Magnetoelectric control of topological phases in graphene

Topological antiferromagnetic (AFM) spintronics is an emerging field of research, which involves the topological electronic states coupled to the AFM order parameter known as the Néel vector. The control of these states is envisioned through manipulation of the Néel vector by spin-orbit torques driven by electric currents. Here we propose a different approach favorable for low-power AFM spintronics, where the control of the topological states in a two-dimensional material, such as graphene, is performed via the proximity effect by the voltage induced switching of the Néel vector in an adjacent magnetoelectric AFM insulator, such as chromia. Mediated by the symmetry protected boundary magnetization and the induced Rashba-type spin-orbit coupling at the interface between graphene and chromia, the emergent topological phases in graphene can be controlled by the Néel vector. Using density functional theory and tight-binding Hamiltonian approaches, we model a graphene/Cr2O3 (0001) interface and demonstrate nontrivial band gap openings in the graphene Dirac bands asymmetric between the K and K′ valleys. This gives rise to an unconventional quantum anomalous Hall effect (QAHE) with a quantized value of 2e^2/h and an additional steplike feature at a value close to e^2/2h, and the emergence of the spin-polarized valley Hall effect (VHE). Furthermore, depending on the Néel vector orientation, we predict the appearance and transformation of different topological phases in graphene across the 180° AFM domain wall, involving the QAHE, the valley-polarized QAHE, and the quantum VHE, and the emergence of the chiral edge states along the domain wall. These topological properties are controlled by voltage through magnetoelectric switching of the AFM insulator with no need for spin-orbit torques

Feature Extraction for Classification in the Data Mining Process

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of “the curse of dimensionality”. Three different eigenvector-based feature extraction approaches are discussed and three different kinds of applications with respect to classification tasks are considered. The summary of obtained results concerning the accuracy of classification schemes is presented with the conclusion about the search for the most appropriate feature extraction method. The problem how to discover knowledge needed to integrate the feature extraction and classification processes is stated. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up the proposed system are considered

A Comparison of Ensemble and Case-Base Maintenance Techniques for Handling Concept Drift in Spam Filtering

The problem of concept drift has recently received con- siderable attention in machine learning research. One important practical problem where concept drift needs to be addressed is spam filtering. The literature on con- cept drift shows that among the most promising ap- proaches are ensembles and a variety of techniques for ensemble construction has been proposed. In this pa- per we compare the ensemble approach to an alternative lazy learning approach to concept drift whereby a sin- gle case-based classifier for spam filtering keeps itself up-to-date through a case-base maintenance protocol. We present an evaluation that shows that the case-base maintenance approach is more effective than a selection of ensemble techniques. The evaluation is complicated by the overriding importance of False Positives (FPs) in spam filtering. The ensemble approaches can have very good performance on FPs because it is possible to bias an ensemble more strongly away from FPs than it is to bias the single classifer. However this comes at consid- erable cost to the overall accurac

Magnetoelectric control of topological phases in graphene

Topological antiferromagnetic (AFM) spintronics is an emerging field of research, which involves the topological electronic states coupled to the AFM order parameter known as the Néel vector. The control of these states is envisioned through manipulation of the Néel vector by spin-orbit torques driven by electric currents. Here we propose a different approach favorable for low-power AFM spintronics, where the control of the topological states in a two-dimensional material, such as graphene, is performed via the proximity effect by the voltage induced switching of the Néel vector in an adjacent magnetoelectric AFM insulator, such as chromia. Mediated by the symmetry protected boundary magnetization and the induced Rashba-type spin-orbit coupling at the interface between graphene and chromia, the emergent topological phases in graphene can be controlled by the Néel vector. Using density functional theory and tight-binding Hamiltonian approaches, we model a graphene/Cr2O3 (0001) interface and demonstrate nontrivial band gap openings in the graphene Dirac bands asymmetric between the K and K′ valleys. This gives rise to an unconventional quantum anomalous Hall effect (QAHE) with a quantized value of 2e^2/h and an additional steplike feature at a value close to e^2/2h, and the emergence of the spin-polarized valley Hall effect (VHE). Furthermore, depending on the Néel vector orientation, we predict the appearance and transformation of different topological phases in graphene across the 180° AFM domain wall, involving the QAHE, the valley-polarized QAHE, and the quantum VHE, and the emergence of the chiral edge states along the domain wall. These topological properties are controlled by voltage through magnetoelectric switching of the AFM insulator with no need for spin-orbit torques

A Case-based Technique for Tracking Concept Drift in Spam Filtering

Clearly, machine learning techniques can play an important role in filtering spam email because ample training data is available to build a robust classifier. However, spam filtering is a particularly challenging task as the data distribution and concept being learned changes over time. This is a particularly awkward form of concept drift as the change is driven by spammers wishing to circumvent the spam filters. In this paper we show that lazy learning techniques are appropriate for such dynamically changing contexts. We present a case-based system for spam filtering called ECUE that can learn dynamically. We evaluate its performance as the case-base is updated with new cases. We also explore the benefit of periodically redoing the feature selection process to bring new features into play. Our evaluation shows that these two levels of model update are effective in tracking concept drift

Reversible spin texture in ferroelectric HfO\u3csub\u3e2\u3c/sub\u3e

Spin-orbit coupling effects occurring in noncentrosymmetric materials are known to be responsible for nontrivial spin configurations and a number of emergent physical phenomena. Ferroelectric materials may be especially interesting in this regard due to reversible spontaneous polarization making possible a nonvolatile electrical control of the spin degrees of freedom. Here, we explore a technologically relevant oxide material, HfO2, which has been shown to exhibit robust ferroelectricity in a noncentrosymmetric orthorhombic phase. Using theoretical modelling based on density-functional theory, we investigate the spin-dependent electronic structure of the ferroelectric HfO2 and demonstrate the appearance of chiral spin textures driven by spin-orbit coupling. We analyze these spin configurations in terms of the Rashba and Dresselhaus effects within the k · p Hamiltonian model and find that the Rashba-type spin texture dominates around the valence-band maximum, while the Dresselhaus-type spin texture prevails around the conduction band minimum. The latter is characterized by a very large Dresselhaus constant λD = 0.578 eV A, which allows using this material as a tunnel barrier to ˚ produce tunneling anomalous and spin Hall effects that are reversible by ferroelectric polarization

Reversible spin texture in ferroelectric HfO2

Spin-orbit coupling effects occurring in non-centrosymmetric materials are known to be responsible for non-trivial spin configurations and a number of emergent physical phenomena. Ferroelectric materials may be especially interesting in this regard due to reversible spontaneous polarization making possible for a non-volatile electrical control of the spin degrees of freedom. Here, we explore a technologically relevant oxide material, HfO2, which has been shown to exhibit robust ferroelectricity in a non-centrosymmetric orthorhombic phase. Using theoretical modelling based on density-functional theory, we investigate the spin-dependent electronic structure of the ferroelectric HfO2 and demonstrate the appearance of chiral spin textures driven by spin-orbit coupling. We analyze these spin configurations in terms of the Rashba and Dresselhaus effects within the k.p Hamiltonian model and find that the Rashba-type spin texture dominates around the valence band maximum, while the Dresselhaus-type spin texture prevails around the conduction band minimum. The latter is characterized by a very large Dresselhaus constant {\alpha}D = 0.578 eV {\AA}, which allows using this material as a tunnel barrier to produce tunneling anomalous and spin Hall effects that are reversible by ferroelectric polarization

Prescience:Probabilistic Guidance on the Retraining Conundrum for Malware Detection

Malware evolves perpetually and relies on increasingly sophisticatedattacks to supersede defense strategies. Datadrivenapproaches to malware detection run the risk of becomingrapidly antiquated. Keeping pace with malwarerequires models that are periodically enriched with freshknowledge, commonly known as retraining. In this work,we propose the use of Venn-Abers predictors for assessingthe quality of binary classification tasks as a first step towardsidentifying antiquated models. One of the key bene-fits behind the use of Venn-Abers predictors is that they areautomatically well calibrated and offer probabilistic guidanceon the identification of nonstationary populations ofmalware. Our framework is agnostic to the underlying classificationalgorithm and can then be used for building betterretraining strategies in the presence of concept drift. Resultsobtained over a timeline-based evaluation with about 90Ksamples show that our framework can identify when modelstend to become obsolete