Tootfinder

PENELLOPE VIII: Revisiting empirical relations to measure accretion luminosity
E. Fiorellino, J. M. Alcal\'a, C. F. Manara, C. Pittman, P. \'Abrah\'am, L. Venuti, S. Cabrit, R. Claes, M. Fang, \'A. Kosp\'al, G. Lodato, K. Mauco, {\L}. Tychoniec
https://arxiv.org/abs/2509.21078

PENELLOPE VIII: Revisiting empirical relations to measure accretion luminosity
The accretion luminosity (Lacc) in young, low-mass stars is crucial for understanding stellar formation, but direct measurements are often hindered by limited spectral coverage and challenges in UV-excess modeling. Empirical relations linking Lacc to various accretion tracers are widely used to overcome these limitations. This work revisits these empirical relations using the PENELLOPE dataset, evaluating their applicability across different star-forming regions and to accreting young objects o…

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Selection, Reflection and Self-Refinement: Revisit Reasoning Tasks via a Causal Lens
Yunlong Deng, Boyang Sun, Yan Li, Lingjing Kong, Zeyu Tang, Kun Zhang, Guangyi Chen
https://arxiv.org/abs/2510.08222

Selection, Reflection and Self-Refinement: Revisit Reasoning Tasks via a Causal Lens
Due to their inherent complexity, reasoning tasks have long been regarded as rigorous benchmarks for assessing the capabilities of machine learning models, especially large language models (LLMs). Although humans can solve these tasks with ease, existing models, even after extensive pre-training and post-training at scale, still fail to perform reasoning reliably. In this paper, we revisit reasoning tasks from a causal perspective, seeking to understand their behavior in latent space and to off…

Instance-Aware Robust Consistency Regularization for Semi-Supervised Nuclei Instance Segmentation
Zenan Lin, Wei Li, Jintao Chen, Zihao Wu, Wenxiong Kang, Changxin Gao, Liansheng Wang, Jin-Gang Yu
https://arxiv.org/abs/2510.09329

Instance-Aware Robust Consistency Regularization for Semi-Supervised Nuclei Instance Segmentation
Nuclei instance segmentation in pathological images is crucial for downstream tasks such as tumor microenvironment analysis. However, the high cost and scarcity of annotated data limit the applicability of fully supervised methods, while existing semi-supervised methods fail to adequately regularize consistency at the instance level, lack leverage of the inherent prior knowledge of pathological structures, and are prone to introducing noisy pseudo-labels during training. In this paper, we propo…

False Alarm Rates in Detecting Gravitational Wave Lensing from Astrophysical Coincidences: Insights with Model-Independent Technique GLANCE
Aniruddha Chakraborty, Suvodip Mukherjee
https://arxiv.org/abs/2510.11790

False Alarm Rates in Detecting Gravitational Wave Lensing from Astrophysical Coincidences: Insights with Model-Independent Technique GLANCE
The strong lensing gravitational waves (GWs) due to intervening massive astrophysical systems between the source and an observer are an inevitable consequence of the general theory of relativity, which can produce multiple GW events in overlapping sky localization error. However, the confirmed detection of such a unique astrophysical phenomenon is challenging due to several sources of contamination, arising from detector noise to astrophysical uncertainties. Robust model-independent search tech…

Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes
Xi Fang, Bingkai Wang, Liangyuan Hu, Fan Li
https://arxiv.org/abs/2510.08438 https://…

Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes
Cluster-randomized trials (CRTs) are experimental designs where groups or clusters of participants, rather than the individual participants themselves, are randomized to intervention groups. Analyzing CRT requires distinguishing between treatment effects at the cluster level and the individual level, which requires a clear definition of the estimands under the potential outcomes framework. For analyzing survival outcomes, it is common to assess the treatment effect by comparing survival functio…

Stability and Robustness of Time-Varying Opinion Dynamics: A Graph-Theoretic Approach
M. Hossein Abedinzadeh, Emrah Akyol
https://arxiv.org/abs/2510.01580 https://

Stability and Robustness of Time-Varying Opinion Dynamics: A Graph-Theoretic Approach
We study the stability of opinion dynamics in the time-varying Friedkin-Johnsen (TVFJ) model, which captures both persistent individual biases and adaptive social influence. We introduce two temporal structures, defected temporal graphs (DTGs) and weakly defected temporal graphs (WDTGs), that serve as graph-theoretic certificates linking stubborn influence and temporal connectivity to contraction of the state-transition matrix. Using these tools, we prove asymptotic stability of TVFJ dynamics u…

NSARM: Next-Scale Autoregressive Modeling for Robust Real-World Image Super-Resolution
Xiangtao Kong, Rongyuan Wu, Shuaizheng Liu, Lingchen Sun, Lei Zhang
https://arxiv.org/abs/2510.00820

NSARM: Next-Scale Autoregressive Modeling for Robust Real-World Image Super-Resolution
Most recent real-world image super-resolution (Real-ISR) methods employ pre-trained text-to-image (T2I) diffusion models to synthesize the high-quality image either from random Gaussian noise, which yields realistic results but is slow due to iterative denoising, or directly from the input low-quality image, which is efficient but at the price of lower output quality. These approaches train ControlNet or LoRA modules while keeping the pre-trained model fixed, which often introduces over-enhance…

Multi-bit Audio Watermarking
Luca A. Lanzend\"orfer, Kyle Fearne, Florian Gr\"otschla, Roger Wattenhofer
https://arxiv.org/abs/2510.01968 https://

Multi-bit Audio Watermarking
We present Timbru, a post-hoc audio watermarking model that achieves state-of-the-art robustness and imperceptibility trade-offs without training an embedder-detector model. Given any 44.1 kHz stereo music snippet, our method performs per-audio gradient optimization to add imperceptible perturbations in the latent space of a pretrained audio VAE, guided by a combined message and perceptual loss. The watermark can then be extracted using a pretrained CLAP model. We evaluate 16-bit watermarking o…

Linking Path-Dependent and Stochastic Volatility Models
Samuel N. Cohen, Cephas Svosve
https://arxiv.org/abs/2510.02024 https://arxiv.org/pdf/2510.02024

Linking Path-Dependent and Stochastic Volatility Models
We explore a link between stochastic volatility (SV) and path-dependent volatility (PDV) models. Using assumed density filtering, we map a given SV model into a corresponding PDV representation. The resulting specification is lightweight, improves in-sample fit, and delivers robust out-of-sample forecasts. We also introduce a calibration procedure for both SV and PDV models that produces standard errors for parameter estimates and supports joint calibration of SPX/VIX smile.