arXiv subjects

Bin Yan

Publications and source records attributed to Bin Yan.

At least 55 records · Page 3Linked to original sources

arXiv2024

Direct Fidelity Estimation for Generic Quantum States

Verifying the proper preparation of quantum states is essential in modern quantum information science. Various protocols have been developed to estimate the fidelity of quantum states produced by different parties. Direct fidelity estimation is a leading approach, as it typically requires a number of measurements that scale linearly with the Hilbert space dimension, making it far more efficient than full state tomography. In this article, we introduce a novel fidelity estimation protocol for generic quantum states, with an overall computational cost that scales only as the square root of the Hilbert space dimension. Furthermore, our protocol significantly reduces the number of required measurements and the communication cost between parties to finite. This protocol leverages the quantum amplitude estimation algorithm in conjunction with classical shadow tomography to achieve these improvements.

quant-ph↗

arXiv2024

Parametric tuning of quantum phase transitions in ultracold reactions

Advances in atomic physics have led to the possibility of a coherent transformation between ultra-cold atoms and molecules including between completely bosonic condensates. Such transformations are enabled by the magneto-association of atoms at a Feshbach resonance which results in a passage through a quantum critical point. In this study, we show that the presence of generic interaction between the constituent atoms and molecules can fundamentally alter the nature of the critical point, change the yield of the reaction and the order of the consequent phase transition. We find that the correlations introduced by this interaction induce nontrivial many-body physics such as coherent oscillations between atoms and molecules, and a selective formation of squeezed molecular quantum states and quantum cat states. We provide analytical and numerical descriptions of these effects, along with scaling laws for the reaction yield in non-adiabatic regimes.

cond-mat.quant-gas↗

arXiv2024

Towards Real-Time Open-Vocabulary Video Instance Segmentation

In this paper, we address the challenge of performing open-vocabulary video instance segmentation (OV-VIS) in real-time. We analyze the computational bottlenecks of state-of-the-art foundation models that performs OV-VIS, and propose a new method, TROY-VIS, that significantly improves processing speed while maintaining high accuracy. We introduce three key techniques: (1) Decoupled Attention Feature Enhancer to speed up information interaction between different modalities and scales; (2) Flash Embedding Memory for obtaining fast text embeddings of object categories; and, (3) Kernel Interpolation for exploiting the temporal continuity in videos. Our experiments demonstrate that TROY-VIS achieves the best trade-off between accuracy and speed on two large-scale OV-VIS benchmarks, BURST and LV-VIS, running 20x faster than GLEE-Lite (25 FPS v.s. 1.25 FPS) with comparable or even better accuracy. These results demonstrate TROY-VIS's potential for real-time applications in dynamic environments such as mobile robotics and augmented reality. Code and model will be released at https://github.com/google-research/troyvis.

cs.CV↗

arXiv2024

Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack

With the development and application of deep learning in signal detection tasks, the vulnerability of neural networks to adversarial attacks has also become a security threat to signal detection networks. This paper defines a signal adversarial examples generation model for signal detection network from the perspective of adding perturbations to the signal. The model uses the inequality relationship of L2-norm between time domain and time-frequency domain to constrain the energy of signal perturbations. Building upon this model, we propose a method for generating signal adversarial examples utilizing gradient-based attacks and Short-Time Fourier Transform. The experimental results show that under the constraint of signal perturbation energy ratio less than 3%, our adversarial attack resulted in a 28.1% reduction in the mean Average Precision (mAP), a 24.7% reduction in recall, and a 30.4% reduction in precision of the signal detection network. Compared to random noise perturbation of equivalent intensity, our adversarial attack demonstrates a significant attack effect.

cs.CV↗

arXiv2024

Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reduction

Metal artifacts in computed tomography (CT) imaging pose significant challenges to accurate clinical diagnosis. The presence of high-density metallic implants results in artifacts that deteriorate image quality, manifesting in the forms of streaking, blurring, or beam hardening effects, etc. Nowadays, various deep learning-based approaches, particularly generative models, have been proposed for metal artifact reduction (MAR). However, these methods have limited perception ability in the diverse morphologies of different metal implants with artifacts, which may generate spurious anatomical structures and exhibit inferior generalization capability. To address the issues, we leverage visual-language model (VLM) to identify these morphological features and introduce them into a dual-domain CLIP-assisted residual optimization perception model (DuDoCROP) for MAR. Specifically, a dual-domain CLIP (DuDoCLIP) is fine-tuned on the image domain and sinogram domain using contrastive learning to extract semantic descriptions from anatomical structures and metal artifacts. Subsequently, a diffusion model is guided by the embeddings of DuDoCLIP, thereby enabling the dual-domain prior generation. Additionally, we design prompt engineering for more precise image-text descriptions that can enhance the model's perception capability. Then, a downstream task is devised for the one-step residual optimization and integration of dual-domain priors, while incorporating raw data fidelity. Ultimately, a new perceptual indicator is proposed to validate the model's perception and generation performance. With the assistance of DuDoCLIP, our DuDoCROP exhibits at least 63.7% higher generalization capability compared to the baseline model. Numerical experiments demonstrate that the proposed method can generate more realistic image structures and outperform other SOTA approaches both qualitatively and quantitatively.

cs.CV↗

arXiv2024

Revisiting strong-coupling determinations from $e^+ e^-$ event shapes

We reassess the theoretical uncertainties of strong-coupling determinations from a global fit to the $e^+ e^-$ thrust data, focusing in particular on the scheme dependence associated with the applied renormalon-cancellation prescription and on the choice of scale parameters that are used to estimate higher-order perturbative corrections in all sectors of the calculation.

hep-ph↗

arXiv2024

Additive engineering for Sb$_2$S$_3$ indoor photovoltaics with efficiency exceeding 17%

Indoor photovoltaics (IPVs) have attracted increasing attention for sustainably powering Internet of Things (IoT) electronics. Sb$_2$S$_3$ is a promising IPV candidate material with a bandgap of ~1.75 eV, which is near the optimal value for indoor energy harvesting. However, the performance of Sb$_2$S$_3$ solar cells is limited by nonradiative recombination, closely associated with the poor-quality absorber films. Additive engineering is an effective strategy to improved the properties of solution-processed films. This work shows that the addition of monoethanolamine (MEA) into the precursor solution allows the nucleation and growth of Sb$_2$S$_3$ films to be controlled, enabling the deposition of high-quality Sb$_2$S$_3$ absorbers with reduced grain boundary density, optimized band positions and increased carrier concentration. Complemented with computations, it is revealed that the incorporation of MEA leads to a more efficient and energetically favorable deposition for enhanced heterogeneous nucleation on the substrate, which increases the grain size and accelerates the deposition rate of Sb$_2$S$_3$ films. Due to suppressed carrier recombination and improved charge-carrier transport in Sb$_2$S$_3$ absorber films, the MEA-modulated Sb$_2$S$_3$ solar cell yields a power conversion efficiency (PCE) of 7.22% under AM1.5G illumination, and an IPV PCE of 17.55% under 1000 lux white light emitting diode (WLED) illumination, which is the highest yet reported for Sb$_2$S$_3$ IPVs. Furthermore, we construct high performance large-area Sb$_2$S$_3$ IPV modules to power IoT wireless sensors, and realize the long-term continuous recording of environmental parameters under WLED illumination in an office. This work highlights the great prospect of Sb$_2$S$_3$ photovoltaics for indoor energy harvesting.

cond-mat.mtrl-sci↗

arXiv2024

Efficient Pretraining Model based on Multi-Scale Local Visual Field Feature Reconstruction for PCB CT Image Element Segmentation

Element segmentation is a key step in nondestructive testing of Printed Circuit Boards (PCB) based on Computed Tomography (CT) technology. In recent years, the rapid development of self-supervised pretraining technology can obtain general image features without labeled samples, and then use a small amount of labeled samples to solve downstream tasks, which has a good potential in PCB element segmentation. At present, Masked Image Modeling (MIM) pretraining model has been initially applied in PCB CT image element segmentation. However, due to the small and regular size of PCB elements such as vias, wires, and pads, the global visual field has redundancy for a single element reconstruction, which may damage the performance of the model. Based on this issue, we propose an efficient pretraining model based on multi-scale local visual field feature reconstruction for PCB CT image element segmentation (EMLR-seg). In this model, the teacher-guided MIM pretraining model is introduced into PCB CT image element segmentation for the first time, and a multi-scale local visual field extraction (MVE) module is proposed to reduce redundancy by focusing on local visual fields. At the same time, a simple 4-Transformer-blocks decoder is used. Experiments show that EMLR-seg can achieve 88.6% mIoU on the PCB CT image dataset we proposed, which exceeds 1.2% by the baseline model, and the training time is reduced by 29.6 hours, a reduction of 17.4% under the same experimental condition, which reflects the advantage of EMLR-seg in terms of performance and efficiency.

cs.CV↗

arXiv2024

Probing the four-fermion operators via the transverse double spin asymmetry at the Electron-Ion Collider

The chirality-flipping operators of light fermions are currently poorly constrained by experimental analyses due to the lack of interference with Standard Model (SM) amplitudes in traditional observables. In this work, we propose to investigate the semi-leptonic scalar/tensor four-fermion operators of electron and quarks through the transverse double spin asymmetry (DSA) at Electron-Ion Collider, where both the electron and proton beams could be highly transversely polarized. Due to the chirality-flipping nature of these operators, we demonstrate that their interference with the SM results in an unsuppressed contribution to the DSA, and could lead to non-trivial azimuthal $\cos2ϕ$ and $\sin2ϕ$ distributions that are linearly dependent on their Wilson coefficients. This new method has the potential to significantly improve the current constraints on these scalar/tensor four-fermion operators without relying on theoretical assumptions about other types of new physics effects, particularly for the tensor type operator of the $u$-quark. Additionally, our findings indicate that both the real and imaginary parts of these operators can be simultaneously constrained and offer a new opportunity for probing potential $CP$-violation effects. However, it is important to note that these results would be sensitive to the quark transversity distributions, which are currently poorly constrained by the experimental data, but could be significantly improved at the upcoming Electron-Ion Collider. Therefore, our work opens up a new avenue to utilize this new spin asymmetry for exploring the new physics effects from the scalar/tensor four-fermion operators.

hep-ph↗

arXiv2024

Spin asymmetry and dipole moments in $τ$-pair production with ultraperipheral heavy ion collisions

The anomalous magnetic (MDM) and electric (EDM) dipole moments of the $τ$ lepton serve as crucial indicators of new physics beyond the Standard Model. Leveraging azimuthal angular asymmetry as a novel tool in ultraperipheral collisions (UPCs), we attain unparalleled precision in the study of these key properties. Driven by the highly linear polarization of coherent photons, this method uniquely enables both the MDM and EDM to contribute to the $\cos2ϕ$ angular distribution in similar magnitudes. Importantly, our approach substantially narrows the parameter space, excluding more than half of it compared to expected UPC-based measurements reliant solely on the total cross-section. This method not only provides improved constraints but also minimizes the need for additional theoretical assumptions.

hep-ph↗

arXiv2024

Probing light quark Yukawa couplings through angularity distributions in Higgs boson decay

We propose to utilize angularity distributions in Higgs boson decay to probe light quark Yukawa couplings at $e^+e^-$ colliders. Angularities $τ_a$ are a class of 2-jet event shapes with variable and tunable sensitivity to the distribution of radiation in hadronic jets in the final state. Using soft-collinear effective theory (SCET), we present a prediction of angularity distributions from Higgs decaying to quark and gluon states at $e^+e^-$ colliders to ${\rm NNLL}+\mathcal{O}(α_s)$ accuracy. Due to the different color structures in quark and gluon jets, the angularity distributions from $H\to q\bar{q}$ and $H\to gg$ show different behaviors and can be used to constrain the light quark Yukawa couplings. We show that the upper limit of light quark Yukawa couplings could be probed to the level of $\sim15\%$ of the bottom quark Yukawa coupling in the Standard Model in a conservative analysis window far away from nonperturbative effects and other uncertainties; the limit can be pushed to $\lesssim 7-9\%$ with better control of the nonperturbative effects especially on gluon angularity distributions and/or with multiple angularities.

hep-ph↗

arXiv2024

Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking

Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e., spatial and appearance information), which exhibit powerful instance-level discrimination. However, when object occlusion and clustering occur, spatial and appearance information will become ambiguous simultaneously due to the high overlap among objects. In this paper, we demonstrate this long-standing challenge in MOT can be efficiently and effectively resolved by incorporating weak cues to compensate for strong cues. Along with velocity direction, we introduce the confidence and height state as potential weak cues. With superior performance, our method still maintains Simple, Online and Real-Time (SORT) characteristics. Also, our method shows strong generalization for diverse trackers and scenarios in a plug-and-play and training-free manner. Significant and consistent improvements are observed when applying our method to 5 different representative trackers. Further, with both strong and weak cues, our method Hybrid-SORT achieves superior performance on diverse benchmarks, including MOT17, MOT20, and especially DanceTrack where interaction and severe occlusion frequently happen with complex motions. The code and models are available at https://github.com/ymzis69/HybridSORT.

cs.CV↗

arXiv2024

Aligned with LLM: a new multi-modal training paradigm for encoding fMRI activity in visual cortex

Recently, there has been a surge in the popularity of pre trained large language models (LLMs) (such as GPT-4), sweeping across the entire Natural Language Processing (NLP) and Computer Vision (CV) communities. These LLMs have demonstrated advanced multi-modal understanding capabilities and showcased strong performance across various benchmarks. The LLM has started to embody traits of artificial general intelligence, which holds vital guidance for enhancing brain-like characteristics within visual encoding models. Hence, This paper proposes a new multi-modal training paradigm, aligning with LLM, for encoding fMRI activity in visual cortex. Based on this paradigm, we trained an encoding model in fMRI data named the LLM-Visual Encoding Model (LLM-VEM). Specifically, we utilize LLM (miniGPT4) to generate descriptive text for all stimulus images, forming a high-quality textual description set. Moreover, we use the pre-trained text encoder (CLIP) to process these detailed descriptions, obtaining the text embedding features. Next, we use the contrast loss function to minimize the distance between the image embedding features and the text embedding features to complete the alignment operation of the stimulus image and text information. With the assistance of the pre-trained LLM, this alignment process facilitates better learning of the visual encoding model, resulting in higher precision. The final experimental results indicate that our training paradigm has significantly aided in enhancing the performance of the visual encoding model.

cs.CV↗

arXiv2023

UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces

The reference-based object segmentation tasks, namely referring image segmentation (RIS), few-shot image segmentation (FSS), referring video object segmentation (RVOS), and video object segmentation (VOS), aim to segment a specific object by utilizing either language or annotated masks as references. Despite significant progress in each respective field, current methods are task-specifically designed and developed in different directions, which hinders the activation of multi-task capabilities for these tasks. In this work, we end the current fragmented situation and propose UniRef++ to unify the four reference-based object segmentation tasks with a single architecture. At the heart of our approach is the proposed UniFusion module which performs multiway-fusion for handling different tasks with respect to their specified references. And a unified Transformer architecture is then adopted for achieving instance-level segmentation. With the unified designs, UniRef++ can be jointly trained on a broad range of benchmarks and can flexibly complete multiple tasks at run-time by specifying the corresponding references. We evaluate our unified models on various benchmarks. Extensive experimental results indicate that our proposed UniRef++ achieves state-of-the-art performance on RIS and RVOS, and performs competitively on FSS and VOS with a parameter-shared network. Moreover, we showcase that the proposed UniFusion module could be easily incorporated into the current advanced foundation model SAM and obtain satisfactory results with parameter-efficient finetuning. Codes and models are available at \url{https://github.com/FoundationVision/UniRef}.

cs.CV↗

arXiv2023

Bell meets Cavendish: a quantum signature of gravity?

The inclusion of gravitation within the framework of quantum theory remains one of the most prominent open problem in physics. To date, the absence of empirical evidence hampers conclusions regarding the fundamental nature of gravity -- whether it adheres to quantum principles or remains a classical field manifests solely in the macroscopic domain. This article presents a though experiment aimed at discerning the quantum signature of gravity through the lens of macroscopic nonlocality. The experiment integrates a standard Bell test with a classical Cavendish experiment. We illustrate that the measurement apparatuses employed in a Bell experiment, despite lacking entanglement, defy classical descriptions; their statistical behaviors resist explanations through local hidden variable models. Extending this argument to encompass the massive objects in the Cavendish experiment allows for further disputing classical models of the gravitational field. Under favorable conditions and in light of corroborating evidence from the recent loophole-free Bell experiments, the quantum character of gravity is essentially substantiated.

quant-ph↗

arXiv2023

Single Transverse Spin Asymmetry as a New Probe of SMEFT Dipole Operators

Electroweak dipole operators in the Standard Model Effective Field Theory (SMEFT) are important indirect probes of quantum effects of new physics beyond the Standard Model (SM), yet they remain poorly constrained by current experimental analyses for lack of interference with the SM amplitudes in constructing cross section observables. In this Letter, we point out that dipole operators flip fermion helicities so are ideally studied through single transverse spin asymmetries. We illustrate this at a future electron-positron collider with transversely polarized beams, where such effect exhibits as azimuthal $\cosϕ$ and $\sinϕ$ distributions which originate from the interference of the electron dipole operators with the SM and are linearly dependent on their Wilson coefficients. This new method can improve the current constraints on the electron dipole couplings by one to two orders of magnitude, without depending on other new physics operators, and can also simultaneously constrain both their real and imaginary parts, offering a new opportunity for probing potential $CP$-violating effects.

hep-ph↗

arXiv2023

Effects of Renormalon Scheme and Perturbative Scale Choices on Determinations of the Strong Coupling from $e^+e^-$ Event Shapes

We study the role of renormalon cancellation schemes and perturbative scale choices in extractions of the strong coupling constant $α_s(m_Z)$ and the leading non-perturbative shift parameter $Ω_1$ from resummed predictions of the $e^+e^-$ event shape thrust. We calculate the thrust distribution to N$^{3}$LL$^\prime$ resummed accuracy in Soft-Collinear Effective Theory (SCET) matched to the fixed-order $\mathcal{O}(α_s^2)$ prediction, and perform a new high-statistics computation of the $\mathcal{O}(α_s^3)$ matching in EERAD3, although we do not include the latter in our final $α_s$ fits due to some observed systematics that require further investigation. We are primarily interested in testing the phenomenological impact sourced from varying amongst three renormalon cancellation schemes and two sets of perturbative scale profile choices. We then perform a global fit to available data spanning center-of-mass energies between 35-207 GeV in each scenario. Relevant subsets of our results are consistent with prior SCET-based extractions of $α_s(m_Z)$, but we are also led to a number of novel observations. Notably, we find that the combined effect of altering the renormalon cancellation scheme and profile parameters can lead to few-percent-level impacts on the extracted values in the $α_s-Ω_1$ plane, indicating a potentially important systematic theory uncertainty that should be accounted for. We also observe that fits performed over windows dominated by dijet events are typically of a higher quality than those that extend into the far tails of the distributions, possibly motivating future fits focused more heavily in this region. Finally, we discuss how different estimates of the three-loop soft matching coefficient $c_{\tilde{S}}^3$ can also lead to measurable changes in the fitted $\lbrace α_s, Ω_1 \rbrace$ values.

hep-ph↗

arXiv2023

Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection

The presence of a large number of bots in Online Social Networks (OSN) leads to undesirable social effects. Graph neural networks (GNNs) are effective in detecting bots as they utilize user interactions. However, class-imbalanced issues can affect bot detection performance. To address this, we propose an over-sampling strategy for GNNs (OS-GNN) that generates samples for the minority class without edge synthesis. First, node features are mapped to a feature space through neighborhood aggregation. Then, we generate samples for the minority class in the feature space. Finally, the augmented features are used to train the classifiers. This framework is general and can be easily extended into different GNN architectures. The proposed framework is evaluated using three real-world bot detection benchmark datasets, and it consistently exhibits superiority over the baselines.

cs.CV↗