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Xin Tong

Publications and source records attributed to Xin Tong.

At least 127 records · Page 7Linked to original sources
arXiv2024

Pressure tunable magnetic skyrmion phase in Co8Zn8Mn4 single crystals

In a magnetic skyrmion phase, magnetic moments form vortex-like topological textures which are of both fundamental and industrial interests. In $β$-Mn-type Co-Zn-Mn alloys, chrial magnetic skyrmions emerge above room temperature, providing a unique system for studying the skrymion physics and exploring spintronics applications. However, the magnetic skyrmion phase is typically confined in a narrow and limited temperature ($T$) and magnetic field ($H$) range. Here, we demonstrate that hydrostatic pressure can expand the skyrmion phase in the $T-H$ phase diagram of single-crystalline Co$_8$Zn$_8$Mn$_4$. At ambient pressure, signatures of skyrmions are seen within $T\sim302-308$ K and $H\sim50-100$ Oe. Applying a moderate pressure of 6 kbar extends this range to $T\sim300-310$ K and $H\sim50-150$ Oe. However, further escalation of pressure to 10 kbar results in a slight contraction of the skyrmion phase. These findings underscore the sensitivity of the skyrmion phase in Co$_8$Zn$_8$Mn$_4$ to external pressures, and hint at the potential of strain engineering, particularly in $β$-Mn-type Co-Zn-Mn thin films, as a promising avenue to customize the skyrmion phase.

cond-mat.mtrl-sci↗
arXiv2024

Are the Signs of Factor Loadings Arbitrary in Confirmatory Factor Analysis? Problems and Solutions

The replication crisis in social and behavioral sciences has raised concerns about the reliability and validity of empirical studies. While research in the literature has explored contributing factors to this crisis, the issues related to analytical tools have received less attention. This study focuses on a widely used analytical tool - confirmatory factor analysis (CFA) - and investigates one issue that is typically overlooked in practice: accurately estimating factor-loading signs. Incorrect loading signs can distort the relationship between observed variables and latent factors, leading to unreliable or invalid results in subsequent analyses. Our study aims to investigate and address the estimation problem of factor-loading signs in CFA models. Based on an empirical demonstration and Monte Carlo simulation studies, we found current methods have drawbacks in estimating loading signs. To address this problem, three solutions are proposed and proven to work effectively. The applications of these solutions are discussed and elaborated.

stat.ME↗
arXiv2024

Arithmetic Hodge-Iwasawa Moduli Stacks

In this article, we are going to construct arithmetic moduli stacks of $G$-bundles after our previous construction on Hodge-Iwasawa theory. These stacks parametrize certain Hodge-Iwasawa structures in a coherent way.

math.AG↗
arXiv2024

Advanced Unstructured Data Processing for ESG Reports: A Methodology for Structured Transformation and Enhanced Analysis

In the evolving field of corporate sustainability, analyzing unstructured Environmental, Social, and Governance (ESG) reports is a complex challenge due to their varied formats and intricate content. This study introduces an innovative methodology utilizing the "Unstructured Core Library", specifically tailored to address these challenges by transforming ESG reports into structured, analyzable formats. Our approach significantly advances the existing research by offering high-precision text cleaning, adept identification and extraction of text from images, and standardization of tables within these reports. Emphasizing its capability to handle diverse data types, including text, images, and tables, the method adeptly manages the nuances of differing page layouts and report styles across industries. This research marks a substantial contribution to the fields of industrial ecology and corporate sustainability assessment, paving the way for the application of advanced NLP technologies and large language models in the analysis of corporate governance and sustainability. Our code is available at https://github.com/linancn/TianGong-AI-Unstructure.git.

cs.CL↗
arXiv2023

A Comparison of Full Information Maximum Likelihood and Machine Learning Missing Data Analytical Methods in Growth Curve Modeling

Missing data are inevitable in longitudinal studies. Traditional methods, such as the full information maximum likelihood (FIML), are commonly used to handle ignorable missing data. However, they may lead to biased model estimation due to missing not at random data that often appear in longitudinal studies. Recently, machine learning methods, such as random forests (RF) and K-nearest neighbors (KNN) imputation methods, have been proposed to cope with missing values. Although machine learning imputation methods have been gaining popularity, few studies have investigated the tenability and utility of these methods in longitudinal research. Through Monte Carlo simulations, this study evaluates and compares the performance of traditional and machine learning approaches (FIML, RF, and KNN) in growth curve modeling. The effects of sample size, the rate of missingness, and the missing data mechanism on model estimation are investigated. Results indicate that FIML is a better choice than the two machine learning imputation methods in terms of model estimation accuracy and efficiency.

stat.AP↗
arXiv2023

Plan, Posture and Go: Towards Open-World Text-to-Motion Generation

Conventional text-to-motion generation methods are usually trained on limited text-motion pairs, making them hard to generalize to open-world scenarios. Some works use the CLIP model to align the motion space and the text space, aiming to enable motion generation from natural language motion descriptions. However, they are still constrained to generate limited and unrealistic in-place motions. To address these issues, we present a divide-and-conquer framework named PRO-Motion, which consists of three modules as motion planner, posture-diffuser and go-diffuser. The motion planner instructs Large Language Models (LLMs) to generate a sequence of scripts describing the key postures in the target motion. Differing from natural languages, the scripts can describe all possible postures following very simple text templates. This significantly reduces the complexity of posture-diffuser, which transforms a script to a posture, paving the way for open-world generation. Finally, go-diffuser, implemented as another diffusion model, estimates whole-body translations and rotations for all postures, resulting in realistic motions. Experimental results have shown the superiority of our method with other counterparts, and demonstrated its capability of generating diverse and realistic motions from complex open-world prompts such as "Experiencing a profound sense of joy". The project page is available at https://moonsliu.github.io/Pro-Motion.

cs.CV↗
arXiv2023

"Centralized or Decentralized?": Concerns and Value Judgments of Stakeholders in the Non-Fungible Tokens (NFTs) Market

Non-fungible tokens (NFTs) are decentralized digital tokens to represent the unique ownership of items. Recently, NFTs have been gaining popularity and at the same time bringing up issues, such as scams, racism, and sexism. Decentralization, a key attribute of NFT, contributes to some of the issues that are easier to regulate under centralized schemes, which are intentionally left out of the NFT marketplace. In this work, we delved into this centralization-decentralization dilemma in the NFT space through mixed quantitative and qualitative methods. Centralization-decentralization dilemma is the dilemma caused by the conflict between the slogan of decentralization and the interests of stakeholders. We first analyzed over 30,000 NFT-related tweets to obtain a high-level understanding of stakeholders' concerns in the NFT space. We then interviewed 15 NFT stakeholders (both creators and collectors) to obtain their in-depth insights into these concerns and potential solutions. Our findings identify concerning issues among users: financial scams, counterfeit NFTs, hacking, and unethical NFTs. We further reflected on the centralization-decentralization dilemma drawing upon the perspectives of the stakeholders in the interviews. Finally, we gave some inferences to solve the centralization-decentralization dilemma in the NFT market and thought about the future of NFT and decentralization.

cs.CY↗
arXiv2023

$\infty$-Categorical Approaches to Hodge-Iwasawa Theory II: $\infty$-Categorical and Derived Hodge-Iwasawa Modules

This paper is a discussion on $\infty$-categorical approaches to Hodge-Iwasawa Theory, which was initiated in our project on the $\infty$-categorical approaches to Hodge-Iwasawa Theory. The theory aims at the serious unification of $p$-adic Hodge Theory and $p$-adic Iwasawa Theory, by taking deformation of Hodge-theoretic constructions along some consideration in Iwasawa Theory beyond the Iwasawa deformation of certain motives in the general sense. The Hodge modules in our current consideration will be essentially within $\infty$-categorical derived categories of inductive Banach modules and $\infty$-categorical derived categories of condensed solidification of certain topological modules.

math.AG↗
arXiv2023

GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds

Recent works have shown that 3D-aware GANs trained on unstructured single image collections can generate multiview images of novel instances. The key underpinnings to achieve this are a 3D radiance field generator and a volume rendering process. However, existing methods either cannot generate high-resolution images (e.g., up to 256X256) due to the high computation cost of neural volume rendering, or rely on 2D CNNs for image-space upsampling which jeopardizes the 3D consistency across different views. This paper proposes a novel 3D-aware GAN that can generate high resolution images (up to 1024X1024) while keeping strict 3D consistency as in volume rendering. Our motivation is to achieve super-resolution directly in the 3D space to preserve 3D consistency. We avoid the otherwise prohibitively-expensive computation cost by applying 2D convolutions on a set of 2D radiance manifolds defined in the recent generative radiance manifold (GRAM) approach, and apply dedicated loss functions for effective GAN training at high resolution. Experiments on FFHQ and AFHQv2 datasets show that our method can produce high-quality 3D-consistent results that significantly outperform existing methods. It makes a significant step towards closing the gap between traditional 2D image generation and 3D-consistent free-view generation.

cs.CV↗
arXiv2023

A Real-time Method for Inserting Virtual Objects into Neural Radiance Fields

We present the first real-time method for inserting a rigid virtual object into a neural radiance field, which produces realistic lighting and shadowing effects, as well as allows interactive manipulation of the object. By exploiting the rich information about lighting and geometry in a NeRF, our method overcomes several challenges of object insertion in augmented reality. For lighting estimation, we produce accurate, robust and 3D spatially-varying incident lighting that combines the near-field lighting from NeRF and an environment lighting to account for sources not covered by the NeRF. For occlusion, we blend the rendered virtual object with the background scene using an opacity map integrated from the NeRF. For shadows, with a precomputed field of spherical signed distance field, we query the visibility term for any point around the virtual object, and cast soft, detailed shadows onto 3D surfaces. Compared with state-of-the-art techniques, our approach can insert virtual object into scenes with superior fidelity, and has a great potential to be further applied to augmented reality systems.

cs.CV↗
arXiv2023

Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data

COVID-19 has a spectrum of disease severity, ranging from asymptomatic to requiring hospitalization. Understanding the mechanisms driving disease severity is crucial for developing effective treatments and reducing mortality rates. One way to gain such understanding is using a multi-class classification framework, in which patients' biological features are used to predict patients' severity classes. In this severity classification problem, it is beneficial to prioritize the identification of more severe classes and control the "under-classification" errors, in which patients are misclassified into less severe categories. The Neyman-Pearson (NP) classification paradigm has been developed to prioritize the designated type of error. However, current NP procedures are either for binary classification or do not provide high probability controls on the prioritized errors in multi-class classification. Here, we propose a hierarchical NP (H-NP) framework and an umbrella algorithm that generally adapts to popular classification methods and controls the under-classification errors with high probability. On an integrated collection of single-cell RNA-seq (scRNA-seq) datasets for 864 patients, we explore ways of featurization and demonstrate the efficacy of the H-NP algorithm in controlling the under-classification errors regardless of featurization. Beyond COVID-19 severity classification, the H-NP algorithm generally applies to multi-class classification problems, where classes have a priority order.

cs.LG↗
arXiv2023

More Than a Wife and a Mom: A Study of Mom Vlogging Practices in China

Mom vloggers are stay-at-home moms who record and share their daily life through short videos. In this exploratory study, we aspire to understand mom vloggers' motivations, practices, and challenges. Our mixed-methods inspection contained interviews with 4 mom vloggers in China and a content analysis of mom vlogs of 5 other mom vloggers. Mom vloggers' primary motivations are to make money, record daily life, and seek their individual identities and values, well meeting their financial and social needs after leaving their paid employment. When creating vlog content, mom bloggers encounter various challenges, such as a lack of video visibility, being stretched by both intensive motherhood and heavy digital work, privacy and self-presentation concerns, and so on. Based on the findings, we propose design implications toward resolving these challenges and benefiting mom vloggers' experiences.

cs.HC↗
arXiv2023

"I'm Not Confident in Debiasing AI Systems Since I Know Too Little": Teaching AI Creators About Gender Bias Through Hands-on Tutorials

Gender bias is rampant in AI systems, causing bad user experience, injustices, and mental harm to women. School curricula fail to educate AI creators on this topic, leaving them unprepared to mitigate gender bias in AI. In this paper, we designed hands-on tutorials to raise AI creators' awareness of gender bias in AI and enhance their knowledge of sources of gender bias and debiasing techniques. The tutorials were evaluated with 18 AI creators, including AI researchers, AI industrial practitioners (i.e., developers and product managers), and students who had learned AI. Their improved awareness and knowledge demonstrated the effectiveness of our tutorials, which have the potential to complement the insufficient AI gender bias education in CS/AI courses. Based on the findings, we synthesize design implications and a rubric to guide future research, education, and design efforts.

cs.HC↗
arXiv2023

AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections

Previous animatable 3D-aware GANs for human generation have primarily focused on either the human head or full body. However, head-only videos are relatively uncommon in real life, and full body generation typically does not deal with facial expression control and still has challenges in generating high-quality results. Towards applicable video avatars, we present an animatable 3D-aware GAN that generates portrait images with controllable facial expression, head pose, and shoulder movements. It is a generative model trained on unstructured 2D image collections without using 3D or video data. For the new task, we base our method on the generative radiance manifold representation and equip it with learnable facial and head-shoulder deformations. A dual-camera rendering and adversarial learning scheme is proposed to improve the quality of the generated faces, which is critical for portrait images. A pose deformation processing network is developed to generate plausible deformations for challenging regions such as long hair. Experiments show that our method, trained on unstructured 2D images, can generate diverse and high-quality 3D portraits with desired control over different properties.

cs.CV↗
arXiv2023

Relighting Neural Radiance Fields with Shadow and Highlight Hints

This paper presents a novel neural implicit radiance representation for free viewpoint relighting from a small set of unstructured photographs of an object lit by a moving point light source different from the view position. We express the shape as a signed distance function modeled by a multi layer perceptron. In contrast to prior relightable implicit neural representations, we do not disentangle the different reflectance components, but model both the local and global reflectance at each point by a second multi layer perceptron that, in addition, to density features, the current position, the normal (from the signed distace function), view direction, and light position, also takes shadow and highlight hints to aid the network in modeling the corresponding high frequency light transport effects. These hints are provided as a suggestion, and we leave it up to the network to decide how to incorporate these in the final relit result. We demonstrate and validate our neural implicit representation on synthetic and real scenes exhibiting a wide variety of shapes, material properties, and global illumination light transport.

cs.CV↗
arXiv2023

Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding

The use of pretrained backbones with fine-tuning has been successful for 2D vision and natural language processing tasks, showing advantages over task-specific networks. In this work, we introduce a pretrained 3D backbone, called {\SST}, for 3D indoor scene understanding. We design a 3D Swin transformer as our backbone network, which enables efficient self-attention on sparse voxels with linear memory complexity, making the backbone scalable to large models and datasets. We also introduce a generalized contextual relative positional embedding scheme to capture various irregularities of point signals for improved network performance. We pretrained a large {\SST} model on a synthetic Structured3D dataset, which is an order of magnitude larger than the ScanNet dataset. Our model pretrained on the synthetic dataset not only generalizes well to downstream segmentation and detection on real 3D point datasets, but also outperforms state-of-the-art methods on downstream tasks with +2.3 mIoU and +2.2 mIoU on S3DIS Area5 and 6-fold semantic segmentation, +1.8 mIoU on ScanNet segmentation (val), +1.9 mAP@0.5 on ScanNet detection, and +8.1 mAP@0.5 on S3DIS detection. A series of extensive ablation studies further validate the scalability, generality, and superior performance enabled by our approach. The code and models are available at https://github.com/microsoft/Swin3D .

cs.CV↗
arXiv2023

RTVis: Research Trend Visualization Toolkit

When researchers are about to start a new project or have just entered a new research field, choosing a proper research topic is always challenging. To help them have an overall understanding of the research trend in real-time and find out the research topic they are interested in, we developed the Research Trend Visualization toolkit (RTVis) to analyze and visualize the research paper information. RTVis consists of a field theme river, a co-occurrence network, a specialized citation bar chart, and a word frequency race diagram, showing the field change through time, cooperating relationship among authors, paper citation numbers in different venues, and the most common words in the abstract part respectively. Moreover, RTVis is open source and easy to deploy. The demo of our toolkit and code with detailed documentation are both available online.

cs.HC↗
arXiv2023

On the Mechanics of NFT Valuation: AI Ethics and Social Media

As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction activities and pricing based on gender and skin tone. Our result is consistent and robust, controlling for the rarity of an NFT based on the set of human-readable attributes, including gender and skin tone. Our research contributes to the interdisciplinary study at the intersection of AI, Ethics, and Society, focusing on the ecosystem of decentralized AI or blockchain. We provide our data and code for replicability as open access on GitHub.

cs.CY↗