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Hao Sun

Publications and source records attributed to Hao Sun.

At least 451 records · Page 25Linked to original sources
arXiv2021

Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks

Deep neural networks recognize objects by analyzing local image details and summarizing their information along the inference layers to derive the final decision. Because of this, they are prone to adversarial attacks. Small sophisticated noise in the input images can accumulate along the network inference path and produce wrong decisions at the network output. On the other hand, human eyes recognize objects based on their global structure and semantic cues, instead of local image textures. Because of this, human eyes can still clearly recognize objects from images which have been heavily damaged by adversarial attacks. This leads to a very interesting approach for defending deep neural networks against adversarial attacks. In this work, we propose to develop a structure-preserving progressive low-rank image completion (SPLIC) method to remove unneeded texture details from the input images and shift the bias of deep neural networks towards global object structures and semantic cues. We formulate the problem into a low-rank matrix completion problem with progressively smoothed rank functions to avoid local minimums during the optimization process. Our experimental results demonstrate that the proposed method is able to successfully remove the insignificant local image details while preserving important global object structures. On black-box, gray-box, and white-box attacks, our method outperforms existing defense methods (by up to 12.6%) and significantly improves the adversarial robustness of the network.

cs.CV↗
arXiv2021

Kotzinian-Mulders effect in semi-inclusive DIS within TMD factorization

In this paper we study the Kotzinian-Mulders effect of a single hadron production in semi-inclusive deep inelastic scattering (SIDIS) within the framework of transverse momentum dependent (TMD) factorization. The asymmetry is contributed by the convolution of the Kotzinian-Mulders function $g_{1T}$ and the unpolarized fragmentation function $D_1$. As a TMD distribution, the Kotzinian-Mulders function in the coordinate space in the perturbative region can be represented as the convolution of the $C$-coefficients and the corresponding collinear correlation function. The Wandzura-Wilczek approximation is used to obtain this correlation function. We perform a detailed phenomenological numerical analysis of the Kotzinian-Mulders effect in the SIDIS process within TMD factorization at the kinematics of the HERMES and COMPASS measurements. It is found that the obtained $x_B$-, $z_h$- and $P_{h\perp}$-dependent Kotzinian-Mulders effect are basically consistent with the HERMES and COMPASS measurements.

hep-ph↗
arXiv2021

Multi-attributed Community Search in Road-social Networks

Given a location-based social network, how to find the communities that are highly relevant to query users and have top overall scores in multiple attributes according to user preferences? Typically, in the face of such a problem setting, we can model the network as a multi-attributed road-social network, in which each user is linked with location information and $d$ ($\geq\! 1$) numerical attributes. In practice, user preferences (i.e., weights) are usually inherently uncertain and can only be estimated with bounded accuracy, because a human user is not able to designate exact values with absolute precision. Inspired by this, we introduce a normative community model suitable for multi-criteria decision making, called multi-attributed community (MAC), based on the concepts of $k$-core and a novel dominance relationship specific to preferences. Given uncertain user preferences, namely, an approximate representation of weights, the MAC search reports the exact communities for each of the possible weight settings. We devise an elegant index structure to maintain the dominance relationships, based on which two algorithms are developed to efficiently compute the top-$j$ MACs. The efficiency and scalability of our algorithms and the effectiveness of MAC model are demonstrated by extensive experiments on both real-world and synthetic road-social networks.

cs.DB↗
arXiv2021

A Combinatorial Approach to Root Multiplicities of a Special Type Rank 3 Kac-Moody Algebras

In this paper, we calculate the dimension of root spaces $\mathfrak{g}_λ$ of a special type rank $3$ Kac-Moody algebras $\mathfrak{g}$. We first introduce a special type of elements in $\mathfrak{g}$, which we call elements in standard form. Then, we prove that any root space is spanned by these elements. By calculating the number of linearly independent elements in standard form, we obtain a formula for the dimension of root spaces $\mathfrak{g}_λ$, which depends on the root $λ$.

math.RT↗
arXiv2021

Electron-Ion Collider in China

Lepton scattering is an established ideal tool for studying inner structure of small particles such as nucleons as well as nuclei. As a future high energy nuclear physics project, an Electron-ion collider in China (EicC) has been proposed. It will be constructed based on an upgraded heavy-ion accelerator, High Intensity heavy-ion Accelerator Facility (HIAF) which is currently under construction, together with a new electron ring. The proposed collider will provide highly polarized electrons (with a polarization of $\sim$80%) and protons (with a polarization of $\sim$70%) with variable center of mass energies from 15 to 20 GeV and the luminosity of (2-3) $\times$ 10$^{33}$ cm$^{-2}$ s$^{-1}$. Polarized deuterons and Helium-3, as well as unpolarized ion beams from Carbon to Uranium, will be also available at the EicC. The main foci of the EicC will be precision measurements of the structure of the nucleon in the sea quark region, including 3D tomography of nucleon; the partonic structure of nuclei and the parton interaction with the nuclear environment; the exotic states, especially those with heavy flavor quark contents. In addition, issues fundamental to understanding the origin of mass could be addressed by measurements of heavy quarkonia near-threshold production at the EicC. In order to achieve the above-mentioned physics goals, a hermetical detector system will be constructed with cutting-edge technologies. This document is the result of collective contributions and valuable inputs from experts across the globe. The EicC physics program complements the ongoing scientific programs at the Jefferson Laboratory and the future EIC project in the United States. The success of this project will also advance both nuclear and particle physics as well as accelerator and detector technology in China.

nucl-ex↗
arXiv2021

Negative Thermal Hall Conductance in Two-Dimer Shastry-Sutherland Model with π-flux Dirac Triplon

We introduce an effective 2-dimer tight-binding model for the family of Shastry-Sutherland models with geometrically tunable triplon excitations. The Rashba pseudospin-orbit coupling induced by the tilted external magnetic field leads to elementary excitations having nontrivial topological properties with π-Berry flux. The interplay between the in-plane and out-of-plane magnetic field thus allows us to effectively engineer the band structure in this bosonic system. In particular, the in-plane magnetic field gives rise to Berry curvature hotspot near the bottom of the triplon band, and at the same time significantly increases the critical magnetic field for the topological triplon band. We calculate explicitly the experimental signature of the thermal Hall effect of triplons in SrCu2(BO3)2, and show a pronounced and tunabled transport signals within the accessible parameter range, particularly with a change of sign of the thermal Hall conductance. The tilted magnetic field is also useful in reducing the bandwidth of the lowest triplon band. We show it can thus be a flexible theoretical and experimental platform for the correlated bosonic topological system.

cond-mat.mes-hall↗
arXiv2021

Physics-informed learning of governing equations from scarce data

Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines. This work introduces a novel physics-informed deep learning framework to discover governing partial differential equations (PDEs) from scarce and noisy data for nonlinear spatiotemporal systems. In particular, this approach seamlessly integrates the strengths of deep neural networks for rich representation learning, physics embedding, automatic differentiation and sparse regression to (1) approximate the solution of system variables, (2) compute essential derivatives, as well as (3) identify the key derivative terms and parameters that form the structure and explicit expression of the PDEs. The efficacy and robustness of this method are demonstrated, both numerically and experimentally, on discovering a variety of PDE systems with different levels of data scarcity and noise accounting for different initial/boundary conditions. The resulting computational framework shows the potential for closed-form model discovery in practical applications where large and accurate datasets are intractable to capture.

cs.LG↗
arXiv2021

Relic density of dark matter in the inert doublet model beyond leading order for the low mass region: 1. Renormalisation and constraints

The present paper is the first in a series that addresses the calculation of the full one-loop corrections of dark matter (DM) annihilation cross-sections in the low mass region of the inert doublet model (IDM). We first review the renormalisation of the model both in a fully on-shell (OS) scheme and a mixed scheme combining on-shell (for the masses) and a $\overline{\rm MS}$ approach when the partial invisible width is closed and does not allow the use of a full OS scheme. The scale dependence introduced by the mixed scheme is shown to be tracked through an analysis of a parametrisation of the tree-level cross-section and the $β$ constant of a specific coupling. We discuss how to minimise the scale dependence. The theoretical uncertainty brought by the scale dependence leads us to introduce a new criterion on the perturbativity of the IDM. This criterion further delimits the allowed parameter space which we investigate carefully by including a host of constraints, both theoretical and experimental, including in particular, new data from the LHC. We come up with a set of benchmark points that cover three different mechanisms for a viable relic density of DM: {\it i)} a dominance of co-annihilation into a fermion pair, { \it ii)} annihilation into 2 vector bosons of which one is off-shell that requires the calculation of a $2 \to 3$ process at one-loop, {\it iii)} annihilation that proceeds through the very narrow standard model Higgs resonance. Since the $2 \to 3$ vector boson channel features in all three channels and is essentially a build up on the simpler annihilation to OS vector bosons, we study the latter in detail in the present paper. We confirm again that the corrected cross-sections involve a parameter that represents rescattering in the dark sector that a tree-level computation in not sensitive to.

hep-ph↗
arXiv2021

Relic density of dark matter in the inert doublet model beyond leading order for the low mass region: 2. Co-annihilation

We examine the relic density of the light mass dark matter region in the inert doublet model (IDM) when the dominant process is due to co-annihilation between the lightest neutral scalars of the model. The full one-loop electroweak corrections are computed in an on-shell scheme and are found to be well approximated as an effective cross-section expressed in terms of $Z$-observables. The electroweak corrections to the subdominant process which consists of an annihilation into an on-shell $W$ and an off-shell $W$, that is calculated as a annihilation into a 3-body final state, is also performed. The latter reveals an important dependence on a parameter that describes the self-interaction of the new scalars (solely within the dark sector), a parameter which is not accessible in tree-level calculations of standard model (SM)-IDM interactions.

hep-ph↗
arXiv2021

Relic density of dark matter in the inert doublet model beyond leading order for the low mass region: 3. Annihilation in 3-body final state

We perform the first one-loop electroweak corrections for $2 \to 3$ processes for dark matter annihilation. These are the dominant processes that enter the computation of the relic density for the low mass region of the inert doublet model (IDM) when annihilations to two on-shell vector bosons are closed. The impact of the one-loop corrections are important as they involve, through rescattering effects, not only a dependence on the parameter controlling the dark sector, not present if a calculation at tree-level is conducted, but also on the renormalisation scale. These combined effects should be taken into account in analyses based on tree-level cross-sections of the relic density calculations, as a theoretical uncertainty which we find to be much larger than the cursory $\pm 10\%$ uncertainty that is routinely assumed, independently of the model parameters.

hep-ph↗
arXiv2021

Relic density of dark matter in the inert doublet model beyond leading order for the low mass region: 4. The Higgs resonance region

One-loop electroweak corrections to the annihilation cross-sections of dark matter in the Higgs resonance region of the inert doublet model (IDM) are investigated. The procedure of how to implement the width of the Higgs in order to regularise the amplitude both at tree-level and at one-loop together with the renormalisation of a key parameter of the model, are thoroughly scrutinised. The discussions go beyond the application to the relic density calculation and also beyond the IDM so that addressing these technical issues can help in a wider context. We look in particular at the dominant channels with the $b \bar b$ final state and the more involved 3-body final state, $W f \bar f^\prime$, where both a resonance and an anti-resonance, due to interference effects, are present. We also discuss how to integrate over such configurations when converting the cross-sections into a calculation of the relic density.

hep-ph↗
arXiv2020

Moduli Space of $Λ$-modules on Projective Deligne-Mumford Stacks

In this paper, we define $Λ$-quot-functors on Deligne-Mumford stacks. We prove that the $Λ$-quot-functor is representable by an algebraic space. Then, we construct the moduli space of $Λ$-modules on a projective Deligne-Mumford stack. We prove that this moduli space is a quasi-projective scheme.

math.AG↗
arXiv2020

On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration

Imitation learning from observation (LfO) is more preferable than imitation learning from demonstration (LfD) due to the nonnecessity of expert actions when reconstructing the expert policy from the expert data. However, previous studies imply that the performance of LfO is inferior to LfD by a tremendous gap, which makes it challenging to employ LfO in practice. By contrast, this paper proves that LfO is almost equivalent to LfD in the deterministic robot environment, and more generally even in the robot environment with bounded randomness. In the deterministic robot environment, from the perspective of the control theory, we show that the inverse dynamics disagreement between LfO and LfD approaches zero, meaning that LfO is almost equivalent to LfD. To further relax the deterministic constraint and better adapt to the practical environment, we consider bounded randomness in the robot environment and prove that the optimizing targets for both LfD and LfO remain almost same in the more generalized setting. Extensive experiments for multiple robot tasks are conducted to empirically demonstrate that LfO achieves comparable performance to LfD. In fact, most common robot systems in reality are the robot environment with bounded randomness (i.e., the environment this paper considered). Hence, our findings greatly extend the potential of LfO and suggest that we can safely apply LfO without sacrificing the performance compared to LfD in practice.

cs.RO↗
arXiv2020

Monodromy of Rank 2 Parabolic Hitchin Systems

We study the monodromy of the Hitchin fibration for moduli spaces of parabolic G-Higgs bundles in the cases when G=SL(2,R), GL(2,R) and PGL(2,R) A calculation of the orbits of the monodromy with Z2-coefficients provides an exact count of the components of the moduli spaces for these groups.

math.AG↗
arXiv2020

A Human Ear Reconstruction Autoencoder

The ear, as an important part of the human head, has received much less attention compared to the human face in the area of computer vision. Inspired by previous work on monocular 3D face reconstruction using an autoencoder structure to achieve self-supervised learning, we aim to utilise such a framework to tackle the 3D ear reconstruction task, where more subtle and difficult curves and features are present on the 2D ear input images. Our Human Ear Reconstruction Autoencoder (HERA) system predicts 3D ear poses and shape parameters for 3D ear meshes, without any supervision to these parameters. To make our approach cover the variance for in-the-wild images, even grayscale images, we propose an in-the-wild ear colour model. The constructed end-to-end self-supervised model is then evaluated both with 2D landmark localisation performance and the appearance of the reconstructed 3D ears.

cs.CV↗
arXiv2020

Extracting full-field subpixel structural displacements from videos via deep learning

This paper develops a deep learning framework based on convolutional neural networks (CNNs) that enable real-time extraction of full-field subpixel structural displacements from videos. In particular, two new CNN architectures are designed and trained on a dataset generated by the phase-based motion extraction method from a single lab-recorded high-speed video of a dynamic structure. As displacement is only reliable in the regions with sufficient texture contrast, the sparsity of motion field induced by the texture mask is considered via the network architecture design and loss function definition. Results show that, with the supervision of full and sparse motion field, the trained network is capable of identifying the pixels with sufficient texture contrast as well as their subpixel motions. The performance of the trained networks is tested on various videos of other structures to extract the full-field motion (e.g., displacement time histories), which indicates that the trained networks have generalizability to accurately extract full-field subtle displacements for pixels with sufficient texture contrast.

cs.CV↗
arXiv2020

An Attention Based Neural Network for Jet Tagging

Convolutional neural networks are basic structures using jet images as input for the jet tagging problems. However, what they have learned during the training process is always difficult to understand just through feature maps. Inspired by the attention mechanism popular in machine learning fields, we propose a novel attention-based neural network (ABNN) to get insight of this problem. The ABNN combines a jet image with average jet images from the signal and the background to generate attention maps which show clearly the relevant importance according to the different origination of jets. Compared with networks in the similar architecture, this network achieves better performance, which indicates the potential of attention mechanism to use in other works.

hep-ph↗