Author: "Maxwell, P" - Searchworks@Jio Institute Digital Library Search Results

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1. Essay X-Ray: Using an In-House Academic Writing Tool to Scaffold Academic Skills Support

Author: Laura Key, Chris Till, and Joe Maxwell
Abstract: This paper introduces a project to develop a digital academic writing tool at Leeds Beckett University (LBU). Essay X-ray is an interactive online tool designed to help students get to grips with the structure and style of academic writing and was developed using the Articulate Storyline 360 platform. The aim was to expand LBU's academic skills support for students tasked with essay assignments, especially at Level 4 (first year, undergraduate), enabling independent learning using a self-paced format available open access and 24/7. This would complement existing academic skills provision (one-to-ones, workshops, drop-ins, static online resources), with the interactive element facilitating active, hands-on learning (Lumpkin, Achen and Dodd, 2015). Following a successful development, review and rollout process, the utility of Essay X-ray as an independent learning tool but also as a classroom resource was reported by students and colleagues. Tentative talks about additional versions (Dissertation X-ray, Report X-ray) have taken place, indicating its potential for rollout to other subject areas and assessment types. Finally, in-house digital academic skills tools like Essay X-ray are posited as a potential response to the recent upsurge in Generative Artificial Intelligence (GenAI) tools. Essay X-ray requires users to think critically about essay structure, style and content to create their own original pieces of writing, thus responding to questions about the maintenance of academic integrity in a digital world. These features enable users to develop their essay writing skills, in contrast to passive engagement with a GenAI programme that merely writes an answer for them.
Published: 2024

2. SALSA: Soup-based Alignment Learning for Stronger Adaptation in RLHF

Author: Chegini, Atoosa, Kazemi, Hamid, Mirzadeh, Iman, Yin, Dong, Horton, Maxwell, Nabi, Moin, Farajtabar, Mehrdad, and Alizadeh, Keivan
Subjects: Computer Science - Machine Learning
Abstract: In Large Language Model (LLM) development, Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning models with human values and preferences. RLHF traditionally relies on the Kullback-Leibler (KL) divergence between the current policy and a frozen initial policy as a reference, which is added as a penalty in policy optimization algorithms like Proximal Policy Optimization (PPO). While this constraint prevents models from deviating too far from the initial checkpoint, it limits exploration of the reward landscape, reducing the model's ability to discover higher-quality solutions. As a result, policy optimization is often trapped in a narrow region of the parameter space, leading to suboptimal alignment and performance. This paper presents SALSA (Soup-based Alignment Learning for Stronger Adaptation), a novel approach designed to overcome these limitations by creating a more flexible and better located reference model through weight-space averaging of two independent supervised fine-tuned (SFT) models. This model soup allows for larger deviation in KL divergence and exploring a promising region of the solution space without sacrificing stability. By leveraging this more robust reference model, SALSA fosters better exploration, achieving higher rewards and improving model robustness, out-of-distribution generalization, and performance. We validate the effectiveness of SALSA through extensive experiments on popular open models (Llama2-7B, Mistral-7B, and Gemma-2B) across various benchmarks (MT-Bench, Arena-Hard, UltraFeedback), where it consistently surpasses PPO by fostering deeper exploration and achieving superior alignment in LLMs.
Published: 2024

3. IO Transformer: Evaluating SwinV2-Based Reward Models for Computer Vision

Author: Meyer, Maxwell and Spruyt, Jack
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Transformers and their derivatives have achieved state-of-the-art performance across text, vision, and speech recognition tasks. However, minimal effort has been made to train transformers capable of evaluating the output quality of other models. This paper examines SwinV2-based reward models, called the Input-Output Transformer (IO Transformer) and the Output Transformer. These reward models can be leveraged for tasks such as inference quality evaluation, data categorization, and policy optimization. Our experiments demonstrate highly accurate model output quality assessment across domains where the output is entirely dependent on the input, with the IO Transformer achieving perfect evaluation accuracy on the Change Dataset 25 (CD25). We also explore modified Swin V2 architectures. Ultimately Swin V2 remains on top with a score of 95.41 % on the IO Segmentation Dataset, outperforming the IO Transformer in scenarios where the output is not entirely dependent on the input. Our work expands the application of transformer architectures to reward modeling in computer vision and provides critical insights into optimizing these models for various tasks., Comment: 15 pages, 3 figures, 2 tables
Published: 2024

4. First Light and Reionisation Epoch Simulations (FLARES) XVII: Learning the galaxy-halo connection at high redshifts

Author: Maltz, Maxwell G. A., Thomas, Peter A., Lovell, Christoper C., Roper, William J., Vijayan, Aswin P., Irodotou, Dimitrios, Liao, Shihong, Seeyave, Louise T. C., and Wilkins, Stephen M.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Understanding the galaxy-halo relationship is not only key for elucidating the interplay between baryonic and dark matter, it is essential for creating large mock galaxy catalogues from N-body simulations. High-resolution hydrodynamical simulations are limited to small volumes by their large computational demands, hindering their use for comparisons with wide-field observational surveys. We overcome this limitation by using the First Light and Reionisation Epoch Simulations (FLARES), a suite of high-resolution (M_gas = 1.8 x 10^6 M_Sun) zoom simulations drawn from a large, (3.2 cGpc)^3 box. We use an extremely randomised trees machine learning approach to model the relationship between galaxies and their subhaloes in a wide range of environments. This allows us to build mock catalogues with dynamic ranges that surpass those obtainable through periodic simulations. The low cost of the zoom simulations facilitates multiple runs of the same regions, differing only in the random number seed of the subgrid models; changing this seed introduces a butterfly effect, leading to random differences in the properties of matching galaxies. This randomness cannot be learnt by a deterministic machine learning model, but by sampling the noise and adding it post-facto to our predictions, we are able to recover the distributions of the galaxy properties we predict (stellar mass, star formation rate, metallicity, and size) remarkably well. We also explore the resolution-dependence of our models' performances and find minimal depreciation down to particle resolutions of order M_DM ~ 10^8 M_Sun, enabling the future application of our models to large dark matter-only boxes., Comment: 19 pages, 12 figures. Submitted to MNRAS
Published: 2024

5. Observation of fractional evolution in nonlinear optics

Author: Hoang, Van Thuy, Widjaja, Justin, Qiang, Y. Long, Liu, Maxwell, Alexander, Tristram J., Runge, Antoine F. J., and de Sterke, C. Martijn
Subjects: Physics - Optics
Abstract: The idea of fractional derivatives has a long history that dates back centuries. Apart from their intriguing mathematical properties, fractional derivatives have been studied widely in physics, for example in quantum mechanics and generally in systems with nonlocal temporal or spatial interactions. However, systematic experiments have been rare due to challenges associated with the physical implementation. Here we report the observation and full characterization of a family of temporal optical solitons that are governed by a nonlinear wave equation with a fractional Laplacian. This equation has solutions with unique properties such as non-exponential tails and a very small time-bandwidth product.
Published: 2024

6. EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography

Author: Yang, Jehan, Soh, Maxwell, Lieu, Vivianna, Weber, Douglas J, and Erickson, Zackory
Subjects: Computer Science - Machine Learning
Abstract: This paper introduces the first generalization and adaptation benchmark using machine learning for evaluating out-of-distribution performance of electromyography (EMG) classification algorithms. The ability of an EMG classifier to handle inputs drawn from a different distribution than the training distribution is critical for real-world deployment as a control interface. By predicting the user's intended gesture using EMG signals, we can create a wearable solution to control assistive technologies, such as computers, prosthetics, and mobile manipulator robots. This new out-of-distribution benchmark consists of two major tasks that have utility for building robust and adaptable control interfaces: 1) intersubject classification and 2) adaptation using train-test splits for time-series. This benchmark spans nine datasets--the largest collection of EMG datasets in a benchmark. Among these, a new dataset is introduced, featuring a novel, easy-to-wear high-density EMG wearable for data collection. The lack of open-source benchmarks has made comparing accuracy results between papers challenging for the EMG research community. This new benchmark provides researchers with a valuable resource for analyzing practical measures of out-of-distribution performance for EMG datasets. Our code and data from our new dataset can be found at emgbench.github.io.
Published: 2024

7. Real classical shadows

Author: West, Maxwell, Mele, Antonio Anna, Larocca, Martin, and Cerezo, M.
Subjects: Quantum Physics
Abstract: Efficiently learning expectation values of a quantum state using classical shadow tomography has become a fundamental task in quantum information theory. In a classical shadows protocol, one measures a state in a chosen basis $\mathcal{W}$ after it has evolved under a unitary transformation randomly sampled from a chosen distribution $\mathcal{U}$. In this work we study the case where $\mathcal{U}$ corresponds to either local or global orthogonal Clifford gates, and $\mathcal{W}$ consists of real-valued vectors. Our results show that for various situations of interest, this ``real'' classical shadow protocol improves the sample complexity over the standard scheme based on general Clifford unitaries. For example, when one is interested in estimating the expectation values of arbitrary real-valued observables, global orthogonal Cliffords decrease the required number of samples by a factor of two. More dramatically, for $k$-local observables composed only of real-valued Pauli operators, sampling local orthogonal Cliffords leads to a reduction by an exponential-in-$k$ factor in the sample complexity over local unitary Cliffords. Finally, we show that by measuring in a basis containing complex-valued vectors, orthogonal shadows can, in the limit of large system size, exactly reproduce the original unitary shadows protocol., Comment: 7+12 pages, 1+1 figures
Published: 2024

8. Dynamic Information Sub-Selection for Decision Support

Author: Huang, Hung-Tien, Lennon, Maxwell, Brahmavar, Shreyas Bhat, Sylvia, Sean, and Oliva, Junier B.
Subjects: Computer Science - Machine Learning
Abstract: We introduce Dynamic Information Sub-Selection (DISS), a novel framework of AI assistance designed to enhance the performance of black-box decision-makers by tailoring their information processing on a per-instance basis. Blackbox decision-makers (e.g., humans or real-time systems) often face challenges in processing all possible information at hand (e.g., due to cognitive biases or resource constraints), which can degrade decision efficacy. DISS addresses these challenges through policies that dynamically select the most effective features and options to forward to the black-box decision-maker for prediction. We develop a scalable frequentist data acquisition strategy and a decision-maker mimicking technique for enhanced budget efficiency. We explore several impactful applications of DISS, including biased decision-maker support, expert assignment optimization, large language model decision support, and interpretability. Empirical validation of our proposed DISS methodology shows superior performance to state-of-the-art methods across various applications.
Published: 2024

9. A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications

Author: Vangaru, Sriniketh, Rosen, Daniel, Green, Dylan, Rodriguez, Raphael, Wiecek, Maxwell, Johnson, Amos, Jones, Alyse M., and Headley, William C.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems, Computer Science - Networking and Internet Architecture
Abstract: Technological trends show that Radio Frequency Reinforcement Learning (RFRL) will play a prominent role in the wireless communication systems of the future. Applications of RFRL range from military communications jamming to enhancing WiFi networks. Before deploying algorithms for these purposes, they must be trained in a simulation environment to ensure adequate performance. For this reason, we previously created the RFRL Gym: a standardized, accessible tool for the development and testing of reinforcement learning (RL) algorithms in the wireless communications space. This environment leveraged the OpenAI Gym framework and featured customizable simulation scenarios within the RF spectrum. However, the RFRL Gym was limited to training a single RL agent per simulation; this is not ideal, as most real-world RF scenarios will contain multiple intelligent agents in cooperative, competitive, or mixed settings, which is a natural consequence of spectrum congestion. Therefore, through integration with Ray RLlib, multi-agent reinforcement learning (MARL) functionality for training and assessment has been added to the RFRL Gym, making it even more of a robust tool for RF spectrum simulation. This paper provides an overview of the updated RFRL Gym environment. In this work, the general framework of the tool is described relative to comparable existing resources, highlighting the significant additions and refactoring we have applied to the Gym. Afterward, results from testing various RF scenarios in the MARL environment and future additions are discussed., Comment: Accepted to IEEE CCNC 2025
Published: 2024

10. User-Centered Design of Socially Assistive Robotic Combined with Non-Immersive Virtual Reality-based Dyadic Activities for Older Adults Residing in Long Term Care Facilities

Author: Ghosh, Ritam, Khan, Nibraas, Migovich, Miroslava, Tate, Judith A., Maxwell, Cathy, Latshaw, Emily, Newhouse, Paul, Scharre, Douglas W., Tan, Alai, Colopietro, Kelley, Mion, Lorraine C., and Sarkar, Nilanjan
Subjects: Computer Science - Human-Computer Interaction, Electrical Engineering and Systems Science - Systems and Control
Abstract: Apathy impairs the quality of life for older adults and their care providers. While few pharmacological remedies exist, current non-pharmacologic approaches are resource intensive. To address these concerns, this study utilizes a user-centered design (UCD) process to develop and test a set of dyadic activities that provide physical, cognitive, and social stimuli to older adults residing in long-term care (LTC) communities. Within the design, a novel framework that combines socially assistive robots and non-immersive virtual reality (SAR-VR) emphasizing human-robot interaction (HRI) and human-computer interaction (HCI) is utilized with the roles of the robots being coach and entertainer. An interdisciplinary team of engineers, nurses, and physicians collaborated with an advisory panel comprising LTC activity coordinators, staff, and residents to prototype the activities. The study resulted in four virtual activities: three with the humanoid robot, Nao, and one with the animal robot, Aibo. Fourteen participants tested the acceptability of the different components of the system and provided feedback at different stages of development. Participant approval increased significantly over successive iterations of the system highlighting the importance of stakeholder feedback. Five LTC staff members successfully set up the system with minimal help from the researchers, demonstrating the usability of the system for caregivers. Rationale for activity selection, design changes, and both quantitative and qualitative results on the acceptability and usability of the system have been presented. The paper discusses the challenges encountered in developing activities for older adults in LTCs and underscores the necessity of the UCD process to address them.
Published: 2024

11. Search for gravitational waves emitted from SN 2023ixf

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., Gong, Y., González, G., Goodarzi, P., Goode, S., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Govorkova, K., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, A., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurs, J., Gutierrez, N., Guzman, F., H, H. -Y., Haba, D., Haberland, M., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Hardison, A. R., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hart, J., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, R., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Henderson-Sapir, O., Hendry, M., Heng, I. S., Hennes, E., Henshaw, C., Hertog, T., Heurs, M., Hewitt, A. L., Heyns, J., Higginbotham, S., Hild, S., Hill, S., Himemoto, Y., Hirata, N., Hirose, C., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Holmes, Z. J., Holz, D. E., Honet, L., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huddart, A. D., Hughey, B., Hui, D. C. 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Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table
Published: 2024

12. Visible-Light High-Contrast Imaging and Polarimetry with SCExAO/VAMPIRES

Author: Lucas, Miles, Norris, Barnaby, Guyon, Olivier, Bottom, Michael, Deo, Vincent, Vievard, Sébastian, Lozi, Julien, Ahn, Kyohoon, Ashcraft, Jaren, Currie, Thayne, Doelman, David, Kudo, Tomoyuki, Leboulleux, Lucie, Lilley, Lucinda, Millar-Blanchaer, Maxwell, Safonov, Boris, Tuthill, Peter, Uyama, Taichi, Walk, Aidan, and Zhang, Manxuan
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: We present significant upgrades to the VAMPIRES instrument, a visible-light (600 nm to 800 nm) high-contrast imaging polarimeter integrated within SCExAO on the Subaru telescope. Key enhancements include new qCMOS detectors, coronagraphs, polarization optics, and a multiband imaging mode, improving sensitivity, resolution, and efficiency. These upgrades position VAMPIRES as a powerful tool for studying sub-stellar companions, accreting protoplanets, circumstellar disks, stellar jets, stellar mass-loss shells, and solar system objects. The instrument achieves angular resolutions from 17 mas to 21 mas and Strehl ratios up to 60\%, with 5$\sigma$ contrast limits of $10^{\text{-}4}$ at 0.1'' to $10^{\text{-}6}$ beyond 0.5''. We demonstrate these capabilities through spectro-polarimetric coronagraphic imaging of the HD 169142 circumstellar disk, ADI+SDI imaging of the sub-stellar companion HD 1160B, narrowband H$\alpha$ imaging of the R Aqr emission nebula, and spectro-polarimetric imaging of Neptune., Comment: 36 pages, 33 figures, accepted to PASP
Published: 2024

13. Browsing without Third-Party Cookies: What Do You See?

Author: Lin, Maxwell, Lin, Shihan, Wu, Helen, Wang, Karen, and Yang, Xiaowei
Subjects: Computer Science - Cryptography and Security
Abstract: Third-party web cookies are often used for privacy-invasive behavior tracking. Partly due to privacy concerns, browser vendors have started to block all third-party cookies in recent years. To understand the effects of such third-party cookieless browsing, we crawled and measured the top 10,000 Tranco websites. We developed a framework to remove third-party cookies and analyze the differences between the appearance of web pages with and without these cookies. We find that disabling third-party cookies has no substantial effect on website appearance including layouts, text, and images. This validates the industry-wide shift towards cookieless browsing as a way to protect user privacy without compromising on the user experience., Comment: To appear in IMC '24
Published: 2024
Full Text: View/download PDF

14. SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators

Author: Shafipour, Rasoul, Harrison, David, Horton, Maxwell, Marker, Jeffrey, Bedayat, Houman, Mehta, Sachin, Rastegari, Mohammad, Najibi, Mahyar, and Naderiparizi, Saman
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Large Language Models (LLMs) have transformed natural language processing, but face significant challenges in widespread deployment due to their high runtime cost. In this paper, we introduce SeedLM, a novel post-training compression method that uses seeds of pseudo-random generators to encode and compress model weights. Specifically, for each block of weights, we find a seed that is fed into a Linear Feedback Shift Register (LFSR) during inference to efficiently generate a random matrix. This matrix is then linearly combined with compressed coefficients to reconstruct the weight block. SeedLM reduces memory access and leverages idle compute cycles during inference, effectively speeding up memory-bound tasks by trading compute for fewer memory accesses. Unlike state-of-the-art compression methods that rely on calibration data, our approach is data-free and generalizes well across diverse tasks. Our experiments with Llama 3 70B, which is particularly challenging to compress, show that SeedLM achieves significantly better zero-shot accuracy retention at 4- and 3-bit than state-of-the-art techniques, while maintaining performance comparable to FP16 baselines. Additionally, FPGA-based tests demonstrate that 4-bit SeedLM, as model size increases to 70B, approaches a 4x speed-up over an FP16 Llama 2/3 baseline.
Published: 2024

15. Oogway: Designing, Implementing, and Testing an AUV for RoboSub 2023

Author: Denton, Will, Chiavetta, Lilly, Bryant, Michael, Shah, Vedarsh, Zhu, Rico, Weerts, Ricky, Xue, Phillip, Chen, Vincent, Le, Hung, Lin, Maxwell, Camacho, Austin, Council, Drew, Horowitz, Ethan, Ong, Jackie, Chu, Morgan, and Pool, Alex
Subjects: Computer Science - Robotics
Abstract: The Duke Robotics Club is proud to present our robot for the 2023 RoboSub Competition: Oogway. Oogway marks one of the largest design overhauls in club history. Beyond a revamped formfactor, some of Oogway's notable features include all-new computer vision software, advanced sonar integration, novel acoustics hardware processing, and upgraded stereoscopic cameras. Oogway was built on the principle of independent, well-integrated, and reliable subsystems. Individual components and subsystems were tested and designed separately. Oogway's most advanced capabilities are a result of the tight integration between these subsystems. Such examples include sonar-assisted computer vision algorithms and robot-agnostic controls configured in part through the robot's 3D model. The success of constructing and testing Oogway in under 2 year's time can be attributed to 20+ contributing club members, supporters within Duke's Pratt School of Engineering, and outside sponsors., Comment: arXiv admin note: text overlap with arXiv:2410.09684
Published: 2024

16. Technical Design Review of Duke Robotics Club's Oogway: An AUV for RoboSub 2024

Author: Denton, Will, Bryant, Michael, Chiavetta, Lilly, Shah, Vedarsh, Zhu, Rico, Xue, Philip, Chen, Vincent, Lin, Maxwell, Le, Hung, Camacho, Austin, Galvez, Raul, Yang, Nathan, Ren, Nathanael, Rose, Tyler, Chu, Mathew, Ergashev, Amir, Arya, Saagar, Pieter, Kaelyn, Horowitz, Ethan, Allampallam, Maanav, Zheng, Patrick, Kaarls, Mia, and Wood, June
Subjects: Computer Science - Robotics
Abstract: The Duke Robotics Club is proud to present our robot for the 2024 RoboSub Competition: Oogway. Now in its second year, Oogway has been dramatically upgraded in both its capabilities and reliability. Oogway was built on the principle of independent, well-integrated, and reliable subsystems. Individual components and subsystems were tested and designed separately. Oogway's most advanced capabilities are a result of the tight integration between these subsystems. Such examples include a re-envisioned controls system, an entirely new electrical stack, advanced sonar integration, additional cameras and system monitoring, a new marker dropper, and a watertight capsule mechanism. These additions enabled Oogway to prequalify for Robosub 2024.
Published: 2024

17. On The MCMC Performance In Bernoulli Group Testing And The Random Max Set-Cover Problem

Author: Lovig, Maxwell and Zadik, Ilias
Subjects: Mathematics - Statistics Theory, Computer Science - Data Structures and Algorithms, Mathematics - Probability
Abstract: The group testing problem is a canonical inference task where one seeks to identify $k$ infected individuals out of a population of $n$ people, based on the outcomes of $m$ group tests. Of particular interest is the case of Bernoulli group testing (BGT), where each individual participates in each test independently and with a fixed probability. BGT is known to be an "information-theoretically" optimal design, as there exists a decoder that can identify with high probability as $n$ grows the infected individuals using $m^*=\log_2 \binom{n}{k}$ BGT tests, which is the minimum required number of tests among \emph{all} group testing designs. An important open question in the field is if a polynomial-time decoder exists for BGT which succeeds also with $m^*$ samples. In a recent paper (Iliopoulos, Zadik COLT '21) some evidence was presented (but no proof) that a simple low-temperature MCMC method could succeed. The evidence was based on a first-moment (or "annealed") analysis of the landscape, as well as simulations that show the MCMC success for $n \approx 1000s$. In this work, we prove that, despite the intriguing success in simulations for small $n$, the class of MCMC methods proposed in previous work for BGT with $m^*$ samples takes super-polynomial-in-$n$ time to identify the infected individuals, when $k=n^{\alpha}$ for $\alpha \in (0,1)$ small enough. Towards obtaining our results, we establish the tight max-satisfiability thresholds of the random $k$-set cover problem, a result of potentially independent interest in the study of random constraint satisfaction problems., Comment: 71 pages
Published: 2024

18. A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R. ., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghonge, S., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., Gong, Y., González, G., Goodarzi, P., Goode, S., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Govorkova, K., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, A., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurs, J., Gutierrez, N., Guzman, F., H, H. -Y., Haba, D., Haberland, M., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Hardison, A. R., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hart, J., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, R., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Henderson-Sapir, O., Hendry, M., Heng, I. S., Hennes, E., Henshaw, C., Hertog, T., Heurs, M., Hewitt, A. L., Heyns, J., Higginbotham, S., Hild, S., Hill, S., Himemoto, Y., Hirata, N., Hirose, C., Ho, W. C. G., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Holmes, Z. J., Holz, D. E., Honet, L., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huxford, R., Huynh-Dinh, T., Iampieri, L., Iandolo, G. A., Ianni, M., Iess, A., Imafuku, H., Inayoshi, K., Inoue, Y., Iorio, G., Iqbal, M. H., Irwin, J., Ishikawa, R., Isi, M., Ismail, M. A., Itoh, Y., Iwanaga, H., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, C., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Kubisz, J., Johanson, C., Johns, G. R., Johnson, N. A., Johnston, M. C., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kaku, I., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, T., Katsavounidis, E., Katzman, W., Kaushik, R., Kawabe, K., Kawamoto, R., Kazemi, A., Keitel, D., Kelley-Derzon, J., Kennington, J., Kesharwani, R., Key, J. S., Khadela, R., Khadka, S., Khalili, F. Y., Khan, F., Khan, I., Khanam, T., Khursheed, M., Khusid, N. M., Kiendrebeogo, W., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, S., Kim, Y. -M., Kimball, C., Kinley-Hanlon, M., Kinnear, M., Kissel, J. S., Klimenko, S., Knee, A. M., Knust, N., Kobayashi, K., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kou, X., Koushik, A., Kouvatsos, N., Kovalam, M., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kruska, K., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. Kulur, Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuntimaddi, N., Kuroyanagi, S., Kurth, N. J., Kuwahara, S., Kwak, K., Kwan, K., Kwok, J., Lacaille, G., Lagabbe, P., Laghi, D., Lai, S., Laity, A. H., Lakkis, M. H., Lalande, E., Lalleman, M., Lalremruati, P. C., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rana, A., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Lawrence, M. N., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, K., Lee, R. -K., Lee, R., Lee, S., Lee, Y., Legred, I. N., Lehmann, J., Lehner, L., Jean, M. Le, Lemaître, A., Lenti, M., Leonardi, M., Lequime, M., Leroy, N., Lesovsky, M., Letendre, N., Lethuillier, M., Levin, S. E., Levin, Y., Leyde, K., Li, A. K. Y., Li, K. L., Li, T. G. F., Li, X., Li, Z., Lihos, A., Lin, C-Y., Lin, C. -Y., Lin, E. T., Lin, F., Lin, H., Lin, L. C. -C., Lin, Y. -C., Linde, F., Linker, S. D., Littenberg, T. B., Liu, A., Liu, G. C., Liu, Jian, Villarreal, F. Llamas, Llobera-Querol, J., Lo, R. K. L., Locquet, J. -P., London, L. T., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Lorenzo-Medina, A., Loriette, V., Lormand, M., Losurdo, G., Lott IV, T. P., Lough, J. D., Loughlin, H. A., Lousto, C. O., Lowry, M. J., Lu, N., Lück, H., Lumaca, D., Lundgren, A. P., Lussier, A. W., Ma, L. -T., Ma, S., Ma'arif, M., Macas, R., Macedo, A., MacInnis, M., Maciy, R. R., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Macri, D., Maeda, K., Maenaut, S., Hernandez, I. Magaña, Magare, S. S., Magazzù, C., Magee, R. M., Maggio, E., Maggiore, R., Magnozzi, M., Mahesh, M., Mahesh, S., Maini, M., Majhi, S., Majorana, E., Makarem, C. N., Makelele, E., Malaquias-Reis, J. A., Mali, U., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B., Mansell, G. L., Mansingh, G., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Márka, S., Márka, Z., Markosyan, A. S., Markowitz, A., Maros, E., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, B. B., Martinez, M., Martinez, V., Martini, A., Martinovic, K., Martins, J. C., Martynov, D. V., Marx, E. J., Massaro, L., Masserot, A., Masso-Reid, M., Mastrodicasa, M., Mastrogiovanni, S., Matcovich, T., Matiushechkina, M., Matsuyama, M., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McCormick, S., McCuller, L., McEachin, S., McElhenny, C., McGhee, G. I., McGinn, J., McGowan, K. B. M., McIver, J., McLeod, A., McRae, T., Meacher, D., Meijer, Q., Melatos, A., Mellaerts, S., Menendez-Vazquez, A., Menoni, C. S., Mera, F., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Mérou, J. R., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyer-Conde, M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Miller, A. L., Miller, S., Millhouse, M., Milotti, E., Milotti, V., Minenkov, Y., Mio, N., Mir, Ll. M., Mirasola, L., Miravet-Tenés, M., Miritescu, C. -A., Mishra, A. K., Mishra, A., Mishra, C., Mishra, T., Mitchell, A. L., Mitchell, J. G., Mitra, S., Mitrofanov, V. P., Mittleman, R., Miyakawa, O., Miyamoto, S., Miyoki, S., Mo, G., Mobilia, L., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Moraru, D., More, A., More, S., Moreno, G., Morgan, C., Morisaki, S., Moriwaki, Y., Morras, G., Moscatello, A., Mourier, P., Mours, B., Mow-Lowry, C. M., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Samanwaya, Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Mundi, J., Mungioli, C. L., Oberg, W. R. Munn, Murakami, Y., Murakoshi, M., Murray, P. G., Muusse, S., Nabari, D., Nadji, S. L., Nagar, A., Nagarajan, N., Nagler, K. N., Nakagaki, K., Nakamura, K., Nakano, H., Nakano, M., Nandi, D., Napolano, V., Narayan, P., Nardecchia, I., Narola, H., Naticchioni, L., Nayak, R. K., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, S., Quynh, L. Nguyen, Nichols, S. A., Nielsen, A. B., Nieradka, G., Niko, A., Nishino, Y., Nishizawa, A., Nissanke, S., Nitoglia, E., Niu, W., Nocera, F., Norman, M., North, C., Novak, J., Siles, J. F. Nuño, Nuttall, L. K., Obayashi, K., Oberling, J., O'Dell, J., Oertel, M., Offermans, A., Oganesyan, G., Oh, J. J., Oh, K., O'Hanlon, T., Ohashi, M., Ohkawa, M., Ohme, F., Oliveira, A. S., Oliveri, R., O'Neal, B., Oohara, K., O'Reilly, B., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., O'Shea, S., Oshima, Y., Oshino, S., Ossokine, S., Osthelder, C., Ota, I., Ottaway, D. J., Ouzriat, A., Overmier, H., Owen, B. J., Pace, A. E., Pagano, R., Page, M. A., Pai, A., Pal, A., Pal, S., Palaia, M. A., Pálfi, M., Palma, P. P., Palomba, C., Palud, P., Pan, H., Pan, J., Pan, K. C., Panai, R., Panda, P. K., Pandey, S., Panebianco, L., Pang, P. T. H., Pannarale, F., Pannone, K. A., Pant, B. C., Panther, F. H., Paoletti, F., Paolone, A., Papalexakis, E. E., Papalini, L., Papigkiotis, G., Paquis, A., Parisi, A., Park, B. -J., Park, J., Parker, W., Pascale, G., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passenger, L., Passuello, D., Patane, O., Pathak, D., Pathak, M., Patra, A., Patricelli, B., Patron, A. S., Paul, K., Paul, S., Payne, E., Pearce, T., Pedraza, M., Pegna, R., Pele, A., Arellano, F. E. Peña, Penn, S., Penuliar, M. D., Perego, A., Pereira, Z., Perez, J. J., Périgois, C., Perna, G., Perreca, A., Perret, J., Perriès, S., Perry, J. W., Pesios, D., Petracca, S., Petrillo, C., Pfeiffer, H. P., Pham, H., Pham, K. A., Phukon, K. S., Phurailatpam, H., Piarulli, M., Piccari, L., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierra, G., Pierro, V., Pietrzak, M., Pillas, M., Pilo, F., Pinard, L., Pinto, I. M., Pinto, M., Piotrzkowski, B. J., Pirello, M., Pitkin, M. D., Placidi, A., Placidi, E., Planas, M. L., Plastino, W., Poggiani, R., Polini, E., Pompili, L., Poon, J., Porcelli, E., Porter, E. K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradhan, B. K., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Prasia, P., Pratten, G., Principe, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Puecher, A., Pullin, J., Punturo, M., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quéméner, G., Quetschke, V., Quigley, C., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raabith, S. S., Raaijmakers, G., Raja, S., Rajan, C., Rajbhandari, B., Ramirez, K. E., Vidal, F. A. Ramis, Ramos-Buades, A., Rana, D., Ranjan, S., Ransom, K., Rapagnani, P., Ratto, B., Rawat, S., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. Recaman, Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Relton, P., Renzini, A. I., Rettegno, P., Revenu, B., Reyes, R., Rezaei, A. S., Ricci, F., Ricci, M., Ricciardone, A., Richardson, J. W., Richardson, M., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Ruhama, N., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Saha, S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Santoliquido, F., Saravanan, T. R., Sarin, N., Sasaoka, S., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. 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R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Sotani, H., Soulard, R., Southgate, A., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Spoon, J. B., Sprague, C. A., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suleiman, L., Sullivan, K. D., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Suzuki, T., Suzuki, Y., Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables
Published: 2024

19. AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

Author: Andriushchenko, Maksym, Souly, Alexandra, Dziemian, Mateusz, Duenas, Derek, Lin, Maxwell, Wang, Justin, Hendrycks, Dan, Zou, Andy, Kolter, Zico, Fredrikson, Matt, Winsor, Eric, Wynne, Jerome, Gal, Yarin, and Davies, Xander
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: The robustness of LLMs to jailbreak attacks, where users design prompts to circumvent safety measures and misuse model capabilities, has been studied primarily for LLMs acting as simple chatbots. Meanwhile, LLM agents -- which use external tools and can execute multi-stage tasks -- may pose a greater risk if misused, but their robustness remains underexplored. To facilitate research on LLM agent misuse, we propose a new benchmark called AgentHarm. The benchmark includes a diverse set of 110 explicitly malicious agent tasks (440 with augmentations), covering 11 harm categories including fraud, cybercrime, and harassment. In addition to measuring whether models refuse harmful agentic requests, scoring well on AgentHarm requires jailbroken agents to maintain their capabilities following an attack to complete a multi-step task. We evaluate a range of leading LLMs, and find (1) leading LLMs are surprisingly compliant with malicious agent requests without jailbreaking, (2) simple universal jailbreak templates can be adapted to effectively jailbreak agents, and (3) these jailbreaks enable coherent and malicious multi-step agent behavior and retain model capabilities. To enable simple and reliable evaluation of attacks and defenses for LLM-based agents, we publicly release AgentHarm at https://huggingface.co/datasets/ai-safety-institute/AgentHarm.
Published: 2024

20. Gauging generalised symmetries in linear gravity

Author: Hull, Chris, Hutt, Maxwell L, and Lindström, Ulf
Subjects: High Energy Physics - Theory, General Relativity and Quantum Cosmology
Abstract: The theory of a free spin-2 field on Minkowski spacetime has 1-form and $(d-3)$-form symmetries associated with conserved currents formed by contractions of the linearised Riemann tensor with conformal Killing-Yano 2-forms. We show that a subset of these can be interpreted as Noether currents for specific shift symmetries of the graviton that involve a Killing vector and a closed 1-form parameter. We give a systematic method to gauge these 1-form symmetries by coupling the currents to background gauge fields and introducing a particular set of counter-terms involving the background fields. The simultaneous gauging of certain pairs of 1-form and $(d-3)$-form symmetries is obstructed by the presence of mixed 't Hooft anomalies. The anomalous pairs of symmetries are those which are related by gravitational duality. The implications of these anomalies are discussed., Comment: 25 pages + appendices
Published: 2024

21. KV Prediction for Improved Time to First Token

Author: Horton, Maxwell, Cao, Qingqing, Sun, Chenfan, Jin, Yanzi, Mehta, Sachin, Rastegari, Mohammad, and Nabi, Moin
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Inference with transformer-based language models begins with a prompt processing step. In this step, the model generates the first output token and stores the KV cache needed for future generation steps. This prompt processing step can be computationally expensive, taking 10s of seconds or more for billion-parameter models on edge devices when prompt lengths or batch sizes rise. This degrades user experience by introducing significant latency into the model's outputs. To reduce the time spent producing the first output (known as the ``time to first token'', or TTFT) of a pretrained model, we introduce a novel method called KV Prediction. In our method, a small auxiliary model is used to process the prompt and produce an approximation of the KV cache used by a base model. This approximated KV cache is then used with the base model for autoregressive generation without the need to query the auxiliary model again. We demonstrate that our method produces a pareto-optimal efficiency-accuracy trade-off when compared to baselines. On TriviaQA, we demonstrate relative accuracy improvements in the range of $15\%-50\%$ across a range of TTFT FLOPs budgets. We also demonstrate accuracy improvements of up to $30\%$ on HumanEval python code completion at fixed TTFT FLOPs budgets. Additionally, we benchmark models on an Apple M2 Pro CPU and demonstrate that our improvement in FLOPs translates to a TTFT speedup on hardware. We release our code at https://github.com/apple/corenet/tree/main/projects/kv-prediction .
Published: 2024

22. An Accessible Planar Ion Trap for Experiential Learning in Quantum Technologies

Author: Thomas, Robert E., Wolfram, Cole E., Warren, Noah B., Fouch, Isaac J., Blinov, Boris B., and Parsons, Maxwell F.
Subjects: Quantum Physics, Physics - Physics Education
Abstract: We describe an inexpensive and accessible instructional setup which explores ion trapping with a planar linear ion trap. The planar trap is constructed using standard printed circuit board manufacturing and is designed to trap macroscopic charged particles in air. Trapping, shuttling, and splitting is demonstrated to students using these particles, visible to the naked eye. Students have control over trap voltages and can compare properties of particle motion to an analytic model of the trap using a computer vision program for particle tracking. Learning outcomes include understanding the design considerations for planar RF traps, mechanisms underpinning ion ejection, the physics of micromotion, and methods of data analysis using standard computer vision libraries., Comment: 22 pages, 5 figures
Published: 2024

23. Unveiling the Face-Dependent Ice Growth Kinetics: Insights from Molecular Dynamics on the Basal and Prism Surfaces

Author: Shi, Jihong, Fulford, Maxwell, Salvalaglio, Matteo, and Molteni, Carla
Subjects: Condensed Matter - Materials Science, Condensed Matter - Soft Condensed Matter
Abstract: Ice nucleation and growth are critical in many fields, including atmospheric science, cryobiology, and aviation. However, understanding the detailed mechanisms of ice crystal growth remains challenging. In this work, crystallization at the ice/quasi-liquid layer (QLL) interface of the basal and primary prism (prism1) surfaces of hexagonal ice (Ih) was investigated using molecular dynamics simulations across a wide range of temperatures for the TIP4P/Ice model, with comparisons to the mW coarse-grained model. Together with elucidating the temperature-dependent mechanisms of crystallization, face-specific growth rates were systematically estimated. While the prism surface generally exhibits faster growth rates than the basal surface, a temperature-dependent crossover in growth rates between the basal and prism surfaces is observed in TIP4P/Ice simulations, which correlates with a crossover in QLL thickness and with the well-known column to platelets transition in ice-crystal habits at low vapour pressure. This observation helps decode the complex dependence between crystal morphology and temperature in ice crystals., Comment: 12 pages, 6 figures
Published: 2024

24. Thermodynamic Bayesian Inference

Author: Aifer, Maxwell, Duffield, Samuel, Donatella, Kaelan, Melanson, Denis, Klett, Phoebe, Belateche, Zach, Crooks, Gavin, Martinez, Antonio J., and Coles, Patrick J.
Subjects: Condensed Matter - Statistical Mechanics, Computer Science - Emerging Technologies, Computer Science - Machine Learning
Abstract: A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks including model selection. However, the intractability of sampling Bayesian posteriors over many parameters inhibits the use of Bayesian methods where they are most needed. Thermodynamic computing has emerged as a paradigm for accelerating operations used in machine learning, such as matrix inversion, and is based on the mapping of Langevin equations to the dynamics of noisy physical systems. Hence, it is natural to consider the implementation of Langevin sampling algorithms on thermodynamic devices. In this work we propose electronic analog devices that sample from Bayesian posteriors by realizing Langevin dynamics physically. Circuit designs are given for sampling the posterior of a Gaussian-Gaussian model and for Bayesian logistic regression, and are validated by simulations. It is shown, under reasonable assumptions, that the Bayesian posteriors for these models can be sampled in time scaling with $\ln(d)$, where $d$ is dimension. For the Gaussian-Gaussian model, the energy cost is shown to scale with $ d \ln(d)$. These results highlight the potential for fast, energy-efficient Bayesian inference using thermodynamic computing., Comment: 20 pages, 8 figures
Published: 2024

25. PyRIT: A Framework for Security Risk Identification and Red Teaming in Generative AI System

Author: Munoz, Gary D. Lopez, Minnich, Amanda J., Lutz, Roman, Lundeen, Richard, Dheekonda, Raja Sekhar Rao, Chikanov, Nina, Jagdagdorj, Bolor-Erdene, Pouliot, Martin, Chawla, Shiven, Maxwell, Whitney, Bullwinkel, Blake, Pratt, Katherine, de Gruyter, Joris, Siska, Charlotte, Bryan, Pete, Westerhoff, Tori, Kawaguchi, Chang, Seifert, Christian, Kumar, Ram Shankar Siva, and Zunger, Yonatan
Subjects: Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Generative Artificial Intelligence (GenAI) is becoming ubiquitous in our daily lives. The increase in computational power and data availability has led to a proliferation of both single- and multi-modal models. As the GenAI ecosystem matures, the need for extensible and model-agnostic risk identification frameworks is growing. To meet this need, we introduce the Python Risk Identification Toolkit (PyRIT), an open-source framework designed to enhance red teaming efforts in GenAI systems. PyRIT is a model- and platform-agnostic tool that enables red teamers to probe for and identify novel harms, risks, and jailbreaks in multimodal generative AI models. Its composable architecture facilitates the reuse of core building blocks and allows for extensibility to future models and modalities. This paper details the challenges specific to red teaming generative AI systems, the development and features of PyRIT, and its practical applications in real-world scenarios.
Published: 2024

26. Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model

Author: Gilson, Aidan, Ai, Xuguang, Xie, Qianqian, Srinivasan, Sahana, Pushpanathan, Krithi, Singer, Maxwell B., Huang, Jimin, Kim, Hyunjae, Long, Erping, Wan, Peixing, Del Priore, Luciano V., Ohno-Machado, Lucila, Xu, Hua, Liu, Dianbo, Adelman, Ron A., Tham, Yih-Chung, and Chen, Qingyu
Subjects: Computer Science - Computation and Language
Abstract: Large Language Models (LLMs) are poised to revolutionize healthcare. Ophthalmology-specific LLMs remain scarce and underexplored. We introduced an open-source, specialized LLM for ophthalmology, termed Language Enhanced Model for Eye (LEME). LEME was initially pre-trained on the Llama2 70B framework and further fine-tuned with a corpus of ~127,000 non-copyrighted training instances curated from ophthalmology-specific case reports, abstracts, and open-source study materials. We benchmarked LEME against eight other LLMs, namely, GPT-3.5, GPT-4, three Llama2 models (7B, 13B, 70B), PMC-LLAMA 13B, Meditron 70B, and EYE-Llama (another ophthalmology-specific LLM). Evaluations included four internal validation tasks: abstract completion, fill-in-the-blank, multiple-choice questions (MCQ), and short-answer QA. External validation tasks encompassed long-form QA, MCQ, patient EHR summarization, and clinical QA. Evaluation metrics included Rouge-L scores, accuracy, and expert evaluation of correctness, completeness, and readability. In internal validations, LEME consistently outperformed its counterparts, achieving Rouge-L scores of 0.20 in abstract completion (all p<0.05), 0.82 in fill-in-the-blank (all p<0.0001), and 0.22 in short-answer QA (all p<0.0001, except versus GPT-4). In external validations, LEME excelled in long-form QA with a Rouge-L of 0.19 (all p<0.0001), ranked second in MCQ accuracy (0.68; all p<0.0001), and scored highest in EHR summarization and clinical QA (ranging from 4.24 to 4.83 out of 5 for correctness, completeness, and readability). LEME's emphasis on robust fine-tuning and the use of non-copyrighted data represents a breakthrough in open-source ophthalmology-specific LLMs, offering the potential to revolutionize execution of clinical tasks while democratizing research collaboration.
Published: 2024

27. A Mathematical Perspective on Neurophenomenology

Author: Da Costa, Lancelot, Sandved-Smith, Lars, Friston, Karl, Ramstead, Maxwell J. D., and Seth, Anil K.
Subjects: Quantitative Biology - Neurons and Cognition
Abstract: In the context of consciousness studies, a key challenge is how to rigorously conceptualise first-person phenomenological descriptions of lived experience and their relation to third-person empirical measurements of the activity or dynamics of the brain and body. Since the 1990s, there has been a coordinated effort to explicitly combine first-person phenomenological methods, generating qualitative data, with neuroscientific techniques used to describe and quantify brain activity under the banner of "neurophenomenology". Here, we take on this challenge and develop an approach to neurophenomenology from a mathematical perspective. We harness recent advances in theoretical neuroscience and the physics of cognitive systems to mathematically conceptualise first-person experience and its correspondence with neural and behavioural dynamics. Throughout, we make the operating assumption that the content of first-person experience can be formalised as (or related to) a belief (i.e. a probability distribution) that encodes an organism's best guesses about the state of its external and internal world (e.g. body or brain) as well as its uncertainty. We mathematically characterise phenomenology, bringing to light a tool-set to quantify individual phenomenological differences and develop several hypotheses including on the metabolic cost of phenomenology and on the subjective experience of time. We conceptualise the form of the generative passages between first- and third-person descriptions, and the mathematical apparatus that mutually constrains them, as well as future research directions. In summary, we formalise and characterise first-person subjective experience and its correspondence with third-person empirical measurements of brain and body, offering hypotheses for quantifying various aspects of phenomenology to be tested in future work., Comment: 15 pages, 4 figures
Published: 2024

28. Giant and Tunable Bosonic Quantum Interference Induced by Two-Dimensional Metals

Author: Zhang, Kunyan, Maniyara, Rinu Abraham, Wang, Yuanxi, Jain, Arpit, Wetherington, Maxwell T., Mai, Thuc T., Dong, Chengye, Bowen, Timothy, Wang, Ke, Rotkin, Slava V., Walker, Angela R. Hight, Crespi, Vincent H., Robinson, Joshua, and Huang, Shengxi
Subjects: Condensed Matter - Materials Science
Abstract: Harnessing quantum interference among bosons provides significant opportunities as bosons often carry longer coherence time than fermions. As an example of quantum interference, Fano resonance involving phonons or photons describes the coupling between discrete and continuous states, signified by an asymmetric spectral lineshape. Utilizing photon-based Fano resonance, molecule sensing with ultra-high sensitivity and ultrafast optical switching has been realized. However, phonon-based Fano resonance, which would expand the application space to a vaster regime, has been less exploited because of the weak coupling between discrete phonons with continuous states such as electronic continuum. In this work, we report the discovery of giant phonon-based Fano resonance in a graphene/2D Ag/SiC heterostructure. The Fano asymmetry, being proportional to the coupling strength, exceeds prior reports by two orders of magnitude. This Fano asymmetry arises from simultaneous frequency and lifetime matching between discrete and continuous phonons of SiC. The introduction of 2D Ag layers restructures SiC at the interface and facilitates resonant scattering to further enhance the Fano asymmetry, which is not achievable with conventional Ag thin films. With these unique properties, we demonstrated that the phonon-based Fano resonance can be used for ultrasensitive molecule detection at the single-molecule level. Our work highlights strong Fano resonance in the phononic system, opening avenues for engineering quantum interference based on bosons. Further, our findings provide opportunities for advancing phonon-related applications, including biochemical sensing, quantum transduction, and superconductor-based quantum computing.
Published: 2024

29. Formally Verified Physics-Informed Neural Control Lyapunov Functions

Author: Liu, Jun, Fitzsimmons, Maxwell, Zhou, Ruikun, and Meng, Yiming
Subjects: Electrical Engineering and Systems Science - Systems and Control, Computer Science - Machine Learning, Mathematics - Optimization and Control
Abstract: Control Lyapunov functions are a central tool in the design and analysis of stabilizing controllers for nonlinear systems. Constructing such functions, however, remains a significant challenge. In this paper, we investigate physics-informed learning and formal verification of neural network control Lyapunov functions. These neural networks solve a transformed Hamilton-Jacobi-Bellman equation, augmented by data generated using Pontryagin's maximum principle. Similar to how Zubov's equation characterizes the domain of attraction for autonomous systems, this equation characterizes the null-controllability set of a controlled system. This principled learning of neural network control Lyapunov functions outperforms alternative approaches, such as sum-of-squares and rational control Lyapunov functions, as demonstrated by numerical examples. As an intermediate step, we also present results on the formal verification of quadratic control Lyapunov functions, which, aided by satisfiability modulo theories solvers, can perform surprisingly well compared to more sophisticated approaches and efficiently produce global certificates of null-controllability.
Published: 2024

30. Observation of spin squeezing with contact interactions in one- and three-dimensional easy-plane magnets

Author: Lee, Yoo Kyung, Block, Maxwell, Lin, Hanzhen, Fedoseev, Vitaly, Crowley, Philip J. D., Yao, Norman Y., and Ketterle, Wolfgang
Subjects: Quantum Physics, Condensed Matter - Quantum Gases, Physics - Atomic Physics
Abstract: Entanglement in a many-particle system can enable measurement sensitivities beyond that achievable by only classical correlations. For an ensemble of spins, all-to-all interactions are known to reshape the quantum projection noise, leading to a form of entanglement known as spin squeezing. Here, we demonstrate spin squeezing with strictly short-range contact interactions. In particular, working with ultracold lithium atoms in optical lattices, we utilize superexchange interactions to realize a nearest-neighbor anisotropic Heisenberg model. We investigate the resulting quench dynamics from an initial product state in both one and three dimensions. In 1D, we observe $1.9^{+0.7}_{-0.5}$ dB of spin squeezing in quantitative agreement with theory. However, in 3D, we observe a maximum of $2.0^{+0.7}_{-0.7}$ dB of squeezing, over an order of magnitude smaller than that expected. We demonstrate that this discrepancy arises from the presence of a finite density of holes; both the motion of the holes as well as direct coupling between spin and density qualitatively alter the spin dynamics. Our observations point to the importance of understanding the complex interplay between motional and spin degrees of freedom in quantum simulators.
Published: 2024

31. Random ensembles of symplectic and unitary states are indistinguishable

Author: West, Maxwell, Mele, Antonio Anna, Larocca, Martin, and Cerezo, M.
Subjects: Quantum Physics, Computer Science - Computational Complexity, Computer Science - Information Theory
Abstract: A unitary state $t$-design is an ensemble of pure quantum states whose moments match up to the $t$-th order those of states uniformly sampled from a $d$-dimensional Hilbert space. Typically, unitary state $t$-designs are obtained by evolving some reference pure state with unitaries from an ensemble that forms a design over the unitary group $\mathbb{U}(d)$, as unitary designs induce state designs. However, in this work we study whether Haar random symplectic states -- i.e., states obtained by evolving some reference state with unitaries sampled according to the Haar measure over $\mathbb{SP}(d/2)$ -- form unitary state $t$-designs. Importantly, we recall that random symplectic unitaries fail to be unitary designs for $t>1$, and that, while it is known that symplectic unitaries are universal, this does not imply that their Haar measure leads to a state design. Notably, our main result states that Haar random symplectic states form unitary $t$-designs for all $t$, meaning that their distribution is unconditionally indistinguishable from that of unitary Haar random states, even with tests that use infinite copies of each state. As such, our work showcases the intriguing possibility of creating state $t$-designs using ensembles of unitaries which do not constitute designs over $\mathbb{U}(d)$ themselves, such as ensembles that form $t$-designs over $\mathbb{SP}(d/2)$., Comment: 6+11 pages, 1+1 figures
Published: 2024

32. Topological and geometric characterization of synthetic aperture sonar collections

Author: Robinson, Michael, Memon, Zander, and Gualtieri, Maxwell
Subjects: Computer Science - Computational Engineering, Finance, and Science
Abstract: This article explores the theoretical underpinnings of -- and practical results for -- topological and geometric features of data collected by synthetic aperture sonar systems. We prove a strong theoretical guarantee about the structure of the space of echos (the signature space) that is relevant for classification, and that this structure is measurable in laboratory conditions. The guarantee makes minimal assumptions about the sonar platform's trajectory, and establishes that the signature space divides neatly into topological features based upon the number of prominent echos and their placement, and geometric features that capture their corresponding sonar cross section., Comment: 34 pages, 16 figures
Published: 2024

33. Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology

Author: Gilson, Aidan, Ai, Xuguang, Arunachalam, Thilaka, Chen, Ziyou, Cheong, Ki Xiong, Dave, Amisha, Duic, Cameron, Kibe, Mercy, Kaminaka, Annette, Prasad, Minali, Siddig, Fares, Singer, Maxwell, Wong, Wendy, Jin, Qiao, Keenan, Tiarnan D. L., Hu, Xia, Chew, Emily Y., Lu, Zhiyong, Xu, Hua, Adelman, Ron A., Tham, Yih-Chung, and Chen, Qingyu
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Despite the potential of Large Language Models (LLMs) in medicine, they may generate responses lacking supporting evidence or based on hallucinated evidence. While Retrieval Augment Generation (RAG) is popular to address this issue, few studies implemented and evaluated RAG in downstream domain-specific applications. We developed a RAG pipeline with 70,000 ophthalmology-specific documents that retrieve relevant documents to augment LLMs during inference time. In a case study on long-form consumer health questions, we systematically evaluated the responses including over 500 references of LLMs with and without RAG on 100 questions with 10 healthcare professionals. The evaluation focuses on factuality of evidence, selection and ranking of evidence, attribution of evidence, and answer accuracy and completeness. LLMs without RAG provided 252 references in total. Of which, 45.3% hallucinated, 34.1% consisted of minor errors, and 20.6% were correct. In contrast, LLMs with RAG significantly improved accuracy (54.5% being correct) and reduced error rates (18.8% with minor hallucinations and 26.7% with errors). 62.5% of the top 10 documents retrieved by RAG were selected as the top references in the LLM response, with an average ranking of 4.9. The use of RAG also improved evidence attribution (increasing from 1.85 to 2.49 on a 5-point scale, P<0.001), albeit with slight decreases in accuracy (from 3.52 to 3.23, P=0.03) and completeness (from 3.47 to 3.27, P=0.17). The results demonstrate that LLMs frequently exhibited hallucinated and erroneous evidence in the responses, raising concerns for downstream applications in the medical domain. RAG substantially reduced the proportion of such evidence but encountered challenges.
Published: 2024

34. Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions

Author: Lin, Ryan Y., Ojha, Siddhartha, Cai, Kevin, and Chen, Maxwell F.
Subjects: Computer Science - Computer Science and Game Theory, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Quantitative Finance - Computational Finance
Abstract: Machine-learning technologies are seeing increased deployment in real-world market scenarios. In this work, we explore the strategic behaviors of large language models (LLMs) when deployed as autonomous agents in multi-commodity markets, specifically within Cournot competition frameworks. We examine whether LLMs can independently engage in anti-competitive practices such as collusion or, more specifically, market division. Our findings demonstrate that LLMs can effectively monopolize specific commodities by dynamically adjusting their pricing and resource allocation strategies, thereby maximizing profitability without direct human input or explicit collusion commands. These results pose unique challenges and opportunities for businesses looking to integrate AI into strategic roles and for regulatory bodies tasked with maintaining fair and competitive markets. The study provides a foundation for further exploration into the ramifications of deferring high-stakes decisions to LLM-based agents.
Published: 2024

35. Optimal Linear Filtering for Discrete-Time Systems with Infinite-Dimensional Measurements

Author: Varley, Maxwell, Molloy, Timothy L., and Nair, Girish N.
Subjects: Electrical Engineering and Systems Science - Systems and Control
Abstract: Systems equipped with modern sensing modalities such as vision and lidar gain access to increasingly high-dimensional measurements with which to enact estimation and control schemes. In this article, we examine the continuum limit of high-dimensional measurements and analyze state estimation in linear time-invariant systems with infinite-dimensional measurements but finite-dimensional states, both corrupted by additive noise. We propose a linear filter and derive the corresponding optimal gain functional in the sense of the minimum mean square error, analogous to the classic Kalman filter. By modeling the measurement noise as a wide-sense stationary random field, we are able to derive the optimal linear filter explicitly, in contrast to previous derivations of Kalman filters in distributed-parameter settings. Interestingly, we find that we need only impose conditions that are finite-dimensional in nature to ensure that the filter is asymptotically stable. The proposed filter is verified via simulation of a linearized system with a pinhole camera sensor., Comment: 15 pages, 3 Figures. This paper will appear in the IEEE Transactions on Automatic Control
Published: 2024

36. Towards Time Series Reasoning with LLMs

Author: Chow, Winnie, Gardiner, Lauren, Hallgrímsson, Haraldur T., Xu, Maxwell A., and Ren, Shirley You
Subjects: Computer Science - Machine Learning
Abstract: Multi-modal large language models (MLLMs) have enabled numerous advances in understanding and reasoning in domains like vision, but we have not yet seen this broad success for time-series. Although prior works on time-series MLLMs have shown promising performance in time-series forecasting, very few works show how an LLM could be used for time-series reasoning in natural language. We propose a novel multi-modal time-series LLM approach that learns generalizable information across various domains with powerful zero-shot performance. First, we train a lightweight time-series encoder on top of an LLM to directly extract time-series information. Then, we fine-tune our model with chain-of-thought augmented time-series tasks to encourage the model to generate reasoning paths. We show that our model learns a latent representation that reflects specific time-series features (e.g. slope, frequency), as well as outperforming GPT-4o on a set of zero-shot reasoning tasks on a variety of domains.
Published: 2024

37. Certifying Anosov representations

Author: Riestenberg, J. Maxwell
Subjects: Mathematics - Group Theory, Mathematics - Geometric Topology, 53C35, 20F65, 22E40
Abstract: By providing new finite criteria which certify that a finitely generated subgroup of $\mathrm{SL}(d,\mathbb{R})$ or $\mathrm{SL}(d,\mathbb{C})$ is projective Anosov, we obtain a practical algorithm to verify the Anosov condition. We demonstrate on a surface group of genus 2 in $\mathrm{SL}(3,\mathbb{R})$ by verifying the criteria for all words of length 8. The previous version required checking all words of length $2$ million., Comment: 12 pages. Comments welcome!
Published: 2024

38. NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls

Author: Basu, Kinjal, Abdelaziz, Ibrahim, Bradford, Kelsey, Crouse, Maxwell, Kate, Kiran, Kumaravel, Sadhana, Goyal, Saurabh, Munawar, Asim, Rizk, Yara, Wang, Xin, Lastras, Luis, and Kapanipathi, Pavan
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Autonomous agent applications powered by large language models (LLMs) have recently risen to prominence as effective tools for addressing complex real-world tasks. At their core, agentic workflows rely on LLMs to plan and execute the use of tools and external Application Programming Interfaces (APIs) in sequence to arrive at the answer to a user's request. Various benchmarks and leaderboards have emerged to evaluate an LLM's capabilities for tool and API use; however, most of these evaluations only track single or multiple isolated API calling capabilities. In this paper, we present NESTFUL, a benchmark to evaluate LLMs on nested sequences of API calls, i.e., sequences where the output of one API call is passed as input to a subsequent call. NESTFUL has a total of 300 human annotated samples divided into two types - executable and non-executable. The executable samples are curated manually by crawling Rapid-APIs whereas the non-executable samples are hand picked by human annotators from data synthetically generated using an LLM. We evaluate state-of-the-art LLMs with function calling abilities on NESTFUL. Our results show that most models do not perform well on nested APIs in NESTFUL as compared to their performance on the simpler problem settings available in existing benchmarks.
Published: 2024

39. LIGO Detector Characterization in the first half of the fourth Observing run

Author: Soni, S., Berger, B. K., Davis, D., Renzo, F. Di., Effler, A., Ferreira, T. A., Glanzer, J., Goetz, E., González, G., Helmling-Cornell, A., Hughey, B., Huxford, R., Mannix, B., Mo, G., Nandi, D., Neunzert, A., Nichols, S., Pham, K., Renzini, A. I., Schofield, R. M. S., Stuver, A, Trevor, M., Álvarez-López, S., Beda, R., Berry, C. P. L., Bhuiyan, S., Bruntz, R., Christensen, N., Blagg, L., Chan, M., Charlton, P., Connolly, G., Dhatri, R., Ding, J., Garg, V., Holley-Bockelmann, K., Hourihane, S., Jani, K., Janssens, K., Jarov, S., Knee, A. M., Lattal, A., Lecoeuche, Y., Littenberg, T., Liyanage, A., Lott, B., Macas, R., Malakar, D., McGowan, K., McIver, J., Millhouse, M., Nuttall, L., Nykamp, D., Ota, I., Rawcliffe, C., Scully, B., Tasson, J., Tejera, A., Thiele, S., Udall, R., Winborn, C., Yarbrough, Z., Zhang, Z., Abbott, R., Abouelfettouh, I., Adhikari, R. X., Ananyeva, A., Appert, S., Arai, K., Aritomi, N., Aston, S. M., Ball, M., Ballmer, S. W., Barker, D., Barsotti, L., Betzwieser, J., Billingsley, G., Biscans, S., Bode, N., Bonilla, E., Bossilkov, V., Branch, A., Brooks, A. F., Brown, D. D., Bryant, J., Cahillane, C., Cao, H., Capote, E., Clara, F., Collins, J., Compton, C. M., Cottingham, R., Coyne, D. C., Crouch, R., Csizmazia, J., Cullen, T. J., Dartez, L. P., Demos, N., Dohmen, E., Driggers, J. C., Dwyer, S. E., Ejlli, A., Etzel, T., Evans, M., Feicht, J., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fulda, P., Fyffe, M., Ganapathy, D., Gateley, B., Giaime, J. A., Giardina, K. D., Goetz, R., Goodwin-Jones, A. W., Gras, S., Gray, C., Griffith, D., Grote, H., Guidry, T., Hall, E. D., Hanks, J., Hanson, J., Heintze, M. C., Holland, N. A., Hoyland, D., Huang, H. Y., Inoue, Y., James, A. L., Jennings, A., Jia, W., Karat, S., Karki, S., Kasprzack, M., Kawabe, K., Kijbunchoo, N., King, P. J., Kissel, J. S., Komori, K., Kontos, A., Kumar, Rahul, Kuns, K., Landry, M., Lantz, B., Laxen, M., Lee, K., Lesovsky, M., Llamas, F., Lormand, M., Loughlin, H. A., MacInnis, M., Makarem, C. N., Mansell, G. L., Martin, R. M., Mason, K., Matichard, F., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McRae, T., Mera, F., Merilh, E. L., Meylahn, F., Mittleman, R., Moraru, D., Moreno, G., Mullavey, A., Nakano, M., Nelson, T. J. N., Notte, J., Oberling, J., O'Hanlon, T., Osthelder, C., Ottaway, D. J., Overmier, H., Parker, W., Pele, A., Pham, H., Pirello, M., Quetschke, V., Ramirez, K. E., Reyes, J., Richardson, J. W., Robinson, M., Rollins, J. G., Romel, C. L., Romie, J. H., Ross, M. P., Ryan, K., Sadecki, T., Sanchez, A., Sanchez, E. J., Sanchez, L. E., Savage, R. L., Schaetzl, D., Schiworski, M. G., Schnabel, R., Schwartz, E., Sellers, D., Shaffer, T., Short, R. W., Sigg, D., Slagmolen, B. J. J., Soike, C., Srivastava, V., Sun, L., Tanner, D. B., Thomas, M., Thomas, P., Thorne, K. A., Torrie, C. I., Traylor, G., Ubhi, A. S., Vajente, G., Vanosky, J., Vecchio, A., Veitch, P. J., Vibhute, A. M., von Reis, E. R. G., Warner, J., Weaver, B., Weiss, R., Whittle, C., Willke, B., Wipf, C. C., Xu, V. A., Yamamoto, H., Zhang, L., and Zucker, M. E.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, General Relativity and Quantum Cosmology
Abstract: Progress in gravitational-wave astronomy depends upon having sensitive detectors with good data quality. Since the end of the LIGO-Virgo-KAGRA third Observing run in March 2020, detector-characterization efforts have lead to increased sensitivity of the detectors, swifter validation of gravitational-wave candidates and improved tools used for data-quality products. In this article, we discuss these efforts in detail and their impact on our ability to detect and study gravitational-waves. These include the multiple instrumental investigations that led to reduction in transient noise, along with the work to improve software tools used to examine the detectors data-quality. We end with a brief discussion on the role and requirements of detector characterization as the sensitivity of our detectors further improves in the future Observing runs., Comment: 35 pages, 18 figures
Published: 2024

40. Experiences of Diversity, Inclusion, and Belonging among Postgraduate Health Sciences Research Students at an Australian University: A Qualitative Study

Author: Sarah J. Egan, Samantha Collegde-Frisby, Rose Stackpole, Caitlin Munro, Matthew McDonald, Bronwyn Myers, Rob Steuart, Anthony Kicic, Arazu Sharif, Chloe Maxwell-Smith, Andrew Maiorana, Timothy A. Carey, Rima Caccetta, Ben Milbourn, and Eleanor Quest
Abstract: Postgraduate research students have poorer mental health than the general community. Improving their experiences of diversity, inclusion, and belonging at university may bolster their overall wellbeing and reduce poor mental health outcomes. The aim was to explore postgraduate research students' views on diversity, inclusion, and belonging, to understand how these experiences impact their mental health and wellbeing, and to identify ways to improve their experiences. Thirty-one postgraduate research students (aged 24-68 years, M = 35.78 years, SD = 10.38; 69% female), enrolled in health sciences degrees at a research-intensive Australian university, completed either an online qualitative survey or participated in a focus group. Content analysis was undertaken to identify core themes. The three main content areas included: diversity (promoting diversity, staff and student training), inclusion (support from supervisors and peers, support in the perinatal period) and belonging (social isolation, suggestions to improve a sense of belonging). Most participants had not received training in diversity, inclusion and belonging, and identified this as an important area of need. Strategies to reduce isolation may potentially improve students experience of inclusion and belonging.
Published: 2024

41. Primary School Learners' Age and Academic Achievement in Ghana. The Moderating Effects of School Types

Author: Maxwell Kwesi Nyatsikor
Abstract: The study explored the extent learners' age variances impacted their achievement in a national education assessment in Ghana and how these were moderated by the types of schools (i.e., private and public) they attended. A multistage sampling method was used, and the data were analyzed using a multilevel modeling technique. The sample comprised 19,210 primary grade 3 and 17,088 primary grade 6 learners from 525 and 499 schools, respectively. Relatively younger learners outperformed their older peers in both subjects except for primary 3 mathematics achievement. Schools marginally reduced the age effect on both subjects except primary 3 mathematics achievement, where there was an increase. Moreover, there was a statistically insignificant difference in private and public schools' impact on age-linked effects on subjects except for primary 3 mathematics. The study concludes that being relatively overage for a specific grade level is not beneficial, especially for English language achievement. Hence, enrolling learners at the prescribed age and school term is highly recommended.
Published: 2024

42. Context-Dependent Social Comparison and Imposter Phenomenon: An Experimental Vignette Approach

Author: Kate E. Snyder, Maxwell I. Bartley, and Allison Fowler
Abstract: Recent research into imposter phenomenon, or internal feelings of questioning competence, has shifted away from conceptualizing the feeling as an individual characteristic that requires an individual solution toward instead examining the role of context. We used a 2 (Generational Status: First Generation vs. Continuing Generation) × 3 (Environment: Classroom vs. Peer vs. Administration) × 2 (Social Comparison: Present vs. Absent) experimental vignette design. Overall, we found that, for both first- and continuing-generation students, social comparison messages differentially impacted imposter feelings depending on context, with the most elevated levels in social comparison messages from administrators and peers. Findings contribute to a better understanding of structural conditions that exacerbate or lessen imposter feelings at highly selective post-secondary institutions.
Published: 2024
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43. Cosmopolitan Nationalism as an Analytical Lens: Four Articulations in Education Policy

Author: Miri Yemini, Claire Maxwell, Ewan Wright, Laura Engel, and Moosung Lee
Abstract: Cosmopolitanism and nationalism are often presented in binary terms, as standing in opposition to each other, especially with regards to education. In this paper, we establish a framework for studying education policy today through the lens of a concept we have developed: 'cosmopolitan nationalism'. Many education systems around the world are grappling with and simultaneously integrating both cosmopolitan and nationalist elements in policy objectives, curricular content and pedagogic approaches. Through an in-depth analysis of three country examples -- China, the US and Israel -- we outline four different manifestations of education policy that can be explained by employing the analytical lens of cosmopolitan nationalism: (i) the increasing prominence and visibility of the International Baccalaureate within these three public education systems; (ii) reforms of specific curricula elements and pedagogies legitimised with reference to approaches assessed as globally leading through international assessments; (iii) efforts to promote national education systems as international beacons of best practice; (iv) creation of alternative education provisions that are promoted globally for very specific purposes and populations. We argue that, given our heterogeneous sample of case study countries, and careful analysis of these, a cosmopolitan nationalism lens offers important insights into education policy-making today which has the potential to be applied in other contexts.
Published: 2024
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44. Social and Emotional Skills Predict Postsecondary Enrollment and Retention. ACT Research. Technical Brief

Author: ACT, Inc., Kate E. Walton, Jeff Allen, Maxwell J. Box, Jeremy Burrus, and Dana Murano
Abstract: Social and emotional (SE) skills are known to be linked with many important life outcomes, including academic performance, performance on standardized college entrance exams, and college enrollment. In this technical brief, the authors evaluated whether the five SE skills measured with the Mosaic™ by ACT® predict postsecondary enrollment and retention above and beyond household income, high school grade point average, and Composite score on the ACT® test.
Published: 2023

45. Plant functional types and tissue stoichiometry explain nutrient transfer in common arbuscular mycorrhizal networks of temperate grasslands

Author: Dawson, Hilary Rose, Shek, Katherine L, Maxwell, Toby M, Reed, Paul B, Bomfim, Barbara, Bridgham, Scott D, Bohannan, Brendan JM, and Silva, Lucas CR
Subjects: Plant Biology, Biological Sciences, Ecology, arbuscular mycorrhizal fungi, carbon, common mycorrhizal networks, ecological stoichiometry, grasslands, nitrogen, plant functional types, Environmental Sciences, Biological sciences, Environmental sciences
Abstract: Plants and mycorrhizal fungi form mutualistic relationships that affect how resources flow between organisms and within ecosystems. Common mycorrhizal networks (CMNs) could facilitate preferential transfer of carbon and limiting nutrients, but this remains difficult to predict. Do CMNs favour fungal resource acquisition at the expense of plant resource demands (a fungi-centric view), or are they passive channels through which plants regulate resource fluxes (a plant-centric view)? We used stable isotope tracers (13CO2 and 15NH3), plant traits, and mycorrhizal DNA to quantify above- and below-ground carbon and nitrogen transfer between 18 plant species along a 520-km latitudinal gradient in the Pacific Northwest, USA. Plant functional type and tissue stoichiometry were the most important predictors of interspecific resource transfer. Of ‘donor’ plants, 98% were 13C-enriched, but we detected transfer in only 2% of ‘receiver’ plants. However, all donors were 15N-enriched and we detected transfer in 81% of receivers. Nitrogen was preferentially transferred to annuals (0.26 ± 0.50 mg N per g leaf mass) compared with perennials (0.13 ± 0.30 mg N per g leaf mass). This corresponded with tissue stoichiometry differences. Synthesis Our findings suggest that plants and fungi that are located closer together in space and with stronger demand for resources over time are more likely to receive larger amounts of those limiting resources. Read the free Plain Language Summary for this article on the Journal blog.
Published: 2024

46. Assessing the Potential for Densification and VMT Reduction in Areas Without Rail Transit Access

Author: Kim, Jae Hong, PhD, Barajas, Jesus M., PhD, Marantz, Nicholas J., PhD, Houston, Douglas, PhD, Herrera, Veronica, Okashita, Alex, and Cabello, Maxwell B.
Subjects: Housing, transit oriented development, land use, bus transit, vehicle miles of travel, travel behavior
Abstract: While transportation infrastructure and efficiency should inform where to build more housing, little is known about how housing allocation and development processes can be coordinated more systematically with transportation. To date, transportation-housing coordination has often relied on the densification of areas near rail transit stations, putting heavy burdens on these locations and their residents. Much less attention has been paid to how densification can be achieved in a more equitable manner by encompassing other sites. This report directs attention to non-rail locations, specifically low vehicle miles traveled (VMT) areas and bus corridors, and examines the challenges that can arise in promoting densification more broadly. It shows that data uncertainties can make it challenging to identify low VMT locations and that prioritizing only low VMT locations for residential development may have limited effectiveness in expanding housing opportunities in high opportunity areas. The report further explores ways to achieve more inclusive densification of non-rail transit areas and highlights the importance of anti-displacement strategies.
Published: 2024

47. What Challenges Can Arise from Coordinating Housing Development with Transportation?

Author: Kim, Jae Hong, Barajas, Jesus M., Marantz, Nicholas J., Houston, Douglas, Herrara, Veronica, Okashita, Alex, and Cabello, Maxwell B.
Abstract: More systematic coordination between transportation and housing development is increasingly recognized as a promising strategy for creating more sustainable communities. In California, the importance of transportation-housing coordination is reflected in recent legislative efforts to address the state’s long-standing housing affordability crisis. One approach is to encourage higher density affordable housing developments near transit or in similarly transportation-efficient areas, such as locations with low vehicle miles traveled (VMT). However, little is known about how transportation access should be considered in guiding housing development, what challenges can arise from coordinating housing development with transportation, and what the state can do to better deal with these challenges and achieve more equitable residential densification.
Published: 2024

48. Generalised symmetries in linear gravity

Author: Hull, Chris, Hutt, Maxwell L, and Lindström, Ulf
Subjects: High Energy Physics - Theory, General Relativity and Quantum Cosmology
Abstract: Linearised gravity has a global symmetry under which the graviton is shifted by a symmetric tensor satisfying a certain flatness condition. There is also a dual symmetry that can be associated with a global shift symmetry of the dual graviton theory. The corresponding conserved charges are shown to satisfy a centrally-extended algebra. We discuss the gauging of these global symmetries, finding an obstruction to the simultaneous gauging of both symmetries which we interpret as a mixed 't Hooft anomaly for the ungauged theory. We discuss the implications of this, analogous to those resulting from a similar structure in Maxwell theory, and interpret the graviton and dual graviton as Nambu-Goldstone modes for these shift symmetries., Comment: 30 pages
Published: 2024

49. Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environments

Author: Schrader, Maxwell, Kumar, Navish, Sørig, Esben, Yoon, Soonmyeong, Srivastava, Akash, Xu, Kai, Astefanoaei, Maria, and Collignon, Nicolas
Subjects: Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract: Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities. Cargo-bike logistics and Light Electric Vehicles (LEVs) have been put forward as a high impact candidate for replacing LGVs. Studies have estimated over half of urban van deliveries being replaceable by cargo-bikes, due to their faster speeds, shorter parking times and more efficient routes across cities. However, the logistics sector suffers from a lack of publicly available data, particularly pertaining to cargo-bike deliveries, thus limiting the understanding of their potential benefits. Specifically, service time (which includes cruising for parking, and walking to destination) is a major, but often overlooked component of delivery time modelling. The aim of this study is to establish a framework for measuring the performance of delivery vehicles, with an initial focus on modelling service times of vans and cargo-bikes across diverse urban environments. We introduce two datasets that allow for in-depth analysis and modelling of service times of cargo bikes and use existing datasets to reason about differences in delivery performance across vehicle types. We introduce a modelling framework to predict the service times of deliveries based on urban context. We employ Uber's H3 index to divide cities into hexagonal cells and aggregate OpenStreetMap tags for each cell, providing a detailed assessment of urban context. Leveraging this spatial grid, we use GeoVex to represent micro-regions as points in a continuous vector space, which then serve as input for predicting vehicle service times. We show that geospatial embeddings can effectively capture urban contexts and facilitate generalizations to new contexts and cities. Our methodology addresses the challenge of limited comparative data available for different vehicle types within the same urban settings., Comment: 37 pages in submission to the Springer Journal of Urban Informatics. arXiv admin note: text overlap with arXiv:2007.06277 by other authors
Published: 2024

50. On the Effects of Modeling on the Sim-to-Real Transfer Gap in Twinning the POWDER Platform

Author: McManus, Maxwell, Cui, Yuqing, Zhang, Zhaoxi, Bentley, Elizabeth Serena, Medley, Michael, Mastronarde, Nicholas, and Guan, Zhangyu
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: Digital Twin (DT) technology is expected to play a pivotal role in NextG wireless systems. However, a key challenge remains in the evaluation of data-driven algorithms within DTs, particularly the transfer of learning from simulations to real-world environments. In this work, we investigate the sim-to-real gap in developing a digital twin for the NSF PAWR Platform, POWDER. We first develop a 3D model of the University of Utah campus, incorporating geographical measurements and all rooftop POWDER nodes. We then assess the accuracy of various path loss models used in training modeling and control policies, examining the impact of each model on sim-to-real link performance predictions. Finally, we discuss the lessons learned from model selection and simulation design, offering guidance for the implementation of DT-enabled wireless networks.
Published: 2024

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