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

作者： Mynatt, Elizabeth D. Kobayashi, Masatomo Pradhan, Alisha Mathur, Niharika Vines, John Seaborn, Katie Buehler, Erin Waycott, Jenny Rudnik, John Zubatiy, Tamara Rozga, Agata Khoury College of Computer Sciences Northeastern University Boston MA United States IBM Research Tokyo Japan Department of Informatics Ying Wu College of Computing New Jersey Institute of Technology Newark NJ United States School of Interactive Computing Georgia Institute of Technology Atlanta GA United States University of Edinburgh Edinburgh United Kingdom Institute of Science Tokyo Japan Google Mountain View CA United States School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Information University of Michigan Ann Arbor MI United States Northeastern University Boston MA United States

ISBN: (纸本)9798400713958

The caregiving environment for an older adult aging in place includes a network of caregivers working with the older adult to support their needs and maintain independence. As older adults experience cognitive and functional changes, their caregiving network expands to include spouses or siblings (who are often older adults themselves), children, friends, neighbors and community members—each bringing unique values, expectations, and goals. In this network of care, technology-enabled support offers the potential to mediate care responsibilities, such as coordinating activities and assisting with everyday tasks. However, designing these systems requires addressing value tensions among caregivers, cultural norms around aging, participatory research practices and balancing autonomy with safety concerns for older adults in later life. This workshop brings together researchers and practitioners to discuss (1) opportunities and challenges for designing technological systems for caregiving for older adults;(2) longitudinal interactions with these systems as older adults progress through stages of functional and cognitive changes;(3) potential for such systems to support caregivers while centering older adults’ privacy and autonomy needs;and (4) the influence of cultural norms on caregiving and technology use. © 2025 Copyright held by the owner/author(s).

关键词： Assistive technology Caregiving Older Adults

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StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands

arXiv

引用

arXiv 2021年

作者： Cho, Won Ik Moon, Sangwhan Kim, Jong In Kim, Seok Min Kim, Nam Soo Department of Electrical and Computer Engineering INMC Seoul National University Korea Republic of Department of Computer Science Tokyo Institute of Technology Japan Google LLC Interdisciplinary Program in Cognitive Science Seoul National University Korea Republic of

Paraphrasing is often performed with less concern for controlled style conversion. Especially for questions and commands, style-variant paraphrasing can be crucial in tone and manner, which also matters with industrial applications such as dialog systems. In this paper, we attack this issue with a corpus construction scheme that simultaneously considers the core content and style of directives, namely intent and formality, for the Korean language. Utilizing manually generated natural language queries on six daily topics, we expand the corpus to formal and informal sentences by human rewriting and transferring. We verify the validity and industrial applicability of our approach by checking the adequate classification and inference performance that fit with conventional fine-tuning approaches, at the same time proposing a supervised formality transfer task. © 2021, CC BY-SA.

关键词： Natural language processing systems

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Curiosity killed or incapacitated the cat and the asymptotically optimal agent

arXiv

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arXiv 2020年

作者： Cohen, Michael K. Catt, Elliot Hutter, Marcus Oxford University Department of Engineering Science United Kingdom Australian National University Research School of Computer Science Australia Australian National University Department of Computer Science Australia Google DeepMind

Reinforcement learners are agents that learn to pick actions that lead to high reward. Ideally, the value of a reinforcement learner’s policy approaches optimality—where the optimal informed policy is the one which maximizes reward. Unfortunately, we show that if an agent is guaranteed to be "asymptotically optimal" in any (stochastically computable) environment, then subject to an assumption about the true environment, this agent will be either "destroyed" or "incapacitated" with probability 1. Much work in reinforcement learning uses an ergodicity assumption to avoid this problem. Often, doing theoretical research under simplifying assumptions prepares us to provide practical solutions even in the absence of those assumptions, but the ergodicity assumption in reinforcement learning may have led us entirely astray in preparing safe and effective exploration strategies for agents in dangerous environments. Rather than assuming away the problem, we present an agent, Mentee, with the modest guarantee of approaching the performance of a mentor, doing safe exploration instead of reckless exploration. Critically, Mentee’s exploration probability depends on the expected information gain from exploring. In a simple non-ergodic environment with a weak mentor, we find Mentee outperforms existing asymptotically optimal agents and its mentor. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

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System of Agentic AI for the Discovery of Metal-Organic Frameworks

arXiv

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arXiv 2025年

作者： Inizan, Théo Jaffrelot Yang, Sherry Kaplan, Aaron Lin, Yen-Hsu Yin, Jian Mirzaei, Saber Abdelgaid, Mona Alawadhi, Ali H. Cho, KwangHwan Zheng, Zhiling Cubuk, Ekin Dogus Borgs, Christian Chayes, Jennifer T. Persson, Kristin A. Yaghi, Omar M. Department of Chemistry University of California BerkeleyCA United States Bakar Institute of Digital Materials for the Planet University of California BerkeleyCA United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Google DeepMind Mountain ViewCA United States Kavli Energy NanoScience Institute BerkeleyCA United States Department of Mathematics University of California BerkeleyCA United States Department of Statistics University of California BerkeleyCA United States School of Information University of California BerkeleyCA United States Department of Materials Science and Engineering University of California BerkeleyCA United States

Generative models and machine learning promise accelerated material discovery in MOFs for CO2 capture and water harvesting but face significant challenges navigating vast chemical spaces while ensuring synthetizability. Here, we present MOFGen, a system of Agentic AI comprising interconnected agents: a large language model that proposes novel MOF compositions, a diffusion model that generates crystal structures, quantum mechanical agents that optimize and filter candidates, and synthetic-feasibility agents guided by expert rules and machine learning. Trained on all experimentally reported MOFs and computational databases, MOFGen generated hundreds of thousands of novel MOF structures and synthesizable organic linkers. Our methodology was validated through high-throughput experiments and the successful synthesis of five"AI-dreamt" MOFs, representing a major step toward automated synthesizable material discovery. © 2025, CC BY.

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Visualizing dynamics of charges and strings in (2 + 1)D lattice gauge theories

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Nature 2025年第8067期642卷 315-320页

作者： T A Cochran B Jobst E Rosenberg Y D Lensky G Gyawali N Eassa M Will A Szasz D Abanin R Acharya L Aghababaie Beni T I Andersen M Ansmann F Arute K Arya A Asfaw J Atalaya R Babbush B Ballard J C Bardin A Bengtsson A Bilmes A Bourassa J Bovaird M Broughton D A Browne B Buchea B B Buckley T Burger B Burkett N Bushnell A Cabrera J Campero H-S Chang Z Chen B Chiaro J Claes A Y Cleland J Cogan R Collins P Conner W Courtney A L Crook B Curtin S Das S Demura L De Lorenzo A Di Paolo P Donohoe I Drozdov A Dunsworth A Eickbusch A Moshe Elbag M Elzouka C Erickson V S Ferreira L Flores Burgos E Forati A G Fowler B Foxen S Ganjam R Gasca É Genois W Giang D Gilboa R Gosula A Grajales Dau D Graumann A Greene J A Gross S Habegger M Hansen M P Harrigan S D Harrington P Heu O Higgott J Hilton H-Y Huang A Huff W Huggins E Jeffrey Z Jiang C Jones C Joshi P Juhas D Kafri H Kang A H Karamlou K Kechedzhi T Khaire T Khattar M Khezri S Kim P Klimov B Kobrin A Korotkov F Kostritsa J Kreikebaum V Kurilovich D Landhuis T Lange-Dei B Langley K-M Lau J Ledford K Lee B Lester L Le Guevel W Li A T Lill W Livingston A Locharla D Lundahl A Lunt S Madhuk A Maloney S Mandrà L Martin O Martin C Maxfield J McClean M McEwen S Meeks A Megrant K Miao R Molavi S Molina S Montazeri R Movassagh C Neill M Newman A Nguyen M Nguyen C-H Ni K Ottosson A Pizzuto R Potter O Pritchard C Quintana G Ramachandran M Reagor D Rhodes G Roberts K Sankaragomathi K Satzinger H Schurkus M Shearn A Shorter N Shutty V Shvarts V Sivak S Small W C Smith S Springer G Sterling J Suchard A Sztein D Thor M Torunbalci A Vaishnav J Vargas S Vdovichev G Vidal C Vollgraff Heidweiller S Waltman S X Wang B Ware T White K Wong B W K Woo C Xing Z Jamie Yao P Yeh B Ying J Yoo N Yosri G Young A Zalcman Y Zhang N Zhu N Zobrist S Boixo J Kelly E Lucero Y Chen V Smelyanskiy H Neven A Gammon-Smith F Pollmann M Knap P Roushan Google Research Mountain View CA USA. Department of Physics Princeton University Princeton NJ USA. Department of Physics TUM School of Natural Sciences Technical University of Munich Garching Germany. Munich Center for Quantum Science and Technology (MCQST) Munich Germany. Department of Physics Cornell University Ithaca NY USA. Laboratory of Atomic and Solid State Physics Cornell University Ithaca NY USA. Department of Physics and Astronomy Purdue University West Lafayette IN USA. Department of Electrical and Computer Engineering University of Massachusetts Amherst MA USA. Department of Physics University of Connecticut Storrs CT USA. Department of Electrical and Computer Engineering University of California Riverside Riverside CA USA. School of Physics and Astronomy University of Nottingham Nottingham UK. Centre for the Mathematics and Theoretical Physics of Quantum Non-Equilibrium Systems (CQNE) University of Nottingham Nottingham UK. Department of Physics TUM School of Natural Sciences Technical University of Munich Garching Germany. frank.pollmann@tum.de. Munich Center for Quantum Science and Technology (MCQST) Munich Germany. frank.pollmann@tum.de. Department of Physics TUM School of Natural Sciences Technical University of Munich Garching Germany. michael.knap@ph.tum.de. Munich Center for Quantum Science and Technology (MCQST) Munich Germany. michael.knap@ph.tum.de. Google Research Mountain View CA USA. pedramr@***.

Lattice gauge theories (LGTs) can be used to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging, as it requires solving many-body problems that are generally beyond perturbative limits. Here we investigate the dynamics of local excitations in a LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit that prepares low-energy states that have a large overlap with the ground state; then we create charge excitations with local gates and simulate their quantum dynamics by means of a discretized time evolution. As the electric field coupling constant is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the electric field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT, from which we uncover two distinct regimes inside the confining phase: for weak confinement, the string fluctuates strongly in the transverse direction, whereas for strong confinement, transverse fluctuations are effectively frozen. We also demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a new set of techniques for investigating emergent excitations and string dynamics.

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Reflections from the Workshop on AI-Assisted Decision Making for Conservation

arXiv

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arXiv 2023年

作者： Xu, Lily Rolf, Esther Beery, Sara Bennett, Joseph R. Berger-Wolf, Tanya Birch, Tanya Bondi-Kelly, Elizabeth Brashares, Justin Chapman, Melissa Corso, Anthony Davies, Andrew Garg, Nikhil Gaylard, Angela Heilmayr, Robert Kerner, Hannah Klemmer, Konstantin Kumar, Vipin MacKey, Lester Monteleoni, Claire Moorcroft, Paul Palmer, Jonathan Perrault, Andrew Thau, David Tambe, Milind School of Engineering and Applied Sciences Harvard University United States Harvard University United States United States Department of Computer Science University of Colorado Boulder United States Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Biology Carleton University Canada Department of Computer Science and Engineering The Ohio State University United States Department of Electrical and Computer Engineering The Ohio State University United States Department of Evolution Ecology and Organismal Biology The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Google United States Electrical Engineering and Computer Science University of Michigan United States Department of Environmental Science Policy and Management University of California Berkeley United States Department of Aeronautics and Astronautics Stanford University United States Department of Organismic and Evolutionary Biology Harvard University United States Operations Research and Information Engineering Cornell Tech United States African Parks South Africa Bren School of Environmental Science and Management University of California Santa Barbara United States School of Computing and Augmented Intelligence Arizona State University United States Microsoft Research United States Department of Computer Science and Engineering University of Minnesota United States Inria Paris France United States Wwf Global Science United States

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Conservation

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MambaLRP: explaining selective state space sequence models 24

MambaLRP: explaining selective state space sequence models

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Farnoush Rezaei Jafari Grégoire Montavon Klaus-Robert Müller Oliver Eberle Machine Learning Group Technische Universität Berlin Berlin Germany and BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Mathematics and Computer Science Freie Universitat Berlin Berlin Germany and BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin Germany and Machine Learning Group Technische Universität Berlin Berlin Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin Germany and Machine Learning Group Technische Universität Berlin Berlin Germany and Department of Artificial Intelligence Korea University Seoul South Korea and Max Planck Institute for Informatics Saarbrücken Germany and Google DeepMind Berlin Germany

ISBN: (纸本)9798331314385

Recent sequence modeling approaches using selective state space sequence models, referred to as Mamba models, have seen a surge of interest. These models allow efficient processing of long sequences in linear time and are rapidly being adopted in a wide range of applications such as language modeling, demonstrating promising performance. To foster their reliable use in real-world scenarios, it is crucial to augment their transparency. Our work bridges this critical gap by bringing explainability, particularly Layer-wise Relevance Propagation (LRP), to the Mamba architecture. Guided by the axiom of relevance conservation, we identify specific components in the Mamba architecture, which cause unfaithful explanations. To remedy this issue, we propose MambaLRP, a novel algorithm within the LRP framework, which ensures a more stable and reliable relevance propagation through these components. Our proposed method is theoretically sound and excels in achieving state-of-the-art explanation performance across a diverse range of models and datasets. Moreover, MambaLRP facilitates a deeper inspection of Mamba architectures, uncovering various biases and evaluating their significance. It also enables the analysis of previous speculations regarding the long-range capabilities of Mamba models.

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Accurate Computation of Quantum Excited States with Neural Networks

arXiv

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arXiv 2023年

作者： Pfau, David Axelrod, Simon Sutterud, Halvard von Glehn, Ingrid Spencer, James S. Google DeepMind S2 8 Handyside Street LondonN1C 4DJ United Kingdom Department of Physics Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom Department of Chemistry and Chemical Biology Harvard University 12 Oxford St. CambridgeMA01238 United States Department of Materials Science and Engineering Massachusetts Institute of Technology 182 Memorial Dr. CambridgeMA01239 United States

We present a variational Monte Carlo algorithm for estimating the lowest excited states of a quantum system which is a natural generalization of the estimation of ground states. The method has no free parameters and requires no explicit orthogonalization of the different states, instead transforming the problem of finding excited states of a given system into that of finding the ground state of an expanded system. Expected values of arbitrary observables can be calculated, including off-diagonal expectations between different states such as the transition dipole moment. Although the method is entirely general, it works particularly well in conjunction with recent work on using neural networks as variational Ansätze for many-electron systems, and we show that by combining this method with the FermiNet and Psiformer Ansätze we can accurately recover vertical excitation energies and oscillator strengths on a range of molecules. Our method is the first deep learning approach to achieve accurate vertical excitation energies, including challenging double excitations, on benzene-scale molecules. Beyond the chemistry examples here, we expect this technique will be of great interest for applications to atomic, nuclear and condensed matter physics. © 2023, CC BY.

关键词： Excitation energy

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Assessing the use cases of persistent memory in high-performance scientific computing

arXiv

引用

arXiv 2021年

作者： Fridman, Yehonatan Snir, Yaniv Rusanovsky, Matan Zvi, Kfir Levin, Harel Hendler, Danny Attiya, Hagit Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Israel Israel Atomic Energy Commission Israel Google Scientific Computing Center Nuclear Research Center – Negev Israel Department of Computer Science Technion – Israel Institute of Technology Israel

As the High Performance Computing (HPC) world moves towards the Exa-Scale era, huge amounts of data should be analyzed, manipulated and stored. In the traditional storage/memory hierarchy, each compute node retains its data objects in its local volatile DRAM. Whenever the DRAM’s capacity becomes insufficient for storing this data, the computation should either be distributed between several compute nodes, or some portion of these data objects must be stored in a non-volatile block device such as a hard disk drive (HDD) or an SSD storage device. These standard block devices offer large and relatively cheap non-volatile storage, but their access times are orders-of-magnitude slower than those of DRAM. Optane™ DataCenter Persistent Memory Module (DCPMM) [1], a new technology introduced by Intel, provides non-volatile memory that can be plugged into standard memory bus slots (DDR DIMMs) and therefore be accessed much faster than standard storage devices. In this work, we present and analyze the results of a comprehensive performance assessment of several ways in which DCPMM can 1) replace standard storage devices, and 2) replace or augment DRAM for improving the performance of HPC scientific computations. To achieve this goal, we have configured an HPC system such that DCPMM can service I/O operations of scientific applications, replace standard storage devices and file systems (specifically for diagnostics and checkpoint-restarting), and serve for expanding applications’ main memory. We focus on keeping the scientific codes with as few changes as possible, while allowing them to access the NVM transparently as if they access persistent storage. Our results show that DCPMM allows scientific applications to fully utilize nodes’ locality by providing them with sufficiently-large main memory. Moreover, it can also be used for providing a high-performance replacement for persistent storage. Thus, the usage of DCPMM has the potential of replacing standard HDD and SSD storage

关键词： Dynamic random access storage

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Decoupling semantic similarity from spatial alignment for neural networks 24

Decoupling semantic similarity from spatial alignment for ne...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Tassilo Wald Constantin Ulrich Gregor Köhler David Zimmerer Stefan Denner Michael Baumgartner Fabian Isensee Priyank Jaini Klaus H. Maier-Hein Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Medical Faculty Heidelberg University of Heidelberg Germany and National Center for Tumor Diseases (NCT) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany Google Deepmind Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany and Medical Faculty Heidelberg University of Heidelberg Germany and Pattern Analysis and Learning Group Department of Radiation Oncology

ISBN: (纸本)9798331314385

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to their internal high dimensionality and complexity. To address this, one approach is to measure the similarity of activation responses to various inputs. Representational Similarity Matrices (RSMs) distill this similarity into scalar values for each input pair. These matrices encapsulate the entire similarity structure of a system, indicating which input leads to similar responses. While the similarity between images is ambiguous, we argue that the spatial location of semantic objects does neither influence human perception nor deep learning classifiers. Thus this should be reflected in the definition of similarity between image responses for computer vision systems. Revisiting the established similarity calculations for RSMs we expose their sensitivity to spatial alignment. In this paper, we propose to solve this through semantic RSMs, which are invariant to spatial permutation. We measure semantic similarity between input responses by formulating it as a set-matching problem. Further, we quantify the superiority of semantic RSMs over spatio-semantic RSMs through image retrieval and by comparing the similarity between representations to the similarity between predicted class probabilities.

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