arXiv subjects

Thomas Gross

Publications and source records attributed to Thomas Gross.

7 recordsLinked to original sources

arXiv2025

Modified Marrone-Treanor model: parameterization and benchmarking for five-species air

We present updated parameters for five-species air (N2, O2, NO, N and O) reactions to be used with the Modified Marrone-Treanor two-temperature model. The vibrational relaxation and chemical reaction rates are derived from quasiclassical trajectory calculations and direct molecular simulations using ab initio potential energy surfaces. The resulting model enables efficient computational fluid dynamics simulations of nonequilibrium air chemistry in hypersonic flows. We show that the model reproduces direct molecular simulation benchmark solutions with high accuracy in zero-dimensional heat baths representative of strong nonequilibrium post-shock conditions. The model's analytical expressions for dissociation rate coefficient and vibrational energy change per reaction ensure that the correct amount of energy is transferred between the vibrational and trans-rotational modes. Detailed balance is imposed for three-body recombination reactions and our simulations exhibit quasi-steady-state dissociation rates and proper approach to thermochemical equilibrium. In direct comparison with the Park TTv model, the Modified Marrone-Treanor model predicts significantly slower conversion of N2 into N below 10000 K and significantly more NO production at all temperatures. This is likely due to its significantly higher Zeldovich reaction rates compared to Park.

physics.chem-ph↗

arXiv2025

SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts

Neural surrogates for Partial Differential Equations (PDEs) often suffer significant performance degradation when evaluated on problem configurations outside their training distribution, such as new initial conditions or structural dimensions. While Unsupervised Domain Adaptation (UDA) techniques have been widely used in vision and language to generalize across domains without additional labeled data, their application to complex engineering simulations remains largely unexplored. In this work, we address this gap through two focused contributions. First, we introduce SIMSHIFT, a novel benchmark dataset and evaluation suite composed of four industrial simulation tasks spanning diverse processes and physics: hot rolling, sheet metal forming, electric motor design and heatsink design. Second, we extend established UDA methods to state-of-the-art neural surrogates and systematically evaluate them. Extensive experiments on SIMSHIFT highlight the challenges of out-of-distribution neural surrogate modeling, demonstrate the potential of UDA in simulation, and reveal open problems in achieving robust neural surrogates under distribution shifts in industrially relevant scenarios. Our codebase is available at https://github.com/psetinek/simshift

cs.LG↗

arXiv2021

Why Most Results of Socio-Technical Security User Studies Are False

Background. In recent years, cyber security user studies have been scrutinized for their reporting completeness, statistical reporting fidelity, statistical reliability and biases. It remains an open question what strength of evidence positive reports of such studies actually yield. We focus on the extent to which positive reports indicate relation true in reality, that is, a probabilistic assessment. Aim. This study aims at establishing the overall strength of evidence in cyber security user studies, with the dimensions -- Positive Predictive Value (PPV) and its complement False Positive Risk (FPR), -- Likelihood Ratio (LR), and -- Reverse-Bayesian Prior (RBP) for a fixed tolerated False Positive Risk. Method. Based on $431$ coded statistical inferences in $146$ cyber security user studies from a published SLR covering the years 2006-2016, we first compute a simulation of the a posteriori false positive risk based on assumed prior and bias thresholds. Second, we establish the observed likelihood ratios for positive reports. Third, we compute the reverse Bayesian argument on the observed positive reports by computing the prior required for a fixed a posteriori false positive rate. Results. We obtain a comprehensive analysis of the strength of evidence including an account of appropriate multiple comparison corrections. The simulations show that even in face of well-controlled conditions and high prior likelihoods, only few studies achieve good a posteriori probabilities. Conclusions. Our work shows that the strength of evidence of the field is weak and that most positive reports are likely false. From this, we learn what to watch out for in studies to advance the knowledge of the field.

cs.CR↗

arXiv2021

Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications

Financial markets are a source of non-stationary multidimensional time series which has been drawing attention for decades. Each financial instrument has its specific changing-over-time properties, making its analysis a complex task. Hence, improvement of understanding and development of more informative, generalisable market representations are essential for the successful operation in financial markets, including risk assessment, diversification, trading, and order execution. In this study, we propose a volume-price-based market representation for making financial time series more suitable for machine learning pipelines. We use a statistical approach for evaluating the representation. Through the research questions, we investigate, i) whether the proposed representation allows the more efficient design of machine learning models; ii) whether the proposed representation leads to increased performance over the price levels market pattern; iii) whether the proposed representation performs better on the liquid markets, and iv) whether SHAP feature interactions are reliable to be used in the considered setting. Our analysis shows that the proposed volume-based method allows successful classification of the financial time series patterns, and also leads to better classification performance than the price levels-based method, excelling specifically on more liquid financial instruments. Finally, we propose an approach for obtaining feature interactions directly from tree-based models and compare the outcomes to those of the SHAP method. This results in the significant similarity between the two methods, hence we claim that SHAP feature interactions are reliable to be used in the setting of financial markets.

cs.LG↗

arXiv2020

GSL: A Cryptographic Library for the strong RSA Graph Signature Scheme

Current cloud and network infrastructures do not employ privacy-preserving methods to protect their assets. Anonymous credential schemes are a cryptographic building block that enables the certification of data structures and prove properties over their representations without disclosing the innards of their data structures in zero-knowledge. The GRaph Signature (GRS) scheme enables the certification and proof methods to sign infrastructure topologies represented as graph data structures and use zero-knowledge to prove properties over their certificates. As such, they represent a powerful privacy-preserving method that proves properties over a signed topology graph to another party without disclosing the blueprint of its topology. In this paper, we report our efforts in designing, implementing and benchmarking a Graph Signature Library (GSL). GSL is a cryptographic library realized in Java that implements the graph signature scheme.

cs.CR↗

arXiv2020

A Survey on Hardware Approaches for Remote Attestation in Network Infrastructures

Remote attestation schemes have been utilized for assuring the integrity of a network node to a remote verifier. In recent years, a number of remote attestation schemes have been proposed for various contexts such as cloud computing, Internet of Things (IoTs) and critical network infrastructures. These attestation schemes provide a different perspective in terms of security objectives, scalability and efficiency. In this report, we focus on remote attestation schemes that use a hardware device and cryptographic primitives to assist with the attestation of nodes in a network infrastructure. We also point towards the open research challenges that await the research community and propose possible avenues of addressing these challenges.

cs.CR↗

arXiv2015

Towards a New Paradigm for Privacy and Security in Cloud Services

The market for cloud computing can be considered as the major growth area in ICT. However, big companies and public authorities are reluctant to entrust their most sensitive data to external parties for storage and processing. The reason for their hesitation is clear: There exist no satisfactory approaches to adequately protect the data during its lifetime in the cloud. The EU Project Prismacloud (Horizon 2020 programme; duration 2/2015-7/2018) addresses these challenges and yields a portfolio of novel technologies to build security enabled cloud services, guaranteeing the required security with the strongest notion possible, namely by means of cryptography. We present a new approach towards a next generation of security and privacy enabled services to be deployed in only partially trusted cloud infrastructures.

cs.CR↗