Tootfinder

Low-Complexity Ordered Reliability Direct Error Pattern Testing (ORDEPT) Decoding with Likelihood Thresholding
Reza Hadavian, Dmitri Truhachev
https://arxiv.org/abs/2506.20079

Low-Complexity Ordered Reliability Direct Error Pattern Testing (ORDEPT) Decoding with Likelihood Thresholding
We propose a reduced complexity approach to pattern-based soft decoding of block codes. We start from the ORDEPT decoding algorithm which tests a list of partial error patterns organized in the order of their likelihood and attempts to complete the patterns creating candidate codewords. We then propose an early termination criterion. Once a candidate codeword is found, its log-likelihood difference to the received sequence is compared to a preset threshold and the decoding decision is instantly…

Identifiability and Maximum Likelihood Estimation for System Identification of Networks of Dynamical Systems
Anders Hansson, Jo\~ao Victor Galv\~ao da Mata, Martin S. Andersen
https://arxiv.org/abs/2506.20628

Identifiability and Maximum Likelihood Estimation for System Identification of Networks of Dynamical Systems
In this paper we investigate identifiability and maximum likelihood estimation for direct system identification of networks of dynamical systems. We provide necessary and sufficient conditions for network identifiability in terms of Gröbner bases. We show that the maximum likelihood approach is both consistent and efficient, which is in contrast to existing prediction error approaches. Moreover, our approach has wider applicability, i.e., it is applicable whenever network identifiability holds…

Accelerating GNN Training through Locality-aware Dropout and Merge
Gongjian Sun, Mingyu Yan, Dengke Han, Runzhen Xue, Duo Wang, Xiaochun Ye, Dongrui Fan
https://arxiv.org/abs/2506.21414

Accelerating GNN Training through Locality-aware Dropout and Merge
Graph Neural Networks (GNNs) have demonstrated significant success in graph learning and are widely adopted across various critical domains. However, the irregular connectivity between vertices leads to inefficient neighbor aggregation, resulting in substantial irregular and coarse-grained DRAM accesses. This lack of data locality presents significant challenges for execution platforms, ultimately degrading performance. While previous accelerator designs have leveraged on-chip memory and data a…

On likelihood of a Condorcet winner for uniformly random and independent voter preferences
Boris Pittel
https://arxiv.org/abs/2506.20613 https://

On likelihood of a Condorcet winner for uniformly random and independent voter preferences
We study a mathematical model of voting contest with $m$ voters and $n$ candidates, with each voter ranking the candidates in order of preference, without ties. A Condorcet winner is a candidate who gets more than $m/2$ votes in pairwise contest with every other candidate. An ``impartial culture'' setting is the case when each voter chooses his/her candidate preference list uniformly at random from all $n!$ preferences, and does it independently of all other voters. For impartial culture case, …

Kirjutasin ERRi artikli. Pole vist kunagi nii kiiresti ühte artiklit kirjutanud, tavaliselt kulub selleks palju rohkem aega. Põhjus selles, et lugesin Kristjan Porti arvamust Novaatoris ning mõtlesin, et miks see "pimeda keskaja" müüt ka ei kao. Sellest on ju nii palju juttu olnud. Meil oli eelmisel semestril TÜs ka ülikoolimõtte ajaloo kursus, kus sai pikalt tegeletud keskaja ning varauusaja ülikoolide ajalooga ning eriti just sellel taustal tundus, et on vaja ikkagi midagi omalt …

In the case of the Iraq war, the administration of George W Bush believed that Saddam Hussein was building weapons of mass destruction, and
– determined to act on that belief
– sought proof of its proposition.
Convinced that he was right,
Bush sought to streamline information that confirmed its bias.
What fell by the wayside were conflicting views.
Because intelligence is ultimately about assessing the likelihood of things which are difficult to know,

Efficient Bayesian Inference for Spatial Point Patterns Using the Palm Likelihood
Kevin M. Collins, Erin M. Schliep
https://arxiv.org/abs/2507.17065 https://

Efficient Bayesian Inference for Spatial Point Patterns Using the Palm Likelihood
Bayesian inference for spatial point patterns is often hindered computationally by intractable likelihoods. In the frequentist literature, estimating equations utilizing pseudolikelihoods have long been used for simulation-free parameter estimation. One such pseudolikelihood based on the process of differences is known as the Palm likelihood. Utilizing notions of Bayesian composite likelihoods and generalized Bayesian inference, we develop a framework for the use of Palm likelihoods in a Bayesi…

Squared Linear Models
Hannah Friedman, Bernd Sturmfels, Maximilian Wiesmann
https://arxiv.org/abs/2505.19351 https://arxiv.org/pdf/25…

Squared Linear Models
We study statistical models that are parametrized by squares of linear forms. All critical points of the likelihood function are real and positive. There is one for each region of the projective hyperplane arrangement. We study the ideal and singular locus of the model, and we give a determinantal presentation for its likelihood correspondence. We characterize tropical degenerations of the MLE, and we describe log-normal polytopes.

Squared Linear Models
Hannah Friedman, Bernd Sturmfels, Maximilian Wiesmann
https://arxiv.org/abs/2505.19351 https://arxiv.org/pdf/25…

Squared Linear Models
We study statistical models that are parametrized by squares of linear forms. All critical points of the likelihood function are real and positive. There is one for each region of the projective hyperplane arrangement. We study the ideal and singular locus of the model, and we give a determinantal presentation for its likelihood correspondence. We characterize tropical degenerations of the MLE, and we describe log-normal polytopes.

Word-Representable Graphs and Locality of Words
Philipp B\"oll, Pamela Fleischmann, Annika Huch, Jana Krei{\ss}, Tim L\"ock, Kajus Park, Max Wiedenh\"oft
https://arxiv.org/abs/2506.19493

Word-Representable Graphs and Locality of Words
In this work, we investigate the relationship between $k$-repre\-sentable graphs and graphs representable by $k$-local words. In particular, we show that every graph representable by a $k$-local word is $(k+1)$-representable. A previous result about graphs represented by $1$-local words is revisited with new insights. Moreover, we investigate both classes of graphs w.r.t. hereditary and in particular the speed as a measure. We prove that the latter ones belong to the factorial layer and that th…