BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Jie Hou

Jie Hou contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Digital LibrariesSocial and Information NetworksArtificial Intelligencecond-mat.mtrl-sciComputer Visioncond-mat.str-elcond-mat.supr-conDistributed, Parallel, and Cluster Computingmath.OCMultimediaphysics.soc-ph

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
11works
0followers
11topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

11 published item(s)

preprint2026arXiv

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence

As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool use, and real environment interaction. Existing agenticinfrastructure remain fragmented across evaluation, data management, and agent evolution, making it difficult to discover risks systematically and improve models in a continuous closed loop. In this report, we present \textbf{Safactory}, a scalable agent factory for trustworthy autonomous intelligence. Safactory integrates three tightly coupled platforms: a \textbf{Parallel Simulation Platform} for trajectory generation, a \textbf{Trustworthy Data Platform} for trajectory storage and experience extraction, and an \textbf{Autonomous Evolution Platform} for asynchronous reinforcement learning and on-policy distillation. As far as we know, Safactory is the first framework to propose a unified evolutionary pipeline for next-generation trustworthy autonomous intelligence.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

EmotionGesture: Audio-Driven Diverse Emotional Co-Speech 3D Gesture Generation

Generating vivid and diverse 3D co-speech gestures is crucial for various applications in animating virtual avatars. While most existing methods can generate gestures from audio directly, they usually overlook that emotion is one of the key factors of authentic co-speech gesture generation. In this work, we propose EmotionGesture, a novel framework for synthesizing vivid and diverse emotional co-speech 3D gestures from audio. Considering emotion is often entangled with the rhythmic beat in speech audio, we first develop an Emotion-Beat Mining module (EBM) to extract the emotion and audio beat features as well as model their correlation via a transcript-based visual-rhythm alignment. Then, we propose an initial pose based Spatial-Temporal Prompter (STP) to generate future gestures from the given initial poses. STP effectively models the spatial-temporal correlations between the initial poses and the future gestures, thus producing the spatial-temporal coherent pose prompt. Once we obtain pose prompts, emotion, and audio beat features, we will generate 3D co-speech gestures through a transformer architecture. However, considering the poses of existing datasets often contain jittering effects, this would lead to generating unstable gestures. To address this issue, we propose an effective objective function, dubbed Motion-Smooth Loss. Specifically, we model motion offset to compensate for jittering ground-truth by forcing gestures to be smooth. Last, we present an emotion-conditioned VAE to sample emotion features, enabling us to generate diverse emotional results. Extensive experiments demonstrate that our framework outperforms the state-of-the-art, achieving vivid and diverse emotional co-speech 3D gestures. Our code and dataset will be released at the project page: https://xingqunqi-lab.github.io/Emotion-Gesture-Web/

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Distributed Momentum-based Frank-Wolfe Algorithm for Stochastic Optimization

This paper considers distributed stochastic optimization, in which a number of agents cooperate to optimize a global objective function through local computations and information exchanges with neighbors over a network. Stochastic optimization problems are usually tackled by variants of projected stochastic gradient descent. However, projecting a point onto a feasible set is often expensive. The Frank-Wolfe (FW) method has well-documented merits in handling convex constraints, but existing stochastic FW algorithms are basically developed for centralized settings. In this context, the present work puts forth a distributed stochastic Frank-Wolfe solver, by judiciously combining Nesterov's momentum and gradient tracking techniques for stochastic convex and nonconvex optimization over networks. It is shown that the convergence rate of the proposed algorithm is $\mathcal{O}(k^{-\frac{1}{2}})$ for convex optimization, and $\mathcal{O}(1/\mathrm{log}_2(k))$ for nonconvex optimization. The efficacy of the algorithm is demonstrated by numerical simulations against a number of competing alternatives.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Gutzwiller approximation approach to the SU(4) $t$-$J$ model

We develop the Gutzwiller approximation method to obtain the renormalized Hamiltonian of the SU(4) $t$-$J$ model with the corresponding renormalization factors. Subsequently, a mean-field theory is employed on the renormalized Hamiltonian of the model on the honeycomb lattice under the scenario of a cooperative condensation of carriers moving in the resonating valence bond state of flavors. In particular, we find that the extended $s$-wave superconducting state is more favorable than the $d\pm id$-wave superconducting state in the doping range close to quarter filling. The pairing states of the SU(4) case reveal the property that the spin-singlet pairing and the spin-triplet pairing can coexist simultaneously. Our results might provide new insights into the twisted bilayer graphene system.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Accurate prediction of nanovoid structures and energetics in bcc metals

Knowledge on structures and energetics of nanovoids is fundamental to understand defect evolution in metals. Yet there remain no reliable methods able to determine essential structural details or to provide accurate assessment of energetics for general nanovoids. Here, we performed systematic first-principles investigations to examine stable structures and energetics of nanovoids in bcc metals, explicitly demonstrated the stable structures can be precisely determined by minimizing their Wigner-Seitz area, and revealed a linear relationship between formation energy and Wigner-Seitz area of nanovoids. We further developed a new physics-based model to accurately predict stable structures and energetics for arbitrary-sized nanovoids. This model was well validated by first-principles calculations and recent nanovoid annealing experiments, and showed distinct advantages over the widely used spherical approximation. The present work offers mechanistic insights that crucial for understanding nanovoid formation and evolution, being a critical step towards predictive control and prevention of nanovoid related damage processes in structural metals.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Hydrogen clustering in bcc metals: atomic origin and strong stress anisotropy

Hydrogen (H) induced damage in metals has been a long-standing woe for many industrial applications. One form of such damage is linked to H clustering, for which the atomic origin remains contended, particularly for non-hydride forming metals. In this work, we systematically studied H clustering behavior in bcc metals represented by W, Fe, Mo, and Cr, combining first-principles calculations, atomistic and Monte Carlo simulations. H clustering has been shown to be energetically favorable, and can be strongly facilitated by anisotropic stress field, dominated by the tensile component along one of the <001> crystalline directions. We showed that the stress effect can be well predicted by the continuum model based on H formation volume tensor, and that H clustering is thermodynamically possible at edge dislocations, evidenced by nanohydride formation at rather low levels of H concentration. Moreover, anisotropy in the stress effect is well reflected in nanohydride morphology around dislocations, with nanohydride growth occurring in the form of thin platelet structures that maximize one <001> tension. In particular, the <001> type edge dislocation, with the <001> tensile component maximized, has been shown to be highly effective in facilitating H aggregation, thus expected to play an important role in H clustering in bcc metals, in close agreement with recent experimental observations. This work explicitly and quantitatively clarifies the anisotropic nature of stress effect on H energetics and H clustering behaviors, offering mechanistic insights critical towards understanding H-induced damages in metals.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Implicit Multi-feature Learning for Dynamic Time Series Prediction of the Impact of Institutions

Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous researches have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors&#39; influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Judging a Book by Its Cover: The Effect of Facial Perception on Centrality in Social Networks

Facial appearance matters in social networks. Individuals frequently make trait judgments from facial clues. Although these face-based impressions lack the evidence to determine validity, they are of vital importance, because they may relate to human network-based social behavior, such as seeking certain individuals for help, advice, dating, and cooperation, and thus they may relate to centrality in social networks. However, little to no work has investigated the apparent facial traits that influence network centrality, despite the large amount of research on attributions of the central position including personality and behavior. In this paper, we examine whether perceived traits based on facial appearance affect network centrality by exploring the initial stage of social network formation in a first-year college residential area. We took face photos of participants who are freshmen living in the same residential area, and we asked them to nominate community members linking to different networks. We then collected facial perception data by requiring other participants to rate facial images for three main attributions: dominance, trustworthiness, and attractiveness. Meanwhile, we proposed a framework to discover how facial appearance affects social networks. Our results revealed that perceived facial traits were correlated with the network centrality and that they were indicative to predict the centrality of people in different networks. Our findings provide psychological evidence regarding the interaction between faces and network centrality. Our findings also offer insights in to a combination of psychological and social network techniques, and they highlight the function of facial bias in cuing and signaling social traits. To the best of our knowledge, we are the first to explore the influence of facial perception on centrality in social networks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Prediction Methods and Applications in the Science of Science: A Survey

Science of science has become a popular topic that attracts great attentions from the research community. The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact. In this paper, we analyse methods and applications in data-driven prediction in the science of science, and discuss their significance. First, we introduce the background and review the current state of the science of science. Second, we review data-driven prediction based on paper citation count, and investigate research issues in this area. Then, we discuss methods to predict scholar impact, and we analyse different approaches to promote the scholarly collaboration in the collaboration network. This paper also discusses open issues and existing challenges, and suggests potential research directions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quantifying Success in Science: An Overview

Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quantifying the Impact of Scholarly Papers Based on Higher-Order Weighted Citations

Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of a scholarly paper. A higher-order weighted quantum PageRank algorithm is also developed to address the behavior of multiple step citation flow. Capturing the citation dynamics with higher-order dependencies reveals the actual impact of papers, including necessary self-citations that are sometimes excluded in prior studies. Quantum PageRank is utilized in this paper to help differentiating nodes whose PageRank values are identical.

0 citations0 reviewsNo code linkedOpen access