arXiv subjects

Xiao Shen

Publications and source records attributed to Xiao Shen.

At least 37 records · Page 2Linked to original sources

arXiv2024

Lower bound for large transversal fluctuations in exactly solvable KPZ models

The study of transversal fluctuation of the optimal path has been a crucial aspect of the Kadar-Parisi-Zhang (KPZ) universality class. In this paper, we establish a new probability lower bound, with optimal exponential order, for the rare event in which a given level of the optimal path has a large transversal fluctuation. We present our results in both zero and positive temperature settings. The previously known lower bounds were obtained in zero temperature models, and they hold for the maximum transversal fluctuation along the entire geodesic (Hammond-Sarkar'20) or the starting portion of the geodesic at a local scale (Agarwal'23). Our result improves upon these as now the rare event can demand where the large fluctuation occurs exactly along the optimal path, on both local and global scales. Our proof utilizes the coupling method: we first obtain a version of the estimate in the semi-infinite setting using duality and then transfer the result to finite paths using planar monotonicity. Our method differs from the previous works (Agarwal'23, Hammond-Sarkar'20), and in fact, we do not require fine information about the left tail moderate deviation, which played a crucial role in (Agarwal'23, Hammond-Sarkar'20).

math.PR↗

arXiv2024

Denoising-Aware Contrastive Learning for Noisy Time Series

Time series self-supervised learning (SSL) aims to exploit unlabeled data for pre-training to mitigate the reliance on labels. Despite the great success in recent years, there is limited discussion on the potential noise in the time series, which can severely impair the performance of existing SSL methods. To mitigate the noise, the de facto strategy is to apply conventional denoising methods before model training. However, this pre-processing approach may not fully eliminate the effect of noise in SSL for two reasons: (i) the diverse types of noise in time series make it difficult to automatically determine suitable denoising methods; (ii) noise can be amplified after mapping raw data into latent space. In this paper, we propose denoising-aware contrastive learning (DECL), which uses contrastive learning objectives to mitigate the noise in the representation and automatically selects suitable denoising methods for every sample. Extensive experiments on various datasets verify the effectiveness of our method. The code is open-sourced.

cs.LG↗

arXiv2024

Towards establishing best practice in the analysis of hydrogen and deuterium by atom probe tomography

As hydrogen is touted as a key player in the decarbonization of modern society, it is critical to enable quantitative H analysis at high spatial resolution, if possible at the atomic scale. Indeed, H has a known deleterious impact on the mechanical properties (strength, ductility, toughness) of most materials that can hinder their use as part of the infrastructure of a hydrogen-based economy. Enabling H mapping, including local hydrogen concentration analyses at specific microstructural features, is essential for understanding the multiple ways that H affect the properties of materials, including for instance embrittlement mechanisms and their synergies, but also spatial mapping and quantification of hydrogen isotopes is essential to accurately predict tritium inventory of future fusion power plants, ensuring their safe and efficient operation for example. Atom probe tomography (APT) has the intrinsic capabilities for detecting hydrogen (H), and deuterium (D), and in principle the capacity for performing quantitative mapping of H within a material's microstructure. Yet the accuracy and precision of H analysis by APT remain affected by the influence of residual hydrogen from the ultra-high vacuum chamber that can obscure the signal of H from within the material, along with a complex field evaporation behavior. The present article reports the essence of discussions at a focused workshop held at the Max-Planck Institute for Sustainable Materials in April 2024. The workshop was organized to pave the way to establishing best practices in reporting APT data for the analysis of H. We first summarize the key aspects of the intricacies of H analysis by APT and propose a path for better reporting of the relevant data to support interpretation of APT-based H analysis in materials.

cond-mat.mtrl-sci↗

arXiv2024

The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications

Future wireless networks will integrate sensing, learning and communication to provide new services beyond communication and to become more resilient. Sensors at the network infrastructure, sensors on the user equipment, and the sensing capability of the communication signal itself provide a new source of data that connects the physical and radio frequency environments. A wireless network that harnesses all these sensing data can not only enable additional sensing services, but also become more resilient to channel-dependent effects like blockage and better support adaptation in dynamic environments as networks reconfigure. In this paper, we provide a vision for integrated sensing and communication (ISAC) networks and an overview of how signal processing, optimization and machine learning techniques can be leveraged to make them a reality in the context of 6G. We also include some examples of the performance of several of these strategies when evaluated using a simulation framework based on a combination of ray tracing measurements and mathematical models that mix the digital and physical worlds.

eess.SP↗

arXiv2024

Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy

Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels. SSDA tasks need to overcome the domain gap between the source and target graphs. However, to date, this challenging research problem has yet to be formally considered by the existing approaches designed for cross-graph node classification. This paper proposes a novel method called SemiGCL to tackle the graph \textbf{Semi}-supervised domain adaptation with \textbf{G}raph \textbf{C}ontrastive \textbf{L}earning and minimax entropy training. SemiGCL generates informative node representations by contrasting the representations learned from a graph's local and global views. Additionally, SemiGCL is adversarially optimized with the entropy loss of unlabeled target nodes to reduce domain divergence. Experimental results on benchmark datasets demonstrate that SemiGCL outperforms the state-of-the-art baselines on the SSDA tasks. The source codes of SemiGCL are publicly available at https://github.com/ JiarenX/SemiGCL.

cs.LG↗

arXiv2024

Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning

Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data. In reality, the real-world graph data typically show dynamics over time, with changing node attributes and edge structure, leading to the severe graph data distribution shift issue. This issue is compounded by the diverse and complex nature of distribution shifts, which can significantly impact the performance of graph learning methods in degraded generalization and adaptation capabilities, posing a substantial challenge to their effectiveness. In this survey, we provide a comprehensive review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. Concretely, according to the observability of distributions in the inference stage and the availability of sufficient supervision information in the training stage, we categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. For each scenario, a detailed taxonomy is proposed, with specific descriptions and discussions of existing progress made in distribution-shifted graph learning. Additionally, we discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field. The survey is positioned to provide general guidance for the development of effective graph learning algorithms in handling graph distribution shifts, and to stimulate future research and advancements in this area.

cs.LG↗

arXiv2024

Decoupled few-femtosecond phase transitions in vanadium dioxide

The nature of the insulator-to-metal phase transition in vanadium dioxide (VO2) is one of the longest-standing problems in condensed-matter physics. Ultrafast spectroscopy has long promised to determine whether the transition is primarily driven by the electronic or structural degree of freedom, but measurements to date have been stymied by their sensitivity to only one of these components and/or their limited temporal resolution. Here we use ultra-broadband few-femtosecond pump-probe spectroscopy to resolve the electronic and structural phase transitions in VO2 at their fundamental time scales. We find that the system transforms into a bad-metallic phase within 10 fs after photoexcitation, but requires another 100 fs to complete the transition, during which we observe electronic oscillations and a partial re-opening of the bandgap, signalling a transient semi-metallic state. Comparisons with tensor-network simulations and density-functional theory calculations show these features originate from oscillations around the equilibrium high-symmetry atomic positions during an unprecedentedly fast structural transition, in which the vanadium dimers separate and untwist with two different timescales. Our results resolve the complete structural and electronic nature of the light-induced phase transition in VO2 and establish ultra-broadband few-femtosecond spectroscopy as a powerful new tool for studying quantum materials out of equilibrium.

cond-mat.str-el↗

arXiv2024

Time correlations in KPZ models with diffusive initial conditions

Temporal correlation for randomly growing interfaces in the KPZ universality class is a topic of recent interest. Most of the works so far have been concentrated on the zero temperature model of exponential last passage percolation, and three special initial conditions, namely droplet, flat and stationary. We focus on studying the time correlation problem for generic random initial conditions with diffusive growth. We formulate our results in terms of the positive temperature exactly solvable model of the inverse-gamma polymer and obtain up to constant upper and lower bounds for the correlation between the free energy of two polymers whose endpoints are close together or far apart. Our proofs apply almost verbatim to the zero temperature set-up of exponential LPP and are valid for a broad class of initial conditions. Our work complements and completes the partial results obtained in (Ferrari-Occelli'19), following the conjectures of (Ferrari-Spohn'16). Moreover, our arguments rely on the one-point moderate deviation estimates which have recently been obtained using stationary polymer techniques and thus do not depend on complicated exact formulae.

math.PR↗

arXiv2023

Independence property of the Busemann function in exactly solvable KPZ models

The study of Kadar-Parsi-Zhang (KPZ) universality class has been a subject of great interest among mathematicians and physicists over the past three decades. A notably successful approach for analyzing KPZ models is the coupling method, which hinges on understanding random growth from stationary initial conditions defined by Busemann functions. To advance in this direction, we investigate the independence property of the Busemann function across multiple directions in various exactly solvable KPZ models. These models encompass the corner growth model, the inverse-gamma polymer, Brownian last-passage percolation, the O'Connell-Yor polymer, the KPZ equation, and the directed landscape. In the context of the corner growth model, our result states that disjoint Busemann increments in different directions along a down-right path are independent, as long as their associated semi-infinite geodesics have nonempty intersections almost surely. The proof for the independence utilizes the queueing representation of the Busemann process developed by Seppäläinen et al. As an application, our independence result yields a near-optimal probability upper bound (missing by a logarithmic factor) for the rare event where the endpoint of a point-to-line inverse-gamma polymer is close to the diagonal.

math.PR↗

arXiv2023

Domain-adaptive Message Passing Graph Neural Network

Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently. To address CNNC, we propose a domain-adaptive message passing graph neural network (DM-GNN), which integrates graph neural network (GNN) with conditional adversarial domain adaptation. DM-GNN is capable of learning informative representations for node classification that are also transferrable across networks. Firstly, a GNN encoder is constructed by dual feature extractors to separate ego-embedding learning from neighbor-embedding learning so as to jointly capture commonality and discrimination between connected nodes. Secondly, a label propagation node classifier is proposed to refine each node's label prediction by combining its own prediction and its neighbors' prediction. In addition, a label-aware propagation scheme is devised for the labeled source network to promote intra-class propagation while avoiding inter-class propagation, thus yielding label-discriminative source embeddings. Thirdly, conditional adversarial domain adaptation is performed to take the neighborhood-refined class-label information into account during adversarial domain adaptation, so that the class-conditional distributions across networks can be better matched. Comparisons with eleven state-of-the-art methods demonstrate the effectiveness of the proposed DM-GNN.

cs.LG↗

arXiv2023

Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification

Graph neural networks (GNNs) have shown great ability in modeling graphs, however, their performance would significantly degrade when there are noisy edges connecting nodes from different classes. To alleviate negative effect of noisy edges on neighborhood aggregation, some recent GNNs propose to predict the label agreement between node pairs within a single network. However, predicting the label agreement of edges across different networks has not been investigated yet. Our work makes the pioneering attempt to study a novel problem of cross-network homophilous and heterophilous edge classification (CNHHEC), and proposes a novel domain-adaptive graph attention-supervised network (DGASN) to effectively tackle the CNHHEC problem. Firstly, DGASN adopts multi-head GAT as the GNN encoder, which jointly trains node embeddings and edge embeddings via the node classification and edge classification losses. As a result, label-discriminative embeddings can be obtained to distinguish homophilous edges from heterophilous edges. In addition, DGASN applies direct supervision on graph attention learning based on the observed edge labels from the source network, thus lowering the negative effects of heterophilous edges while enlarging the positive effects of homophilous edges during neighborhood aggregation. To facilitate knowledge transfer across networks, DGASN employs adversarial domain adaptation to mitigate domain divergence. Extensive experiments on real-world benchmark datasets demonstrate that the proposed DGASN achieves the state-of-the-art performance in CNHHEC.

cs.SI↗

arXiv2023

On the Performance Tradeoff of an ISAC System with Finite Blocklength

Integrated sensing and communication (ISAC) has been proposed as a promising paradigm in the future wireless networks, where the spectral and hardware resources are shared to provide a considerable performance gain. It is essential to understand how sensing and communication (S\&C) influences each other to guide the practical algorithm and system design in ISAC. In this paper, we investigate the performance tradeoff between S\&C in a single-input single-output (SISO) ISAC system with finite blocklength. In particular, we present the system model and the ISAC scheme, after which the rate-error tradeoff is introduced as the performance metric. Then we derive the achievability and converse bounds for the rate-error tradeoff, determining the boundary of the joint S\&C performance. Furthermore, we develop the asymptotic analysis at large blocklength regime, where the performance tradeoff between S\&C is proved to vanish as the blocklength tends to infinity. Finally, our theoretical analysis is consolidated by simulation results.

cs.IT↗

arXiv2023

Neighbor Contrastive Learning on Learnable Graph Augmentation

Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various graph datasets. In addition, the contrastive losses originally developed in computer vision have been directly applied to graph data, where the neighboring nodes are regarded as negatives and consequently pushed far apart from the anchor. However, this is contradictory with the homophily assumption of networks that connected nodes often belong to the same class and should be close to each other. In this work, we propose an end-to-end automatic GCL method, named NCLA to apply neighbor contrastive learning on learnable graph augmentation. Several graph augmented views with adaptive topology are automatically learned by the multi-head graph attention mechanism, which can be compatible with various graph datasets without prior domain knowledge. In addition, a neighbor contrastive loss is devised to allow multiple positives per anchor by taking network topology as the supervised signals. Both augmentations and embeddings are learned end-to-end in the proposed NCLA. Extensive experiments on the benchmark datasets demonstrate that NCLA yields the state-of-the-art node classification performance on self-supervised GCL and even exceeds the supervised ones, when the labels are extremely limited. Our code is released at https://github.com/shenxiaocam/NCLA.

cs.SI↗

arXiv2023

Coalescence and total-variation distance of semi-infinite inverse-gamma polymers

We show that two semi-infinite positive temperature polymers coalesce on the scale predicted by KPZ (Kardar-Parisi-Zhang) universality. The two polymer paths have the same asymptotic direction and evolve in the same environment, independently until coalescence. If they start at distance $k$ apart, their coalescence occurs on the scale $k^{3/2}$. It follows that the total variation distance of two semi-infinite polymer measures decays on this same scale. Our results are upper and lower bounds on probabilities and expectations that match, up to constant factors and occasional logarithmic corrections. Our proofs are done in the context of the solvable inverse-gamma polymer model, but without appeal to integrable probability. With minor modifications, our proofs give also bounds on transversal fluctuations of the polymer path. Since the free energy of a directed polymer is a discretization of a stochastically forced viscous Hamilton-Jacobi equation, our results suggest that the hyperbolicity phenomenon of such equations obeys the KPZ exponent.

math.PR↗

arXiv2022

Ferroelectric 2D Antimony Oxides with Wide Bandgaps

The first two-dimensional (2D) polymorphs of antimony dioxide, namely, $γ$-Sb$_2$O$_4$ and $δ$-Sb$_2$O$_4$, are predicted using the evolutionary algorithm combined with first-principles density functional theory (DFT) calculations. Out-of-plane ferroelectricity is found in $γ$-Sb$_2$O$_4$, while in-plane ferroelectricity is found in $δ$-Sb$_2$O$_4$. The predicted dipole moments of $γ$-Sb$_2$O$_4$ and $δ$-Sb$_2$O$_4$ phases are 36.63 and 14.96 eÅ, respectively, implying that they can be good candidates for making ferroelectric devices. The calculations show that doping with other group V elements or applying strain can lower the switching energy barriers and thus facilitate switching. Results from GW calculations show indirect band gaps of 5.51 and 3.39 eV for $γ$-Sb$_2$O$_4$ and $δ$-Sb$_2$O$_4$ in their monolayers, respectively. Raman spectra are calculated to facilitate the experimental investigation of the predicted structures. The existence of both in-plane and out-of-plane 2D ferroelectricity and the large band gaps make this material system particularly interesting for potential applications.

cond-mat.mtrl-sci↗

arXiv2022

Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution

This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel \textcolor{black}{graph} transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains. The source code for reproducing the experimental results is available at https://github.com/daiquanyu/AdaGCN.

cs.LG↗

arXiv2022

On the number and size of holes in the growing ball of first-passage percolation

First-passage percolation is a random growth model defined on $\mathbb{Z}^d$ using i.i.d. nonnegative weights $(τ_e)$ on the edges. Letting $T(x,y)$ be the distance between vertices $x$ and $y$ induced by the weights, we study the random ball of radius $t$ centered at the origin, $B(t) = \{x \in \mathbb{Z}^d : T(0,x) \leq t\}$. It is known that for all such $τ_e$, the number of vertices (volume) of $B(t)$ is at least order $t^d$, and under mild conditions on $τ_e$, this volume grows like a deterministic constant times $t^d$. Defining a hole in $B(t)$ to be a bounded component of the complement $B(t)^c$, we prove that if $τ_e$ is not deterministic, then a.s., for all large $t$, $B(t)$ has at least $ct^{d-1}$ many holes, and the maximal volume of any hole is at least $c\log t$. Conditionally on the (unproved) uniform curvature assumption, we prove that a.s., for all large $t$, the number of holes is at most $(\log t)^C t^{d-1}$, and for $d=2$, no hole in $B(t)$ has volume larger than $(\log t)^C$. Without curvature, we show that no hole has volume larger than $Ct \log t$.

math.PR↗

arXiv2022

Realization of A Strong Ferroelectric Metal by Nb-doping in Strained EuTiO$_{3}$

Ferroelectric (FE) metals have been attracting attention as they possess both metallicity and ferroelectricity, the two seemingly incompatible physical properties. An important problem for both fundamental research and potential applications is how to realize a strong FE metal. One strategy is to dope a strong FE insulator into an FE metal provided that the induced free electrons do not destroy FE distortion. Strained EuTiO$_{3}$ film is a strong FE ferromagnet and a promising candidate for such a strategy. Here, we calculate the structural, electronic, and polarization properties of Nb-doped strained EuTiO$_{3}$ film using the hybrid density-functional theory. The results show that the strained EuTi$_{0.875}$Nb$_{0.125}$O$_{3}$ film is a strong FE metal as the Nb doping induces metallicity without weakening the FE distortion. The underlying atomistic mechanism of the coexistence of metallicity and strong ferroelectricity is discussed. The findings show that combining doping and strain engineering is a promising way to realize new EuTiO$_{3}$-based strong FE metals and may be used in other materials as well.

cond-mat.mtrl-sci↗