950,118 results on '"Neil, A."'
Search Results
2. 'Magnetic Reading': Effects on Reading Achievement for Grades 3-5. Final Report
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NORC at the University of Chicago and Neil Seftor
- Abstract
"Magnetic Reading" is an evidence-based reading comprehension program for students in grades 3 through 5 built on four pedagogical pillars: knowledge-rich learning, culturally and linguistically responsive pedagogy, scaffolds to support learner variability, and data to inform instruction. This study uses a prospective matched comparison group quasi-experimental design to examine the impact of "Magnetic Reading." At the beginning of the 2022-23 school year, we used coarsened exact matching to match students in schools that used "Magnetic Reading" to students in schools that did not using student demographic characteristics and a fall achievement test score. We subsequently conducted an analysis of spring state achievement test scores on the post-attrition sample after establishing that the groups were similar on baseline characteristics. Our HLM analysis found a statistically significant impact of 8.4 points--an effect size of 0.22 standard deviations--on reading scores. [This report was produced by NORC Education and presented to Curriculum Associates.]
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- 2024
3. The Karp Dataset
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DiCicco, Mason, Worden, Eamon, Olsen, Conner, Gangaram, Nikhil, Reichman, Daniel, and Heffernan, Neil
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Computer Science - Machine Learning ,Computer Science - Computation and Language - Abstract
Understanding the mathematical reasoning capabilities of Large Language Models (LLMs) is a central topic in the study of artificial intelligence. This new domain necessitates the creation of datasets of reasoning tasks for both training and benchmarking the performance of LLMs. To this end, we introduce the Karp dataset: The first dataset composed of detailed proofs of NP-completeness reductions. The reductions vary in difficulty, ranging from simple exercises of undergraduate courses to more challenging reductions from academic papers. We compare the performance of state-of-the-art models on this task and demonstrate the effect of fine-tuning with the Karp dataset on reasoning capacity., Comment: Accepted to the 4th workshop on mathematical reasoning and AI at NeurIPS 2024
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- 2025
4. GraphRAG under Fire
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Liang, Jiacheng, Wang, Yuhui, Li, Changjiang, Zhu, Rongyi, Jiang, Tanqiu, Gong, Neil, and Wang, Ting
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Cryptography and Security - Abstract
GraphRAG advances retrieval-augmented generation (RAG) by structuring external knowledge as multi-scale knowledge graphs, enabling language models to integrate both broad context and granular details in their reasoning. While GraphRAG has demonstrated success across domains, its security implications remain largely unexplored. To bridge this gap, this work examines GraphRAG's vulnerability to poisoning attacks, uncovering an intriguing security paradox: compared to conventional RAG, GraphRAG's graph-based indexing and retrieval enhance resilience against simple poisoning attacks; meanwhile, the same features also create new attack surfaces. We present GRAGPoison, a novel attack that exploits shared relations in the knowledge graph to craft poisoning text capable of compromising multiple queries simultaneously. GRAGPoison employs three key strategies: i) relation injection to introduce false knowledge, ii) relation enhancement to amplify poisoning influence, and iii) narrative generation to embed malicious content within coherent text. Empirical evaluation across diverse datasets and models shows that GRAGPoison substantially outperforms existing attacks in terms of effectiveness (up to 98% success rate) and scalability (using less than 68% poisoning text). We also explore potential defensive measures and their limitations, identifying promising directions for future research., Comment: 13 pages
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- 2025
5. Is fixed-node diffusion quantum Monte Carlo reproducible?
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Della Pia, Flaviano, Shi, Benjamin, Al-Hamdani, Yasmine S., Alfè, Dario, Anderson, Tyler, Barborini, Matteo, Benali, Anouar, Casula, Michele, Drummond, Neil, Dubecký, Matúš, Filippi, Claudia, Kent, Paul, Krogel, Jaron, Rios, Pablo Lopez, Lüchow, Arne, Luo, Ye, Michaelides, Angelos, Mitas, Lubos, Nakano, Kosuke, Needs, Richard, Per, Manolo, Scemama, Anthony, Schultze, Jil, Shinde, Ravindra, Slootman, Emiel, Sorella, Sandro, Tkatchenko, Alexandre, Towler, Mike, Umrigar, Cyrus, Wagner, Lucas, Wheeler, William Ashwin, Zhou, Haihan, and Zen, Andrea
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Physics - Computational Physics ,Condensed Matter - Materials Science ,Physics - Chemical Physics - Abstract
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schr\"{o}dinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power makes FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method's inherent stochastic nature. This study represents a community-wide effort to address the titular question, affirming that: Yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move (TM), the determinant locality approximation (DLA), or the determinant T-move (DTM) schemes, while the older locality approximation (LA) leads to considerable variability in results. This work lays the foundation to establish accurate and reproducible FN-DMC estimates for all future studies across applications in materials science, physics, chemistry, and biology.
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- 2025
6. Additive subgroups of a module that are saturated with respect to a fixed subset of the ring
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Elliott, Jesse and Epstein, Neil
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Mathematics - Rings and Algebras ,Mathematics - Commutative Algebra - Abstract
Let $T$ be a subset of a ring $A$, and let $M$ be an $A$-module. We study the additive subgroups $F$ of $M$ such that, for all $x \in M$, if $tx \in F$ for some $t \in T$, then $x \in F$. We call any such subset $F$ of $M$ a $T$-factroid of $M$, which is a kind of dual to the notion of a $T$-submodule of $M$. We connect the notion with the zero-divisors on $M$, various classes of primary and prime ideals of $A$, Euclidean domains, and the recent concepts of unit-additive commutative rings and of Egyptian fractions with respect to a multiplicative subset of a commutative ring. We also introduce a common generalization of local rings and unit-additive rings, called *sublocalizing* rings, and relate them to $T$-factroids., Comment: Removed color from erroneously colored text. Removed a nonequivalent condition from 8.13. 42 pages; comments very welcome!
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- 2025
7. GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models
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Lou, Jiadong, Yuan, Xu, Zhang, Rui, Yuan, Xingliang, Gong, Neil, and Tzeng, Nian-Feng
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Computer Science - Machine Learning ,Computer Science - Cryptography and Security - Abstract
Graph neural networks (GNNs) have exhibited superior performance in various classification tasks on graph-structured data. However, they encounter the potential vulnerability from the link stealing attacks, which can infer the presence of a link between two nodes via measuring the similarity of its incident nodes' prediction vectors produced by a GNN model. Such attacks pose severe security and privacy threats to the training graph used in GNN models. In this work, we propose a novel solution, called Graph Link Disguise (GRID), to defend against link stealing attacks with the formal guarantee of GNN model utility for retaining prediction accuracy. The key idea of GRID is to add carefully crafted noises to the nodes' prediction vectors for disguising adjacent nodes as n-hop indirect neighboring nodes. We take into account the graph topology and select only a subset of nodes (called core nodes) covering all links for adding noises, which can avert the noises offset and have the further advantages of reducing both the distortion loss and the computation cost. Our crafted noises can ensure 1) the noisy prediction vectors of any two adjacent nodes have their similarity level like that of two non-adjacent nodes and 2) the model prediction is unchanged to ensure zero utility loss. Extensive experiments on five datasets are conducted to show the effectiveness of our proposed GRID solution against different representative link-stealing attacks under transductive settings and inductive settings respectively, as well as two influence-based attacks. Meanwhile, it achieves a much better privacy-utility trade-off than existing methods when extended to GNNs.
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- 2025
8. The putative center in NGC 1052
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Baczko, Anne-Kathrin, Kadler, Matthias, Ros, Eduardo, Fromm, Christian M., Wielgus, Maciek, Perucho, Manel, Krichbaum, Thomas P., Baloković, Mislav, Blackburn, Lindy, Chan, Chi-kwan, Issaoun, Sara, Janssen, Michael, Ricci, Luca, Akiyama, Kazunori, Albentosa-Ruíz, Ezequiel, Alberdi, Antxon, Alef, Walter, Algaba, Juan Carlos, Anantua, Richard, Asada, Keiichi, Azulay, Rebecca, Bach, Uwe, Ball, David, Bandyopadhyay, Bidisha, Barrett, John, Bauböck, Michi, Benson, Bradford A., Bintley, Dan, Blundell, Raymond, Bouman, Katherine L., Bower, Geoffrey C., Boyce, Hope, Bremer, Michael, Brinkerink, Christiaan D., Brissenden, Roger, Britzen, Silke, Broderick, Avery E., Broguiere, Dominique, Bronzwaer, Thomas, Bustamante, Sandra, Byun, Do-Young, Carlstrom, John E., Ceccobello, Chiara, Chael, Andrew, Chang, Dominic O., Chatterjee, Koushik, Chatterjee, Shami, Chen, Ming-Tang, Chen, Yongjun, Cheng, Xiaopeng, Cho, Ilje, Christian, Pierre, Conroy, Nicholas S., Conway, John E., Cordes, James M., Crawford, Thomas M., Crew, Geoffrey B., Cruz-Osorio, Alejandro, Cui, Yuzhu, Dahale, Rohan, Davelaar, Jordy, De Laurentis, Mariafelicia, Deane, Roger, Dempsey, Jessica, Desvignes, Gregory, Dexter, Jason, Dhruv, Vedant, Dihingia, Indu K., Doeleman, Sheperd S., Dougall, Sean Taylor, Dzib, Sergio A., Eatough, Ralph P., Emami, Razieh, Falcke, Heino, Farah, Joseph, Fish, Vincent L., Fomalont, Edward, Ford, H. Alyson, Foschi, Marianna, Fraga-Encinas, Raquel, Freeman, William T., Friberg, Per, Fuentes, Antonio, Galison, Peter, Gammie, Charles F., García, Roberto, Gentaz, Olivier, Georgiev, Boris, Goddi, Ciriaco, Gold, Roman, Gómez-Ruiz, Arturo I., Gómez, José L., Gu, Minfeng, Gurwell, Mark, Hada, Kazuhiro, Haggard, Daryl, Haworth, Kari, Hecht, Michael H., Hesper, Ronald, Heumann, Dirk, Ho, Luis C., Ho, Paul, Honma, Mareki, Huang, Chih-Wei L., Huang, Lei, Hughes, David H., Impellizzeri, C. M. Violette, Inoue, Makoto, James, David J., Jannuzi, Buell T., Jeter, Britton, Jiang, Wu, Jiménez-Rosales, Alejandra, Johnson, Michael D., Jorstad, Svetlana, Joshi, Abhishek V., Jung, Taehyun, Karami, Mansour, Karuppusamy, Ramesh, Kawashima, Tomohisa, Keating, Garrett K., Kettenis, Mark, Kim, Dong-Jin, Kim, Jae-Young, Kim, Jongsoo, Kim, Junhan, Kino, Motoki, Koay, Jun Yi, Kocherlakota, Prashant, Kofuji, Yutaro, Koyama, Shoko, Kramer, Carsten, Kramer, Joana A., Kramer, Michael, Kuo, Cheng-Yu, La Bella, Noemi, Lauer, Tod R., Lee, Daeyoung, Lee, Sang-Sung, Leung, Po Kin, Levis, Aviad, Li, Zhiyuan, Lico, Rocco, Lindahl, Greg, Lindqvist, Michael, Lisakov, Mikhail, Liu, Jun, Liu, Kuo, Liuzzo, Elisabetta, Lo, Wen-Ping, Lobanov, Andrei P., Loinard, Laurent, Lonsdale, Colin J., Lowitz, Amy E., Lu, Ru-Sen, MacDonald, Nicholas R., Mao, Jirong, Marchili, Nicola, Markoff, Sera, Marrone, Daniel P., Marscher, Alan P., Martí-Vidal, Iván, Matsushita, Satoki, Matthews, Lynn D., Medeiros, Lia, Menten, Karl M., Michalik, Daniel, Mizuno, Izumi, Mizuno, Yosuke, Moran, James M., Moriyama, Kotaro, Moscibrodzka, Monika, Mulaudzi, Wanga, Müller, Cornelia, Müller, Hendrik, Mus, Alejandro, Musoke, Gibwa, Myserlis, Ioannis, Nadolski, Andrew, Nagai, Hiroshi, Nagar, Neil M., Nair, Dhanya G., Nakamura, Masanori, Narayanan, Gopal, Natarajan, Iniyan, Nathanail, Antonios, Fuentes, Santiago Navarro, Neilsen, Joey, Neri, Roberto, Ni, Chunchong, Noutsos, Aristeidis, Nowak, Michael A., Oh, Junghwan, Okino, Hiroki, Sánchez, Héctor Raúl Olivares, Ortiz-León, Gisela N., Oyama, Tomoaki, Özel, Feryal, Palumbo, Daniel C. M., Paraschos, Georgios Filippos, Park, Jongho, Parsons, Harriet, Patel, Nimesh, Pen, Ue-Li, Pesce, Dominic W., Piétu, Vincent, Plambeck, Richard, PopStefanija, Aleksandar, Porth, Oliver, Pötzl, Felix M., Prather, Ben, Preciado-López, Jorge A., Principe, Giacomo, Psaltis, Dimitrios, Pu, Hung-Yi, Ramakrishnan, Venkatessh, Rao, Ramprasad, Rawlings, Mark G., Raymond, Alexander W., Ricarte, Angelo, Ripperda, Bart, Roelofs, Freek, Rogers, Alan, Romero-Cañizales, Cristina, Roshanineshat, Arash, Rottmann, Helge, Roy, Alan L., Ruiz, Ignacio, Ruszczyk, Chet, Rygl, Kazi L. J., Sánchez, Salvador, Sánchez-Argüelles, David, Sánchez-Portal, Miguel, Sasada, Mahito, Satapathy, Kaushik, Savolainen, Tuomas, Schloerb, F. Peter, Schonfeld, Jonathan, Schuster, Karl-Friedrich, Shao, Lijing, Shen, Zhiqiang, Small, Des, Sohn, Bong Won, SooHoo, Jason, Salas, León David Sosapanta, Souccar, Kamal, Stanway, Joshua S., Sun, He, Tazaki, Fumie, Tetarenko, Alexandra J., Tiede, Paul, Tilanus, Remo P. J., Titus, Michael, Torne, Pablo, Toscano, Teresa, Traianou, Efthalia, Trent, Tyler, Trippe, Sascha, Turk, Matthew, van Bemmel, Ilse, van Langevelde, Huib Jan, van Rossum, Daniel R., Vos, Jesse, Wagner, Jan, Ward-Thompson, Derek, Wardle, John, Washington, Jasmin E., Weintroub, Jonathan, Wharton, Robert, Wiik, Kaj, Witzel, Gunther, Wondrak, Michael F., Wong, George N., Wu, Qingwen, Yadlapalli, Nitika, Yamaguchi, Paul, Yfantis, Aristomenis, Yoon, Doosoo, Young, André, Young, Ken, Younsi, Ziri, Yu, Wei, Yuan, Feng, Yuan, Ye-Fei, Zensus, J. Anton, Zhang, Shuo, and Zhao, Guang-Yao
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Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Astrophysics of Galaxies - Abstract
Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A
- Published
- 2025
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- View/download PDF
9. Static Segmentation by Tracking: A Frustratingly Label-Efficient Approach to Fine-Grained Segmentation
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Feng, Zhenyang, Wang, Zihe, Bueno, Saul Ibaven, Frelek, Tomasz, Ramesh, Advikaa, Bai, Jingyan, Wang, Lemeng, Huang, Zanming, Gu, Jianyang, Yoo, Jinsu, Pan, Tai-Yu, Chowdhury, Arpita, Ramirez, Michelle, Campolongo, Elizabeth G., Thompson, Matthew J., Lawrence, Christopher G., Record, Sydne, Rosser, Neil, Karpatne, Anuj, Rubenstein, Daniel, Lapp, Hilmar, Stewart, Charles V., Berger-Wolf, Tanya, Su, Yu, and Chao, Wei-Lun
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
We study image segmentation in the biological domain, particularly trait and part segmentation from specimen images (e.g., butterfly wing stripes or beetle body parts). This is a crucial, fine-grained task that aids in understanding the biology of organisms. The conventional approach involves hand-labeling masks, often for hundreds of images per species, and training a segmentation model to generalize these labels to other images, which can be exceedingly laborious. We present a label-efficient method named Static Segmentation by Tracking (SST). SST is built upon the insight: while specimens of the same species have inherent variations, the traits and parts we aim to segment show up consistently. This motivates us to concatenate specimen images into a ``pseudo-video'' and reframe trait and part segmentation as a tracking problem. Concretely, SST generates masks for unlabeled images by propagating annotated or predicted masks from the ``pseudo-preceding'' images. Powered by Segment Anything Model 2 (SAM~2) initially developed for video segmentation, we show that SST can achieve high-quality trait and part segmentation with merely one labeled image per species -- a breakthrough for analyzing specimen images. We further develop a cycle-consistent loss to fine-tune the model, again using one labeled image. Additionally, we highlight the broader potential of SST, including one-shot instance segmentation on images taken in the wild and trait-based image retrieval.
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- 2025
10. CuRLA: Curriculum Learning Based Deep Reinforcement Learning for Autonomous Driving
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Uppuluri, Bhargava, Patel, Anjel, Mehta, Neil, Kamath, Sridhar, and Chakraborty, Pratyush
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Computer Science - Robotics ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
In autonomous driving, traditional Computer Vision (CV) agents often struggle in unfamiliar situations due to biases in the training data. Deep Reinforcement Learning (DRL) agents address this by learning from experience and maximizing rewards, which helps them adapt to dynamic environments. However, ensuring their generalization remains challenging, especially with static training environments. Additionally, DRL models lack transparency, making it difficult to guarantee safety in all scenarios, particularly those not seen during training. To tackle these issues, we propose a method that combines DRL with Curriculum Learning for autonomous driving. Our approach uses a Proximal Policy Optimization (PPO) agent and a Variational Autoencoder (VAE) to learn safe driving in the CARLA simulator. The agent is trained using two-fold curriculum learning, progressively increasing environment difficulty and incorporating a collision penalty in the reward function to promote safety. This method improves the agent's adaptability and reliability in complex environments, and understand the nuances of balancing multiple reward components from different feedback signals in a single scalar reward function. Keywords: Computer Vision, Deep Reinforcement Learning, Variational Autoencoder, Proximal Policy Optimization, Curriculum Learning, Autonomous Driving., Comment: To be published in the 17th International Conference on Agents and Artificial Intelligence (ICAART), Feb 2025
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- 2025
11. A multi-frequency study of sub-parsec jets with the Event Horizon Telescope
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Röder, Jan, Wielgus, Maciek, Lobanov, Andrei P., Krichbaum, Thomas P., Nair, Dhanya G., Lee, Sang-Sung, Ros, Eduardo, Fish, Vincent L., Blackburn, Lindy, Chan, Chi-kwan, Issaoun, Sara, Janssen, Michael, Johnson, Michael D., Doeleman, Sheperd S., Bower, Geoffrey C., Crew, Geoffrey B., Tilanus, Remo P. J., Savolainen, Tuomas, Impellizzeri, C. M. Violette, Alberdi, Antxon, Baczko, Anne-Kathrin, Gómez, José L., Lu, Ru-Sen, Paraschos, Georgios F., Traianou, Efthalia, Goddi, Ciriaco, Kim, Daewon, Lisakov, Mikhail, Kovalev, Yuri Y., Voitsik, Petr A., Sokolovsky, Kirill V., Akiyama, Kazunori, Albentosa-Ruíz, Ezequiel, Alef, Walter, Algaba, Juan Carlos, Anantua, Richard, Asada, Keiichi, Azulay, Rebecca, Bach, Uwe, Ball, David, Baloković, Mislav, Bandyopadhyay, Bidisha, Barrett, John, Bauböck, Michi, Benson, Bradford A., Bintley, Dan, Blundell, Raymond, Bouman, Katherine L., Bremer, Michael, Brinkerink, Christiaan D., Brissenden, Roger, Britzen, Silke, Broderick, Avery E., Broguiere, Dominique, Bronzwaer, Thomas, Bustamante, Sandra, Byun, Do-Young, Carlstrom, John E., Ceccobello, Chiara, Chael, Andrew, Chang, Dominic O., Chatterjee, Koushik, Chatterjee, Shami, Chen, Ming-Tang, Chen, Yongjun, Cheng, Xiaopeng, Cho, Ilje, Christian, Pierre, Conroy, Nicholas S., Conway, John E., Cordes, James M., Crawford, Thomas M., Cruz-Osorio, Alejandro, Cui, Yuzhu, Curd, Brandon, Dahale, Rohan, Davelaar, Jordy, De Laurentis, Mariafelicia, Deane, Roger, Dempsey, Jessica, Desvignes, Gregory, Dexter, Jason, Dhruv, Vedant, Dihingia, Indu K., Dougall, Sean Taylor, Dzib, Sergio A., Eatough, Ralph P., Emami, Razieh, Falcke, Heino, Farah, Joseph, Fomalont, Edward, Ford, H. Alyson, Foschi, Marianna, Fraga-Encinas, Raquel, Freeman, William T., Friberg, Per, Fromm, Christian M., Fuentes, Antonio, Galison, Peter, Gammie, Charles F., García, Roberto, Gentaz, Olivier, Georgiev, Boris, Gold, Roman, Gómez-Ruiz, Arturo I., Gu, Minfeng, Gurwell, Mark, Hada, Kazuhiro, Haggard, Daryl, Haworth, Kari, Hecht, Michael H., Hesper, Ronald, Heumann, Dirk, Ho, Luis C., Ho, Paul, Honma, Mareki, Huang, Chih-Wei L., Huang, Lei, Hughes, David H., Ikeda, Shiro, Inoue, Makoto, James, David J., Jannuzi, Buell T., Jeter, Britton, Jiang, Wu, Jiménez-Rosales, Alejandra, Jorstad, Svetlana, Joshi, Abhishek V., Jung, Taehyun, Karami, Mansour, Karuppusamy, Ramesh, Kawashima, Tomohisa, Keating, Garrett K., Kettenis, Mark, Kim, Dong-Jin, Kim, Jae-Young, Kim, Jongsoo, Kim, Junhan, Kino, Motoki, Koay, Jun Yi, Kocherlakota, Prashant, Kofuji, Yutaro, Koyama, Shoko, Kramer, Carsten, Kramer, Joana A., Kramer, Michael, Kuo, Cheng-Yu, La Bella, Noemi, Lauer, Tod R., Lee, Daeyoung, Leung, Po Kin, Levis, Aviad, Li, Zhiyuan, Lico, Rocco, Lindahl, Greg, Lindqvist, Michael, Liu, Jun, Liu, Kuo, Liuzzo, Elisabetta, Lo, Wen-Ping, Loinard, Laurent, Lonsdale, Colin J., Lowitz, Amy E., MacDonald, Nicholas R., Mao, Jirong, Marchili, Nicola, Markoff, Sera, Marrone, Daniel P., Marscher, Alan P., Martí-Vidal, Iván, Matsushita, Satoki, Matthews, Lynn D., Medeiros, Lia, Menten, Karl M., Michalik, Daniel, Mizuno, Izumi, Mizuno, Yosuke, Moran, James M., Moriyama, Kotaro, Moscibrodzka, Monika, Mulaudzi, Wanga, Müller, Cornelia, Müller, Hendrik, Mus, Alejandro, Musoke, Gibwa, Myserlis, Ioannis, Nadolski, Andrew, Nagai, Hiroshi, Nagar, Neil M., Nakamura, Masanori, Narayanan, Gopal, Natarajan, Iniyan, Nathanail, Antonios, Fuentes, Santiago Navarro, Neilsen, Joey, Neri, Roberto, Ni, Chunchong, Noutsos, Aristeidis, Nowak, Michael A., Oh, Junghwan, Okino, Hiroki, Sánchez, Héctor R. Olivares, Ortiz-León, Gisela N., Oyama, Tomoaki, özel, Feryal, Palumbo, Daniel C. M., Park, Jongho, Parsons, Harriet, Patel, Nimesh, Pen, Ue-Li, Pesce, Dominic W., Piétu, Vincent, Plambeck, Richard, PopStefanija, Aleksandar, Porth, Oliver, Pötzl, Felix M., Prather, Ben, Preciado-López, Jorge A., Principe, Giacomo, Psaltis, Dimitrios, Pu, Hung-Yi, Ramakrishnan, Venkatessh, Rao, Ramprasad, Rawlings, Mark G., Ricarte, Angelo, Ripperda, Bart, Roelofs, Freek, Rogers, Alan, Romero-Cañizales, Cristina, Roshanineshat, Arash, Rottmann, Helge, Roy, Alan L., Ruiz, Ignacio, Ruszczyk, Chet, Rygl, Kazi L. J., Sánchez, Salvador, Sánchez-Argüelles, David, Sánchez-Portal, Miguel, Sasada, Mahito, Satapathy, Kaushik, Schloerb, F. Peter, Schonfeld, Jonathan, Schuster, Karl-Friedrich, Shao, Lijing, Shen, Zhiqiang, Small, Des, Sohn, Bong Won, SooHoo, Jason, Salas, León David Sosapanta, Souccar, Kamal, Stanway, Joshua S., Sun, He, Tazaki, Fumie, Tetarenko, Alexandra J., Tiede, Paul, Titus, Michael, Torne, Pablo, Toscano, Teresa, Trent, Tyler, Trippe, Sascha, Turk, Matthew, van Bemmel, Ilse, van Langevelde, Huib J., van Rossum, Daniel R., Vos, Jesse, Wagner, Jan, Ward-Thompson, Derek, Wardle, John, Washington, Jasmin E., Weintroub, Jonathan, Wharton, Robert, Wiik, Kaj, Witzel, Gunther, Wondrak, Michael F., Wong, George N., Wu, Qingwen, Yadlapalli, Nitika, Yamaguchi, Paul, Yfantis, Aristomenis, Yoon, Doosoo, Young, André, Young, Ken, Younsi, Ziri, Yu, Wei, Yuan, Feng, Yuan, Ye-Fei, Zensus, J. Anton, Zhang, Shuo, Zhao, Guang-Yao, and Zhao, Shan-Shan
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Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Astrophysics of Galaxies ,Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.
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- 2025
12. Optimal Scheduling in a Quantum Switch
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Bhambay, Sanidhay, Vasantam, Thirupathaiah, and Walton, Neil
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Quantum Physics ,Computer Science - Networking and Internet Architecture ,Computer Science - Performance ,Mathematics - Probability - Abstract
With a growing number of quantum networks in operation, there is a pressing need for performance analysis of quantum switching technologies. A quantum switch establishes, distributes, and maintains entanglements across a network. In contrast to a classical switching fabric, a quantum switch is a two sided queueing network. The switch generates Link Level Entanglements (LLEs), which are then fused to process the networks entanglement requests. Our proof techniques analyse a two time scale separation phenomenon at the fluid scale for a general switch topology. This allows us to demonstrate that the optimal fluid dynamics are given by a scheduling algorithm that solves a certain average reward Markov Decision Process.
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- 2025
13. Roadmap on Atomic-scale Semiconductor Devices
- Author
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Schofield, Steven R., Fisher, Andrew J., Ginossar, Eran, Lyding, Joseph W., Silver, Richard, Fei, Fan, Namboodiri, Pradeep, Wyrick, Jonathan, Masteghin, M. G., Cox, D. C., Murdin, B. N., Clowes, S. K, Keizer, Joris G., Simmons, Michelle Y., Stemp, Holly G., Morello, Andrea, Voisin, Benoit, Rogge, Sven, Wolkow, Robert A., Livadaru, Lucian, Pitters, Jason, Stock, Taylor J. Z., Curson, Neil J., Butera, Robert E., Pavlova, Tatiana V., Jakob, A. M., Spemann, D., Räcke, P., Schmidt-Kaler, F., Jamieson, D. N., Pratiush, Utkarsh, Duscher, Gerd, Kalinin, Sergei V., Kazazis, Dimitrios, Constantinou, Procopios, Aeppli, Gabriel, Ekinci, Yasin, Owen, James H. G., Fowler, Emma, Moheimani, S. O. Reza, Randall, John N., Misra, Shashank, Ivie, Jeffrey, Allemang, Christopher R., Anderson, Evan M., Bussmann, Ezra, Campbell, Quinn, Gao, Xujiao, Lu, Tzu-Ming, and Schmucker, Scott W.
- Subjects
Quantum Physics ,Physics - Applied Physics - Abstract
Spin states in semiconductors provide exceptionally stable and noise-resistant environments for qubits, positioning them as optimal candidates for reliable quantum computing technologies. The proposal to use nuclear and electronic spins of donor atoms in silicon, introduced by Kane in 1998, sparked a new research field focused on the precise positioning of individual impurity atoms for quantum devices, utilising scanning tunnelling microscopy and ion implantation. This roadmap article reviews the advancements in the 25 years since Kane's proposal, the current challenges, and the future directions in atomic-scale semiconductor device fabrication and measurement. It covers the quest to create a silicon-based quantum computer and expands to include diverse material systems and fabrication techniques, highlighting the potential for a broad range of semiconductor quantum technological applications. Key developments include phosphorus in silicon devices such as single-atom transistors, arrayed few-donor devices, one- and two-qubit gates, three-dimensional architectures, and the development of a toolbox for future quantum integrated circuits. The roadmap also explores new impurity species like arsenic and antimony for enhanced scalability and higher-dimensional spin systems, new chemistry for dopant precursors and lithographic resists, and the potential for germanium-based devices. Emerging methods, such as photon-based lithography and electron beam manipulation, are discussed for their disruptive potential. This roadmap charts the path toward scalable quantum computing and advanced semiconductor quantum technologies, emphasising the critical intersections of experiment, technological development, and theory., Comment: 94 pages
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- 2025
14. A common framework for test ideals, closure operations, and their duals
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Epstein, Neil, G., Rebecca R., and Vassilev, Janet
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Mathematics - Commutative Algebra - Abstract
Closure operations such as tight and integral closure and test ideals have appeared frequently in the study of commutative algebra. This articles serves as a survey of the authors' prior results connecting closure operations, test ideals, and interior operations via the more general structure of pair operations. Specifically, we describe a duality between closure and interior operations generalizing the duality between tight closure and its test ideal, provide methods for creating pair operations that are compatible with taking quotient modules or submodules, and describe a generalization of core and its dual. Throughout, we discuss how these ideas connect to common constructions in commutative algebra., Comment: 24 pages. Comments welcome
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- 2025
15. Rapid Experimentation with Python Considering Optional and Hierarchical Inputs
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Ranly, Neil and Wagner, Torrey
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Computer Science - Mathematical Software ,Statistics - Methodology ,62K99 ,G.4 - Abstract
Space-filling experimental design techniques are commonly used in many computer modeling and simulation studies to explore the effects of inputs on outputs. This research presents raxpy, a Python package that leverages expressive annotation of Python functions and classes to simplify space-filling experimentation. It incorporates code introspection to derive a Python function's input space and novel algorithms to automate the design of space-filling experiments for spaces with optional and hierarchical input dimensions. In this paper, we review the criteria for design evaluation given these types of dimensions and compare the proposed algorithms with numerical experiments. The results demonstrate the ability of the proposed algorithms to create improved space-filling experiment designs. The package includes support for parallelism and distributed execution. raxpy is available as free and open-source software under a MIT license.
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- 2025
16. Human and Machine: How Software Engineers Perceive and Engage with AI-Assisted Code Reviews Compared to Their Peers
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Alami, Adam and Ernst, Neil A.
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Computer Science - Software Engineering - Abstract
The integration of artificial intelligence (AI) continues to increase and evolve, including in software engineering (SE). This integration involves processes traditionally entrusted to humans, such as coding. However, the impact on socio-technical processes like code review remains underexplored. In this interview-based study (20 interviewees), we investigate how software engineers perceive and engage with Large Language Model (LLM)-assisted code reviews compared to human peer-led reviews. In this inherently human-centric process, we aim to understand how software engineers navigate the introduction of AI into collaborative workflows. We found that engagement in code review is multi-dimensional, spanning cognitive, emotional, and behavioral dimensions. The introduction of LLM-assisted review impacts some of these attributes. For example, there is less need for emotional regulation and coping mechanisms when dealing with an LLM compared to peers. However, the cognitive load sometimes is higher in dealing with LLM-generated feedback due to its excessive details. Software engineers use a similar sense-making process to evaluate and adopt feedback suggestions from their peers and the LLM. However, the LLM feedback adoption is constrained by trust and lack of context in the review. Our findings contribute to a deeper understanding of how AI tools are impacting SE socio-technical processes and provide insights into the future of AI-human collaboration in SE practices., Comment: Accepted at CHASE 2025
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- 2025
17. Modulo Sampling: Performance Guarantees in The Presence of Quantization
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Bernardo, Neil Irwin, Shah, Shaik Basheeruddin, and Eldar, Yonina C.
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Electrical Engineering and Systems Science - Signal Processing - Abstract
In this paper, we investigate the relationship between the dynamic range and quantization noise power in modulo analog-to-digital converters (ADCs). Two modulo ADC systems are considered: (1) a modulo ADC which outputs the folded samples and an additional 1-bit folding information signal, and (2) a modulo ADC without the 1-bit information. A recovery algorithm that unfolds the quantized modulo samples using the extra 1-bit folding information is analyzed. Using the dithered quantization framework, we show that an oversampling factor of $\mathrm{OF} > 3$ and a quantizer resolution of $b > 3$ are sufficient conditions to unfold the modulo samples. When these conditions are met, we demonstrate that the mean squared error (MSE) performance of modulo ADC with an extra 1-bit folding information signal is better than that of a conventional ADC with the same number of bits used for amplitude quantization. Since folding information is typically not available in modulo ADCs, we also propose and analyze a recovery algorithm based on orthogonal matching pursuit (OMP) that does not require the 1-bit folding information. In this case, we prove that $\mathrm{OF} > 3$ and $b > 3 + \log_2(\delta)$ for some $\delta > 1$ are sufficient conditions to unfold the modulo samples. For the two systems considered, we show that, with sufficient number of bits for amplitude quantization, the mean squared error (MSE) of a modulo ADC is $\mathcal{O}\left(\frac{1}{\mathrm{OF}^3}\right)$ whereas that of a conventional ADC is only $\mathcal{O}\left(\frac{1}{\mathrm{OF}}\right)$. We extend the analysis to the case of simultaneous acquisition of weak and strong signals occupying different frequency bands. Finally, numerical results are presented to validate the derived performance guarantees.
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- 2025
18. Position building in competition is a game with incomplete information
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Chriss, Neil A.
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Quantitative Finance - Trading and Market Microstructure - Abstract
This paper examines strategic trading under incomplete information, where firms lack full knowledge of key aspects of their competitors' trading strategies such as target sizes and market impact models. We extend previous work on competitive trading equilibria by incorporating uncertainty through the framework of Bayesian games. This allows us to analyze scenarios where firms have diverse beliefs about market conditions and each other's strategies. We derive optimal trading strategies in this setting and demonstrate how uncertainty significantly impacts these strategies compared to the complete information case. Furthermore, we introduce a novel approach to model the presence of non-strategic traders, even when strategic firms disagree on their characteristics. Our analysis reveals the complex interplay of beliefs and strategic adjustments required in such an environment. Finally, we discuss limitations of the current model, including the reliance on linear market impact and the lack of dynamic strategy adjustments, outlining directions for future research., Comment: 38 pages, 8 figures
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- 2025
19. Negativity in Self-Admitted Technical Debt: How Sentiment Influences Prioritization
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Cassee, Nathan, Ernst, Neil, Novielli, Nicole, and Serebrenik, Alexander
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Computer Science - Software Engineering - Abstract
Self-Admitted Technical Debt, or SATD, is a self-admission of technical debt present in a software system. To effectively manage SATD, developers need to estimate its priority and assess the effort required to fix the described technical debt. About a quarter of descriptions of SATD in software systems express some form of negativity or negative emotions when describing technical debt. In this paper, we report on an experiment conducted with 59 respondents to study whether negativity expressed in the description of SATD \textbf{actually} affects the prioritization of SATD. The respondents are a mix of professional developers and students, and in the experiment, we asked participants to prioritize four vignettes: two expressing negativity and two expressing neutral sentiment. To ensure realism, vignettes were based on existing SATD. We find that negativity causes between one-third and half of developers to prioritize SATD, in which negativity is expressed as having more priority. Developers affected by negativity when prioritizing SATD are twice as likely to increase their estimation of urgency and 1.5 times as likely to increase their estimation of importance and effort for SATD compared to the likelihood of decreasing these prioritization scores. Our findings show how developers actively use negativity in SATD to determine how urgently a particular instance of TD should be addressed. However, our study also describes a gap in the actions and belief of developers. Even if 33% to 50% use negativity to prioritize SATD, 67% of developers believe that using negativity as a proxy for priority is unacceptable. Therefore, we would not recommend using negativity as a proxy for priority. However, we also recognize that developers might unavoidably express negativity when describing technical debt.
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- 2025
20. Retrieval-Augmented Generation with Graphs (GraphRAG)
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Han, Haoyu, Wang, Yu, Shomer, Harry, Guo, Kai, Ding, Jiayuan, Lei, Yongjia, Halappanavar, Mahantesh, Rossi, Ryan A., Mukherjee, Subhabrata, Tang, Xianfeng, He, Qi, Hua, Zhigang, Long, Bo, Zhao, Tong, Shah, Neil, Javari, Amin, Xia, Yinglong, and Tang, Jiliang
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Computer Science - Information Retrieval ,Computer Science - Computation and Language ,Computer Science - Machine Learning - Abstract
Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information, making it a golden resource for RAG in tremendous real-world applications. As a result, we have recently witnessed increasing attention on equipping RAG with Graph, i.e., GraphRAG. However, unlike conventional RAG, where the retriever, generator, and external data sources can be uniformly designed in the neural-embedding space, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains. Given the broad applicability, the associated design challenges, and the recent surge in GraphRAG, a systematic and up-to-date survey of its key concepts and techniques is urgently desired. Following this motivation, we present a comprehensive and up-to-date survey on GraphRAG. Our survey first proposes a holistic GraphRAG framework by defining its key components, including query processor, retriever, organizer, generator, and data source. Furthermore, recognizing that graphs in different domains exhibit distinct relational patterns and require dedicated designs, we review GraphRAG techniques uniquely tailored to each domain. Finally, we discuss research challenges and brainstorm directions to inspire cross-disciplinary opportunities. Our survey repository is publicly maintained at https://github.com/Graph-RAG/GraphRAG/.
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- 2024
21. Novel SiC UV Instrumentation Development with Potential Applications for the Habitable Worlds Observatory
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Saxena, Prabal, Dilli, Zeynep, Snapp, Peter, Youngblood, Allison, Hewagama, Tilak, Aslam, Shahid, Chullhee, Cho, Waczynski, Augustyn, Abuhassan, Nader, La, Ahn T., Place, Bryan K., Hansico, Thomas F., Stauffer, Ryan, Bower, Dina, Akturk, Akin, Goldsman, Neil, Galey, Bryce, Mountfort, Ethan, Gross, Mitchell, Purcell, Ryan, Khalid, Usama, Kamali, Yekta, Darmody, Chris, Washington, Robert, Livengood, Tim, Moriarty, Daniel P., Kotecki, Carl A., Prasad, Narasimha S., and Wilkins, Joseph
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Astrophysics - Instrumentation and Methods for Astrophysics ,Astrophysics - Earth and Planetary Astrophysics ,Astrophysics - Astrophysics of Galaxies ,Astrophysics - Solar and Stellar Astrophysics - Abstract
In this paper, we detail recent and current work that is being carried out to fabricate and advance novel SiC UV instrumentation that is aimed at enabling more sensitive measurements across numerous disciplines, with a short discussion of the promise such detectors may hold for the Habitable Worlds Observatory. We discuss SiC instrument development progress that is being carried out under multiple NASA grants, including several PICASSO and SBIR grants, as well as an ECI grant. Testing of pixel design, properties and layout as well as maturation of the integration scheme developed through these efforts provide key technology and engineering advancement for potential HWO detectors. Achieving desired noise characteristics, responsivity, and validating operation of SiC detectors using standard read out techniques offers a compelling platform for operation of denser and higher dimensionality SiC photodiode arrays of interest for use in potential HWO Coronagraph, Spectrograph, and High Resolution Imaging Instruments. We incorporate these SiC detector properties into a simulation of potential NUV exoplanet observations by HWO using SiC detectors and also discuss potential application to HWO., Comment: 8 figures, Submitted to JATIS
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- 2024
22. Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics
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De La Fuente, Neil, Alonso, Miquel Noguer i, and Casadellà, Guim
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Computer Science - Multiagent Systems ,Computer Science - Artificial Intelligence ,Computer Science - Computer Science and Game Theory - Abstract
This paper explores advanced topics in complex multi-agent systems building upon our previous work. We examine four fundamental challenges in Multi-Agent Reinforcement Learning (MARL): non-stationarity, partial observability, scalability with large agent populations, and decentralized learning. The paper provides mathematical formulations and analysis of recent algorithmic advancements designed to address these challenges, with a particular focus on their integration with game-theoretic concepts. We investigate how Nash equilibria, evolutionary game theory, correlated equilibrium, and adversarial dynamics can be effectively incorporated into MARL algorithms to improve learning outcomes. Through this comprehensive analysis, we demonstrate how the synthesis of game theory and MARL can enhance the robustness and effectiveness of multi-agent systems in complex, dynamic environments., Comment: 22 pages
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- 2024
23. Demographics of black holes at $<$100 R$_{\rm g}$ scales: accretion flows, jets, and shadows
- Author
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Nair, Dhanya G., Nagar, Neil M., Ramakrishnan, Venkatessh, Wielgus, Maciek, Arratia, Vicente, Krichbaum, Thomas P., Zhang, Xinyue A., Ricarte, Angelo, S., Silpa, Hernández-Yévenes, Joaquín, Ford, Nicole M., Bandyopadhyay, Bidisha, Gurwell, Mark, Burridge, Roman, Pesce, Dominic W., Doeleman, Sheperd S., Kim, Jae-Young, Kim, Daewon, Janssen, Michael, von Fellenberg, Sebastiano D., Fromm, Christian M., Lee, Deokhyeong, Falcke, Heino, Wagner, Jan, Bower, Geoffrey C., Baczko, Anne-Kathrin, Kim, Dong-Jin, Akiyama, Kazunori, Asada, Keiichi, Arevalo, Patricia, Bignall, Hayley, Blackburn, Lindy, Broderick, Avery E., Brunthaler, Andreas, Chan, Chi-kwan, Doi, Akihiro, Fish, Vincent L., Fomalont, Edward, Gómez, José L., Haggard, Daryl, Hada, Kazuhiro, Herrera-Camus, Rodrigo, Hoak, Daniel, Hughes, David, Hlavacek-Larrondo, Julie, Jorstad, Svetlana, Johnson, Michael D., Kawashima, Tomohisa, Keating, Garrett K., Kharb, Preeti, Koay, Jun Yi, Koyama, Shoko, Kuo, Cheng-Yu, Leigh, Nathan W. C., Lira, Paulina, Lindqvist, Michael, Lobanov, Andrei P., Lo, Wen-Ping, Lu, Ru-Sen, Markoff, Sera, MacDonald, Nicholas R., Martínez-Aldama, Mary Loli, Matthews, Lynn D., Matsushita, Satoki, Mezcua, Mar, Moscibrodzka, Monika, Müller, Hendrik, Nagai, Hiroshi, Nakamura, Masanori, Natarajan, Priyamvada, Narayanan, Gopal, Nowak, Michael A., Sánchez, Héctor Raúl Olivares, Park, Jongho, Psaltis, Dimitrios, Pu, Hung-Yi, Porth, Oliver, Rao, Ramprasad, Reynolds, Cormac, Reeves, Rodrigo, Romero-Cañizales, Cristina, Ros, Eduardo, Rottmann, Helge, Roy, Alan L., Schleicher, Dominik, Savolainen, Tuomas, Impellizzeri, C. M. Violette, Treister, Ezequiel, Wiik, Kaj, and Zensus, J. Anton
- Subjects
Astrophysics - Astrophysics of Galaxies - Abstract
Using the Event Horizon Telescope (EHT), the gravitationally lensed rings around the supermassive black holes (SMBHs) in Messier 87 (M87) and Sagittarius A* (Sgr A*) have now been successfully imaged at a resolution under 10 gravitational radii (R$_{\rm g}$ $ = \rm{GM/c^2}$). To expand studies beyond M87 and Sgr A*, we have constructed the Event Horizon and Environs (ETHER) sample, a comprehensive database encompassing approximately 3.15 million SMBH mass estimates, $\sim$ 20,000 Very-Long Baseline Interferometry (VLBI) radio flux densities, and $\sim$ 36,000 hard X-ray flux densities. This database is designed to identify and optimize target selection for the EHT and its upgrades on the ground and in space. We have identified a Gold Sample (GS) of nearby low-luminosity Active Galactic Nuclei (AGNs) within it that are ideal for studying jet bases and potentially imaging black hole shadows. We observed 27 of these AGNs using the EHT from 2022 to 2024, providing an opportunity to resolve and image accretion flows and jets at resolutions of $\leq$ 100 R$_{\rm g}$. Only a few SMBHs have sufficiently high enough flux density to be imaged at scales of $\leq$ 50 R$_{\rm g}$ with the present EHT. Among these are M87, Sgr A*, NGC4594 (Sombrero/M104), NGC4261, and NGC4374 (Messier 84/M84). Of these, NGC4261, Sombrero, and M84 have been observed and/or are scheduled for deep imaging with EHT+ALMA from 2023 to 2025. Sombrero, NGC4261, M84, NGC4278, and NGC5232 are clearly detected in our EHT+ALMA observations in 2022, indicating that the 230 GHz flux density from the accretion flows is significantly high. Ongoing imaging of the ETHER GS will enable measurements of black hole mass and spin, help constrain General Relativity, and enrich our understanding of jet launching and accretion inflows across a broad multi-parameter space, including black hole mass, spin, accretion rate, and orientation., Comment: 9 pages, 6 figures, 1 table, published in Proceedings of the 16th EVN Symposium, Ed. E. Ros, P. Benke, S.A. Dzib, I. Rottmann, & J.A. Zensus, Bonn: Max-Planck-Institut f\"ur Radioastronomie, 2024, pages 75-84, https://cloud.mpifr-bonn.mpg.de/index.php/s/BkX2CC2Xjn2aKR4
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- 2024
24. Advancing NAM-to-Speech Conversion with Novel Methods and the MultiNAM Dataset
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Shah, Neil, Karande, Shirish, and Gandhi, Vineet
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Computer Science - Sound ,Computer Science - Artificial Intelligence ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Current Non-Audible Murmur (NAM)-to-speech techniques rely on voice cloning to simulate ground-truth speech from paired whispers. However, the simulated speech often lacks intelligibility and fails to generalize well across different speakers. To address this issue, we focus on learning phoneme-level alignments from paired whispers and text and employ a Text-to-Speech (TTS) system to simulate the ground-truth. To reduce dependence on whispers, we learn phoneme alignments directly from NAMs, though the quality is constrained by the available training data. To further mitigate reliance on NAM/whisper data for ground-truth simulation, we propose incorporating the lip modality to infer speech and introduce a novel diffusion-based method that leverages recent advancements in lip-to-speech technology. Additionally, we release the MultiNAM dataset with over 7.96 hours of paired NAM, whisper, video, and text data from two speakers and benchmark all methods on this dataset. Speech samples and the dataset are available at https://diff-nam.github.io/DiffNAM/, Comment: Accepted at IEEE ICASSP 2025
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- 2024
25. MRI2Speech: Speech Synthesis from Articulatory Movements Recorded by Real-time MRI
- Author
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Shah, Neil, Kashyap, Ayan, Karande, Shirish, and Gandhi, Vineet
- Subjects
Computer Science - Sound ,Computer Science - Artificial Intelligence ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Previous real-time MRI (rtMRI)-based speech synthesis models depend heavily on noisy ground-truth speech. Applying loss directly over ground truth mel-spectrograms entangles speech content with MRI noise, resulting in poor intelligibility. We introduce a novel approach that adapts the multi-modal self-supervised AV-HuBERT model for text prediction from rtMRI and incorporates a new flow-based duration predictor for speaker-specific alignment. The predicted text and durations are then used by a speech decoder to synthesize aligned speech in any novel voice. We conduct thorough experiments on two datasets and demonstrate our method's generalization ability to unseen speakers. We assess our framework's performance by masking parts of the rtMRI video to evaluate the impact of different articulators on text prediction. Our method achieves a $15.18\%$ Word Error Rate (WER) on the USC-TIMIT MRI corpus, marking a huge improvement over the current state-of-the-art. Speech samples are available at https://mri2speech.github.io/MRI2Speech/, Comment: Accepted at IEEE ICASSP 2025
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- 2024
26. The moments of the spectral form factor in SYK
- Author
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Legramandi, Andrea and Talwar, Neil
- Subjects
High Energy Physics - Theory ,Condensed Matter - Disordered Systems and Neural Networks ,Condensed Matter - Statistical Mechanics ,Condensed Matter - Strongly Correlated Electrons ,Quantum Physics - Abstract
In chaotic quantum systems the spectral form factor exhibits a universal linear ramp and plateau structure with superimposed erratic oscillations. The mean signal and the statistics of the noise can be probed by the moments of the spectral form factor, also known as higher-point spectral form factors. We identify saddle points in the SYK model that describe the moments during the ramp region. Perturbative corrections around the saddle point indicate that SYK mimics random matrix statistics for the low order moments, while large deviations for the high order moments arise from fluctuations near the edge of the spectrum. The leading correction scales inversely with the number of random parameters in the SYK Hamiltonian and is amplified in a sparsified version of the SYK model, which we study numerically, even in regimes where a linear ramp persists. Finally, we study the $q=2$ SYK model, whose spectral form factor exhibits an exponential ramp with increased noise. These findings reveal how deviations from random matrix universality arise in disordered systems and motivate their interpretation from a bulk gravitational perspective., Comment: 43 pages plus appendices
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- 2024
27. Hadronic vacuum polarization for the muon $g-2$ from lattice QCD: Long-distance and full light-quark connected contribution
- Author
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Bazavov, Alexei, Bernard, Claude W., Clarke, David A., Davies, Christine, DeTar, Carleton, El-Khadra, Aida X., Gámiz, Elvira, Gottlieb, Steven, Grebe, Anthony V., Hostetler, Leon, Jay, William I., Jeong, Hwancheol, Kronfeld, Andreas S., Lahert, Shaun, Laiho, Jack, Lepage, G. Peter, Lynch, Michael, Lytle, Andrew T., McNeile, Craig, Neil, Ethan T., Peterson, Curtis T., Simone, James N., Sitison, Jacob W., Van de Water, Ruth S., and Vaquero, Alejandro
- Subjects
High Energy Physics - Lattice - Abstract
We present results for the dominant light-quark connected contribution to the long-distance window (LD) of the hadronic vacuum polarization contribution (HVP) to the muon $g-2$ from lattice quantum chromodynamics (QCD). Specifically, with a new determination of the lattice scale on MILC's physical-mass HISQ ensembles, using the $\Omega^-$ baryon mass, we obtain a result of $a^{ll,\,{\mathrm{LD}}}_{\mu}(\mathrm{conn.}) = 401.2(2.3)_{\mathrm{stat}}(3.6)_{\mathrm{syst}}[4.3]_{\mathrm{total}} \times 10^{-10}$. In addition, following up on our recent work on the short- (SD) and intermediate-distance (W) windows, we report updated values for these quantities with this new scale-setting determination. Summing these individual window contributions enables a sub-percent precision determination of the light-quark-connected contribution to HVP of $a^{ll}_{\mu}(\mathrm{conn.}) = 656.2(1.9)_{\mathrm{stat}}(4.0)_{\mathrm{syst}}[4.4]_{\mathrm{total}} \times 10^{-10}$. Finally, as a consistency check, we verify that an independent analysis of the full contribution is in agreement with the sum of individual windows. We discuss our future plans for improvements of our HVP calculations to meet the target precision of the Fermilab $g-2$ experiment., Comment: arXiv admin note: text overlap with arXiv:2411.09656
- Published
- 2024
28. An Ensemble Approach to Short-form Video Quality Assessment Using Multimodal LLM
- Author
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Wen, Wen, Wang, Yilin, Birkbeck, Neil, and Adsumilli, Balu
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
The rise of short-form videos, characterized by diverse content, editing styles, and artifacts, poses substantial challenges for learning-based blind video quality assessment (BVQA) models. Multimodal large language models (MLLMs), renowned for their superior generalization capabilities, present a promising solution. This paper focuses on effectively leveraging a pretrained MLLM for short-form video quality assessment, regarding the impacts of pre-processing and response variability, and insights on combining the MLLM with BVQA models. We first investigated how frame pre-processing and sampling techniques influence the MLLM's performance. Then, we introduced a lightweight learning-based ensemble method that adaptively integrates predictions from the MLLM and state-of-the-art BVQA models. Our results demonstrated superior generalization performance with the proposed ensemble approach. Furthermore, the analysis of content-aware ensemble weights highlighted that some video characteristics are not fully represented by existing BVQA models, revealing potential directions to improve BVQA models further., Comment: Accepted by ICASSP 2025
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- 2024
29. GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems
- Author
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Wu, Xinyi, Loveland, Donald, Chen, Runjin, Liu, Yozen, Chen, Xin, Neves, Leonardo, Jadbabaie, Ali, Ju, Clark Mingxuan, Shah, Neil, and Zhao, Tong
- Subjects
Computer Science - Information Retrieval ,Computer Science - Social and Information Networks - Abstract
Deep recommender systems rely heavily on large embedding tables to handle high-cardinality categorical features such as user/item identifiers, and face significant memory constraints at scale. To tackle this challenge, hashing techniques are often employed to map multiple entities to the same embedding and thus reduce the size of the embedding tables. Concurrently, graph-based collaborative signals have emerged as powerful tools in recommender systems, yet their potential for optimizing embedding table reduction remains unexplored. This paper introduces GraphHash, the first graph-based approach that leverages modularity-based bipartite graph clustering on user-item interaction graphs to reduce embedding table sizes. We demonstrate that the modularity objective has a theoretical connection to message-passing, which provides a foundation for our method. By employing fast clustering algorithms, GraphHash serves as a computationally efficient proxy for message-passing during preprocessing and a plug-and-play graph-based alternative to traditional ID hashing. Extensive experiments show that GraphHash substantially outperforms diverse hashing baselines on both retrieval and click-through-rate prediction tasks. In particular, GraphHash achieves on average a 101.52% improvement in recall when reducing the embedding table size by more than 75%, highlighting the value of graph-based collaborative information for model reduction.
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- 2024
30. Enhancing Item Tokenization for Generative Recommendation through Self-Improvement
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Chen, Runjin, Ju, Mingxuan, Bui, Ngoc, Antypas, Dimosthenis, Cai, Stanley, Wu, Xiaopeng, Neves, Leonardo, Wang, Zhangyang, Shah, Neil, and Zhao, Tong
- Subjects
Computer Science - Machine Learning ,Computer Science - Information Retrieval - Abstract
Generative recommendation systems, driven by large language models (LLMs), present an innovative approach to predicting user preferences by modeling items as token sequences and generating recommendations in a generative manner. A critical challenge in this approach is the effective tokenization of items, ensuring that they are represented in a form compatible with LLMs. Current item tokenization methods include using text descriptions, numerical strings, or sequences of discrete tokens. While text-based representations integrate seamlessly with LLM tokenization, they are often too lengthy, leading to inefficiencies and complicating accurate generation. Numerical strings, while concise, lack semantic depth and fail to capture meaningful item relationships. Tokenizing items as sequences of newly defined tokens has gained traction, but it often requires external models or algorithms for token assignment. These external processes may not align with the LLM's internal pretrained tokenization schema, leading to inconsistencies and reduced model performance. To address these limitations, we propose a self-improving item tokenization method that allows the LLM to refine its own item tokenizations during training process. Our approach starts with item tokenizations generated by any external model and periodically adjusts these tokenizations based on the LLM's learned patterns. Such alignment process ensures consistency between the tokenization and the LLM's internal understanding of the items, leading to more accurate recommendations. Furthermore, our method is simple to implement and can be integrated as a plug-and-play enhancement into existing generative recommendation systems. Experimental results on multiple datasets and using various initial tokenization strategies demonstrate the effectiveness of our method, with an average improvement of 8\% in recommendation performance.
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- 2024
31. OpenAI o1 System Card
- Author
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OpenAI, Jaech, Aaron, Kalai, Adam, Lerer, Adam, Richardson, Adam, El-Kishky, Ahmed, Low, Aiden, Helyar, Alec, Madry, Aleksander, Beutel, Alex, Carney, Alex, Iftimie, Alex, Karpenko, Alex, Passos, Alex Tachard, Neitz, Alexander, Prokofiev, Alexander, Wei, Alexander, Tam, Allison, Bennett, Ally, Kumar, Ananya, Saraiva, Andre, Vallone, Andrea, Duberstein, Andrew, Kondrich, Andrew, Mishchenko, Andrey, Applebaum, Andy, Jiang, Angela, Nair, Ashvin, Zoph, Barret, Ghorbani, Behrooz, Rossen, Ben, Sokolowsky, Benjamin, Barak, Boaz, McGrew, Bob, Minaiev, Borys, Hao, Botao, Baker, Bowen, Houghton, Brandon, McKinzie, Brandon, Eastman, Brydon, Lugaresi, Camillo, Bassin, Cary, Hudson, Cary, Li, Chak Ming, de Bourcy, Charles, Voss, Chelsea, Shen, Chen, Zhang, Chong, Koch, Chris, Orsinger, Chris, Hesse, Christopher, Fischer, Claudia, Chan, Clive, Roberts, Dan, Kappler, Daniel, Levy, Daniel, Selsam, Daniel, Dohan, David, Farhi, David, Mely, David, Robinson, David, Tsipras, Dimitris, Li, Doug, Oprica, Dragos, Freeman, Eben, Zhang, Eddie, Wong, Edmund, Proehl, Elizabeth, Cheung, Enoch, Mitchell, Eric, Wallace, Eric, Ritter, Erik, Mays, Evan, Wang, Fan, Such, Felipe Petroski, Raso, Filippo, Leoni, Florencia, Tsimpourlas, Foivos, Song, Francis, von Lohmann, Fred, Sulit, Freddie, Salmon, Geoff, Parascandolo, Giambattista, Chabot, Gildas, Zhao, Grace, Brockman, Greg, Leclerc, Guillaume, Salman, Hadi, Bao, Haiming, Sheng, Hao, Andrin, Hart, Bagherinezhad, Hessam, Ren, Hongyu, Lightman, Hunter, Chung, Hyung Won, Kivlichan, Ian, O'Connell, Ian, Osband, Ian, Gilaberte, Ignasi Clavera, Akkaya, Ilge, Kostrikov, Ilya, Sutskever, Ilya, Kofman, Irina, Pachocki, Jakub, Lennon, James, Wei, Jason, Harb, Jean, Twore, Jerry, Feng, Jiacheng, Yu, Jiahui, Weng, Jiayi, Tang, Jie, Yu, Jieqi, Candela, Joaquin Quiñonero, Palermo, Joe, Parish, Joel, Heidecke, Johannes, Hallman, John, Rizzo, John, Gordon, Jonathan, Uesato, Jonathan, Ward, Jonathan, Huizinga, Joost, Wang, Julie, Chen, Kai, Xiao, Kai, Singhal, Karan, Nguyen, Karina, Cobbe, Karl, Shi, Katy, Wood, Kayla, Rimbach, Kendra, Gu-Lemberg, Keren, Liu, Kevin, Lu, Kevin, Stone, Kevin, Yu, Kevin, Ahmad, Lama, Yang, Lauren, Liu, Leo, Maksin, Leon, Ho, Leyton, Fedus, Liam, Weng, Lilian, Li, Linden, McCallum, Lindsay, Held, Lindsey, Kuhn, Lorenz, Kondraciuk, Lukas, Kaiser, Lukasz, Metz, Luke, Boyd, Madelaine, Trebacz, Maja, Joglekar, Manas, Chen, Mark, Tintor, Marko, Meyer, Mason, Jones, Matt, Kaufer, Matt, Schwarzer, Max, Shah, Meghan, Yatbaz, Mehmet, Guan, Melody Y., Xu, Mengyuan, Yan, Mengyuan, Glaese, Mia, Chen, Mianna, Lampe, Michael, Malek, Michael, Wang, Michele, Fradin, Michelle, McClay, Mike, Pavlov, Mikhail, Wang, Miles, Wang, Mingxuan, Murati, Mira, Bavarian, Mo, Rohaninejad, Mostafa, McAleese, Nat, Chowdhury, Neil, Ryder, Nick, Tezak, Nikolas, Brown, Noam, Nachum, Ofir, Boiko, Oleg, Murk, Oleg, Watkins, Olivia, Chao, Patrick, Ashbourne, Paul, Izmailov, Pavel, Zhokhov, Peter, Dias, Rachel, Arora, Rahul, Lin, Randall, Lopes, Rapha Gontijo, Gaon, Raz, Miyara, Reah, Leike, Reimar, Hwang, Renny, Garg, Rhythm, Brown, Robin, James, Roshan, Shu, Rui, Cheu, Ryan, Greene, Ryan, Jain, Saachi, Altman, Sam, Toizer, Sam, Toyer, Sam, Miserendino, Samuel, Agarwal, Sandhini, Hernandez, Santiago, Baker, Sasha, McKinney, Scott, Yan, Scottie, Zhao, Shengjia, Hu, Shengli, Santurkar, Shibani, Chaudhuri, Shraman Ray, Zhang, Shuyuan, Fu, Siyuan, Papay, Spencer, Lin, Steph, Balaji, Suchir, Sanjeev, Suvansh, Sidor, Szymon, Broda, Tal, Clark, Aidan, Wang, Tao, Gordon, Taylor, Sanders, Ted, Patwardhan, Tejal, Sottiaux, Thibault, Degry, Thomas, Dimson, Thomas, Zheng, Tianhao, Garipov, Timur, Stasi, Tom, Bansal, Trapit, Creech, Trevor, Peterson, Troy, Eloundou, Tyna, Qi, Valerie, Kosaraju, Vineet, Monaco, Vinnie, Pong, Vitchyr, Fomenko, Vlad, Zheng, Weiyi, Zhou, Wenda, McCabe, Wes, Zaremba, Wojciech, Dubois, Yann, Lu, Yinghai, Chen, Yining, Cha, Young, Bai, Yu, He, Yuchen, Zhang, Yuchen, Wang, Yunyun, Shao, Zheng, and Li, Zhuohan
- Subjects
Computer Science - Artificial Intelligence - Abstract
The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.
- Published
- 2024
32. WiP: Deception-in-Depth Using Multiple Layers of Deception
- Author
-
Landsborough, Jason, Rowe, Neil C., Nguyen, Thuy D., and Fugate, Sunny
- Subjects
Computer Science - Cryptography and Security - Abstract
Deception is being increasingly explored as a cyberdefense strategy to protect operational systems. We are studying implementation of deception-in-depth strategies with initially three logical layers: network, host, and data. We draw ideas from military deception, network orchestration, software deception, file deception, fake honeypots, and moving-target defenses. We are building a prototype representing our ideas and will be testing it in several adversarial environments. We hope to show that deploying a broad range of deception techniques can be more effective in protecting systems than deploying single techniques. Unlike traditional deception methods that try to encourage active engagement from attackers to collect intelligence, we focus on deceptions that can be used on real machines to discourage attacks., Comment: Presented at HoTSoS 2024
- Published
- 2024
33. Counting Defiers in Health Care with a Design-Based Likelihood for the Joint Distribution of Potential Outcomes
- Author
-
Christy, Neil and Kowalski, Amanda Ellen
- Subjects
Economics - Econometrics - Abstract
We present a design-based model of a randomized experiment in which the observed outcomes are informative about the joint distribution of potential outcomes within the experimental sample. We derive a likelihood function that maintains curvature with respect to the joint distribution of potential outcomes, even when holding the marginal distributions of potential outcomes constant -- curvature that is not maintained in a sampling-based likelihood that imposes a large sample assumption. Our proposed decision rule guesses the joint distribution of potential outcomes in the sample as the distribution that maximizes the likelihood. We show that this decision rule is Bayes optimal under a uniform prior. Our optimal decision rule differs from and significantly outperforms a ``monotonicity'' decision rule that assumes no defiers or no compliers. In sample sizes ranging from 2 to 40, we show that the Bayes expected utility of the optimal rule increases relative to the monotonicity rule as the sample size increases. In two experiments in health care, we show that the joint distribution of potential outcomes that maximizes the likelihood need not include compliers even when the average outcome in the intervention group exceeds the average outcome in the control group, and that the maximizer of the likelihood may include both compliers and defiers, even when the average intervention effect is large and statistically significant., Comment: 22 pages, 5 figures
- Published
- 2024
34. What determines the breakup length of a jet?
- Author
-
Kooij, Stefan, Jordan, Daniel T. A., van Rijn, Cees J. M., Ribe, Neil, and Bonn, Daniel
- Subjects
Physics - Fluid Dynamics - Abstract
The breakup of a capillary jet into drops is believed to be governed by initial disturbances on the surface of the jet that grow exponentially. The disturbances are often assumed to be due to external sources of noise, to turbulence, or to imperfections of the nozzle. However, even in conditions where external perturbations are minimal, the jet's length cannot grow indefinitely, suggesting that its fragmentation cannot be entirely attributed to such factors. Here we show that the initial disturbances are thermal capillary waves. By extrapolating the observed growth of the instability back in time, we demonstrate that the initiating disturbances must be of the order of an {\aa}ngstr\"om, consistent with fluctuations induced by thermal noise. Further, by performing many experiments with different nozzles, we find no significant variation in breakup length linked to nozzle type, shape or inner roughness. By systematically varying the jet diameter and velocity, and the surface tension, viscosity, and density of the fluid, we validate our thermal disturbance model over four orders of magnitude in jet length; seven orders of magnitude if simulations of nanojets are included., Comment: 6 pages, 5 figures
- Published
- 2024
35. Hyper Stealth Dark Matter and Long-Lived Particles
- Author
-
Fleming, George T., Kribs, Graham D., Neil, Ethan T., Schaich, David, and Vranas, Pavlos M.
- Subjects
High Energy Physics - Phenomenology ,High Energy Physics - Lattice - Abstract
A new dark matter candidate is proposed that arises as the lightest baryon from a confining $SU(N)$ gauge theory which equilibrates with the Standard Model only through electroweak interactions. Surprisingly, this candidate can be as light as a few GeV. The lower bound arises from the intersection of two competing requirements: i) the equilibration sector of the model must be sufficiently heavy, at least several TeV, to avoid bounds from colliders, and ii) the lightest dark meson (that may be the dark $\eta'$, $\sigma$, or the lightest glueball) has suppressed interactions with the SM, and must decay before BBN. The low energy dark sector consists of one flavor that is electrically neutral and an almost electroweak singlet. The dark matter candidate is the lightest baryon consisting of $N$ of these light flavors leading to a highly suppressed elastic scattering rate with the SM. The equilibration sector consists of vector-like dark quarks that transform under the electroweak group, ensuring that the dark sector can reach thermal equilibrium with the SM in the early Universe. The lightest dark meson lifetimes vary between $10^{-3} \lesssim c \tau \lesssim 10^7$~meters, providing an outstanding target for LHC production and experimental detection. We delineate the interplay between the lifetime of the light mesons, the suppressed direct detection cross section of the lightest baryon, and the scale of equilibration sector that can be probed at the LHC., Comment: 20 pages, 8 figures
- Published
- 2024
36. When Copilot Becomes Autopilot: Generative AI's Critical Risk to Knowledge Work and a Critical Solution
- Author
-
Sarkar, Advait, Xiaotong, Xu, Toronto, Neil, Drosos, Ian, and Poelitz, Christian
- Subjects
Computer Science - Human-Computer Interaction - Abstract
Generative AI, with its tendency to "hallucinate" incorrect results, may pose a risk to knowledge work by introducing errors. On the other hand, it may also provide unprecedented opportunities for users, particularly non-experts, to learn and apply advanced software features and greatly increase the scope and complexity of tasks they can successfully achieve. As an example of a complex knowledge workflow that is subject to risks and opportunities from generative AI, we consider the spreadsheet. AI hallucinations are an important challenge, but they are not the greatest risk posed by generative AI to spreadsheet workflows. Rather, as more work can be safely delegated to AI, the risk is that human critical thinking -- the ability to holistically and rigorously evaluate a problem and its solutions -- is degraded in the process. The solution is to design the interfaces of generative AI systems deliberately to foster and encourage critical thinking in knowledge work, building primarily on a long history of research on critical thinking tools for education. We discuss a prototype system for the activity of critical shortlisting in spreadsheets. The system uses generative AI to suggest shortlisting criteria and applies these criteria to sort rows in a spreadsheet. It also generates "provocations": short text snippets that critique the AI-generated criteria, highlighting risks, shortcomings, and alternatives. Our prototype opens up a rich and completely unexplored design space of critical thinking tools for modern AI-assisted knowledge work. We outline a research agenda for AI as a critic or provocateur, including questions about where and when provocations should appear, their form and content, and potential design trade-offs.
- Published
- 2024
37. Lorentzian Residual Neural Networks
- Author
-
He, Neil, Yang, Menglin, and Ying, Rex
- Subjects
Computer Science - Machine Learning - Abstract
Hyperbolic neural networks have emerged as a powerful tool for modeling hierarchical data structures prevalent in real-world datasets. Notably, residual connections, which facilitate the direct flow of information across layers, have been instrumental in the success of deep neural networks. However, current methods for constructing hyperbolic residual networks suffer from limitations such as increased model complexity, numerical instability, and errors due to multiple mappings to and from the tangent space. To address these limitations, we introduce LResNet, a novel Lorentzian residual neural network based on the weighted Lorentzian centroid in the Lorentz model of hyperbolic geometry. Our method enables the efficient integration of residual connections in Lorentz hyperbolic neural networks while preserving their hierarchical representation capabilities. We demonstrate that our method can theoretically derive previous methods while offering improved stability, efficiency, and effectiveness. Extensive experiments on both graph and vision tasks showcase the superior performance and robustness of our method compared to state-of-the-art Euclidean and hyperbolic alternatives. Our findings highlight the potential of LResNet for building more expressive neural networks in hyperbolic embedding space as a generally applicable method to multiple architectures, including CNNs, GNNs, and graph Transformers., Comment: 12 pages, 3 figures, KDD 2025
- Published
- 2024
38. Race Discrimination in Internet Advertising: Evidence From a Field Experiment
- Author
-
Sehgal, Neil K. R. and Svirsky, Dan
- Subjects
Computer Science - Computers and Society ,Computer Science - Human-Computer Interaction ,Computer Science - Social and Information Networks ,Economics - General Economics - Abstract
We present the results of an experiment documenting racial bias on Meta's Advertising Platform in Brazil and the United States. We find that darker skin complexions are penalized, leading to real economic consequences. For every \$1,000 an advertiser spends on ads with models with light-skin complexions, that advertiser would have to spend \$1,159 to achieve the same level of engagement using photos of darker skin complexion models. Meta's budget optimization tool reinforces these viewer biases. When pictures of models with light and dark complexions are allocated a shared budget, Meta funnels roughly 64\% of the budget towards photos featuring lighter skin complexions.
- Published
- 2024
39. Residual paramodularity of a certain Calabi-Yau threefold
- Author
-
Dummigan, Neil and Tornaría, Gonzalo
- Subjects
Mathematics - Number Theory ,11F33, 11F41, 11F46, 11G40, 14J32 - Abstract
We prove congruences of Hecke eigenvalues between cuspidal Hilbert newforms $f_{79}$ and $h_{79}$ over $F=\mathbb Q(\sqrt{5})$, of weights (2,2) and (2,4) respectively, level of norm 79. In the main example, the modulus is a divisor of 5 in some coefficient field, in the secondary example a divisor of 2. The former allows us to prove that the 4-dimensional mod-5 representation of $\mathrm{Gal}(\overline{\mathbb Q} / \mathbb Q)$ on the 3rd cohomology of a certain Calabi-Yau threefold comes from a Siegel modular form $F_{79}$ of genus 2, weight 3 and paramodular level 79. This is a weak form of a conjecture of Golyshev and van Straten. In aid of this, we prove also a congruence of Hecke eigenvalues between $F_{79}$ and the Johnson-Leung-Roberts lift $\mathrm{JR}(h_{79})$, which has weight 3 and paramodular level $79\times 5^2$., Comment: 16 pages, submitted; companion code and data available at https://www.cmat.edu.uy/cnt/cy79/
- Published
- 2024
40. F\o{}lner, Banach, and translation density are equal and other new results about density in left amenable semigroups
- Author
-
Glasscock, Daniel, Hindman, Neil, and Strauss, Dona
- Subjects
Mathematics - Combinatorics ,20M10, 05C42 - Abstract
In any semigroup $S$ satisfying the Strong Folner Condition, there are three natural notions of density for a subset $A$ of $S$: Folner density $d(A)$, Banach density $d^*(A)$, and translation density $d_t(A)$. If $S$ is commutative or left cancellative, it is known that these three notions coincide. We shall show that these notions coincide for every semigroup $S$ which satisfies the Strong Folner Condition. Using this fact, we solve a problem that has been open for decades, showing that the set of ultrafilters every member of which has positive Folner density is a two sided ideal of $\beta S$. We also show that, if $S$ is a left amenable semigroup, then the set of ultrafilters every member of which has positive Banach density is a two sided ideal of $\beta S$. We investigate the density properties of subsets of $S$ in the case in which the minimal left ideals of the Stone-\v{C}ech compactification $\beta S$ are singletons. This occurs in many familiar examples, including all semilattices and all semigroups which have a right zero. We show that this is equivalent to the statement that $S$ satisfies the Strong Folner Condition and that, for every subset $A$ of $S$, $d(A)\in \{0,1\}$. We also examine the relation between the density properties of two semigroups when one is a quotient of the other. The Folner density of a subset of $S$ is always determined by some Folner net in $S$. We show that an arbitrary Folner net in $S$ determines the density of all of the subsets of $S$. And we prove that, if $S$ and $T$ are left amenable semigroups, then $d^*(A\times B)=d^*(A)d^*(B)$ for every subset $A$ of $S$ and every subset $B$ of $T$., Comment: 24 pages
- Published
- 2024
41. Rapid inference for individual binaries and a stochastic background with Pulsar Timing Array data
- Author
-
Gundersen, Aiden and Cornish, Neil
- Subjects
General Relativity and Quantum Cosmology - Abstract
The analysis of pulsar timing array data has provided evidence for a gravitational wave background in the NanoHertz band. This raises the question of what is the source of the signal, is it astrophysical or cosmological in origin? If the signal originates from a population of supermassive black hole binaries, as is generally assumed, we can expect to see evidence for both anisotropy and to be able to resolve signals from individual binaries as more data are collected. The anisotropy and resolvable systems are caused by a small number of loud signals that stand out from the crowd. Here we focus on the joint detection of individual signals and a stochastic background. While methods have previously been developed to perform such an analysis, they are currently held back by the cost of computing the joint likelihood function. Each individual source is described by $N=8+2N_p$ parameters, where $N_p$ are the number of pulsars in the array. With the latest combined data sets having over one hundred pulsars, the parameter space is very large, and consequently, it takes a large number of likelihood evaluations to explore these models. Here we present a new approach that extends the Fourier basis method, previously introduced to accelerate analyses for stochastic signals, to also include deterministic signals. Key elements of the method are that the likelihood evaluations are per-pulsar, avoiding expensive operations on large matrices, and the templates for individual binaries can be computed analytically or using fast Fourier methods on a sparsely sampled grid of time samples. The net result is an analysis that is orders of magnitude faster than previous approaches., Comment: 9 pages, 5 figures, 1 table
- Published
- 2024
42. A compact scalable phase modulator with zero static power consumption for visible integrated photonics
- Author
-
MacFarlane, Neil and Aflatouni, Firooz
- Subjects
Physics - Optics ,Physics - Applied Physics - Abstract
Optical modulators in the visible regime have far-reaching applications from biophotonics to quantum science. Implementations of such optical phase modulators on a complementary metal-oxide-semiconductor (CMOS) compatible platform have been mainly limited to utilization of the thermo-optic effect, liquid crystal technology, as well as piezo-optomechanical effects. Despite excellent performance, the demonstrations using the thermo-optic effect and liquid crystal technology both suffer from limited modulation speed. Moreover, the demonstrations utilizing piezo-optomechanical effects, require very large footprints due to a weak modulation efficiency. Here, we report the demonstration of the first highly scalable compact CMOS-compatible phase modulator in the visible regime based on altering the refractive index of an indium-tin oxide capacitive stack over a Si${_3}$N${_4}$ waveguide through the charge accumulation effect. The implemented modulator achieves a two orders-of-magnitude larger bandwidth compared to thermo-optic and liquid crystal based counterparts and close to 3 orders-of-magnitude higher modulation efficiency with about two orders-of-magnitude smaller footprint compared to piezo-optomechanical modulators. The 50 ${\mu}$m long phase modulator achieves a modulation efficiency, V$_{\pi}$L, of 0.06 V$.$cm at a zero static power consumption and a 31 MHz bandwidth at 637.9 nm., Comment: 24 pages, 4 figures
- Published
- 2024
43. Online optimisation for dynamic electrical impedance tomography
- Author
-
Dizon, Neil, Jauhiainen, Jyrki, and Valkonen, Tuomo
- Subjects
Mathematics - Optimization and Control ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Online optimisation studies the convergence of optimisation methods as the data embedded in the problem changes. Based on this idea, we propose a primal dual online method for nonlinear time-discrete inverse problems. We analyse the method through regret theory and demonstrate its performance in real-time monitoring of moving bodies in a fluid with Electrical Impedance Tomography (EIT). To do so, we also prove the second-order differentiability of the Complete Electrode Model (CEM) solution operator on $L^\infty$.
- Published
- 2024
44. How to fit large complexity classes into TFNP
- Author
-
Thapen, Neil
- Subjects
Computer Science - Computational Complexity ,Computer Science - Logic in Computer Science - Abstract
Subclasses of TFNP (total functional NP) are usually defined by specifying a complete problem, which is necessarily in TFNP, and including all problems many-one reducible to it. We study two notions of how a TFNP problem can be reducible to an object, such as a complexity class, outside TFNP. This gives rise to subclasses of TFNP which capture some properties of that outside object. We show that well-known subclasses can arise in this way, for example PPA from reducibility to parity P and PLS from reducibility to P^NP. We study subclasses arising from PSPACE and the polynomial hierarchy, and show that they are characterized by the propositional proof systems Frege and constant-depth Frege, extending the known pairings between natural TFNP subclasses and proof systems. We study approximate counting from this point of view, and look for a subclass of TFNP that gives a natural home to combinatorial principles such as Ramsey which can be proved using approximate counting. We relate this to the recently-studied Long choice and Short choice problems., Comment: 55 pages
- Published
- 2024
45. Black Mirrors: CPT-Symmetric Alternatives to Black Holes
- Author
-
Tzanavaris, Kostas, Boyle, Latham, and Turok, Neil
- Subjects
High Energy Physics - Theory ,General Relativity and Quantum Cosmology - Abstract
Einstein's equations imply that a gravitationally collapsed object forms an event horizon. But what lies on the other side of this horizon? In this paper, we question the reality of the conventional solution (the black hole), and point out another, topologically distinct solution: the \textit{black mirror}. In the black hole solution, the horizon connects the exterior metric to an interior metric which contains a curvature singularity. In the black mirror, the horizon instead connects the exterior metric to its own CPT mirror image, yielding a solution with no curvature singularities. We give the general stationary (charged, rotating) black mirror solution explicitly, and also describe the general black mirror formed by gravitational collapse. The black mirror is the relevant stationary point when the quantum path integral is equipped with suitably CPT-symmetric boundary conditions, that we propose. It appears to avoid many vexing puzzles which plague the conventional black hole., Comment: 6+3 pages, 6 figures
- Published
- 2024
46. SIRI-2 Detection of the Gamma-ray Burst 221009A
- Author
-
Mitchell, Lee J., Finke, Justin D., Phlips, Bernard, Johnson, W. Neil, and Kong, Emily
- Subjects
Astrophysics - High Energy Astrophysical Phenomena - Abstract
SIRI-2 is a collection of Strontium Iodide gamma-ray detectors sensitive at approximately 400 keV to 10 MeV, launched on the Department of Defense's STPSat-6 to geosynchronous orbit. SIRI-2 detected the gamma-ray burst (GRB) 221009A and, unlike most GRB detectors, was not saturated and did not require any pulse pile-up corrections. The energetics of this burst as measured by SIRI-2 are consistent with those found by other instruments, and the Band function fits to the spectra are consistent with that from the unsaturated Insight and GECAM instruments, and similar to corrected spectra from the Fermi Gamma-ray Burst Monitor, but softer than those found by Konus-Wind when that instrument was saturated. The total fluence measured with SIRI-2 was measured to be 0.140 +/- 0.002 erg cm-2, lower than other instruments, likely due to the increasing background of SIRI-2 forcing the calculation to use a smaller time interval. An extrapolation of the distributions of fluences from GRBs to the fluence of 221009A measured with SIRI-2 indicates bursts brighter than this one should occur about once every 4,000 years., Comment: 9 pages, 8 figures. Accepted by ApJ
- Published
- 2024
47. Arithmetic localisation and completion of spectra
- Author
-
Strickland, Neil
- Subjects
Mathematics - Algebraic Topology ,55P60 - Abstract
This is an exposition of facts about p-local spectra, p-complete spectra and modules over the p-complete sphere spectrum, including homological criteria for finiteness. Most things are well-known to the experts, with a couple of potential exceptions: every dualisable p-complete spectrum is the p-completion of a finite spectrum, and the category of modules over the p-complete sphere has homological Brown representability.
- Published
- 2024
48. Visions in Quantum Gravity
- Author
-
Buoninfante, Luca, Knorr, Benjamin, Kumar, K. Sravan, Platania, Alessia, Anselmi, Damiano, Basile, Ivano, Bjerrum-Bohr, N. Emil J., Brandenberger, Robert, González, Mariana Carrillo, Davis, Anne-Christine, Dittrich, Bianca, Di Vecchia, Paolo, Donoghue, John F., Dowker, Fay, Dvali, Gia, Eichhorn, Astrid, Giddings, Steven B., Gnecchi, Alessandra, Gubitosi, Giulia, Heisenberg, Lavinia, Kallosh, Renata, Koshelev, Alexey S., Liberati, Stefano, Modesto, Leonardo, Moniz, Paulo, Oriti, Daniele, Papadoulaki, Olga, Pawlowski, Jan M., Percacci, Roberto, Rachwał, Lesław, Sakellariadou, Mairi, Salvio, Alberto, Stelle, Kellogg, Surya, Sumati, Tseytlin, Arkady, Turok, Neil, Van Riet, Thomas, and Woodard, Richard P.
- Subjects
High Energy Physics - Theory ,Astrophysics - Cosmology and Nongalactic Astrophysics ,General Relativity and Quantum Cosmology ,High Energy Physics - Phenomenology ,Quantum Physics - Abstract
To deepen our understanding of Quantum Gravity and its connections with black holes and cosmology, building a common language and exchanging ideas across different approaches is crucial. The Nordita Program "Quantum Gravity: from gravitational effective field theories to ultraviolet complete approaches" created a platform for extensive discussions, aimed at pinpointing both common grounds and sources of disagreements, with the hope of generating ideas and driving progress in the field. This contribution summarizes the twelve topical discussions held during the program and collects individual thoughts of speakers and panelists on the future of the field in light of these discussions., Comment: Collection of summaries of twelve topical panel discussions and individual thoughts of speakers and panelists, Nordita Scientific Program "Quantum Gravity: from gravitational EFTs to UV complete approaches". 62 pages + references, no figures
- Published
- 2024
49. Adaptation of Wallace's Approach to the Specific Heat of Elemental Solids with Significant Intrinsic Anharmonicity, Particularly the Light Actinide Metals
- Author
-
Mizzi, Christopher A., Phelan, W. Adam, Cook, Matthew S., Chappell, Greta L., Tobash, Paul H., Arellano, David C., Prada, Derek V., Maiorov, Boris, and Harrison, Neil
- Subjects
Condensed Matter - Materials Science - Abstract
The quasiharmonic approximation is the most common method for modeling the specific heat of solids; however, it fails to capture the effects of intrinsic anharmonicity in phonons. In this study, we introduce the "elastic softening approximation," an alternative approach to modelling intrinsic anharmonic effects on thermodynamic quantities which is grounded in Wallace's thermodynamic framework (Thermodynamics of Crystals, 1972) and focused on tracking entropy changes due to the continuous softening of phonons as a function of temperature. A key finding of our study is a direct correlation between Poisson's ratio and the differential rate of phonon softening at finite frequencies, compared to lower frequencies relevant to elastic moduli measurements. We observe that elemental solids such as alpha-Be, diamond, Al, Cu, In, W, Au, and Pb, which span a wide range of Poisson's ratios and exhibit varying degrees of intrinsic anharmonicity, consistently follow this trend. When applied to alpha-U, alpha-Pu, and delta-Pu, our method reveals unusually large anharmonic phonon contributions at elevated temperatures across all three light actinide metals. These findings are attributed to the unique combination of enhanced covalency and softer elastic moduli inherent in the actinides, potentially influenced by their 5f-electron bonding., Comment: 18 pages with 13 figures
- Published
- 2024
50. Efficient user history modeling with amortized inference for deep learning recommendation models
- Author
-
Hertel, Lars, Daftary, Neil, Borisyuk, Fedor, Gupta, Aman, and Mazumder, Rahul
- Subjects
Computer Science - Machine Learning ,Computer Science - Information Retrieval - Abstract
We study user history modeling via Transformer encoders in deep learning recommendation models (DLRM). Such architectures can significantly improve recommendation quality, but usually incur high latency cost necessitating infrastructure upgrades or very small Transformer models. An important part of user history modeling is early fusion of the candidate item and various methods have been studied. We revisit early fusion and compare concatenation of the candidate to each history item against appending it to the end of the list as a separate item. Using the latter method, allows us to reformulate the recently proposed amortized history inference algorithm M-FALCON \cite{zhai2024actions} for the case of DLRM models. We show via experimental results that appending with cross-attention performs on par with concatenation and that amortization significantly reduces inference costs. We conclude with results from deploying this model on the LinkedIn Feed and Ads surfaces, where amortization reduces latency by 30\% compared to non-amortized inference., Comment: 5 pages, 3 figures, WWW 2025
- Published
- 2024
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