arXiv subjects

Xinyuan Zhang

Publications and source records attributed to Xinyuan Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2023

A Near-Linear Time Sampler for the Ising Model with External Field

We give a near-linear time sampler for the Gibbs distribution of the ferromagnetic Ising models with edge activities $\boldsymbolβ > 1$ and external fields $\boldsymbolλ<1$ (or symmetrically, $\boldsymbolλ>1$) on general graphs with bounded or unbounded maximum degree. Our algorithm is based on the field dynamics given in [CFYZ21]. We prove the correctness and efficiency of our algorithm by establishing spectral independence of distribution of the random cluster model and the rapid mixing of Glauber dynamics on the random cluster model in a low-temperature regime, which may be of independent interest.

math.PR↗

arXiv2023

AI-Generated Network Design: A Diffusion Model-based Learning Approach

The future networks pose intense demands for intelligent and customized designs to cope with the surging network scale, dynamically time-varying environments, diverse user requirements, and complicated manual configuration. However, traditional rule-based solutions heavily rely on human efforts and expertise, while data-driven intelligent algorithms still lack interpretability and generalization. In this paper, we propose the AIGN (AI-Generated Network), a novel intention-driven paradigm for network design, which allows operators to quickly generate a variety of customized network solutions and achieve expert-free problem optimization. Driven by the diffusion model-based learning approach, AIGN has great potential to learn the reward-maximizing trajectories, automatically satisfy multiple constraints, adapt to different objectives and scenarios, or even intelligently create novel designs and mechanisms unseen in existing network environments. Finally, we conduct a use case to demonstrate that AIGN can effectively guide the design of transmit power allocation in digital twin-based access networks.

cs.NI↗

arXiv2022

Multiplication of freestanding semiconductor membranes from a single wafer by advanced remote epitaxy

Freestanding single-crystalline membranes are an important building block for functional electronics. Especially, compounds semiconductor membranes such as III-N and III-V offer great opportunities for optoelectronics, high-power electronics, and high-speed computing. Despite huge efforts to produce such membranes by detaching epitaxial layers from donor wafers, however, it is still challenging to harvest epitaxial layers using practical processes. Here, we demonstrate a method to grow and harvest multiple epitaxial membranes with extremely high throughput at the wafer scale. For this, 2D materials are directly formed on III-N and III-V substrates in epitaxy systems, which enables an advanced remote epitaxy scheme comprised of multiple alternating layers of 2D materials and epitaxial layers that can be formed by a single epitaxy run. Each epilayer in the multi-stack structure is then harvested by layer-by-layer peeling, producing multiple freestanding membranes with unprecedented throughput from a single wafer. Because 2D materials allow peeling at the interface without damaging the epilayer or the substrate, wafers can be reused for subsequent membrane production. Therefore, this work represents a meaningful step toward high-throughput and low-cost production of single-crystal membranes that can be heterointegrated.

physics.app-ph↗

arXiv2022

GIU-GANs: Global Information Utilization for Generative Adversarial Networks

In recent years, with the rapid development of artificial intelligence, image generation based on deep learning has dramatically advanced. Image generation based on Generative Adversarial Networks (GANs) is a promising study. However, since convolutions are limited by spatial-agnostic and channel-specific, features extracted by traditional GANs based on convolution are constrained. Therefore, GANs are unable to capture any more details per image. On the other hand, straightforwardly stacking of convolutions causes too many parameters and layers in GANs, which will lead to a high risk of overfitting. To overcome the aforementioned limitations, in this paper, we propose a new GANs called Involution Generative Adversarial Networks (GIU-GANs). GIU-GANs leverages a brand new module called the Global Information Utilization (GIU) module, which integrates Squeeze-and-Excitation Networks (SENet) and involution to focus on global information by channel attention mechanism, leading to a higher quality of generated images. Meanwhile, Batch Normalization(BN) inevitably ignores the representation differences among noise sampled by the generator, and thus degrade the generated image quality. Thus we introduce Representative Batch Normalization(RBN) to the GANs architecture for this issue. The CIFAR-10 and CelebA datasets are employed to demonstrate the effectiveness of our proposed model. A large number of experiments prove that our model achieves state-of-the-art competitive performance.

cs.LG↗

arXiv2022

Optimal mixing for two-state anti-ferromagnetic spin systems

We prove an optimal $Ω(n^{-1})$ lower bound for modified log-Sobolev (MLS) constant of the Glauber dynamics for anti-ferromagnetic two-spin systems with $n$ vertices in the tree uniqueness regime. Specifically, this optimal MLS bound holds for the following classes of two-spin systems in the tree uniqueness regime: $\bullet$ all strictly anti-ferromagnetic two-spin systems (where both edge parameters $β,γ<1$), which cover the hardcore models and the anti-ferromagnetic Ising models; $\bullet$ general anti-ferromagnetic two-spin systems on regular graphs. Consequently, an optimal $O(n\log n)$ mixing time holds for these anti-ferromagnetic two-spin systems when the uniqueness condition is satisfied. These MLS and mixing time bounds hold for any bounded or unbounded maximum degree, and the constant factors in the bounds depend only on the gap to the uniqueness threshold. We prove this by showing a boosting theorem for MLS constant for distributions satisfying certain spectral independence and marginal stability properties.

math-ph↗

arXiv2022

GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network

Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and techniques that support inspiration usually only focus on example browsing with limited user control or similarity-based example retrieval, leading to undesirable design outcomes such as focus drift and design fixation. To address these issues, we propose the GANSpiration approach that suggests design examples for both targeted and serendipitous inspiration, leveraging a style-based Generative Adversarial Network. A quantitative evaluation revealed that the outputs of GANSpiration-based example suggestion approaches are relevant to the input design, and at the same time include diverse instances. A user study with professional UI/UX practitioners showed that the examples suggested by our approach serve as viable sources of inspiration for overall design concepts and specific design elements. Overall, our work paves the road of using advanced generative machine learning techniques in supporting the creative design practice.

cs.HC↗

arXiv2022

Cognitive Semantic Communication Systems Driven by Knowledge Graph

Semantic communication is envisioned as a promising technique to break through the Shannon limit. However, the existing semantic communication frameworks do not involve inference and error correction, which limits the achievable performance. In this paper, in order to tackle this issue, a cognitive semantic communication framework is proposed by exploiting knowledge graph. Moreover, a simple, general and interpretable solution for semantic information detection is developed by exploiting triples as semantic symbols. It also allows the receiver to correct errors occurring at the symbolic level. Furthermore, the pre-trained model is fine-tuned to recover semantic information, which overcomes the drawback that a fixed bit length coding is used to encode sentences of different lengths. Simulation results on the public WebNLG corpus show that our proposed system is superior to other benchmark systems in terms of the data compression rate and the reliability of communication.

cs.AI↗

arXiv2022

Polynomial-Time Approximation of Zero-Free Partition Functions

Zero-free based algorithm is a major technique for deterministic approximate counting. In Barvinok's original framework[Bar17], by calculating truncated Taylor expansions, a quasi-polynomial time algorithm was given for estimating zero-free partition functions. Patel and Regts[PR17] later gave a refinement of Barvinok's framework, which gave a polynomial-time algorithm for a class of zero-free graph polynomials that can be expressed as counting induced subgraphs in bounded-degree graphs. In this paper, we give a polynomial-time algorithm for estimating classical and quantum partition functions specified by local Hamiltonians with bounded maximum degree, assuming a zero-free property for the temperature. Consequently, when the inverse temperature is close enough to zero by a constant gap, we have polynomial-time approximation algorithm for all such partition functions. Our result is based on a new abstract framework that extends and generalizes the approach of Patel and Regts.

cs.DS↗

arXiv2021

KDCTime: Knowledge Distillation with Calibration on InceptionTime for Time-series Classification

Time-series classification approaches based on deep neural networks are easy to be overfitting on UCR datasets, which is caused by the few-shot problem of those datasets. Therefore, in order to alleviate the overfitting phenomenon for further improving the accuracy, we first propose Label Smoothing for InceptionTime (LSTime), which adopts the information of soft labels compared to just hard labels. Next, instead of manually adjusting soft labels by LSTime, Knowledge Distillation for InceptionTime (KDTime) is proposed in order to automatically generate soft labels by the teacher model. At last, in order to rectify the incorrect predicted soft labels from the teacher model, Knowledge Distillation with Calibration for InceptionTime (KDCTime) is proposed, where it contains two optional calibrating strategies, i.e. KDC by Translating (KDCT) and KDC by Reordering (KDCR). The experimental results show that the accuracy of KDCTime is promising, while its inference time is two orders of magnitude faster than ROCKET with an acceptable training time overhead.

cs.LG↗

arXiv2021

Rapid mixing of Glauber dynamics via spectral independence for all degrees

We prove an optimal $Ω(n^{-1})$ lower bound on the spectral gap of Glauber dynamics for anti-ferromagnetic two-spin systems with $n$ vertices in the tree uniqueness regime. This spectral gap holds for all, including unbounded, maximum degree $Δ$. Consequently, we have the following mixing time bounds for the models satisfying the uniqueness condition with a slack $δ\in(0,1)$: $\bullet$ $C(δ) n^2\log n$ mixing time for the hardcore model with fugacity $λ\le (1-δ)λ_c(Δ)= (1-δ)\frac{(Δ- 1)^{Δ- 1}}{(Δ- 2)^Δ}$; $\bullet$ $C(δ) n^2$ mixing time for the Ising model with edge activity $β\in\left[\frac{Δ-2+δ}{Δ-δ},\frac{Δ-δ}{Δ-2+δ}\right]$; where the maximum degree $Δ$ may depend on the number of vertices $n$, and $C(δ)$ depends only on $δ$. Our proof is built upon the recently developed connections between the Glauber dynamics for spin systems and the high-dimensional expander walks. In particular, we prove a stronger notion of spectral independence, called the complete spectral independence, and use a novel Markov chain called the field dynamics to connect this stronger spectral independence to the rapid mixing of Glauber dynamics for all degrees.

cs.DS↗

arXiv2021

Optimal Mixing Time for the Ising Model in the Uniqueness Regime

We prove an optimal $O(n \log n)$ mixing time of the Glauber dynamics for the Ising models with edge activity $β\in \left(\frac{Δ-2}Δ, \fracΔ{Δ-2}\right)$. This mixing time bound holds even if the maximum degree $Δ$ is unbounded. We refine the boosting technique developed in [CFYZ21], and prove a new boosting theorem by utilizing the entropic independence defined in [AJK+21]. The theorem relates the modified log-Sobolev (MLS) constant of the Glauber dynamics for a near-critical Ising model to that for an Ising model in a sub-critical regime.

math.PR↗

arXiv2021

$LnCu_3(OH)_6Cl_3 (Ln = Gd, Tb, Dy)$: Heavy Lanthanides on Spin-1/2 Kagome Magnets

The spin-1/2 kagome antiferromagnets are key prototype materials for studying frustrated magnetism. Three isostructural kagome antiferromagnets LnCu$_3$(OH)$_6$Cl$_3$ (Ln = Gd, Tb, Dy) have been successfully synthesized by the hydrothermal method. LnCu$_3$(OH)$_6$Cl$_3$ adopts space group $P\overline{3}m1$ and features the layered Cu-kagome lattice with lanthanide Ln$^{3+}$ cations sitting at the center of the hexagons. Although heavy lanthanides (Ln = Gd, Tb, Dy) in LnCu$_3$(OH)$_6$Cl$_3$ provide a large effective magnetic moment and ferromagnetic-like spin correlations compared to light-lanthanides (Nd, Sm, Eu) analogues, Cu-kagome holds an antiferromagnetically ordered state at around 17 K like YCu$_3$(OH)$_6$Cl$_3$.

cond-mat.str-el↗

arXiv2021

Deep-Learning-Enabled Inverse Engineering of Multi-Wavelength Invisibility-to-Superscattering Switching with Phase-Change Materials

Inverse design of nanoparticles for desired scattering spectra and dynamic switching between the two opposite scattering anomalies, i.e. superscattering and invisibility, is important in realizing cloaking, sensing and functional devices. However, traditionally the design process is quite complicated, which involves complex structures with many choices of synthetic constituents and dispersions. Here, we demonstrate that a well-trained deep-learning neural network can handle these issues efficiently, which can not only forwardly predict scattering spectra of multilayer nanoparticles with high precision, but also inversely design the required structural and material parameters efficiently. Moreover, we show that the neural network is capable of finding out multi-wavelength invisibility-to-superscattering switching points at the desired wavelengths in multilayer nanoparticles composed of metals and phase-change materials. Our work provides a useful solution of deep learning for inverse design of nanoparticles with dynamic scattering spectra by using phase-change materials.

physics.optics↗

arXiv2021

Flexible daytime radiative cooling enhanced by enabling three-phase composites with scattering interfaces between silica-microspheres and hierarchical porous coatings

Daytime radiative cooling has attracted considerable attention recently due to its tremendous potential for passively exploiting the coldness of deep-sky as clean and renewable energy. Many advanced materials with novel photonic micro-nanostructures have already been developed to enable highly efficient daytime radiative coolers, among which the flexible hierarchical porous coatings (HPCs) are a more distinguished category. However, it is still hard to precisely control the size distribution of the randomized pores within the HPCs, usually resulting in a deficient solar reflection at the near-infrared optical regime under diverse fabrication conditions of the coatings. We report here a three-phase (i.e., air pore-phase, microsphere-phase and polymer-phase) self-assembled hybrid porous composite coating which dramatically increases the average solar reflectance and yields a remarkable temperature drop of ~10 degC and 30 degC compared to the ambient circumstance and black paint, respectively, according to the rooftop measurements. Mie theory and Monte Carlo simulations reveal the origin of the low reflectivity of as-prepared two-phase porous HPCs, and the optical cooling improvement of the three-phase porous composite coatings is attributed to the newly generated interfaces possessing the high scattering efficiency between the hierarchical pores and silica microspheres hybridized with appropriate mass fractions. As a result, the hybrid porous composite approach enhances the whole performance of the coatings, which provides a promising alternative to the flexible daytime radiative cooler.

physics.app-ph↗

arXiv2021

Alloy Engineering of Polar (Si,Ge)2N2O System for Controllable Second Harmonic Performance

Although silicon oxynitrides are important semiconductors for many practical applications, their potential second-order nonlinear optical (NLO) applications, regardless of balanced or controllable performance, have never been systemically explored. Using the first-principles calculations, in this article, we discover that the sinoite (i.e., typical silicon oxynitride Si2N2O) can simultaneously exhibit wide optical bandgap, strong second-harmonic generation (SHG) effect, and large birefringence, which are further confirmed by our preliminary experimental data. Importantly, we propose that alloying engineering can be further applied to control the balanced NLO properties in the Si2N2O system. Combining first-principles calculations and cluster expansion theory, we demonstrate that alloying Ge into Si2N2O can easily form low formation energy Si2(1-x)Ge2xN2O alloys, which can in turn achieve controllable phase-matching harmonic output with high SHG efficiency at different energy ranges. Therefore, alloy engineering could provide a unique approach to effectively control the balanced NLO performance of Si2(1-x)Ge2xN2O, making this polar alloy system holding potential applications in tunable laser frequency conversion and controllable all-optical devices.

cond-mat.mtrl-sci↗

arXiv2020

Mixing Strategies in Cryptocurrencies and An Alternative Implementation

Since the initial launch of Bitcoin by Satoshi Nakamoto in 2009, decentralized digital currencies have long been of major interest in both the academia and the industry. Till today, there are more than 3000 different cryptocurrencies over the internet. Each one relies on mathematical soundness and cryptographic wit to provide unique properties in addition to securing basic correctness. A common misbelief for cryptocurrencies is that they provide complete anonymity by replacing people's real-life identity with a randomly generated wallet address in payments. However, this "pseudonymity" is easily breakable under the public ledger. Many attacks demonstrate ways to deanonymize people through observing the transaction patterns or network interactions. Thus, cryptocurrency fungibility has become a popular topic in the research community. This report reviews a partial list of existing schemes and describes an alternative implementation, Eth-Tumbler. Eth-Tumbler utilizes layered encryption and multiple signatures and thus efficiently hides a user under k-anonymity.

cs.CR↗

arXiv2020

Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes

High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). Unlike standard text summarization, a dialogue summarization method should consider the multi-speaker scenario where the speakers have different roles, goals, and language styles. In a tete-a-tete, such as a customer-agent conversation, SuTaT aims to summarize for each speaker by modeling the customer utterances and the agent utterances separately while retaining their correlations. SuTaT consists of a conditional generative module and two unsupervised summarization modules. The conditional generative module contains two encoders and two decoders in a variational autoencoder framework where the dependencies between two latent spaces are captured. With the same encoders and decoders, two unsupervised summarization modules equipped with sentence-level self-attention mechanisms generate summaries without using any annotations. Experimental results show that SuTaT is superior on unsupervised dialogue summarization for both automatic and human evaluations, and is capable of dialogue classification and single-turn conversation generation.

cs.CL↗

arXiv2020

Fractional matching preclusion of fault Hamiltonian graphs

Matching preclusion is a measure of robustness in the event of edge failure in interconnection networks. As a generalization of matching preclusion, the fractional matching preclusion number (FMP number for short) of a graph is the minimum number of edges whose deletion results in a graph that has no fractional perfect matchings, and the fractional strong matching preclusion number (FSMP number for short) of a graph is the minimum number of edges and/or vertices whose deletion leaves a resulting graph with no fractional perfect matchings. A graph $G$ is said to be $f$-fault Hamiltonian if there exists a Hamiltonian cycle in $G-F$ for any set $F$ of vertices and/or edges with $|F|\leq f$. In this paper, we establish the FMP number and FSMP number of $(δ-2)$-fault Hamiltonian graphs with minimum degree $δ\geq 3$. As applications, the FMP number and FSMP number of some well-known networks are determined.

math.CO↗