arXiv subjects

Leen Lambers

Publications and source records attributed to Leen Lambers.

8 recordsLinked to original sources

arXiv2026

Design and performance of the Fast Beam Condition Monitor for luminosity and background measurement at the CMS Experiment in LHC Run 3

The Fast Beam Condition Monitor (BCM1F) has been used at the CMS Experiment since the first LHC circulating beams in 2008. Originally meant as a beam-induced background monitor for fast beam losses detection, it showed a potential also for luminosity measurements in 2012 running, and has been used for luminosity measurements since the beginning of Run 2 data taking in 2015 as a part of the Beam Radiation, Instrumentation and Luminosity (BRIL) system. Over the years, the system has undergone various upgrades to the sensors, the front-end and back-end electronics, providing improvements in the precision of the measurements, that remain valid in the higher pileup conditions of LHC Run 3 (2022-2026). Based on the experience of all BCM1F Run 2 upgrades, the detector was completely rebuilt prior to LHC Run 3 using AC-coupled silicon-pad diodes and active cooling. This latest detector version exhibits excellent linearity with instantaneous luminosity and achieves nanosecond-level timing precision, enabling improved systematic corrections for luminosity and background measurements. This paper presents a detailed overview of the detector system for LHC Run 3, including the selection and qualification of sensors as well as a summary of the readout system. It also outlines the processing and calibration strategy for luminosity data, discussing operational hurdles and comparing BCM1F measurements to other CMS luminosity measurements to assess the system's performance as a luminometer. Lastly, the implications for the design of a future luminosity detector to be used in the envisioned HL-LHC upgrade are discussed.

physics.ins-det↗

arXiv2026

Conflict Essences for Transformation Rules with Nested Application Conditions -- Long Version

Conflict and dependency analysis is an important static analysis tool that provides an overview of the potential interactions of (graph) transformation rules. This analysis is based on critical pairs and initial conflicts, which represent conflicting transformations in a minimal context. However, the crucial information about a conflicting transformation pair is contained in much smaller structures, called disabling/conflict essences in existing research. Recently, we introduced disabling essences for rules with application conditions which contain the information on how an application condition can be violated by another rule. In this paper, we extend the notion of disabling essences to support not only application conditions in Alternating Quantifier Normal Form, but also arbitrary nested conditions. We introduce (symbolic) conflict essences that are constructed from disabling essences and which capture the interaction between two rules. We show that a transformation pair is parallel dependent if and only if a symbolic conflict essence can be embedded into it and relate symbolic conflict essences to initial conflicts for transformation rules with application conditions. We present our results for adhesive HLR categories, which includes several types of graph-like structures.

cs.SE↗

arXiv2026

Proceedings 16th International Workshop on Graph Computation Models

This volume contains the post-proceedings of the Sixteenth International Workshop on Graph Computation Models (GCM 2025). The workshops took place in Koblenz, Germany on June 10 as part of STAF (Software Technologies: Applications and Foundations). Graphs are common mathematical structures that are visual and intuitive. They constitute a natural and seamless way for system modeling in science, engineering, and beyond, including computer science, biology, and business process modeling. Graph computation models constitute a class of very high-level models where graphs are first-class citizens. The aim of the International GCM Workshop series is to bring together researchers interested in all aspects of computation models based on graphs and graph transformation. It promotes the cross-fertilizing exchange of ideas and experiences among senior and young researchers from the different communities interested in the foundations, applications, and implementations of graph computation models and related areas.

cs.LO↗

arXiv2025

Exploring a Graph-based Approach to Offline Reinforcement Learning for Sepsis Treatment

Sepsis is a serious, life-threatening condition. When treating sepsis, it is challenging to determine the correct amount of intravenous fluids and vasopressors for a given patient. While automated reinforcement learning (RL)-based methods have been used to support these decisions with promising results, previous studies have relied on relational data. Given the complexity of modern healthcare data, representing data as a graph may provide a more natural and effective approach. This study models patient data from the well-known MIMIC-III dataset as a heterogeneous graph that evolves over time. Subsequently, we explore two Graph Neural Network architectures - GraphSAGE and GATv2 - for learning patient state representations, adopting the approach of decoupling representation learning from policy learning. The encoders are trained to produce latent state representations, jointly with decoders that predict the next patient state. These representations are then used for policy learning with the dBCQ algorithm. The results of our experimental evaluation confirm the potential of a graph-based approach, while highlighting the complexity of representation learning in this domain.

cs.LG↗

arXiv2025

Taint Analysis for Graph APIs Focusing on Broken Access Control

We present the first systematic approach to static and dynamic taint analysis for Graph APIs focusing on broken access control. The approach comprises the following. We taint nodes of the Graph API if they represent data requiring specific privileges in order to be retrieved or manipulated, and identify API calls which are related to sources and sinks. Then, we statically analyze whether a tainted information flow between API source and sink calls occurs. To this end, we model the API calls using graph transformation rules. We subsequently use Critical Pair Analysis to automatically analyze potential dependencies between rules representing source calls and rules representing sink calls. We distinguish direct from indirect tainted information flow and argue under which conditions the Critical Pair Analysis is able to detect not only direct, but also indirect tainted flow. The static taint analysis (i) identifies flows that need to be further reviewed, since tainted nodes may be created by an API call and used or manipulated by another API call later without having the necessary privileges, and (ii) can be used to systematically design dynamic security tests for broken access control. The dynamic taint analysis checks if potential broken access control risks detected during the static taint analysis really occur. We apply the approach to a part of the GitHub GraphQL API. The application illustrates that our analysis supports the detection of two types of broken access control systematically: the case where users of the API may not be able to access or manipulate information, although they should be able to do so; and the case where users (or attackers) of the API may be able to access/manipulate information that they should not.

cs.CR↗

arXiv2024

Coinductive Techniques for Checking Satisfiability of Generalized Nested Conditions

We study nested conditions, a generalization of first-order logic to a categorical setting, and provide a tableau-based (semi-decision) procedure for checking (un)satisfiability and finite model generation. This generalizes earlier results on graph conditions. Furthermore we introduce a notion of witnesses, allowing the detection of infinite models in some cases. To ensure completeness, paths in a tableau must be fair, where fairness requires that all parts of a condition are processed eventually. Since the correctness arguments are non-trivial, we rely on coinductive proof methods and up-to techniques that structure the arguments. We distinguish between two types of categories: categories where all sections are isomorphisms, allowing for a simpler tableau calculus that includes finite model generation; in categories where this requirement does not hold, model generation does not work, but we still obtain a sound and complete calculus.

cs.LO↗

arXiv2020

Initial Conflicts for Transformation Rules with Nested Application Conditions

We extend the theory of initial conflicts in the framework of M-adhesive categories to transformation rules with ACs. We first show that for rules with ACs, conflicts are in general neither inherited from a bigger context any more, nor is it possible to find a finite and complete subset of finite conflicts as illustrated for the category of graphs. We define initial conflicts to be special so-called symbolic transformation pairs, and show that they are minimally complete (and in the case of graphs also finite) in this symbolic way. We show that initial conflicts represent a proper subset of critical pairs again. We moreover demonstrate that (analogous to the case of rules without ACs) for each conflict a unique initial conflict exists representing it. We conclude with presenting a sufficient condition illustrating important special cases for rules with ACs, where we do not only have initial conflicts being complete in a symbolic way, but also find complete (and in the case of graphs also finite) subsets of conflicts in the classical sense.

cs.LO↗

arXiv2019

Analysis of Graph Transformation Systems: Native vs Translation-based Techniques

The paper summarises the contributions in a session at GCM 2019 presenting and discussing the use of native and translation-based solutions to common analysis problems for Graph Transformation Systems (GTSs). In addition to a comparison of native and translation-based techniques in this area, we explore design choices for the latter, s.a. choice of logic and encoding method, which have a considerable impact on the overall quality and complexity of the analysis. We substantiate our arguments by citing literature on application of theorem provers, model checkers, and SAT/SMT solver in GTSs, and conclude with a general discussion from a software engineering perspective, including comments from the workshop participants, and recommendations on how to investigate important design choices in the future.

cs.SE↗