Rough backward SDEs with discontinuous Young drivers

Dirk Becherer, Yuchen Sun

We study solutions to backward differential equations that are driven hybridly by a deterministic discontinuous rough path $W$ of finite $q$-variation for $q \in [1, 2)$ and by Brownian motion $B$. To distinguish between integration of jumps in a forward- or Marcus-sense, we refer to these equations as forward- respectively Marcus-type rough backward stochastic differential equations (RBSDEs). We establish global well-posedness by proving global apriori bounds for solutions and employing fixed-point arguments locally. Furthermore, we lift the RBSDE solution and the driving rough noise to the space of decorated paths endowed with a Skorokhod-type metric and show stability of solutions with respect to perturbations of the rough noise. Finally, we prove well-posedness for a new class of backward doubly stochastic differential equations (BDSDEs), which are jointly driven by a Brownian martingale $B$ and an independent discontinuous stochastic process $L$ of finite $q$-variation. We explain, how our RBSDEs can be understood as conditional solutions to such BDSDEs, conditioned on the information generated by the path of $L$.

Real-time Dynamics of Soft Manipulators with Cross-section Inflation: Application to the Octopus Muscular Hydrostat

Yuchen Sun, Anup Teejo Mathew, Imran Afgan, Federico Renda, Cecilia Laschi

Inspired by the embodied intelligence of biological creatures like the octopus, the soft robotic arm utilizes its highly flexible structure to perform various tasks in the complex environment. While the classic Cosserat rod theory investigates the bending, twisting, shearing, and stretching of the soft arm, it fails to capture the in-plane deformation that occurs during certain tasks, particularly those involving active lateral traction. This paper introduces an extended Cosserat rod theory addressing these limitations by incorporating an extra strain variable reflecting the in-plane inflation ratio. To accurately describe the viscoelasticity effect of the soft body in dynamics, the proposed model enhances the constitutive law by integrating the Saint-Venant Kirchhoff hyperelastic and Kelvin-Voigt viscous models. The active and environmental loads are accounted for the equations of motion, which are numerically solved by adapting the Geometric Variable Strain (GVS) approach to balance the accuracy and computational efficiency. Our contributions include the derivation of the extended Cosserat rod theory in dynamic context, and the development of a reduced-order numerical method that enables rapid and precise solutions. We demonstrate applications of the model in stiffness tuning of a soft robotic arm and the study of complex octopus' arm motions.

Deep Intellectual Property Protection: A Survey

Yuchen Sun, Tianpeng Liu, Panhe Hu, Qing Liao, Shaojing Fu, Nenghai Yu, Deke Guo, Yongxiang Liu, Li Liu

Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made revolutionary progress in recent years, and are widely used in various fields. The high performance of DNNs requires a huge amount of high-quality data, expensive computing hardware, and excellent DNN architectures that are costly to obtain. Therefore, trained DNNs are becoming valuable assets and must be considered the Intellectual Property (IP) of the legitimate owner who created them, in order to protect trained DNN models from illegal reproduction, stealing, redistribution, or abuse. Although being a new emerging and interdisciplinary field, numerous DNN model IP protection methods have been proposed. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of two mainstream DNN IP protection methods: deep watermarking and deep fingerprinting, with a proposed taxonomy. More than 190 research contributions are included in this survey, covering many aspects of Deep IP Protection: problem definition, main threats and challenges, merits and demerits of deep watermarking and deep fingerprinting methods, evaluation metrics, and performance discussion. We finish the survey by identifying promising directions for future research.

EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion

Yuchen Sun, Qianqian Xu, Zitai Wang, Zhiyong Yang, Junwei He

Multi-label Out-Of-Distribution (OOD) detection aims to discriminate the OOD samples from the multi-label In-Distribution (ID) ones. Compared with its multiclass counterpart, it is crucial to model the joint information among classes. To this end, JointEnergy, which is a representative multi-label OOD inference criterion, summarizes the logits of all the classes. However, we find that JointEnergy can produce an imbalance problem in OOD detection, especially when the model lacks enough discrimination ability. Specifically, we find that the samples only related to minority classes tend to be classified as OOD samples due to the ambiguous energy decision boundary. Besides, imbalanced multi-label learning methods, originally designed for ID ones, would not be suitable for OOD detection scenarios, even producing a serious negative transfer effect. In this paper, we resort to auxiliary outlier exposure (OE) and propose an unknown-aware multi-label learning framework to reshape the uncertainty energy space layout. In this framework, the energy score is separately optimized for tail ID samples and unknown samples, and the energy distribution gap between them is expanded, such that the tail ID samples can have a significantly larger energy score than the OOD ones. What's more, a simple yet effective measure is designed to select more informative OE datasets. Finally, comprehensive experimental results on multiple multi-label and OOD datasets reveal the effectiveness of the proposed method.

GLOMIA-Pro: A Generalizable Longitudinal Medical Image Analysis Framework for Disease Progression Prediction

Shuaitong Zhang, Yuchen Sun, Yong Ao, Xuehuan Zhang, Ruoshui Yang, Jiantao Xu, Zuwu Ai, Haike Zhang, Xiang Yang, Yao Xu, Kunwei Li, Duanduan Chen

Longitudinal medical images are essential for monitoring disease progression by capturing spatiotemporal changes associated with dynamic biological processes. While current methods have made progress in modeling spatiotemporal patterns, they face three key limitations: (1) lack of generalizable framework applicable to diverse disease progression prediction tasks; (2) frequent overlook of the ordinal nature inherent in disease staging; (3) susceptibility to representation collapse due to structural similarities between adjacent time points, which can obscure subtle but discriminative progression biomarkers. To address these limitations, we propose a Generalizable LOngitudinal Medical Image Analysis framework for disease Progression prediction (GLOMIA-Pro). GLOMIA-Pro consists of two core components: progression representation extraction and progression-aware fusion. The progression representation extraction module introduces a piecewise orthogonal attention mechanism and employs a novel ordinal progression constraint to disentangle finegrained temporal imaging variations relevant to disease progression. The progression-aware fusion module incorporates a redesigned skip connection architecture which integrates the learned progression representation with current imaging representation, effectively mitigating representation collapse during cross-temporal fusion. Validated on two distinct clinical applications: knee osteoarthritis severity prediction and esophageal cancer treatment response assessment, GLOMIA-Pro consistently outperforms seven state-of-the-art longitudinal analysis methods. Ablation studies further confirm the contribution of individual components, demonstrating the robustness and generalizability of GLOMIA-Pro across diverse clinical scenarios.

Penetration-free Solid-Fluid Interaction on Shells and Rods

Jinyuan Liu, Yuchen Sun, Yin Yang, Chenfanfu Jiang, Minchen Li, Bo Zhu

We introduce a novel approach to simulate the interaction between fluids and thin elastic solids without any penetration. Our approach is centered around an optimization system augmented with barriers, which aims to find a configuration that ensures the absence of penetration while enforcing incompressibility for the fluids and minimizing elastic potentials for the solids. Unlike previous methods that primarily focus on velocity coherence at the fluid-solid interfaces, we demonstrate the effectiveness and flexibility of explicitly resolving positional constraints, including both explicit representation of solid positions and the implicit representation of fluid level-set interface. To preserve the volume of the fluid, we propose a simple yet efficient approach that adjusts the associated level-set values. Additionally, we develop a distance metric capable of measuring the separation between an implicitly represented surface and a Lagrangian object of arbitrary codimension. By integrating the inertia, solid elastic potential, damping, barrier potential, and fluid incompressibility within a unified system, we are able to robustly simulate a wide range of processes involving fluid interactions with lower-dimensional objects such as shells and rods. These processes include topology changes, bouncing, splashing, sliding, rolling, floating, and more.

When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-$k$ Multi-Label Learning

Yuchen Sun, Qianqian Xu, Zitai Wang, Qingming Huang

With the great success of deep neural networks, adversarial learning has received widespread attention in various studies, ranging from multi-class learning to multi-label learning. However, existing adversarial attacks toward multi-label learning only pursue the traditional visual imperceptibility but ignore the new perceptible problem coming from measures such as Precision@$k$ and mAP@$k$. Specifically, when a well-trained multi-label classifier performs far below the expectation on some samples, the victim can easily realize that this performance degeneration stems from attack, rather than the model itself. Therefore, an ideal multi-labeling adversarial attack should manage to not only deceive visual perception but also evade monitoring of measures. To this end, this paper first proposes the concept of measure imperceptibility. Then, a novel loss function is devised to generate such adversarial perturbations that could achieve both visual and measure imperceptibility. Furthermore, an efficient algorithm, which enjoys a convex objective, is established to optimize this objective. Finally, extensive experiments on large-scale benchmark datasets, such as PASCAL VOC 2012, MS COCO, and NUS WIDE, demonstrate the superiority of our proposed method in attacking the top-$k$ multi-label systems.

GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration

Yuchen Sun, Shanhui Zhao, Tao Yu, Hao Wen, Samith Va, Mengwei Xu, Yuanchun Li, Chongyang Zhang

GUI agents hold significant potential to enhance the experience and efficiency of human-device interaction. However, current methods face challenges in generalizing across applications (apps) and tasks, primarily due to two fundamental limitations in existing datasets. First, these datasets overlook developer-induced structural variations among apps, limiting the transferability of knowledge across diverse software environments. Second, many of them focus solely on navigation tasks, which restricts their capacity to represent comprehensive software architectures and complex user interactions. To address these challenges, we introduce GUI-Xplore, a dataset meticulously designed to enhance cross-application and cross-task generalization via an exploration-and-reasoning framework. GUI-Xplore integrates pre-recorded exploration videos providing contextual insights, alongside five hierarchically structured downstream tasks designed to comprehensively evaluate GUI agent capabilities. To fully exploit GUI-Xplore's unique features, we propose Xplore-Agent, a GUI agent framework that combines Action-aware GUI Modeling with Graph-Guided Environment Reasoning. Further experiments indicate that Xplore-Agent achieves a 10% improvement over existing methods in unfamiliar environments, yet there remains significant potential for further enhancement towards truly generalizable GUI agents.

Multi-frame Interferometric Imaging with a Femtosecond Stroboscopic Pulse Train for Observing Irreversible Phenomena

Dmitro Martynowych, David Veysset, Alexei A. Maznev, Yuchen Sun, Steven E. Kooi, Keith. A. Nelson

We describe a high-speed single-shot multi-frame interferometric imaging technique enabling multiple interferometric images with femtosecond exposure time over a 50 ns event window to be recorded following a single laser-induced excitation event. The stroboscopic illumination of a framing camera is made possible through the use of a doubling cavity which produces a femtosecond pulse train that is synchronized to the gated exposure windows of the individual frames of the camera. The imaging system utilizes a Michelson interferometer to extract phase and ultimately displacement information. We demonstrate the method by monitoring laser-induced deformation and the propagation of high-amplitude acoustic waves in a silicon nitride membrane. The method is applicable to a wide range of fast irreversible phenomena such as crack branching, shock-induced material damage, cavitation and dielectric breakdown.

Can Graph Neural Networks Learn to Solve MaxSAT Problem?

Minghao Liu, Fuqi Jia, Pei Huang, Fan Zhang, Yuchen Sun, Shaowei Cai, Feifei Ma, Jian Zhang

With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability (SAT), which shows the potential in bridging the gap between machine learning and symbolic reasoning. However, the quality of solutions predicted by GNNs has not been well investigated in the literature. In this paper, we study the capability of GNNs in learning to solve Maximum Satisfiability (MaxSAT) problem, both from theoretical and practical perspectives. We build two kinds of GNN models to learn the solution of MaxSAT instances from benchmarks, and show that GNNs have attractive potential to solve MaxSAT problem through experimental evaluation. We also present a theoretical explanation of the effect that GNNs can learn to solve MaxSAT problem to some extent for the first time, based on the algorithmic alignment theory.

Eulerian-Lagrangian Fluid Simulation on Particle Flow Maps

Junwei Zhou, Duowen Chen, Molin Deng, Yitong Deng, Yuchen Sun, Sinan Wang, Shiying Xiong, Bo Zhu

We propose a novel Particle Flow Map (PFM) method to enable accurate long-range advection for incompressible fluid simulation. The foundation of our method is the observation that a particle trajectory generated in a forward simulation naturally embodies a perfect flow map. Centered on this concept, we have developed an Eulerian-Lagrangian framework comprising four essential components: Lagrangian particles for a natural and precise representation of bidirectional flow maps; a dual-scale map representation to accommodate the mapping of various flow quantities; a particle-to-grid interpolation scheme for accurate quantity transfer from particles to grid nodes; and a hybrid impulse-based solver to enforce incompressibility on the grid. The efficacy of PFM has been demonstrated through various simulation scenarios, highlighting the evolution of complex vortical structures and the details of turbulent flows. Notably, compared to NFM, PFM reduces computing time by up to 49 times and memory consumption by up to 41%, while enhancing vorticity preservation as evidenced in various tests like leapfrog, vortex tube, and turbulent flow.

MAGI-1: Autoregressive Video Generation at Scale

Sand. ai, Hansi Teng, Hongyu Jia, Lei Sun, Lingzhi Li, Maolin Li, Mingqiu Tang, Shuai Han, Tianning Zhang, W. Q. Zhang, Weifeng Luo, Xiaoyang Kang, Yuchen Sun, Yue Cao, Yunpeng Huang, Yutong Lin, Yuxin Fang, Zewei Tao, Zheng Zhang, Zhongshu Wang, Zixun Liu, Dai Shi, Guoli Su, Hanwen Sun, Hong Pan, Jie Wang, Jiexin Sheng, Min Cui, Min Hu, Ming Yan, Shucheng Yin, Siran Zhang, Tingting Liu, Xianping Yin, Xiaoyu Yang, Xin Song, Xuan Hu, Yankai Zhang, Yuqiao Li

We present MAGI-1, a world model that generates videos by autoregressively predicting a sequence of video chunks, defined as fixed-length segments of consecutive frames. Trained to denoise per-chunk noise that increases monotonically over time, MAGI-1 enables causal temporal modeling and naturally supports streaming generation. It achieves strong performance on image-to-video (I2V) tasks conditioned on text instructions, providing high temporal consistency and scalability, which are made possible by several algorithmic innovations and a dedicated infrastructure stack. MAGI-1 facilitates controllable generation via chunk-wise prompting and supports real-time, memory-efficient deployment by maintaining constant peak inference cost, regardless of video length. The largest variant of MAGI-1 comprises 24 billion parameters and supports context lengths of up to 4 million tokens, demonstrating the scalability and robustness of our approach. The code and models are available at https://github.com/SandAI-org/MAGI-1 and https://github.com/SandAI-org/MagiAttention. The product can be accessed at https://sand.ai.

Identification and Development of Therapeutics for COVID-19

Halie M. Rando, Nils Wellhausen, Soumita Ghosh, Alexandra J. Lee, Anna Ada Dattoli, Fengling Hu, James Brian Byrd, Diane N. Rafizadeh, Ronan Lordan, Yanjun Qi, Yuchen Sun, Christian Brueffer, Jeffrey M. Field, Marouen Ben Guebila, Nafisa M. Jadavji, Ashwin N. Skelly, Bharath Ramsundar, Jinhui Wang, Rishi Raj Goel, YoSon Park, the COVID-19 Review Consortium, Simina M. Boca, Anthony Gitter, Casey S. Greene

After emerging in China in late 2019, the novel Severe acute respiratory syndrome-like coronavirus 2 (SARS-CoV-2) spread worldwide and as of early 2021, continues to significantly impact most countries. Only a small number of coronaviruses are known to infect humans, and only two are associated with the severe outcomes associated with SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a closely related species of SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Both of these previous epidemics were controlled fairly rapidly through public health measures, and no vaccines or robust therapeutic interventions were identified. However, previous insights into the immune response to coronaviruses gained during the outbreaks of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have proved beneficial to identifying approaches to the treatment and prophylaxis of novel coronavirus disease 2019 (COVID-19). A number of potential therapeutics against SARS-CoV-2 and the resultant COVID-19 illness were rapidly identified, leading to a large number of clinical trials investigating a variety of possible therapeutic approaches being initiated early on in the pandemic. As a result, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA) in the United States, and many other therapeutics remain under investigation. Here, we describe a range of approaches for the treatment of COVID-19, along with their proposed mechanisms of action and the current status of clinical investigation into each candidate. The status of these investigations will continue to evolve, and this review will be updated as progress is made.

Trajectory Consistency for One-Step Generation on Euler Mean Flows

Zhiqi Li, Yuchen Sun, Duowen Chen, Jinjin He, Bo Zhu

We propose \emph{Euler Mean Flows (EMF)}, a flow-based generative framework for one-step and few-step generation that enforces long-range trajectory consistency with minimal sampling cost. The key idea of EMF is to replace the trajectory consistency constraint, which is difficult to supervise and optimize over long time scales, with a principled linear surrogate that enables direct data supervision for long-horizon flow-map compositions. We derive this approximation from the semigroup formulation of flow-based models and show that, under mild regularity assumptions, it faithfully approximates the original consistency objective while being substantially easier to optimize. This formulation leads to a unified, JVP-free training framework that supports both $u$-prediction and $x_1$-prediction variants, avoiding explicit Jacobian computations and significantly reducing memory and computational overhead. Experiments on image synthesis, particle-based geometry generation, and functional generation demonstrate improved optimization stability and sample quality under fixed sampling budgets, together with approximately $50\%$ reductions in training time and memory consumption compared to existing one-step methods for image generation.

The Amazon Nova Family of Models: Technical Report and Model Card

Amazon AGI, Aaron Langford, Aayush Shah, Abhanshu Gupta, Abhimanyu Bhatter, Abhinav Goyal, Abhinav Mathur, Abhinav Mohanty, Abhishek Kumar, Abhishek Sethi, Abi Komma, Abner Pena, Achin Jain, Adam Kunysz, Adam Opyrchal, Adarsh Singh, Aditya Rawal, Adok Achar Budihal Prasad, Adrià de Gispert, Agnika Kumar, Aishwarya Aryamane, Ajay Nair, Akilan M, Akshaya Iyengar, Akshaya Vishnu Kudlu Shanbhogue, Alan He, Alessandra Cervone, Alex Loeb, Alex Zhang, Alexander Fu, Alexander Lisnichenko, Alexander Zhipa, Alexandros Potamianos, Ali Kebarighotbi, Aliakbar Daronkolaei, Alok Parmesh, Amanjot Kaur Samra, Ameen Khan, Amer Rez, Amir Saffari, Amit Agarwalla, Amit Jhindal, Amith Mamidala, Ammar Asmro, Amulya Ballakur, Anand Mishra, Anand Sridharan, Anastasiia Dubinina, Andre Lenz, Andreas Doerr, Andrew Keating, Andrew Leaver, Andrew Smith, Andrew Wirth, Andy Davey, Andy Rosenbaum, Andy Sohn, Angela Chan, Aniket Chakrabarti, Anil Ramakrishna, Anirban Roy, Anita Iyer, Anjali Narayan-Chen, Ankith Yennu, Anna Dabrowska, Anna Gawlowska, Anna Rumshisky, Anna Turek, Anoop Deoras, Anton Bezruchkin, Anup Prasad, Anupam Dewan, Anwith Kiran, Apoorv Gupta, Aram Galstyan, Aravind Manoharan, Arijit Biswas, Arindam Mandal, Arpit Gupta, Arsamkhan Pathan, Arun Nagarajan, Arushan Rajasekaram, Arvind Sundararajan, Ashwin Ganesan, Ashwin Swaminathan, Athanasios Mouchtaris, Audrey Champeau, Avik Ray, Ayush Jaiswal, Ayush Sharma, Bailey Keefer, Balamurugan Muthiah, Beatriz Leon-Millan, Ben Koopman, Ben Li, Benjamin Biggs, Benjamin Ott, Bhanu Vinzamuri, Bharath Venkatesh, Bhavana Ganesh, Bhoomit Vasani, Bill Byrne, Bill Hsu, Bincheng Wang, Blake King, Blazej Gorny, Bo Feng, Bo Zheng, Bodhisattwa Paul, Bofan Sun, Bofeng Luo, Bowen Chen, Bowen Xie, Boya Yu, Brendan Jugan, Brett Panosh, Brian Collins, Brian Thompson, Can Karakus, Can Liu, Carl Lambrecht, Carly Lin, Carolyn Wang, Carrie Yuan, Casey Loyda, Cezary Walczak, Chalapathi Choppa, Chandana Satya Prakash, Chankrisna Richy Meas, Charith Peris, Charles Recaido, Charlie Xu, Charul Sharma, Chase Kernan, Chayut Thanapirom, Chengwei Su, Chenhao Xu, Chenhao Yin, Chentao Ye, Chenyang Tao, Chethan Parameshwara, Ching-Yun Chang, Chong Li, Chris Hench, Chris Tran, Christophe Dupuy, Christopher Davis, Christopher DiPersio, Christos Christodoulopoulos, Christy Li, Chun Chen, Claudio Delli Bovi, Clement Chung, Cole Hawkins, Connor Harris, Corey Ropell, Cynthia He, DK Joo, Dae Yon Hwang, Dan Rosen, Daniel Elkind, Daniel Pressel, Daniel Zhang, Danielle Kimball, Daniil Sorokin, Dave Goodell, Davide Modolo, Dawei Zhu, Deepikaa Suresh, Deepti Ragha, Denis Filimonov, Denis Foo Kune, Denis Romasanta Rodriguez, Devamanyu Hazarika, Dhananjay Ram, Dhawal Parkar, Dhawal Patel, Dhwanil Desai, Dinesh Singh Rajput, Disha Sule, Diwakar Singh, Dmitriy Genzel, Dolly Goldenberg, Dongyi He, Dumitru Hanciu, Dushan Tharmal, Dzmitry Siankovich, Edi Cikovic, Edwin Abraham, Ekraam Sabir, Elliott Olson, Emmett Steven, Emre Barut, Eric Jackson, Ethan Wu, Evelyn Chen, Ezhilan Mahalingam, Fabian Triefenbach, Fan Yang, Fangyu Liu, Fanzi Wu, Faraz Tavakoli, Farhad Khozeimeh, Feiyang Niu, Felix Hieber, Feng Li, Firat Elbey, Florian Krebs, Florian Saupe, Florian Sprünken, Frank Fan, Furqan Khan, Gabriela De Vincenzo, Gagandeep Kang, George Ding, George He, George Yeung, Ghada Qaddoumi, Giannis Karamanolakis, Goeric Huybrechts, Gokul Maddali, Gonzalo Iglesias, Gordon McShane, Gozde Sahin, Guangtai Huang, Gukyeong Kwon, Gunnar A. Sigurdsson, Gurpreet Chadha, Gururaj Kosuru, Hagen Fuerstenau, Hah Hah, Haja Maideen, Hajime Hosokawa, Han Liu, Han-Kai Hsu, Hann Wang, Hao Li, Hao Yang, Haofeng Zhu, Haozheng Fan, Harman Singh, Harshavardhan Kaluvala, Hashim Saeed, He Xie, Helian Feng, Hendrix Luo, Hengzhi Pei, Henrik Nielsen, Hesam Ilati, Himanshu Patel, Hongshan Li, Hongzhou Lin, Hussain Raza, Ian Cullinan, Imre Kiss, Inbarasan Thangamani, Indrayani Fadnavis, Ionut Teodor Sorodoc, Irem Ertuerk, Iryna Yemialyanava, Ishan Soni, Ismail Jelal, Ivan Tse, Jack FitzGerald, Jack Zhao, Jackson Rothgeb, Jacky Lee, Jake Jung, Jakub Debski, Jakub Tomczak, James Jeun, James Sanders, Jason Crowley, Jay Lee, Jayakrishna Anvesh Paidy, Jayant Tiwari, Jean Farmer, Jeff Solinsky, Jenna Lau, Jeremy Savareese, Jerzy Zagorski, Ji Dai, Jiacheng, Gu, Jiahui Li, Jian, Zheng, Jianhua Lu, Jianhua Wang, Jiawei Dai, Jiawei Mo, Jiaxi Xu, Jie Liang, Jie Yang, Jim Logan, Jimit Majmudar, Jing Liu, Jinghong Miao, Jingru Yi, Jingyang Jin, Jiun-Yu Kao, Jixuan Wang, Jiyang Wang, Joe Pemberton, Joel Carlson, Joey Blundell, John Chin-Jew, John He, Jonathan Ho, Jonathan Hueser, Jonathan Lunt, Jooyoung Lee, Joshua Tan, Joyjit Chatterjee, Judith Gaspers, Jue Wang, Jun Fang, Jun Tang, Jun Wan, Jun Wu, Junlei Wang, Junyi Shi, Justin Chiu, Justin Satriano, Justin Yee, Jwala Dhamala, Jyoti Bansal, Kai Zhen, Kai-Wei Chang, Kaixiang Lin, Kalyan Raman, Kanthashree Mysore Sathyendra, Karabo Moroe, Karan Bhandarkar, Karan Kothari, Karolina Owczarzak, Karthick Gopalswamy, Karthick Ravi, Karthik Ramakrishnan, Karthika Arumugam, Kartik Mehta, Katarzyna Konczalska, Kavya Ravikumar, Ke Tran, Kechen Qin, Kelin Li, Kelvin Li, Ketan Kulkarni, Kevin Angelo Rodrigues, Keyur Patel, Khadige Abboud, Kiana Hajebi, Klaus Reiter, Kris Schultz, Krishna Anisetty, Krishna Kotnana, Kristen Li, Kruthi Channamallikarjuna, Krzysztof Jakubczyk, Kuba Pierewoj, Kunal Pal, Kunwar Srivastav, Kyle Bannerman, Lahari Poddar, Lakshmi Prasad, Larry Tseng, Laxmikant Naik, Leena Chennuru Vankadara, Lenon Minorics, Leo Liu, Leonard Lausen, Leonardo F. R. Ribeiro, Li Zhang, Lili Gehorsam, Ling Qi, Lisa Bauer, Lori Knapp, Lu Zeng, Lucas Tong, Lulu Wong, Luoxin Chen, Maciej Rudnicki, Mahdi Namazifar, Mahesh Jaliminche, Maira Ladeira Tanke, Manasi Gupta, Mandeep Ahlawat, Mani Khanuja, Mani Sundaram, Marcin Leyk, Mariusz Momotko, Markus Boese, Markus Dreyer, Markus Mueller, Mason Fu, Mateusz Górski, Mateusz Mastalerczyk, Matias Mora, Matt Johnson, Matt Scott, Matthew Wen, Max Barysau, Maya Boumerdassi, Maya Krishnan, Mayank Gupta, Mayank Hirani, Mayank Kulkarni, Meganathan Narayanasamy, Melanie Bradford, Melanie Gens, Melissa Burke, Meng Jin, Miao Chen, Michael Denkowski, Michael Heymel, Michael Krestyaninov, Michal Obirek, Michalina Wichorowska, Michał Miotk, Milosz Watroba, Mingyi Hong, Mingzhi Yu, Miranda Liu, Mohamed Gouda, Mohammad El-Shabani, Mohammad Ghavamzadeh, Mohit Bansal, Morteza Ziyadi, Nan Xia, Nathan Susanj, Nav Bhasin, Neha Goswami, Nehal Belgamwar, Nicolas Anastassacos, Nicolas Bergeron, Nidhi Jain, Nihal Jain, Niharika Chopparapu, Nik Xu, Nikko Strom, Nikolaos Malandrakis, Nimisha Mishra, Ninad Parkhi, Ninareh Mehrabi, Nishita Sant, Nishtha Gupta, Nitesh Sekhar, Nithin Rajeev, Nithish Raja Chidambaram, Nitish Dhar, Noor Bhagwagar, Noy Konforty, Omar Babu, Omid Razavi, Orchid Majumder, Osama Dar, Oscar Hsu, Pablo Kvitca, Pallavi Pandey, Parker Seegmiller, Patrick Lange, Paul Ferraro, Payal Motwani, Pegah Kharazmi, Pei Wang, Pengfei Liu, Peter Bradtke, Peter Götz, Peter Zhou, Pichao Wang, Piotr Poskart, Pooja Sonawane, Pradeep Natarajan, Pradyun Ramadorai, Pralam Shah, Prasad Nirantar, Prasanthi Chavali, Prashan Wanigasekara, Prashant Saraf, Prashun Dey, Pratyush Pant, Prerak Pradhan, Preyaa Patel, Priyanka Dadlani, Prudhvee Narasimha Sadha, Qi Dong, Qian Hu, Qiaozi, Gao, Qing Liu, Quinn Lam, Quynh Do, R. Manmatha, Rachel Willis, Rafael Liu, Rafal Ellert, Rafal Kalinski, Rafi Al Attrach, Ragha Prasad, Ragini Prasad, Raguvir Kunani, Rahul Gupta, Rahul Sharma, Rahul Tewari, Rajaganesh Baskaran, Rajan Singh, Rajiv Gupta, Rajiv Reddy, Rajshekhar Das, Rakesh Chada, Rakesh Vaideeswaran Mahesh, Ram Chandrasekaran, Ramesh Nallapati, Ran Xue, Rashmi Gangadharaiah, Ravi Rachakonda, Renxian Zhang, Rexhina Blloshmi, Rishabh Agrawal, Robert Enyedi, Robert Lowe, Robik Shrestha, Robinson Piramuthu, Rohail Asad, Rohan Khanna, Rohan Mukherjee, Rohit Mittal, Rohit Prasad, Rohith Mysore Vijaya Kumar, Ron Diamant, Ruchita Gupta, Ruiwen Li, Ruoying Li, Rushabh Fegade, Ruxu Zhang, Ryan Arbow, Ryan Chen, Ryan Gabbard, Ryan Hoium, Ryan King, Sabarishkumar Iyer, Sachal Malick, Sahar Movaghati, Sai Balakavi, Sai Jakka, Sai Kashyap Paruvelli, Sai Muralidhar Jayanthi, Saicharan Shriram Mujumdar, Sainyam Kapoor, Sajjad Beygi, Saket Dingliwal, Saleh Soltan, Sam Ricklin, Sam Tucker, Sameer Sinha, Samridhi Choudhary, Samson Tan, Samuel Broscheit, Samuel Schulter, Sanchit Agarwal, Sandeep Atluri, Sander Valstar, Sanjana Shankar, Sanyukta Sanyukta, Sarthak Khanna, Sarvpriye Khetrapal, Satish Janakiraman, Saumil Shah, Saurabh Akolkar, Saurabh Giri, Saurabh Khandelwal, Saurabh Pawar, Saurabh Sahu, Sean Huang, Sejun Ra, Senthilkumar Gopal, Sergei Dobroshinsky, Shadi Saba, Shamik Roy, Shamit Lal, Shankar Ananthakrishnan, Sharon Li, Shashwat Srijan, Shekhar Bhide, Sheng Long Tang, Sheng Zha, Shereen Oraby, Sherif Mostafa, Shiqi Li, Shishir Bharathi, Shivam Prakash, Shiyuan Huang, Shreya Yembarwar, Shreyas Pansare, Shreyas Subramanian, Shrijeet Joshi, Shuai Liu, Shuai Tang, Shubham Chandak, Shubham Garg, Shubham Katiyar, Shubham Mehta, Shubham Srivastav, Shuo Yang, Siddalingesha D S, Siddharth Choudhary, Siddharth Singh Senger, Simon Babb, Sina Moeini, Siqi Deng, Siva Loganathan, Slawomir Domagala, Sneha Narkar, Sneha Wadhwa, Songyang Zhang, Songyao Jiang, Sony Trenous, Soumajyoti Sarkar, Soumya Saha, Sourabh Reddy, Sourav Dokania, Spurthideepika Sandiri, Spyros Matsoukas, Sravan Bodapati, Sri Harsha Reddy Wdaru, Sridevi Yagati Venkateshdatta, Srikanth Ronanki, Srinivasan R Veeravanallur, Sriram Venkatapathy, Sriramprabhu Sankaraguru, Sruthi Gorantla, Sruthi Karuturi, Stefan Schroedl, Subendhu Rongali, Subhasis Kundu, Suhaila Shakiah, Sukriti Tiwari, Sumit Bharti, Sumita Sami, Sumith Mathew, Sunny Yu, Sunwoo Kim, Suraj Bajirao Malode, Susana Cumplido Riel, Swapnil Palod, Swastik Roy, Syed Furqhan, Tagyoung Chung, Takuma Yoshitani, Taojiannan Yang, Tejaswi Chillakura, Tejwant Bajwa, Temi Lajumoke, Thanh Tran, Thomas Gueudre, Thomas Jung, Tianhui Li, Tim Seemman, Timothy Leffel, Tingting Xiang, Tirth Patel, Tobias Domhan, Tobias Falke, Toby Guo, Tom Li, Tomasz Horszczaruk, Tomasz Jedynak, Tushar Kulkarni, Tyst Marin, Tytus Metrycki, Tzu-Yen Wang, Umang Jain, Upendra Singh, Utkarsh Chirimar, Vaibhav Gupta, Vanshil Shah, Varad Deshpande, Varad Gunjal, Varsha Srikeshava, Varsha Vivek, Varun Bharadwaj, Varun Gangal, Varun Kumar, Venkatesh Elango, Vicente Ordonez, Victor Soto, Vignesh Radhakrishnan, Vihang Patel, Vikram Singh, Vinay Varma Kolanuvada, Vinayshekhar Bannihatti Kumar, Vincent Auvray, Vincent Cartillier, Vincent Ponzo, Violet Peng, Vishal Khandelwal, Vishal Naik, Vishvesh Sahasrabudhe, Vitaliy Korolev, Vivek Gokuladas, Vivek Madan, Vivek Subramanian, Volkan Cevher, Vrinda Gupta, Wael Hamza, Wei Zhang, Weitong Ruan, Weiwei Cheng, Wen Zhang, Wenbo Zhao, Wenyan Yao, Wenzhuo Ouyang, Wesley Dashner, William Campbell, William Lin, Willian Martin, Wyatt Pearson, Xiang Jiang, Xiangxing Lu, Xiangyang Shi, Xianwen Peng, Xiaofeng Gao, Xiaoge Jiang, Xiaohan Fei, Xiaohui Wang, Xiaozhou Joey Zhou, Xin Feng, Xinyan Zhao, Xinyao Wang, Xinyu Li, Xu Zhang, Xuan Wang, Xuandi Fu, Xueling Yuan, Xuning Wang, Yadunandana Rao, Yair Tavizon, Yan Rossiytsev, Yanbei Chen, Yang Liu, Yang Zou, Yangsook Park, Yannick Versley, Yanyan Zhang, Yash Patel, Yen-Cheng Lu, Yi Pan, Yi-Hsiang, Lai, Yichen Hu, Yida Wang, Yiheng Zhou, Yilin Xiang, Ying Shi, Ying Wang, Yishai Galatzer, Yongxin Wang, Yorick Shen, Yuchen Sun, Yudi Purwatama, Yue, Wu, Yue Gu, Yuechun Wang, Yujun Zeng, Yuncong Chen, Yunke Zhou, Yusheng Xie, Yvon Guy, Zbigniew Ambrozinski, Zhaowei Cai, Zhen Zhang, Zheng Wang, Zhenghui Jin, Zhewei Zhao, Zhiheng Li, Zhiheng Luo, Zhikang Zhang, Zhilin Fang, Zhiqi Bu, Zhiyuan Wang, Zhizhong Li, Zijian Wang, Zimeng, Qiu, Zishi Li

Laser-Driven High-Velocity Microparticle Launcher In Atmosphere And Under Vacuum

David Veysset, Yuchen Sun, Steven E. Kooi, Jet Lem, Keith A. Nelson

This paper presents a novel approach to launch single microparticles at high velocities under low vacuum conditions. In an all-optical table-top method, microparticles with sizes ranging from a few microns to tens of microns are accelerated to supersonic velocities depending on the particle mass. The acceleration is performed through a laser ablation process and the particles are monitored in free space using an ultra-high-speed multi-frame camera with nanosecond time resolution. Under low vacuum, we evaluate the current platform performance by measuring particle velocities for a range of particle types and sizes, and demonstrate blast wave suppression and drag reduction under vacuum. Showing an impact on polyethylene, we demonstrate the capability of the experimental setup to study materials behavior under high-velocity impact. The present method is relevant to space applications, particularly to rendezvous missions where velocities range from tens of m/s to a few km/s, as well as to a wide range of terrestrial applications including impact bonding and impact-induced erosion.

Fluid Simulation on Vortex Particle Flow Maps

Sinan Wang, Junwei Zhou, Fan Feng, Zhiqi Li, Yuchen Sun, Duowen Chen, Greg Turk, Bo Zhu

We propose the Vortex Particle Flow Map (VPFM) method to simulate incompressible flow with complex vortical evolution in the presence of dynamic solid boundaries. The core insight of our approach is that vorticity is an ideal quantity for evolution on particle flow maps, enabling significantly longer flow map distances compared to other fluid quantities like velocity or impulse. To achieve this goal, we developed a hybrid Eulerian-Lagrangian representation that evolves vorticity and flow map quantities on vortex particles, while reconstructing velocity on a background grid. The method integrates three key components: (1) a vorticity-based particle flow map framework, (2) an accurate Hessian evolution scheme on particles, and (3) a solid boundary treatment for no-through and no-slip conditions in VPFM. These components collectively allow a substantially longer flow map length (3-12 times longer) than the state-of-the-art, enhancing vorticity preservation over extended spatiotemporal domains. We validated the performance of VPFM through diverse simulations, demonstrating its effectiveness in capturing complex vortex dynamics and turbulence phenomena.

An Impulse-formed Navier-Stokes Solver based on Long-range Particle Flow Maps

Zhiqi Li, Duowen Chen, Junwei Zhou, Sinan Wang, Yuchen Sun, Bo Zhu

We present a particle-grid characteristic-mapping framework that extends long-range characteristic mapping from inviscid flows to general Navier-Stokes dynamics with viscosity, body forces, and complex boundaries. Unlike traditional grid-based and vorticity-centered characteristic methods, our method is built on the observation that particle trajectories naturally provide the long-range flow map, enabling geometric quantities and their gradients to be transported in a direct and effective manner. We identify the impulse, the gauge variable of the velocity field, as the primary quantity mapped along characteristics while remaining compatible with standard velocity-based incompressible solvers. Using the 1-form representation of the impulse equation, we derive an integral formulation that decomposes the impulse evolution into a component transported geometrically along the particle flow map and a complementary component generated by viscosity and body forces evaluated through path integrals accumulated along particle trajectories. These components together yield a unified characteristic-mapping solver capable of handling incompressible Navier-Stokes flows with viscosity and body forces while maintaining the accuracy and geometric fidelity of characteristic transport.

Fine-Tuning Vision-Language Models for Visual Navigation Assistance

Xiao Li, Bharat Gandhi, Ming Zhan, Mohit Nehra, Zhicheng Zhang, Yuchen Sun, Meijia Song, Naisheng Zhang, Xi Wang

We address vision-language-driven indoor navigation to assist visually impaired individuals in reaching a target location using images and natural language guidance. Traditional navigation systems are ineffective indoors due to the lack of precise location data. Our approach integrates vision and language models to generate step-by-step navigational instructions, enhancing accessibility and independence. We fine-tune the BLIP-2 model with Low Rank Adaptation (LoRA) on a manually annotated indoor navigation dataset. We propose an evaluation metric that refines the BERT F1 score by emphasizing directional and sequential variables, providing a more comprehensive measure of navigational performance. After applying LoRA, the model significantly improved in generating directional instructions, overcoming limitations in the original BLIP-2 model.

WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based Environments

Xi Chen, Tianyu Shi, Qingpeng Zhao, Yuchen Sun, Yunfei Gao, Xiangjun Wang

Recent advances in deep reinforcement learning (RL) have demonstrated complex decision-making capabilities in simulation environments such as Arcade Learning Environment, MuJoCo, and ViZDoom. However, they are hardly extensible to more complicated problems, mainly due to the lack of complexity and variations in the environments they are trained and tested on. Furthermore, they are not extensible to an open-world environment to facilitate long-term exploration research. To learn realistic task-solving capabilities, we need to develop an environment with greater diversity and complexity. We developed WILD-SCAV, a powerful and extensible environment based on a 3D open-world FPS (First-Person Shooter) game to bridge the gap. It provides realistic 3D environments of variable complexity, various tasks, and multiple modes of interaction, where agents can learn to perceive 3D environments, navigate and plan, compete and cooperate in a human-like manner. WILD-SCAV also supports different complexities, such as configurable maps with different terrains, building structures and distributions, and multi-agent settings with cooperative and competitive tasks. The experimental results on configurable complexity, multi-tasking, and multi-agent scenarios demonstrate the effectiveness of WILD-SCAV in benchmarking various RL algorithms, as well as it is potential to give rise to intelligent agents with generalized task-solving abilities. The link to our open-sourced code can be found here https://github.com/inspirai/wilderness-scavenger.