations of existing approaches.

cs.CV↗

arXiv2024

Gaussian Object Carver: Object-Compositional Gaussian Splatting with surfaces completion

3D scene reconstruction is a foundational problem in computer vision. Despite recent advancements in Neural Implicit Representations (NIR), existing methods often lack editability and compositional flexibility, limiting their use in scenarios requiring high interactivity and object-level manipulation. In this paper, we introduce the Gaussian Object Carver (GOC), a novel, efficient, and scalable framework for object-compositional 3D scene reconstruction. GOC leverages 3D Gaussian Splatting (GS), enriched with monocular geometry priors and multi-view geometry regularization, to achieve high-quality and flexible reconstruction. Furthermore, we propose a zero-shot Object Surface Completion (OSC) model, which uses 3D priors from 3d object data to reconstruct unobserved surfaces, ensuring object completeness even in occluded areas. Experimental results demonstrate that GOC improves reconstruction efficiency and geometric fidelity. It holds promise for advancing the practical application of digital twins in embodied AI, AR/VR, and interactive simulation environments.

cs.CV↗

arXiv2024

Ultra-low-loss slow-light thin-film lithium-niobate optical modulator

Electro-optic modulators for next-generation optical interconnects require low loss-efficiency products, compact footprints, high modulation efficiency, broad bandwidths, and low losses. Here we propose and demonstrate a low-loss high-efficiency thin-film lithium-niobate Mach Zehnder modulator enabled by a novel ultralow-loss slow-light structure based on apodized gratings in cascade. The present loss-engineered slow-light structure achieves excess losses as low as 0.6 dB/mm experimentally, which is tens of times lower than conventional slow-light structures, and a high modulation bandwidth up to 320GHz in theory is achieved with optimally-designed capacitively-loaded traveling-wave electrodes. Experimentally, the fabricated slow-light modulator with a 2.8-mm-long modulation region has an ultra-low loss-efficiency product of 7.4 VdB and a flat electro-optic response up to 67 GHz, enabling 100-Gbps on-off keying with high ERs of 4.5 dB at a low driving voltage of 2Vpp, while 200-Gbps PAM4 and 150-Gbps PAM8 signals are also generated to show great promise for advanced modulation formats. In particular, it has also achieved the highest figure-of-merit(FOM) of 182 for high-speed optical modulation , including the bit rate, the extinction ratio normalized with respective to Vpp, the modulation efficiency. The outstanding performance of the present apodized-grating-based slow-light modulator shows great potential and paves the way for developing high-speed optical interconnects for both data-centers and high-performance computing systems.

physics.optics↗

arXiv2024

EZSR: Event-based Zero-Shot Recognition

This paper studies zero-shot object recognition using event camera data. Guided by CLIP, which is pre-trained on RGB images, existing approaches achieve zero-shot object recognition by optimizing embedding similarities between event data and RGB images respectively encoded by an event encoder and the CLIP image encoder. Alternatively, several methods learn RGB frame reconstructions from event data for the CLIP image encoder. However, they often result in suboptimal zero-shot performance. This study develops an event encoder without relying on additional reconstruction networks. We theoretically analyze the performance bottlenecks of previous approaches: the embedding optimization objectives are prone to suffer from the spatial sparsity of event data, causing semantic misalignments between the learned event embedding space and the CLIP text embedding space. To mitigate the issue, we explore a scalar-wise modulation strategy. Furthermore, to scale up the number of events and RGB data pairs for training, we also study a pipeline for synthesizing event data from static RGB images in mass. Experimentally, we demonstrate an attractive scaling property in the number of parameters and synthesized data. We achieve superior zero-shot object recognition performance on extensive standard benchmark datasets, even compared with past supervised learning approaches. For example, our model with a ViT/B-16 backbone achieves 47.84% zero-shot accuracy on the N-ImageNet dataset.

cs.CV↗

arXiv2024

PZT Optical Memristors

Optical memristors represent a monumental leap in the fusion of photonics and electronics, heralding a new era of applications from neuromorphic computing to artificial intelligence. However, current technologies are hindered by complex fabrication, limited endurance, high optical loss or low modulation depth. For the first time, we reveal optical non-volatility in thin-film Lead Zirconate Titanate (PZT) by electrically manipulating the ferroelectric domains to control the refractive index, providing a brand-new routine for optical memristors. The developed PZT optical memristors offer unprecedented advantages more than exceptional performance metrics like low loss of <2 dB/cm, high precision exceeding 6-bits, large modulation depth with an index change as large as 4.6x10-3. Additionally, these devices offer impressive stability, maintaining minimal wavelength variation for over three weeks and enduring more than 10,000 cycles, and require a mere 0.8 pJ of energy for non-volatile operation. The wafer-scale sol-gel fabrication process also ensures compatible with standardized mass fabrication processes and high scalability for photonic integration. Specially, these devices also demonstrate unique functional duality: setting above a threshold voltage enables non-volatile behaviors, below this threshold allows volatile high-speed optical modulation. This marks the first-ever optical memristor capable of performing high-speed (48 Gbps) and energy-efficient (450 fJ/bit) signal processing and non-volatile retention on a single platform, and is also the inaugural demonstration of scalable functional systems. The PZT optical memristors developed here facilitate the realization of novel paradigms for high-speed and energy-efficient optical interconnects, programmable PICs, quantum computing, neural networks, in-memory computing and brain-like architecture.

physics.optics↗

arXiv2024

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs

Large Language Models (LLMs) deployed on edge devices, known as edge LLMs, need to continuously fine-tune their model parameters from user-generated data under limited resource constraints. However, most existing learning methods are not applicable for edge LLMs because of their reliance on high resources and low learning capacity. Prompt tuning (PT) has recently emerged as an effective fine-tuning method for edge LLMs by only modifying a small portion of LLM parameters, but it suffers from user domain shifts, resulting in repetitive training and losing resource efficiency. Conventional techniques to address domain shift issues often involve complex neural networks and sophisticated training, which are incompatible for PT for edge LLMs. Therefore, an open research question is how to address domain shift issues for edge LLMs with limited resources. In this paper, we propose a prompt tuning framework for edge LLMs, exploiting the benefits offered by non-volatile computing-in-memory (NVCiM) architectures. We introduce a novel NVCiM-assisted PT framework, where we narrow down the core operations to matrix-matrix multiplication, which can then be accelerated by performing in-situ computation on NVCiM. To the best of our knowledge, this is the first work employing NVCiM to improve the edge LLM PT performance.

cs.LG↗

arXiv2024

Storyboard guided Alignment for Fine-grained Video Action Recognition

Fine-grained video action recognition can be conceptualized as a video-text matching problem. Previous approaches often rely on global video semantics to consolidate video embeddings, which can lead to misalignment in video-text pairs due to a lack of understanding of action semantics at an atomic granularity level. To tackle this challenge, we propose a multi-granularity framework based on two observations: (i) videos with different global semantics may share similar atomic actions or appearances, and (ii) atomic actions within a video can be momentary, slow, or even non-directly related to the global video semantics. Inspired by the concept of storyboarding, which disassembles a script into individual shots, we enhance global video semantics by generating fine-grained descriptions using a pre-trained large language model. These detailed descriptions capture common atomic actions depicted in videos. A filtering metric is proposed to select the descriptions that correspond to the atomic actions present in both the videos and the descriptions. By employing global semantics and fine-grained descriptions, we can identify key frames in videos and utilize them to aggregate embeddings, thereby making the embedding more accurate. Extensive experiments on various video action recognition datasets demonstrate superior performance of our proposed method in supervised, few-shot, and zero-shot settings.

cs.CV↗

arXiv2024

Azimuthal imaging of rock fractures by incorporating single borehole radar and optical data

Single borehole radar detection suffers from azimuthal ambiguity, while borehole optical tests only provide information about the borehole wall. These limitations prevent either detection method from revealing the complete spatial patterns of rock fractures on their own. In this paper, we address these challenges by proposing a joint imaging method that combines the advantages of both borehole detection methods. Geological azimuthal parameters are extracted from optical images by fitting the fracture curves to sinusoidal functions. A 2D Kirchhoff time migration is then implemented using radar common offset gather. Up-dip and down-dip events are separated by the f-k transform or z-s transform, depending on their geometric relation. The complete fracture planes, including trend, dip angle, gap width, and extension length, are finally reconstructed in 3D space by mapping the migration profile using azimuthal information from optical images. The method is proven reliable and high-resolution through both numerical tests and real field data.

physics.geo-ph↗

arXiv2024

RPMArt: Towards Robust Perception and Manipulation for Articulated Objects

Articulated objects are commonly found in daily life. It is essential that robots can exhibit robust perception and manipulation skills for articulated objects in real-world robotic applications. However, existing methods for articulated objects insufficiently address noise in point clouds and struggle to bridge the gap between simulation and reality, thus limiting the practical deployment in real-world scenarios. To tackle these challenges, we propose a framework towards Robust Perception and Manipulation for Articulated Objects (RPMArt), which learns to estimate the articulation parameters and manipulate the articulation part from the noisy point cloud. Our primary contribution is a Robust Articulation Network (RoArtNet) that is able to predict both joint parameters and affordable points robustly by local feature learning and point tuple voting. Moreover, we introduce an articulation-aware classification scheme to enhance its ability for sim-to-real transfer. Finally, with the estimated affordable point and articulation joint constraint, the robot can generate robust actions to manipulate articulated objects. After learning only from synthetic data, RPMArt is able to transfer zero-shot to real-world articulated objects. Experimental results confirm our approach's effectiveness, with our framework achieving state-of-the-art performance in both noise-added simulation and real-world environments. Code, data and more results can be found on the project website at https://r-pmart.github.io.

cs.RO↗

arXiv2024

Event Camera Data Dense Pre-training

This paper introduces a self-supervised learning framework designed for pre-training neural networks tailored to dense prediction tasks using event camera data. Our approach utilizes solely event data for training. Transferring achievements from dense RGB pre-training directly to event camera data yields subpar performance. This is attributed to the spatial sparsity inherent in an event image (converted from event data), where many pixels do not contain information. To mitigate this sparsity issue, we encode an event image into event patch features, automatically mine contextual similarity relationships among patches, group the patch features into distinctive contexts, and enforce context-to-context similarities to learn discriminative event features. For training our framework, we curate a synthetic event camera dataset featuring diverse scene and motion patterns. Transfer learning performance on downstream dense prediction tasks illustrates the superiority of our method over state-of-the-art approaches.

cs.CV↗