Tootfinder

Heterogeneous Exposures to Systematic and Idiosyncratic Risk across Crypto Assets: A Divide-and-Conquer Approach
Nektarios Aslanidis, Aurelio Bariviera, George Kapetanios, Vasilis Sarafidis
https://arxiv.org/abs/2506.21100

Heterogeneous Exposures to Systematic and Idiosyncratic Risk across Crypto Assets: A Divide-and-Conquer Approach
This paper analyzes realized return behavior across a broad set of crypto assets by estimating heterogeneous exposures to idiosyncratic and systematic risk. A key challenge arises from the latent nature of broader economy-wide risk sources: macro-financial proxies are unavailable at high-frequencies, while the abundance of low-frequency candidates offers limited guidance on empirical relevance. To address this, we develop a two-stage ``divide-and-conquer'' approach. The first stage estimates ex…

Modeling European Electricity Market Integration during turbulent times
Francesco Ravazzolo, Luca Rossini, Andrea Viselli
https://arxiv.org/abs/2506.23289 …

Modeling European Electricity Market Integration during turbulent times
This paper introduces a novel Bayesian reverse unrestricted mixed-frequency model applied to a panel of nine European electricity markets. Our model analyzes the impact of daily fossil fuel prices and hourly renewable energy generation on hourly electricity prices, employing a hierarchical structure to capture cross-country interdependencies and idiosyncratic factors. The inclusion of random effects demonstrates that electricity market integration both mitigates and amplifies shocks. Our result…

Non-Discriminatory Personalized Pricing
Philipp Strack, Kai Hao Yang
https://arxiv.org/abs/2506.20925 https://arxiv.org/pdf/2506.2092…

Non-Discriminatory Personalized Pricing
A monopolist offers personalized prices to consumers with unit demand, heterogeneous values, and idiosyncratic costs, who differ in a protected characteristic, such as race or gender. The seller is subject to a non-discrimination constraint: consumers with the same cost, but different characteristics must face identical prices. Such constraints arise in regulated markets like credit or insurance. The setting reduces to an optimal transport, and we characterize the optimal pricing rule. Under th…

Abt. Wirksamkeit der Tibetologie (Kudos an Dagmar Schwerk!).
https://www.blankforms.org/publications/blank-forms-10-alien-roots-eliane-radigue

Blank Forms 10: Alien Roots: Éliane Radigue
Release date: June 20, 2025The tenth and final anthology from Blank Forms explores the early electronic work of French composer Éliane Radigue, whose radical approach to feedback, analog synthesis, and composition on tape has long evaded historical and technical interpretation. Combining key texts, newly translated primary documents, interviews, and commissioned essays, this compendium interrogates the composer’s idiosyncratic compositional practice, which both embraces and confounds the it…

Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders
Matyas Bohacek, Thomas Fel, Maneesh Agrawala, Ekdeep Singh Lubana
https://arxiv.org/abs/2506.19708

Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders
Despite their impressive performance, generative image models trained on large-scale datasets frequently fail to produce images with seemingly simple concepts -- e.g., human hands or objects appearing in groups of four -- that are reasonably expected to appear in the training data. These failure modes have largely been documented anecdotally, leaving open the question of whether they reflect idiosyncratic anomalies or more structural limitations of these models. To address this, we introduce a …

This https://arxiv.org/abs/2506.05339 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Flattery, Fluff, and Fog: Diagnosing and Mitigating Idiosyncratic Biases in Preference Models
Language models serve as proxies for human preference judgements in alignment and evaluation, yet they exhibit systematic miscalibration, prioritizing superficial patterns over substantive qualities. This bias manifests as overreliance on features like length, structure, and style, leading to issues like reward hacking and unreliable evaluations. Evidence suggests these biases originate in artifacts in human training data. In this work, we systematically investigate the relationship between tra…

Credit risk for large portfolios of green and brown loans: extending the ASRF model
Alessandro Ramponi, Sergio Scarlatti
https://arxiv.org/abs/2506.12510 h…

Credit risk for large portfolios of green and brown loans: extending the ASRF model
We propose a credit risk model for portfolios composed of green and brown loans, extending the ASRF framework via a two-factor copula structure. Systematic risk is modeled using potentially skewed distributions, allowing for asymmetric creditworthiness effects, while idiosyncratic risk remains Gaussian. Under a non-uniform exposure setting, we establish convergence in quadratic mean of the portfolio loss to a limit reflecting the distinct characteristics of the two loan segments. Numerical resu…

When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text
Hillary Dawkins, Kathleen C. Fraser, Svetlana Kiritchenko
https://arxiv.org/abs/2506.09975

When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text
Detecting AI-generated text is a difficult problem to begin with; detecting AI-generated text on social media is made even more difficult due to the short text length and informal, idiosyncratic language of the internet. It is nonetheless important to tackle this problem, as social media represents a significant attack vector in online influence campaigns, which may be bolstered through the use of mass-produced AI-generated posts supporting (or opposing) particular policies, decisions, or event…

Rhythm Features for Speaker Identification
Nick Mehlman, Thomas Thebaud, Dani Byrd, Shri Narayanan
https://arxiv.org/abs/2506.06834 https://

Rhythm Features for Speaker Identification
While deep learning models have demonstrated robust performance in speaker recognition tasks, they primarily rely on low-level audio features learned empirically from spectrograms or raw waveforms. However, prior work has indicated that idiosyncratic speaking styles heavily influence the temporal structure of linguistic units in speech signals (rhythm). This makes rhythm a strong yet largely overlooked candidate for a speech identity feature. In this paper, we test this hypothesis by applying d…