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Apache Flink is uniquely positioned to serve as the backbone for AI agents, equipping them with the powerful new tool of stream processing. Join Steffen Hoellinger at this year's Berlin Buzzwords to explore how Flink jobs can be transformed into “Agents”—autonomous, goal-driven entities that dynamically interact with data streams, trigger actions, and adapt their behaviour based on real-time insights.
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The half-space KPZ line ensemble and its scaling limit
Sayan Das, Christian Serio
https://arxiv.org/abs/2506.07939 https://arxiv.org/…

The half-space KPZ line ensemble and its scaling limit
For each $α\in \mathbb{R}$, $t \geq 1$, we show that there exists a unique $\mathbb{N}$-indexed line ensemble of random continuous curves $\mathbb{R}_{\le 0} \to \mathbb{R}$ with the following properties: (1) The top curve is distributed as the time-$t$ Cole--Hopf solution to the half-space KPZ equation with narrow wedge initial condition and Neumann boundary condition with parameter $α$. (2) The line ensemble satisfies a one-sided resampling invariance property, involving softly non-inte…

NFL news roundup: Lions signing OL Trystan Colon following Frank Ragnow's retirement https://www.nfl.com/news/nfl-news-roundup-latest-league-updates-from-wednesday-june-4

Roles of Non-switchable Domains and Internal Bias in Electrocaloric and Pyroelectric effects
Jun Usami, Yuki Okamoto, Hisashi Inoue, Takeshi Kobayashi, Hiroyuki Yamada
https://arxiv.org/abs/2506.07573

Roles of Non-switchable Domains and Internal Bias in Electrocaloric and Pyroelectric effects
Solid-state cooling and energy harvesting via pyroelectric effect (PEE) and electrocaloric effect (ECE) in ferroelectric thin films could be enhanced beyond their intrinsic ferroelectric response by exploiting the recently observed direction-dependent enhancement of the PEE; however, its microscopic origin remains unknown. Herein, we report direct hysteresis measurements of pyrocurrent ($I_{\rm p}$) and ECE-induced temperature change versus bias voltage in 1-$μ$m-thick Pb(Zr$_{0.65}$Ti$_{0.35}…

Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing
Chen Liu, Hengyu Tang, Zhixiao Yang, Ke Zhou, Sangwhan Cha
https://arxiv.org/abs/2506.02008 …

Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing
In the age of digital finance, detecting fraudulent transactions and money laundering is critical for financial institutions. This paper presents a scalable and efficient solution using Big Data tools and machine learning models. We utilize realtime data streaming platforms like Apache Kafka and Flink, distributed processing frameworks such as Apache Spark, and cloud storage services AWS S3 and RDS. A synthetic dataset representing real-world Anti-Money Laundering (AML) challenges is employed t…

Optimization of the multiple sampling and signal extraction in non-destructive exposures
B. Kubik, R. Barbier, A. Castera, E. Chabanat, S. Ferriol, G. Smadja
https://arxiv.org/abs/2506.04904

Optimization of the multiple sampling and signal extraction in non-destructive exposures
We derive the full covariance matrix formulae are derived for proper treatment of correlations in signal fitting procedures, extending the results from previous publications. The straight line fits performed with these matrices demonstrate that a significantly higher signal to noise is obtained when the fluence exceeds 1 e/sec/pix in particular in long (several hundreds of seconds) spectroscopic exposures. The improvement arising from the covariance matrix is particularly strong for the initial…