Tootfinder

Training Spatial Ability in Virtual Reality
Yiannos Demetriou, Manasvi Parikh, Sara Eskandari, Westley Weimer, Madeline Endres
https://arxiv.org/abs/2508.10195 https://

Training Spatial Ability in Virtual Reality
Background: Spatial reasoning has been identified as a critical skill for success in STEM. Unfortunately, under-represented groups often have lower incoming spatial ability. Courses that improve spatial skills exist but are not widely used. Virtual reality (VR) has been suggested as a possible tool for teaching spatial reasoning since students are more accurate and complete spatial tasks more quickly in three dimensions. However, no prior work has developed or evaluated a fully-structured VR sp…

A retrospective on DISPEED -- Leveraging heterogeneity in a drone swarm for IDS execution
Vincent Lannurien, Cam\'elia Slimani, Louis Morge-Rollet, Laurent Lemarchand, David Espes, Fr\'ed\'eric Le Roy, Jalil Boukhobza
https://arxiv.org/abs/2506.11800

A retrospective on DISPEED -- Leveraging heterogeneity in a drone swarm for IDS execution
Swarms of drones are gaining more and more autonomy and efficiency during their missions. However, security threats can disrupt their missions' progression. To overcome this problem, Network Intrusion Detection Systems ((N)IDS) are promising solutions to detect malicious behavior on network traffic. However, modern NIDS rely on resource-hungry machine learning techniques, that can be difficult to deploy on a swarm of drones. The goal of the DISPEED project is to leverage the heterogeneity (exec…

Communication Efficient Robotic Mixed Reality with Gaussian Splatting Cross-Layer Optimization
Chenxuan Liu, He Li, Zongze Li, Shuai Wang, Wei Xu, Kejiang Ye, Derrick Wing Kwan Ng, Chengzhong Xu
https://arxiv.org/abs/2508.08624

Communication Efficient Robotic Mixed Reality with Gaussian Splatting Cross-Layer Optimization
Realizing low-cost communication in robotic mixed reality (RoboMR) systems presents a challenge, due to the necessity of uploading high-resolution images through wireless channels. This paper proposes Gaussian splatting (GS) RoboMR (GSMR), which enables the simulator to opportunistically render a photo-realistic view from the robot's pose by calling ``memory'' from a GS model, thus reducing the need for excessive image uploads. However, the GS model may involve discrepancies compared to the act…

Dead Zone of Accountability: Why Social Claims in Machine Learning Research Should Be Articulated and Defended
Tianqi Kou, Dana Calacci, Cindy Lin
https://arxiv.org/abs/2508.08739

Dead Zone of Accountability: Why Social Claims in Machine Learning Research Should Be Articulated and Defended
Many Machine Learning research studies use language that describes potential social benefits or technical affordances of new methods and technologies. Such language, which we call "social claims", can help garner substantial resources and influence for those involved in ML research and technology production. However, there exists a gap between social claims and reality (the claim-reality gap): ML methods often fail to deliver the claimed functionality or social impacts. This paper investigates …

The pearl-clutching centrist crowd likes to imagine that shaming and shunning are the slippery slope that leads to political violence.
They have it backwards.
Shaming and shunning are the •alternative• to political violence. So is meaningful democracy. So is a free press that remains steadfastly grounded in reality. So is a judiciary that applies the law, however flawed, with actual judgement and justice.
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InGaN Nanopixel Arrays on Single Crystal GaN Substrate
Nirmal Anand, Sadat Tahmeed Azad, Christy Giji Jenson, Dipon Kumar Ghosh, Md Zunaid Baten, Pei-Cheng Ku, Grzegorz Muziol, Sharif Sadaf
https://arxiv.org/abs/2506.11408

InGaN Nanopixel Arrays on Single Crystal GaN Substrate
Indium gallium nitride (InGaN) quantum well (QW) micro- and nano-scale light-emitting diodes (LEDs) are vital to the next-generation optoelectronic applications such as ultrahigh-speed optical interconnects and near-eye augmented/virtual reality displays. However, scaling to nanoscale dimensions introduces several challenges, including an increased nonradiative recombination from surface states and defect-related emission degradation. In this work, we present a high-performance, strain-relaxed …

Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation
Digvijay Wadekar, Arush Pimpalkar, Mark Ho-Yeuk Cheung, Benjamin Wandelt, Emanuele Berti, Ajit Kumar Mehta, Tejaswi Venumadhav, Javier Roulet, Tousif Islam, Barak Zackay, Jonathan Mushkin, Matias Zaldarriaga
https://…

Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation
We introduce a machine learning (ML) framework called $\texttt{TIER}$ for improving the sensitivity of gravitational wave search pipelines. Typically, search pipelines only use a small region of strain data in the vicinity of a candidate signal to construct the detection statistic. However, extended strain data ($\sim 10$ s) in the candidate's vicinity can also carry valuable complementary information. We show that this information can be efficiently captured by ML classifier models trained on …

ReconDreamer-RL: Enhancing Reinforcement Learning via Diffusion-based Scene Reconstruction
Chaojun Ni, Guosheng Zhao, Xiaofeng Wang, Zheng Zhu, Wenkang Qin, Xinze Chen, Guanghong Jia, Guan Huang, Wenjun Mei
https://arxiv.org/abs/2508.08170

ReconDreamer-RL: Enhancing Reinforcement Learning via Diffusion-based Scene Reconstruction
Reinforcement learning for training end-to-end autonomous driving models in closed-loop simulations is gaining growing attention. However, most simulation environments differ significantly from real-world conditions, creating a substantial simulation-to-reality (sim2real) gap. To bridge this gap, some approaches utilize scene reconstruction techniques to create photorealistic environments as a simulator. While this improves realistic sensor simulation, these methods are inherently constrained b…

AZRA: Extending the Affective Capabilities of Zoomorphic Robots using Augmented Reality
Shaun Macdonald, Salma ElSayed, Mark McGill
https://arxiv.org/abs/2508.08507 https://

AZRA: Extending the Affective Capabilities of Zoomorphic Robots using Augmented Reality
Zoomorphic robots could serve as accessible and practical alternatives for users unable or unwilling to keep pets. However, their affective interactions are often simplistic and short-lived, limiting their potential for domestic adoption. In order to facilitate more dynamic and nuanced affective interactions and relationships between users and zoomorphic robots we present AZRA, a novel augmented reality (AR) framework that extends the affective capabilities of these robots without physical modi…

Pushing the Boundaries of Immersion and Storytelling: A Technical Review of Unreal Engine
Oleksandra Sobchyshak, Santiago Berrezueta-Guzman, Stefan Wagner
https://arxiv.org/abs/2507.08142

Pushing the Boundaries of Immersion and Storytelling: A Technical Review of Unreal Engine
Unreal Engine is a platform that has influenced immersive storytelling and virtual reality (VR) through its advanced features and diverse applications. This paper provides an in-depth technical review of Unreal Engine. It analyzes its key innovations in creating hyper-realistic environments and emotionally engaging narratives, with significant applications in gaming, virtual production, education, cultural preservation, and healthcare. The findings of this article highlight Unreal Engine's tran…