arXiv subjects

Tao Wang

Publications and source records attributed to Tao Wang.

At least 325 records · Page 18Linked to original sources

arXiv2024

Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images

Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. To address this issue, we developed TRUECAM, a framework designed to ensure both data and model trustworthiness in non-small cell lung cancer subtyping with whole-slide images. TRUECAM integrates 1) a spectral-normalized neural Gaussian process for identifying out-of-scope inputs and 2) an ambiguity-guided elimination of tiles to filter out highly ambiguous regions, addressing data trustworthiness, as well as 3) conformal prediction to ensure controlled error rates. We systematically evaluated the framework across multiple large-scale cancer datasets, leveraging both task-specific and foundation models, illustrate that an AI model wrapped with TRUECAM significantly outperforms models that lack such guidance, in terms of classification accuracy, robustness, interpretability, and data efficiency, while also achieving improvements in fairness. These findings highlight TRUECAM as a versatile wrapper framework for digital pathology AI models with diverse architectural designs, promoting their responsible and effective applications in real-world settings.

eess.IV↗

arXiv2024

WMCodec: End-to-End Neural Speech Codec with Deep Watermarking for Authenticity Verification

Recent advances in speech spoofing necessitate stronger verification mechanisms in neural speech codecs to ensure authenticity. Current methods embed numerical watermarks before compression and extract them from reconstructed speech for verification, but face limitations such as separate training processes for the watermark and codec, and insufficient cross-modal information integration, leading to reduced watermark imperceptibility, extraction accuracy, and capacity. To address these issues, we propose WMCodec, the first neural speech codec to jointly train compression-reconstruction and watermark embedding-extraction in an end-to-end manner, optimizing both imperceptibility and extractability of the watermark. Furthermore, We design an iterative Attention Imprint Unit (AIU) for deeper feature integration of watermark and speech, reducing the impact of quantization noise on the watermark. Experimental results show WMCodec outperforms AudioSeal with Encodec in most quality metrics for watermark imperceptibility and consistently exceeds both AudioSeal with Encodec and reinforced TraceableSpeech in extraction accuracy of watermark. At bandwidth of 6 kbps with a watermark capacity of 16 bps, WMCodec maintains over 99% extraction accuracy under common attacks, demonstrating strong robustness.

cs.SD↗

arXiv2024

LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detection

Explainable fake news detection predicts the authenticity of news items with annotated explanations. Today, Large Language Models (LLMs) are known for their powerful natural language understanding and explanation generation abilities. However, presenting LLMs for explainable fake news detection remains two main challenges. Firstly, fake news appears reasonable and could easily mislead LLMs, leaving them unable to understand the complex news-faking process. Secondly, utilizing LLMs for this task would generate both correct and incorrect explanations, which necessitates abundant labor in the loop. In this paper, we propose LLM-GAN, a novel framework that utilizes prompting mechanisms to enable an LLM to become Generator and Detector and for realistic fake news generation and detection. Our results demonstrate LLM-GAN's effectiveness in both prediction performance and explanation quality. We further showcase the integration of LLM-GAN to a cloud-native AI platform to provide better fake news detection service in the cloud.

cs.CL↗

arXiv2024

General Relativistic Fluctuation Theorems

Using the recently proposed covariant framework of general relativistic stochastic mechanics and stochastic thermodynamics, we proved the detailed and integral fluctuation theorems in curved spacetime. The time-reversal transformation is described as a transformation from the perspective of future-directed observer to that of the corresponding past-directed observer, which enables us to maintain general covariance throughout the construction.

gr-qc↗

arXiv2024

SemDP: Semantic-level Differential Privacy Protection for Face Datasets

While large-scale face datasets have advanced deep learning-based face analysis, they also raise privacy concerns due to the sensitive personal information they contain. Recent schemes have implemented differential privacy to protect face datasets. However, these schemes generally treat each image as a separate database, which does not fully meet the core requirements of differential privacy. In this paper, we propose a semantic-level differential privacy protection scheme that applies to the entire face dataset. Unlike pixel-level differential privacy approaches, our scheme guarantees that semantic privacy in faces is not compromised. The key idea is to convert unstructured data into structured data to enable the application of differential privacy. Specifically, we first extract semantic information from the face dataset to build an attribute database, then apply differential perturbations to obscure this attribute data, and finally use an image synthesis model to generate a protected face dataset. Extensive experimental results show that our scheme can maintain visual naturalness and balance the privacy-utility trade-off compared to the mainstream schemes.

cs.CV↗

arXiv2024

Ultralight dark matter detection with levitated ferromagnets

Levitated ferromagnets act as ultraprecise magnetometers, which can exhibit high quality factors due to their excellent isolation from the environment. These instruments can be utilized in searches for ultralight dark matter candidates, such as axionlike dark matter or dark-photon dark matter. In addition to being sensitive to an axion-photon coupling or kinetic mixing, which produce physical magnetic fields, ferromagnets are also sensitive to the effective magnetic field (or "axion wind") produced by an axion-electron coupling. While the dynamics of a levitated ferromagnet in response to a DC magnetic field have been well studied, all of these couplings would produce AC fields. In this work, we study the response of a ferromagnet to an applied AC magnetic field and use these results to project their sensitivity to axion and dark-photon dark matter. We pay special attention to the direction of motion induced by an applied AC field, in particular, whether it precesses around the applied field (similar to an electron spin) or librates in the plane of the field (similar to a compass needle). We show that existing levitated ferromagnet setups can already have comparable sensitivity to an axion-electron coupling as comagnetometer or torsion balance experiments. In addition, future setups can become sensitive probes of axion-electron coupling, dark-photon kinetic mixing, and axion-photon coupling, for ultralight dark matter masses $m_\mathrm{DM}\lesssim\mathrm{feV}$.

hep-ph↗

arXiv2024

A hierarchical splines-based $h$-adaptive isogeometric solver for all-electron Kohn--Sham equation

In this paper, a novel $h$-adaptive isogeometric solver utilizing high-order hierarchical splines is proposed to solve the all-electron Kohn--Sham equation. In virtue of the smooth nature of Kohn--Sham wavefunctions across the domain, except at the nuclear positions, high-order globally regular basis functions such as B-splines are well suited for achieving high accuracy. To further handle the singularities in the external potential at the nuclear positions, an $h$-adaptive framework based on the hierarchical splines is presented with a specially designed residual-type error indicator, allowing for different resolutions on the domain. The generalized eigenvalue problem raising from the discretized Kohn--Sham equation is effectively solved by the locally optimal block preconditioned conjugate gradient (LOBPCG) method with an elliptic preconditioner, and it is found that the eigensolver's convergence is independent of the spline basis order. A series of numerical experiments confirm the effectiveness of the $h$-adaptive framework, with a notable experiment that the numerical accuracy $10^{-3} \mathrm{~Hartree/particle}$ in the all-electron simulation of a methane molecule is achieved using only $6355$ degrees of freedom, demonstrating the competitiveness of our solver for the all-electron Kohn--Sham equation.

physics.comp-ph↗

arXiv2024

Minor-merger induced star formation rejuvenation in an elliptical radio-loud quasar host, 3C 59

We report a rare case where an elliptical radio-loud quasar host, 3C 59, rejuvenates star formation activity through minor mergers with its nearby satellite galaxies. The inferred star formation history of 3C 59 shows significant star formation rejuvenation within the past 500 Myr, before which remains rather quiescent for most of the cosmic time. Three nearest satellite galaxies of 3C 59 exhibit significant morphological disturbances, and two of them present strong tidal tails pointing towards 3C 59. In addition, all the satellite galaxies within a projected distance of 200 kpc show low star formation activities. They also have systematically lower effective radius ($R_{\rm e}$) than local late-type galaxies, while 3C 59 has significantly larger $R_{\rm e}$ than both early- and late-type galaxies. All these features suggest that ongoing minor mergers between 3C 59 and its nearby satellites could be causing gas to flow into 3C 59, which induces the star formation rejuvenation, and possibly also triggers the quasar activity. The enormous power from the large-scale radio jet of 3C 59 may in turn help keep the halo hot, prevent gas cooling, and further reduce star formation in its satellite galaxies. These results provide important insights into the mass and size growth of central galaxies and star formation quenching of satellite galaxies in galaxy groups.

astro-ph.GA↗

arXiv2024

WFCAT: Augmenting Website Fingerprinting with Channel-wise Attention on Timing Features

Website Fingerprinting (WF) aims to deanonymize users on the Tor network by analyzing encrypted network traffic. Recent deep-learning-based attacks show high accuracy on undefended traces. However, they struggle against modern defenses that use tactics like injecting dummy packets and delaying real packets, which significantly degrade classification performance. Our analysis reveals that current attacks inadequately leverage the timing information inherent in traffic traces, which persists as a source of leakage even under robust defenses. Addressing this shortfall, we introduce a novel feature representation named the Inter-Arrival Time (IAT) histogram, which quantifies the frequencies of packet inter-arrival times across predetermined time slots. Complementing this feature, we propose a new CNN-based attack, WFCAT, enhanced with two innovative architectural blocks designed to optimally extract and utilize timing information. Our approach uses kernels of varying sizes to capture multi-scale features, which are then integrated using a weighted sum across all feature channels to enhance the model's efficacy in identifying temporal patterns. Our experiments validate that WFCAT substantially outperforms existing methods on defended traces in both closed- and open-world scenarios. Notably, WFCAT achieves over 59% accuracy against Surakav, a recently developed robust defense, marking an improvement of over 28% and 48% against the state-of-the-art attacks RF and Tik-Tok, respectively, in the closed-world scenario.

cs.CR↗

arXiv2024

Evidence for the Sombrero Galaxy as an Accelerator of the Highest-Energy Cosmic Rays

Ultrahigh-energy cosmic rays (UHECRs) are the highest energy messenger from space, with energies exceeding 1 EeV. Although UHECRs were discovered over 60 years ago, their origin still remains a mystery. Pinpointing sources of UHECRs is crucial for understanding the extreme astrophysical processes that accelerate particles to such extraordinary energies. We searched for UHECR multiplets via analyzing 17 years of data with energies greater than 40 EeV from the Pierre Auger Observatory. A spatial association is found between a multiplet of $25.7^{+6.2}_{-7.0}$ cosmic rays and the Sombrero galaxy with a local (global) significance of $4.5~σ~(3.3~σ)$. The Sombrero galaxy hosts a supermassive central black hole with a mass of $\sim1\times 10^9 M_{\odot}$ and exhibits large-scale radio lobes and jets. Our finding provides critical evidence on active supermassive black holes as the source of the highest-energy cosmic rays.

astro-ph.HE↗

arXiv2024

Simultaneous Multiband Photometry of the Early Optical Afterglow of GRB 240825A with Mephisto

Gamma-ray bursts (GRBs) are the most luminous transients in the universe. The interaction of the relativistic jet with the circumburst medium produces an afterglow and generates multiwavelength emission. In this work, we present simultaneous multiband photometry of GRB~240825A with the Multi-channel Photometric Survey Telescope (Mephisto) and analyze its temporal and spectral properties. The measurement began 128 seconds after the GRB trigger and continued until the fourth day when the afterglow essentially diminished and the measured brightness was close to that of the host galaxy. Based on the multiband light curves in the $uvgriz$ bands, we find that the optical flux density satisfies $F_{ν,{\rm obs}}\propto t^{-1.34}ν^{-2.48}$ with a spectral index of $2.48$ much larger than those of most other GRBs. To reconcile the measured much softer spectral energy distribution (SED) with that predicted by the standard afterglow model, an extra host-galaxy extinction of $E_{B-V}\sim(0.37-0.57)$ mag is required. We interpreted this excess as arising from a dense circumburst medium. We further find that the SED of the optical afterglow hardened as the afterglow decayed and the color excess $E_{B-V}$ decreased $\sim0.26$ mag from 100 seconds to 3000 seconds after the GRB trigger. Finally, we analyze the properties of the host galaxy of GRB~240825A based on data from the SDSS, PanSTARRS and HSC-SSP surveys. For a host redshift of $z=0.659$, the stellar mass and star formation rate of the host galaxy are estimated to be $\log(M_*/M_\odot)=10.0^{+0.3}_{-0.3}$ and $\log({\rm SFR}/M_{\odot}{\rm yr}^{-1})= 0.6^{+0.8}_{-3.3}$, respectively, pointing to a gas-rich, star-forming, medium-size galaxy.

astro-ph.HE↗

arXiv2024

An empirical model of the extragalactic radio background

Radio observations provide a powerful tool to constrain the assembly of galaxies over cosmic time. Recent deep and wide radio continuum surveys have improved significantly our understanding on radio emission properties of AGNs and SFGs across $0 < z < 4$. This allows us to derive an empirical model of the radio continuum emission of galaxies based on their SFR and the probability of hosting an radio AGN. We make use of the Empirical Galaxy Generator (EGG) to generate a near-infrared-selected, flux-limited multi-wavelength catalog to mimic real observations. Then we assign radio continuum flux densities to galaxies based on their SFRs and the probability of hosting a radio-AGN of specific 1.4 GHz luminosity. We also apply special treatments to reproduce the clustering signal of radio AGNs.Our empirical model successfully recovers the observed 1.4 GHz radio luminosity functions (RLFs) of both AGN and SFG populations, as well as the differential number counts at various radio bands. The uniqueness of this approach also allows us to directly link radio flux densities of galaxies to other properties, including redshifts, stellar masses, and magnitudes at various photometric bands. We find that roughly half of the radio continuum sources to be detected by SKA at $z \sim 4-6$ will be too faint to be detected in the optical survey ($r \sim 27.5$) carried out by Rubin observatory. Unlike previous studies which utilized RLFs to reproduce ERB, our work starts from a simulated galaxy catalog with realistic physical properties. It has the potential to simultaneously, and self-consistently reproduce physical properties of galaxies across a wide range of wavelengths, from optical, NIR, FIR to radio wavelengths. Our empirical model can shed light on the contribution of different galaxies to the extragalactic background light, and greatly facilitates designing future multiwavelength galaxy surveys.

astro-ph.GA↗

arXiv2024

Dynamic Disentangled Fusion Network for RGBT Tracking

RGBT tracking usually suffers from various challenging factors of low resolution, similar appearance, extreme illumination, thermal crossover and occlusion, to name a few. Existing works often study complex fusion models to handle challenging scenarios, but can not well adapt to various challenges, which might limit tracking performance. To handle this problem, we propose a novel Dynamic Disentangled Fusion Network called DDFNet, which disentangles the fusion process into several dynamic fusion models via the challenge attributes to adapt to various challenging scenarios, for robust RGBT tracking. In particular, we design six attribute-based fusion models to integrate RGB and thermal features under the six challenging scenarios respectively.Since each fusion model is to deal with the corresponding challenges, such disentangled fusion scheme could increase the fusion capacity without the dependence on large-scale training data. Considering that every challenging scenario also has different levels of difficulty, we propose to optimize the combination of multiple fusion units to form each attribute-based fusion model in a dynamic manner, which could well adapt to the difficulty of the corresponding challenging scenario. To address the issue that which fusion models should be activated in the tracking process, we design an adaptive aggregation fusion module to integrate all features from attribute-based fusion models in an adaptive manner with a three-stage training algorithm. In addition, we design an enhancement fusion module to further strengthen the aggregated feature and modality-specific features. Experimental results on benchmark datasets demonstrate the effectiveness of our DDFNet against other state-of-the-art methods.

cs.CV↗

arXiv2024

Mel-Refine: A Plug-and-Play Approach to Refine Mel-Spectrogram in Audio Generation

Text-to-audio (TTA) model is capable of generating diverse audio from textual prompts. However, most mainstream TTA models, which predominantly rely on Mel-spectrograms, still face challenges in producing audio with rich content. The intricate details and texture required in Mel-spectrograms for such audio often surpass the models' capacity, leading to outputs that are blurred or lack coherence. In this paper, we begin by investigating the critical role of U-Net in Mel-spectrogram generation. Our analysis shows that in U-Net structure, high-frequency components in skip-connections and the backbone influence texture and detail, while low-frequency components in the backbone are critical for the diffusion denoising process. We further propose ``Mel-Refine'', a plug-and-play approach that enhances Mel-spectrogram texture and detail by adjusting different component weights during inference. Our method requires no additional training or fine-tuning and is fully compatible with any diffusion-based TTA architecture. Experimental results show that our approach boosts performance metrics of the latest TTA model Tango2 by 25\%, demonstrating its effectiveness.

cs.SD↗

arXiv2024

Image Privacy Protection: A Survey

Images serve as a crucial medium for communication, presenting information in a visually engaging format that facilitates rapid comprehension of key points. Meanwhile, during transmission and storage, they contain significant sensitive information. If not managed properly, this information may be vulnerable to exploitation for personal gain, potentially infringing on privacy rights and other legal entitlements. Consequently, researchers continue to propose some approaches for preserving image privacy and publish reviews that provide comprehensive and methodical summaries of these approaches. However, existing reviews tend to categorize either by specific scenarios, or by specific privacy objectives. This classification somewhat restricts the reader's ability to grasp a holistic view of image privacy protection and poses challenges in developing a total understanding of the subject that transcends different scenarios and privacy objectives. Instead of examining image privacy protection from a single aspect, it is more desirable to consider user needs for a comprehensive understanding. To fill this gap, we conduct a systematic review of image privacy protection approaches based on privacy protection goals. Specifically, we define the attribute known as privacy sensitive domains and use it as the core classification dimension to construct a comprehensive framework for image privacy protection that encompasses various scenarios and privacy objectives. This framework offers a deep understanding of the multi-layered aspects of image privacy, categorizing its protection into three primary levels: data-level, content-level, and feature-level. For each category, we analyze the main approaches and features of image privacy protection and systematically review representative solutions. Finally, we discuss the challenges and future directions of image privacy protection.

cs.CR↗

arXiv2024

MV4PG: Materialized Views for Property Graphs

Graph databases are getting more and more attention in the highly interconnected data domain, and the demand for efficient querying of big data is increasing. We noticed that there are duplicate patterns in graph database queries, and the results of these patterns can be stored as materialized views first, which can speed up the query rate. So we propose materialized views on property graphs, including three parts: view creation, view maintenance, and query optimization using views, and we propose for the first time an efficient templated view maintenance method for containing variable-length edges, which can be applied to multiple graph databases. In order to verify the effect of materialized views, we prototype on TuGraph and experiment on both TuGraph and Neo4j. The experiment results show that our query optimization on read statements is much higher than the additional view maintenance cost brought by write statements. The speedup ratio of the whole workload reaches up to 28.71x, and the speedup ratio of a single query reaches up to nearly 100x.

cs.DB↗

arXiv2024

Considerations for Single-Arm Trials to Support Accelerated Approval of Oncology Drugs

In the last two decades, single-arm trials (SATs) have been effectively used to study anticancer therapies in well-defined patient populations using durable response rates as an objective and interpretable clinical endpoints. With a growing trend of regulatory accelerated approval (AA) requiring randomized controlled trials (RCTs), some confusions have arisen about the roles of SATs in AA. This paper is intended to elucidate conditions under which an SAT may be considered reasonable for AA. Specifically, the paper describes (1) two necessary conditions for designing an SAT, (2) three sufficient conditions that help either optimize the study design or interpret the study results, (3) four conditions that demonstrate substantial evidence of clinical benefits of the drug, and (4) a plan of a confirmatory RCT to verify the clinical benefits. Some further considerations are discussed to help design a scientifically sound SAT and communicate with regulatory agencies. Conditions presented in this paper may serve as a set of references for sponsors using SATs for regulatory decision.

stat.AP↗

arXiv2024

TraceableSpeech: Towards Proactively Traceable Text-to-Speech with Watermarking

Various threats posed by the progress in text-to-speech (TTS) have prompted the need to reliably trace synthesized speech. However, contemporary approaches to this task involve adding watermarks to the audio separately after generation, a process that hurts both speech quality and watermark imperceptibility. In addition, these approaches are limited in robustness and flexibility. To address these problems, we propose TraceableSpeech, a novel TTS model that directly generates watermarked speech, improving watermark imperceptibility and speech quality. Furthermore, We design the frame-wise imprinting and extraction of watermarks, achieving higher robustness against resplicing attacks and temporal flexibility in operation. Experimental results show that TraceableSpeech outperforms the strong baseline where VALL-E or HiFicodec individually uses WavMark in watermark imperceptibility, speech quality and resilience against resplicing attacks. It also can apply to speech of various durations. The code is avaliable at https://github.com/zjzser/TraceableSpeech

cs.SD↗