arXiv subjects

Yiran Zhang

Publications and source records attributed to Yiran Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2024

Nematicity and Orbital Depairing in Superconducting Bernal Bilayer Graphene with Strong Spin Orbit Coupling

Superconductivity (SC) is a ubiquitous feature of graphite allotropes, having been observed in Bernal bilayers[1], rhombohedral trilayers[2], and a wide variety of angle-misaligned multilayers[3-6]. Despite significant differences in the electronic structure across these systems, supporting the graphite layer on a WSe$_2$ substrate has been consistently observed to expand the range of SC in carrier density and temperature[7-10]. Here, we report the observation of two distinct superconducting states (denoted SC$_1$ and SC$_2$) in Bernal bilayer graphene with strong proximity-induced Ising spin-orbit coupling. Quantum oscillations show that while the normal state of SC$_1$ is consistent with the single-particle band structure, SC$_2$ emerges from a nematic normal state with broken rotational symmetry. Both superconductors are robust to in-plane magnetic fields, violating the paramagnetic limit; however, neither reach fields expected for spin-valley locked Ising superconductors. We use our knowledge of the Fermi surface geometry of SC$_1$ to argue that superconductivity is limited by orbital depairing arising from the imperfect layer polarization of the electron wavefunctions. Finally, a comparative analysis of transport and thermodynamic compressibility measurements in SC$_2$ shows that the proximity to the observed isospin phase boundaries, observed in other rhombohedral graphene allotropes, is likely coincidental, constraining theories of unconventional superconducting pairing mechanisms in theses systems.

cond-mat.supr-con↗

arXiv2024

Monochromatic cycles in 2-edge-colored bipartite graphs with large minimum degree

For graphs $G_0$, $G_1$ and $G_2$, write $G_0\longmapsto(G_1, G_2)$ if each red-blue-edge-coloring of $G_0$ yields a red $G_1$ or a blue $G_2$. The Ramsey number $r(G_1, G_2)$ is the minimum number $n$ such that the complete graph $K_n\longmapsto(G_1, G_2)$. In [Discrete Math. 312(2012)], Schelp formulated the following question: for which graphs $H$ there is a constant $0 c|V(G)|$, $G\longmapsto(H, H)$. In this paper, we prove that for any $m>n$, if $G$ is a balanced bipartite graph of order $2(m+n-1)$ with $δ(G)>\frac{3}{4}(m+n-1)$, then $G\longmapsto(CM_m, CM_n)$, where $CM_i$ is a matching with $i$ edges contained in a connected component. By Szeméredi's Regularity Lemma, using a similar idea as introduced by [J. Combin. Theory Ser. B 75(1999)], we show that for every $η>0$, there is an integer $N_0>0$ such that for any $N>N_0$ the following holds: Let $α_1>α_2>0$ such that $α_1+α_2=1$. Let $G[X, Y]$ be a balanced bipartite graph on $2(N-1)$ vertices with $δ(G)\geq(\frac{3}{4}+3η)(N-1)$. Then for each red-blue-edge-coloring of $G$, either there exist red even cycles of each length in $\{4, 6, 8, \ldots, (2-3η^2)α_1N\}$, or there exist blue even cycles of each length in $\{4, 6, 8, \ldots, (2-3η^2)α_2N\}$. Furthermore, the bound $δ(G)\geq(\frac{3}{4}+3η)(N-1)$ is asymptotically tight. Previous studies on Schelp's question on cycles are on diagonal case, we obtain an asymptotic result of Schelp's question for all non-diagonal cases.

math.CO↗

arXiv2024

Game-MUG: Multimodal Oriented Game Situation Understanding and Commentary Generation Dataset

The dynamic nature of esports makes the situation relatively complicated for average viewers. Esports broadcasting involves game expert casters, but the caster-dependent game commentary is not enough to fully understand the game situation. It will be richer by including diverse multimodal esports information, including audiences' talks/emotions, game audio, and game match event information. This paper introduces GAME-MUG, a new multimodal game situation understanding and audience-engaged commentary generation dataset and its strong baseline. Our dataset is collected from 2020-2022 LOL game live streams from YouTube and Twitch, and includes multimodal esports game information, including text, audio, and time-series event logs, for detecting the game situation. In addition, we also propose a new audience conversation augmented commentary dataset by covering the game situation and audience conversation understanding, and introducing a robust joint multimodal dual learning model as a baseline. We examine the model's game situation/event understanding ability and commentary generation capability to show the effectiveness of the multimodal aspects coverage and the joint integration learning approach.

cs.CL↗

arXiv2024

Observation of Cooper-pair density modulation state

Superconducting states that break space-group symmetries of the underlying crystal can exhibit nontrivial spatial modulation of the order parameter. Previously, such remarkable states were intimately associated with the breaking of translational symmetry, giving rise to the density-wave orders, with wavelengths spanning several unit cells. However, a related basic concept has been long overlooked: when only intra-unit-cell symmetries of the space group are broken, the superconducting states can display a distinct type of nontrivial modulation preserving long-range lattice translation. Here, we refer to this new concept as the pair density modulation (PDM), and report the first observation of a PDM state in exfoliated thin flakes of iron-based superconductor FeTe$_{\text{0.55}}$Se$_{\text{0.45}}$. Using scanning tunneling microscopy, we discover robust superconducting gap modulation with the wavelength corresponding to the lattice periodicity and the amplitude exceeding 30% of the gap average. Importantly, we find that the observed modulation originates from the large difference in superconducting gaps on the two nominally equivalent iron sublattices. The experimental findings, backed up by model calculations, suggest that in contrast to the density-wave orders, the PDM state is driven by the interplay of sublattice symmetry breaking and a peculiar nematic distortion specific to the thin flakes. Our results establish new frontiers for exploring the intertwined orders in strong-correlated electronic systems and open a new chapter for iron-based superconductors.

cond-mat.supr-con↗

arXiv2024

Deciphering the Enigma of Satellite Computing with COTS Devices: Measurement and Analysis

In the wake of the rapid deployment of large-scale low-Earth orbit satellite constellations, exploiting the full computing potential of Commercial Off-The-Shelf (COTS) devices in these environments has become a pressing issue. However, understanding this problem is far from straightforward due to the inherent differences between the terrestrial infrastructure and the satellite platform in space. In this paper, we take an important step towards closing this knowledge gap by presenting the first measurement study on the thermal control, power management, and performance of COTS computing devices on satellites. Our measurements reveal that the satellite platform and COTS computing devices significantly interplay in terms of the temperature and energy, forming the main constraints on satellite computing. Further, we analyze the critical factors that shape the characteristics of onboard COTS computing devices. We provide guidelines for future research on optimizing the use of such devices for computing purposes. Finally, we have released the datasets to facilitate further study in satellite computing.

cs.NI↗

arXiv2024

FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission

Communication overhead is a significant bottleneck in federated learning (FL), which has been exaggerated with the increasing size of AI models. In this paper, we propose FedRDMA, a communication-efficient cross-silo FL system that integrates RDMA into the FL communication protocol. To overcome the limitations of RDMA in wide-area networks (WANs), FedRDMA divides the updated model into chunks and designs a series of optimization techniques to improve the efficiency and robustness of RDMA-based communication. We implement FedRDMA atop the industrial federated learning framework and evaluate it on a real-world cross-silo FL scenario. The experimental results show that \sys can achieve up to 3.8$\times$ speedup in communication efficiency compared to traditional TCP/IP-based FL systems.

cs.LG↗

arXiv2024

Small-Scale Anisotropies of Cosmic Rays from Turbulent Flow

Within the classical convection--diffusion approximation, we show that the angular distribution of cosmic rays (CRs) in a highly turbulent flow may exhibit significant small-scale anisotropies. The CR intensity angular power spectrum $ C_\ell $ is then a direct reflection of interstellar turbulence, from which one expects $ C_\ell\propto\ell^{-γ-1} $ for $ \ell\gg 1 $, where $ γ$ is the power-law turbulence spectral index. Observations by IceCube and HAWC at TeV energies can be explained approximately with the Kolmogorov law $ γ=5/3 $ with a convection velocity dispersion of 20 km/s on the scale of 10 pc.

astro-ph.HE↗

arXiv2024

Resource-efficient In-orbit Detection of Earth Objects

With the rapid proliferation of large Low Earth Orbit (LEO) satellite constellations, a huge amount of in-orbit data is generated and needs to be transmitted to the ground for processing. However, traditional LEO satellite constellations, which downlink raw data to the ground, are significantly restricted in transmission capability. Orbital edge computing (OEC), which exploits the computation capacities of LEO satellites and processes the raw data in orbit, is envisioned as a promising solution to relieve the downlink burden. Yet, with OEC, the bottleneck is shifted to the inelastic computation capacities. The computational bottleneck arises from two primary challenges that existing satellite systems have not adequately addressed: the inability to process all captured images and the limited energy supply available for satellite operations. In this work, we seek to fully exploit the scarce satellite computation and communication resources to achieve satellite-ground collaboration and present a satellite-ground collaborative system named TargetFuse for onboard object detection. TargetFuse incorporates a combination of techniques to minimize detection errors under energy and bandwidth constraints. Extensive experiments show that TargetFuse can reduce detection errors by 3.4 times on average, compared to onboard computing. TargetFuse achieves a 9.6 times improvement in bandwidth efficiency compared to the vanilla baseline under the limited bandwidth budget constraint.

cs.NI↗

arXiv2023

Marginal Structural Illness-Death Models for Semi-Competing Risks Data

The three state illness death model has been established as a general approach for regression analysis of semi competing risks data. For observational data the marginal structural models (MSM) are a useful tool, under the potential outcomes framework to define and estimate parameters with causal interpretations. In this paper we introduce a class of marginal structural illness death models for the analysis of observational semi competing risks data. We consider two specific such models, the Markov illness death MSM and the frailty based Markov illness death MSM. For interpretation purposes, risk contrasts under the MSMs are defined. Inference under the illness death MSM can be carried out using estimating equations with inverse probability weighting, while inference under the frailty based illness death MSM requires a weighted EM algorithm. We study the inference procedures under both MSMs using extensive simulations, and apply them to the analysis of mid life alcohol exposure on late life cognitive impairment as well as mortality using the Honolulu Asia Aging Study data set. The R codes developed in this work have been implemented in the R package semicmprskcoxmsm that is publicly available on CRAN.

stat.ME↗

arXiv2023

Energy and Time-Aware Inference Offloading for DNN-based Applications in LEO Satellites

In recent years, Low Earth Orbit (LEO) satellites have witnessed rapid development, with inference based on Deep Neural Network (DNN) models emerging as the prevailing technology for remote sensing satellite image recognition. However, the substantial computation capability and energy demands of DNN models, coupled with the instability of the satellite-ground link, pose significant challenges, burdening satellites with limited power intake and hindering the timely completion of tasks. Existing approaches, such as transmitting all images to the ground for processing or executing DNN models on the satellite, is unable to effectively address this issue. By exploiting the internal hierarchical structure of DNNs and treating each layer as an independent subtask, we propose a satellite-ground collaborative computation partial offloading approach to address this challenge. We formulate the problem of minimizing the inference task execution time and onboard energy consumption through offloading as an integer linear programming (ILP) model. The complexity in solving the problem arises from the combinatorial explosion in the discrete solution space. To address this, we have designed an improved optimization algorithm based on branch and bound. Simulation results illustrate that, compared to the existing approaches, our algorithm improve the performance by 10%-18%

cs.DC↗

arXiv2023

Software Architecture Recovery with Information Fusion

Understanding the architecture is vital for effectively maintaining and managing large software systems. However, as software systems evolve over time, their architectures inevitably change. To keep up with the change, architects need to track the implementation-level changes and update the architectural documentation accordingly, which is time-consuming and error-prone. Therefore, many automatic architecture recovery techniques have been proposed to ease this process. Despite efforts have been made to improve the accuracy of architecture recovery, existing solutions still suffer from two limitations. First, most of them only use one or two type of information for the recovery, ignoring the potential usefulness of other sources. Second, they tend to use the information in a coarse-grained manner, overlooking important details within it. To address these limitations, we propose SARIF, a fully automated architecture recovery technique, which incorporates three types of comprehensive information, including dependencies, code text and folder structure. SARIF can recover architecture more accurately by thoroughly analyzing the details of each type of information and adaptively fusing them based on their relevance and quality. To evaluate SARIF, we collected six projects with published ground-truth architectures and three open-source projects labeled by our industrial collaborators. We compared SARIF with nine state-of-the-art techniques using three commonly-used architecture similarity metrics and two new metrics. The experimental results show that SARIF is 36.1% more accurate than the best of the previous techniques on average. By providing comprehensive architecture, SARIF can help users understand systems effectively and reduce the manual effort of obtaining ground-truth architectures.

cs.SE↗

arXiv2023

Who is the Real Hero? Measuring Developer Contribution via Multi-dimensional Data Integration

Proper incentives are important for motivating developers in open-source communities, which is crucial for maintaining the development of open-source software healthy. To provide such incentives, an accurate and objective developer contribution measurement method is needed. However, existing methods rely heavily on manual peer review, lacking objectivity and transparency. The metrics of some automated works about effort estimation use only syntax-level or even text-level information, such as changed lines of code, which lack robustness. Furthermore, some works about identifying core developers provide only a qualitative understanding without a quantitative score or have some project-specific parameters, which makes them not practical in real-world projects. To this end, we propose CValue, a multidimensional information fusion-based approach to measure developer contributions. CValue extracts both syntax and semantic information from the source code changes in four dimensions: modification amount, understandability, inter-function and intra-function impact of modification. It fuses the information to produce the contribution score for each of the commits in the projects. Experimental results show that CValue outperforms other approaches by 19.59% on 10 real-world projects with manually labeled ground truth. We validated and proved that the performance of CValue, which takes 83.39 seconds per commit, is acceptable to be applied in real-world projects. Furthermore, we performed a large-scale experiment on 174 projects and detected 2,282 developers having inflated commits. Of these, 2,050 developers did not make any syntax contribution; and 103 were identified as bots.

cs.SE↗

arXiv2023

Mitigating Persistence of Open-Source Vulnerabilities in Maven Ecosystem

Vulnerabilities from third-party libraries (TPLs) have been unveiled to threaten the Maven ecosystem. Despite patches being released promptly after vulnerabilities are disclosed, the libraries and applications in the community still use the vulnerable versions, which makes the vulnerabilities persistent in the Maven ecosystem (e.g., the notorious Log4Shell still greatly influences the Maven ecosystem nowadays from 2021). Both academic and industrial researchers have proposed user-oriented standards and solutions to address vulnerabilities, while such solutions fail to tackle the ecosystem-wide persistent vulnerabilities because it requires a collective effort from the community to timely adopt patches without introducing breaking issues. To seek an ecosystem-wide solution, we first carried out an empirical study to examine the prevalence of persistent vulnerabilities in the Maven ecosystem. Then, we identified affected libraries for alerts by implementing an algorithm monitoring downstream dependents of vulnerabilities based on an up-to-date dependency graph. Based on them, we further quantitatively revealed that patches blocked by upstream libraries caused the persistence of vulnerabilities. After reviewing the drawbacks of existing countermeasures, to address them, we proposed a solution for range restoration (Ranger) to automatically restore the compatible and secure version ranges of dependencies for downstream dependents. The automatic restoration requires no manual effort from the community, and the code-centric compatibility assurance ensures smooth upgrades to patched versions. Moreover, Ranger along with the ecosystem monitoring can timely alert developers of blocking libraries and suggest flexible version ranges to rapidly unblock patch versions. By evaluation, Ranger could restore 75.64% of ranges which automatically remediated 90.32% of vulnerable downstream projects.

cs.SE↗

arXiv2023

Satellite Computing: A Case Study of Cloud-Native Satellites

The on-orbit processing of massive satellite-native data relies on powerful computing power. Satellite computing has started to gain attention, with researchers proposing various algorithms, applications, and simulation testbeds. Unfortunately, a practical platform for deploying satellite computing is currently lacking. As a result, the industry needs to make relentless efforts to achieve this goal. We suggest using cloud-native technology to enhance the computing power of LEO satellites. The first main satellite of the Tiansuan constellation, BUPT-1, is a significant example of a cloud-native satellite. Prior to delving into the details of BUPT-1, we define the essential concepts of cloud-native satellites, i.e., the cloud-native load and cloud-native platform. Afterwards, we present the design scheme of cloud-native satellites, including the architecture of BUPT-1 and the experimental subjects it can support. Two validation tests are shown to reflect the operation and capability of BUPT-1. Besides, we predict possible research fields that could shape the future of satellites in the next decade.

cs.DC↗

arXiv2023

The electrostatic fate of $N$-layer moiré graphene

Twisted $N$-layer graphene (TNG) moiré structures have recently been shown to exhibit robust superconductivity similar to twisted bilayer graphene (TBG). In particular for $N=4$ and $N=5$, the phase diagram features a superconducting pocket that extends beyond the nominal full filling of the flat band. These observations are seemingly at odds with the canonical understanding of the low-energy theory of TNG, wherein the TNG Hamiltonian consists of one flat-band sector and accompanying dispersive bands. Using a self-consistent Hartee-Fock treatment, we explain how the phenomenology of TNG can be understood through an interplay of in-plane Hartree and inhomogeneous layer potentials, which cause a reshuffling of electronic bands. We extend our understanding beyond the case of N = 5 realized in experiment so far. We decribe how the Hartree and layer potentials control the phase diagram for devices with N $>$ 5 and tend to preclude exchange-driven correlated phenomena in this limit. To circumvent these electrostatic constraints, we propose a new flat-band paradigm that could be realized in large-N devices by taking advantage of two nearly flat sectors acting together to enhance the importance of exchange effects.

cond-mat.mes-hall↗

arXiv2023

Imaging inter-valley coherent order in magic-angle twisted trilayer graphene

Magic-angle twisted trilayer graphene (MATTG) exhibits a range of strongly correlated electronic phases that spontaneously break its underlying symmetries. The microscopic nature of these phases and their residual symmetries stands as a key outstanding puzzle whose resolution promises to shed light on the origin of superconductivity in twisted materials. Here we investigate correlated phases of MATTG using scanning tunneling microscopy and identify striking signatures of interaction-driven spatial symmetry breaking. In low-strain samples, over a filling range of about 2-3 electrons or holes per moiré unit cell, we observe atomic-scale reconstruction of the graphene lattice that accompanies a correlated gap in the tunneling spectrum. This short-scale restructuring appears as a Kekulé supercell -- implying spontaneous inter-valley coherence between electrons -- and persists in a wide range of magnetic fields and temperatures that coincide with the development of the gap. Large-scale maps covering several moiré unit cells further reveal a slow evolution of the Kekulé pattern, indicating that atomic-scale reconstruction coexists with translation symmetry breaking at the much longer moiré scale. We employ auto-correlation and Fourier analyses to extract the intrinsic periodicity of these phases and find that they are consistent with the theoretically proposed incommensurate Kekulé spiral order. Moreover, we find that the wavelength characterizing moiré-scale modulations monotonically decreases with hole doping away from half-filling of the bands and depends only weakly on the magnetic field. Our results provide essential insights into the nature of MATTG correlated phases in the presence of strain and imply that superconductivity emerges from an inter-valley coherent parent state.

cond-mat.str-el↗

arXiv2022

Cosmic-Ray Convection-Diffusion Anisotropy

Under nonuniform convection, the distribution of diffusive particles can exhibit dipole and quadrupole anisotropy induced by the fluid inertial and shear force, respectively. These convection-related anisotropies, unlike the Compton-Getting effect, typically increase with the cosmic-ray (CR) energy, and are thus candidate contributors for the CR anisotropy. In consideration of the inertial effect, CR observational data can be used to set an upper limit on the average acceleration of the local interstellar medium in the equatorial plane to be on the order of 100 $ μ\text{m}/\text{s}^2 $. Using Oort constants, the quadrupole anisotropy above 200 TeV may be modeled with the shear effect arising from the Galactic differential rotation.

astro-ph.HE↗

arXiv2022

Hierarchy of Symmetry Breaking Correlated Phases in Twisted Bilayer Graphene

Twisted bilayer graphene (TBG) near the magic twist angle of $\sim1.1^{o}$ exhibits a rich phase diagram. However, the interplay between different phases and their dependence on twist angle is still elusive. Here, we explore the stability of various TBG phases and demonstrate that superconductivity near filling of two electrons per moiré unit cell alongside Fermi surface reconstructions, as well as entropy-driven high-temperature phase transitions and linear-in-T resistance occur over a range of twist angles which extends far beyond those exhibiting correlated insulating phases. In the vicinity of the magic angle, we also find a metallic phase that displays a hysteretic anomalous Hall effect and incipient Chern insulating behaviour. Such a metallic phase can be rationalized in terms of the interplay between interaction-driven deformations of TBG bands leading to Berry curvature redistribution and Fermi surface reconstruction. Our results provide an extensive perspective on the hierarchy of correlated phases in TBG as classified by their robustness against deviations from the magic angle or, equivalently, their electronic interaction requirements.

cond-mat.str-el↗