Andrei Patrascu

Sparsifying transforms became in the last decades widely known tools for finding structured sparse representations of signals in certain transform domains. Despite the popularity of classical transforms such as DCT and Wavelet, learning optimal transforms that guarantee good representations of data into the sparse domain has been recently analyzed in a series of papers. Typically, the conditioning number and representation ability are complementary key features of learning square transforms that may not be explicitly controlled in a given optimization model. Unlike the existing approaches from the literature, in our paper, we consider a new sparsifying transform model that enforces explicit control over the data representation quality and the condition number of the learned transforms. We confirm through numerical experiments that our model presents better numerical behavior than the state-of-the-art.

Cyberthreats are a permanent concern in our modern technological world. In the recent years, sophisticated traffic analysis techniques and anomaly detection (AD) algorithms have been employed to face the more and more subversive adversarial attacks. A malicious intrusion, defined as an invasive action in-tending to illegally exploit private resources, manifests through unusual data traffic and/or abnormal connectivity pattern. Despite the plethora of statistical or signature-based detectors currently provided in the literature, the topological connectivity component of a malicious flow is less exploited. Furthermore, a great proportion of the existing statistical intrusion detectors are based on supervised learning, that relies on labeled data. By viewing network flows as weighted directed interactions between a pair of nodes, in this paper we present a simple method that facilitate the use of connectivity graph features in unsupervised anomaly detection algorithms. We test our methodology on real network traffic datasets and observe several improvements over standard AD.

Cyberthreats are a permanent concern in our modern technological world. In the recent years, sophisticated traffic analysis techniques and anomaly detection (AD) algorithms have been employed to face the more and more subversive adversarial attacks. A malicious intrusion, defined as an invasive action intending to illegally exploit private resources, manifests through unusual data traffic and/or abnormal connectivity pattern. Despite the plethora of statistical or signature-based detectors currently provided in the literature, the topological connectivity component of a malicious flow is less exploited. Furthermore, a great proportion of the existing statistical intrusion detectors are based on supervised learning, that relies on labeled data. By viewing network flows as weighted directed interactions between a pair of nodes, in this paper we present a simple method that facilitate the use of connectivity graph features in unsupervised anomaly detection algorithms. We test our methodology on real network traffic datasets and observe several improvements over standard AD.

Many applications like audio and image processing show that sparse representations are a powerful and efficient signal modeling technique. Finding an optimal dictionary that generates at the same time the sparsest representations of data and the smallest approximation error is a hard problem approached by dictionary learning (DL). We study how DL performs in detecting abnormal samples in a dataset of signals. In this paper we use a particular DL formulation that seeks uniform sparse representations model to detect the underlying subspace of the majority of samples in a dataset, using a K-SVD-type algorithm. Numerical simulations show that one can efficiently use this resulted subspace to discriminate the anomalies over the regular data points.

Two metallacyclophanes have been assembled by self-assembly processes involving the trianionic compartmental ligand (L^3-), nickel(II) and sodium or potassium ions: [LNiNa]₂·C₂H₅OH·2H₂O (1) and [LNiK]₂·C₂H₅OH·2H₂O (2) (the Schiff-base proligand, H₃L, is obtained by condensation of o-vanillin and DL-2,3-diaminopropionic acid). The structures of 1 and 2 can be viewed as resulting from two enantiomeric fragments, {LNiNa/K} connected to each other by the two carboxylato groups. The intramolecular distance between the phenyl rings is 3.64 Å. The sodium/potassium ions are coordinated by five oxygens in a planar pentagon. Weak intermolecular sodium/potassium···CH interactions are observed in the packing diagrams of the two compounds. The intra- and intermolecular non-covalent interactions are analyzed through several methods based on electron density, Bader’s QTAIM, non-covalent interactions index and natural bond orbitals. They confirm the in-plane five coordination of the alkali ion, which is of an electrostatic, non-covalent nature and the intermolecular interaction with σ C-H orbitals as well. The diffuse reflectance spectra of the two compounds show characteristic features of square-planar nickel(II) chromophores.