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heise | JavaScript: Einen Discord-Bot für die eigene Gaming-Community programmieren
Ein Discord-Bot begrüßt User, zeigt Daten an oder schmeißt Spammer raus. Wir zeigen, wie Sie mit dem Framework discord.js einen simplen Bot programmieren.

Discrete and Continuous Difference of Submodular Minimization
George Orfanides, Tim Hoheisel, Marwa El Halabi
https://arxiv.org/abs/2506.07952 https://

Discrete and Continuous Difference of Submodular Minimization
Submodular functions, defined on continuous or discrete domains, arise in numerous applications. We study the minimization of the difference of two submodular (DS) functions, over both domains, extending prior work restricted to set functions. We show that all functions on discrete domains and all smooth functions on continuous domains are DS. For discrete domains, we observe that DS minimization is equivalent to minimizing the difference of two convex (DC) functions, as in the set function cas…

Simulating discrete-time quantum walk with urn model
Surajit Saha
https://arxiv.org/abs/2506.06896 https://arxiv.org/pdf/2506.06896…

Simulating discrete-time quantum walk with urn model
Urn models have long been used to study stochastic processes, probability distributions, and reinforcement dynamics. Meanwhile, discrete time quantum walks (DTQW) serve as fundamental components in quantum computation and quantum information theory. This work explores a novel connection between an urn model and discrete-time quantum walks, focusing on how urn-based stochastic processes can provide insights into quantum state evolution and algorithmic behavior. Importantly, our model operates en…

Been doing some game testing that's required me to use Discord more again. And I mean, I knew Discord was bad, but admittedly, I'd forgotten how TRULY awful is

This https://arxiv.org/abs/2502.13574 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior
Denoising diffusion probabilistic models (DDPMs) can be utilized to recover a clean signal from its degraded observation(s) by conditioning the model on the degraded signal. The degraded signals are themselves contaminated versions of the clean signals; due to this correlation, they may encompass certain useful information about the target clean data distribution. However, existing adoption of the standard Gaussian as the prior distribution in turn discards such information when shaping the pri…

Centrality Change Proneness: an Early Indicator of Microservice Architectural Degradation
Alexander Bakhtin, Matteo Esposito, Valentina Lenarduzzi, Davide Taibi
https://arxiv.org/abs/2506.07690

Centrality Change Proneness: an Early Indicator of Microservice Architectural Degradation
Over the past decade, the wide adoption of Microservice Architecture has required the identification of various patterns and anti-patterns to prevent Microservice Architectural Degradation. Frequently, the systems are modelled as a network of connected services. Recently, the study of temporal networks has emerged as a way to describe and analyze evolving networks. Previous research has explored how software metrics such as size, complexity, and quality are related to microservice centrality in…

Controlled Reach-avoid Set Computation for Discrete-time Polynomial Systems via Convex Optimization
Taoran Wu, Yiling Xue, Dejin Ren, Arvind Easwaran, Martin Fr\"anzle, Bai Xue
https://arxiv.org/abs/2506.06679

Controlled Reach-avoid Set Computation for Discrete-time Polynomial Systems via Convex Optimization
This paper addresses the computation of controlled reach-avoid sets (CRASs) for discrete-time polynomial systems subject to control inputs. A CRAS is a set encompassing initial states from which there exist control inputs driving the system into a target set while avoiding unsafe sets. However, efficiently computing CRASs remains an open problem, especially for discrete-time systems. In this paper, we propose a novel framework for computing CRASs which takes advantage of a probabilistic perspec…

Inf-sup stable space-time discretization of the wave equation based on a first-order-in-time variational formulation
Matteo Ferrari, Ilaria Perugia, Enrico Zampa
https://arxiv.org/abs/2506.05886

Inf-sup stable space-time discretization of the wave equation based on a first-order-in-time variational formulation
In this paper, we present a conforming space-time discretization of the wave equation based on a first-order-in-time variational formulation with exponential weights in time. We analyze the method, showing its stability without imposing any restrictions on the mesh size or time step, and proving quasi-optimal convergence for any choice of space-time tensor product discrete spaces that satisfies standard approximation assumptions. Numerical examples are provided to support the theoretical findin…