arXiv subjects

Peng Lei

Publications and source records attributed to Peng Lei.

13 recordsLinked to original sources

arXiv2025

Real-time 3D Ultrasonic Needle Tracking with a Photoacoustic Beacon

Many minimally invasive procedures, such as core needle biopsy of focal liver lesions, nerve blocks, and fetal and vascular interventions, are typically performed under ultrasound guidance, which provides real-time, high-resolution visualisation of tissue anatomy. Accurate and efficient localisation of the needle tip relative to patient anatomy is essential for guiding the needle towards the procedure target, avoiding adverse events and reducing the need for repeat procedures. However, the 3D nature of the procedure and poor image contrast of the needle in heterogeneous tissue or at steep insertion angles often lead to confusion over the true location of the tip within the 2D guidance images, and existing methods to enhance needle visibility largely remain limited to 2D. Here, we present a novel interventional ultrasound system capable of 2D B-mode imaging and 3D needle tracking. The tip location is determined from the time-of-flight of ultrasound generated by a photoacoustic beacon embedded in the needle bevel and received by a sparse receiver array distributed around the imaging system's curvilinear ultrasound probe. The measured tracking accuracy was better than 2 mm for depths up to 140 mm in water, and approximately 2 mm on average in an ex vivo tissue phantom, with referenced positions derived from X-ray computed tomography. In a usability study involving 12 clinicians performing biopsy procedures in a ex vivo tissue phantom, the failure rate was reduced by 35%, from 15.8% to 10.3% after only a few minutes of training. These results demonstrate that the proposed system has strong potential to support a wide range of minimally invasive procedures by enabling clinicians to accurately target small anatomical structures, improving the efficiency and effectiveness of diagnostic sampling and therapeutic delivery or ablation, and reducing the risk of adverse events.

physics.med-ph↗

arXiv2024

Joint Optimization of Buffer Delay and HARQ for Video Communications

To improve the quality of experience (QoE) in video communication over lossy networks, this paper presents a transmission method that jointly optimizes buffer delay and Hybrid Automatic Repeat request (HARQ), referred to as BD-HARQ. This method operates on packet group and employs dynamic buffer delay combined with HARQ strategy for transmission. By defining the QoE based on metrics such as buffer delay, Forward Error Correction (FEC) redundancy, and data recovery rate, the proposed method derives its closed-form expression through rigorous mathematical modeling and analysis. The optimal transmission parameters, i.e., the buffer delay and the FEC redundancy, are then determined and implemented, guaranteeing the real-time performance, transmission efficiency, and data recovery rate of video communication. Experimental results demonstrate that the proposed method aligns well with its theoretical expectations, and that it can provide up to 13.7% higher QoE compared to existing methods and increase the tolerance for packet loss rate from 15%-22% to up to 31% while maintaining a high QoE.

cs.MM↗

arXiv2024

Loading-effect-based 3-D microfabrication empowers on-chip Brillouin optomechanics

The acousto-optic interaction known as stimulated Brillouin scattering (SBS) has emerged as fundamental principles for realizing crucial components and functionalities in integrated photonics. However, the main challenge of integrated Brillouin devices is how to effectively confine both optical and acoustic waves. Apart from that, the manufacturing processes for these devices need to be compatible with standard fabrication platforms, and streamlined to facilitate their large-scale integration. Here, we demonstrate a novel suspended nanowire structure that can tightly confine photons and phonons. Furthermore, tailored for this structure, we introduce a loading-effect-based three-dimensional microfabrication technique, compatible with complementary metal-oxide-semiconductor (CMOS) technology. This innovative technique allows for the fabrication of the entire structure using a single-step lithography exposure, significantly streamlining the fabrication process. Leveraging this structure and fabrication scheme, we have achieved a Brillouin gain coefficient of 1100 1/W/m on the silicon-on-insulator platform within a compact footprint. It can support a Brillouin net gain over 4.1 dB with modest pump powers. We believe that this structure can significantly advance the development of SBS on chip, unlocking new opportunities for the large-scale integration of Brillouin-based photonic devices.

physics.optics↗

arXiv2024

Anti-resonant acoustic waveguides enabled tailorable Brillouin scattering on chip

Empowering independent control of optical and acoustic modes and enhancing the photon-phonon interaction, integrated photonics boosts the advancements of on-chip stimulated Brillouin scattering (SBS). However, achieving acoustic waveguides with low loss, tailorability, and easy fabrication remains a challenge. Here, inspired by the optical anti-resonance in hollow-core fibers, we propose suspended anti-resonant acoustic waveguides (SARAWs) with superior confinement and high selectivity of acoustic modes, supporting both forward and backward SBS on chip. Furthermore, this structure streamlines the design and fabrication processes. Leveraging the advantages of SARAWs, we have showcased a series of record-breaking results for SBS within a compact footprint on the silicon-on-insulator platform. For forward SBS, a centimeter-scale SARAW supports a large net gain exceeding 6.4 dB. For backward SBS, we have observed an unprecedented Brillouin frequency shift of 27.6 GHz and a mechanical quality factor of up to 1,960 in silicon waveguides. This paradigm of acoustic waveguide propels SBS into a new era, unlocking new opportunities in the fields of optomechanics, phononic circuits, and hybrid quantum systems.

physics.optics↗

arXiv2021

BARS: Joint Search of Cell Topology and Layout for Accurate and Efficient Binary ARchitectures

Binary Neural Networks (BNNs) have received significant attention due to their promising efficiency. Currently, most BNN studies directly adopt widely-used CNN architectures, which can be suboptimal for BNNs. This paper proposes a novel Binary ARchitecture Search (BARS) flow to discover superior binary architecture in a large design space. Specifically, we analyze the information bottlenecks that are related to both the topology and layout architecture design choices. And we propose to automatically search for the optimal information flow. To achieve that, we design a two-level (Macro & Micro) search space tailored for BNNs and apply a differentiable neural architecture search (NAS) to explore this search space efficiently. The macro-level search space includes width and depth decisions, which is required for better balancing the model performance and complexity. We also design the micro-level search space to strengthen the information flow for BNN. %A notable challenge of BNN architecture search lies in that binary operations exacerbate the "collapse" problem of differentiable NAS, for which we incorporate various search and derive strategies to stabilize the search process. On CIFAR-10, BARS achieves 1.5% higher accuracy with 2/3 binary operations and 1/10 floating-point operations comparing with existing BNN NAS studies. On ImageNet, with similar resource consumption, BARS-discovered architecture achieves a 6% accuracy gain than hand-crafted binary ResNet-18 architectures and outperforms other binary architectures while fully binarizing the architecture backbone.

cs.AI↗

arXiv2020

DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation

Budgeted pruning is the problem of pruning under resource constraints. In budgeted pruning, how to distribute the resources across layers (i.e., sparsity allocation) is the key problem. Traditional methods solve it by discretely searching for the layer-wise pruning ratios, which lacks efficiency. In this paper, we propose Differentiable Sparsity Allocation (DSA), an efficient end-to-end budgeted pruning flow. Utilizing a novel differentiable pruning process, DSA finds the layer-wise pruning ratios with gradient-based optimization. It allocates sparsity in continuous space, which is more efficient than methods based on discrete evaluation and search. Furthermore, DSA could work in a pruning-from-scratch manner, whereas traditional budgeted pruning methods are applied to pre-trained models. Experimental results on CIFAR-10 and ImageNet show that DSA could achieve superior performance than current iterative budgeted pruning methods, and shorten the time cost of the overall pruning process by at least 1.5x in the meantime.

cs.CV↗

arXiv2020

Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment

To leverage deep learning for image aesthetics assessment, one critical but unsolved issue is how to seamlessly incorporate the information of image aspect ratios to learn more robust models. In this paper, an adaptive fractional dilated convolution (AFDC), which is aspect-ratio-embedded, composition-preserving and parameter-free, is developed to tackle this issue natively in convolutional kernel level. Specifically, the fractional dilated kernel is adaptively constructed according to the image aspect ratios, where the interpolation of nearest two integers dilated kernels is used to cope with the misalignment of fractional sampling. Moreover, we provide a concise formulation for mini-batch training and utilize a grouping strategy to reduce computational overhead. As a result, it can be easily implemented by common deep learning libraries and plugged into popular CNN architectures in a computation-efficient manner. Our experimental results demonstrate that our proposed method achieves state-of-the-art performance on image aesthetics assessment over the AVA dataset.

cs.CV↗

arXiv2019

Weakly Supervised Energy-Based Learning for Action Segmentation

This paper is about labeling video frames with action classes under weak supervision in training, where we have access to a temporal ordering of actions, but their start and end frames in training videos are unknown. Following prior work, we use an HMM grounded on a Gated Recurrent Unit (GRU) for frame labeling. Our key contribution is a new constrained discriminative forward loss (CDFL) that we use for training the HMM and GRU under weak supervision. While prior work typically estimates the loss on a single, inferred video segmentation, our CDFL discriminates between the energy of all valid and invalid frame labelings of a training video. A valid frame labeling satisfies the ground-truth temporal ordering of actions, whereas an invalid one violates the ground truth. We specify an efficient recursive algorithm for computing the CDFL in terms of the logadd function of the segmentation energy. Our evaluation on action segmentation and alignment gives superior results to those of the state of the art on the benchmark Breakfast Action, Hollywood Extended, and 50Salads datasets.

cs.CV↗

arXiv2018

Left counital Hopf algebras on free Nijenhuis algebras

Factorization in algebra is an important problem. In this paper, we first obtain a unique factorization in free Nijenhuis algebras. By using of this unique factorization, we then define a coproduct and a left counital bialgebraic structure on a free Nijenhuis algebra. Finally, we prove that this left counital bialgebra is connected and hence obtain a left counital Hopf algebra on a free Nijenhuis algebra.

math.RA↗

arXiv2018

Boundary Flow: A Siamese Network that Predicts Boundary Motion without Training on Motion

Using deep learning, this paper addresses the problem of joint object boundary detection and boundary motion estimation in videos, which we named boundary flow estimation. Boundary flow is an important mid-level visual cue as boundaries characterize objects spatial extents, and the flow indicates objects motions and interactions. Yet, most prior work on motion estimation has focused on dense object motion or feature points that may not necessarily reside on boundaries. For boundary flow estimation, we specify a new fully convolutional Siamese network (FCSN) that jointly estimates object-level boundaries in two consecutive frames. Boundary correspondences in the two frames are predicted by the same FCSN with a new, unconventional deconvolution approach. Finally, the boundary flow estimate is improved with an edgelet-based filtering. Evaluation is conducted on three tasks: boundary detection in videos, boundary flow estimation, and optical flow estimation. On boundary detection, we achieve the state-of-the-art performance on the benchmark VSB100 dataset. On boundary flow estimation, we present the first results on the Sintel training dataset. For optical flow estimation, we run the recent approach CPMFlow but on the augmented input with our boundary-flow matches, and achieve significant performance improvement on the Sintel benchmark.

cs.CV↗

arXiv2014

Applications of shuffle product to restricted decomposition formulas for multiple zeta values

In this paper we obtain a recursive formula for the shuffle product and apply it to derive two restricted decomposition formulas for multiple zeta values (MZVs). The first formula generalizes the decomposition formula of Euler and is similar to the restricted formula of Eie and Wei for MZVs with one strings of 1's. The second formula generalizes the previous results to the product of two MZVs with one and two strings of 1's respectively.

math.NT↗

arXiv2014

Families of weighted sum formulas for multiple zeta values

Euler's sum formula and its multi-variable and weighted generalizations form a large class of the identities of multiple zeta values. In this paper we prove a family of identities involving Bernoulli numbers and apply them to obtain infinitely many weighted sum formulas for double zeta values and triple zeta values where the weight coefficients are given by symmetric polynomials. We give a general conjecture in arbitrary depth at the end of the paper.

math.NT↗

arXiv2012

Nijenhuis algebras, NS algebras and N-dendriform algebras

In this paper we study (associative) Nijenhuis algebras, with emphasis on the relationship between the category of Nijenhuis algebras and the categories of NS algebras. This is in analogy to the well-known theory of the adjoint functor from the category of Lie algebras to that of associative algebras, and the more recent results on the adjoint functor from the categories of dendriform and tridendriform algebras to that of Rota-Baxter algebras. We first give an explicit construction of free Nijenhuis algebras and then apply it to obtain the universal enveloping Nijenhuis algebra of an NS algebra. We further apply the construction to determine the binary quadratic nonsymmetric algebra, called the N-dendriform algebra, that is compatible with the Nijenhuis algebra. As it turns out, the N-dendriform algebra has more relations than the NS algebra.

math.RA↗