Tootfinder

Characterizing $A_\sigma$-minimizer graphs: given order and independence number
Shuchao Li, Jiaqi Zhang
https://arxiv.org/abs/2508.09770 https://arxiv.org/…

Characterizing $A_σ$-minimizer graphs: given order and independence number
For a given graph \( G \), let \( A(G) \), \( Q(G) \), and \( D(G) \) denote the adjacency matrix, signless Laplacian matrix, and diagonal degree matrix of \( G \), respectively. The \( A_σ(G) \) matrix, proposed by Nikiforov, is defined as \( A_σ(G)=σD(G)+(1 - σ)A(G) \), where \( σ\in[0,1] \). This matrix captures the gradual transition from \( A(G) \) to \( Q(G) \). Let \( \mathcal{G}_{n,α} \) denote the family of all connected graphs with \( n \) vertices and independence number \( α\…

I refuse to replace capitalist hierarchies with red-tinted authoritarianism, because in the end, I want liberation, not another power structure draped in red with the hammer and sickle.
#Authoritarianism #Anarchism

Tight Regret Upper and Lower Bounds for Optimistic Hedge in Two-Player Zero-Sum Games
Taira Tsuchiya
https://arxiv.org/abs/2510.11691 https://arxiv.org/pdf…

Tight Regret Upper and Lower Bounds for Optimistic Hedge in Two-Player Zero-Sum Games
In two-player zero-sum games, the learning dynamic based on optimistic Hedge achieves one of the best-known regret upper bounds among strongly-uncoupled learning dynamics. With an appropriately chosen learning rate, the social and individual regrets can be bounded by $O(\log(mn))$ in terms of the numbers of actions $m$ and $n$ of the two players. This study investigates the optimality of the dependence on $m$ and $n$ in the regret of optimistic Hedge. To this end, we begin by refining existing …

Daisy family flower color graded to take out most of the greens of the surrounding foliage but leave the exquisite yellows and oranges intact
#photo #photography #yellow

Utilizing dynamic sparsity on pretrained DETR
Reza Sedghi, Anand Subramoney, David Kappel
https://arxiv.org/abs/2510.09380 https://arxiv.org/pdf/2510.09380…

Utilizing dynamic sparsity on pretrained DETR
Efficient inference with transformer-based models remains a challenge, especially in vision tasks like object detection. We analyze the inherent sparsity in the MLP layers of DETR and introduce two methods to exploit it without retraining. First, we propose Static Indicator-Based Sparsification (SIBS), a heuristic method that predicts neuron inactivity based on fixed activation patterns. While simple, SIBS offers limited gains due to the input-dependent nature of sparsity. To address this, we i…

Stable hyperplane arrangements
Toshio Oshima
https://arxiv.org/abs/2510.11099 https://arxiv.org/pdf/2510.11099

Stable hyperplane arrangements
We classify complex hyperplane arrangements $\mathcal A$ whose intersection posets $L(\mathcal A)$ satisfy $L(\mathcal A)=π_i^{-1}\circπ_i\bigl(L(\mathcal A)\bigr)$ for $i=1,\dots,n$. Here $π_i$ denotes the projection from $\mathbb C^n$ onto $\mathbb C^{n-1}$ defined by that forgets the coordinate $x_i$ of $(x_1,\dots,x_n)\in\mathbb C^n$, and $π_i\bigl(L(\mathcal A)\bigr)=\{π_i(S)\mid S\in L(\mathcal A)\}$. We show that such arrangements $\mathcal A$ arise as pullbacks of the mirror hyperp…

CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way
Ankush Pratap Singh, Houwei Cao, Yong Liu
https://arxiv.org/abs/2510.09382 https://arxiv.org…

CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way
Curriculum learning (CL) structures training from simple to complex samples, facilitating progressive learning. However, existing CL approaches for emotion recognition often rely on heuristic, data-driven, or model-based definitions of sample difficulty, neglecting the difficulty for human perception, a critical factor in subjective tasks like emotion recognition. We propose CHUCKLE (Crowdsourced Human Understanding Curriculum for Knowledge Led Emotion Recognition), a perception-driven CL frame…

Remoteness, order, size and connectivity constraints in digraphs
Sufiyan Mallu
https://arxiv.org/abs/2510.08841 https://arxiv.org/pdf/2510.08841

Remoteness, order, size and connectivity constraints in digraphs
Let \( D \) be a strongly connected digraph. The average distance of a vertex \( v \) in \( D \) is defined as the arithmetic mean of the distances from \( v \) to all other vertices in \( D \). The remoteness \( ρ(D) \) of \( D \) is the maximum of the average distances of the vertices in \( D \). In this paper, we provide a sharp upper bound on the remoteness of a strong digraph with given order, size, and vertex-connectivity. We then characterise the extremal digraphs that maximise remote…

Chronologically Consistent Generative AI
Songrun He, Linying Lv, Asaf Manela, Jimmy Wu
https://arxiv.org/abs/2510.11677 https://arxiv.org/pdf/2510.11677

Chronologically Consistent Generative AI
We introduce a family of chronologically consistent, instruction-following large language models to eliminate lookahead bias. Each model is trained only on data available before a clearly defined knowledge-cutoff date, ensuring strict temporal separation from any post-cutoff data. The resulting framework offers (i) a simple, conversational chat interface, (ii) fully open, fixed model weights that guarantee replicability, and (iii) a conservative lower bound on forecast accuracy, isolating the s…