Tootfinder

Forward and inverse problems for a mixed-type equation with the Caputo fractional derivative and Dezin-type non-local condition
Ravshan Ashurov, Umida Dusanova, Navbahor Nuraliyeva
https://arxiv.org/abs/2507.12129

Forward and inverse problems for a mixed-type equation with the Caputo fractional derivative and Dezin-type non-local condition
This work is dedicated to the study of a mixed-type partial differential equation involving a Caputo fractional derivative in the time domain $t > 0$ and a classical parabolic equation in the domain $t < 0$, along with Dezin-type non-local boundary and gluing conditions. The forward and inverse problems are studied in detail. For the forward problem, the existence and uniqueness of solutions are established using the Fourier method, under appropriate assumptions on the initial data and the righ…

Hamilton-Jacobi Reachability for Viability Analysis of Constrained Waste-to-Energy Systems under Adversarial Uncertainty
Achraf Bouhmady, Othman Cherkaoui Dekkaki
https://arxiv.org/abs/2510.11396

Hamilton-Jacobi Reachability for Viability Analysis of Constrained Waste-to-Energy Systems under Adversarial Uncertainty
This paper investigates the problem of maintaining the safe operation of Waste-to-Energy (WtE) systems under operational constraints and uncertain waste inflows. We model this as a robust viability problem, formulated as a zero-sum differential game between a control policy and an adversarial disturbance. Within a Hamilton-Jacobi framework, the viability kernel is characterized as the zero sublevel set of a value function satisfying a constrained Hamilton-Jacobi-Bellman (HJB) equation in the vi…

AgriVLN: Vision-and-Language Navigation for Agricultural Robots
Xiaobei Zhao, Xingqi Lyu, Xiang Li
https://arxiv.org/abs/2508.07406 https://arxiv.org/pdf/2…

AgriVLN: Vision-and-Language Navigation for Agricultural Robots
Agricultural robots have emerged as powerful members in agricultural tasks, nevertheless, still heavily rely on manual operation or untransportable railway for movement, resulting in limited mobility and poor adaptability. Vision-and-Language Navigation (VLN) enables robots to navigate to the target destinations following natural language instructions, demonstrating strong performance on several domains. However, none of the existing benchmarks or methods is specifically designed for agricultur…

"Our government is being run by aggrieved elites who were thrown onto social platforms with everyone else in the 2010s, where they could finally read what we all think about them. And it caused them so much psychic damage that they have decided to destroy both the internet and democracy to make sure nothing like it will ever happen again."
(Original title: Petty, powerful, and pathologically online)

Petty, powerful, and pathologically online
Read to the end for an academic paper on puppygirls

I can't decide who's less likable - Mike Johnson or JD Vance. Vance has a more punchable face, but Johnson is much smoother at being a despicable piece of shit.
Vance is that edgelord that everyone hates but at least you know what they are; Johnson is that evil manager who seems agreeable and nice but then quietly reports you to higher-ups as a problem and makes sure you never get any real opportunities or advance anywhere in your career, and tells any future employers what a…

Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report
Riccardo Mereu, Aidan Scannell, Yuxin Hou, Yi Zhao, Aditya Jitta, Antonio Dominguez, Luigi Acerbi, Amos Storkey, Paul Chang
https://arxiv.org/abs/2510.07092

Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report
World models are a powerful paradigm in AI and robotics, enabling agents to reason about the future by predicting visual observations or compact latent states. The 1X World Model Challenge introduces an open-source benchmark of real-world humanoid interaction, with two complementary tracks: sampling, focused on forecasting future image frames, and compression, focused on predicting future discrete latent codes. For the sampling track, we adapt the video generation foundation model Wan-2.2 TI2V-…

Toward Accessible Dermatology: Skin Lesion Classification Using Deep Learning Models on Mobile-Acquired Images
Asif Newaz, Masum Mushfiq Ishti, A Z M Ashraful Azam, Asif Ur Rahman Adib
https://arxiv.org/abs/2509.04800

Toward Accessible Dermatology: Skin Lesion Classification Using Deep Learning Models on Mobile-Acquired Images
Skin diseases are among the most prevalent health concerns worldwide, yet conventional diagnostic methods are often costly, complex, and unavailable in low-resource settings. Automated classification using deep learning has emerged as a promising alternative, but existing studies are mostly limited to dermoscopic datasets and a narrow range of disease classes. In this work, we curate a large dataset of over 50 skin disease categories captured with mobile devices, making it more representative o…

Correction of weighted and shifted seven-step BDF for parabolic equations with nonsmooth data
Minghua Chen, Jiankang Shi, Fan Yu, Zhi Zhou
https://arxiv.org/abs/2509.02307 https…

Correction of weighted and shifted seven-step BDF for parabolic equations with nonsmooth data
It is well known that the seven-step backward difference formula (BDF) is unstable for the parabolic equations, since it is not even zero-stable. However, a linear combination of two non zero-stable schemes, namely the seven-step BDF and its shifted counterpart, can yield $A(α)$ stable. Based on this observation, the authors [Akrivis, Chen, and Yu, IMA J. Numer. Anal., DOI:10.1093/imanum/drae089] propose the weighted and shifted seven-step BDF methods for the parabolic equations, which stabili…

T-araVLN: Translator for Agricultural Robotic Agents on Vision-and-Language Navigation
Xiaobei Zhao, Xingqi Lyu, Xiang Li
https://arxiv.org/abs/2509.06644 https://

T-araVLN: Translator for Agricultural Robotic Agents on Vision-and-Language Navigation
Agricultural robotic agents have been becoming powerful helpers in a wide range of agricultural tasks, nevertheless, still heavily rely on manual operation or untransportable railway for movement. The AgriVLN method and the A2A benchmark pioneeringly extend Vision-and-Language Navigation (VLN) to the agricultural domain, enabling agents navigate to the target position following the natural language instructions. AgriVLN effectively understands the simple instructions, however, often misundersta…

Learning Real-World Acrobatic Flight from Human Preferences
Colin Merk, Ismail Geles, Jiaxu Xing, Angel Romero, Giorgia Ramponi, Davide Scaramuzza
https://arxiv.org/abs/2508.18817

Learning Real-World Acrobatic Flight from Human Preferences
Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making it well-suited for tasks where objectives are difficult to formalize or inherently subjective. Acrobatic flight poses a particularly challenging problem due to its complex dynamics, rapid movements, and the importance of precise execution. In this work, we explore the use of PbRL for agile drone control, focusing on the execution of dynamic maneuve…