arXiv subjects

Jing Ren

Publications and source records attributed to Jing Ren.

At least 91 records · Page 5Linked to original sources

arXiv2020

Structured Regularization of Functional Map Computations

We consider the problem of non-rigid shape matching using the functional map framework. Specifically, we analyze a commonly used approach for regularizing functional maps, which consists in penalizing the failure of the unknown map to commute with the Laplace-Beltrami operators on the source and target shapes. We show that this approach has certain undesirable fundamental theoretical limitations, and can be undefined even for trivial maps in the smooth setting. Instead we propose a novel, theoretically well-justified approach for regularizing functional maps, by using the notion of the resolvent of the Laplacian operator. In addition, we provide a natural one-parameter family of regularizers, that can be easily tuned depending on the expected approximate isometry of the input shape pair. We show on a wide range of shape correspondence scenarios that our novel regularization leads to an improvement in the quality of the estimated functional, and ultimately pointwise correspondences before and after commonly-used refinement techniques.

cs.GR↗

arXiv2020

MapTree: Recovering Multiple Solutions in the Space of Maps

In this paper we propose an approach for computing multiple high-quality near-isometric dense correspondences between a pair of 3D shapes. Our method is fully automatic and does not rely on user-provided landmarks or descriptors. This allows us to analyze the full space of maps and extract multiple diverse and accurate solutions, rather than optimizing for a single optimal correspondence as done in most previous approaches. To achieve this, we propose a compact tree structure based on the spectral map representation for encoding and enumerating possible rough initializations, and a novel efficient approach for refining them to dense pointwise maps. This leads to a new method capable of both producing multiple high-quality correspondences across shapes and revealing the symmetry structure of a shape without a priori information. In addition, we demonstrate through extensive experiments that our method is robust and results in more accurate correspondences than state-of-the-art for shape matching and symmetry detection.

cs.GR↗

arXiv2020

MODEL: Motif-based Deep Feature Learning for Link Prediction

Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this paper, we propose a novel embedding algorithm that incorporates network motifs to capture higher-order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms by 20% and the state-of-the-art embedding-based algorithms by 19%.

cs.SI↗

arXiv2020

Multivariate Relations Aggregation Learning in Social Networks

Multivariate relations are general in various types of networks, such as biological networks, social networks, transportation networks, and academic networks. Due to the principle of ternary closures and the trend of group formation, the multivariate relationships in social networks are complex and rich. Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important. Existing graph learning methods are based on the neighborhood information diffusion mechanism, which often leads to partial omission or even lack of multivariate relationship information, and ultimately affects the accuracy and execution efficiency of the task. To address these challenges, this paper proposes the multivariate relationship aggregation learning (MORE) method, which can effectively capture the multivariate relationship information in the network environment. By aggregating node attribute features and structural features, MORE achieves higher accuracy and faster convergence speed. We conducted experiments on one citation network and five social networks. The experimental results show that the MORE model has higher accuracy than the GCN (Graph Convolutional Network) model in node classification tasks, and can significantly reduce time cost.

cs.SI↗

arXiv2020

MGCN: Descriptor Learning using Multiscale GCNs

We propose a novel framework for computing descriptors for characterizing points on three-dimensional surfaces. First, we present a new non-learned feature that uses graph wavelets to decompose the Dirichlet energy on a surface. We call this new feature wavelet energy decomposition signature (WEDS). Second, we propose a new multiscale graph convolutional network (MGCN) to transform a non-learned feature to a more discriminative descriptor. Our results show that the new descriptor WEDS is more discriminative than the current state-of-the-art non-learned descriptors and that the combination of WEDS and MGCN is better than the state-of-the-art learned descriptors. An important design criterion for our descriptor is the robustness to different surface discretizations including triangulations with varying numbers of vertices. Our results demonstrate that previous graph convolutional networks significantly overfit to a particular resolution or even a particular triangulation, but MGCN generalizes well to different surface discretizations. In addition, MGCN is compatible with previous descriptors and it can also be used to improve the performance of other descriptors, such as the heat kernel signature, the wave kernel signature, or the local point signature.

cs.GR↗

arXiv2020

Quantum nucleation of up-down quark matter and astrophysical implications

Quark matter with only $u$ and $d$ quarks ($ud$QM) might be the ground state of baryonic matter at large baryon number $A>A_{\rm min}$. With $A_{\rm min}\gtrsim 300$, this has no direct conflict with the stability of ordinary nuclei. An intriguing test of this scenario is to look for quantum nucleation of $ud$QM inside neutron stars due to their large baryon densities. In this paper, we study the transition rate of cold neutron stars to $ud$ quark stars ($ud$QSs) and the astrophysical implications, considering the relevant theoretical uncertainties and observational constraints. It turns out that a large portion of parameter space predicts an instantaneous transition, and so the observed neutron stars are mostly $ud$QSs. We find this possibility still viable under the recent gravitational wave and pulsar observations, although there are debates on its compatibility with some observations that involve complicated structure of quark matter. The tension could be partially relieved in the two-families scenario, where the high-mass stars ($M\gtrsim2 M_{\odot}$) are all $ud$QSs and the low-mass ones ($M\sim1.4\, M_{\odot}$) are mostly hadronic stars. In this case, the slow transition of the low-mass hadronic stars points to a very specific class of hadronic models with moderately stiff EOSs, and $ud$QM properties are also strongly constrained.

hep-ph↗

arXiv2020

Not quite black holes as dark matter

Primordial black holes that survive until the present have been considered as a dark matter candidate. In this paper we argue that primordial 2-2-hole remnants provide a more promising and testable option. 2-2-holes arise in quadratic gravity as a new family of classical solutions for ultracompact matter distributions and they possess the black hole exterior without an event horizon. They may serve as the endpoint of gravitational collapse, providing a resolution for the information loss problem. Intriguing thermodynamic behavior is found for these objects when sourced by a thermal gas. A large 2-2-hole radiates with a Hawking-like temperature and exhibits an entropy-area law. At a late stage, the evaporation slows down and essentially stops as the mass asymptotically approaches a minimal value. This remnant mass is determined by a fundamental scale in quadratic gravity. We study the cosmological and astrophysical implications of having these remnants as dark matter and derive the corresponding constraints. A distinctive phenomenon associated with remnant mergers occurs, predicting fluxes of high-energy astrophysical particles due to the spectacular evaporation of the merger product. Measurements of high-energy photon and neutrino fluxes could possibly bound the remnant mass to be not far above the Planck mass. Early-universe physics, on the other hand, requires that 2-2-holes quickly evolve into the remnant state after formation, putting an upper bound on the formation mass.

gr-qc↗

arXiv2020

An Immunology-Inspired Network Security Architecture

The coming 5G networks have been enabling the creation of a wide variety of new services and applications which demand a new network security architecture. Immunology is the study of the immune system in vertebrates (including humans) which protects us from infection through various lines of defence. By studying the resemblance between the immune system and network security system, we acquire some inspirations from immunology and distill some guidelines for the design of network security architecture. We present a philosophical design principle, that is maintaining the balance between security and availability. Then, we derive two methodological principles: 1) achieving situation-awareness and fast response through community cooperation among heterogeneous nodes, and 2) Enhancing defense capability through consistently contesting with invaders in a real environment and actively mutating/evolving attack strategies. We also present a reference architecture designed based on the principles.

cs.CR↗

arXiv2019

Anatomy of a thermal black hole mimicker

We are entering a new era to test the strong gravity regime around astrophysical black holes. The possibility that they are actually horizonless ultracompact objects and then free from the information loss paradox can be examined more closely with observational data. In this paper, we systematically develop a thermal gas model of the 2-2-hole in quadratic gravity, as one step further to look for more tractable models of black hole mimickers. Concrete predictions for departures from black holes are made all the way down to the high curvature interior. The simple form of matter further enables an explicit study of the relation between geometry and thermodynamics. Within this unified framework, we identify notably different behaviors at two limits. On one side is the astrophysically large 2-2-hole, as characterized by a minuscule deviation outside the would-be horizon and a highly squeezed interior along the radial direction. Anomalous features of black hole thermodynamics emerge from the ordinary gas. On the other side is the minimal 2-2-hole with an isotropic and shrinking interior, which behaves more like a normal thermodynamic system. This brings a new perspective to the related theoretical questions as well as phenomenological implications.

gr-qc↗

arXiv2019

ZoomOut: Spectral Upsampling for Efficient Shape Correspondence

We present a simple and efficient method for refining maps or correspondences by iterative upsampling in the spectral domain that can be implemented in a few lines of code. Our main observation is that high quality maps can be obtained even if the input correspondences are noisy or are encoded by a small number of coefficients in a spectral basis. We show how this approach can be used in conjunction with existing initialization techniques across a range of application scenarios, including symmetry detection, map refinement across complete shapes, non-rigid partial shape matching and function transfer. In each application we demonstrate an improvement with respect to both the quality of the results and the computational speed compared to the best competing methods, with up to two orders of magnitude speed-up in some applications. We also demonstrate that our method is both robust to noisy input and is scalable with respect to shape complexity. Finally, we present a theoretical justification for our approach, shedding light on structural properties of functional maps.

cs.GR↗

arXiv2019

Cybertwin: An Origin of Next Generation Network Architecture

With fast development of Internet of Everything (IoE) and its applications, the ever increasing mobile internet traffic and services bring unprecedented challenges including scalability, mobility, availability, and security which cannot be addressed by the current clean-slate network architecture. In this paper, a cybertwin based next generation network architecture is proposed to accomodate the evolution from end-to-end connection to cloud-to-end connection in the future network. As a digital representation of human or things in the virtual cyberspace, cybertwin serves in multiple capacities, such as communications assistant, network data logger, and digital asset owner. The new and unique characteristics of the cybertwin make the proposed network to be flexible, scalable, reliable, and secure. Further, we advocate a new cloud network operating system which can work in a distributed way through a real-time multi-agent trading platform to allocate 3C (computing, caching, communications) resources. We also propose cloud operator, a new operator that can provide and manage the resources to the end users and offer location and authentication services for human and things in the cyberspace. Some promising and open research topics are discussed to envision the challenges and opportunities of the cybertwin in the future network architecture.

cs.NI↗

arXiv2018

Continuous and Orientation-preserving Correspondences via Functional Maps

We propose a method for efficiently computing orientation-preserving and approximately continuous correspondences between non-rigid shapes, using the functional maps framework. We first show how orientation preservation can be formulated directly in the functional (spectral) domain without using landmark or region correspondences and without relying on external symmetry information. This allows us to obtain functional maps that promote orientation preservation, even when using descriptors, that are invariant to orientation changes. We then show how higher quality, approximately continuous and bijective pointwise correspondences can be obtained from initial functional maps by introducing a novel refinement technique that aims to simultaneously improve the maps both in the spectral and spatial domains. This leads to a general pipeline for computing correspondences between shapes that results in high-quality maps, while admitting an efficient optimization scheme. We show through extensive evaluation that our approach improves upon state-of-the-art results on challenging isometric and non-isometric correspondence benchmarks according to both measures of continuity and coverage as well as producing semantically meaningful correspondences as measured by the distance to ground truth maps.

cs.GR↗

arXiv2018

Gravitational wave echoes through new windows

There has been a striking realization that physics resolving the black hole information paradox could imply postmerger gravitational wave echoes. We here report on evidence for echoes from the LIGO compact binary merger events, GW151226, GW170104, GW170608, GW170814, as well as the neutron star merger GW170817. There is a signal for each event with a $p$-value of order 1% or sometimes significantly less. Our study begins with the comparison of echoes from a variety of horizonless exotic compact objects. Next we investigate the effects of spin. The identification of the more generic features of echoes then leads to the development of relatively simple windowing methods, in both time and frequency space, to extract a signal from noise. The time delay between echoes is inversely related to the spacing between the spectral resonances, and it is advantageous to look directly for this resonance structure. We find time delays for the first four events that are consistent with a simple model that accounts for mass and spin of the final object, while for the neutron star merger the final mass and spin are constrained.

gr-qc↗

arXiv2018

LHC Search of New Higgs Boson via Resonant Di-Higgs Production with Decays into 4W

Searching for new Higgs particle beyond the observed light Higgs boson h(125GeV) will unambiguously point to new physics beyond the standard model. We study the resonant production of a CP-even heavy Higgs state $H^0$ in the di-Higgs channel via, $gg\to H^0\to h^0h^0\to WW^*WW^*$, at the LHC Run-2 and the high luminosity LHC (HL-LHC). We analyze two types of the $4W$ decay modes, one with the same-sign di-leptons ($4W\to\ell^\pmν\ell^\pmν4q$) and the other with tri-leptons ($4W\to\ell^\pmν\ell^\mpν\ell^\pmν2q$). We perform a full simulation for the signals and backgrounds, and estimate the discovery potential of the heavy Higgs state at the LHC Run-2 and the HL-LHC, in the context of generical two-Higgs-doublet models (2HDM). We determine the viable parameter space of the 2HDM as allowed by the theoretical constraints and the current experimental limits. We systematically analyze the allowed parameter space of the 2HDM which can be effectively probed by the heavy Higgs searches of the LHC, and further compare this with the viable parameter region under the current theoretical and experimental bounds.

hep-ph↗

arXiv2018

Quark matter may not be strange

If quark matter is energetically favored over nuclear matter at zero temperature and pressure then it has long been expected to take the form of strange quark matter (SQM), with comparable amounts of $u$, $d$, $s$ quarks. The possibility of quark matter with only $u$, $d$ quarks ($ud$QM) is usually dismissed because of the observed stability of ordinary nuclei. However we find that $ud$QM generally has lower bulk energy per baryon than normal nuclei and SQM. This emerges in a phenomenological model that describes the spectra of the lightest pseudoscalar and scalar meson nonets. Taking into account the finite size effects, $ud$QM can be the ground state of baryonic matter only for baryon number $A>A_\textrm{min}$ with $A_\textrm{min}\gtrsim 300$. This ensures the stability of ordinary nuclei and points to a new form of stable matter just beyond the periodic table.

hep-ph↗

arXiv2018

Probing Quartic Higgs Self-Interaction

The Higgs self-interactions play a crucial role for exploring the underlying mechanism of electroweak symmetry breaking and the nature of the phase transition involved. In this article, we propose to probe the quartic Higgs self-interaction at lepton and hadron colliders, via the di-Higgs productions. We analyze the contributions of the quartic Higgs coupling, including the renormalization of the cubic Higgs coupling and the modification of the $VVhh$ form factor, to the vector-boson-fusion and the vector-boson associated di-Higgs productions at one-loop level. Such an effect is independent of the choice of gauge-fixing, if the quartic Higgs coupling is decoupled from the other ones in the contexts considered. Notably, a combination of these two di-Higgs productions is important for optimizing the collider sensitivities to probe the quartic Higgs coupling. With this guideline, we explore the ILC and CLIC sensitivities, and find that the ILC has a potential to measure the quartic Higgs coupling, normalized by its SM value, with a precision of $\sim \pm 25$ (500 GeV, 4 ab$^{-1}$ + 1 TeV, 2.5 ab$^{-1}$) and $\sim \pm20$ (500 GeV, 4 ab$^{-1}$ + 1 TeV, 8 ab$^{-1}$), at $1σ$ C.L., after marginalizing the cubic Higgs coupling in the $χ^2$ analysis. The dependence on the renormalization scheme of the cubic Higgs coupling is discussed.

hep-ph↗

arXiv2017

Not quite a black hole

Astrophysical black hole candidates, although long thought to have a horizon, could be horizonless ultra-compact objects. This intriguing possibility is motivated by the black hole information paradox and a plausible fundamental connection with quantum gravity. Asymptotically free quadratic gravity is considered here as the UV completion of general relativity. A classical theory that captures its main features is used to search for solutions as sourced by matter. We find that sufficiently dense matter produces a novel horizonless configuration, the 2-2-hole, which closely matches the exterior Schwarzschild solution down to about a Planck proper length of the would-be horizon. The 2-2-hole is characterized by an interior with a shrinking volume and a seemingly innocuous timelike curvature singularity. The interior also has a novel scaling behavior with respect to the physical mass of the 2-2-hole. This leads to an extremely deep gravitational potential in which particles get efficiently trapped via collisions. As a generic static solution, the 2-2-hole may then be the nearly black endpoint of gravitational collapse. There is a considerable time delay for external probes of the 2-2-hole interior, and this determines the spacing of echoes in a post-merger gravitational wave signal.

gr-qc↗

arXiv2016

A QCD analogy for quantum gravity

Quadratic gravity presents us with a renormalizable, asymptotically free theory of quantum gravity. When its couplings grow strong at some scale, as in QCD, then this strong scale sets the Planck mass. QCD has a gluon that does not appear in the physical spectrum. Quadratic gravity has a spin-2 ghost that we conjecture does not appear in the physical spectrum. We discuss how the QCD analogy leads to this conjecture and to the possible emergence of general relativity. Certain aspects of the QCD path integral and its measure are also similar for quadratic gravity. With the addition of the Einstein-Hilbert term, quadratic gravity has a dimensionful parameter that seems to control a quantum phase transition and the size of a mass gap in the strong phase.

hep-th↗