Tootfinder

Leveraging Network Methods for Hub-like Microservice Detection
Alexander Bakhtin, Matteo Esposito, Valentina Lenarduzzi, Davide Taibi
https://arxiv.org/abs/2506.07683

Leveraging Network Methods for Hub-like Microservice Detection
Context: Microservice Architecture is a popular architectural paradigm that facilitates flexibility by decomposing applications into small, independently deployable services. Catalogs of architectural anti-patterns have been proposed to highlight the negative aspects of flawed microservice design. In particular, the Hub-like anti-pattern lacks an unambiguous definition and detection method. Aim: In this work, we aim to find a robust detection approach for the Hub-like microservice anti-pattern …

Receiving RISs: Enabling Channel Estimation and Autonomous Configuration
George C. Alexandropoulos, Konstantinos D. Katsanos, Evangelos Vlachos
https://arxiv.org/abs/2506.10662

Receiving RISs: Enabling Channel Estimation and Autonomous Configuration
This chapter focuses on a hardware architecture for semi-passive Reconfigurable Intelligent Surfaces (RISs) and investigates its consideration for boosting the performance of Multiple-Input Multiple-Output (MIMO) communication systems. The architecture incorporates a single or multiple radio-frequency chains to receive pilot signals via tunable absorption phase profiles realized by the metasurface front end, as well as a controller encompassing a baseband processing unit to carry out channel es…

WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection
Alexander Stepikin, Evgenia Romanenkova, Alexey Zaytsev
https://arxiv.org/abs/2506.08066

WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection
Change Point Detection (CPD) aims to identify moments of abrupt distribution shifts in data streams. Real-world high-dimensional CPD remains challenging due to data pattern complexity and violation of common assumptions. Resorting to standalone deep neural networks, the current state-of-the-art detectors have yet to achieve perfect quality. Concurrently, ensembling provides more robust solutions, boosting the performance. In this paper, we investigate ensembles of deep change point detectors an…

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#Alzheimer #Frühdemenz #Krankheit

Cancer model with moving extinction threshold reproduces real cancer data
Frank Bastian, Hassan Alkhayuon, Kieran Mulchrone, Micheal O'Riordain, Sebastian Wieczorek
https://arxiv.org/abs/2506.05992

Cancer model with moving extinction threshold reproduces real cancer data
We propose a simple dynamic model of cancer development that captures carcinogenesis and subsequent cancer progression. A central idea of the model is to include the immune system as an extinction threshold, similar to the strong Allee effect in population biology. We first identify the limitations of commonly used Allee effect models in reproducing typical cancer progression. We then address these limitations by deriving a new model that incorporates: (i) random mutations of stem cells at a ra…

The branching fraction measurements of $J/\psi$ decay into $\rho \eta$ and $\phi \eta$ final states
KEDR Collaboration, V. V. Anashin, O. V. Anchugov, K. V. Astrelina, V. M. Aulchenko, V. V. Balakin, E. M. Baldin, G. N. Baranov, A. K. Barladyan, A. Yu. Barnyakov, M. Yu. Barnyakov, I. Yu. Basok, E. A. Bekhtenev, O. V. Belikov, D. E. Berkaev, A. E. Blinov, V. E. Blinov, A. V. Bobrov, V. S. Bobrovnikov, A. V. Bogomyagkov, D. Yu. Bolkhovityanov, A. E. Bondar, V. M. Borin, A. R. Buzykaev, V…

The branching fraction measurements of $J/ψ$ decay into $ρη$ and $ϕη$ final states
We present measurements of the branching fractions for the $J/ψ$ meson decays into the $ρη$ and $ϕη$ final states, based on data collected with the KEDR detector at the VEPP-4M collider. The data set consisted of 4.93 million $J/ψ$ events. The resulting branching fractions are: - $\mathcal{B}(J/ψ\to ρη) = (2.05 \pm 0.47 \pm 0.23)\times 10^{-4}$, - $\mathcal{B}(J/ψ\to ϕη) = (8.70 \pm 1.22 \pm 0.40) \times 10^{-4}$, where the first uncertainty is statistical and the second one i…

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SNIFR : Boosting Fine-Grained Child Harmful Content Detection Through Audio-Visual Alignment with Cascaded Cross-Transformer
Orchid Chetia Phukan, Mohd Mujtaba Akhtar, Girish, Swarup Ranjan Behera, Abu Osama Siddiqui, Sarthak Jain, Priyabrata Mallick, Jaya Sai Kiran Patibandla, Pailla Balakrishna Reddy, Arun Balaji Buduru, Rajesh Sharma
https://

SNIFR : Boosting Fine-Grained Child Harmful Content Detection Through Audio-Visual Alignment with Cascaded Cross-Transformer
As video-sharing platforms have grown over the past decade, child viewership has surged, increasing the need for precise detection of harmful content like violence or explicit scenes. Malicious users exploit moderation systems by embedding unsafe content in minimal frames to evade detection. While prior research has focused on visual cues and advanced such fine-grained detection, audio features remain underexplored. In this study, we embed audio cues with visual for fine-grained child harmful c…

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…

Synthesizing Performance Constraints for Evaluating and Improving Code Efficiency
Jun Yang, Cheng-Chi Wang, Bogdan Alexandru Stoica, Kexin Pei
https://arxiv.org/abs/2505.23471

Synthesizing Performance Constraints for Evaluating and Improving Code Efficiency
Large Language Models (LLMs) have been increasingly used to optimize code efficiency. Evaluating their effectiveness and further suggesting optimization opportunities often rely on high-quality tests to demonstrate the performance bottlenecks presented in the program. However, existing approaches rely on a limited set of hand-curated inputs or LLM-generated uninteresting length-stressing tests, failing to reveal more nuanced optimization opportunities. We present WEDGE, a framework for generati…