arXiv subjects

Kai Li

Publications and source records attributed to Kai Li.

At least 361 records · Page 20Linked to original sources

arXiv2021

Visuality in a Cross-disciplinary Battleground: Analysis of Inscriptions in Digital Humanities Journal Publications

Like the old saying, "a graph is worth a thousand words," the non-verbal language, encapsulated in the concept of inscription, is a fundamental rhetorical device in the construction of knowledge represented by research outputs. As many inscriptions are deeply situated in a scientific and data-driven research paradigm, they can be used to understand the relationships between research traditions involved in the field of digital humanities (DH), a highly cross-disciplinary field that is frequently regarded as a battleground between these distinct research traditions, especially the humanities and STEM fields. This paper presents a quantitative, community-focused examination of how inscriptions are used in English-language research articles in DH journals. We randomly selected 252 articles published between 2011 and 2020 from a representative DH journal list, and manually coded and classified inscriptions and author domains in these publications. We found that inscriptions have been increasingly used during the past decade and their uses are more intensive in publications led by STEM authors comparing to other domains. This study offers a timely survey of the disciplinary landscape of DH from the perspective of inscriptions and sheds light on how different research approaches collaborated and combated in the field of DH.

cs.DL↗

arXiv2021

The first photometric investigation and spectroscopic analysis of two contact binaries: ASAS J124343+1531.7 and LINEAR 2323566

The multi-color passband CCD light curves of ASAS J124343+1531.7 and LINEAR 2323566 were first obtained by the 0.84-m Ritchey-Chrétien telescope with follow up observations by the WIYN 0.90m Cassegrain telescope. The data from the $Transiting\quad Exoplanet\quad Survey\quad Satellite\quad (TESS)$ of ASAS J124343+1531.7 was also applied for subsequent analysis. By analyzing the data through the W-D program, their mass ratios and fill-out factors were determined as 3.758, 1.438 and 31.8$\%$, 14.9$\%$, respectively. ASAS J124343+1531.7 is a W-subtype median contact binary, while LINEAR 2323566 is a W-subtype shallow contact binary, and the asymmetric light curves prove that they both have the O'Connell effect, which is generally explained by magnetic activity. The equivalent widths (EWs) of H$_α$ lines were calculated, which show they certainly have magnetic activity. Moreover, LINEAR 2323566 has a stronger magnetic activity. The analysis of orbital period changes shows that ASAS J124343+1531.7 has a trend of secular period increase, which is generally explained by the mass transfer from the less massive to the more massive star. According to the estimated absolute parameters, their evolutionary states are discussed. The two components of ASAS J124343+1531.7 are both main sequence stars. While for LINEAR 2323566, the more massive star is a main sequence star, the less massive star has evolved out of main sequence and is over-luminous and over-sized.

astro-ph.SR↗

arXiv2021

The first photometric analysis of the totally eclipsing contact binary V811 Cep

The first photometric analysis of V811 Cep was carried out. The first complete light curves of V, R and I bands are given. The analysis was carried out by Wilson-Devinney (W-D) program, and the results show that V811 Cep is a median-contact binary ($f=33.9(\pm4.9)\%$) with a mass ratio of 0.285. It is a W-subtype contact binary, that is, the component with less mass is hotter than the component with more mass, and the light curves are asymmetric (O'Connell effect), which can be explained by the existence of a hot spot on the component with less mass. The orbital inclination is $i=88.3^{\circ}$, indicating that it is a totally eclipsing binary, so the parameters obtained are reliable. Through the O-C analyzing, it is found that the orbital period decreases at the rate of $\dot{P}=-3.90(\pm0.06)\times 10^{-7}d \cdot yr^{-1}$, which indicates that the mass transfer occurs from the more massive component to the less massive one.

astro-ph.SR↗

arXiv2021

InstaHide: Instance-hiding Schemes for Private Distributed Learning

How can multiple distributed entities collaboratively train a shared deep net on their private data while preserving privacy? This paper introduces InstaHide, a simple encryption of training images, which can be plugged into existing distributed deep learning pipelines. The encryption is efficient and applying it during training has minor effect on test accuracy. InstaHide encrypts each training image with a "one-time secret key" which consists of mixing a number of randomly chosen images and applying a random pixel-wise mask. Other contributions of this paper include: (a) Using a large public dataset (e.g. ImageNet) for mixing during its encryption, which improves security. (b) Experimental results to show effectiveness in preserving privacy against known attacks with only minor effects on accuracy. (c) Theoretical analysis showing that successfully attacking privacy requires attackers to solve a difficult computational problem. (d) Demonstrating that use of the pixel-wise mask is important for security, since Mixup alone is shown to be insecure to some some efficient attacks. (e) Release of a challenge dataset https://github.com/Hazelsuko07/InstaHide_Challenge Our code is available at https://github.com/Hazelsuko07/InstaHide

cs.CR↗

arXiv2021

L2E: Learning to Exploit Your Opponent

Opponent modeling is essential to exploit sub-optimal opponents in strategic interactions. Most previous works focus on building explicit models to directly predict the opponents' styles or strategies, which require a large amount of data to train the model and lack adaptability to unknown opponents. In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling. L2E acquires the ability to exploit opponents by a few interactions with different opponents during training, thus can adapt to new opponents with unknown styles during testing quickly. We propose a novel opponent strategy generation algorithm that produces effective opponents for training automatically. We evaluate L2E on two poker games and one grid soccer game, which are the commonly used benchmarks for opponent modeling. Comprehensive experimental results indicate that L2E quickly adapts to diverse styles of unknown opponents.

cs.LG↗

arXiv2021

VINS: Visual Search for Mobile User Interface Design

Searching for relative mobile user interface (UI) design examples can aid interface designers in gaining inspiration and comparing design alternatives. However, finding such design examples is challenging, especially as current search systems rely on only text-based queries and do not consider the UI structure and content into account. This paper introduces VINS, a visual search framework, that takes as input a UI image (wireframe, high-fidelity) and retrieves visually similar design examples. We first survey interface designers to better understand their example finding process. We then develop a large-scale UI dataset that provides an accurate specification of the interface's view hierarchy (i.e., all the UI components and their specific location). By utilizing this dataset, we propose an object-detection based image retrieval framework that models the UI context and hierarchical structure. The framework achieves a mean Average Precision of 76.39\% for the UI detection and high performance in querying similar UI designs.

cs.HC↗

arXiv2021

Higher-order Topological Anderson Insulators

We study disorder effects in a two-dimensional system with chiral symmetry and find that disorder can induce a quadrupole topological insulating phase (a higher-order topological phase with quadrupole moments) from a topologically trivial phase. Their topological properties manifest in a topological invariant defined based on effective boundary Hamiltonians, the quadrupole moment, and zero-energy corner modes. We find gapped and gapless topological phases and a Griffiths regime. In the gapless topological phase, all the states are localized, while in the Griffiths regime, the states at zero energy become multifractal. We further apply the self-consistent Born approximation to show that the induced topological phase arises from disorder renormalized masses. We finally introduce a practical experimental scheme with topoelectrical circuits where the predicted topological phenomena can be observed by impedance measurements. Our work opens the door to studying higher-order topological Anderson insulators and their localization properties.

cond-mat.mes-hall↗

arXiv2021

Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details

With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography. Images captured under low-light conditions often suffer from poor visibility and blur. Solely brightening the dark regions will inevitably amplify the blur, thus may lead to detail loss. In this paper, we propose a simple yet effective two-stream framework named NEID to tune up the brightness and enhance the details simultaneously without introducing many computational costs. Precisely, the proposed method consists of three parts: Light Enhancement (LE), Detail Refinement (DR) and Feature Fusing (FF) module, which can aggregate composite features oriented to multiple tasks based on channel attention mechanism. Extensive experiments conducted on several benchmark datasets demonstrate the efficacy of our method and its superiority over state-of-the-art methods.

eess.IV↗

arXiv2021

Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References

It is suggested that low-light image enhancement realizes one-to-many mapping since we have different definitions of NORMAL-light given application scenarios or users' aesthetic. However, most existing methods ignore subjectivity of the task, and simply produce one result with fixed brightness. This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference. Inspired by style transfer, our method decomposes an image into two low-coupling feature components in the latent space, which allows the concatenation feasibility of the content components from low-light images and the luminance components from reference images. In such a way, the network learns to extract scene-invariant and brightness-specific information from a set of image pairs instead of learning brightness differences. Moreover, information except for the brightness is preserved to the greatest extent to alleviate color distortion. Extensive results show strong capacity and superiority of our network against existing methods.

cs.CV↗

arXiv2020

The first photometric analysis and period investigation of the K-type W UMa type binary system V0842 Cep

V0842 Cep is a W UMa-type binary star that has been neglected since its discovery. We analysed the VR$_c$I$_c$ light curves, obtained by the 1 m telescope at the Weihai Observatory of Shandong University, using the Wilson-Devinney code. V0842 Cep was found to be a shallow contact binary system (f=8.7$\%$) with a mass ratio of 2.281. Because its orbital inclination is greater than 80$^\circ$, the photometric results are reliable. A period study is included which reveals a continually decreasing orbital period ($\frac{\mathrm{d}p}{\mathrm{d}t}$=1.50($\pm$0.42)$\times$10$^{-7}$dyr$^{-1}$). This trend could be attributed to the angular momentum loss via stellar wind.

astro-ph.SR↗

arXiv2020

Consistency of a kind of general noncanonical warm inflation

The framework of a kind of noncanonical warm inflation is introduced, and the dynamical equations of this scenario are presented. We propose the slow roll approximations and give some redefining slow roll parameters in this scenario which remain dimensionless. Performing systemic stability analysis, we calculate the slow roll conditions to guarantee that slow roll approximations hold. The slow roll conditions suggest slow roll inflation in general noncanonical warm inflationary scenario can still exist, and in addition, the slow roll approximations are more easily to be satisfied. Then, a concrete Dirac-Born-Infeld warm inflationary model is studied.

gr-qc↗

arXiv2020

AIM 2020 Challenge on Learned Image Signal Processing Pipeline

This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to-RGB mapping problem, where to goal was to map the original low-quality RAW images captured by the Huawei P20 device to the same photos obtained with the Canon 5D DSLR camera. The considered task embraced a number of complex computer vision subtasks, such as image demosaicing, denoising, white balancing, color and contrast correction, demoireing, etc. The target metric used in this challenge combined fidelity scores (PSNR and SSIM) with solutions' perceptual results measured in a user study. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical image signal processing pipeline modeling.

cs.CV↗

arXiv2020

$η$-pairing in correlated fermion models with spin-orbit coupling

We generalize the $η$-pairing theory in Hubbard models to the ones with spin-orbit coupling (SOC) and obtain the conditions under which the $η$-pairing operator is an eigenoperator of the Hamiltonian. The $η$ pairing thus reveals an exact $SU(2)$ pseudospin symmetry in our spin-orbit coupled Hubbard model, even though the $SU(2)$ spin symmetry is explicitly broken by the SOC. In particular, these exact results can be applied to a variety of Hubbard models with SOC on either bipartite or non-bipartite lattices, whose noninteracting limit can be a Dirac semimetal, a Weyl semimetal, a nodal-line semimetal, and a Chern insulator. The $η$ pairing conditions also impose constraints on the band topology of these systems. We then construct and focus on an interacting Dirac-semimetal model, which exhibits an exact pseudospin symmetry with fine-tuned parameters. The stability regions for the exact $η$-pairing ground states (with momentum $\bmπ$ or $\bm{0}$) and the exact charge-density-wave ground states are established. Between these distinct symmetry-breaking phases, there exists an exactly solvable multicritical line. In the end, we discuss possible experimental realizations of our results.

cond-mat.str-el↗

arXiv2020

Adversarial Feature Hallucination Networks for Few-Shot Learning

The recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data. Nonetheless, massive supervision remains a luxury for many real applications, boosting great interest in label-scarce techniques such as few-shot learning (FSL), which aims to learn concept of new classes with a few labeled samples. A natural approach to FSL is data augmentation and many recent works have proved the feasibility by proposing various data synthesis models. However, these models fail to well secure the discriminability and diversity of the synthesized data and thus often produce undesirable results. In this paper, we propose Adversarial Feature Hallucination Networks (AFHN) which is based on conditional Wasserstein Generative Adversarial networks (cWGAN) and hallucinates diverse and discriminative features conditioned on the few labeled samples. Two novel regularizers, i.e., the classification regularizer and the anti-collapse regularizer, are incorporated into AFHN to encourage discriminability and diversity of the synthesized features, respectively. Ablation study verifies the effectiveness of the proposed cWGAN based feature hallucination framework and the proposed regularizers. Comparative results on three common benchmark datasets substantiate the superiority of AFHN to existing data augmentation based FSL approaches and other state-of-the-art ones.

cs.CV↗

arXiv2020

Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization

Matching information across image and text modalities is a fundamental challenge for many applications that involve both vision and natural language processing. The objective is to find efficient similarity metrics to compare the similarity between visual and textual information. Existing approaches mainly match the local visual objects and the sentence words in a shared space with attention mechanisms. The matching performance is still limited because the similarity computation is based on simple comparisons of the matching features, ignoring the characteristics of their distribution in the data. In this paper, we address this limitation with an efficient learning objective that considers the discriminative feature distributions between the visual objects and sentence words. Specifically, we propose a novel Adversarial Discriminative Domain Regularization (ADDR) learning framework, beyond the paradigm metric learning objective, to construct a set of discriminative data domains within each image-text pairs. Our approach can generally improve the learning efficiency and the performance of existing metrics learning frameworks by regulating the distribution of the hidden space between the matching pairs. The experimental results show that this new approach significantly improves the overall performance of several popular cross-modal matching techniques (SCAN, VSRN, BFAN) on the MS-COCO and Flickr30K benchmarks.

cs.CV↗

arXiv2020

MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery

To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data representations, as a way to control the trade-off between data utility and vulnerability to inversion attacks. Our method is motivated by the theoretical insights of data separability in neural networking training and results on the hardness of model inversion. Empirically, by adjusting the separability of data representation, we show that there exist sweet-spots for data separability such that it is difficult to recover data during inference while maintaining data utility.

cs.LG↗

arXiv2020

Pushing The Limit of Type I Codebook For FDD Massive MIMO Beamforming: A Channel Covariance Reconstruction Approach

There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems. In particular, two types of codebooks (namely Type I and Type II codebooks) are introduced with different resolution and overhead requirements. Although the Type I codebook based scheme requires lower feedback overhead, its channel state information (CSI) reconstruction and beamforming performance are not as good as those from the Type II codebook based scheme. However, since the Type I codebook based scheme has been widely used in 4G systems for many years, replacing it by the Type II codebook based scheme overnight is too costly to be an option. Therefore, in this paper, using Type I codebook, we leverage advances in cutting plane method to optimize the CSI reconstruction at the base station (BS), in order to close the gap between these two codebook based beamforming schemes. Numerical results based on channel samples from QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) are presented to show the excellent performance of the proposed algorithm in terms of beamforming vector acquisition.

eess.SP↗

arXiv2020

The (re-)instrumentalization of the Diagnostic and Statistical Manual of Mental Disorders (DSM) in psychological publications: a citation context analysis

Research instruments play significant roles in the construction of scientific knowledge, even though we have only acquired very limited knowledge about their lifecycles from quantitative studies. This paper aims to address this gap by quantitatively examining the citation contexts of an exemplary research instrument, the Diagnostic and Statistical Manual of Mental Disorders (DSM), in full-text psychological publications. We investigated the relationship between the citation contexts of the DSM and its status as a valid instrument being used and described by psychological researchers. We specifically focused on how this relationship has changed over the DSM's citation histories, especially through the temporal framework of its versions. We found that a new version of the DSM is increasingly regarded as a valid instrument after its publication; this is reflected in various key citation contexts, such as the use of hedges, attention markers, and the verb profile in sentences where the DSM is cited. We call this process the re-instrumentalization of the DSM in the space of scientific publications. Our findings bridge an important gap between quantitative and qualitative science studies and shed light on an aspect of the social process of scientific instrument development that is not addressed by the current qualitative literature.

cs.DL↗