arXiv subjects

Yuanyuan Zhao

Publications and source records attributed to Yuanyuan Zhao.

At least 37 records · Page 2Linked to original sources

arXiv2024

Experts' cognition-driven ensemble deep learning for external validation of predicting pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer

In breast cancer, neoadjuvant chemotherapy (NAC) provides a standard treatment option for patients who have locally advanced cancer and some large operable tumors. A patient will have better prognosis when he has achieved a pathological complete response (pCR) with the treatment of NAC. There has been a trend to directly predict pCR to NAC from histological images based on deep learning (DL). However, the DL-based predictive models numerically have better performances in internal validation than in external validation. In this paper, we aim to alleviate this situation with an intrinsic approach. We propose an experts' cognition-driven ensemble deep learning (ECDEDL) approach. Taking the cognition of both pathology and artificial intelligence experts into consideration to improve the generalization of the predictive model to the external validation, ECDEDL can intrinsically approximate the working paradigm of a human being which will refer to his various working experiences to make decisions. ECDEDL was validated with 695 WSIs collected from the same center as the primary dataset to develop the predictive model and perform the internal validation, and was also validated with 340 WSIs collected from other three centers as the external dataset to perform the external validation. In external validation, ECDEDL improves the AUCs of pCR prediction from 61.52(59.80-63.26) to 67.75(66.74-68.80) and the Accuracies of pCR prediction from 56.09(49.39-62.79) to 71.01(69.44-72.58). ECDEDL was quite effective for external validation of predicting pCR to NAC from histological images in breast cancer, numerically approximating the internal validation.

physics.med-ph↗

arXiv2024

An evaluation of source-blending impact on the calibration of SKA EoR experiments

Twenty-one-centimetre signals from the Epoch of Reionization (EoR) are expected to be detected in the low-frequency radio window by the next-generation interferometers, particularly the Square Kilometre Array (SKA). However, precision data analysis pipelines are required to minimize the systematics within an infinitesimal error budget. Consequently, there is a growing need to characterize the sources of errors in EoR analysis. In this study, we identify one such error origin, namely source blending, which is introduced by the overlap of objects in the densely populated observing sky under SKA1-Low's unprecedented sensitivity and resolution, and evaluate its two-fold impact in both the spatial and frequency domains using a novel hybrid evaluation (HEVAL) pipeline combining end-to-end simulation with an analytic method to mimic EoR analysis pipelines. Sky models corrupted by source blending induce small but severe frequency-dependent calibration errors when coupled with astronomical foregrounds, impeding EoR parameter inference with strong additive residuals in the two-dimensional power spectrum space. We report that additive residuals from poor calibration against sky models with blending ratios of 5 and 0.5 per cent significantly contaminate the EoR window. In contrast, the sky model with a 0.05 per cent blending ratio leaves little residual imprint within the EoR window, therefore identifying a blending tolerance at approximately 0.05 per cent. Given that the SKA observing sky is estimated to suffer from an extended level of blending, strategies involving de-blending, frequency-dependent error mitigation, or a combination of both, are required to effectively attenuate the calibration impact of source-blending defects.

astro-ph.IM↗

arXiv2024

Chiral kagome superconductivity modulations with residual Fermi arcs in KV3Sb5 and CsV3Sb5

Superconductivity involving finite momentum pairing can lead to spatial gap and pair density modulations, as well as Bogoliubov Fermi states within the superconducting gap. However, the experimental realization of their intertwined relations has been challenging. Here, we detect chiral kagome superconductivity modulations with residual Fermi arcs in KV3Sb5 and CsV3Sb5 by normal and Josephson scanning tunneling microscopy down to 30mK with resolved electronic energy difference at microelectronvolt level. We observe a U-shaped superconducting gap with flat residual in-gap states. This gap exhibits chiral 2 by 2 spatial modulations with magnetic field tunable chirality, which align with the chiral 2 by 2 pair density modulations observed through Josephson tunneling. These findings demonstrate a chiral pair density wave (PDW) that breaks time-reversal symmetry. Quasiparticle interference imaging of the in-gap zero-energy states reveals segmented arcs, with high-temperature data linking them to parts of the reconstructed V d-orbital states within the charge order. The detected residual Fermi arcs can be explained by the partial suppression of these d-orbital states through an interorbital 2 by 2 PDW and thus serve as candidate Bogoliubov Fermi states. Additionally, we differentiate the observed PDW order from impurity-induced gap modulations. Our observations not only uncover a chiral PDW order with orbital-selectivity, but also illuminate the fundamental space-momentum correspondence inherent in finite momentum paired superconductivity.

cond-mat.supr-con↗

arXiv2024

Jointly Recognizing Speech and Singing Voices Based on Multi-Task Audio Source Separation

In short video and live broadcasts, speech, singing voice, and background music often overlap and obscure each other. This complexity creates difficulties in structuring and recognizing the audio content, which may impair subsequent ASR and music understanding applications. This paper proposes a multi-task audio source separation (MTASS) based ASR model called JRSV, which Jointly Recognizes Speech and singing Voices. Specifically, the MTASS module separates the mixed audio into distinct speech and singing voice tracks while removing background music. The CTC/attention hybrid recognition module recognizes both tracks. Online distillation is proposed to improve the robustness of recognition further. To evaluate the proposed methods, a benchmark dataset is constructed and released. Experimental results demonstrate that JRSV can significantly improve recognition accuracy on each track of the mixed audio.

cs.SD↗

arXiv2024

Apollonion: Profile-centric Dialog Agent

The emergence of Large Language Models (LLMs) has innovated the development of dialog agents. Specially, a well-trained LLM, as a central process unit, is capable of providing fluent and reasonable response for user's request. Besides, auxiliary tools such as external knowledge retrieval, personalized character for vivid response, short/long-term memory for ultra long context management are developed, completing the usage experience for LLM-based dialog agents. However, the above-mentioned techniques does not solve the issue of \textbf{personalization from user perspective}: agents response in a same fashion to different users, without consideration of their features, such as habits, interests and past experience. In another words, current implementation of dialog agents fail in ``knowing the user''. The capacity of well-description and representation of user is under development. In this work, we proposed a framework for dialog agent to incorporate user profiling (initialization, update): user's query and response is analyzed and organized into a structural user profile, which is latter served to provide personal and more precise response. Besides, we proposed a series of evaluation protocols for personalization: to what extend the response is personal to the different users. The framework is named as \method{}, inspired by inscription of ``Know Yourself'' in the temple of Apollo (also known as \method{}) in Ancient Greek. Few works have been conducted on incorporating personalization into LLM, \method{} is a pioneer work on guiding LLM's response to meet individuation via the application of dialog agents, with a set of evaluation methods for measurement in personalization.

cs.IR↗

arXiv2023

Enlargement of Memory Window of Si Channel FeFET by Inserting Al2O3 Interlayer on Ferroelectric Hf0.5Zr0.5O2

In this work, we demonstrate the enlargement of the memory window of Si channel FeFET with ferroelectric Hf0.5Zr0.5O2 by gate-side dielectric interlayer engineering. By inserting an Al2O3 dielectric interlayer between TiN gate metal and ferroelectric Hf0.5Zr0.5O2, we achieve a memory window of 3.2 V with endurance of ~105 cycles and retention over 10 years. The physical origin of memory window enlargement is clarified to be charge trapping at the Al2O3/Hf0.5Zr0.5O2 interface, which has an opposite charge polarity to the trapped charges at the Hf0.5Zr0.5O2/SiOx interface.

cond-mat.mtrl-sci↗

arXiv2023

Constraints on Hairy Kerr black hole with quasi-periodic oscillations

The Hairy Kerr black hole is a novel black hole solution that depicts a rotating space-time encompassed by an axisymmetric fluid. It has significant observational importance and is an excellent candidate for an astrophysical black hole. Our study investigates the impact of the hairy charge on the quasi-periodic oscillations (QPOs) of X-ray binaries in the Hairy Kerr black hole (HKBH) space-time. The relativistic precession model is employed to compute the three principal frequencies of the accretion disk encircling the HKBH. We compare our outcomes with the observations of five X-ray binaries and employ a Markov chain Monte Carlo (MCMC) simulation for restricting the hairy charge parameters. There is no substantial evidence for the existence of hairy charge in the HKBH space-time. Therefore, we are placing observational constraints on the deformation parameters with $0<α<0.07697$ and hairy charge values ranging from $0.27182<l_0/M<2.0$.

astro-ph.HE↗

arXiv2023

Constraints on the rotating self-dual black hole with quasi-periodic oscillations

An impressive feature of loop quantum gravity (LQG) is that it can elegantly resolve both the big bang and black hole singularities. By using the Newman-Janis algorithm, a regular and effective rotating self-dual black hole(SDBH) metric could be constructed, which alters the Kerr geometry with a polymeric function $P$ from the quantum effects of LQG geometry. In this paper, we investigate its impact on the frequency characteristics of the X-ray quasi-periodic oscillations(QPOs) from 5 X-ray binaries and contrast it with the existing results of the orbital, periastron precession and nodal precession frequencies within the relativistic precession model. We apply a Monte Carlo Markov Chain (MCMC) simulation to examine the possible LQG effects on the X-ray QPOs. We found that the best constraint result for the rotating self-dual geometry from LQG came from the QPOs of X-ray binary GRO J1655-40, which establish an upper bound on the polymeric function $P$ less than $6.17\times 10^{-3}$ at 95\% confidence level. This bound leads to a restriction on the polymeric parameter $δ$ of LQG to be 0.67.

gr-qc↗

arXiv2023

Scientific Objectives of the Hot Universe Baryon Surveyor (HUBS) Mission

The Hot Universe Baryon Surveyor (HUBS) is a proposed space-based X-ray telescope for detecting X-ray emissions from the hot gas content in our universe. With its unprecedented spatially-resolved high-resolution spectroscopy and large field of view, the HUBS mission will be uniquely qualified to measure the physical and chemical properties of the hot gas in the interstellar medium, the circumgalactic medium, the intergalactic medium, and the intracluster medium. These measurements will be valuable for two key scientific goals of HUBS, namely to unravel the AGN and stellar feedback physics that governs the formation and evolution of galaxies, and to probe the baryon budget and multi-phase states from galactic to cosmological scales. In addition to these two goals, the HUBS mission will also help us solve some problems in the fields of galaxy clusters, AGNs, diffuse X-ray backgrounds, supernova remnants, and compact objects. This paper discusses the perspective of advancing these fields using the HUBS telescope.

astro-ph.GA↗

arXiv2023

Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats

Advanced persistent threats (APTs) have novel features such as multi-stage penetration, highly-tailored intention, and evasive tactics. APTs defense requires fusing multi-dimensional Cyber threat intelligence data to identify attack intentions and conducts efficient knowledge discovery strategies by data-driven machine learning to recognize entity relationships. However, data-driven machine learning lacks generalization ability on fresh or unknown samples, reducing the accuracy and practicality of the defense model. Besides, the private deployment of these APT defense models on heterogeneous environments and various network devices requires significant investment in context awareness (such as known attack entities, continuous network states, and current security strategies). In this paper, we propose a few-shot multi-domain knowledge rearming (FMKR) scheme for context-aware defense against APTs. By completing multiple small tasks that are generated from different network domains with meta-learning, the FMKR firstly trains a model with good discrimination and generalization ability for fresh and unknown APT attacks. In each FMKR task, both threat intelligence and local entities are fused into the support/query sets in meta-learning to identify possible attack stages. Secondly, to rearm current security strategies, an finetuning-based deployment mechanism is proposed to transfer learned knowledge into the student model, while minimizing the defense cost. Compared to multiple model replacement strategies, the FMKR provides a faster response to attack behaviors while consuming less scheduling cost. Based on the feedback from multiple real users of the Industrial Internet of Things (IIoT) over 2 months, we demonstrate that the proposed scheme can improve the defense satisfaction rate.

cs.CR↗

arXiv2023

DHBE: Data-free Holistic Backdoor Erasing in Deep Neural Networks via Restricted Adversarial Distillation

Backdoor attacks have emerged as an urgent threat to Deep Neural Networks (DNNs), where victim DNNs are furtively implanted with malicious neurons that could be triggered by the adversary. To defend against backdoor attacks, many works establish a staged pipeline to remove backdoors from victim DNNs: inspecting, locating, and erasing. However, in a scenario where a few clean data can be accessible, such pipeline is fragile and cannot erase backdoors completely without sacrificing model accuracy. To address this issue, in this paper, we propose a novel data-free holistic backdoor erasing (DHBE) framework. Instead of the staged pipeline, the DHBE treats the backdoor erasing task as a unified adversarial procedure, which seeks equilibrium between two different competing processes: distillation and backdoor regularization. In distillation, the backdoored DNN is distilled into a proxy model, transferring its knowledge about clean data, yet backdoors are simultaneously transferred. In backdoor regularization, the proxy model is holistically regularized to prevent from infecting any possible backdoor transferred from distillation. These two processes jointly proceed with data-free adversarial optimization until a clean, high-accuracy proxy model is obtained. With the novel adversarial design, our framework demonstrates its superiority in three aspects: 1) minimal detriment to model accuracy, 2) high tolerance for hyperparameters, and 3) no demand for clean data. Extensive experiments on various backdoor attacks and datasets are performed to verify the effectiveness of the proposed framework. Code is available at \url{https://github.com/yanzhicong/DHBE}

cs.LG↗

arXiv2023

CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications

Federated learning (FL) provides an emerging approach for collaboratively training semantic encoder/decoder models of semantic communication systems, without private user data leaving the devices. Most existing studies on trustworthy FL aim to eliminate data poisoning threats that are produced by malicious clients, but in many cases, eliminating model poisoning attacks brought by fake servers is also an important objective. In this paper, a certificateless authentication-based trustworthy federated learning (CATFL) framework is proposed, which mutually authenticates the identity of clients and server. In CATFL, each client verifies the server's signature information before accepting the delivered global model to ensure that the global model is not delivered by false servers. On the contrary, the server also verifies the server's signature information before accepting the delivered model updates to ensure that they are submitted by authorized clients. Compared to PKI-based methods, the CATFL can avoid too high certificate management overheads. Meanwhile, the anonymity of clients shields data poisoning attacks, while real-name registration may suffer from user-specific privacy leakage risks. Therefore, a pseudonym generation strategy is also presented in CATFL to achieve a trade-off between identity traceability and user anonymity, which is essential to conditionally prevent from user-specific privacy leakage. Theoretical security analysis and evaluation results validate the superiority of CATFL.

cs.CR↗

arXiv2022

Metal-insulator phase separation in KTaO3-based two-dimensional electron gas

Electronic phase separation (EPS) originates from an incomplete transformation between electronic phases, causing the inhomogeneous spatial distribution of electronic properties. In the system of two-dimensional electron gas (2DEG), the EPS is usually identified based on a percolative metal-to-superconductor transition. Here, we report a metal-insulator transition (MIT) in KTaO3-based 2DEG with the width of conductive channel decreasing into micrometer scale. Hysteretic resistance-temperature relations are observed due to the competition between metallic and insulating phases, which is tunable by magnetic field. Such a size-dependent MIT effect is attributed to the coexistence and separation of metallic and insulating phases. Combining density functional theory calculation, we propose a theoretical model to simulate the dynamic process of the EPS using the percolation theory, demonstrating the mechanism of size-dependent MIT. Our work suggests a clear and simple 2DEG platform to achieve the spatial coexistence of metallic and insulating phases.

cond-mat.mtrl-sci↗

arXiv2022

Shape, Velocity, and Exact Controllability for the Wave Equation on a Graph with Cycle

Exact controllability is proven on a graph with cycle. The controls can be a mix of controls applied at the boundary and interior vertices. The method of proof first uses a dynamical argument to prove shape controllability and velocity controllability, thereby solving their associated moment problems. This enables one to solve the moment problem associated to exact controllability. In the case of a single control, either boundary or interior, it is shown that exact controllability fails.

math.OC↗

arXiv2022

A deep learning approach to solve forward differential problems on graphs

We propose a novel deep learning (DL) approach to solve one-dimensional non-linear elliptic, parabolic, and hyperbolic problems on graphs. A system of physics-informed neural network (PINN) models is used to solve the differential equations, by assigning each PINN model to a specific edge of the graph. Kirkhoff-Neumann (KN) nodal conditions are imposed in a weak form by adding a penalization term to the training loss function. Through the penalization term that imposes the KN conditions, PINN models associated with edges that share a node coordinate with each other to ensure continuity of the solution and of its directional derivatives computed along the respective edges. Using individual PINN models for each edge of the graph allows our approach to fulfill necessary requirements for parallelization by enabling different PINN models to be trained on distributed compute resources. Numerical results show that the system of PINN models accurately approximate the solutions of the differential problems across the entire graph for a broad set of graph topologies.

cs.LG↗

arXiv2022

A Semi-Analytical Model for the Formation and Evolution of Radio Relics in Galaxy Clusters

Radio relics are Mpc-sized synchrotron sources located in the peripheral regions of galaxy clusters. Models based on the diffuse shock acceleration (DSA) scenario have been widely accepted to explain the formation of radio relics. However, a critical challenge to these models is that most observed shocks seem too weak to generate detectable emission, unless fossil electrons, a population of mildly energetic electrons that have been accelerated previously, are included in the models. To address this issue, we present a new semi-analytical model to describe the formation and evolution of radio relics by incorporating fossil relativistic electrons into DSA theory, which is constrained by a sample of 14 observed relics, and employ the Press-Schechter formalism to simulate the relics in a $20^{\circ} \times 20^{\circ}$ sky field at 50, 158, and 1400 MHz, respectively. Results show that fossil electrons contribute significantly to the radio emission, which can generate radiation four orders of magnitude brighter than that solely produced by thermal electrons at 158 MHz, and the power distribution of our simulated radio relic catalog can reconcile the observed $P_{1400}-M_{\mathrm{vir}}$ relation. We predict that $7.1\%$ clusters with $M_{\mathrm{vir}} > 1.2\times 10^{14}\,\mathrm{M}_{\odot}$ would host relics at 158 MHz, which is consistent with the result of $10 \pm 6\%$ given by the LoTSS DR2. It is also found that radio relics are expected to cause severe foreground contamination in future EoR experiments, similar to that of radio halos. The possibility of AGN providing seed fossil relativistic electrons is evaluated by calculating the number of radio-loud AGNs that a shock is expected to encounter during its propagation.

astro-ph.CO↗

arXiv2021

The Merger Dynamics of the Galaxy Cluster Abell 1775: New Insights from Chandra and XMM-Newton for a Cluster Simultaneously Hosting a WAT and a NAT Radio Sources

We present a new study of the merger dynamics of Abell~1775 by analyzing the high-quality Chandra and XMM-Newton archival data. We confirm/identify an arc-shaped edge (i.e., the head) at $\sim48$~kpc west of the X-ray peak, a split cold gas tail that extends eastward to $\sim163$~kpc, and a plume of spiral-like X-ray excess (within about $81-324$~kpc northeast of the cluster core) that connects to the end of the tail. The head, across which the projected gas temperature rises outward from $3.39_{-0.18}^{+0.28}$~keV to $5.30_{-0.43}^{+0.54}$~keV, is found to be a cold front with a Mach number of $\mathcal{M}\sim0.79$. Along the surfaces of the cold front and tail, typical KHI features (noses and wings, etc.) are found and are used to constrain the upper limit of the magnetic field ($\sim11.2~μ$G) and the viscosity suppression factor ($\sim0.01$). Combining optical and radio evidence we propose a two-body merger (instead of systematic motion in a large-scale gas environment) scenario and have carried out idealized hydrodynamic simulations to verify it. We find that the observed X-ray emission and temperature distributions can be best reproduced with a merger mass ratio of 5 after the first pericentric passage. The NAT radio galaxy is thus more likely to be a single galaxy falling into the cluster center at a relative velocity of 2800~$\rm km~s^{-1}$, a speed constrained by its radio morphology. The infalling subcluster is expected to have a relatively low gas content, because only a gas-poor subcluster can cause central-only disturbances as observed in such an off-axis merger.

astro-ph.CO↗

arXiv2021

A Study of Gas Entropy Profiles of 47 Galaxy Clusters and Groups Out to the Virial Radius

Some observations such as those presented in Walker et al. show that the observed entropy profiles of the intra-cluster medium (ICM) deviate from the power-law prediction of adiabatic simulations. This implies that non-gravitational processes, which are absent in the simulations, may be important in the evolution of the ICM, and by quantifying the deviation, we may be able to estimate the feedback energy in the ICM and use it as a probe of the non-gravitational processes. To address this issue we calculate the ICM entropy profiles in a sample of 47 galaxy clusters and groups, which have been observed out to at least $\sim r_{500}$ with Chandra, XMM-Newton and/or Suzaku, by constructing a physical model to incorporate the effects of both gravity and non-gravitational processes to fit the observed gas temperature and surface brightness profiles via Bayesian statistics. After carefully evaluating the effects of systematic errors, we find that the gas entropy profiles derived with best-fit results of our model are consistent with the simulation-predicted power-law profile near the virial radius, while the flattened profiles reported previously can be explained by introducing the gas clumping effect, the existence of which is confirmed in 19 luminous targets in our sample. We calculate the total feedback energy per particle and find that it decreases from $\sim 10$ keV at the center to about zero at $\sim 0.35$$r_{200}$ and is consistent with zero outside $\sim 0.35$$r_{200}$, implying the upper limit of the feedback efficiency $\sim 0.02$ for the super-massive black holes hosted in the brightest cluster galaxies.

astro-ph.CO↗