arXiv subjects

Tao Jiang

Publications and source records attributed to Tao Jiang.

At least 127 records · Page 7Linked to original sources

arXiv2024

In situ Qubit Frequency Tuning Circuit for Scalable Superconducting Quantum Computing: Scheme and Experiment

Frequency tunable qubit plays a significant role for scalable superconducting quantum processors. The state-of-the-art room-temperature electronics for tuning qubit frequency suffers from unscalable limit, such as heating problem, linear growth of control cables, etc. Here we propose a scalable scheme to tune the qubit frequency by using in situ superconducting circuit, which is based on radio frequency superconducting quantum interference device (rf-SQUID). We demonstrate both theoretically and experimentally that the qubit frequency could be modulated by inputting several single pulses into rf-SQUID. Compared with the traditional scheme, our scheme not only solves the heating problem, but also provides the potential to exponentially reduce the number of cables inside the dilute refrigerator and the room-temperature electronics resource for tuning qubit frequency, which is achieved by a time-division-multiplex (TDM) scheme combining rf-SQUID with switch arrays. With such TDM scheme, the number of cables could be reduced from the usual $\sim 3n$ to $\sim \log_2{(3n)} + 1$ for two-dimensional quantum processors comprising $n$ qubits and $\sim 2n$ couplers. Our work paves the way for large-scale control of superconducting quantum processor.

quant-ph↗

arXiv2024

A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models

Membership Inference Attack (MIA) identifies whether a record exists in a machine learning model's training set by querying the model. MIAs on the classic classification models have been well-studied, and recent works have started to explore how to transplant MIA onto generative models. Our investigation indicates that existing MIAs designed for generative models mainly depend on the overfitting in target models. However, overfitting can be avoided by employing various regularization techniques, whereas existing MIAs demonstrate poor performance in practice. Unlike overfitting, memorization is essential for deep learning models to attain optimal performance, making it a more prevalent phenomenon. Memorization in generative models leads to an increasing trend in the probability distribution of generating records around the member record. Therefore, we propose a Probabilistic Fluctuation Assessing Membership Inference Attack (PFAMI), a black-box MIA that infers memberships by detecting these trends via analyzing the overall probabilistic fluctuations around given records. We conduct extensive experiments across multiple generative models and datasets, which demonstrate PFAMI can improve the attack success rate (ASR) by about 27.9% when compared with the best baseline.

cs.LG↗

arXiv2024

Bessel functions and Weyl's law for balls and spherical shells

The purpose of this paper is twofold. One is to investigate the properties of the zeros of cross-products of Bessel functions or derivatives of ultraspherical Bessel functions, as well as the properties of the zeros of the derivative of the first-kind ultraspherical Bessel function. The properties we study include asymptotics (with uniform and nonuniform remainder estimates), upper and lower bounds and so on. In addition, we provide the number of zeros of a certain cross-product within a large circle and show that all its zeros are real and simple. These results may be of independent interest. The other is to investigate the Dirichlet/Neumann Laplacian on balls and spherical shells in $\mathbb{R}^d$ ($d\geq 2$) and the remainder of the associated Weyl's law. We obtain new upper bounds in all dimensions, both in the Dirichlet and Neumann cases. The proof relies on our studies of Bessel functions and the latest development in the Gauss circle problem, which was driven by the application of the emerging decoupling theory of harmonic analysis.

math.CA↗

arXiv2024

Spatiotemporal Blind-Spot Network with Calibrated Flow Alignment for Self-Supervised Video Denoising

Self-supervised video denoising aims to remove noise from videos without relying on ground truth data, leveraging the video itself to recover clean frames. Existing methods often rely on simplistic feature stacking or apply optical flow without thorough analysis. This results in suboptimal utilization of both inter-frame and intra-frame information, and it also neglects the potential of optical flow alignment under self-supervised conditions, leading to biased and insufficient denoising outcomes. To this end, we first explore the practicality of optical flow in the self-supervised setting and introduce a SpatioTemporal Blind-spot Network (STBN) for global frame feature utilization. In the temporal domain, we utilize bidirectional blind-spot feature propagation through the proposed blind-spot alignment block to ensure accurate temporal alignment and effectively capture long-range dependencies. In the spatial domain, we introduce the spatial receptive field expansion module, which enhances the receptive field and improves global perception capabilities. Additionally, to reduce the sensitivity of optical flow estimation to noise, we propose an unsupervised optical flow distillation mechanism that refines fine-grained inter-frame interactions during optical flow alignment. Our method demonstrates superior performance across both synthetic and real-world video denoising datasets. The source code is publicly available at https://github.com/ZKCCZ/STBN.

cs.CV↗

arXiv2024

Establishing a New Benchmark in Quantum Computational Advantage with 105-qubit Zuchongzhi 3.0 Processor

In the relentless pursuit of quantum computational advantage, we present a significant advancement with the development of Zuchongzhi 3.0. This superconducting quantum computer prototype, comprising 105 qubits, achieves high operational fidelities, with single-qubit gates, two-qubit gates, and readout fidelity at 99.90%, 99.62% and 99.18%, respectively. Our experiments with an 83-qubit, 32-cycle random circuit sampling on Zuchongzhi 3.0 highlight its superior performance, achieving one million samples in just a few hundred seconds. This task is estimated to be infeasible on the most powerful classical supercomputers, Frontier, which would require approximately $6.4\times 10^9$ years to replicate the task. This leap in processing power places the classical simulation cost six orders of magnitude beyond Google's SYC-67 and SYC-70 experiments [Nature 634, 328(2024)], firmly establishing a new benchmark in quantum computational advantage. Our work not only advances the frontiers of quantum computing but also lays the groundwork for a new era where quantum processors play an essential role in tackling sophisticated real-world challenges.

quant-ph↗

arXiv2024

Experimental Analysis and Modeling of Penetration Loss for Building Materials in FR1 and FR3 bands

This study focuses on analysis and modeling of the penetration loss of typical building materials in the FR1 (450 MHz-6 GHz) and FR3 (7-24 GHz) bands based on experimental measurements. Firstly, we measure the penetration loss characteristics of four different typical building materials from 4 to 16 GHz, including wood, glass, foam and concrete, by using a penetration loss measurement platform based on the vector network analyzer (VNA). Next, we analyze the frequency dependence and thickness dependence of penetration loss. Finally, the linear model is applied to fit the curve of the measured penetration loss, and new model parameters for the penetration loss of different building materials are given, which are compared with that in the third generation partnership project (3GPP) technical report (TR) 38.901. The analysis results and new model parameters may provides insight into understanding propagation characteristics in FR1 and FR3 bands and 3GPP channel model standardisation.

eess.SP↗

arXiv2024

Cascaded channel modeling and experimental validation for RIS assisted communication system

Reconfigurable Intelligent Surface (RIS) is considered as a promising technology for 6G due to its ability to actively modify the electromagnetic propagation environment. Accurate channel modeling is essential for the design and evaluation of RIS assisted communication systems. Most current research models the RIS channel as a cascade of Tx-RIS and RIS-Rx sub-channels. However, most validation efforts regarding this assumption focus on large-scale path loss. To further explore this, in this paper, we derive and extend a convolution expression of RIS cascaded channel model based on the previously proposed Geometry-based Stochastic Model (GBSM)-based RIS cascaded channels. This model follows the 3GPP standard framework and leverages parameters such as angles, delays, and path powers defined in the GBSM model to more accurately reflect the smallscale characteristics of RIS multipath cascades. To verify the accuracy of this model, we conduct measurements of the TxRIS-Rx channel, Tx-RIS, and RIS-Rx sub-channels in a factory environment at 6.9 GHz, using the measured data to demonstrate the models validity and applicability in real-world scenarios. Validation with measured data shows that the proposed model accurately describes the characteristics of the RIS cascaded channel in terms of delay, angle, and power in complex multipath environments, providing important references for the design and deployment of RIS systems.

eess.SP↗

arXiv2024

Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not. Existing MIAs designed for large language models (LLMs) can be bifurcated into two types: reference-free and reference-based attacks. Although reference-based attacks appear promising performance by calibrating the probability measured on the target model with reference models, this illusion of privacy risk heavily depends on a reference dataset that closely resembles the training set. Both two types of attacks are predicated on the hypothesis that training records consistently maintain a higher probability of being sampled. However, this hypothesis heavily relies on the overfitting of target models, which will be mitigated by multiple regularization methods and the generalization of LLMs. Thus, these reasons lead to high false-positive rates of MIAs in practical scenarios. We propose a Membership Inference Attack based on Self-calibrated Probabilistic Variation (SPV-MIA). Specifically, we introduce a self-prompt approach, which constructs the dataset to fine-tune the reference model by prompting the target LLM itself. In this manner, the adversary can collect a dataset with a similar distribution from public APIs. Furthermore, we introduce probabilistic variation, a more reliable membership signal based on LLM memorization rather than overfitting, from which we rediscover the neighbour attack with theoretical grounding. Comprehensive evaluation conducted on three datasets and four exemplary LLMs shows that SPV-MIA raises the AUC of MIAs from 0.7 to a significantly high level of 0.9. Our code and dataset are available at: https://github.com/tsinghua-fib-lab/NeurIPS2024_SPV-MIA

cs.CL↗

arXiv2024

Active Sensing for Multiuser Beam Tracking with Reconfigurable Intelligent Surface

This paper studies a beam tracking problem in which an access point (AP), in collaboration with a reconfigurable intelligent surface (RIS), dynamically adjusts its downlink beamformers and the reflection pattern at the RIS in order to maintain reliable communications with multiple mobile user equipments (UEs). Specifically, the mobile UEs send uplink pilots to the AP periodically during the channel sensing intervals, the AP then adaptively configures the beamformers and the RIS reflection coefficients for subsequent data transmission based on the received pilots. This is an active sensing problem, because channel sensing involves configuring the RIS coefficients during the pilot stage and the optimal sensing strategy should exploit the trajectory of channel state information (CSI) from previously received pilots. Analytical solution to such an active sensing problem is very challenging. In this paper, we propose a deep learning framework utilizing a recurrent neural network (RNN) to automatically summarize the time-varying CSI obtained from the periodically received pilots into state vectors. These state vectors are then mapped to the AP beamformers and RIS reflection coefficients for subsequent downlink data transmissions, as well as the RIS reflection coefficients for the next round of uplink channel sensing. The mappings from the state vectors to the downlink beamformers and the RIS reflection coefficients for both channel sensing and downlink data transmission are performed using graph neural networks (GNNs) to account for the interference among the UEs. Simulations demonstrate significant and interpretable performance improvement of the proposed approach over the existing data-driven methods with nonadaptive channel sensing schemes.

eess.SP↗

arXiv2024

Polarization-independent metasurfaces based on bound states in the continuum with high Q-factor and resonance modulation

Metasurfaces offer a powerful platform for effective light manipulation, which is crucial for advanced optical technologies. While designs of polarization-independent structures have reduced the need for polarized illumination, they are often limited by either low Q factors or low resonance modulation. Here, we design and experimentally demonstrate a metasurface with polarization-independent quasi-bound state in the continuum (quasi-BIC), where the unit cell consists of four silicon squares arranged in a two-dimensional array and the resonance properties can be controlled by adjusting the edge length difference between different squares. Our metasurface experimentally achieves a Q factor of approximately 100 and a resonance modulation of around 50%. This work addresses a common limitation in previous designs, which either achieved high Q factors exceeding 200 with a resonance modulation of less than 10%, leading to challenging signal-to-noise ratio requirements, or achieved strong resonance modulation with Q factors of only around 10, limiting light confinement and fine-tuning capabilities. In contrast, our metasurface ensures that the polarization-independent signal is sharp and distinct within the system, reducing the demands on signal-to-noise ratio and improving robustness. Experiments show the consistent performance across different polarization angles. This work contributes to the development of versatile optical devices, enhancing the potential for the practical application of BIC-based designs in areas such as optical filtering and sensing.

physics.optics↗

arXiv2024

Phase sensitivity for an SU(1,1) interferometer via multiphoton subtraction at the output port

In the field of quantum precision measurement, enhancing phase sensitivity is crucial for various applications, including quantum metrology and quantum sensing technologies. We theoretically investigate the improvement in phase sensitivity and quantum Fisher information achieved through multiphoton subtraction operations at the output port of an SU(1,1) interferometer under conditions of photon loss. We use vacuum and coherent states as the inputs and detect the outputs by intensity detection. The results indicate that internal photon losses within the SU(1,1) interferometer have a more significant impact on the phase sensitivity compared to external photon losses. Moreover, increasing the number of photon subtractions m effectively enhances both the phase sensitivity and the quantum Fisher information. Notably, even under conditions of severe photon loss, the multiphoton subtraction operations can enable the phase sensitivity to surpass the standard quantum limit, approaching both the Heisenberg limit and the quantum Cramér-Rao bound. This study provides a new theoretical basis for enhancing the phase sensitivity in the SU(1,1) interferometer.

quant-ph↗

arXiv2024

Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving

3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the availability of large-scale annotated datasets, which is time-consuming and expensive to compile, especially for 3D bounding box annotation. In this work, we investigate diversity-based active learning (AL) as a potential solution to alleviate the annotation burden. Given limited annotation budget, only the most informative frames and objects are automatically selected for human to annotate. Technically, we take the advantage of the multimodal information provided in an AV dataset, and propose a novel acquisition function that enforces spatial and temporal diversity in the selected samples. We benchmark the proposed method against other AL strategies under realistic annotation cost measurement, where the realistic costs for annotating a frame and a 3D bounding box are both taken into consideration. We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly. Code is available at https://github.com/Linkon87/Exploring-Diversity-based-Active-Learning-for-3D-Object-Detection-in-Autonomous-Driving

cs.CV↗

arXiv2024

PGCS: Physical Law embedded Generative Cloud Synthesis in Remote Sensing Images

Data quantity and quality are both critical for information extraction and analyzation in remote sensing. However, the current remote sensing datasets often fail to meet these two requirements, for which cloud is a primary factor degrading the data quantity and quality. This limitation affects the precision of results in remote sensing application, particularly those derived from data-driven techniques. In this paper, a physical law embedded generative cloud synthesis method (PGCS) is proposed to generate diverse realistic cloud images to enhance real data and promote the development of algorithms for subsequent tasks, such as cloud correction, cloud detection, and data augmentation for classification, recognition, and segmentation. The PGCS method involves two key phases: spatial synthesis and spectral synthesis. In the spatial synthesis phase, a style-based generative adversarial network is utilized to simulate the spatial characteristics, generating an infinite number of single-channel clouds. In the spectral synthesis phase, the atmospheric scattering law is embedded through a local statistics and global fitting method, converting the single-channel clouds into multi-spectral clouds. The experimental results demonstrate that PGCS achieves a high accuracy in both phases and performs better than three other existing cloud synthesis methods. Two cloud correction methods are developed from PGCS and exhibits a superior performance compared to state-of-the-art methods in the cloud correction task. Furthermore, the application of PGCS with data from various sensors was investigated and successfully extended. Code will be provided at https://github.com/Liying-Xu/PGCS.

cs.CV↗

arXiv2024

SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving

Monocular scene understanding is a foundational component of autonomous systems. Within the spectrum of monocular perception topics, one crucial and useful task for holistic 3D scene understanding is semantic scene completion (SSC), which jointly completes semantic information and geometric details from RGB input. However, progress in SSC, particularly in large-scale street views, is hindered by the scarcity of high-quality datasets. To address this issue, we introduce SSCBench, a comprehensive benchmark that integrates scenes from widely used automotive datasets (e.g., KITTI-360, nuScenes, and Waymo). SSCBench follows an established setup and format in the community, facilitating the easy exploration of SSC methods in various street views. We benchmark models using monocular, trinocular, and point cloud input to assess the performance gap resulting from sensor coverage and modality. Moreover, we have unified semantic labels across diverse datasets to simplify cross-domain generalization testing. We commit to including more datasets and SSC models to drive further advancements in this field.

cs.CV↗

arXiv2024

Wireless Environment Information Sensing, Feature, Semantic, and Knowledge: Four Steps Towards 6G AI-Enabled Air Interface

The air interface technology plays a crucial role in optimizing the communication quality for users. To address the challenges brought by the radio channel variations to air interface design, this article proposes a framework of wireless environment information-aided 6G AI-enabled air interface (WEI-6G AI$^{2}$), which actively acquires real-time environment details to facilitate channel fading prediction and communication technology optimization. Specifically, we first outline the role of WEI in supporting the 6G AI$^{2}$ in scenario adaptability, real-time inference, and proactive action. Then, WEI is delineated into four progressive steps: raw sensing data, features obtained by data dimensionality reduction, semantics tailored to tasks, and knowledge that quantifies the environmental impact on the channel. To validate the availability and compare the effect of different types of WEI, a path loss prediction use case is designed. The results demonstrate that leveraging environment knowledge requires only 2.2 ms of model inference time, which can effectively support real-time design for future 6G AI$^{2}$. Additionally, WEI can reduce the pilot overhead by 25\%. Finally, several open issues are pointed out, including multi-modal sensing data synchronization and information extraction method construction.

eess.SP↗

arXiv2024

CVT-Occ: Cost Volume Temporal Fusion for 3D Occupancy Prediction

Vision-based 3D occupancy prediction is significantly challenged by the inherent limitations of monocular vision in depth estimation. This paper introduces CVT-Occ, a novel approach that leverages temporal fusion through the geometric correspondence of voxels over time to improve the accuracy of 3D occupancy predictions. By sampling points along the line of sight of each voxel and integrating the features of these points from historical frames, we construct a cost volume feature map that refines current volume features for improved prediction outcomes. Our method takes advantage of parallax cues from historical observations and employs a data-driven approach to learn the cost volume. We validate the effectiveness of CVT-Occ through rigorous experiments on the Occ3D-Waymo dataset, where it outperforms state-of-the-art methods in 3D occupancy prediction with minimal additional computational cost. The code is released at \url{https://github.com/Tsinghua-MARS-Lab/CVT-Occ}.

cs.CV↗

arXiv2024

Selective Excitation of Bloch Modes in Canalized Polaritonic Crystals

Polaritonic crystals (PoCs) have experienced significant advancements through involving hyperbolic polaritons in anisotropic materials such as $α$-MoO$_{\rm 3}$, offering a promising approach for nanoscale light control and improved light-matter interactions. Notably, twisted bilayer $α$-MoO$_{\rm 3}$ enables tunable iso-frequency contours (IFCs), especially generating flat IFCs at certain twist angles, which could enhance mode selectivity in their PoCs through the highly collimated and canalized polaritons. This study unveils the selective excitation of Bloch modes in PoCs with square-lattice structures on twisted bilayer $α$-MoO$_{\rm 3}$ with canalized phonon polaritons. Through the optimization of the square lattice design, there is an effective redistribution of canalized polaritons into the reciprocal lattices of PoCs. Fine-tuning the periodicity and orientation of the hole lattice enables momentum matching between flat IFCs and co-linear reciprocal points, allowing precise and directional control over desired Bragg resonances and Bloch modes. This research establishes a versatile platform for tunable polaritonic devices and paves the way for advanced photonic applications.

physics.optics↗

arXiv2024

Extraction of Typical Operating Scenarios of New Power System Based on Deep Time Series Aggregation

Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system. This study proposed a novel deep time series aggregation scheme (DTSAs) to generate typical operational scenarios, considering the large amount of historical operational snapshot data. Specifically, DTSAs analyze the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios. A gramian angular summation field (GASF) based operational scenario image encoder was designed to convert operational scenario sequences into high-dimensional spaces. This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models. The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots. Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional featurescreening methods. In addition, experiments with different new energy access ratios were conducted to verify the robustness of the proposed method. DTSAs enables dispatchers to master the operation experience of the power system in advance, and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.

cs.LG↗