@article{MTMT:34779723,
	title = {Olfactory genes affect major depression in highly educated, emotionally stable, lean women: a bridge between animal models and precision medicine},
	url = {https://m2.mtmt.hu/api/publication/34779723},
	author = {Eszlári, Nóra and Hullám, Gábor István and Gál, Zsófia and Török, Dóra and Nagy, Tamás and Millinghoffer, András Dániel and Baksa, Dániel and Gonda, Xénia and Antal, Péter and Bagdy, György and Juhász, Gabriella},
	doi = {10.1038/s41398-024-02867-2},
	journal-iso = {TRANSL PSYCHIAT},
	journal = {TRANSLATIONAL PSYCHIATRY},
	volume = {14},
	unique-id = {34779723},
	issn = {2158-3188},
	abstract = {Most current approaches to establish subgroups of depressed patients for precision medicine aim to rely on biomarkers that require highly specialized assessment. Our present aim was to stratify participants of the UK Biobank cohort based on three readily measurable common independent risk factors, and to investigate depression genomics in each group to discover common and separate biological etiology. Two-step cluster analysis was run separately in males ( n  = 149,879) and females ( n  = 174,572), with neuroticism (a tendency to experience negative emotions), body fat percentage, and years spent in education as input variables. Genome-wide association analyses were implemented within each of the resulting clusters, for the lifetime occurrence of either a depressive episode or recurrent depressive disorder as the outcome. Variant-based, gene-based, gene set-based, and tissue-specific gene expression test were applied. Phenotypically distinct clusters with high genetic intercorrelations in depression genomics were found. A two-cluster solution was the best model in each sex with some differences including the less important role of neuroticism in males. In females, in case of a protective pattern of low neuroticism, low body fat percentage, and high level of education, depression was associated with pathways related to olfactory function. While also in females but in a risk pattern of high neuroticism, high body fat percentage, and less years spent in education, depression showed association with complement system genes. Our results, on one hand, indicate that alteration of olfactory pathways, that can be paralleled to the well-known rodent depression models of olfactory bulbectomy, might be a novel target towards precision psychiatry in females with less other risk factors for depression. On the other hand, our results in multi-risk females may provide a special case of immunometabolic depression.},
	year = {2024},
	eissn = {2158-3188},
	orcid-numbers = {Eszlári, Nóra/0000-0003-4913-028X; Hullám, Gábor István/0000-0002-4765-2351; Gál, Zsófia/0000-0002-9441-1497; Török, Dóra/0000-0001-9213-4345; Nagy, Tamás/0000-0002-0137-4341; Baksa, Dániel/0000-0002-7826-9179; Gonda, Xénia/0000-0001-9015-4203; Bagdy, György/0000-0001-8141-3410; Juhász, Gabriella/0000-0002-5975-4267}
}

@article{MTMT:34768972,
	title = {ConcurrentWitness2Test: Test-Harnessing the Power of Concurrency (Competition Contribution)},
	url = {https://m2.mtmt.hu/api/publication/34768972},
	author = {Bajczi, Levente and Ádám, Zsófia and Micskei, Zoltán Imre},
	doi = {10.1007/978-3-031-57256-2_16},
	journal-iso = {LNCS},
	journal = {LECTURE NOTES IN COMPUTER SCIENCE},
	volume = {14572},
	unique-id = {34768972},
	issn = {0302-9743},
	abstract = {ConcurrentWitness2Test is a violation witness validator for concurrent software. Taking both nondeterminism of data and interleaving-based nondeterminism into account, the tool aims to use the metadata described in the violation witnesses to synthesize an executable test harness. While plagued by some initial challenges yet to overcome, the validation performance of ConcurrentWitness2Test corroborates the usefulness of the proposed approach.},
	year = {2024},
	eissn = {1611-3349},
	pages = {330-334},
	orcid-numbers = {Bajczi, Levente/0000-0002-6551-5860; Ádám, Zsófia/0000-0003-2354-1750; Micskei, Zoltán Imre/0000-0003-1846-261X}
}

@article{MTMT:34768428,
	title = {Theta: Abstraction Based Techniques for Verifying Concurrency (Competition Contribution)},
	url = {https://m2.mtmt.hu/api/publication/34768428},
	author = {Bajczi, Levente and Telbisz, Csanád Ferenc and Somorjai, Márk and Ádám, Zsófia and Dobos-Kovács, Mihály and Szekeres, Dániel and Mondok, Milán and Molnár, Vince},
	doi = {10.1007/978-3-031-57256-2_30},
	journal-iso = {LNCS},
	journal = {LECTURE NOTES IN COMPUTER SCIENCE},
	volume = {14572},
	unique-id = {34768428},
	issn = {0302-9743},
	abstract = {Theta is a model checking framework, with a strong emphasis on effectively handling concurrency in software using abstraction refinement algorithms. In SV-COMP 2024, we use 1) an abstraction-aware partial order reduction; 2) a dynamic statement reduction technique; and 3) enhanced support for call stacks to handle recursive programs. We integrate these techniques in an improved architecture with inherent support for portfolio-based verification using dynamic algorithm selection, with a diverse selection of supported SMT solvers as well. In this paper we detail the advances of Theta regarding concurrent and recursive software support.},
	year = {2024},
	eissn = {1611-3349},
	pages = {412-417},
	orcid-numbers = {Bajczi, Levente/0000-0002-6551-5860; Telbisz, Csanád Ferenc/0000-0002-6260-5908; Ádám, Zsófia/0000-0003-2354-1750; Dobos-Kovács, Mihály/0000-0002-0064-2965; Szekeres, Dániel/0000-0002-2912-028X; Mondok, Milán/0000-0001-5396-2172; Molnár, Vince/0000-0002-8204-7595}
}

@article{MTMT:34768422,
	title = {EmergenTheta: Verification Beyond Abstraction Refinement (Competition Contribution)},
	url = {https://m2.mtmt.hu/api/publication/34768422},
	author = {Bajczi, Levente and Szekeres, Dániel and Mondok, Milán and Ádám, Zsófia and Somorjai, Márk and Telbisz, Csanád Ferenc and Dobos-Kovács, Mihály and Molnár, Vince},
	doi = {10.1007/978-3-031-57256-2_23},
	journal-iso = {LNCS},
	journal = {LECTURE NOTES IN COMPUTER SCIENCE},
	volume = {14572},
	unique-id = {34768422},
	issn = {0302-9743},
	abstract = {Theta is a model checking framework conventionally based on abstraction refinement techniques. While abstraction is useful for a large number of verification problems, the over-reliance on the technique led to Theta being unable to meaningfully adapt. Identifying this problem in previous years of SV-COMP has led us to create EmergenTheta , a sandbox for the new approaches we want Theta to support. By differentiating between mature and emerging techniques, we can experiment more freely without hurting the reliability of the overall framework. In this paper we detail the development route to EmergenTheta , and its first debut on SV-COMP’24 in the ReachSafety category.},
	year = {2024},
	eissn = {1611-3349},
	pages = {371-375},
	orcid-numbers = {Bajczi, Levente/0000-0002-6551-5860; Szekeres, Dániel/0000-0002-2912-028X; Mondok, Milán/0000-0001-5396-2172; Ádám, Zsófia/0000-0003-2354-1750; Telbisz, Csanád Ferenc/0000-0002-6260-5908; Dobos-Kovács, Mihály/0000-0002-0064-2965; Molnár, Vince/0000-0002-8204-7595}
}

@article{MTMT:34751205,
	title = {Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space},
	url = {https://m2.mtmt.hu/api/publication/34751205},
	author = {Pogány, Domonkos and Antal, Péter},
	doi = {10.1371/journal.pone.0300906},
	journal-iso = {PLOS ONE},
	journal = {PLOS ONE},
	volume = {19},
	unique-id = {34751205},
	issn = {1932-6203},
	abstract = {Given the prolonged timelines and high costs associated with traditional approaches, accelerating drug development is crucial. Computational methods, particularly drug-target interaction prediction, have emerged as efficient tools, yet the explainability of machine learning models remains a challenge. Our work aims to provide more interpretable interaction prediction models using similarity-based prediction in a latent space aligned to biological hierarchies. We investigated integrating drug and protein hierarchies into a joint-embedding drug-target latent space via embedding regularization by conducting a comparative analysis between models employing traditional flat Euclidean vector spaces and those utilizing hyperbolic embeddings. Besides, we provided a latent space analysis as an example to show how we can gain visual insights into the trained model with the help of dimensionality reduction. Our results demonstrate that hierarchy regularization improves interpretability without compromising predictive performance. Furthermore, integrating hyperbolic embeddings, coupled with regularization, enhances the quality of the embedded hierarchy trees. Our approach enables a more informed and insightful application of interaction prediction models in drug discovery by constructing an interpretable hyperbolic latent space, simultaneously incorporating drug and target hierarchies and pairing them with available interaction information. Moreover, compatible with pairwise methods, the approach allows for additional transparency through existing explainable AI solutions.},
	year = {2024},
	eissn = {1932-6203},
	orcid-numbers = {Pogány, Domonkos/0000-0003-4968-7504; Antal, Péter/0000-0002-4370-2198}
}

@article{MTMT:34727894,
	title = {Application of Mutation testing in Safety-Critical Embedded Systems: A Case Study},
	url = {https://m2.mtmt.hu/api/publication/34727894},
	author = {Serban, Andrada Alexia and Micskei, Zoltán Imre},
	doi = {10.12700/APH.21.8.2024.8.5},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {21},
	unique-id = {34727894},
	issn = {1785-8860},
	year = {2024},
	eissn = {1785-8860},
	pages = {87-106},
	orcid-numbers = {Micskei, Zoltán Imre/0000-0003-1846-261X}
}

@misc{MTMT:34726765,
	title = {Design Optimization of a Current Sensing Trace with respect to Skin Effect by FEM Simulations},
	url = {https://m2.mtmt.hu/api/publication/34726765},
	author = {Ákos, Ferenc Hegedűs and Dabóczi, Tamás},
	unique-id = {34726765},
	year = {2024},
	orcid-numbers = {Dabóczi, Tamás/0000-0002-7371-2186}
}

@article{MTMT:34720081,
	title = {Analysis of Quantization Noise in Fixed-Point HDFT Algorithms},
	url = {https://m2.mtmt.hu/api/publication/34720081},
	author = {Alrwashdeh, Monther and Czifra, Balazs and Kollár, Zsolt},
	doi = {10.1109/LSP.2024.3372782},
	journal-iso = {IEEE SIGNAL PROC LET},
	journal = {IEEE SIGNAL PROCESSING LETTERS},
	volume = {31},
	unique-id = {34720081},
	issn = {1070-9908},
	abstract = {The Discrete Fourier Transform (DFT) algorithm is widely used in signal processing and communication systems to transform the signal to the frequency-domain. As real-time signal analysis is required for fast processing, several recursive algorithms were proposed to perform the calculation with overlapping sequences in a sliding manner. One Sliding DFT (SDFT) method is the Hopping DFT (HDFT), where the DFT calculations are not evaluated sample-by-sample but with longer steps, thus further reducing the computational complexity compared to the other SDFT algorithms. This letter analyses the effect of fixed-point roundoff error in the HDFT algorithm, including the Updating Vector Transform (UVT) block. A closed-form expression for the resulting quantization noise power at the output of the HDFT algorithm is provided, which is validated through simulations. The results show that the roundoff error can be determined based on the number and size of the hops, the window size, and the number of fractional bits used in the quantization process.},
	year = {2024},
	eissn = {1558-2361},
	pages = {756-760},
	orcid-numbers = {Kollár, Zsolt/0000-0001-6384-265X}
}

@article{MTMT:34714521,
	title = {Floating-Point Quantization Analysis of Multi-Layer Perceptron Artificial Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/34714521},
	author = {Al-Rikabi, Hussein and Renczes, Balázs},
	doi = {10.1007/s11265-024-01911-0},
	journal-iso = {J SIGNAL PROCESS SYS},
	journal = {JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY},
	unique-id = {34714521},
	issn = {1939-8018},
	abstract = {The impact of quantization in Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANNs) is presented in this paper. In this architecture, the constant increase in size and the demand to decrease bit precision are two factors that contribute to the significant enlargement of quantization errors. We introduce an analytical tool that models the propagation of Quantization Noise Power (QNP) in floating-point MLP ANNs. Contrary to the state-of-the-art approach, which compares the exact and quantized data experimentally, the proposed algorithm can predict the QNP theoretically when the effect of operation quantization and Coefficient Quantization Error (CQE) are considered. This supports decisions in determining the required precision during the hardware design. The algorithm is flexible in handling MLP ANNs of user-defined parameters, such as size and type of activation function. Additionally, a simulation environment is built that can perform each operation on an adjustable bit precision. The accuracy of the QNP calculation is verified with two publicly available benchmarked datasets, using the default precision simulation environment as a reference. © The Author(s) 2024.},
	year = {2024},
	eissn = {1939-8115},
	orcid-numbers = {Renczes, Balázs/0000-0003-4259-5172}
}

@CONFERENCE{MTMT:34714453,
	title = {Calibrated Sinefit Based on Quantized Data},
	url = {https://m2.mtmt.hu/api/publication/34714453},
	author = {Paolo, Carbone and Renczes, Balázs and Alessio, De Angelis and Antonio, Moschitta},
	booktitle = {Proceedings of the IEEE I2MTC conference},
	unique-id = {34714453},
	year = {2024},
	orcid-numbers = {Renczes, Balázs/0000-0003-4259-5172}
}