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Bridging Gaps in HCI: Advancing Education, Research, and Careers in Asia

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Bridging Gaps in HCI: Advancing Education, Research, and Car...

2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Gamage, Dilrukshi Cheng, Shiwei Mudliar, Preeti Lu, Zhicong Zhao, Shengdong Ahmed, Nova Ma, Xiaojuan Lee, Uichin Wang, Ding Department of Computation and Intelligent Systems University of Colombo School of Computing Colombo Sri Lanka Zhejiang University of Technology Hangzhou China International Institute of Information Technology Bangalore Bangalore India Department of Computer Science City University of Hong Kong Hong Kong School of Creative Media City University of Hong Kong Hong Kong Design Inclusion and Access Lab. Department of Electrical and Computer Engineering North South University Dhaka Dhaka Bangladesh Hong Kong University of Science and Technology Hong Kong Hong Kong School of Computing KAIST Daejeon South Korea Google Atlanta GA United States

ISBN: (纸本)9798400713958

Asia’s Human-computer Interaction (HCI) landscape is rapidly evolving, yet it faces distinct challenges in curriculum development, research establishment, and career navigation. This panel discussion, hosted by Asia SIGCHI Committee (ASC) at CHI2025, will bring together distinguished experts to address these challenges and explore strategies for fostering a robust and inclusive HCI community in Asia. Panelists will discuss the integration of culturally relevant content in HCI education, approaches to enhancing research visibility on international platforms, and initiatives to bridge gaps between academic training and industry expectations. Through an engaging discussion and audience interaction, the panel aims to identify actionable solutions, promote collaboration, and inspire future initiatives that strengthen HCI’s growth across the region. Participants will gain valuable insights into overcoming systemic barriers while building sustainable and impactful HCI programs tailored to Asia’s unique sociocultural dynamics. This panel seeks to advance the global relevance and contributions of HCI in Asia. © 2025 Copyright held by the owner/author(s).

关键词： Asia Curricula HCI HCI Education HCI Teaching

Functional Connectivity Network Augmentation with Patch-entirety Self-attention GAN for ADHD Identification

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Pei, Shengbing Jiang, Wencong Cao, Shuai Fan, Cunhang Liang, Wen Lv, Zhao Guan, Jihong Zhang, Chao Anhui University Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Google Company Mountain View94043 United States Civil Aviation Flight University of China Guanghan618307 China Tongji University Department of Computer Science and Technology Shanghai201800 China

Functional connectivity network (FCN) describes the cooperative state of the brain and is often used to identify neurological diseases. However, the performance of FCN-based recognition models is limited by small sample size for the high cost of patient data acquisition and labeling. Generative adversarial networks (GAN) can generate diverse samples to alleviate this problem. Due to the symmetry of FCN matrix, existing GAN-based data augmentation methods mainly adopt fully connected layers to learn flattened FCN data distribution, neglecting the functional collaboration structure within FCN. Here, we propose a novel patch-entirety self-attention GAN (PEGAN) for FCN data augmentation, thereby helping to improve brain disorder identification. PEGAN is a fully convolutional structure, and contains a two-stage attention mechanism to learn the collaboration of brain regions, at the first stage, the FCN is partitioned into multiple patches, each patch is encoded by itself with self-attention, this is to learn local consistency characteristic within a specific brain sub-function;at the second stage, the global self-attention involving all functional connections is performed, this is to learn the global collaborative characteristic of the entire brain. Meanwhile, a symmetry constraint is designed in the optimization objective of GAN to overcome the issue that the convolutional GAN model is not applicable to FCN data. With the sufficient augmentation data, a dual-stream network is trained for classification. Experimental results on ADHD-200 dataset indicates that 1) the proposed identification method with PEGAN demonstrates its superiority by comparing with existing ADHD identification methods;2) the improved optimization objective function effectively solves the problem that FCN data is not suitable for convolution processing;3) the reliability of the generated FCN data with PEGAN is verified from both qualitative and quantitative perspectives;4) the augmented FCN data can

关键词： Neurodegenerative diseases

On the Dichotomy Between Privacy and Traceability in p Stochastic Convex Optimization

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arXiv 2025年

作者： Voitovych, Sasha Haghifam, Mahdi Attias, Idan Dziugaite, Gintare Karolina Livni, Roi Roy, Daniel M. Institute for Data Systems and Society Massachusetts Institute of Technology United States Khoury College of Computer Sciences Northeastern University United States University of Illinois at Chicago Toyota Technological Institute Chicago United States Google DeepMind United Kingdom Department of Electrical Engineering Tel Aviv University Department of Statistical Sciences University of Toronto Vector Institute Canada

In this paper, we investigate the necessity of memorization in stochastic convex optimization (SCO) under p geometries. Informally, we say a learning algorithm memorizes m samples (or is m-traceable) if, by analyzing its output, it is possible to identify at least m of its training samples. Our main results uncover a fundamental tradeoff between traceability and excess risk in SCO. For every p ∈ [1, ∞), we establish the existence of a risk threshold below which any sample-efficient learner must memorize a constant fraction of its sample. For p ∈ [1, 2], this threshold coincides with best risk of differentially private (DP) algorithms, i.e., above this threshold, there are algorithms that do not memorize even a single sample. This establishes a sharp dichotomy between privacy and traceability for p ∈ [1, 2]. For p ∈ (2, ∞), this threshold instead gives novel lower bounds for DP learning, partially closing an open problem in this setup. En route of proving these results, we introduce a complexity notion we term trace value of a problem, which unifies privacy lower bounds and traceability results, and prove a sparse variant of the fingerprinting lemma. © 2025, CC BY.

关键词： Differential privacy

Understanding the Cluster LP for Correlation Clustering

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arXiv 2024年

作者： Cao, Nairen Li, Shi Cohen-Addad, Vincent Lee, Euiwoong Newman, Alantha Vogl, Lukas Boston College United States Nanjing University China Google Research University of Michigan United States Université Grenoble Alpes France EPFL Switzerland Department of Computer Science and Technology Nanjing University China

In the classic Correlation Clustering problem introduced by Bansal, Blum, and Chawla [BBC04], the input is a complete graph where edges are labeled either + or −, and the goal is to find a partition of the vertices that minimizes the sum of the +edges across parts plus the sum of the −edges within parts. In recent years, Chawla, Makarychev, Schramm and Yaroslavtsev [CMSY15] gave a 2.06-approximation by providing a near-optimal rounding of the standard LP, and Cohen-Addad, Lee, Li, and Newman [CLN22, CLLN23] finally bypassed the integrality gap of 2 for this LP giving a 1.73-approximation for the problem. While introducing new ideas for Correlation Clustering, their algorithm is more complicated than typical approximation algorithms in the following two aspects: (1) It is based on two different relaxations with separate rounding algorithms connected by the round-or-cut procedure. (2) Each of the rounding algorithms has to separately handle seemingly inevitable correlated rounding errors, coming from correlated rounding of Sherali-Adams and other strong LP relaxations [GS11, BRS11, RT12]. In order to create a simple and unified framework for Correlation Clustering similar to those for typical approximate optimization tasks, we propose the cluster LP as a strong linear program that might tightly capture the approximability of Correlation Clustering. It unifies all the previous relaxations for the problem. It is exponential-sized, but we show that it can be (1 + Ε)-approximately solved in polynomial time for any Ε > 0, providing the framework for designing rounding algorithms without worrying about correlated rounding errors;these errors are handled uniformly in solving the relaxation. We demonstrate the power of the cluster LP by presenting a simple rounding algorithm, and providing two analyses, one analytically proving a 1.49-approximation and the other solving a factor-revealing SDP to show a 1.437-approximation. Both proofs introduce principled methods by which to

关键词： Approximation algorithms

Hashtag Re-Appropriation for Audience Control on Recommendation-Driven Social Media Xiaohongshu (rednote) 25

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Hashtag Re-Appropriation for Audience Control on Recommendat...

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Wan, Ruyuan Tong, Lingbo Knearem, Tiffany Li, Toby Jia-Jun Huang, Ting-Hao 'Kenneth' Wu, Qunfang College of Information Sciences and Technology The Pennsylvania State University University Park United States University of Notre Dame Notre Dame IN United States Google Cambridge MA United States Department of Computer Science and Engineering University of Notre Dame Notre Dame IN United States Harvard University Cambridge MA United States

ISBN: (纸本)9798400713941

Algorithms have played a central role in personalized recommendations on social media. However, they also present significant obstacles for content creators trying to predict and manage their audience reach. This issue is particularly challenging for marginalized groups seeking to maintain safe spaces. Our study explores how women on Xiaohongshu (rednote), a recommendation-driven social platform, proactively re-appropriate hashtags (e.g., #å®å®è3/4...é£?, Baby Supplemental Food) by using them in posts unrelated to their literal meaning. The hashtags were strategically chosen from topics that would be uninteresting to the male audience they wanted to block. Through a mixed-methods approach, we analyzed the practice of hashtag re-appropriation based on 5,800 collected posts and interviewed 24 active users from diverse backgrounds to uncover users' motivations and reactions towards the re-appropriation. This practice highlights how users can reclaim agency over content distribution on recommendation-driven platforms, offering insights into self-governance within algorithmic-centered power structures. © 2025 Copyright held by the owner/author(s).

关键词： Content Moderation Feminist HCI Hashtag Activism Platform Design Social Media Governance

The Quantum Approximate Optimization Algorithm at High Depth for MaxCut on Large-Girth Regular Graphs and the Sherrington-Kirkpatrick Model 17

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The Quantum Approximate Optimization Algorithm at High Depth...

17th Conference on the Theory of Quantum Computation, Communication and Cryptography, TQC 2022

作者： Basso, Joao Farhi, Edward Marwaha, Kunal Villalonga, Benjamin Zhou, Leo Google Quantum AI VeniceCA United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA United States Department of Computer Science University of Chicago IL United States Walter Burke Institute for Theoretical Physics California Institute of Technology PasadenaCA United States

ISBN: (纸本)9783959772372

The Quantum Approximate Optimization Algorithm (QAOA) finds approximate solutions to combinatorial optimization problems. Its performance monotonically improves with its depth p. We apply the QAOA to MaxCut on large-girth D-regular graphs. We give an iterative formula to evaluate performance for any D at any depth p. Looking at random D-regular graphs, at optimal parameters and as D goes to infinity, we find that the p = 11 QAOA beats all classical algorithms (known to the authors) that are free of unproven conjectures. While the iterative formula for these D-regular graphs is derived by looking at a single tree subgraph, we prove that it also gives the ensemble-averaged performance of the QAOA on the Sherrington-Kirkpatrick (SK) model defined on the complete graph. We also generalize our formula to Max-q-XORSAT on large-girth regular hypergraphs. Our iteration is a compact procedure, but its computational complexity grows as O(p24p). This iteration is more efficient than the previous procedure for analyzing QAOA performance on the SK model, and we are able to numerically go to p = 20. Encouraged by our findings, we make the optimistic conjecture that the QAOA, as p goes to infinity, will achieve the Parisi value. We analyze the performance of the quantum algorithm, but one needs to run it on a quantum computer to produce a string with the guaranteed performance. © Joao Basso, Edward Farhi, Kunal Marwaha, Benjamin Villalonga, and Leo Zhou;licensed under Creative Commons License CC-BY 4.0

关键词： Approximation algorithms

Multi-channel Autobidding with Budget and ROI Constraints

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arXiv 2023年

作者： Deng, Yuan Golrezaei, Negin Jaillet, Patrick Liang, Jason Cheuk Nam Mirrokni, Vahab Google Research Sloan School of Management Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Operations Research Center Massachusetts Institute of Technology United States

In digital online advertising, advertisers procure ad impressions simultaneously on multiple platforms, or so-called channels, such as google Ads, Meta Ads Manager, etc. We study how an advertiser maximizes their total conversion (e.g. ad clicks) while satisfying aggregate return-on-investment (ROI) and budget constraints across all channels. In practice, an advertiser does not have control over, and thus cannot globally optimize, which individual ad auctions she participates in for each channel, and instead authorizes a channel to procure impressions on her behalf: the advertiser can only utilize two levers on each channel, namely setting a per-channel budget and per-channel target ROI. In this work, we first analyze the effectiveness of each of these levers for solving the advertiser’s global multi-channel problem. We show that when an advertiser only optimizes over per-channel ROIs, her total conversion can be arbitrarily worse than what she could have obtained in the global problem. Further, we show that the advertiser can achieve the global optimal conversion when she only optimizes over per-channel budgets. In light of this finding, under a bandit feedback setting that mimics real-world scenarios where advertisers have limited information on ad auctions in each channels and how channels procure ads, we present an efficient learning algorithm that produces per-channel budgets whose resulting conversion approximates that of the global optimal problem. Finally, we conduct numerical studies to demonstrate that our proposed algorithm accurately approximates optimal per-channel budgets in practical setups. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

Quartic quantum speedups for planted inference

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arXiv 2024年

作者： Schmidhuber, Alexander O'Donnell, Ryan Kothari, Robin Babbush, Ryan Google Quantum AI VeniceCA United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA United States Computer Science Department Carnegie Mellon University PittsburghPA United States

We describe a quantum algorithm for the Planted Noisy kXOR problem (also known as sparse Learning Parity with Noise) that achieves a nearly quartic (4th power) speedup over the best known classical algorithm while also only using logarithmically many qubits. Our work generalizes and simplifies prior work of Hastings [Has20], by building on his quantum algorithm for the Tensor Principal Component Analysis (PCA) problem. We achieve our quantum speedup using a general framework based on the Kikuchi Method (recovering the quartic speedup for Tensor PCA), and we anticipate it will yield similar speedups for further planted inference problems. These speedups rely on the fact that planted inference problems naturally instantiate the Guided Sparse Hamiltonian problem. Since the Planted Noisy kXOR problem has been used as a component of certain cryptographic constructions, our work suggests that some of these are susceptible to super-quadratic quantum attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

Neural Wave Functions for Superfluids

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Physical Review X 2024年第2期14卷 021030-021030页

作者： Wan Tong Lou Halvard Sutterud Gino Cassella W. M. C. Foulkes Johannes Knolle David Pfau James S. Spencer Department of Physics Imperial College London South Kensington Campus London SW7 2AZ United Kingdom Department of Physics TQM Technische Universität München James-Franck-Straße 1 D-85748 Garching Germany Munich Center for Quantum Science and Technology (MCQST) 80799 Munich Germany Google DeepMind 6 Pancras Square London N1C 4AG United Kingdom

Understanding superfluidity remains a major goal of condensed matter physics. Here, we tackle this challenge utilizing the recently developed fermionic neural network (FermiNet) wave function Ansatz [D. Pfau et al., Phys. Rev. Res. 2, 033429 (2020).] for variational Monte Carlo calculations. We study the unitary Fermi gas, a system with strong, short-range, two-body interactions known to possess a superfluid ground state but difficult to describe quantitatively. We demonstrate key limitations of the FermiNet Ansatz in studying the unitary Fermi gas and propose a simple modification based on the idea of an antisymmetric geminal power singlet (AGPs) wave function. The new AGPs FermiNet outperforms the original FermiNet significantly in paired systems, giving results which are more accurate than fixed-node diffusion Monte Carlo and are consistent with experiment. We prove mathematically that the new Ansatz, which differs from the original Ansatz only by the method of antisymmetrization, is a strict generalization of the original FermiNet architecture, despite the use of fewer parameters. Our approach shares several advantages with the original FermiNet: The use of a neural network removes the need for an underlying basis set; sand the flexibility of the network yields extremely accurate results within a variational quantum Monte Carlo framework that provides access to unbiased estimates of arbitrary ground-state expectation values. We discuss how the method can be extended to study other superfluid.

关键词： Fermi gases Fermions Superfluids Artificial neural networks