### New talk in the Logic Seminar

Antonio Di Nola (Università degli Studi di Salerno)
will give on
Thursday, May 26, at 10:00 the talk
**Non Archimedean Random Variables and Łukasiewicz Logics**
in the Logic Seminar.

Antonio Di Nola (Università degli Studi di Salerno)
will give on
Thursday, May 26, at 10:00 the talk
**Non Archimedean Random Variables and Łukasiewicz Logics**
in the Logic Seminar.

Horațiu Cheval and Laurențiu Leuştean published the paper **
Quadratic rates of asymptotic regularity for the Tikhonov-Mann iteration**
in Optimization Methods and Software.

Isabela Drămnesc (West University of Timişoara)
will give on
Thursday, May 5, at 10:00 the talk
**Deductive synthesis of sorting algorithms on lists and on binary trees in Theorema **
in the Logic Seminar.

The Research Center for Logic, Optimization and Security (LOS) was founded in October 2020 by
Laurenţiu Leuştean (head), Paul Irofti and Andrei Pătraşcu.

Our main objective is to stimulate interdisciplinary research in the fields of logic,
optimization and security. We are interested both in fundamental research as well as in
industrial applications. We focus on proof mining and applications to optimization, ergodic theory
and nonlinear analysis, convex optimization for machine learning, signal processing and
matrix factorization (dictionary learning), security, anomaly detection and anti-money laundry.

Contact

Hall 317, Faculty of Mathematics and Computer Science,

Academiei 14, 010014 Bucharest, Romania

Email: los@fmi.unibuc.ro

NetAlert aims to create a hardware-software sensor solution for detecting anomalies in computer networks
based on the monitoring and analysis of data packets.
The network-mounted sensor will provide real-time alerts on abnormal traffic behaviors
using two complementary approaches:

(i) static analysis based on rules and behavioral patterns;

(ii) machine learning (ML) analysis without prior expert knowledge.

The main goal of this project, called DDNET, is to adapt and propose
new dictionary learning methods for solving untractable
fault detection and isolation problems found in distribution networks.
Given a large dataset of sensor measurements from the distribution network,
the dictionary learning algorithms should be able to produce the subset
of network nodes where faults exist.

The proposed project, called Graphomaly, aims to create a
Python software package for anomaly detection in graphs
that model financial transactions,
with the purpose of discovering fraudulent behavior like money laundering,
illegal networks, tax evasion, scams, etc.
Such a toolbox is necessary in banks, where fraud detection departments
still use mostly human experts.

StOpAnomaly (143PD/2020)

StOpAnomaly aims to create, analyze and implement numerical optimization algorithms for large-scale optimization focusing on robust anomaly detection models based on decomposition and one-class classification. The research will be directed towards development of a toolbox containing scalable stochastic algorithms that can be used to detect several classes of anomalies in noisy large datasets.

Scientific seminars organized by LOS members

The working seminar of the LOS research center.

The seminar features talks on mathematical logic, philosophical logic and logical aspects of computer science.

The seminar presents recent results on proof mining.

- H. Cheval, L. Leuştean, Quadratic rates of
asymptotic regularity for
the Tikhonov-Mann iteration, Optimization Methods and Software (2022).

DOI: 10.1080/10556788.2022.2060974. - P. Irofti, C. Rusu, A. Pătraşcu,
Dictionary Learning with Uniform Sparse Representations
for Anomaly Detection, in Proceedings of 2022 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2022),
IEEE Computer Society, 3378-3382
(2022)

DOI: 10.1109/ICASSP43922.2022.9747365. - A. Sipoş,
On extracting variable Herbrand disjunctions,
Studia Logica (2022),

DOI: 10.1007/s11225-022-09990-5. - H. Cheval, U. Kohlenbach, L. Leuştean, On modified Halpern and Tikhonov-Mann iterations, arXiv:2203.11003 [math.OC] (2022).
- C. Rusu, C. Rosasco, Fast approximation of
orthogonal matrices and application to PCA, Signal Processing 194, 108451 (2022),

DOI: 10.1016/j.sigpro.2021.108451. - C. Rusu, An iterative coordinate descent algorithm
to compute sparse low-rank approximations,
IEEE Signal Processing Letters (2021),

DOI: 10.1109/LSP.2021.3132276. - A. Sipoş, Bounds on strong unicity for
Chebyshev approximation with bounded coefficients, Mathematische Nachrichten 294, 2425–2440 (2021),

DOI: 10.1002/mana.201900439. - C. Rusu, P. Irofti, Efficient and Parallel Separable Dictionary Learning, in Proceedings of the IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS 2021), IEEE Computer Society, 1-6 (2021).
- P. Irofti, L. Romero-Ben, F. Stoican, V. Puig, Data-driven Leak Localization in Water Distribution Networks via Dictionary Learning and Graph-based Interpolation, arXiv:2110.06372 [cs.LG] (2021).
- I. Leuştean, B. Macovei, DELP: Dynamic Epistemic Logic for Security Protocols, in Proceedings of the 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2021), 275-282 (2021)
- A. Sipoş, A quantitative multiparameter
mean ergodic theorem, Pacific Journal of Mathematics 314, 209 - 218 (2021),

DOI: 10.2140/pjm.2021.314.209 - A. Sipoş, Quantitative inconsistent feasibility
for averaged mappings, Optimization Letters (2021),

DOI: 10.1007/s11590-021-01812-2. - C. Rusu, L. Rosasco,
Constructing fast approximate eigenspaces with application to the fast graph Fourier transforms,
IEEE Transactions on Signal Processing (2021),

DOI: 10.1109/TSP.2021.3107629. - A. Sipoş, Abstract strongly convergent variants of the proximal point algorithm , arXiv:2108.13994 [math.OC] (2021).
- A. Pătraşcu, P. Irofti, Computational complexity of Inexact Proximal Point Algorithm for Convex Optimization under Holderian Growth, arXiv:2108.04482 [cs.LG] (2021).
- C. Rusu, An iterative Jacobi-like algorithm to compute a few sparse eigenvalue-eigenvector pairs, arXiv:2105.14642 [math.NA] (2021)
- A. Sipoş,
Construction of Fixed Points of Asymptotically Nonexpansive Mappings in Uniformly Convex Hyperbolic Spaces
, Numerical Functional Analysis and Optimization 42, 696-711 (2021),

DOI: 10.1080/01630563.2021.1924780. - A. Sipoş, Rates of metastability for iterations
on the unit interval, Journal of Mathematical Analysis and Applications 502, 125235 (2021),

DOI: 10.1016/j.jmaa.2021.125235. - A. Sipoş, Revisiting jointly firmly nonexpansive
families of mappings, Optimization (2021),

DOI: 10.1080/02331934.2021.1915312. - L. Leuştean, P. Pinto,
Quantitative results on a Halpern-type proximal point algorithm,
Computational Optimization and Applications 79, 101–125 (2021),

DOI: 10.1007/s10589-021-00263-w. - A. Pătraşcu, P. Irofti,
Stochastic proximal splitting algorithm for composite minimization,
Optimization Letters 15, 2255–2273 (2021),

DOI: 10.1007/s11590-021-01702-7.