Tootfinder

Attention (as Discrete-Time Markov) Chains
Yotam Erel, Olaf D\"unkel, Rishabh Dabral, Vladislav Golyanik, Christian Theobalt, Amit H. Bermano
https://arxiv.org/abs/2507.17657

Attention (as Discrete-Time Markov) Chains
We introduce a new interpretation of the attention matrix as a discrete-time Markov chain. Our interpretation sheds light on common operations involving attention scores such as selection, summation, and averaging in a unified framework. It further extends them by considering indirect attention, propagated through the Markov chain, as opposed to previous studies that only model immediate effects. Our main observation is that tokens corresponding to semantically similar regions form a set of met…

Die #Marzipanfabrik ist ein Parkplatz mit Bürointegration. All das Geld für schicke Fassaden und die städtische Lage können nicht die Hässlichkeit kompensieren, die so ein Konzept aus dem hinterländlichen Raum erzeugt.
An den Rand gequetschte Fußwege, getrennt durch Wände aus senkrechtem Parken und überbreite Fahrbahnen, versiegelte Flächen, die zur Flucht einladen. Hippe Büros umgebe…

Solving Zero-Sum Convex Markov Games
Fivos Kalogiannis, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Ian Gemp, Georgios Piliouras
https://arxiv.org/abs/2506.16120

Solving Zero-Sum Convex Markov Games
We contribute the first provable guarantees of global convergence to Nash equilibria (NE) in two-player zero-sum convex Markov games (cMGs) by using independent policy gradient methods. Convex Markov games, recently defined by Gemp et al. (2024), extend Markov decision processes to multi-agent settings with preferences that are convex over occupancy measures, offering a broad framework for modeling generic strategic interactions. However, even the fundamental min-max case of cMGs presents signi…

Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning
Lucas de Lara (IECL)
https://arxiv.org/abs/2507.16370

Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning
Counterfactual reasoning aims at answering contrary-to-fact questions like ''Would have Alice recovered had she taken aspirin?'' and corresponds to the most fine-grained layer of causation. Critically, while many counterfactual statements cannot be falsified -- even by randomized experiments -- they underpin fundamental concepts like individual-wise fairness. Therefore, providing models to formalize and implement counterfactual beliefs remains a fundamental scientific problem. In the Markovian …

Welche Auswirkungen hat das Abschmelzen des grönländischen Eisschildes auf den globalen #Meeresspiegel? 🌍🌡️ Muss man sich da Sorgen machen? Wie stark wird der Meeresspiegel ansteigen und was bedeutet das für unsere Küstenstädte? 🧊📈
#Klimawandel

Klimawandel: Das Abschmelzen des grönländischen Eisschildes
Der grönländische Eisschild ist der zweitgrößte der Welt. Sein Abschmelzen würde den Meeresspiegel um mehrere Meter ansteigen lassen und hätte verheerende Folgen für die Menschheit.

House Dems launch multi-committee Epstein attack on Republicans on last day before recess (Politico)
https://www.politico.com/live-updates/2025/07/23/congress/house-democrats-committee-markups-epstein-attack-republicans-recess-00471666
http://www.memeorandum.com/250723/p150#a250723p150

Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data
Arif Pathan
https://arxiv.org/abs/2506.17244

Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data
Short-term sentiment forecasting in financial markets (e.g., stocks, indices) is challenging due to volatility, non-linearity, and noise in OHLC (Open, High, Low, Close) data. This paper introduces a novel CMG (Chaos-Markov-Gaussian) framework that integrates chaos theory, Markov property, and Gaussian processes to improve prediction accuracy. Chaos theory captures nonlinear dynamics; the Markov chain models regime shifts; Gaussian processes add probabilistic reasoning. We enhance the framework…

Die ePetition 178586 an den Bundestag aus dem Bereich Kontrolle von Kriegswaffen und sonstigen Rüstungsgütern mit dem Titel
Keine Lieferung von Taurus-Marschflugkörpern an die Ukraine
kann bis zum Montag 04. August 2025 01:00 UTC mitgezeichnet werden und lautet
Mit der Petition wird gefordert, keine Taurus-Marschflu…

Die ePetition 178586 an den Bundestag aus dem Bereich Kontrolle von Kriegswaffen und sonstigen Rüstungsgütern mit dem Titel
Keine Lieferung von Taurus-Marschflugkörpern an die Ukraine
kann bis zum Montag 04. August 2025 01:00 UTC mitgezeichnet werden und lautet
Mit der Petition wird gefordert, keine Taurus-Marschflu…

Finite-Time Ruin for the Compound Markov Binomial Risk Model
Zbigniew Palmowski, Lewis Ramsden, Apostolos D. Papaioannou
https://arxiv.org/abs/2507.16448 h…

Finite-Time Ruin for the Compound Markov Binomial Risk Model
In this paper, we study finite-time ruin probabilities for the compound Markov binomial risk model - a discrete-time model where claim sizes are modulated by a finite-state ergodic Markov chain. In the classic (non-modulated) case, the risk process has interchangeable increments and consequently, its finite-time ruin probability can be obtained in terms of Takács' famous Ballot Theorem results. Unfortunately, due to the dependency of our process on the state(s) of the modulating chain these do…