Influence of the Dzyaloshinskii-Moriya exchange interaction on quantum phase interference of spins

Magnetization measurements of a Mn12mda wheel single-molecule magnet with a spin ground state of S = 7 show resonant tunneling and quantum phase interference, which are established by studying the tunnel rates as a function of a transverse field applied along the hard magnetization axis. Dzyaloshinskii-Moriya (DM) exchange interaction allows the tunneling between different spin multiplets. It is shown that the quantum phase interference of these transitions is strongly dependent on the direction of the DM vector.Comment: 5 pages, 5 figure

Bis(μ2-pyridine-2-carboxamide oximato)bis­[(pyridine-2-carboxamide oxime)zinc] dinitrate

In the title dinuclear compound, [Zn2(C6H6N3O)2(C6H7N3O)2](NO3)2, the ZnII cation is N,N′-chelated by one pyridine-2-carboxamide oximate anion and one pyridine-2-carboxamide oxime mol­ecule, and is further bridged by an oxime O atom from the adjacent pyridine-2-carboxamide oximate anion, forming a distorted trigonal bipyramidal coordination. Two pyridine-2-carboxamide oximate anions bridge two ZnII cations to form the centrosymmetric dinuclear mol­ecule. Extensive O—H⋯O, N—H⋯O and O—H⋯N hydrogen bonds are present in the crystal structure

Simian immunodeficiency virus (SIV(mac)251) membrane lipid mixing with human CD4\u3csup\u3e+\u3c/sup\u3e and CD4\u3csup\u3e-\u3c/sup\u3e cell lines in vitro does not necessarily result in internalization of the viral core proteins and productive infection

The cell binding site of simian immunodeficiency virus (SIV) is believed to be the CD4 molecule. Several CD4+ cell lines are, however, resistant to infection by SIV(mac)251 in vitro and additional cell membrane molecules have been implicated in SIV(mac)251 entry. We investigated the binding, envelope fusion and entry of the viral core proteins (p27) of SIV(mac)251 into two human CD4+ cell lines (H9 and Sup-T1) which are infectible, and one CD4+ (A3.01) and two CD4- cell lines (K562 and Raji) that are resistant to infection. The fusion of the viral and cellular membranes was monitored by a fluorescence assay for lipid mixing. Cell entry of the viral core was evaluated following virus-cell incubation and cell surface trypsinization. We found that SIV(mac)251 can bind to and fuse (membrane lipid mixing) in a temperature dependent but pH-independent fashion with CD4+ and CD4- human-derived cell lines. In contrast. lipid mixing with CD4 expressing EL-4 mouse T cells or Mv-1-lu mink lung fibroblasts was absent or limited, suggesting that certain components of human cell membranes in addition to CD4 are involved in SIV(mac) envelope-cell fusion. Lipid mixing with the human cells was inhibited partially by soluble CD4. Anti-CD4 antibodies inhibited the lipid inter-mixing with H9, but not with Raji cells, whereas neutralizing anti-SIV(mac) sera inhibited fusion with both CD4+ and CD4- cells. Out of the five human cell lines tested, efficient entry of p27 and productive infection took place only with H9 and Sup-T1 cells. In these two cases, the amounts of p27 internalized during virus-cell fusion correlated with the extent of infection

DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks

In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malware, rendering traditional authorship attribution algorithms useless. Furthermore, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. Finally, the dataset of such available APTs is extremely small. In this paper we describe how deep neural networks (DNN) could be successfully employed for nation-state APT attribution. We use sandbox reports (recording the behavior of the APT when run dynamically) as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. Using a test set of 1,000 Chinese and Russian developed APTs, we achieved an accuracy rate of 94.6%

A Profile-Based Method for Authorship Verification

Abstract. Authorship verification is one of the most challenging tasks in stylebased text categorization. Given a set of documents, all by the same author, and another document of unknown authorship the question is whether or not the latter is also by that author. Recently, in the framework of the PAN-2013 evaluation lab, a competition in authorship verification was organized and the vast majority of submitted approaches, including the best performing models, followed the instance-based paradigm where each text sample by one author is treated separately. In this paper, we show that the profile-based paradigm (where all samples by one author are treated cumulatively) can be very effective surpassing the performance of PAN-2013 winners without using any information from external sources. The proposed approach is fully-trainable and we demonstrate an appropriate tuning of parameter settings for PAN-2013 corpora achieving accurate answers especially when the cost of false negatives is high.