arXiv subjects

Jian Wang

Publications and source records attributed to Jian Wang.

At least 451 records · Page 25Linked to original sources

arXiv2024

A note on Lévy-driven McKean-Vlasov SDEs under monotonicity

In this note, under a weak monotonicity and a weak coercivity, we address strong well-posedness of McKean-Vlasov stochastic differential equations (SDEs) driven by Lévy jump processes, where the coefficients are Lipschitz continuous (with respect to the measure variable) under the $L^β$-Wasserstein distance for $β\in[1,2].$ Moreover, the issue on the weak propagation of chaos (i.e., convergence in distribution via the convergence of the empirical measure) and the strong propagation of chaos (i.e., at the level paths by coupling) is explored simultaneously. To treat the strong well-posedness of McKean-Vlasov SDEs we are interested in, we investigate strong well-posedness of classical time-inhomogeneous SDEs with jumps under a local weak monotonicity and a global weak coercivity. Such a result is of independent interest, and, most importantly, can provide an available reference on strong well-posedness of Lévy-driven SDEs under the monotone condition, which nevertheless is missing for a long time. Based on the theory derived, along with the interlacing technique and the Banach fixed point theorem, the strong well-posedness of McKean-Vlasov SDEs driven by Lévy jump processes can be established. Additionally, as a potential extension, strong well-posedness and conditional propagation of chaos are treated for Lévy-driven McKean-Vlasov SDEs with common noise under a weak monotonicity.

math.PR↗

arXiv2024

Influencing Factors of the FLASH Effect: Unveiling the Importance of Free Radicals

Purpose: Our aim was to elucidate the critical factors responsible for inducing the FLASH effect, focusing on the role of free radicals through simulation and experimental approaches. Methods and Materials: The whole abdomen of C57BL/6 mice was irradiated with 6 MeV electron beam. The endpoint was acute intestinal toxicity quantified by histological score. Total doses ranging from 6 to 15 Gy were evaluated. The impact of the mean dose rate (MDR) was assessed in the range of 40 to 900 Gy/s. Dose per pulse (DPP) of 0.5 Gy and 3 Gy were compared. The recombination of peroxyl radicals were simulated. Further comparisons were conducted by incorporating the antioxidant amifostine. Results: When varying total doses with a constant MDR of 900 Gy/s, the FLASH effect was not observed until the dose reached 15 Gy. For a total dose of 15 Gy and varying MDR, the FLASH effect was observed only when MDR reached 100 Gy/s. For a dose of 15 Gy and an MDR of 150 Gy/s, no significant difference in biological effect was observed between low DPP and high DPP. The simulation results indicated that the fraction of peroxyl radicals recombination remained nearly zero at conventional dose rates. For FLASH irradiation, the recombination fraction increased linearly with the dose. Notably, the dose delivery time corresponding to 50% change in the recombination fraction was approximately 300 ms. The addition of amifostine effectively eliminated the difference between FLASH group and CONV group. Conclusions: The critical requirement for observing the sparing effect at the biological endpoint is the administration of an adequate dose within the time window of the radical reaction. Additionally, the important role of free radical was verified after introducing antioxidants, suggesting that the generation and recombination of free radicals are pivotal factors influencing the FLASH sparing effect.

physics.med-ph↗

arXiv2024

Quantum Griffiths singularity in a three-dimensional superconductor to Anderson critical insulator transition

Disorder is ubiquitous in real materials and can have dramatic effects on quantum phase transitions. Originating from the disorder enhanced quantum fluctuation, quantum Griffiths singularity (QGS) has been revealed as a universal phenomenon in quantum criticality of low-dimensional superconductors. However, due to the weak fluctuation effect, QGS is very challenging to detect experimentally in three-dimensional (3D) superconducting systems. Here we report the discovery of QGS associated with the quantum phase transition from 3D superconductor to Anderson critical insulator in a spinel oxide MgTi2O4 (MTO). Under both perpendicular and parallel magnetic field, the dynamical critical exponent diverges when approaching the quantum critical point, demonstrating the existence of 3D QGS. Among 3D superconductors, MTO shows a relatively strong fluctuation effect featured as a wide superconducting transition region. The enhanced fluctuation, which may arise from the mobility edge of Anderson localization, finally leads to the occurrence of 3D quantum phase transition and QGS. Our findings offer a new perspective to understand quantum phase transitions in strongly disordered 3D systems.

cond-mat.supr-con↗

arXiv2024

Polarization and Orbital Angular Momentum Encoded Quantum Toffoli Gate Enabled by Diffractive Neural Networks

Controlled quantum gates play a crucial role in enabling quantum universal operations by facilitating interactions between qubits. Direct implementation of three-qubit gates simplifies the design of quantum circuits, thereby being conducive to performing complex quantum algorithms. Here, we propose and present an experimental demonstration of a quantum Toffoli gate fully exploiting the polarization and orbital angular momentum of a single photon. The Toffoli gate is implemented using the polarized diffractive neural networks scheme, achieving a mean truth table visibility of $97.27\pm0.20\%$. We characterize the gate's performance through quantum state tomography on 216 different input states and quantum process tomography, which yields a process fidelity of $94.05\pm 0.02\%$. Our method offers a novel approach for realizing the Toffoli gate without requiring exponential optical elements while maintaining extensibility to the implementation of other three-qubit gates.

quant-ph↗

arXiv2024

Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks

While the spatial mode of photons is widely used in quantum cryptography, its potential for quantum computation remains largely unexplored. Here, we showcase the use of the multi-dimensional spatial mode of photons to construct a series of high-dimensional quantum gates, achieved through the use of diffractive deep neural networks (D2NNs). Notably, our gates demonstrate high fidelity of up to 99.6(2)%, as characterized by quantum process tomography. Our experimental implementation of these gates involves a programmable array of phase layers in a compact and scalable device, capable of performing complex operations or even quantum circuits. We also demonstrate the efficacy of the D2NN gates by successfully implementing the Deutsch algorithm and propose an intelligent deployment protocol that involves self-configuration and self-optimization. Moreover, we conduct a comparative analysis of the D2NN gate's performance to the wave-front matching approach. Overall, our work opens a door for designing specific quantum gates using deep learning, with the potential for reliable execution of quantum computation.

quant-ph↗

arXiv2024

Emerging optical spin and chirality by 3D evanescent field coupling

Optical spin and chirality play key roles in engineering photonic emission and light-matter interactions. Here we show that 3D evanescent coupling of guided modes by strongly confined waveguides can extrinsically produce optical spin and chirality. These emerging optical quantities are essentially derived from an intrinsic phase retardation of π/2 between two coupled modes. For a directional coupler with 2*2 input ports, the produced optical spin and chirality are inherently characterized by the side-locked and path-locked phenomena, respectively. We numerically demonstrate that these optical spin and chirality can be ubiquitously produced by higher-order mode coupling and other structural waveguides. These new quantities in optical waveguides may pave the way to exploit near-field photonic spin and on-chip chiral light-matter interactions.

physics.optics↗

arXiv2024

Ultra-High-Efficiency Dual-Band Thin-Film Lithium Niobate Modulator Incorporating Low-k Underfill with 220 GHz Extrapolated Bandwidth for 390 Gbit/s PAM8 Transmission

High-performance electro-optic modulators play a critical role in modern telecommunication networks and intra-datacenter interconnects. Low driving voltage, large electro-optic bandwidth, compact device size, and multi-band operation ability are essential for various application scenarios, especially energy-efficient high-speed data transmission. However, it is challenging to meet all these requirements simultaneously. Here, we demonstrate a high-performance dual-band thin-film lithium niobate electro-optic modulator with low-k underfill to achieve overall performance improvement. The low-k material helps reduce the RF loss of the modulator and achieve perfect velocity matching with narrow electrode gap to overcome the voltage-bandwidth limitation, extending electro-optic bandwidth and enhancing modulation efficiency simultaneously. The fabricated 7-mm-long modulator exhibits a low half-wave voltage of 1.9 V at C-band and 1.54 V at O-band, featuring a low half-wave voltage-length product of 1.33 V*cm and 1.08 V*cm, respectively. Meanwhile, the novel design yields an ultra-wide extrapolated 3 dB bandwidth of 220 GHz (218 GHz) in the C-band (O-band). High-speed data transmission in both C- and O-bands using the same device has been demonstrated for the first time by PAM8 with data rates up to 390 Gbit/s, corresponding to a record-low energy consumption of 0.69 fJ/bit for next-generation cost-effective ultra-high-speed optical communications.

physics.optics↗

arXiv2024

MultiKG: Multi-Source Threat Intelligence Aggregation for High-Quality Knowledge Graph Representation of Attack Techniques

The construction of attack technique knowledge graphs aims to transform various types of attack knowledge into structured representations for more effective attack procedure modeling. Existing methods typically rely on textual data, such as Cyber Threat Intelligence (CTI) reports, which are often coarse-grained and unstructured, resulting in incomplete and inaccurate knowledge graphs. To address these issues, we expand attack knowledge sources by incorporating audit logs and static code analysis alongside CTI reports, providing finer-grained data for constructing attack technique knowledge graphs. We propose MultiKG, a fully automated framework that integrates multiple threat knowledge sources. MultiKG processes data from CTI reports, dynamic logs, and static code separately, then merges them into a unified attack knowledge graph. Through system design and the utilization of the Large Language Model (LLM), MultiKG automates the analysis, construction, and merging of attack graphs across these sources, producing a fine-grained, multi-source attack knowledge graph. We implemented MultiKG and evaluated it using 1,015 real attack techniques and 9,006 attack intelligence entries from CTI reports. Results show that MultiKG effectively extracts attack knowledge graphs from diverse sources and aggregates them into accurate, comprehensive representations. Through case studies, we demonstrate that our approach directly benefits security tasks such as attack reconstruction and detection.

cs.CR↗

arXiv2024

Positive codegree Andrásfai--Erdős--Sós theorem for the generalized triangle

The celebrated Andrásfai--Erdős--Sós Theorem from 1974 shows that every $n$-vertex triangle-free graph with minimum degree greater than $2n/5$ must be bipartite. We establish a positive codegree extension of this result for the $r$-uniform generalized triangle $\mathrm{T}_{r} = \left\{\{1,\ldots, r-1,r\}, \{1,\ldots, r-1,r+1\},\{r,r+1, \ldots, 2r-1\}\right\}$$\colon$ For every $n \ge (r-1)(2r+1)/2$, if $\mathcal{H}$ is an $n$-vertex $\mathrm{T}_{r}$-free $r$-uniform hypergraph in which each $(r-1)$-tuple of vertices is contained in either zero edges or more than $2n/(2r+1)$ edges of $\mathcal{H}$, then $\mathcal{H}$ is $r$-partite. This result provides the first tight positive codegree Andr{á}sfai--Erdős--Sós type theorem for hypergraphs. It also immediately implies that the positive codegree Turán number of $\mathrm{T}_{r}$ is $\lfloor n/r \rfloor$ for all $r$. Additionally, for $r=3$, our result answers one of the questions posed by Hou et al.~\cite{HLYZZ22} in a strong form.

math.CO↗

arXiv2024

Next-to-Next-to-Leading-Order QCD Prediction for the Pion Form Factor

We accomplish for the first time the two-loop computation of the leading-twist contribution to the pion electromagnetic form factor by employing the effective field theory formalism rigorously. The next-to-next-to-leading-order short-distance matching coefficient is determined by evaluating the appropriate $5$-point QCD amplitude with the modern multi-loop technique and subsequently by implementing the ultraviolet renormalization and infrared subtractions with the inclusion of evanescent operators. The renormalization/factorization scale independence of the obtained form factor is then validated explicitly at ${\cal O}(α_s^3)$. The yielding two-loop QCD correction to this fundamental quantity turns out to be numerically significant at experimentally accessible momentum transfers. We further demonstrate that the newly computed two-loop radiative correction is highly beneficial for an improved determination of the leading-twist pion distribution amplitude.

hep-ph↗

arXiv2024

PARIS: A Practical, Adaptive Trace-Fetching and Real-Time Malicious Behavior Detection System

The escalating sophistication of cyber-attacks and the widespread utilization of stealth tactics have led to significant security threats globally. Nevertheless, the existing static detection methods exhibit limited coverage, and traditional dynamic monitoring approaches encounter challenges in bypassing evasion techniques. Thus, it has become imperative to implement nuanced and dynamic analysis to achieve precise behavior detection in real time. There are two pressing concerns associated with current dynamic malware behavior detection solutions. Firstly, the collection and processing of data entail a significant amount of overhead, making it challenging to be employed for real-time detection on the end host. Secondly, these approaches tend to treat malware as a singular entity, thereby overlooking varied behaviors within one instance. To fill these gaps, we propose PARIS, an adaptive trace fetching, lightweight, real-time malicious behavior detection system. Specifically, we monitor malicious behavior with Event Tracing for Windows (ETW) and learn to selectively collect maliciousness-related APIs or call stacks, significantly reducing the data collection overhead. As a result, we can monitor a wider range of APIs and detect more intricate attack behavior. We implemented a prototype of PARIS and evaluated the system overhead, the accuracy of comparative behavior recognition, and the impact of different models and parameters. The result demonstrates that PARIS can reduce over 98.8% of data compared to the raw ETW trace and hence decreases the overhead on the host in terms of memory, bandwidth, and CPU usage with a similar detection accuracy to the baselines that suffer from the high overhead. Furthermore, a breakdown evaluation shows that 80% of the memory and bandwidth savings and a complete reduction in CPU usage can be attributed to our adaptive trace-fetching collector.

cs.CR↗

arXiv2024

Andrásfai--Erdős--Sós theorem for the generalized triangle

The celebrated Andrásfai--Erdős--Sós Theorem from 1974 shows that every $n$-vertex triangle-free graph with minimum degree greater than $2n/5$ must be bipartite. Its extensions to $3$-uniform hypergraphs without the generalized triangle $F_5 = \{abc, abd, cde\}$ have been explored in several previous works such as~\cite{LMR23unif,HLZ24}, demonstrating the existence of $\varepsilon > 0$ such that for large $n$, every $n$-vertex $F_5$-free $3$-graph with minimum degree greater than $(1/9-\varepsilon) n^2$ must be $3$-partite. We determine the optimal value for $\varepsilon$ by showing that for $n \ge 5000$, every $n$-vertex $F_5$-free $3$-graph with minimum degree greater than $4n^2/45$ must be $3$-partite, thus establishing the first tight Andrásfai--Erdős--Sós type theorem for hypergraphs. As a corollary, for all positive $n$, every $n$-vertex cancellative $3$-graph with minimum degree greater than $4n^2/45$ must be $3$-partite. This result is also optimal and considerably strengthens prior work, such as that by Bollobás~\cite{Bol74} and Keevash--Mubayi~\cite{KM04Cancel}.

math.CO↗

arXiv2024

Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification

Mitral regurgitation (MR) is a serious heart valve disease. Early and accurate diagnosis of MR via ultrasound video is critical for timely clinical decision-making and surgical intervention. However, manual MR diagnosis heavily relies on the operator's experience, which may cause misdiagnosis and inter-observer variability. Since MR data is limited and has large intra-class variability, we propose an unsupervised out-of-distribution (OOD) detection method to identify MR rather than building a deep classifier. To our knowledge, we are the first to explore OOD in MR ultrasound videos. Our method consists of a feature extractor, a feature reconstruction model, and a residual accumulation amplification algorithm. The feature extractor obtains features from the video clips and feeds them into the feature reconstruction model to restore the original features. The residual accumulation amplification algorithm then iteratively performs noise feature reconstruction, amplifying the reconstructed error of OOD features. This algorithm is straightforward yet efficient and can seamlessly integrate as a plug-and-play component in reconstruction-based OOD detection methods. We validated the proposed method on a large ultrasound dataset containing 893 non-MR and 267 MR videos. Experimental results show that our OOD detection method can effectively identify MR samples.

cs.CV↗

arXiv2024

Spectral forms and de-Rham Hodge operator

Motivated by the trilinear functional of differential one-forms, spectral triple and spectral torsion for the Hodge-Dirac operator, we introduce a multilinear functional of differential one-forms for a finitely summable regular spectral triple with a noncommutative residue, which generalize the spectral torsion defined by Dabrowski-Sitarz-Zalecki. The main results of this paper recover two forms, torsion of the linear connection and four forms by the noncommutative residue and perturbed de-Rham Hodge operators, and provides an explicit computation of generalized spectral torsion associated with the perturbed de-Rham Hodge Dirac triple.

math.DG↗

arXiv2024

EgoAvatar: Egocentric View-Driven and Photorealistic Full-body Avatars

Immersive VR telepresence ideally means being able to interact and communicate with digital avatars that are indistinguishable from and precisely reflect the behaviour of their real counterparts. The core technical challenge is two fold: Creating a digital double that faithfully reflects the real human and tracking the real human solely from egocentric sensing devices that are lightweight and have a low energy consumption, e.g. a single RGB camera. Up to date, no unified solution to this problem exists as recent works solely focus on egocentric motion capture, only model the head, or build avatars from multi-view captures. In this work, we, for the first time in literature, propose a person-specific egocentric telepresence approach, which jointly models the photoreal digital avatar while also driving it from a single egocentric video. We first present a character model that is animatible, i.e. can be solely driven by skeletal motion, while being capable of modeling geometry and appearance. Then, we introduce a personalized egocentric motion capture component, which recovers full-body motion from an egocentric video. Finally, we apply the recovered pose to our character model and perform a test-time mesh refinement such that the geometry faithfully projects onto the egocentric view. To validate our design choices, we propose a new and challenging benchmark, which provides paired egocentric and dense multi-view videos of real humans performing various motions. Our experiments demonstrate a clear step towards egocentric and photoreal telepresence as our method outperforms baselines as well as competing methods. For more details, code, and data, we refer to our project page.

cs.CV↗

arXiv2024

Delving Deep into Engagement Prediction of Short Videos

Understanding and modeling the popularity of User Generated Content (UGC) short videos on social media platforms presents a critical challenge with broad implications for content creators and recommendation systems. This study delves deep into the intricacies of predicting engagement for newly published videos with limited user interactions. Surprisingly, our findings reveal that Mean Opinion Scores from previous video quality assessment datasets do not strongly correlate with video engagement levels. To address this, we introduce a substantial dataset comprising 90,000 real-world UGC short videos from Snapchat. Rather than relying on view count, average watch time, or rate of likes, we propose two metrics: normalized average watch percentage (NAWP) and engagement continuation rate (ECR) to describe the engagement levels of short videos. Comprehensive multi-modal features, including visual content, background music, and text data, are investigated to enhance engagement prediction. With the proposed dataset and two key metrics, our method demonstrates its ability to predict engagements of short videos purely from video content.

cs.CV↗

arXiv2024

E2CL: Exploration-based Error Correction Learning for Embodied Agents

Language models are exhibiting increasing capability in knowledge utilization and reasoning. However, when applied as agents in embodied environments, they often suffer from misalignment between their intrinsic knowledge and environmental knowledge, leading to infeasible actions. Traditional environment alignment methods, such as supervised learning on expert trajectories and reinforcement learning, encounter limitations in covering environmental knowledge and achieving efficient convergence, respectively. Inspired by human learning, we propose Exploration-based Error Correction Learning (E2CL), a novel framework that leverages exploration-induced errors and environmental feedback to enhance environment alignment for embodied agents. E2CL incorporates teacher-guided and teacher-free explorations to gather environmental feedback and correct erroneous actions. The agent learns to provide feedback and self-correct, thereby enhancing its adaptability to target environments. Extensive experiments in the VirtualHome environment demonstrate that E2CL-trained agents outperform those trained by baseline methods and exhibit superior self-correction capabilities.

cs.CL↗

arXiv2024

Pseudo-Riemannian metric: a new perspective on the quantum realm

As a fundamental concept in condensed matter physics, quantum geometry within the Riemannian metric elucidates various exotic phenomena, including the Hall effects driven by Berry curvature and quantum metric. In this work, we propose novel quantum geometries within a pseudo-Riemannian framework to explore unique characteristic of quantum matter. By defining distinct distances on pseudo-Riemannian manifolds and incorporating spin degree of freedom, we introduce the Pauli quantum geometric tensor. The imaginary part of this tensor corresponds to the Pauli Berry curvature, leading to the discovery a novel quantum phase: Pauli semimetal in PT-symmetric systems. This phase, characterized by the topological Pauli Chern number, manifests as a two-dimensional Pauli Chern insulator with helical edge states. These topological phases, uniquely revealed by the Pauli-Riemannian metric, go beyond the familiar Riemannian metric, where Berry curvature vanishes due to PT-symmetry. Pauli Chern number can classify helical topological insulator with or without time reversal symmetry. Pseudo-Riemannian metrics offer new insights into quantum materials and extend the scope of quantum geometry.

cond-mat.mes-hall↗