Tootfinder

Feature Space Analysis by Guided Diffusion Model
Kimiaki Shirahama, Miki Yanobu, Kaduki Yamashita, Miho Ohsaki
https://arxiv.org/abs/2509.07936 https://arx…

Feature Space Analysis by Guided Diffusion Model
One of the key issues in Deep Neural Networks (DNNs) is the black-box nature of their internal feature extraction process. Targeting vision-related domains, this paper focuses on analysing the feature space of a DNN by proposing a decoder that can generate images whose features are guaranteed to closely match a user-specified feature. Owing to this guarantee that is missed in past studies, our decoder allows us to evidence which of various attributes in an image are encoded into a feature by th…

The Small Magellanic Cloud through the lens of the James Webb Space Telescope : binaries and mass function within the galaxy outskirts
M. V. Legnardi, F. Muratore, A. P. Milone, G. Cordoni, T. Ziliotto, E. Dondoglio, A. F. Marino, A. Mastrobuono-Battisti, E. Bortolan, E. P. Lagioia, M. Tailo
https://arxiv.org/abs/2509.08687

The Small Magellanic Cloud through the lens of the James Webb Space Telescope : binaries and mass function within the galaxy outskirts
The stellar initial mass function (IMF) and the fraction of binary systems are fundamental ingredients that govern the formation and evolution of galaxies. Whether the IMF is universal or varies with environment remains one of the central open questions in astrophysics. Dwarf galaxies such as the Small Magellanic Cloud (SMC), with their low metallicity and diffuse star-forming regions, offer critical laboratories to address this issue. In this work, we exploit ultra-deep photometry from the Jam…

Multi-Actor Multi-Critic Deep Deterministic Reinforcement Learning with a Novel Q-Ensemble Method
Andy Wu, Chun-Cheng Lin, Rung-Tzuo Liaw, Yuehua Huang, Chihjung Kuo, Chia Tong Weng
https://arxiv.org/abs/2510.01083

Multi-Actor Multi-Critic Deep Deterministic Reinforcement Learning with a Novel Q-Ensemble Method
Reinforcement learning has gathered much attention in recent years due to its rapid development and rich applications, especially on control systems and robotics. When tackling real-world applications with reinforcement learning method, the corresponded Markov decision process may have huge discrete or even continuous state/action space. Deep reinforcement learning has been studied for handling these issues through deep learning for years, and one promising branch is the actor-critic architectu…

S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG
Huaicheng Zhang, Ruoxin Wang, Chenlian Zhou, Jiguang Shi, Yue Ge, Zhoutong Li, Sheng Chang, Hao Wang, Jin He, Qijun Huang
https://arxiv.org/abs/2509.03066

S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG
As one of the most effective methods for cardiovascular disease (CVD) diagnosis, multi-lead Electrocardiogram (ECG) signals present a characteristic multi-sensor information fusion challenge that has been continuously researched in deep learning domains. Despite the numerous algorithms proposed with different DL architectures, maintaining a balance among performance, computational complexity, and multi-source ECG feature fusion remains challenging. Recently, state space models (SSMs), particula…

In #StarTrek, humanity did not just wait for other species to come visit them, but actively sought them out by sending out a number of probes into deep space. And one of them is the topic of today's #TrekTriviaTuesday question.
As always no googling and no spoiling the answer for othe…

Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution
Wankun Chen, Feng Gao, Yanhai Gan, Jingchao Cao, Junyu Dong, Qian Du
https://arxiv.org/abs/2509.24334

Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution
Sea surface temperature (SST) is an essential indicator of global climate change and one of the most intuitive factors reflecting ocean conditions. Obtaining high-resolution SST data remains challenging due to limitations in physical imaging, and super-resolution via deep neural networks is a promising solution. Recently, Mamba-based approaches leveraging State Space Models (SSM) have demonstrated significant potential for long-range dependency modeling with linear complexity. However, their ap…

DiffAxE: Diffusion-driven Hardware Accelerator Generation and Design Space Exploration
Arkapravo Ghosh, Abhishek Moitra, Abhiroop Bhattacharjee, Ruokai Yin, Priyadarshini Panda
https://arxiv.org/abs/2508.10303

DiffAxE: Diffusion-driven Hardware Accelerator Generation and Design Space Exploration
Design space exploration (DSE) is critical for developing optimized hardware architectures, especially for AI workloads such as deep neural networks (DNNs) and large language models (LLMs), which require specialized acceleration. As model complexity grows, accelerator design spaces have expanded to O(10^17), becoming highly irregular, non-convex, and exhibiting many-to-one mappings from design configurations to performance metrics. This complexity renders direct inverse derivation infeasible an…

A Variational Physics-Informed Neural Network Framework Using Petrov-Galerkin Method for Solving Singularly Perturbed Boundary Value Problems
Vijay Kumar, Gautam Singh
https://arxiv.org/abs/2509.12271 …

A Variational Physics-Informed Neural Network Framework Using Petrov-Galerkin Method for Solving Singularly Perturbed Boundary Value Problems
This work proposes a Variational Physics-Informed Neural Network (VPINN) framework that integrates the Petrov-Galerkin formulation with deep neural networks (DNNs) for solving one-dimensional singularly perturbed boundary value problems (BVPs) and parabolic partial differential equations (PDEs) involving one or two small parameters. The method adopts a nonlinear approximation in which the trial space is defined by neural network functions, while the test space is constructed from hat functions.…

When recalling in-context, Transformers are not SSMs
Destiny Okpekpe, Antonio Orvieto
https://arxiv.org/abs/2508.19029 https://arxiv.org/pdf/2508.19029

When recalling in-context, Transformers are not SSMs
Despite the advantageous subquadratic complexity of modern recurrent deep learning models -- such as state-space models (SSMs) -- recent studies have highlighted their potential shortcomings compared to transformers on reasoning and memorization tasks. In this paper, we dive deeper into one of such benchmarks: associative recall (AR), which has been shown to correlate well with language modeling performance, and inspect in detail the effects of scaling and optimization issues in recently propos…

The X-ray Source Population of the Metal-Rich Globular Cluster NGC 6528
Bernard Leal, Kwangmin Oh, Jay Strader, Steve E. Zepf, Kristen Dage, S. Kim, C. Y. Hui
https://arxiv.org/abs/2508.10105

The X-ray Source Population of the Metal-Rich Globular Cluster NGC 6528
We present the first study of the X-ray sources in one of the most metal-rich globular clusters in the Galaxy, NGC 6528. Using relatively deep (66 ksec) archival imaging from the Chandra X-ray Observatory, we identify 18 sources within the half-light radius of the cluster, all in the range $L_X \sim 10^{31}$-$10^{32}$erg s$^{-1}$ (0.5-7 keV). By combining these data with photometry from the Hubble Space Telescope and other sources, we classify the X-ray sources as a likely mix of cataclysmic va…