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Qian Wang

Publications and source records attributed to Qian Wang.

At least 343 records · Page 19Linked to original sources
arXiv2023

Deep Joint Source-Channel Coding for Wireless Image Transmission with Entropy-Aware Adaptive Rate Control

Adaptive rate control for deep joint source and channel coding (JSCC) is considered as an effective approach to transmit sufficient information in scenarios with limited communication resources. We propose a deep JSCC scheme for wireless image transmission with entropy-aware adaptive rate control, using a single deep neural network to support multiple rates and automatically adjust the rate based on the feature maps of the input image and their entropy, as well as the channel conditions. In particular, we maximize the entropy of the feature maps to increase the average information carried by each transmitted symbol during the training. We further decide which feature maps should be activated based on their entropy, which improves the efficiency of the transmitted symbols. We also propose a pruning module to remove less important pixels in the activated feature maps in order to further improve transmission efficiency. The experimental results demonstrate that our proposed scheme learns an effective rate control strategy that reduces the required channel bandwidth while preserving the quality of the reconstructed images.

eess.IV↗
arXiv2023

Downstream-agnostic Adversarial Examples

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial use. In this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder.

cs.CV↗
arXiv2023

Coupling kinetic and continuum using data-driven maximum entropy distribution

An important class of multi-scale flow scenarios deals with an interplay between kinetic and continuum phenomena. While hybrid solvers provide a natural way to cope with these settings, two issues restrict their performance. Foremost, the inverse problem implied by estimating distributions has to be addressed, to provide boundary conditions for the kinetic solver. The next issue comes from defining a robust yet accurate switching criterion between the two solvers. This study introduces a data-driven kinetic-continuum coupling, where the Maximum-Entropy-Distribution (MED) is employed to parametrize distributions arising from continuum field variables. Two regression methodologies of Gaussian-Processes (GPs) and Artificial-Neural-Networks (ANNs) are utilized to predict MEDs efficiently. Hence the MED estimates are employed to carry out the coupling, besides providing a switching criterion. To achieve the latter, a continuum breakdown parameter is defined by means of the Fisher information distance computed from the MED estimates. We test the performance of our devised MED estimators by recovering bi-modal densities. Next, MED estimates are integrated into a hybrid kinetic-continuum solution algorithm. Here Direct Simulation Monte-Carlo (DSMC) and Smoothed-Particle Hydrodynamics (SPH) are chosen as kinetic and continuum solvers, respectively. The problem of monatomic gas inside Sod's shock tube is investigated, where DSMC-SPH coupling is realized by applying the devised MED estimates. Very good agreements with respect to benchmark solutions along with a promising speed-up are observed in our reported test cases.

physics.flu-dyn↗
arXiv2023

The 2175 Angstrom Interstellar Extinction Bump: Is the Wavelength Variable?

The most striking characteristics of the mysterious 2175 Angstrom extinction bump, the strongest spectroscopic absorption feature seen on the interstellar extinction curve, are the invariant central wavelength and variable bandwidth: its peak position at 2175 Angstrom is remarkably constant while its bandwidth varies from one line of sight to another. However, recent studies of the lines of sight toward a number of Herbig Ae/Be stars have revealed that the extinction bump exhibits substantial shifts from the canonical wavelength of 2175 Angstrom. In this work we revisit these lines of sight and take a physical approach to determine the ultraviolet (UV) extinction curve for each line of sight. It is found that the wavelengths of the derived UV extinction bumps are around 2200 Angstrom and the scatters are considerably smaller than that of the previous study based on the same set of Herbig Ae/Be stars, consistent with the conventional wisdom. Nevertheless, the scatters are still appreciably larger than that associated with the nominal bump position of 2175 Angstrom. This is discussed in the context that Herbig Ae/Be stars are not well-suited for interstellar extinction studies.

astro-ph.GA↗
arXiv2023

Power-law decay of the fraction of the mixed eigenstates in kicked top model with mixed-type classical phase space

The properties of mixed eigenstates in a generic quantum system with classical counterpart that has mixed-type phase space, although important to understand several fundamental questions that arise in both theoretical and experimental studies, are still not clear. Here, following a recent work [Č.~Lozej {\it et al}. Phys. Rev. E {\bf 106}, 054203 (2022)], we perform an analysis of the features of mixed eigenstates in a time-dependent Hamiltonian system, the celebrated kicked top model. As a paradigmatic model for studying quantum chaos, kicked top model is known to exhibit both classical and quantum chaos. The types of eigenstates are identified by means of the phase space overlap index, which is defined as the overlap of the Husimi function with regular and chaotic regions in classical phase space. We show that the mixed eigenstates appear due to various tunneling precesses between different phase space structures, while the regular and chaotic eigenstates are, respectively, associated with invariant tori and chaotic component in phase space. We examine how the probability distribution of the phase space overlap index evolves with increasing system size for different kicking strengths. In particular, we find that the relative fraction of mixed states exhibits a power-law decay as the system size increases, indicating that only purely regular and chaotic eigenstates are left in the strict semiclassical limit. We thus provide further verification of the principle of uniform semiclassical condensation of Husimi functions and confirm the correctness of the Berry-Robnik picture.

quant-ph↗
arXiv2023

One-step Iterative Estimation of Effective Atomic Number and Electron Density for Dual Energy CT

Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. For proton therapy treatment planning, besides material-selective images, maps of effective atomic number (Z) and relative electron density to that of water ($ρ_e$) can also be achieved and further employed to improve stopping power ratio accuracy and reduce range uncertainty. In this work, we propose a one-step iterative estimation method, which employs multi-domain gradient $L_0$-norm minimization, for Z and $ρ_e$ maps reconstruction. The algorithm was implemented on GPU to accelerate the predictive procedure and to support potential real-time adaptive treatment planning. The performance of the proposed method is demonstrated via both phantom and patient studies.

physics.med-ph↗
arXiv2023

Possibility of generating the $^3_{Λ_c}$H in the quark-delocalization color-screening model

We probe the existence of the $^3_{Λ_c}$H where the $NΛ_c$ potentials are derived from the quark-delocalization color-screening model (QDCSM). The $NΛ_c$ system is studied and the $NΛ_c$ scattering length so as the effective range are obtained in the QDCSM. We construct effective Gaussian-type $NΛ_c$ potentials which reproduce the $NΛ_c$ scattering data given by the QDCSM. By solving the $NNΛ_c$ three body Schrödinger equation with the Gaussian expansion method, we calculate the energies of the $^3_{Λ_c}$H with isospin $I=0$, $J^P=1/2^+$ and $I=0$, $J^P=3/2^+$ under different color screening parameter $μ$. The $J^P=1/2^+$ and $J^P=3/2^+$ states are both bound when the color screening parameter $μ$ is set to 1.0 or 1.2, where the $J^P=1/2^+$ state is bound by $0.08\sim0.85$ MeV and the $J^P=3/2^+$ state is bound by $0.15\sim1.31$ MeV with respect to the deuteron-$Λ_c$ threshold.

nucl-th↗
arXiv2023

CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image Classification

Automatic examination of thin-prep cytologic test (TCT) slides can assist pathologists in finding cervical abnormality for accurate and efficient cancer screening. Current solutions mostly need to localize suspicious cells and classify abnormality based on local patches, concerning the fact that whole slide images of TCT are extremely large. It thus requires many annotations of normal and abnormal cervical cells, to supervise the training of the patch-level classifier for promising performance. In this paper, we propose CellGAN to synthesize cytopathological images of various cervical cell types for augmenting patch-level cell classification. Built upon a lightweight backbone, CellGAN is equipped with a non-linear class mapping network to effectively incorporate cell type information into image generation. We also propose the Skip-layer Global Context module to model the complex spatial relationship of the cells, and attain high fidelity of the synthesized images through adversarial learning. Our experiments demonstrate that CellGAN can produce visually plausible TCT cytopathological images for different cell types. We also validate the effectiveness of using CellGAN to greatly augment patch-level cell classification performance.

cs.CV↗
arXiv2023

High density loading and collisional loss of laser cooled molecules in an optical trap

We report optical trapping of laser-cooled molecules at sufficient density to observe molecule-molecule collisions for the first time in a bulk gas. SrF molecules from a red-detuned magneto-optical trap (MOT) are compressed and cooled in a blue-detuned MOT. Roughly 30% of these molecules are loaded into an optical dipole trap with peak number density $n_0 \approx 3\times 10^{10} \text{ cm}^{-3}$ and temperature $T\approx40$ $μ$K. We observe two-body loss with rate coefficient $β= 2.7^{+1.2}_{-0.8}\times 10^{-10} \text{ cm}^3 \text{ s}^{-1}$. Achieving this density and temperature opens a path to evaporative cooling towards quantum degeneracy of laser-cooled molecules.

physics.atom-ph↗
arXiv2023

Multi-Scenario Ranking with Adaptive Feature Learning

Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost. These efforts produce different MSL paradigms by searching more optimal network structure, such as Auxiliary Network, Expert Network, and Multi-Tower Network. It is intuitive that different scenarios could hold their specific characteristics, activating the user's intents quite differently. In other words, different kinds of auxiliary features would bear varying importance under different scenarios. With more discriminative feature representations refined in a scenario-aware manner, better ranking performance could be easily obtained without expensive search for the optimal network structure. Unfortunately, this simple idea is mainly overlooked but much desired in real-world systems.Further analysis also validates the rationality of adaptive feature learning under a multi-scenario scheme. Moreover, our A/B test results on the Alibaba search advertising platform also demonstrate that Maria is superior in production environments.

cs.IR↗
arXiv2023

Modeling realistic multiphase flows using a non-orthogonal multiple-relaxation-time lattice Boltzmann method

In this paper, we develop a three-dimensional multiple-relaxation-time lattice Boltzmann method (MRT-LBM) based on a set of non-orthogonal basis vectors. Compared with the classical MRT-LBM based on a set of orthogonal basis vectors, the present non-orthogonal MRT-LBM simplifies the transformation between the discrete velocity space and the moment space, and exhibits better portability across different lattices. The proposed method is then extended to multiphase flows at large density ratio with tunable surface tension, and its numerical stability and accuracy are well demonstrated by some benchmark cases. Using the proposed method, a practical case of a fuel droplet impacting on a dry surface at high Reynolds and Weber numbers is simulated and the evolution of the spreading film diameter agrees well with the experimental data. Furthermore, another realistic case of a droplet impacting on a super-hydrophobic wall with a cylindrical obstacle is reproduced, which confirms the experimental finding of Liu \textit{et al.} [``Symmetry breaking in drop bouncing on curved surfaces," Nature communications 6, 10034 (2015)] that the contact time is minimized when the cylinder radius is comparable with the droplet cylinder.

physics.flu-dyn↗
arXiv2023

A general positivity-preserving algorithm for implicit high-order finite volume schemes solving the Euler and Navier-Stokes equations

This paper presents a general positivity-preserving algorithm for implicit high-order finite volume schemes solving Euler and Navier-Stokes equations. Previous positivity-preserving algorithms are mainly based on mathematical analyses, being highly dependent on the existence of low-order positivity-preserving numerical schemes for specific governing equations. This dependency poses serious restrictions on extending these algorithms to temporally implicit schemes, since it is difficult to know if a low-order implicit scheme is positivity-preserving. The present positivity-preserving algorithm is based on an asymptotic analysis of the solutions near local vacuum minimum points. The asymptotic analysis shows that the solutions decay exponentially with time to maintain non-negative density and pressure at a local vacuum minimum point. In its neighborhood, the exponential evolution leads to a modification of the linear evolution process, which can be modelled by a direct correction of the linear residual to ensure positivity. This correction however destroys the conservation of the numerical scheme. Therefore, a second correction procedure is proposed to recover conservation. The proposed positivity-preserving algorithm is considerably less restrictive than existing algorithms. It does not rely on the existence of low-order positivity-preserving baseline schemes and the convex decomposition of volume integrals of flow quantities. It does not need to reduce the time step size for maintaining the stability either. Furthermore, it can be implemented iteratively in the implicit dual time-stepping schemes to preserve positivity of the intermediate and converged states of the sub-iterations. It is proved that the present positivity-preserving algorithm is accuracy-preserving. Numerical results demonstrate that the proposed algorithm preserves the positive density and pressure in all test cases.

physics.comp-ph↗
arXiv2023

Fresh-Fi: Enhancing Information Freshness in Commodity WiFi Systems via Customizing Lower Layers

Enhancing information freshness in wireless networks has gained significant attention in recent years. To optimize or analyze information freshness, which is often characterized by the age of information (AoI) metric, extensive theoretical studies have been conducted on various wireless networks. Early research has demonstrated the significance of last-come-first-served (LCFS) packet scheduling and controlled status sampling (i.e., packet generation) in improving information freshness. These mechanisms have been widely adopted in subsequent studies. However, the effective implementation of these mechanisms in commercial off-the-shelf (COTS) wireless devices has not been thoroughly investigated, which could limit the practical application of information freshness-oriented protocols in real-world systems. Our work aims to address the gap by exploring the effective implementation of the information freshness-oriented mechanisms mentioned above in COTS WiFi devices that use the Linux operating system. Our attempts reveal that implementing these mechanisms in COTS systems is not a straightforward task. Specifically, we found that the physical layer queue of WiFi devices operates on a first-come-first-served (FCFS) basis, and the packet generation process cannot be precisely controlled by default. To overcome these challenges, we develop Fresh-Fi, an information freshness-oriented protocol stack that involves careful customization to the lower layers of the Linux networking protocol stack. Fresh-Fi mainly incorporates a mac80211 subsystem-based LCFS queue and a real-time kernel-based cross-layer tunnel between the mac80211 subsystem and the application layer for triggered packet generation.

cs.NI↗
arXiv2023

Age of Information in Reservation Multi-Access Networks with Stochastic Arrivals: Analysis and Optimization

This paper analyzes and optimizes the average Age of Information (AAoI) of Frame Slotted ALOHA with Reservation and Data slots (FSA-RD) in a multi-access network, where multiple users transmit their randomly generated status updates to a common access point in a framed manner. Each frame consists of one reservation slot and several data slots. The reservation slot is further split into several mini-slots. In each reservation slot, users that want to transmit a status update will randomly send short reservation packets in one of the mini-slots to contend for data slots of the current frame. The reservation is successful only if one reservation packet is sent in a mini-slot. The data slots are then allocated to those users that succeed in the reservation slot. In the considered FSA-RD scheme, one user with a status update for transmission, termed active user, may need to perform multiple reservation attempts before successfully delivering it. As such, the number of active user(s) in different frames are dependent and thus the probability of making a successful reservation varies from frame to frame, making the AAoI analysis non-trivial. We manage to derive an analytical expression of AAoI for FSA-RD by characterizing the evolution of the number of active user(s) in each frame as a discrete-time Markov chain. We then consider the FSA-RD scheme with one reservation attempt per status update, termed FSA-RD-One. Thanks to the independent frame behaviors of FSA-RD-One, we attain a closed-form expression for its AAoI, which is further used to find the near-optimal reservation probability. Our analysis reveals the impact of key protocol parameters, such as frame size and reservation probability, on the AAoI. Simulation results validate our analysis and show that the optimized FSA-RD outperforms the optimized slotted ALOHA.

cs.IT↗
arXiv2023

Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets

In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and bidding on behalf of their clients. Typically, an agency usually has information on multiple advertisers, so she can potentially coordinate bids to help her clients achieve higher utilities than those under independent bidding. In this paper, we study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding. We show that these algorithms achieve maximal coalition welfare and discuss bidders' incentives to misreport their budgets, in symmetric cases. Our proofs combine the techniques of online learning and equilibrium analysis, overcoming the difficulty of competing with a multi-dimensional benchmark. The performance of our algorithms is further evaluated by experiments on both synthetic and real data. To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.

cs.GT↗
arXiv2023

Prediction of nanobubble-assisted focused ultrasound-induced blood-brain barrier opening with machine learning

Novel approaches for predicting the outcomes of blood-brain barrier (BBB) opening with focused ultrasound (FUS) and microbubbles are highly desired. This study aims to explore machine learning-based methods for reliably predicting the FUS-induced BBB opening efficacy and safety. Methods: Sixteen female rats were used in this study. An acoustic feedback-controlled FUS system (f0: 0.5MHz) was used for the BBB opening with the infusion of custom-made nanobubbles/Definity. Evans Blue was injected for the BBB opening efficacy verification and the brain tissue was harvested for the safety assessment. Acoustic emissions were recorded, preprocessed and fed into three machine learning models for BBB opening outcomes prediction. Conventional stable and inertial cavitation dose were also calculated. Results: Among the tested machine learning models, a modified Support Vector Data Description (mSVDD) model achieved the best performance in the BBB opening efficacy and safety prediction with an accuracy of 85.0+/-16.6% and 62.5+/-12.8%, respectively. Conventional stable and inertial cavitation dose-based prediction has a prediction accuracy of 80.0% in efficacy and 34.3% in safety, respectively. The mSVDD model trained with the overall bubble response (0-2 MHz) performed better than that trained with the ultra-harmonic bubble response (0.7-0.8 MHz) in both efficacy prediction (85.0+/-16.6% vs 76.0+/-8.0%, p=0.04) and safety prediction (62.5+/-12.8% vs 55.0+/-10.7%, p>0.05). It is also found that the mSVDD model trained with nanobubble data cannot be directly applied to Definity. Conclusion: Our investigations demonstrated that it is feasible to achieve a reliable prediction of FUS-induced BBB opening outcomes with machine learning and acoustic signals from stimulated nanobubbles. This study provided a new approach for the prediction of FUS-BBB opening outcomes with a clinical translation potential.

physics.med-ph↗
arXiv2023

Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach

Video scene parsing incorporates temporal information, which can enhance the consistency and accuracy of predictions compared to image scene parsing. The added temporal dimension enables a more comprehensive understanding of the scene, leading to more reliable results. This paper presents the winning solution of the CVPR2023 workshop for video semantic segmentation, focusing on enhancing Spatial-Temporal correlations with contrastive loss. We also explore the influence of multi-dataset training by utilizing a label-mapping technique. And the final result is aggregating the output of the above two models. Our approach achieves 65.95% mIoU performance on the VSPW dataset, ranked 1st place on the VSPW challenge at CVPR 2023.

cs.CV↗
arXiv2023

InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructions

Recent works have explored text-guided image editing using diffusion models and generated edited images based on text prompts. However, the models struggle to accurately locate the regions to be edited and faithfully perform precise edits. In this work, we propose a framework termed InstructEdit that can do fine-grained editing based on user instructions. Our proposed framework has three components: language processor, segmenter, and image editor. The first component, the language processor, processes the user instruction using a large language model. The goal of this processing is to parse the user instruction and output prompts for the segmenter and captions for the image editor. We adopt ChatGPT and optionally BLIP2 for this step. The second component, the segmenter, uses the segmentation prompt provided by the language processor. We employ a state-of-the-art segmentation framework Grounded Segment Anything to automatically generate a high-quality mask based on the segmentation prompt. The third component, the image editor, uses the captions from the language processor and the masks from the segmenter to compute the edited image. We adopt Stable Diffusion and the mask-guided generation from DiffEdit for this purpose. Experiments show that our method outperforms previous editing methods in fine-grained editing applications where the input image contains a complex object or multiple objects. We improve the mask quality over DiffEdit and thus improve the quality of edited images. We also show that our framework can accept multiple forms of user instructions as input. We provide the code at https://github.com/QianWangX/InstructEdit.

cs.CV↗